Analysis of musculoskeletal pain in dentistry students

Objetives

# Packages loading.
library(tidyverse)
library(readxl)
library(knitr)
library(broom)

# Data loading.
df <- read_excel("Data/Cleaned Data.xlsx")

# Rename some variables
df <- df |>
    rename(
        NMQ = `Likert NMQ`,
        Activity = `Activity Level`,
        PHQ = `PHQ LEVEL`,
    )  |> 
# Convert some variables to factors
    mutate(
        Activity = factor(Activity, levels = c("Low", "Moderate", "High"), ordered = TRUE),
        PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
        ) 

Sample size

# Sample size
n = nrow(df)

Frequency tables

Gender

# Frequency tables.
df |> 
    count(Sex) |>
    kable()

Sex	n
Female	253
Male	75

df |> 
    ggplot(aes(x = Sex, fill = Sex)) + 
    geom_bar()

df |>
  count(Sex) |>
  mutate(pct = scales::percent(n / sum(n))) |>
  knitr::kable()

Sex	n	pct
Female	253	77%
Male	75	23%

Age

df |> 
      summarize(
        min = min(Age),
        max = max(Age),
        mean = mean(Age),
        sd = sd(Age),
    ) |>
    kable()

min	max	mean	sd
19	39	21.94207	2.982056

Course

# Frequency tables.
df |> 
    count(Year_Study) |>
    kable()

Year_Study	n
1º	84
2º	75
3º	56
4º	64
5º	49

df |> 
    ggplot(aes(x = Year_Study, fill = Year_Study)) + 
    geom_bar()

df |>
  count(Year_Study) |>
  mutate(pct = scales::percent(n / sum(n))) |>
  knitr::kable()

Year_Study	n	pct
1º	84	25.6%
2º	75	22.9%
3º	56	17.1%
4º	64	19.5%
5º	49	14.9%

Weight

df |> 
    summarize(
        min = min(`Weight (Kg)`),
        max = max(`Weight (Kg)`),
        mean = mean(`Weight (Kg)`),
        sd = sd(`Weight (Kg)`),
    ) |>
    kable()

min	max	mean	sd
28	140	61.80701	12.49072

df |> 
    group_by(Sex) |> 
    summarize(
        min = min(`Weight (Kg)`),
        max = max(`Weight (Kg)`),
        mean = mean(`Weight (Kg)`),
        sd = sd(`Weight (Kg)`),
    ) |>
    kable()

Sex	min	max	mean	sd
Female	28	100	57.96047	8.979608
Male	54	140	74.78267	13.914482

Height

df |> 
    summarize(
        min = min(`Height (cm)`),
        max = max(`Height (cm)`),
        mean = mean(`Height (cm)`),
        sd = sd(`Height (cm)`),
    ) |>
    kable()

min	max	mean	sd
150	196	168.5322	8.50053

df |> 
    group_by(Sex) |> 
    summarize(
        min = min(`Height (cm)`),
        max = max(`Height (cm)`),
        mean = mean(`Height (cm)`),
        sd = sd(`Height (cm)`),
    ) |>
    kable()

Sex	min	max	mean	sd
Female	150	185	165.524	6.406686
Male	162	196	178.680	6.649853

BMI

df |> 
    group_by(Sex) |> 
    summarize(
        min = min(BMI),
        max = max(BMI),
        mean = mean(BMI),
        sd = sd(BMI),
    ) |>
    kable()

Sex	min	max	mean	sd
Female	9.69	34.60	21.15866	3.063567
Male	16.98	40.04	23.36373	3.744563

df  |> 
    ggplot(aes(x = BMI)) +
    geom_histogram(binwidth = 1,
                     fill = myred, 
                   color = "white")

df  |> 
    ggplot(aes(x = BMI, fill = Sex)) +
    geom_histogram(binwidth = 1, 
                   color = "white") +
    facet_wrap(~Sex) 

df  |> 
    ggplot(aes(x = BMI)) +
    geom_boxplot(fill = myred)

Dominant Hand

# Frequency tables.
df |> 
    count(Dom_Hand) |>
    kable()

Dom_Hand	n
Left-handed	25
Right-handed	303

df |> 
    ggplot(aes(x = Dom_Hand, fill = Dom_Hand)) + 
    geom_bar()

df |>
  count(Dom_Hand) |>
  mutate(pct = scales::percent(n / sum(n))) |>
  knitr::kable()

Dom_Hand	n	pct
Left-handed	25	8%
Right-handed	303	92%

Descriptive Statistics

Hours of Work per Week

df |> 
    summarize(
        min = min(`Work_Hr/Wk`),
        max = max(`Work_Hr/Wk`),
        mean = mean(`Work_Hr/Wk`),
        sd = sd(`Work_Hr/Wk`),
    ) |>
    kable()

min	max	mean	sd
0	90	23.02439	17.15372

df  |> 
    ggplot(aes(x = `Work_Hr/Wk`)) +
    geom_histogram(binwidth = 1,
                     fill = myred, 
                   color = "white")

df  |> 
    ggplot(aes(x = `Work_Hr/Wk`)) +
    geom_boxplot(fill = myred)

PHQ

With the categories in increasing order of severity.

df |> 
  mutate(PHQ = factor(PHQ, levels = c("Mild", "Minimal", "Moderate", "Moderately Severe", "Severe"))) |>
  count(PHQ) |> 
  kable()

PHQ	n
Mild	120
Minimal	117
Moderate	60
Moderately Severe	26
Severe	5

df |> 
  mutate(PHQ = factor(PHQ, levels = c("Mild", "Minimal", "Moderate", "Moderately Severe", "Severe"))) |>
  ggplot(aes(x = PHQ, fill = PHQ)) + 
  geom_bar() +
    facet_wrap(~Year_Study)

df |> 
  mutate(PHQ = factor(PHQ, levels = c("Mild", "Minimal", "Moderate", "Moderately Severe", "Severe"))) |>
  ggplot(aes(x = PHQ, fill = PHQ)) + 
  geom_bar() +
    facet_wrap(~Sex) +
  scale_fill_hue()

df |> 
  mutate(PHQ = factor(PHQ, levels = c("Mild", "Minimal", "Moderate", "Moderately Severe", "Severe"))) |>
  ggplot(aes(x = PHQ, fill = PHQ)) + 
  geom_bar() +
  facet_wrap(~Sex) +
  scale_fill_hue() + 
  labs(fill = "Depression Level", x = "PHQ Severity", y = "Number of Students") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

df |> 
  mutate(PHQ = factor(PHQ, levels = c("Mild", "Minimal", "Moderate", "Moderately Severe", "Severe"))) |>
  count(Sex, PHQ) |> 
  group_by(Sex) |>
  mutate(pct = n / sum(n)) |>
  ggplot(aes(x = PHQ, y = pct, fill = PHQ)) +
  geom_col() +
  facet_wrap(~Sex) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_hue() + 
  labs(fill = "Depression Level", x = "PHQ Severity", y = "Relative Frequency") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

df |> 
  mutate(PHQ = factor(PHQ, levels = c("Mild", "Minimal", "Moderate", "Moderately Severe", "Severe"))) |>
  ggplot(aes(x = PHQ, fill = PHQ)) + 
  geom_bar() +
  labs(x = "PHQ Severity", y = "Number of Students") +
  scale_fill_hue()

df |>
  count(PHQ) |>
  mutate(pct = scales::percent(n / sum(n))) |>
  knitr::kable()

PHQ	n	pct
Minimal	117	35.67%
Mild	120	36.59%
Moderate	60	18.29%
Moderately Severe	26	7.93%
Severe	5	1.52%

# Bar chart using relative frequency
df |> 
  count(PHQ) |>
  mutate(pct = n / sum(n)) |>
  ggplot(aes(x = PHQ, y = pct, fill = PHQ)) +
  geom_col() +
  scale_y_continuous(labels = scales::percent_format()) +
  labs(x = "PHQ Severity", y = "% of Students") +
  scale_fill_hue()

Activity Level

With the categories in increasing order of activity.

df |> 
  mutate(Activity = factor(Activity, levels = c("Low", "Moderate", "High"))) |>
  count(Activity) |> 
  kable()

Activity	n
Low	90
Moderate	131
High	107

df |> 
  mutate(Activity = factor(Activity, levels = c("Low", "Moderate", "High"))) |>
  ggplot(aes(x = Activity, fill = Activity)) + 
  geom_bar() +
  labs(x = "Activity Level", y = "Number of Students") +
  scale_fill_hue()

# Bar chart with relative frequency
df |> 
  count(Activity) |>
  mutate(pct = n / sum(n)) |>
  ggplot(aes(x = Activity, y = pct, fill = Activity)) +
  geom_col() +
  scale_y_continuous(labels = scales::percent_format()) +
  labs(x = "Activity Level", y = "% of Students") +
  scale_fill_hue() 

df |> 
  mutate(Activity = factor(Activity, levels = c("Low", "Moderate", "High"))) |>
  ggplot(aes(x = Activity, fill = Activity)) + 
  geom_bar() +
    facet_wrap(~Year_Study)

df |> 
  mutate(Activity = factor(Activity, levels = c("Low", "Moderate", "High"))) |>
  ggplot(aes(x = Activity, fill = Activity)) + 
  geom_bar() +
    facet_wrap(~Sex) +
  labs(x = "Activity Level", y = "Relative Frequency") +
  scale_fill_hue()

df |>
  count(Activity) |>
  mutate(pct = scales::percent(n / sum(n))) |>
  knitr::kable()

Activity	n	pct
Low	90	27.4%
Moderate	131	39.9%
High	107	32.6%

df |>
  mutate(Activity = factor(Activity, levels = c("Low", "Moderate", "High"))) |>
  count(Sex, Activity) |>
  group_by(Sex) |>
  mutate(pct = n / sum(n)) |>
  ggplot(aes(x = Activity, y = pct, fill = Activity)) +
  geom_col() +
  facet_wrap(~Sex) +
  scale_y_continuous(labels = scales::percent_format()) +
  labs(x = "Activity Level", y = "% of Students") +
  scale_fill_hue() 

Jenkins score

df  |> 
    summarise(
        min = min(JENKINS),
        max = max(JENKINS),
        mean = mean(JENKINS),
        sd = sd(JENKINS),
    ) |>
    kable()

min	max	mean	sd
0	20	5.079268	4.664899

There’s one outlier in the Jenkins boxplot.

df  |> 
    ggplot(aes(x = JENKINS)) +
    geom_histogram(binwidth = 1,
                     fill = myred, 
                   color = "white") +
    labs(x = "Severity of Sleep Disturbance", y = "Number of students")

# Histogram with relative frequency
df |> 
  ggplot(aes(x = JENKINS)) +
  geom_histogram(aes(y = after_stat(density)), 
                 binwidth = 1,
                 fill = myred, 
                 color = "white") +
  scale_y_continuous(labels = scales::percent_format()) +
  labs(x = "Severity of Sleep Disturbance", y = "% of Students") 

df  |> 
    ggplot(aes(x = JENKINS)) +
    geom_histogram(binwidth = 1,
                     fill = myred, 
                   color = "white") +
    facet_wrap(~Year_Study) 

df  |> 
    ggplot(aes(x = JENKINS)) +
    geom_histogram(binwidth = 1,
                     fill = myred, 
                   color = "white") +
    facet_wrap(~Sex) 

df  |> 
    ggplot(aes(x = JENKINS)) +
    geom_boxplot(fill = myred)

Pain in the last 12 months

# Frequency tables.
df |> 
    count(AnyPain12) |>
    kable()

AnyPain12	n
No	46
Yes	282

df |> 
    ggplot(aes(x = AnyPain12, fill = AnyPain12)) + 
    geom_bar()

Pain in the last 7 days

# Frequency tables.
df |> 
    count(AnyPain7) |>
    kable()

AnyPain7	n
No	138
Yes	190

df |> 
    ggplot(aes(x = AnyPain7, fill = AnyPain7)) + 
    geom_bar()

Work Affected by Pain

# Frequency tables.
df |> 
    count(WorkAffected) |>
    kable()

WorkAffected	n
No	197
Yes	131

df |> 
    count(WorkAffected) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

WorkAffected	n	Percentage
No	197	60.06098
Yes	131	39.93902

Estimation of pain in Neck12 Produces Confidence Interval, apply to all frequency tables

freq <- table(df$WorkAffected)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.3993902	12.8811	0.0003319	1	0.3463729	0.4548036	1-sample proportions test with continuity correction	two.sided

Pain Sites 12 months

df  |> 
    summarise(
        min = min(TotalSites12),
        max = max(TotalSites12),
        mean = mean(TotalSites12),
        sd = sd(TotalSites12),
    ) |>
    kable()

min	max	mean	sd
0	7	2.573171	1.769815

Pain Sites 7 days

df  |> 
    summarise(
        min = min(TotalSites7),
        max = max(TotalSites7),
        mean = mean(TotalSites7),
        sd = sd(TotalSites7),
    ) |>
    kable()

min	max	mean	sd
0	7	1.185976	1.409508

SNQ score

df  |> 
    summarise(
        min = min(NMQ),
        max = max(NMQ),
        mean = mean(NMQ),
        sd = sd(NMQ),
    ) |>
    kable()

min	max	mean	sd
0	4	2.237805	1.443589

df  |> 
    ggplot(aes(x = NMQ)) +
    geom_histogram(binwidth = 1,
                     fill = myred, 
                   color = "white")

df  |> 
    ggplot(aes(x = NMQ)) +
    geom_histogram(binwidth = 1,
                     fill = myred, 
                   color = "white") +
    labs(x = "SNQ score", y = "Number of students")

df |> 
  ggplot(aes(x = NMQ)) +
  geom_histogram(aes(y = after_stat(density)), 
                 binwidth = 1,
                 fill = myred, 
                 color = "white") +
  scale_y_continuous(labels = scales::percent_format()) +
  labs(x = "SNQ Score", y = "% of Students") 

df  |> 
    ggplot(aes(x = NMQ)) +
    geom_boxplot(fill = myred)

df  |> 
    ggplot(aes(x = NMQ, fill = Year_Study)) +
    geom_histogram(binwidth = 1, 
                   color = "white") +
    facet_wrap(~Year_Study) 

df  |> 
    ggplot(aes(x = NMQ, fill = Sex)) +
    geom_histogram(binwidth = 1, 
                   color = "white") +
    facet_wrap(~Sex) 

TUESDAY. APRIL 29.

Neck12 analysis

Frequency table

df |> 
    group_by(Year_Study) |>
    count(Neck12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Neck12	n	Percentage
1º	No	41	48.80952
1º	Yes	43	51.19048
2º	No	30	40.00000
2º	Yes	45	60.00000
3º	No	14	25.00000
3º	Yes	42	75.00000
4º	No	26	40.62500
4º	Yes	38	59.37500
5º	No	16	32.65306
5º	Yes	33	67.34694

Estimation of pain in Neck12 Produces Confidence Interval, apply to all frequency tables

freq <- table(df$Neck12)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.6128049	16.24695	5.56e-05	1	0.5575294	0.6653936	1-sample proportions test with continuity correction	two.sided

Shoulder12 analysis

Frequency table

df |> 
    count(Shoulder12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Shoulder12	n	Percentage
No	209	63.719512
Yes, in BOTH shoulders	78	23.780488
Yes, in the LEFT shoulder	15	4.573171
Yes, in the RIGHT shoulder	26	7.926829

Estimation of pain in Shoulder12 For body parts that have more than one Yes option Proportion of No is calculated than subtracted

freq <- table(df$Shoulder12)["No"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(n-freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.3628049	24.14939	9e-07	1	0.3111769	0.4177017	1-sample proportions test with continuity correction	two.sided

Check normality of JENKINS score

Shapiro Test of Normality Perform for all continuous variables

# Check normality of JENKINS score
shapiro.test(df$JENKINS) |> 
    tidy() |> 
    kable()

statistic	p.value	method
0.8900651	0	Shapiro-Wilk normality test

As the p-value is less than 0.05, we reject the null hypothesis of normality.

Check normality of NMQ score

# Check normality of NMQ score
shapiro.test(df$NMQ) |> 
    tidy() |> 
    kable()

statistic	p.value	method
0.8593169	0	Shapiro-Wilk normality test

As the p-value is less than 0.05, we reject the null hypothesis of normality.

Association between NMQ and JENKINS

As both NMQ and JENKINS scores are not normally distributed, we will use the Kendall correlation test.

# Association between NMQ and JENKINS
cor.test(df$NMQ, df$JENKINS, method = "kendall") |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	method	alternative
0.1751685	4.11401	3.89e-05	Kendall’s rank correlation tau	two.sided

Interpretation of Kendall correlation test: - No correlation: less than 0.1 - Weak correlation: 0.1 to 0.3 - Moderate correlation: 0.3 to 0.6 - Strong correlation: 0.6 to 0.8 - Very strong correlation: 0.8 to 1

Association between NMQ and Activity

# Association between NMQ and Activity
cor.test(df$NMQ, as.numeric(df$Activity), method = "kendall") |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	method	alternative
0.0580097	1.227131	0.2197733	Kendall’s rank correlation tau	two.sided

Association between NMQ and PHQ

# Association between NMQ and PHQ
cor.test(df$NMQ, as.numeric(df$PHQ), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.2090054	4.507812	6.5e-06	Kendall’s rank correlation tau	two.sided

FRIDAY MAY 2

Sex

df |> 
    count(Sex) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Sex	n	Percentage
Female	253	77.13415
Male	75	22.86585

freq <- table(df$Sex)["Male"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.2286585	95.51524	0	1	0.1851032	0.2787051	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Sex)["Female"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.7713415	95.51524	0	1	0.7212949	0.8148968	1-sample proportions test with continuity correction	two.sided

Year of study

df |> 
    count(Year_Study) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	n	Percentage
1º	84	25.60976
2º	75	22.86585
3º	56	17.07317
4º	64	19.51220
5º	49	14.93902

freq <- table(df$Year_Study)["1º"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.2560976	77.07622	0	1	0.2104586	0.3075643	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Year_Study)["2º"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.2286585	95.51524	0	1	0.1851032	0.2787051	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Year_Study)["3º"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1707317	140.9299	0	1	0.132515	0.2168542	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Year_Study)["4º"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.195122	120.7348	0	1	0.1544851	0.243066	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Year_Study)["5º"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1493902	159.8811	0	1	0.1135334	0.1936816	1-sample proportions test with continuity correction	two.sided

Dominant Hand

df |> 
    count(Dom_Hand) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Dom_Hand	n	Percentage
Left-handed	25	7.621951
Right-handed	303	92.378049

freq <- table(df$Dom_Hand)["Right-handed"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.9237805	233.9299	0	1	0.8881463	0.9490927	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Dom_Hand)["Left-handed"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0762195	233.9299	0	1	0.0509073	0.1118537	1-sample proportions test with continuity correction	two.sided

Activity Level

df |> 
    count(Activity) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Activity	n	Percentage
Low	90	27.43902
Moderate	131	39.93902
High	107	32.62195

freq <- table(df$Activity)["Low"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.2743902	65.8811	0	1	0.2274979	0.3266697	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Activity)["Moderate"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.3993902	12.8811	0.0003319	1	0.3463729	0.4548036	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Activity)["High"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.3262195	38.92988	0	1	0.2763022	0.3802804	1-sample proportions test with continuity correction	two.sided

Depression

df |> 
    count(PHQ) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

PHQ	n	Percentage
Minimal	117	35.670732
Mild	120	36.585366
Moderate	60	18.292683
Moderately Severe	26	7.926829
Severe	5	1.524390

freq <- table(df$PHQ)["Minimal"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.3567073	26.3689	3e-07	1	0.3053416	0.4114876	1-sample proportions test with continuity correction	two.sided

freq <- table(df$PHQ)["Mild"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.3658537	23.07622	1.6e-06	1	0.3140979	0.4208055	1-sample proportions test with continuity correction	two.sided

freq <- table(df$PHQ)["Moderate"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1829268	130.6372	0	1	0.1434659	0.2299937	1-sample proportions test with continuity correction	two.sided

freq <- table(df$PHQ)["Moderately Severe"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0792683	230.564	0	1	0.0534104	0.1153652	1-sample proportions test with continuity correction	two.sided

freq <- table(df$PHQ)["Severe"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0152439	306.3689	0	1	0.0056255	0.037267	1-sample proportions test with continuity correction	two.sided

Pain 12 months

df |> 
    count(AnyPain12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

AnyPain12	n	Percentage
No	46	14.02439
Yes	282	85.97561

freq <- table(df$AnyPain12)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.8597561	168.3689	0	1	0.8163267	0.8945225	1-sample proportions test with continuity correction	two.sided

Pain 7 days

df |> 
    count(AnyPain7) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

AnyPain7	n	Percentage
No	138	42.07317
Yes	190	57.92683

freq <- table(df$AnyPain7)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.5792683	7.929878	0.0048625	1	0.5236942	0.632955	1-sample proportions test with continuity correction	two.sided

Neck

df |> 
    count(Neck12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Neck12	n	Percentage
No	127	38.71951
Yes	201	61.28049

freq <- table(df$Neck12)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.6128049	16.24695	5.56e-05	1	0.5575294	0.6653936	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Neck_Work...24) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Neck_Work…24	n	Percentage
No	269	82.0122
Yes	59	17.9878

freq <- table(df$Neck_Work...24)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.179878	133.1738	0	1	0.1407216	0.2267153	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Neck7) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Neck7	n	Percentage
No	227	69.20732
Yes	101	30.79268

freq <- table(df$Neck7)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.3079268	47.6372	0	1	0.2589928	0.3614425	1-sample proportions test with continuity correction	two.sided

Shoulder

df |> 
    count(Shoulder_Work...28) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Shoulder_Work…28	n	Percentage
No	306	93.292683
Yes	22	6.707317

freq <- table(df$Shoulder_Work...28)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0670732	244.1738	0	1	0.043479	0.1012423	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Shoulder7) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Shoulder7	n	Percentage
No	272	82.92683
Yes	56	17.07317

freq <- table(df$Shoulder7)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1707317	140.9299	0	1	0.132515	0.2168542	1-sample proportions test with continuity correction	two.sided

Elbow

df |> 
    count(Elbow12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Elbow12	n	Percentage
No	310	94.5121951
Yes, in BOTH elbows	4	1.2195122
Yes, in the LEFT elbow	3	0.9146341
Yes, in the RIGHT elbow	11	3.3536585

freq <- table(df$Elbow12)["No"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(n-freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.054878	258.1738	0	1	0.0337974	0.0868846	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Elbow_Work) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Elbow_Work	n	Percentage
No	320	97.560976
Yes	8	2.439024

freq <- table(df$Elbow_Work)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0243902	294.8811	0	1	0.0113847	0.0493565	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Elbow7) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Elbow7	n	Percentage
No	320	97.560976
Yes	8	2.439024

freq <- table(df$Elbow7)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0243902	294.8811	0	1	0.0113847	0.0493565	1-sample proportions test with continuity correction	two.sided

Wrist

df |> 
    count(Wrist12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Wrist12	n	Percentage
No	251	76.524390
Yes, in both wrists/hands	21	6.402439
Yes, in the LEFT wrist/hand	10	3.048780
Yes, in the RIGHT wrist/hand	46	14.024390

freq <- table(df$Wrist12)["No"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(n-freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.2347561	91.24695	0	1	0.1907155	0.2851402	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Wrist_Work) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Wrist_Work	n	Percentage
No	306	93.292683
Yes	22	6.707317

freq <- table(df$Wrist_Work)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0670732	244.1738	0	1	0.043479	0.1012423	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Wrist7) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Wrist7	n	Percentage
No	303	92.378049
Yes	25	7.621951

freq <- table(df$Wrist7)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0762195	233.9299	0	1	0.0509073	0.1118537	1-sample proportions test with continuity correction	two.sided

Upper Back

df |> 
    count(UBack12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

UBack12	n	Percentage
No	194	59.14634
Yes	134	40.85366

freq <- table(df$UBack12)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.4085366	10.6128	0.0011231	1	0.3552198	0.4640312	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Uback_Work) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Uback_Work	n	Percentage
No	292	89.02439
Yes	36	10.97561

freq <- table(df$Uback_Work)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1097561	198.247	0	1	0.0790345	0.149914	1-sample proportions test with continuity correction	two.sided

df |> 
    count(UBack7) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

UBack7	n	Percentage
No	270	82.31707
Yes	58	17.68293

freq <- table(df$UBack7)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1768293	135.7348	0	1	0.1379816	0.2234326	1-sample proportions test with continuity correction	two.sided

Lower Back

df |> 
    count(LBack12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

LBack12	n	Percentage
No	174	53.04878
Yes	154	46.95122

freq <- table(df$LBack12)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.4695122	1.10061	0.2941323	1	0.4146711	0.5250789	1-sample proportions test with continuity correction	two.sided

df |> 
    count(LBack_Work) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

LBack_Work	n	Percentage
No	287	87.5
Yes	41	12.5

freq <- table(df$LBack_Work)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.125	183.003	0	1	0.0921709	0.1668762	1-sample proportions test with continuity correction	two.sided

df |> 
    count(LBack7) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

LBack7	n	Percentage
No	254	77.43902
Yes	74	22.56098

freq <- table(df$LBack7)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.2256098	97.68598	0	1	0.182302	0.2754826	1-sample proportions test with continuity correction	two.sided

Hip

df |> 
    count(Hip12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Hip12	n	Percentage
No	298	90.853658
Yes	30	9.146342

freq <- table(df$Hip12)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0914634	217.3445	0	1	0.0635404	0.1292993	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Hip_Work) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Hip_Work	n	Percentage
No	318	96.95122
Yes	10	3.04878

freq <- table(df$Hip_Work)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0304878	287.3445	0	1	0.0155666	0.0571291	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Hip7) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Hip7	n	Percentage
No	319	97.256098
Yes	9	2.743902

freq <- table(df$Hip7)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.027439	291.1006	0	1	0.013449	0.0532658	1-sample proportions test with continuity correction	two.sided

Knees

df |> 
    count(Knee12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Knee12	n	Percentage
No	259	78.96341
Yes	69	21.03659

freq <- table(df$Knee12)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.2103659	108.9055	0	1	0.1683479	0.2593193	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Knee_Work) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Knee_Work	n	Percentage
No	294	89.63415
Yes	34	10.36585

freq <- table(df$Knee_Work)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1036585	204.5152	0	1	0.0738343	0.1430766	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Knee7) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Knee7	n	Percentage
No	293	89.32927
Yes	35	10.67073

freq <- table(df$Knee7)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1067073	201.3689	0	1	0.0764302	0.1464993	1-sample proportions test with continuity correction	two.sided

Feet

df |> 
    count(Feet12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Feet12	n	Percentage
No	286	87.19512
Yes	42	12.80488

freq <- table(df$Feet12)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1280488	180.0274	0	1	0.0948194	0.1702481	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Feet_Work) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Feet_Work	n	Percentage
No	307	93.597561
Yes	21	6.402439

freq <- table(df$Feet_Work)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0640244	247.6372	0	1	0.0410327	0.0976771	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Feet7) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Feet7	n	Percentage
No	305	92.987805
Yes	23	7.012195

freq <- table(df$Feet7)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.070122	240.7348	0	1	0.0459408	0.1047929	1-sample proportions test with continuity correction	two.sided

Specific Nordic Questionnaire

Low Back

df |> 
    count(`Low Back`) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Low Back	n	Percentage
No	121	36.89024
Yes	207	63.10976

freq <- table(df$`Low Back`)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.6310976	22.02744	2.7e-06	1	0.576093	0.6829788	1-sample proportions test with continuity correction	two.sided

df |> 
    count(LB_Hosp) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

LB_Hosp	n	Percentage
No	322	98.170732
Yes	6	1.829268

freq <- table(df$LB_Hosp)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0182927	302.5152	0	1	0.0074576	0.0413682	1-sample proportions test with continuity correction	two.sided

df |> 
    count(LB_Job) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

LB_Job	n	Percentage
No	309	94.207317
Yes	19	5.792683

freq <- table(df$LB_Job)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0579268	254.6372	0	1	0.0361906	0.0904993	1-sample proportions test with continuity correction	two.sided

df |> 
    count(LB_Work) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

LB_Work	n	Percentage
No	307	93.597561
Yes	21	6.402439

freq <- table(df$LB_Work)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0640244	247.6372	0	1	0.0410327	0.0976771	1-sample proportions test with continuity correction	two.sided

df |> 
    count(LB_Leisure) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

LB_Leisure	n	Percentage
No	290	88.41463
Yes	38	11.58537

freq <- table(df$LB_Leisure)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1158537	192.0762	0	1	0.0842668	0.1567203	1-sample proportions test with continuity correction	two.sided

df |> 
    count(LB_Tx) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

LB_Tx	n	Percentage
No	283	86.28049
Yes	45	13.71951

freq <- table(df$LB_Tx)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1371951	171.247	0	1	0.1028038	0.180326	1-sample proportions test with continuity correction	two.sided

Neck

df |> 
    count(Neck) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Neck	n	Percentage
No	119	36.28049
Yes	209	63.71951

freq <- table(df$Neck)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.6371951	24.14939	9e-07	1	0.5822983	0.6888231	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Neck_Acc) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Neck_Acc	n	Percentage
No	301	91.768293
Yes	27	8.231707

freq <- table(df$Neck_Acc)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0823171	227.2226	0	1	0.0559258	0.1188649	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Neck_Job) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Neck_Job	n	Percentage
No	299	91.158537
Yes	29	8.841463

freq <- table(df$Neck_Job)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0884146	220.6128	0	1	0.0609914	0.1258315	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Neck_Work...24) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Neck_Work…24	n	Percentage
No	269	82.0122
Yes	59	17.9878

freq <- table(df$Neck_Work...66)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1128049	195.1494	0	1	0.0816468	0.1533209	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Neck_Leisure) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Neck_Leisure	n	Percentage
No	281	85.67073
Yes	47	14.32927

freq <- table(df$Neck_Leisure)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1432927	165.5152	0	1	0.1081572	0.187015	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Neck_Tx) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Neck_Tx	n	Percentage
No	277	84.45122
Yes	51	15.54878

freq <- table(df$Neck_Tx)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1554878	154.3445	0	1	0.1189315	0.2003271	1-sample proportions test with continuity correction	two.sided

Shoulder

df |> 
    count(Shoulder) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Shoulder	n	Percentage
No	237	72.2561
Yes	91	27.7439

freq <- table(df$Shoulder)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.277439	64.10061	0	1	0.2303476	0.3298442	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Shoulder_Job) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Shoulder_Job	n	Percentage
No	321	97.865854
Yes	7	2.134146

freq <- table(df$Shoulder_Job)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0213415	298.686	0	1	0.0093833	0.0453938	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Shoulder_Work...76) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Shoulder_Work…76	n	Percentage
No	320	97.560976
Yes	8	2.439024

freq <- table(df$Shoulder_Work...76)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0243902	294.8811	0	1	0.0113847	0.0493565	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Shoulder_Leisure) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Shoulder_Leisure	n	Percentage
No	317	96.646342
Yes	11	3.353658

freq <- table(df$Shoulder_Leisure)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0335366	283.6128	0	1	0.0177303	0.060952	1-sample proportions test with continuity correction	two.sided

df |> 
    count(Shoulder_Tx) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Shoulder_Tx	n	Percentage
No	309	94.207317
Yes	19	5.792683

freq <- table(df$Shoulder_Tx)["Yes"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0579268	254.6372	0	1	0.0361906	0.0904993	1-sample proportions test with continuity correction	two.sided

TEST OF NORMALITY

Shapiro Test of Normality Perform for ALL variables

Jenkins

shapiro.test(df$JENKINS) |> 
    tidy() |> 
    kable()

statistic	p.value	method
0.8900651	0	Shapiro-Wilk normality test

As the p-value is less than 0.05, we reject the null hypothesis of normality.

PHQ

shapiro.test(df$`PHQ-9`) |> 
    tidy() |> 
    kable()

statistic	p.value	method
0.9430184	0	Shapiro-Wilk normality test

Activity

shapiro.test(df$MET_SUM) |> 
    tidy() |> 
    kable()

statistic	p.value	method
0.8476052	0	Shapiro-Wilk normality test

Age

shapiro.test(df$Age) |> 
    tidy() |> 
    kable()

statistic	p.value	method
0.8080176	0	Shapiro-Wilk normality test

Work Hour per Week

shapiro.test(df$`Work_Hr/Wk`) |> 
    tidy() |> 
    kable()

statistic	p.value	method
0.9150633	0	Shapiro-Wilk normality test

BMI

shapiro.test(df$BMI) |> 
    tidy() |> 
    kable()

statistic	p.value	method
0.920777	0	Shapiro-Wilk normality test

NMQ

shapiro.test(df$NMQ) |> 
    tidy() |> 
    kable()

statistic	p.value	method
0.8593169	0	Shapiro-Wilk normality test

Total Sites 12

shapiro.test(df$TotalSites12) |> 
    tidy() |> 
    kable()

statistic	p.value	method
0.9432966	0	Shapiro-Wilk normality test

Total Sites 7

shapiro.test(df$TotalSites7) |> 
    tidy() |> 
    kable()

statistic	p.value	method
0.7997039	0	Shapiro-Wilk normality test

Association

Association between NMQ and JENKINS

As both NMQ and JENKINS scores are not normally distributed, we will use the Kendall correlation test.

# Association between NMQ and JENKINS
cor.test(df$NMQ, df$JENKINS, method = "kendall") |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	method	alternative
0.1751685	4.11401	3.89e-05	Kendall’s rank correlation tau	two.sided

Interpretation of Kendall correlation test: - No correlation: less than 0.1 - Weak correlation: 0.1 to 0.3 - Moderate correlation: 0.3 to 0.6 - Strong correlation: 0.6 to 0.8 - Very strong correlation: 0.8 to 1

Association between NMQ and Activity

# Association between NMQ and Activity
cor.test(df$NMQ, as.numeric(df$Activity), method = "kendall") |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	method	alternative
0.0580097	1.227131	0.2197733	Kendall’s rank correlation tau	two.sided

Association between NMQ and Met_Sum

# Association between NMQ and MET_SUM
cor.test(df$NMQ, df$MET_SUM, method = "kendall") |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	method	alternative
0.0490709	1.179627	0.2381485	Kendall’s rank correlation tau	two.sided

Association between NMQ and PHQ

# Association between NMQ and PHQ
cor.test(df$NMQ, as.numeric(df$PHQ), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.2090054	4.507812	6.5e-06	Kendall’s rank correlation tau	two.sided

Association between NMQ and Sex

df <- df %>%
  mutate(Sex = factor(Sex, levels = c("Male", "Female"), ordered = TRUE))

# Association between NMQ and Sex
cor.test(df$NMQ, as.numeric(df$Sex), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.0152315	0.3042428	0.7609429	Kendall’s rank correlation tau	two.sided

Association between NMQ and Age

cor.test(df$NMQ, df$Age, method = "kendall") |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	method	alternative
0.0686514	1.57406	0.1154737	Kendall’s rank correlation tau	two.sided

Association between NMQ and Year of Study

df <- df %>%
  mutate(Year_Study = factor(Year_Study, levels = c("1º", "2º", "3º", "4º", "5º"), ordered = TRUE))

cor.test(df$NMQ, as.numeric(df$Year_Study), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.0833706	1.857258	0.0632745	Kendall’s rank correlation tau	two.sided

NMQ x Work Hours per week

cor.test(df$NMQ, df$`Work_Hr/Wk`, method = "kendall") |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	method	alternative
0.1254633	3.004789	0.0026576	Kendall’s rank correlation tau	two.sided

NMQ X Weight

cor.test(df$NMQ, df$`Weight (Kg)`, method = "kendall") |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	method	alternative
0.020397	0.4893496	0.6245942	Kendall’s rank correlation tau	two.sided

NMQ x Height

cor.test(df$NMQ, df$`Height (cm)`, method = "kendall") |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	method	alternative
-0.0093777	-0.2246466	0.8222542	Kendall’s rank correlation tau	two.sided

NMQ x BMI

df <- df %>%
  mutate(BMI_Category = factor(BMI_Category, levels = c("Underweight", "Normal weight","Overweight", "Obesity Class I", "Obesity Class II", "Obesity Class III"), ordered = TRUE))

cor.test(df$NMQ, as.numeric(df$BMI_Category), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.0380265	0.7857168	0.4320334	Kendall’s rank correlation tau	two.sided

cor.test(df$NMQ, df$BMI, method = "kendall") |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	method	alternative
0.0413366	1.007822	0.3135401	Kendall’s rank correlation tau	two.sided

NMQ x Dominant Hand

df <- df %>%
  mutate(Dom_Hand = factor(Dom_Hand, levels = c("Left-handed", "Right-handed"), ordered = TRUE))

cor.test(df$NMQ, as.numeric(df$Dom_Hand), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.0031197	0.0623152	0.9503119	Kendall’s rank correlation tau	two.sided

NMQ X AnyPain12

df <- df %>%
  mutate(AnyPain12 = factor(AnyPain12, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$NMQ, as.numeric(df$AnyPain12), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.5622732	11.23119	0	Kendall’s rank correlation tau	two.sided

NMQ X AnyPain 7

df <- df %>%
  mutate(AnyPain7 = factor(AnyPain7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$NMQ, as.numeric(df$AnyPain7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.6713437	13.40983	0	Kendall’s rank correlation tau	two.sided

NMQ X Work Affected

df <- df %>%
  mutate(WorkAffected = factor(WorkAffected, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$NMQ, as.numeric(df$WorkAffected), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.7930722	15.84131	0	Kendall’s rank correlation tau	two.sided

NMQ x Total Sites 12

cor.test(df$NMQ, df$TotalSites12, method = "kendall") |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	method	alternative
0.6109764	13.90236	0	Kendall’s rank correlation tau	two.sided

NMQ x Total Sites 7

cor.test(df$NMQ, df$TotalSites7, method = "kendall") |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	method	alternative
0.5947138	12.98996	0	Kendall’s rank correlation tau	two.sided

Activity x AnyPain12

df <- df %>%
  mutate(AnyPain12 = factor(AnyPain12, levels = c("No", "Yes"), ordered = TRUE))

library(dplyr)
library(broom)
library(knitr)

# Step 1: Recode Activity
df <- df %>%
  mutate(Activity = factor(Activity, levels = c("Low", "Moderate", "High"), ordered = TRUE))

# Step 2: Correlation test
cor.test(as.numeric(df$Activity), as.numeric(df$AnyPain12), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.0333017	0.6378048	0.5236007	Kendall’s rank correlation tau	two.sided

Activity x AnyPain7

df <- df %>%
  mutate(AnyPain7 = factor(AnyPain7, levels = c("No", "Yes"), ordered = TRUE))

library(dplyr)
library(broom)
library(knitr)

# Step 1: Recode Activity
df <- df %>%
  mutate(Activity = factor(Activity, levels = c("Low", "Moderate", "High"), ordered = TRUE))

# Step 2: Correlation test
cor.test(as.numeric(df$Activity), as.numeric(df$AnyPain7), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0	0	1	Kendall’s rank correlation tau	two.sided

Activity x Total Sites 12

library(dplyr)
library(broom)
library(knitr)

# Step 1: Recode Activity
df <- df %>%
  mutate(Activity = factor(Activity, levels = c("Low", "Moderate", "High"), ordered = TRUE))

# Step 2: Correlation test
cor.test(as.numeric(df$Activity), df$TotalSites12, method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
-0.009409	-0.2053009	0.837337	Kendall’s rank correlation tau	two.sided

Activity x Total Sites 7

library(dplyr)
library(broom)
library(knitr)

# Step 1: Recode Activity
df <- df %>%
  mutate(Activity = factor(Activity, levels = c("Low", "Moderate", "High"), ordered = TRUE))

# Step 2: Correlation test
cor.test(as.numeric(df$Activity), as.numeric(df$TotalSites7), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
-0.020623	-0.4319377	0.6657867	Kendall’s rank correlation tau	two.sided

Activity x Neck12

library(dplyr)
library(broom)
library(knitr)

# Recode Neck12 and Activity
df <- df %>%
  mutate(
    Neck12 = factor(Neck12, levels = c("No", "Yes"), ordered = TRUE),
    Activity = factor(Activity, levels = c("Low", "Moderate", "High"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$Activity), as.numeric(df$Neck12), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
-0.0209133	-0.4005381	0.6887602	Kendall’s rank correlation tau	two.sided

Activity x Neck7

library(dplyr)
library(broom)
library(knitr)

# Recode Neck7 and Activity
df <- df %>%
  mutate(
    Neck7 = factor(Neck7, levels = c("No", "Yes"), ordered = TRUE),
    Activity = factor(Activity, levels = c("Low", "Moderate", "High"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$Activity), as.numeric(df$Neck7), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
-0.0113318	-0.2170311	0.8281841	Kendall’s rank correlation tau	two.sided

Activity x Year Study

df <- df %>%
  mutate(Year_Study = factor(Year_Study, levels = c("1º", "2º", "3º", "4º", "5º"), ordered = TRUE),
         Activity = factor(Activity, levels = c("Low", "Moderate", "High"), ordered = TRUE))

cor.test(as.numeric(df$Activity), as.numeric(df$Year_Study), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.0767692	1.639916	0.1010225	Kendall’s rank correlation tau	two.sided

Activity x Sex

df <- df %>%
  mutate(Sex = factor(Sex, levels = c("Female", "Male"), ordered = TRUE),
         Activity = factor(Activity, levels = c("Low", "Moderate", "High"), ordered = TRUE))

cor.test(as.numeric(df$Activity), as.numeric(df$Sex), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.1094828	2.096851	0.0360068	Kendall’s rank correlation tau	two.sided

Mann-Whitney U test

wilcox.test(as.numeric(Activity) ~ Sex, data = df) 

    Wilcoxon rank sum test with continuity correction

data:  as.numeric(Activity) by Sex
W = 8068, p-value = 0.03607
alternative hypothesis: true location shift is not equal to 0

PHQ x BMI

library(dplyr)
library(broom)
library(knitr)

# Recode PHQ and BMI
df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    BMI_Category = factor(BMI_Category, levels = c("Underweight", "Normal weight", "Overweight", "Obesity Class I", "Obesity Class II", "Obesity Class III"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$BMI_Category), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.0728875	1.47235	0.1409264	Kendall’s rank correlation tau	two.sided

PHQ x Neck 12

library(dplyr)
library(broom)
library(knitr)

# Recode PHQ and BMI
df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Neck12 = factor(Neck12, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$Neck12), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.0948042	1.851313	0.0641245	Kendall’s rank correlation tau	two.sided

PHQ x Neck 7

library(dplyr)
library(broom)
library(knitr)

# Recode PHQ and BMI
df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Neck7 = factor(Neck7, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$Neck7), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.1346408	2.629233	0.0085578	Kendall’s rank correlation tau	two.sided

PHQ x Neck Work Affected

library(dplyr)
library(broom)
library(knitr)

# Recode PHQ and BMI
df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Neck_Work...24 = factor(Neck_Work...24, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$Neck_Work...24), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.0829803	1.620419	0.1051422	Kendall’s rank correlation tau	two.sided

PHQ x Shoulder 12

Need to fix the levels of Shoulder12, cannot have unordered levels of left shoulder / right shoulder / both shoulders

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Shoulder12YN = factor(Shoulder12YN, levels = c("No", "Yes"), ordered = TRUE)
  )

cor.test(as.numeric(df$PHQ), as.numeric(df$Shoulder12YN), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.1514111	2.95672	0.0031093	Kendall’s rank correlation tau	two.sided

PHQ x Shoulder Work Affected

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Shoulder_Work...28 = factor(Shoulder_Work...28, levels = c("No", "Yes"), ordered = TRUE)
  )

cor.test(as.numeric(df$PHQ), as.numeric(df$Shoulder_Work...28), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.097805	1.909912	0.0561445	Kendall’s rank correlation tau	two.sided

PHQ x Shoulder 7

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Shoulder7 = factor(Shoulder7, levels = c("No", "Yes"), ordered = TRUE)
  )

cor.test(as.numeric(df$PHQ), as.numeric(df$Shoulder7), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.1424113	2.780974	0.0054196	Kendall’s rank correlation tau	two.sided

PHQ x Elbow Work Affected

library(dplyr)
library(broom)
library(knitr)

# Recode PHQ and BMI
df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Elbow_Work = factor(Elbow_Work, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$Elbow_Work), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
-0.0329235	-0.6429221	0.5202746	Kendall’s rank correlation tau	two.sided

PHQ x Elbow 12

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Elbow12YN = factor(Elbow12YN, levels = c("No", "Yes"), ordered = TRUE)
  )

cor.test(as.numeric(df$PHQ), as.numeric(df$Elbow12YN), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
-0.0370059	-0.7226425	0.4698995	Kendall’s rank correlation tau	two.sided

PHQ x Elbow 7

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Elbow7 = factor(Elbow7, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$Elbow7), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.0095815	0.1871043	0.8515789	Kendall’s rank correlation tau	two.sided

PHQ x Wrist Work

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Wrist_Work = factor(Wrist_Work, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$Wrist_Work), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.129799	2.534684	0.0112549	Kendall’s rank correlation tau	two.sided

PHQ x Wrist 12

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Wrist12YN = factor(Wrist12YN, levels = c("No", "Yes"), ordered = TRUE)
  )

cor.test(as.numeric(df$PHQ), as.numeric(df$Wrist12YN), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.1480178	2.890457	0.0038468	Kendall’s rank correlation tau	two.sided

PHQ x Wrist 7

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Wrist7 = factor(Wrist7, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$Wrist7), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.0706331	1.379306	0.1678003	Kendall’s rank correlation tau	two.sided

PHQ x Upper back 12

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    UBack12 = factor(UBack12, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$UBack12), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.1603816	3.131893	0.0017368	Kendall’s rank correlation tau	two.sided

PHQ x Upper Back work affected

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Uback_Work = factor(Uback_Work, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$Uback_Work), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.1594557	3.113812	0.0018469	Kendall’s rank correlation tau	two.sided

PHQ x Upper Back 7

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    UBack7 = factor(UBack7, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$UBack7), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.1984787	3.875845	0.0001063	Kendall’s rank correlation tau	two.sided

PHQ x Low Back 12

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    LBack12 = factor(LBack12, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$LBack12), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.0720219	1.406427	0.1595975	Kendall’s rank correlation tau	two.sided

PHQ x LBack 7

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    LBack7 = factor(LBack7, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$LBack7), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.174696	3.411421	0.0006463	Kendall’s rank correlation tau	two.sided

PHQ x Low Back Work

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    LBack_Work = factor(LBack_Work, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$LBack_Work), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.1248491	2.438022	0.0147679	Kendall’s rank correlation tau	two.sided

PHQ x Hip 12

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Hip12 = factor(Hip12, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$Hip12), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.0881992	1.722333	0.0850092	Kendall’s rank correlation tau	two.sided

PHQ x Hip_Work

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Hip_Work = factor(Hip_Work, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$Hip_Work), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.0376797	0.7357995	0.4618527	Kendall’s rank correlation tau	two.sided

PHQ x Hip 7

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Hip7 = factor(Hip7, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$Hip7), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.050917	0.9942942	0.3200797	Kendall’s rank correlation tau	two.sided

PHQ x Knee 12

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Knee12 = factor(Knee12, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$Knee12), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.1023496	1.998659	0.0456453	Kendall’s rank correlation tau	two.sided

PHQ x Knee_Work

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Knee_Work = factor(Knee_Work, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$Knee_Work), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.0874339	1.707389	0.0877498	Kendall’s rank correlation tau	two.sided

PHQ x Knee 7

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Knee7 = factor(Knee7, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$Knee7), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.0891238	1.740388	0.0817909	Kendall’s rank correlation tau	two.sided

PHQ x Feet 12

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Feet12 = factor(Feet12, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$Feet12), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.1247927	2.436922	0.0148129	Kendall’s rank correlation tau	two.sided

PHQ x Feet Work Affected

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Feet_Work = factor(Feet_Work, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$Feet_Work), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.1478597	2.887369	0.0038848	Kendall’s rank correlation tau	two.sided

PHQ x Feet 7

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Feet7 = factor(Feet7, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$Feet7), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.0774007	1.511462	0.1306708	Kendall’s rank correlation tau	two.sided

PHQ x Hip_Work

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    Hip_Work = factor(Hip_Work, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$Hip_Work), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.0376797	0.7357995	0.4618527	Kendall’s rank correlation tau	two.sided

PHQ x Any Pain 12

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    AnyPain12 = factor(AnyPain12, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$AnyPain12), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.1007059	1.96656	0.049234	Kendall’s rank correlation tau	two.sided

PHQ x Any Pain 7

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
    AnyPain7 = factor(AnyPain7, levels = c("No", "Yes"), ordered = TRUE)
  )

# Correlation test
cor.test(as.numeric(df$PHQ), as.numeric(df$AnyPain7), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.1993799	3.893443	9.88e-05	Kendall’s rank correlation tau	two.sided

PHQ x Total Sites 12

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
  )

# Correlation test
cor.test(as.numeric(df$PHQ), df$TotalSites12, method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.1935985	4.306867	1.66e-05	Kendall’s rank correlation tau	two.sided

PHQ x Total Sites 7

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(
    PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
  )

# Correlation test
cor.test(as.numeric(df$PHQ), df$TotalSites7, method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.2245382	4.794886	1.6e-06	Kendall’s rank correlation tau	two.sided

PHQ x Year Study

df <- df %>%
  mutate(Year_Study = factor(Year_Study, levels = c("1º", "2º", "3º", "4º", "5º"), ordered = TRUE),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE))

cor.test(as.numeric(df$PHQ), as.numeric(df$Year_Study), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
-0.0040213	-0.0875825	0.9302086	Kendall’s rank correlation tau	two.sided

PHQ x Sex

df <- df %>%
  mutate(Sex = factor(Sex, levels = c("Female", "Male"), ordered = TRUE),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE))

cor.test(as.numeric(df$PHQ), as.numeric(df$Sex), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
-0.1001512	-1.955729	0.0504971	Kendall’s rank correlation tau	two.sided

Jenkins x Any Pain 12

df <- df %>%
  mutate(AnyPain12 = factor(AnyPain12, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$JENKINS, as.numeric(df$AnyPain12), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.1053638	2.240829	0.0250372	Kendall’s rank correlation tau	two.sided

Jenkins x Any Pain 7

df <- df %>%
  mutate(AnyPain7 = factor(AnyPain7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$JENKINS, as.numeric(df$AnyPain7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.215505	4.583259	4.6e-06	Kendall’s rank correlation tau	two.sided

Jenkins x Total Sites 12

cor.test(df$JENKINS, df$TotalSites12, method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.1910357	4.627565	3.7e-06	Kendall’s rank correlation tau	two.sided

Jenkins x Total Sites 7

cor.test(df$JENKINS, df$TotalSites7, method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.2371315	5.514236	0	Kendall’s rank correlation tau	two.sided

Jenkins x Neck 12

df <- df %>%
  mutate(Neck12 = factor(Neck12, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$JENKINS, as.numeric(df$Neck12), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.143414	3.050063	0.0022879	Kendall’s rank correlation tau	two.sided

Jenkins x Neck 7

df <- df %>%
  mutate(Neck7 = factor(Neck7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$JENKINS, as.numeric(df$Neck7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.1182256	2.514366	0.0119247	Kendall’s rank correlation tau	two.sided

Jenkins x Shoulder 12

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(Shoulder12YN = factor(Shoulder12YN, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$JENKINS, as.numeric(df$Shoulder12YN), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.0882801	1.877499	0.0604497	Kendall’s rank correlation tau	two.sided

Jenkins x Shoulder 7

df <- df %>%
  mutate(Shoulder7 = factor(Shoulder7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$JENKINS, as.numeric(df$Shoulder7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.1330566	2.829785	0.0046579	Kendall’s rank correlation tau	two.sided

Jenkins x Elbow 12

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(Elbow12YN = factor(Elbow12YN, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$JENKINS, as.numeric(df$Elbow12YN), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
-0.0208964	-0.4444158	0.656742	Kendall’s rank correlation tau	two.sided

Jenkins x Elbow 7

df <- df %>%
  mutate(Elbow7 = factor(Elbow7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$JENKINS, as.numeric(df$Elbow7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.0100756	0.2142836	0.8303259	Kendall’s rank correlation tau	two.sided

Jenkins x Wrist 12

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(Wrist12YN = factor(Wrist12YN, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$JENKINS, as.numeric(df$Wrist12YN), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.1840309	3.913883	9.08e-05	Kendall’s rank correlation tau	two.sided

Jenkins x Wrist 7

df <- df %>%
  mutate(Wrist7 = factor(Wrist7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$JENKINS, as.numeric(df$Wrist7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.1416647	3.012859	0.002588	Kendall’s rank correlation tau	two.sided

Jenkins x Upper Back 12

df <- df %>%
  mutate(UBack12 = factor(UBack12, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$JENKINS, as.numeric(df$UBack12), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.1843375	3.920404	8.84e-05	Kendall’s rank correlation tau	two.sided

Jenkins x Upper Back 7

df <- df %>%
  mutate(UBack7 = factor(UBack7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$JENKINS, as.numeric(df$UBack7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.1570743	3.340582	0.000836	Kendall’s rank correlation tau	two.sided

Jenkins x Low back 12

df <- df %>%
  mutate(LBack12 = factor(LBack12, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$JENKINS, as.numeric(df$LBack12), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.107787	2.292362	0.0218847	Kendall’s rank correlation tau	two.sided

Jenkins x Low back 7

df <- df %>%
  mutate(LBack7 = factor(LBack7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$JENKINS, as.numeric(df$LBack7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.1836836	3.906498	9.36e-05	Kendall’s rank correlation tau	two.sided

Jenkins x Hip 12

df <- df %>%
  mutate(Hip12 = factor(Hip12, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$JENKINS, as.numeric(df$Hip12), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
-0.0009543	-0.0202951	0.9838079	Kendall’s rank correlation tau	two.sided

Jenkins x Hip 7

df <- df %>%
  mutate(Hip7 = factor(Hip7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$JENKINS, as.numeric(df$Hip7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.0390674	0.8308676	0.4060484	Kendall’s rank correlation tau	two.sided

Jenkins x Knee 12

df <- df %>%
  mutate(Knee12 = factor(Knee12, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$JENKINS, as.numeric(df$Knee12), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.059159	1.258166	0.2083317	Kendall’s rank correlation tau	two.sided

Jenkins x Knee 7

df <- df %>%
  mutate(Knee7 = factor(Knee7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$JENKINS, as.numeric(df$Knee7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.0874958	1.860819	0.0627698	Kendall’s rank correlation tau	two.sided

Jenkins x Feet 12

df <- df %>%
  mutate(Feet12 = factor(Feet12, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$JENKINS, as.numeric(df$Feet12), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.0543762	1.156447	0.2474985	Kendall’s rank correlation tau	two.sided

Jenkins x Feet 7

df <- df %>%
  mutate(Feet7 = factor(Feet7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$JENKINS, as.numeric(df$Feet7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.0496088	1.055058	0.2913989	Kendall’s rank correlation tau	two.sided

Jenkins x Year Study

df <- df %>%
  mutate(Year_Study = factor(Year_Study, levels = c("1º", "2º", "3º", "4º", "5º"), ordered = TRUE),
         )

cor.test((df$JENKINS), as.numeric(df$Year_Study), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
-0.0323364	-0.7668969	0.4431428	Kendall’s rank correlation tau	two.sided

Jenkins x Sex

df <- df %>%
  mutate(Sex = factor(Sex, levels = c("Female", "Male"), ordered = TRUE),
         )

cor.test((df$JENKINS), as.numeric(df$Sex), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
-0.0582637	-1.239126	0.2152988	Kendall’s rank correlation tau	two.sided

WorkHrs x Any Pain 12

df <- df %>%
  mutate(AnyPain12 = factor(AnyPain12, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$`Work_Hr/Wk`, as.numeric(df$AnyPain12), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.0907639	1.968494	0.0490112	Kendall’s rank correlation tau	two.sided

WorkHrs x Any Pain 7

df <- df %>%
  mutate(AnyPain7 = factor(AnyPain7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$`Work_Hr/Wk`, as.numeric(df$AnyPain7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.160678	3.484798	0.0004925	Kendall’s rank correlation tau	two.sided

WorkHrs x Total Sites 12

cor.test(df$`Work_Hr/Wk`, df$TotalSites12, method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.1454548	3.592926	0.000327	Kendall’s rank correlation tau	two.sided

WorkHrs x Total Sites 7

cor.test(df$`Work_Hr/Wk`, df$TotalSites7, method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.1511149	3.583382	0.0003392	Kendall’s rank correlation tau	two.sided

WorkHrs x Neck 12

df <- df %>%
  mutate(Neck12 = factor(Neck12, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$`Work_Hr/Wk`, as.numeric(df$Neck12), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.0389866	0.8455457	0.3978063	Kendall’s rank correlation tau	two.sided

WorkHrs x Neck 7

df <- df %>%
  mutate(Neck7 = factor(Neck7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$`Work_Hr/Wk`, as.numeric(df$Neck7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.0710087	1.540043	0.1235498	Kendall’s rank correlation tau	two.sided

WorkHrs x Shoulder 12

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(Shoulder12YN = factor(Shoulder12YN, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$`Work_Hr/Wk`, as.numeric(df$Shoulder12YN), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.067087	1.454988	0.1456726	Kendall’s rank correlation tau	two.sided

WorkHrs x Shoulder 7

df <- df %>%
  mutate(Shoulder7 = factor(Shoulder7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$`Work_Hr/Wk`, as.numeric(df$Shoulder7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.1071202	2.323233	0.0201666	Kendall’s rank correlation tau	two.sided

WorkHrs x Elbow 12

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(Elbow12YN = factor(Elbow12YN, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$`Work_Hr/Wk`, as.numeric(df$Elbow12YN), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
-0.033166	-0.7193065	0.4719521	Kendall’s rank correlation tau	two.sided

WorkHrs x Elbow 7

df <- df %>%
  mutate(Elbow7 = factor(Elbow7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$`Work_Hr/Wk`, as.numeric(df$Elbow7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
-0.0637771	-1.383203	0.1666027	Kendall’s rank correlation tau	two.sided

WorkHrs x Wrist 12

library(dplyr)
library(broom)
library(knitr)

df <- df %>%
  mutate(Wrist12YN = factor(Wrist12YN, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$`Work_Hr/Wk`, as.numeric(df$Wrist12YN), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.1116179	2.42078	0.0154873	Kendall’s rank correlation tau	two.sided

WorkHrs x Wrist 7

df <- df %>%
  mutate(Wrist7 = factor(Wrist7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$`Work_Hr/Wk`, as.numeric(df$Wrist7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.0844847	1.832312	0.0669049	Kendall’s rank correlation tau	two.sided

WorkHrs x Upper Back 12

df <- df %>%
  mutate(UBack12 = factor(UBack12, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$`Work_Hr/Wk`, as.numeric(df$UBack12), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.1602751	3.476061	0.0005088	Kendall’s rank correlation tau	two.sided

WorkHrs x Upper Back 7

df <- df %>%
  mutate(UBack7 = factor(UBack7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$`Work_Hr/Wk`, as.numeric(df$UBack7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.0931758	2.020806	0.0432999	Kendall’s rank correlation tau	two.sided

WorkHrs x Low back 12

df <- df %>%
  mutate(LBack12 = factor(LBack12, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$`Work_Hr/Wk`, as.numeric(df$LBack12), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.1467423	3.18256	0.0014598	Kendall’s rank correlation tau	two.sided

WorkHrs x Low back 7

df <- df %>%
  mutate(LBack7 = factor(LBack7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$`Work_Hr/Wk`, as.numeric(df$LBack7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.1074923	2.331301	0.0197375	Kendall’s rank correlation tau	two.sided

WorkHrs x Hip 12

df <- df %>%
  mutate(Hip12 = factor(Hip12, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$`Work_Hr/Wk`, as.numeric(df$Hip12), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
-0.0301188	-0.6532186	0.5136154	Kendall’s rank correlation tau	two.sided

WorkHrs x Hip 7

df <- df %>%
  mutate(Hip7 = factor(Hip7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$`Work_Hr/Wk`, as.numeric(df$Hip7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.0019746	0.0428242	0.9658417	Kendall’s rank correlation tau	two.sided

WorkHrs x Knee 12

df <- df %>%
  mutate(Knee12 = factor(Knee12, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$`Work_Hr/Wk`, as.numeric(df$Knee12), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.1068095	2.316495	0.0205313	Kendall’s rank correlation tau	two.sided

WorkHrs x Knee 7

df <- df %>%
  mutate(Knee7 = factor(Knee7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$`Work_Hr/Wk`, as.numeric(df$Knee7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.0683448	1.482268	0.1382691	Kendall’s rank correlation tau	two.sided

WorkHrs x Feet 12

df <- df %>%
  mutate(Feet12 = factor(Feet12, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$`Work_Hr/Wk`, as.numeric(df$Feet12), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.0827773	1.795281	0.0726089	Kendall’s rank correlation tau	two.sided

WorkHrs x Feet 7

df <- df %>%
  mutate(Feet7 = factor(Feet7, levels = c("No", "Yes"), ordered = TRUE))

cor.test(df$`Work_Hr/Wk`, as.numeric(df$Feet7), method = "kendall") |> 
    tidy() |> 
    kable() 

estimate	statistic	p.value	method	alternative
0.0543175	1.178042	0.23878	Kendall’s rank correlation tau	two.sided

WorkHrs x Year Study

df <- df %>%
  mutate(Year_Study = factor(Year_Study, levels = c("1º", "2º", "3º", "4º", "5º"), ordered = TRUE),
         )

cor.test((df$`Work_Hr/Wk`), as.numeric(df$Year_Study), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.1063212	2.57129	0.0101321	Kendall’s rank correlation tau	two.sided

WorkHrs x Sex

df <- df %>%
  mutate(Sex = factor(Sex, levels = c("Female", "Male"), ordered = TRUE),
         )

cor.test((df$`Work_Hr/Wk`), as.numeric(df$Sex), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
-0.0462435	-1.002933	0.3158934	Kendall’s rank correlation tau	two.sided

WorkHrs x Depression

df <- df %>%
  mutate(PHQ = factor(PHQ, levels = c("Minimal", "Mild","Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
         )

cor.test((df$`Work_Hr/Wk`), as.numeric(df$PHQ), method = "kendall") |>
  tidy() |>
  kable()

estimate	statistic	p.value	method	alternative
0.0215756	0.505203	0.6134163	Kendall’s rank correlation tau	two.sided

May 7

Specific Nordic Questionnaire - Low back

freq <- table(df$LB_Freq12)["1-7 days"]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.3780488	19.02744	1.29e-05	1	0.325804	0.4331985	1-sample proportions test with continuity correction	two.sided

freq <- table(df$LB_Freq12)["0 days"]
prop.test(n-freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.5762195	7.320122	0.0068187	1	0.5206307	0.6299936	1-sample proportions test with continuity correction	two.sided

freq <- table(df$LB_Freq12)["8-30 days"]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1006098	207.686	0	1	0.071247	0.1396456	1-sample proportions test with continuity correction	two.sided

freq <- table(df$LB_Freq12)["More than 30 days, but not every day"]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0762195	233.9299	0	1	0.0509073	0.1118537	1-sample proportions test with continuity correction	two.sided

freq <- table(df$LB_Freq12)["Every day "]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0213415	298.686	0	1	0.0093833	0.0453938	1-sample proportions test with continuity correction	two.sided

freq <- table(df$LB_Freq12)

df |> 
    count(LB_Freq12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

LB_Freq12	n	Percentage
0 days	139	42.378049
1-7 days	124	37.804878
8-30 days	33	10.060976
Every day	7	2.134146
More than 30 days, but not every day	25	7.621951

freq <- table(df$LB_Freq12)

Number of days of work affected

df |> 
    count(LB_Freq_Work) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

LB_Freq_Work	n	Percentage
0 days	272	82.9268293
1-7 days	45	13.7195122
8-30 days	8	2.4390244
Mas de 30 days	3	0.9146341

freq <- table(df$LB_Freq_Work)["1-7 days"]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1371951	171.247	0	1	0.1028038	0.180326	1-sample proportions test with continuity correction	two.sided

freq <- table(df$LB_Freq_Work)["0 days"]
prop.test(n-freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1707317	140.9299	0	1	0.132515	0.2168542	1-sample proportions test with continuity correction	two.sided

freq <- table(df$LB_Freq_Work)["8-30 days"]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0243902	294.8811	0	1	0.0113847	0.0493565	1-sample proportions test with continuity correction	two.sided

freq <- table(df$LB_Freq_Work)["Mas de 30 days"]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0091463	314.1494	0	1	0.0023651	0.0287569	1-sample proportions test with continuity correction	two.sided

Specific Nordic Questionnaire - Neck

freq <- table(df$Neck_Freq12)["1-7 days"]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.304878	49.17378	0	1	0.2561166	0.3582943	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Neck_Freq12)["0 days"]
prop.test(n-freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.5640244	5.125	0.0235836	1	0.5083972	0.6181274	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Neck_Freq12)["8-30 days"]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1402439	168.3689	0	1	0.1054775	0.1836733	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Neck_Freq12)["More than 30 days, but not every day"]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.097561	210.8811	0	1	0.0686686	0.1362059	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Neck_Freq12)["Every day"]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0213415	298.686	0	1	0.0093833	0.0453938	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Neck_Freq12)

df |> 
    count(Neck_Freq12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Neck_Freq12	n	Percentage
0 days	143	43.597561
1-7 days	100	30.487805
8-30 days	46	14.024390
Every day	7	2.134146
More than 30 days, but not every day	32	9.756098

Number of days of work affected

df |> 
    count(Neck_Freq_Work) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Neck_Freq_Work	n	Percentage
0 days	250	76.219512
1-7 days	62	18.902439
8-30 days	12	3.658537
More than 30 days	4	1.219512

freq <- table(df$Neck_Freq_Work)["1-7 days"]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1890244	125.6372	0	1	0.1489673	0.2365379	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Neck_Freq_Work)["0 days"]
prop.test(n-freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.2378049	89.14939	0	1	0.1935265	0.2883529	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Neck_Freq_Work)["8-30 days"]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0365854	279.9055	0	1	0.0199344	0.0647391	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Neck_Freq_Work)["More than 30 days"]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0121951	310.247	0	1	0.0039137	0.0330721	1-sample proportions test with continuity correction	two.sided

Specific Nordic Questionnaire - Shoulders

freq <- table(df$Shoulder_Freq12)["1-7 days"]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0945122	214.1006	0	1	0.0660996	0.1327573	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Shoulder_Freq12)["0 days"]
prop.test(n-freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.195122	120.7348	0	1	0.1544851	0.243066	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Shoulder_Freq12)["8-30 days"]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0487805	265.3201	0	1	0.0290736	0.0795968	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Shoulder_Freq12)["More than 30 days, but not every day"]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0396341	276.2226	0	1	0.0221742	0.0684943	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Shoulder_Freq12)["Every day"]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0121951	310.247	0	1	0.0039137	0.0330721	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Shoulder_Freq12)

df |> 
    count(Shoulder_Freq12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Shoulder_Freq12	n	Percentage
0 days	264	80.487805
1-7 days	31	9.451220
8-30 days	16	4.878049
Every day	4	1.219512
More than 30 days, but not every day	13	3.963415

Number of days of work affected

df |> 
    count(Shoulder_Freq_Work) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Shoulder_Freq_Work	n	Percentage
0 days	310	94.5121951
1-7 days	14	4.2682927
8-30 days	3	0.9146341
More than 30 days	1	0.3048780

freq <- table(df$Shoulder_Freq_Work)["1-7 days"]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0426829	272.564	0	1	0.024446	0.0722206	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Shoulder_Freq_Work)["0 days"]
prop.test(n-freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.054878	258.1738	0	1	0.0337974	0.0868846	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Shoulder_Freq_Work)["8-30 days"]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0091463	314.1494	0	1	0.0023651	0.0287569	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Shoulder_Freq_Work)["More than 30 days"]
prop.test(freq, n) |> 
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0030488	322.0274	0	1	0.0001592	0.0195598	1-sample proportions test with continuity correction	two.sided

freq <- table(df$Shoulder_Acc)["No"]
# Proportion test (require frequency of "Yes", sample size)
prop.test(n-freq, n) |> 
    tidy() |> 
    kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.070122	240.7348	0	1	0.0459408	0.1047929	1-sample proportions test with continuity correction	two.sided

Stratification of SNQ by Year

Neck

df |> 
    group_by(Year_Study) |>
    count(Neck12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Neck12	n	Percentage
1º	No	41	48.80952
1º	Yes	43	51.19048
2º	No	30	40.00000
2º	Yes	45	60.00000
3º	No	14	25.00000
3º	Yes	42	75.00000
4º	No	26	40.62500
4º	Yes	38	59.37500
5º	No	16	32.65306
5º	Yes	33	67.34694

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Neck12 == "Yes", na.rm = TRUE),
    n = sum(!is.na(Neck12))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	43	84	0.512	0.401	0.622	0.913000
2º	45	75	0.600	0.480	0.709	0.106000
3º	42	56	0.750	0.614	0.852	0.000309
4º	38	64	0.594	0.464	0.712	0.169000
5º	33	49	0.673	0.523	0.796	0.022300

df |> 
  group_by(Year_Study) |>  
  count(Neck_Work...24) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Neck_Work…24	n	Percentage
1º	No	77	91.666667
1º	Yes	7	8.333333
2º	No	58	77.333333
2º	Yes	17	22.666667
3º	No	44	78.571429
3º	Yes	12	21.428571
4º	No	50	78.125000
4º	Yes	14	21.875000
5º	No	40	81.632653
5º	Yes	9	18.367347

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Neck_Work...24 == "Yes", na.rm = TRUE),
    n = sum(!is.na(Neck_Work...24))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	7	84	0.083	0.037	0.170	0.00e+00
2º	17	75	0.227	0.141	0.341	3.90e-06
3º	12	56	0.214	0.120	0.348	3.43e-05
4º	14	64	0.219	0.129	0.343	1.21e-05
5º	9	49	0.184	0.092	0.325	1.82e-05

df |> 
  group_by(Year_Study) |>  
  count(Neck7) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Neck7	n	Percentage
1º	No	61	72.61905
1º	Yes	23	27.38095
2º	No	53	70.66667
2º	Yes	22	29.33333
3º	No	33	58.92857
3º	Yes	23	41.07143
4º	No	41	64.06250
4º	Yes	23	35.93750
5º	No	39	79.59184
5º	Yes	10	20.40816

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Neck7 == "Yes", na.rm = TRUE),
    n = sum(!is.na(Neck7))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	23	84	0.274	0.185	0.384	5.41e-05
2º	22	75	0.293	0.197	0.411	5.32e-04
3º	23	56	0.411	0.284	0.550	2.29e-01
4º	23	64	0.359	0.246	0.490	3.36e-02
5º	10	49	0.204	0.107	0.348	6.33e-05

Shoulder

df |> 
  mutate(Shoulder12 = ifelse(Shoulder12 == "No", "No", "Yes")) |> 
  group_by(Year_Study, Shoulder12) |> 
  summarise(n = n(), .groups = "drop") |> 
  group_by(Year_Study) |>  # Group again to compute percentage within each year
  mutate(Percentage = n / sum(n) * 100) |> 
  kable()

Year_Study	Shoulder12	n	Percentage
1º	No	59	70.23810
1º	Yes	25	29.76190
2º	No	47	62.66667
2º	Yes	28	37.33333
3º	No	34	60.71429
3º	Yes	22	39.28571
4º	No	37	57.81250
4º	Yes	27	42.18750
5º	No	32	65.30612
5º	Yes	17	34.69388

df |> 
    group_by(Year_Study) |>
    count(Shoulder12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Shoulder12	n	Percentage
1º	No	59	70.238095
1º	Yes, in BOTH shoulders	16	19.047619
1º	Yes, in the LEFT shoulder	2	2.380952
1º	Yes, in the RIGHT shoulder	7	8.333333
2º	No	47	62.666667
2º	Yes, in BOTH shoulders	19	25.333333
2º	Yes, in the LEFT shoulder	5	6.666667
2º	Yes, in the RIGHT shoulder	4	5.333333
3º	No	34	60.714286
3º	Yes, in BOTH shoulders	16	28.571429
3º	Yes, in the LEFT shoulder	1	1.785714
3º	Yes, in the RIGHT shoulder	5	8.928571
4º	No	37	57.812500
4º	Yes, in BOTH shoulders	17	26.562500
4º	Yes, in the LEFT shoulder	4	6.250000
4º	Yes, in the RIGHT shoulder	6	9.375000
5º	No	32	65.306122
5º	Yes, in BOTH shoulders	10	20.408163
5º	Yes, in the LEFT shoulder	3	6.122449
5º	Yes, in the RIGHT shoulder	4	8.163265

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Shoulder12 == "No", na.rm = TRUE),
    n = sum(!is.na(Shoulder12))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(n-freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	59	84	0.298	0.205	0.409	0.000317
2º	47	75	0.373	0.267	0.493	0.037700
3º	34	56	0.393	0.268	0.532	0.142000
4º	37	64	0.422	0.302	0.552	0.261000
5º	32	49	0.347	0.221	0.497	0.045500

df |> 
    group_by(Year_Study) |>
    count(Shoulder_Work...28) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Shoulder_Work…28	n	Percentage
1º	No	80	95.238095
1º	Yes	4	4.761905
2º	No	70	93.333333
2º	Yes	5	6.666667
3º	No	53	94.642857
3º	Yes	3	5.357143
4º	No	54	84.375000
4º	Yes	10	15.625000
5º	No	49	100.000000

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Shoulder_Work...28 == "Yes", na.rm = TRUE),
    n = sum(!is.na(Shoulder_Work...28))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	4	84	0.048	0.015	0.124	0e+00
2º	5	75	0.067	0.025	0.155	0e+00
3º	3	56	0.054	0.014	0.158	0e+00
4º	10	64	0.156	0.081	0.273	1e-07
5º	0	49	0.000	0.000	0.091	0e+00

df |> 
    group_by(Year_Study) |>
    count(Shoulder7) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Shoulder7	n	Percentage
1º	No	70	83.33333
1º	Yes	14	16.66667
2º	No	63	84.00000
2º	Yes	12	16.00000
3º	No	49	87.50000
3º	Yes	7	12.50000
4º	No	48	75.00000
4º	Yes	16	25.00000
5º	No	42	85.71429
5º	Yes	7	14.28571

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Shoulder7 == "Yes", na.rm = TRUE),
    n = sum(!is.na(Shoulder7))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	14	84	0.167	0.097	0.267	0.00e+00
2º	12	75	0.160	0.089	0.267	0.00e+00
3º	7	56	0.125	0.056	0.247	0.00e+00
4º	16	64	0.250	0.154	0.377	1.07e-04
5º	7	49	0.143	0.064	0.279	1.20e-06

Elbow

df |> 
  mutate(Elbow12 = ifelse(Elbow12 == "No", "No", "Yes")) |> 
  group_by(Year_Study, Elbow12) |> 
  summarise(n = n(), .groups = "drop") |> 
  group_by(Year_Study) |>  # Group again to compute percentage within each year
  mutate(Percentage = n / sum(n) * 100) |> 
  kable()

Year_Study	Elbow12	n	Percentage
1º	No	81	96.428571
1º	Yes	3	3.571429
2º	No	70	93.333333
2º	Yes	5	6.666667
3º	No	55	98.214286
3º	Yes	1	1.785714
4º	No	59	92.187500
4º	Yes	5	7.812500
5º	No	45	91.836735
5º	Yes	4	8.163265

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  # Recode all "Yes" variations into a single "Yes"
  mutate(Elbow12 = ifelse(Elbow12 == "No", "No", "Yes")) %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Elbow12 == "Yes" & !is.na(Elbow12)),
    n = sum(!is.na(Elbow12))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))  # Testing proportion of "Yes"
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	3	84	0.036	0.009	0.108	0
2º	5	75	0.067	0.025	0.155	0
3º	1	56	0.018	0.001	0.108	0
4º	5	64	0.078	0.029	0.180	0
5º	4	49	0.082	0.026	0.205	0

df |> 
    group_by(Year_Study) |>
    count(Elbow_Work) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Elbow_Work	n	Percentage
1º	No	83	98.809524
1º	Yes	1	1.190476
2º	No	72	96.000000
2º	Yes	3	4.000000
3º	No	56	100.000000
4º	No	62	96.875000
4º	Yes	2	3.125000
5º	No	47	95.918367
5º	Yes	2	4.081633

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Elbow_Work == "Yes", na.rm = TRUE),
    n = sum(!is.na(Elbow_Work))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	1	84	0.012	0.001	0.074	0
2º	3	75	0.040	0.010	0.120	0
3º	0	56	0.000	0.000	0.080	0
4º	2	64	0.031	0.005	0.118	0
5º	2	49	0.041	0.007	0.151	0

df |> 
    group_by(Year_Study) |>
    count(Elbow7) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Elbow7	n	Percentage
1º	No	83	98.809524
1º	Yes	1	1.190476
2º	No	72	96.000000
2º	Yes	3	4.000000
3º	No	56	100.000000
4º	No	61	95.312500
4º	Yes	3	4.687500
5º	No	48	97.959184
5º	Yes	1	2.040816

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Elbow7 == "Yes", na.rm = TRUE),
    n = sum(!is.na(Elbow7))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	1	84	0.012	0.001	0.074	0
2º	3	75	0.040	0.010	0.120	0
3º	0	56	0.000	0.000	0.080	0
4º	3	64	0.047	0.012	0.140	0
5º	1	49	0.020	0.001	0.122	0

Wrist

df |> 
  mutate(Wrist12 = ifelse(Wrist12 == "No", "No", "Yes")) |> 
  group_by(Year_Study, Wrist12) |> 
  summarise(n = n(), .groups = "drop") |> 
  group_by(Year_Study) |>  # Group again to compute percentage within each year
  mutate(Percentage = n / sum(n) * 100) |> 
  kable()

Year_Study	Wrist12	n	Percentage
1º	No	64	76.19048
1º	Yes	20	23.80952
2º	No	66	88.00000
2º	Yes	9	12.00000
3º	No	39	69.64286
3º	Yes	17	30.35714
4º	No	51	79.68750
4º	Yes	13	20.31250
5º	No	31	63.26531
5º	Yes	18	36.73469

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  # Recode all "Yes" variations into a single "Yes"
  mutate(Wrist12 = ifelse(Wrist12 == "No", "No", "Yes")) %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Wrist12 == "Yes" & !is.na(Wrist12)),
    n = sum(!is.na(Wrist12))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))  # Testing proportion of "Yes"
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	20	84	0.238	0.155	0.346	2.70e-06
2º	9	75	0.120	0.060	0.220	0.00e+00
3º	17	56	0.304	0.192	0.443	5.01e-03
4º	13	64	0.203	0.117	0.326	3.80e-06
5º	18	49	0.367	0.238	0.517	8.65e-02

df |> 
    group_by(Year_Study) |>
    count(Wrist_Work) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Wrist_Work	n	Percentage
1º	No	81	96.428571
1º	Yes	3	3.571429
2º	No	70	93.333333
2º	Yes	5	6.666667
3º	No	49	87.500000
3º	Yes	7	12.500000
4º	No	60	93.750000
4º	Yes	4	6.250000
5º	No	46	93.877551
5º	Yes	3	6.122449

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Wrist_Work == "Yes", na.rm = TRUE),
    n = sum(!is.na(Wrist_Work))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	3	84	0.036	0.009	0.108	0
2º	5	75	0.067	0.025	0.155	0
3º	7	56	0.125	0.056	0.247	0
4º	4	64	0.062	0.020	0.160	0
5º	3	49	0.061	0.016	0.179	0

df |> 
    group_by(Year_Study) |>
    count(Wrist7) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Wrist7	n	Percentage
1º	No	79	94.047619
1º	Yes	5	5.952381
2º	No	71	94.666667
2º	Yes	4	5.333333
3º	No	49	87.500000
3º	Yes	7	12.500000
4º	No	61	95.312500
4º	Yes	3	4.687500
5º	No	43	87.755102
5º	Yes	6	12.244898

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Wrist7 == "Yes", na.rm = TRUE),
    n = sum(!is.na(Wrist7))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	5	84	0.060	0.022	0.140	0e+00
2º	4	75	0.053	0.017	0.138	0e+00
3º	7	56	0.125	0.056	0.247	0e+00
4º	3	64	0.047	0.012	0.140	0e+00
5º	6	49	0.122	0.051	0.255	3e-07

Upper back

df |> 
    group_by(Year_Study) |>
    count(UBack12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	UBack12	n	Percentage
1º	No	49	58.33333
1º	Yes	35	41.66667
2º	No	52	69.33333
2º	Yes	23	30.66667
3º	No	26	46.42857
3º	Yes	30	53.57143
4º	No	36	56.25000
4º	Yes	28	43.75000
5º	No	31	63.26531
5º	Yes	18	36.73469

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(UBack12 == "Yes", na.rm = TRUE),
    n = sum(!is.na(UBack12))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	35	84	0.417	0.312	0.529	0.15600
2º	23	75	0.307	0.208	0.425	0.00122
3º	30	56	0.536	0.399	0.668	0.68800
4º	28	64	0.438	0.316	0.567	0.38200
5º	18	49	0.367	0.238	0.517	0.08650

df |> 
  group_by(Year_Study) |>  
  count(Uback_Work) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Uback_Work	n	Percentage
1º	No	79	94.047619
1º	Yes	5	5.952381
2º	No	69	92.000000
2º	Yes	6	8.000000
3º	No	44	78.571429
3º	Yes	12	21.428571
4º	No	54	84.375000
4º	Yes	10	15.625000
5º	No	46	93.877551
5º	Yes	3	6.122449

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Uback_Work == "Yes", na.rm = TRUE),
    n = sum(!is.na(Uback_Work))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	5	84	0.060	0.022	0.140	0.00e+00
2º	6	75	0.080	0.033	0.172	0.00e+00
3º	12	56	0.214	0.120	0.348	3.43e-05
4º	10	64	0.156	0.081	0.273	1.00e-07
5º	3	49	0.061	0.016	0.179	0.00e+00

df |> 
  group_by(Year_Study) |>  
  count(UBack7) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	UBack7	n	Percentage
1º	No	70	83.33333
1º	Yes	14	16.66667
2º	No	64	85.33333
2º	Yes	11	14.66667
3º	No	41	73.21429
3º	Yes	15	26.78571
4º	No	53	82.81250
4º	Yes	11	17.18750
5º	No	42	85.71429
5º	Yes	7	14.28571

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(UBack7 == "Yes", na.rm = TRUE),
    n = sum(!is.na(UBack7))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	14	84	0.167	0.097	0.267	0.00e+00
2º	11	75	0.147	0.079	0.252	0.00e+00
3º	15	56	0.268	0.162	0.405	8.35e-04
4º	11	64	0.172	0.093	0.291	3.00e-07
5º	7	49	0.143	0.064	0.279	1.20e-06

Lower Back

df |> 
    group_by(Year_Study) |>
    count(LBack12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	LBack12	n	Percentage
1º	No	42	50.00000
1º	Yes	42	50.00000
2º	No	50	66.66667
2º	Yes	25	33.33333
3º	No	23	41.07143
3º	Yes	33	58.92857
4º	No	33	51.56250
4º	Yes	31	48.43750
5º	No	26	53.06122
5º	Yes	23	46.93878

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(LBack12 == "Yes", na.rm = TRUE),
    n = sum(!is.na(LBack12))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	42	84	0.500	0.395	0.605	1.00000
2º	25	75	0.333	0.231	0.453	0.00558
3º	33	56	0.589	0.450	0.716	0.22900
4º	31	64	0.484	0.359	0.612	0.90100
5º	23	49	0.469	0.328	0.616	0.77500

df |> 
  group_by(Year_Study) |>  
  count(LBack_Work) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	LBack_Work	n	Percentage
1º	No	70	83.333333
1º	Yes	14	16.666667
2º	No	72	96.000000
2º	Yes	3	4.000000
3º	No	44	78.571429
3º	Yes	12	21.428571
4º	No	53	82.812500
4º	Yes	11	17.187500
5º	No	48	97.959184
5º	Yes	1	2.040816

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(LBack_Work == "Yes", na.rm = TRUE),
    n = sum(!is.na(LBack_Work))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	14	84	0.167	0.097	0.267	0.00e+00
2º	3	75	0.040	0.010	0.120	0.00e+00
3º	12	56	0.214	0.120	0.348	3.43e-05
4º	11	64	0.172	0.093	0.291	3.00e-07
5º	1	49	0.020	0.001	0.122	0.00e+00

df |> 
  group_by(Year_Study) |>  
  count(LBack7) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	LBack7	n	Percentage
1º	No	59	70.23810
1º	Yes	25	29.76190
2º	No	61	81.33333
2º	Yes	14	18.66667
3º	No	42	75.00000
3º	Yes	14	25.00000
4º	No	53	82.81250
4º	Yes	11	17.18750
5º	No	39	79.59184
5º	Yes	10	20.40816

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(LBack7 == "Yes", na.rm = TRUE),
    n = sum(!is.na(LBack7))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	25	84	0.298	0.205	0.409	3.17e-04
2º	14	75	0.187	0.109	0.297	1.00e-07
3º	14	56	0.250	0.148	0.386	3.09e-04
4º	11	64	0.172	0.093	0.291	3.00e-07
5º	10	49	0.204	0.107	0.348	6.33e-05

Hips

df |> 
    group_by(Year_Study) |>
    count(Hip12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Hip12	n	Percentage
1º	No	77	91.666667
1º	Yes	7	8.333333
2º	No	67	89.333333
2º	Yes	8	10.666667
3º	No	52	92.857143
3º	Yes	4	7.142857
4º	No	58	90.625000
4º	Yes	6	9.375000
5º	No	44	89.795918
5º	Yes	5	10.204082

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Hip12 == "Yes", na.rm = TRUE),
    n = sum(!is.na(Hip12))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	7	84	0.083	0.037	0.170	0e+00
2º	8	75	0.107	0.050	0.205	0e+00
3º	4	56	0.071	0.023	0.181	0e+00
4º	6	64	0.094	0.039	0.199	0e+00
5º	5	49	0.102	0.038	0.230	1e-07

df |> 
  group_by(Year_Study) |>  
  count(Hip_Work) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Hip_Work	n	Percentage
1º	No	83	98.809524
1º	Yes	1	1.190476
2º	No	73	97.333333
2º	Yes	2	2.666667
3º	No	54	96.428571
3º	Yes	2	3.571429
4º	No	60	93.750000
4º	Yes	4	6.250000
5º	No	48	97.959184
5º	Yes	1	2.040816

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Hip_Work == "Yes", na.rm = TRUE),
    n = sum(!is.na(Hip_Work))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	1	84	0.012	0.001	0.074	0
2º	2	75	0.027	0.005	0.102	0
3º	2	56	0.036	0.006	0.134	0
4º	4	64	0.062	0.020	0.160	0
5º	1	49	0.020	0.001	0.122	0

df |> 
  group_by(Year_Study) |>  
  count(Hip7) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Hip7	n	Percentage
1º	No	82	97.619048
1º	Yes	2	2.380952
2º	No	73	97.333333
2º	Yes	2	2.666667
3º	No	55	98.214286
3º	Yes	1	1.785714
4º	No	61	95.312500
4º	Yes	3	4.687500
5º	No	48	97.959184
5º	Yes	1	2.040816

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Hip7 == "Yes", na.rm = TRUE),
    n = sum(!is.na(Hip7))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	2	84	0.024	0.004	0.091	0
2º	2	75	0.027	0.005	0.102	0
3º	1	56	0.018	0.001	0.108	0
4º	3	64	0.047	0.012	0.140	0
5º	1	49	0.020	0.001	0.122	0

Knees

df |> 
    group_by(Year_Study) |>
    count(Knee12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Knee12	n	Percentage
1º	No	66	78.57143
1º	Yes	18	21.42857
2º	No	59	78.66667
2º	Yes	16	21.33333
3º	No	46	82.14286
3º	Yes	10	17.85714
4º	No	48	75.00000
4º	Yes	16	25.00000
5º	No	40	81.63265
5º	Yes	9	18.36735

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Knee12 == "Yes", na.rm = TRUE),
    n = sum(!is.na(Knee12))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	18	84	0.214	0.135	0.320	3.00e-07
2º	16	75	0.213	0.130	0.326	1.20e-06
3º	10	56	0.179	0.093	0.308	2.90e-06
4º	16	64	0.250	0.154	0.377	1.07e-04
5º	9	49	0.184	0.092	0.325	1.82e-05

df |> 
  group_by(Year_Study) |>  
  count(Knee_Work) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Knee_Work	n	Percentage
1º	No	75	89.285714
1º	Yes	9	10.714286
2º	No	70	93.333333
2º	Yes	5	6.666667
3º	No	51	91.071429
3º	Yes	5	8.928571
4º	No	52	81.250000
4º	Yes	12	18.750000
5º	No	46	93.877551
5º	Yes	3	6.122449

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Knee_Work == "Yes", na.rm = TRUE),
    n = sum(!is.na(Knee_Work))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	9	84	0.107	0.053	0.198	0.0e+00
2º	5	75	0.067	0.025	0.155	0.0e+00
3º	5	56	0.089	0.033	0.204	0.0e+00
4º	12	64	0.188	0.105	0.308	1.1e-06
5º	3	49	0.061	0.016	0.179	0.0e+00

df |> 
  group_by(Year_Study) |>  
  count(Knee7) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Knee7	n	Percentage
1º	No	77	91.666667
1º	Yes	7	8.333333
2º	No	68	90.666667
2º	Yes	7	9.333333
3º	No	52	92.857143
3º	Yes	4	7.142857
4º	No	53	82.812500
4º	Yes	11	17.187500
5º	No	43	87.755102
5º	Yes	6	12.244898

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Knee7 == "Yes", na.rm = TRUE),
    n = sum(!is.na(Knee7))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	7	84	0.083	0.037	0.170	0e+00
2º	7	75	0.093	0.042	0.189	0e+00
3º	4	56	0.071	0.023	0.181	0e+00
4º	11	64	0.172	0.093	0.291	3e-07
5º	6	49	0.122	0.051	0.255	3e-07

Feet

df |> 
    group_by(Year_Study) |>
    count(Feet12) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Feet12	n	Percentage
1º	No	75	89.285714
1º	Yes	9	10.714286
2º	No	61	81.333333
2º	Yes	14	18.666667
3º	No	51	91.071429
3º	Yes	5	8.928571
4º	No	56	87.500000
4º	Yes	8	12.500000
5º	No	43	87.755102
5º	Yes	6	12.244898

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Feet12 == "Yes", na.rm = TRUE),
    n = sum(!is.na(Feet12))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	9	84	0.107	0.053	0.198	0e+00
2º	14	75	0.187	0.109	0.297	1e-07
3º	5	56	0.089	0.033	0.204	0e+00
4º	8	64	0.125	0.059	0.237	0e+00
5º	6	49	0.122	0.051	0.255	3e-07

df |> 
  group_by(Year_Study) |>  
  count(Feet_Work) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Feet_Work	n	Percentage
1º	No	79	94.047619
1º	Yes	5	5.952381
2º	No	69	92.000000
2º	Yes	6	8.000000
3º	No	52	92.857143
3º	Yes	4	7.142857
4º	No	59	92.187500
4º	Yes	5	7.812500
5º	No	48	97.959184
5º	Yes	1	2.040816

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Feet_Work == "Yes", na.rm = TRUE),
    n = sum(!is.na(Feet_Work
    ))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	5	84	0.060	0.022	0.140	0
2º	6	75	0.080	0.033	0.172	0
3º	4	56	0.071	0.023	0.181	0
4º	5	64	0.078	0.029	0.180	0
5º	1	49	0.020	0.001	0.122	0

df |> 
  group_by(Year_Study) |>  
  count(Feet7) |>
    mutate(Percentage = n/sum(n)*100) |>
    kable()

Year_Study	Feet7	n	Percentage
1º	No	80	95.238095
1º	Yes	4	4.761905
2º	No	66	88.000000
2º	Yes	9	12.000000
3º	No	52	92.857143
3º	Yes	4	7.142857
4º	No	61	95.312500
4º	Yes	3	4.687500
5º	No	46	93.877551
5º	Yes	3	6.122449

library(dplyr)
library(broom)
library(tidyr)
library(knitr)

df %>%
  group_by(Year_Study) %>%
  summarise(
    freq = sum(Feet7 == "Yes", na.rm = TRUE),
    n = sum(!is.na(Feet7
    ))
  ) %>%
  rowwise() %>%
  mutate(
    prop_test = list(tidy(prop.test(freq, n)))
  ) %>%
  unnest(prop_test) %>%
  ungroup() %>%
  select(Year_Study, freq, n, estimate, conf.low, conf.high, p.value) %>%
  mutate(
    estimate = round(estimate, 3),
    conf.low = round(conf.low, 3),
    conf.high = round(conf.high, 3),
    p.value = signif(p.value, 3)
  ) %>%
  kable(col.names = c("Year", "Yes", "Total", "Proportion", "95% CI (Low)", "95% CI (High)", "p-value"))

Year	Yes	Total	Proportion	95% CI (Low)	95% CI (High)	p-value
1º	4	84	0.048	0.015	0.124	0
2º	9	75	0.120	0.060	0.220	0
3º	4	56	0.071	0.023	0.181	0
4º	3	64	0.047	0.012	0.140	0
5º	3	49	0.061	0.016	0.179	0

May 17

# Packages loading.
library(tidyverse)
library(readxl)
library(knitr)
library(broom)

# Data loading.
df <- read_excel("Data/Cleaned Data.xlsx")

# Rename some variables
df <- df |>
    rename(
        NMQ = `Likert NMQ`,
        Activity = `Activity Level`,
        PHQ = `PHQ LEVEL`,
    )  |> 
# Convert some variables to factors
    mutate(
        Activity = factor(Activity, levels = c("Low", "Moderate", "High"), ordered = TRUE),
        PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),
        ) 

Point Biserial Correlation

PHQ x Pain 12

df <- df |>
  mutate(AnyPain12 = if_else(AnyPain12 == "Yes", 1, 0),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),)

cor.test(as.numeric(df$PHQ), df$AnyPain12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1092786	1.984963	0.0479864	326	0.0009974	0.2150268	Pearson’s product-moment correlation	two.sided

PHQ x Pain 7

df <- df |>
  mutate(AnyPain7 = if_else(AnyPain7 == "Yes", 1, 0),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),)

cor.test(as.numeric(df$PHQ), df$AnyPain7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.2305915	4.278747	2.47e-05	326	0.1254308	0.3306282	Pearson’s product-moment correlation	two.sided

PHQ x Neck 12

df <- df |>
  mutate(Neck12 = if_else(Neck12 == "Yes", 1, 0),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),)

cor.test(as.numeric(df$PHQ), df$Neck12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0934113	1.693992	0.091222	326	-0.0150337	0.1996842	Pearson’s product-moment correlation	two.sided

PHQ x Neck 7

df <- df |>
  mutate(Neck7 = if_else(Neck7 == "Yes", 1, 0),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),)

cor.test(as.numeric(df$PHQ), df$Neck7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1452823	2.65127	0.0084106	326	0.0375807	0.2496475	Pearson’s product-moment correlation	two.sided

PHQ x Shoulder12YN

df <- df |>
  mutate(Shoulder12YN = if_else(Shoulder12YN == "Yes", 1, 0),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),)

cor.test(as.numeric(df$PHQ), df$Shoulder12YN) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1487235	2.715472	0.0069708	326	0.0410924	0.2529426	Pearson’s product-moment correlation	two.sided

PHQ x Shoulder 7

df <- df |>
  mutate(Shoulder7 = if_else(Shoulder7 == "Yes", 1, 0),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),)

cor.test(as.numeric(df$PHQ), df$Shoulder7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.156872	2.867906	0.0044012	326	0.0494186	0.2607355	Pearson’s product-moment correlation	two.sided

PHQ x Elbow 12

df <- df |>
  mutate(Elbow12YN = if_else(Elbow12YN == "Yes", 1, 0),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),)

cor.test(as.numeric(df$PHQ), df$Elbow12YN) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
-0.034241	-0.6186004	0.5366116	326	-0.1420074	0.0743275	Pearson’s product-moment correlation	two.sided

PHQ x Elbow 7

df <- df |>
  mutate(Elbow7 = if_else(Elbow7 == "Yes", 1, 0),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),)

cor.test(as.numeric(df$PHQ), df$Elbow7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
-0.0048376	-0.0873457	0.9304504	326	-0.1130712	0.1035096	Pearson’s product-moment correlation	two.sided

PHQ x Wrist 12

df <- df |>
  mutate(Wrist12YN = if_else(Wrist12YN == "Yes", 1, 0),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),)

cor.test(as.numeric(df$PHQ), df$Wrist12YN) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1490804	2.722135	0.0068349	326	0.0414567	0.2532842	Pearson’s product-moment correlation	two.sided

PHQ x Wrist 7

df <- df |>
  mutate(Wrist7 = if_else(Wrist7 == "Yes", 1, 0),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),)

cor.test(as.numeric(df$PHQ), df$Wrist7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0719237	1.301987	0.1938399	326	-0.0366547	0.1788237	Pearson’s product-moment correlation	two.sided

PHQ x Upper back 12

df <- df |>
  mutate(UBack12 = if_else(UBack12 == "Yes", 1, 0),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),)

cor.test(as.numeric(df$PHQ), df$UBack12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1737441	3.185479	0.0015848	326	0.0667062	0.2768284	Pearson’s product-moment correlation	two.sided

PHQ x Upper back 7

df <- df |>
  mutate(UBack7 = if_else(UBack7 == "Yes", 1, 0),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),)

cor.test(as.numeric(df$PHQ), df$UBack7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.226398	4.19669	3.5e-05	326	0.1210735	0.3266815	Pearson’s product-moment correlation	two.sided

PHQ x Lower back 12

df <- df |>
  mutate(LBack12 = if_else(LBack12 == "Yes", 1, 0),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),)

cor.test(as.numeric(df$PHQ), df$LBack12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0570436	1.031629	0.3030107	326	-0.0515679	0.1643214	Pearson’s product-moment correlation	two.sided

PHQ x Lower back 7

df <- df |>
  mutate(LBack7 = if_else(LBack7 == "Yes", 1, 0),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),)

cor.test(as.numeric(df$PHQ), df$LBack7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1884399	3.464438	0.0006024	326	0.0818166	0.2907986	Pearson’s product-moment correlation	two.sided

PHQ x Hip 12

df <- df |>
  mutate(Hip12 = if_else(Hip12 == "Yes", 1, 0),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),)

cor.test(as.numeric(df$PHQ), df$Hip12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0964284	1.749211	0.0811955	326	-0.0119897	0.2026056	Pearson’s product-moment correlation	two.sided

PHQ x Hip 7

df <- df |>
  mutate(Hip7 = if_else(Hip7 == "Yes", 1, 0),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),)

cor.test(as.numeric(df$PHQ), df$Hip7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.051048	0.9228981	0.3567428	326	-0.0575632	0.1584649	Pearson’s product-moment correlation	two.sided

PHQ x Knee 12

df <- df |>
  mutate(Knee12 = if_else(Knee12 == "Yes", 1, 0),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),)

cor.test(as.numeric(df$PHQ), df$Knee12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1043175	1.893835	0.059132	326	-0.0040208	0.2102355	Pearson’s product-moment correlation	two.sided

PHQ x Knee 7

df <- df |>
  mutate(Knee7 = if_else(Knee7 == "Yes", 1, 0),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),)

cor.test(as.numeric(df$PHQ), df$Knee7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1083428	1.967763	0.0499427	326	5.04e-05	0.2141234	Pearson’s product-moment correlation	two.sided

PHQ x Feet 12

df <- df |>
  mutate(Feet12 = if_else(Feet12 == "Yes", 1, 0),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),)

cor.test(as.numeric(df$PHQ), df$Feet12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1347774	2.455877	0.0145755	326	0.0268768	0.2395736	Pearson’s product-moment correlation	two.sided

PHQ x Feet 7

df <- df |>
  mutate(Feet7 = if_else(Feet7 == "Yes", 1, 0),
         PHQ = factor(PHQ, levels = c("Minimal", "Mild", "Moderate", "Moderately Severe", "Severe"), ordered = TRUE),)

cor.test(as.numeric(df$PHQ), df$Feet7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0994333	1.804256	0.072114	326	-0.0089561	0.2055132	Pearson’s product-moment correlation	two.sided

J x pain 12

cor.test(df$JENKINS, df$AnyPain12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.116205	2.112446	0.0354083	326	0.0080129	0.2217079	Pearson’s product-moment correlation	two.sided

J x pain 7

cor.test(df$JENKINS, df$AnyPain7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.2505106	4.672061	4.4e-06	326	0.1461835	0.3493268	Pearson’s product-moment correlation	two.sided

JENKINS x Neck 12

cor.test(df$JENKINS, df$Neck12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1573098	2.876111	0.0042911	326	0.0498664	0.2611538	Pearson’s product-moment correlation	two.sided

J x Neck 7

cor.test(df$JENKINS, df$Neck7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1361103	2.48062	0.0136196	326	0.0282335	0.240853	Pearson’s product-moment correlation	two.sided

J x shoulder 12

cor.test(df$JENKINS, df$Shoulder12YN) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0702015	1.270657	0.2047568	326	-0.0383832	0.1771477	Pearson’s product-moment correlation	two.sided

J x shoulder 7

cor.test(df$JENKINS, df$Shoulder7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1279646	2.329613	0.0204369	326	0.0199481	0.2330283	Pearson’s product-moment correlation	two.sided

J x elbow 12

cor.test(df$JENKINS, df$Elbow12YN) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
-0.0184714	-0.3335672	0.7389206	326	-0.1265113	0.0900015	Pearson’s product-moment correlation	two.sided

J x elbow 7

cor.test(df$JENKINS, df$Elbow7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0142822	0.2578987	0.7966478	326	-0.0941563	0.1223858	Pearson’s product-moment correlation	two.sided

J x wrist 12

cor.test(df$JENKINS, df$Wrist12YN) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.2098681	3.875577	0.0001286	326	0.1039374	0.3110907	Pearson’s product-moment correlation	two.sided

J x wrist 7

cor.test(df$JENKINS, df$Wrist7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1283174	2.336144	0.0200894	326	0.0203067	0.2333675	Pearson’s product-moment correlation	two.sided

J x upper back 12

cor.test(df$JENKINS, df$UBack12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1975774	3.639089	0.000318	326	0.0912366	0.2994629	Pearson’s product-moment correlation	two.sided

J x upper back 7

cor.test(df$JENKINS, df$UBack7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.223723	4.144475	4.35e-05	326	0.1182961	0.3241622	Pearson’s product-moment correlation	two.sided

J x lower back 12

cor.test(df$JENKINS, df$LBack12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1230141	2.238075	0.025891	326	0.0149199	0.2282661	Pearson’s product-moment correlation	two.sided

J x lower back 7

cor.test(df$JENKINS, df$LBack7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.2006564	3.69816	0.0002548	326	0.0944151	0.3023787	Pearson’s product-moment correlation	two.sided

J x hip 12

cor.test(df$JENKINS, df$Hip12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
-0.016753	-0.3025264	0.7624437	326	-0.1248195	0.0917062	Pearson’s product-moment correlation	two.sided

J x hip 7

cor.test(df$JENKINS, df$Hip7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0291963	0.5273783	0.5982896	326	-0.0793475	0.1370559	Pearson’s product-moment correlation	two.sided

J x knee 12

cor.test(df$JENKINS, df$Knee12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0634861	1.148588	0.2515679	326	-0.045117	0.1706061	Pearson’s product-moment correlation	two.sided

J x knee 7

cor.test(df$JENKINS, df$Knee7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1170829	2.128627	0.0340346	326	0.0089029	0.222554	Pearson’s product-moment correlation	two.sided

J x feet 12

cor.test(df$JENKINS, df$Feet12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0914235	1.657636	0.0983529	326	-0.0170381	0.1977585	Pearson’s product-moment correlation	two.sided

J x feet 7

cor.test(df$JENKINS, df$Feet7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0696636	1.260872	0.2082566	326	-0.038923	0.176624	Pearson’s product-moment correlation	two.sided

Hrs Work x pain 12

cor.test(df$`Work_Hr/Wk`, df$AnyPain12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1179667	2.14492	0.0326979	326	0.0097989	0.2234056	Pearson’s product-moment correlation	two.sided

Hrs Work x pain 7

cor.test(df$`Work_Hr/Wk`, df$AnyPain7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1890701	3.476451	0.0005769	326	0.0824656	0.2913966	Pearson’s product-moment correlation	two.sided

Hrs Work x neck 12

cor.test(df$`Work_Hr/Wk`, df$Neck12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0493688	0.8924646	0.3728022	326	-0.0592409	0.1568233	Pearson’s product-moment correlation	two.sided

Hrs Work x neck 7

cor.test(df$`Work_Hr/Wk`, df$Neck7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0869657	1.576178	0.1159545	326	-0.02153	0.1934369	Pearson’s product-moment correlation	two.sided

Hrs Work x shoulder 12

cor.test(df$`Work_Hr/Wk`, df$Shoulder12YN) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0781522	1.415404	0.1579046	326	-0.030398	0.1848804	Pearson’s product-moment correlation	two.sided

Hrs Work x shoulder 7

cor.test(df$`Work_Hr/Wk`, df$Shoulder7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1195139	2.173457	0.0304654	326	0.0113681	0.224896	Pearson’s product-moment correlation	two.sided

Hrs Work x elbow 12

cor.test(df$`Work_Hr/Wk`, df$Elbow12YN) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
-0.0355154	-0.6416519	0.5215501	326	-0.1432573	0.0730585	Pearson’s product-moment correlation	two.sided

Hrs Work x elbow 7

cor.test(df$`Work_Hr/Wk`, df$Elbow7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
-0.085617	-1.551552	0.1217396	326	-0.1921286	0.0228881	Pearson’s product-moment correlation	two.sided

Hrs Work x wrist 12

cor.test(df$`Work_Hr/Wk`, df$Wrist12YN) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1189039	2.162205	0.0313295	326	0.0107494	0.2243085	Pearson’s product-moment correlation	two.sided

Hrs Work x wrist 7

cor.test(df$`Work_Hr/Wk`, df$Wrist7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0921657	1.671208	0.0956402	326	-0.0162898	0.1984776	Pearson’s product-moment correlation	two.sided

Hrs Work x u back 12

cor.test(df$`Work_Hr/Wk`, df$UBack12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1936366	3.563647	0.0004204	326	0.0871716	0.2957282	Pearson’s product-moment correlation	two.sided

Hrs Work x u back 7

cor.test(df$`Work_Hr/Wk`, df$UBack7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1276444	2.323687	0.0207568	326	0.0196227	0.2327204	Pearson’s product-moment correlation	two.sided

Hrs Work x l back 12

cor.test(df$`Work_Hr/Wk`, df$LBack12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.1880534	3.457071	0.0006185	326	0.0814186	0.2904317	Pearson’s product-moment correlation	two.sided

Hrs Work x l back 7

cor.test(df$`Work_Hr/Wk`, df$LBack7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.151691	2.770918	0.0059106	326	0.0441229	0.2557822	Pearson’s product-moment correlation	two.sided

Hrs Work x hip 12

cor.test(df$`Work_Hr/Wk`, df$Hip12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
-0.0492342	-0.8900262	0.3741081	326	-0.1566917	0.0593753	Pearson’s product-moment correlation	two.sided

Hrs Work x hip 7

cor.test(df$`Work_Hr/Wk`, df$Hip7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
-0.013315	-0.2404303	0.8101478	326	-0.1214328	0.095115	Pearson’s product-moment correlation	two.sided

Hrs Work x knee 12

cor.test(df$`Work_Hr/Wk`, df$Knee12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0927289	1.681509	0.0936217	326	-0.0157219	0.1990233	Pearson’s product-moment correlation	two.sided

Hrs Work x knee 7

cor.test(df$`Work_Hr/Wk`, df$Knee7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0513973	0.9292307	0.353457	326	-0.057214	0.1588063	Pearson’s product-moment correlation	two.sided

Hrs Work x feet 12

cor.test(df$`Work_Hr/Wk`, df$Feet12) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0990725	1.797644	0.0731587	326	-0.0093204	0.2051642	Pearson’s product-moment correlation	two.sided

Hrs Work x feet 7

cor.test(df$`Work_Hr/Wk`, df$Feet7) |>
  tidy() |>
  kable()

estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.0693183	1.254592	0.2105257	326	-0.0392694	0.1762878	Pearson’s product-moment correlation	two.sided