Question #79 Do you
experience joint pain and body aches?
A Chi-square test of independence was conducted to assess the
relationship between experiencing joint aches and pain and menopausal
status. A statistically significant association was found,
X2(df=2)=42.999, p<.00001. Cramer’s V was moderate at V=0.11, 95%
CI(0.073, 0.133).
Yes No Prefer Not to Answer
3210 931 0
<NA>
7
status
aches pre-menopause peri-menopause post-menopause
Yes 328 1421 1461
No 167 400 364
status
aches pre-menopause peri-menopause post-menopause
Yes 0.6626263 0.7803405 0.8005479
No 0.3373737 0.2196595 0.1994521
Pearson's Chi-squared test
data: x
X-squared = 42.999, df = 2, p-value = 0.0000000004602
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.1019003
Confidence interval:
2.5% 97.5%
0.07338194 0.13313993
Pairwise comparisons using Pairwise comparison of proportions
data: b$num out of b$den
1 2
2 0.00000018386 -
3 0.00000000041 0.14
P value adjustment method: holm
Question #80 Please
rate the extent to which the joint pain/aches negatively impact your
training
A chi-square goodness-of-fit test was conducted to examine who
said “Often/Always” to Q79 were equally distributed across three stages.
The results indicated that the observed frequencies differed
significantly from the expected frequencies, χ²(2) = 23.515, p
=.0000078.
Additionally, A chi-square goodness-of-fit test was conducted to
examine who said “Rarely/Never” to Q79 were equally distributed across
three stages. The results indicated that the observed frequencies
differed significantly from the expected frequencies, χ²(2) = 17.776,
p=.000138
Always Often Sometimes Rarely Never <NA>
225 627 1481 805 72 938
pre-menopause peri-menopause post-menopause <NA>
496 1823 1829 0
status
aches_imp pre-menopause peri-menopause post-menopause
Always 0.04878049 0.07037298 0.07460643
Often 0.10670732 0.21534131 0.19575633
Sometimes 0.48170732 0.46586911 0.45242984
Rarely 0.33841463 0.22378607 0.25735797
Never 0.02439024 0.02463054 0.01984942
status
aches_imp pre-menopause peri-menopause post-menopause
Always 16 100 109
Often 35 306 286
Sometimes 158 662 661
Rarely 111 318 376
Never 8 35 29
pre-menopause peri-menopause post-menopause
328 1421 1461
Oftenalways other
852 2358
status
achesimp pre-menopause peri-menopause post-menopause
Oftenalways 51 406 395
other 277 1015 1066
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.08559009
Confidence interval:
2.5% 97.5%
0.05462145 0.12123391
Pairwise comparisons using Pairwise comparison of proportions
data: b$num out of b$den
1 2
2 0.0000056 -
3 0.0000382 0.38
P value adjustment method: holm
other rarelynever
2333 877
status
achesimp pre-menopause peri-menopause post-menopause
other 209 1068 1056
rarelynever 119 353 405
Pearson's Chi-squared test
data: x
X-squared = 17.776, df = 2, p-value = 0.000138
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.07441656
Confidence interval:
2.5% 97.5%
0.04451325 0.11013040
Pairwise comparisons using Pairwise comparison of proportions
data: b$num out of b$den
1 2
2 0.00011 -
3 0.00520 0.08675
P value adjustment method: holm
Question #81 . Do you
have frozen shoulder?
A Chi-square test of independence was conducted to assess the
relationship between frozen shoulder and menopausal status. A
statistically significant association was found, X2(df=2)=22.605,
p=.00015. Cramer’s V was trivial at V=0.052, 95% CI(0.0339,
0.0741).
Yes No I don't know
328 3598 222
Prefer Not to Answer <NA>
0 0
status
frozen pre-menopause peri-menopause post-menopause
I don't know 36 109 77
No 441 1553 1604
Yes 19 161 148
status
frozen pre-menopause peri-menopause post-menopause
I don't know 0.07258065 0.05979155 0.04209951
No 0.88911290 0.85189248 0.87698196
Yes 0.03830645 0.08831596 0.08091853
Pearson's Chi-squared test
data: x
X-squared = 22.605, df = 4, p-value = 0.0001518
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.05220021
Confidence interval:
2.5% 97.5%
0.03388257 0.07406649
Pairwise comparisons using Pairwise comparison of proportions
data: b$num out of b$den
1 2
2 0.00097 -
3 0.00313 0.45706
P value adjustment method: holm
Question #82 Please
rate the extent to which frozen shoulder negatively impact your
training.
A chi-square goodness-of-fit test was conducted to examine who
said “Often/Always” to Q81 were equally distributed across three stages.
The results indicated that the observed frequencies did not diff
significantly from the expected frequencies, χ²(2) = 4.497, p =.1056.
Additionally, A chi-square goodness-of-fit test was conducted to
examine who said “Rarely/Never” to Q81 were equally distributed across
three stages. The results indicated that the observed frequencies did
not differ significantly from the expected frequencies, χ²(2) = 1.1287,
p=.5687.
Always Often Sometimes Rarely Never <NA>
48 97 140 36 7 3820
status
fr_impact pre-menopause peri-menopause post-menopause
Always 0.00000000 0.14285714 0.16891892
Often 0.21052632 0.30434783 0.29729730
Sometimes 0.57894737 0.42857143 0.40540541
Rarely 0.15789474 0.10559006 0.10810811
Never 0.05263158 0.01863354 0.02027027
status
fr_impact pre-menopause peri-menopause post-menopause
Always 0 23 25
Often 4 49 44
Sometimes 11 69 60
Rarely 3 17 16
Never 1 3 3
pre-menopause peri-menopause post-menopause <NA>
19 161 148 0
Oftenalways other
145 183
status
frozenimp pre-menopause peri-menopause post-menopause
Oftenalways 4 72 69
other 15 89 79
Pearson's Chi-squared test
data: x
X-squared = 4.497, df = 2, p-value = 0.1056
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.1170919
Confidence interval:
2.5% 97.5%
0.0000000 0.2293522
other rarelynever
285 43
status
frozenimp pre-menopause peri-menopause post-menopause
other 15 141 129
rarelynever 4 20 19
Pearson's Chi-squared test
data: x
X-squared = 1.1287, df = 2, p-value = 0.5687
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.0586614
Confidence interval:
2.5% 97.5%
0.0000000 0.1690329
Question #83. Do you
have plantar fasciitis?
A Chi-square test of independence was conducted to assess the
relationship between plantar fasciitis frozen shoulder and menopausal
status. A statistically significant association was found,
X2(df=2)=21.506, p=.00025. Cramer’s V was trivial at V=0.051, 95%
CI(0.0328, 0.0728).
I don't know No Prefer not to answer
124 3489 1
Yes <NA>
534 0
1 2 4 <NA>
534 3489 124 0
I don't know No Yes
124 3489 534
status
plantar pre-menopause peri-menopause post-menopause
I don't know 21 70 33
No 413 1496 1580
Yes 61 257 216
status
plantar pre-menopause peri-menopause post-menopause
I don't know 0.04242424 0.03839824 0.01804265
No 0.83434343 0.82062534 0.86386003
Yes 0.12323232 0.14097641 0.11809732
Pearson's Chi-squared test
data: x
X-squared = 21.506, df = 4, p-value = 0.0002513
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.05092122
Confidence interval:
2.5% 97.5%
0.03276740 0.07278492
Pairwise comparisons using Pairwise comparison of proportions
data: b$num out of b$den
1 2
2 0.69 -
3 0.81 0.13
P value adjustment method: holm
Question #84. Please
rate the extent to which the plantar fasciitis negatively impact your
training.
A chi-square goodness-of-fit test was conducted to examine who
said “Often/Always” to Q84 were equally distributed across three stages.
The results indicated that the observed frequencies differed
significantly from the expected frequencies, χ²(2) = 7.4466, p =.02415.
Cramer’s V was trivial at V=0.037, 95% CI(0.0000, 0.0688).
Additionally, A chi-square goodness-of-fit test was conducted to
examine who said “Rarely/Never” to Q84 were equally distributed across
three stages. The results indicated that the observed frequencies did
not differ significantly from the expected frequencies, χ²(2) = 3.4859,
p=.175.
Always Often Sometimes Rarely Never <NA>
29 96 222 166 21 3614
status
pf_impact pre-menopause peri-menopause post-menopause
Always 0.04918033 0.05058366 0.06018519
Often 0.08196721 0.22957198 0.14814815
Sometimes 0.45901639 0.40856031 0.41203704
Rarely 0.36065574 0.29182879 0.31944444
Never 0.04918033 0.01945525 0.06018519
status
pf_impact pre-menopause peri-menopause post-menopause
Always 3 13 13
Often 5 59 32
Sometimes 28 105 89
Rarely 22 75 69
Never 3 5 13
pre-menopause peri-menopause post-menopause <NA>
61 257 216 0
Oftenalways other
125 409
status
plantarimp pre-menopause peri-menopause post-menopause
Oftenalways 8 72 45
other 53 185 171
Pearson's Chi-squared test
data: x
X-squared = 7.4466, df = 2, p-value = 0.02415
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.1180888
Confidence interval:
2.5% 97.5%
0.06148507 0.20603650
Pairwise comparisons using Pairwise comparison of proportions
data: b$num out of b$den
1 2
2 0.074 -
3 0.242 0.180
P value adjustment method: holm
other rarelynever
347 187
status
plantarimp pre-menopause peri-menopause post-menopause
other 36 177 134
rarelynever 25 80 82
Pearson's Chi-squared test
data: x
X-squared = 3.4859, df = 2, p-value = 0.175
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.08079535
Confidence interval:
2.5% 97.5%
0.0000000 0.1687117
Question #85. How often
do you alter your training program due to joint pain, frozen
shoulder,
A chi-square goodness-of-fit test was conducted to examine who
said “Often/Always” to Q85 were equally distributed across three stages.
The results indicated that the observed frequencies differed
significantly from the expected frequencies, χ²(2) = 7.4466, p = .02415.
Cramer’s V was trivial at V=0.037, 95% CI(0.0000, 0.0688).
Additionally, A chi-square goodness-of-fit test was conducted to
examine who said “Rarely/Never” to Q84 were equally distributed across
three stages. The results indicated that the observed frequencies did
not differ significantly from the expected frequencies, χ²(2) = 3.4859,
p= .175.
Always Often Sometimes Rarely Never <NA>
175 485 1321 1133 181 853
status
aches_alter pre-menopause peri-menopause post-menopause
Always 0.03508772 0.05122951 0.05910007
Often 0.10233918 0.16256831 0.14237743
Sometimes 0.38304094 0.39412568 0.41168570
Rarely 0.37719298 0.33948087 0.34049698
Never 0.10233918 0.05259563 0.04633983
status
aches_alter pre-menopause peri-menopause post-menopause
Always 12 75 88
Often 35 238 212
Sometimes 131 577 613
Rarely 129 497 507
Never 35 77 69
pre-menopause peri-menopause post-menopause <NA>
342 1464 1489 0
Oftenalways other
660 2635
status
achesimp pre-menopause peri-menopause post-menopause
Oftenalways 47 313 300
other 295 1151 1189
Pearson's Chi-squared test
data: x
X-squared = 10.118, df = 2, p-value = 0.006352
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.05541411
Confidence interval:
2.5% 97.5%
0.02950641 0.09077899
Pairwise comparisons using Pairwise comparison of proportions
data: b$num out of b$den
1 2
2 0.0057 -
3 0.0161 0.4354
P value adjustment method: holm
other rarelynever
347 187
status
achesimp pre-menopause peri-menopause post-menopause
other 36 177 134
rarelynever 25 80 82
Pearson's Chi-squared test
data: x
X-squared = 3.4859, df = 2, p-value = 0.175
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.08079535
Confidence interval:
2.5% 97.5%
0.0000000 0.1687117
Question #86. From one
workout to the next, how often do you feel fully recovered?
A chi-square goodness-of-fit test was conducted to examine who
said “Often/Always” to Q86 were equally distributed across three stages.
The results indicated that the observed frequencies did not differ
significantly from the expected frequencies, χ²(2) = 44.517,
p<.000001.
Additionally, A chi-square goodness-of-fit test was conducted to
examine who said “Rarely/Never” to Q86 were equally distributed across
three stages. The results indicated that the observed frequencies did
not differ significantly from the expected frequencies, χ²(2) = 17.739,
p= .00014.
Always Often Sometimes Rarely Never <NA>
326 2044 1365 372 41 0
status
recover pre-menopause peri-menopause post-menopause
Always 0.060483871 0.055951728 0.106068890
Often 0.580645161 0.458584750 0.503007108
Sometimes 0.298387097 0.366428963 0.300164024
Rarely 0.052419355 0.105869446 0.083652269
Never 0.008064516 0.013165112 0.007107709
status
recover pre-menopause peri-menopause post-menopause
Always 30 102 194
Often 288 836 920
Sometimes 148 668 549
Rarely 26 193 153
Never 4 24 13
pre-menopause peri-menopause post-menopause <NA>
496 1823 1829 0
Oftenalways other
2370 1778
status
rec pre-menopause peri-menopause post-menopause
Oftenalways 318 938 1114
other 178 885 715
Pearson's Chi-squared test
data: x
X-squared = 44.517, df = 2, p-value = 0.0000000002155
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.1035956
Confidence interval:
2.5% 97.5%
0.07505037 0.13480054
Pairwise comparisons using Pairwise comparison of proportions
data: b$num out of b$den
1 2
2 0.000001367 -
3 0.22 0.000000028
P value adjustment method: holm
other rarelynever
3735 413
status
rec pre-menopause peri-menopause post-menopause
other 466 1606 1663
rarelynever 30 217 166
Pearson's Chi-squared test
data: x
X-squared = 17.739, df = 2, p-value = 0.0001406
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.06539596
Confidence interval:
2.5% 97.5%
0.03909832 0.09681374
Pairwise comparisons using Pairwise comparison of proportions
data: b$num out of b$den
1 2
2 0.074 -
3 0.242 0.180
P value adjustment method: holm
Question #87. Do you
experience urinary incontinence?
A Chi-square test of independence was conducted to assess the
relationship between incontinence and menopausal status. A statistically
significant association was not found, X2(df=2)=21.699, p=.000019.
Cramer’s V was trivial at V=0.072, 95% CI(0.0454, 0.1038).
1 2 3
1526 2614 8
Yes No Prefer Not to Answer
1526 2614 0
<NA>
8
status
incont pre-menopause peri-menopause post-menopause
No 351 1094 1169
Yes 142 727 657
status
incont pre-menopause peri-menopause post-menopause
No 0.7119675 0.6007688 0.6401972
Yes 0.2880325 0.3992312 0.3598028
Pearson's Chi-squared test
data: x
X-squared = 21.699, df = 2, p-value = 0.00001942
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.07239642
Confidence interval:
2.5% 97.5%
0.04536575 0.10380066
Pairwise comparisons using Pairwise comparison of proportions
data: b$num out of b$den
1 2
2 0.000023 -
3 0.007 0.016
P value adjustment method: holm
Question #88. What do
you believe was the cause of your urinary incontinence?
1 2 3 4
508 693 104 221
Menopause Childbirth Other I don't know <NA>
508 693 104 221 2622
status
cause pre-menopause peri-menopause post-menopause
Childbirth 90 367 236
I don't know 34 109 78
Menopause 9 200 299
Other 9 51 44
status
cause pre-menopause peri-menopause post-menopause
Childbirth 0.63380282 0.50481431 0.35920852
I don't know 0.23943662 0.14993122 0.11872146
Menopause 0.06338028 0.27510316 0.45509893
Other 0.06338028 0.07015131 0.06697108
Pearson's Chi-squared test
data: x
X-squared = 107.1, df = 6, p-value < 0.00000000000000022
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.1873243
Confidence interval:
2.5% 97.5%
0.1534578 0.2232019
Question #89. Please
rate the extent to which urinary incontinence impacts your desire to
engage in exercise.
A chi-square goodness-of-fit test was conducted to examine who
said “Often/Always” to Q89 were equally distributed across three stages.
The results indicated that the observed frequencies differed
significantly from the expected frequencies, χ²(2) = 8.3636, p = .01527.
Cramer’s V was moderate at V=0.187, 95% CI(0.1535, 0.2232).
Additionally, A chi-square goodness-of-fit test was conducted to
examine who said “Rarely/Never” to Q89 were equally distributed across
three stages. The results indicated that the observed frequencies did
not differ significantly from the expected frequencies, χ²(2) = 2.3826,
p= .3038.
Always Often Sometimes Rarely Never <NA>
36 124 311 563 492 2622
status
in_impact pre-menopause peri-menopause post-menopause
Always 0.02816901 0.02475928 0.02130898
Often 0.08450704 0.10178817 0.05783866
Sometimes 0.19014085 0.20082531 0.21004566
Rarely 0.37323944 0.37414030 0.36225266
Never 0.32394366 0.29848693 0.34855403
status
in_impact pre-menopause peri-menopause post-menopause
Always 4 18 14
Often 12 74 38
Sometimes 27 146 138
Rarely 53 272 238
Never 46 217 229
pre-menopause peri-menopause post-menopause <NA>
142 727 657 0
Oftenalways other
160 1366
status
incimp pre-menopause peri-menopause post-menopause
Oftenalways 16 92 52
other 126 635 605
Pearson's Chi-squared test
data: x
X-squared = 8.3636, df = 2, p-value = 0.01527
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.07403208
Confidence interval:
2.5% 97.5%
0.03867997 0.12603885
other rarelynever
471 1055
status
incimp pre-menopause peri-menopause post-menopause
other 43 238 190
rarelynever 99 489 467
Pearson's Chi-squared test
data: x
X-squared = 2.3825, df = 2, p-value = 0.3038
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.03951297
Confidence interval:
2.5% 97.5%
0.00000000 0.09138077
---
title: "FFMS Manuscript 3 Statistical Analysis"
author: ""
date: ""
output:
  html_document: 
    toc: yes
    toc_depth: 4
    toc_float: yes
    number_sections: yes
    toc_collapsed: yes
    code_folding: hide
    code_download: yes
    smooth_scroll: yes
    theme: lumen
  pdf_document: 
    toc: yes
    toc_depth: 4
    fig_caption: yes
    number_sections: yes
    fig_width: 3
    fig_height: 3
  word_document: 
    toc: yes
    toc_depth: 4
    fig_caption: yes
    keep_md: yes
editor_options: 
  chunk_output_type: inline
---

```{css, echo = FALSE}
#TOC::before {
  content: "Table of Contents";
  font-weight: bold;
  font-size: 1.2em;
  display: block;
  color: navy;
  margin-bottom: 10px;
}


div#TOC li {     /* table of content  */
    list-style:upper-roman;
    background-image:none;
    background-repeat:none;
    background-position:0;
}

h1.title {    /* level 1 header of title  */
  font-size: 22px;
  font-weight: bold;
  color: DarkRed;
  text-align: center;
  font-family: "Gill Sans", sans-serif;
}

h4.author { /* Header 4 - and the author and data headers use this too  */
  font-size: 15px;
  font-weight: bold;
  font-family: system-ui;
  color: navy;
  text-align: center;
}

h4.date { /* Header 4 - and the author and data headers use this too  */
  font-size: 18px;
  font-weight: bold;
  font-family: "Gill Sans", sans-serif;
  color: DarkBlue;
  text-align: center;
}

h1 { /* Header 1 - and the author and data headers use this too  */
    font-size: 20px;
    font-weight: bold;
    font-family: "Times New Roman", Times, serif;
    color: darkred;
    text-align: left;
}

h2 { /* Header 2 - and the author and data headers use this too  */
    font-size: 18px;
    font-weight: bold;
    font-family: "Times New Roman", Times, serif;
    color: navy;
    text-align: left;
}

h3 { /* Header 3 - and the author and data headers use this too  */
    font-size: 16px;
    font-weight: bold;
    font-family: "Times New Roman", Times, serif;
    color: navy;
    text-align: left;
}

h4 { /* Header 4 - and the author and data headers use this too  */
    font-size: 14px;
  font-weight: bold;
    font-family: "Times New Roman", Times, serif;
    color: darkred;
    text-align: left;
}

/* Add dots after numbered headers */
.header-section-number::after {
  content: ".";

body { background-color:white; }

.highlightme { background-color:yellow; }

p { background-color:white; }

}
```

```{r setup, include=F}

knitr::opts_chunk$set(echo = F, comment=NA, warning=F, results=T, message=F)

setwd("C:/Users/75LPYOTT/OneDrive - West Chester University of PA/FFMS")

library(FSA)
library(dplyr)
library(kableExtra)
library(splitstackshape)
library(summarytools)
library(tidyverse)
library(tidyr)
library(VIM)
library(zoo)
library(jtools)
library(broom)
library(vcd)
library(visdat)
library(skimr)
library(janitor)
library(ggplot2)
library(gmodels)
library(dunn.test)
library(rstatix)
library(DescTools)
library(effectsize)
library(confintr)
library(chisquare)
library(forcats)
options(scipen=999)

#wrangling

first.data=read.csv("FFMS demog vars.csv", header=T)

first.data = first.data %>%
  filter(WTKG<180)

first.data = first.data %>%
  mutate(status=case_when(MENOSTATUS==1~"pre-menopause",
                           MENOSTATUS==2~"peri-menopause",
                           MENOSTATUS==3~"post-menopause", TRUE~NA))

first.data$status=fct_inorder(first.data$status)
```

# Question #79 Do you experience joint pain and body aches?

*A Chi-square test of independence was conducted to assess the relationship between experiencing joint aches and pain and menopausal status. A statistically significant association was found, X2(df=2)=42.999, p<.00001. Cramer's V was moderate at V=0.11, 95% CI(0.073, 0.133).*

```{r Q79, include=T}

first.data = first.data %>%
  mutate(aches=case_when(ACHES==1~"Yes",
                          ACHES==2~"No", 
                          ACHES==3~"Prefer not to answer", TRUE~NA))
first.data$aches <- factor(first.data$aches, 
                                levels = c("Yes", "No", "Prefer Not to Answer"))
table(first.data$aches, useNA = "always")
#Remove prefer not to answer=0 and missing=7
data = first.data %>%
  filter(ACHES<3)

data$aches <- factor(data$aches, levels = c("Yes", "No"))

x=xtabs(~aches+status, data=data)
x
prop.table(x,2)
result=chisq.test(x)
result
ci_cramersv(result)

b=data.frame(
num=c(328, 1421, 1461),
den=c(328+167, 1421+400 ,1461+364),
stage=c("pre", "peri", "post")
)

pairwise.prop.test(x=b$num, n=b$den, p.adjust.method = "holm")

```

# Question #80 Please rate the extent to which the joint pain/aches negatively impact your training

*A chi-square goodness-of-fit test was conducted to examine who said "Often/Always" to Q79 were equally distributed across three stages. The results indicated that the observed frequencies differed significantly from the expected frequencies, χ²(2) = 23.515, p =.0000078. *

*Additionally, A chi-square goodness-of-fit test was conducted to examine who said "Rarely/Never" to Q79 were equally distributed across three stages. The results indicated that the observed frequencies differed significantly from the expected frequencies, χ²(2) = 17.776, p=.000138*

```{r Q80, include=T}

first.data = first.data %>%
    mutate(aches_imp=case_when(ACHES_IMPACT==1~"Always",
                          ACHES_IMPACT==2~"Often", 
                          ACHES_IMPACT==3~"Sometimes",
                          ACHES_IMPACT==4~"Rarely", 
                          ACHES_IMPACT==5~"Never", TRUE~NA))
first.data$aches_imp <- factor(first.data$aches_imp, 
                                levels = c("Always", "Often", "Sometimes", "Rarely", "Never"))
table(first.data$aches_imp, useNA = "always")
table(first.data$status, useNA = "always")
#Remove missing N=938, skip logic
data = first.data %>%
  filter(ACHES_IMPACT<6)
x=xtabs(~aches_imp+status, data=data)
prop.table(x,2)
x
table(data$status)

#often and always-combine responses for x2 test
often = data %>%
  mutate(achesimp=case_when(ACHES_IMPACT<=2~"Oftenalways",
                               ACHES_IMPACT>2~"other"))

table(often$achesimp)
x=xtabs(~achesimp+status, data=often)
x
result=chisq.test(x)
ci_cramersv(result)

b=data.frame(
num=c(51, 406, 395),
den=c(51+277, 406+1015, 395+1066),
stage=c("pre", "peri", "post")
)

pairwise.prop.test(x=b$num, n=b$den, p.adjust.method = "holm")

#never and rarely-combine responses for x2 test
never = data %>%
  mutate(achesimp=case_when(ACHES_IMPACT>=4~"rarelynever",
                               ACHES_IMPACT<4~"other"))

table(never$achesimp)
x=xtabs(~achesimp+status, data=never)
x
result=chisq.test(x)
result
ci_cramersv(result)

b=data.frame(
num=c(119, 353, 405),
den=c(119+209, 353+1068, 405+1056),
stage=c("pre", "peri", "post")
)

pairwise.prop.test(x=b$num, n=b$den, p.adjust.method = "holm")

```

# Question #81 . Do you have frozen shoulder?

*A Chi-square test of independence was conducted to assess the relationship between frozen shoulder and menopausal status. A statistically significant association was found, X2(df=2)=22.605, p=.00015. Cramer's V was trivial at V=0.052, 95% CI(0.0339, 0.0741).*

```{r Q81, include=T}

first.data = first.data %>%
  mutate(frozen=case_when(FROZEN==1~"Yes",
                          FROZEN==2~"No", 
                          FROZEN==4~"I don't know", 
                          FROZEN==3~"Prefer not to answer", TRUE~NA))
first.data$frozen <- factor(first.data$frozen, 
                                levels = c("Yes", "No", "I don't know", "Prefer Not to Answer"))
table(first.data$frozen, useNA = "always")
#Remove missing N=0, PNTA=0
data = first.data %>%
  filter(FROZEN==1 | FROZEN==2 | FROZEN==4)

data$frozen <- as.character(data$frozen)

x=xtabs(~frozen+status, data=data)
x
prop.table(x,2)
result=chisq.test(x)
result
ci_cramersv(result)

b=data.frame(
num=c(19, 161, 148),
den=c(19+441+36, 161+1553+109, 148+1604+77),
stage=c("pre", "peri", "post")
)

pairwise.prop.test(x=b$num, n=b$den, p.adjust.method = "holm")

```

# Question #82 Please rate the extent to which frozen shoulder negatively impact your training. 

*A chi-square goodness-of-fit test was conducted to examine who said "Often/Always" to Q81 were equally distributed across three stages. The results indicated that the observed frequencies did not diff significantly from the expected frequencies, χ²(2) = 4.497, p =.1056. *

*Additionally, A chi-square goodness-of-fit test was conducted to examine who said "Rarely/Never" to Q81 were equally distributed across three stages. The results indicated that the observed frequencies did not differ significantly from the expected frequencies, χ²(2) = 1.1287, p=.5687. *


```{r Q82, include=T}

first.data = first.data %>%
  mutate(fr_impact=case_when(FROZEN_IMPACT==1~"Always",
                          FROZEN_IMPACT==2~"Often", 
                          FROZEN_IMPACT==3~"Sometimes", 
                          FROZEN_IMPACT==4~"Rarely", 
                          FROZEN_IMPACT==5~"Never", TRUE~NA))
first.data$fr_impact <- factor(first.data$fr_impact, 
                                levels = c("Always", "Often", "Sometimes", "Rarely", "Never"))
table(first.data$fr_impact, useNA = "always")


#Remove missing N=3820, skip logic
data = first.data %>%
  filter(FROZEN_IMPACT<6)
x=xtabs(~fr_impact+status, data=data)
prop.table(x,2)
x
table(data$status, useNA="always")

#often and always-combine responses for x2 test

often = data %>%
  mutate(frozenimp=case_when(FROZEN_IMPACT<=2~"Oftenalways",
                               FROZEN_IMPACT>2~"other"))

table(often$frozenimp)
x=xtabs(~frozenimp+status, data=often)
x
result=chisq.test(x)
result
ci_cramersv(result)

#never and rarely-combine responses for x2 test

never = data %>%
  mutate(frozenimp=case_when(FROZEN_IMPACT>=4~"rarelynever",
                               FROZEN_IMPACT<4~"other"))

table(never$frozenimp)
x=xtabs(~frozenimp+status, data=never)
x
result=chisq.test(x)
result
ci_cramersv(result)
```

# Question #83. Do you have plantar fasciitis?

*A Chi-square test of independence was conducted to assess the relationship between plantar fasciitis frozen shoulder and menopausal status. A statistically significant association was found, X2(df=2)=21.506, p=.00025. Cramer's V was trivial at V=0.051, 95% CI(0.0328, 0.0728). *


```{r Q83}

first.data = first.data %>%
  mutate(plantar=case_when(PLANTAR==1~"Yes",
                          PLANTAR==2~"No", 
                          PLANTAR==4~"I don't know", 
                          PLANTAR==3~"Prefer not to answer", TRUE~NA))
#first.data$plantar <- factor(first.data$plantar, 
                               # levels = c("Yes", "No", "I don't know", "Prefer Not to Answer"))
table(first.data$plantar, useNA = "always")
#Remove missing N=1, PNTA=0
data = first.data %>%
  filter(PLANTAR==1 | PLANTAR==2 | PLANTAR==4)

table(data$PLANTAR, useNA="always")
table(data$plantar)
data$plantar <- as.character(data$plantar)

x=xtabs(~plantar+status, data=data)
x
prop.table(x,2)
result=chisq.test(x)
result
ci_cramersv(result)

b=data.frame(
num=c(61, 257, 216),
den=c(61+21+413, 257+1496+70, 216+33+1580),
stage=c("pre", "peri", "post")
)

pairwise.prop.test(x=b$num, n=b$den, p.adjust.method = "holm")


```

# Question #84. Please rate the extent to which the plantar fasciitis negatively impact your training.

*A chi-square goodness-of-fit test was conducted to examine who said "Often/Always" to Q84 were equally distributed across three stages. The results indicated that the observed frequencies differed significantly from the expected frequencies, χ²(2) = 7.4466, p =.02415. Cramer's V was trivial at V=0.037, 95% CI(0.0000, 0.0688). *

*Additionally, A chi-square goodness-of-fit test was conducted to examine who said "Rarely/Never" to Q84 were equally distributed across three stages. The results indicated that the observed frequencies did not differ significantly from the expected frequencies, χ²(2) = 3.4859, p=.175. *

```{r Q84, include=T}

first.data = first.data %>%
  mutate(pf_impact=case_when(PLANTAR_IMPACT==1~"Always",
                          PLANTAR_IMPACT==2~"Often", 
                          PLANTAR_IMPACT==3~"Sometimes", 
                          PLANTAR_IMPACT==4~"Rarely", 
                          PLANTAR_IMPACT==5~"Never", TRUE~NA))
first.data$pf_impact <- factor(first.data$pf_impact, 
                                levels = c("Always", "Often", "Sometimes", "Rarely", "Never"))
table(first.data$pf_impact, useNA = "always")


#Remove missing N=3614, skip logic
data = first.data %>%
  filter(PLANTAR_IMPACT<6)
x=xtabs(~pf_impact+status, data=data)
prop.table(x,2)
x
table(data$status, useNA="always")

#often and always-combine responses for x2 test
often = data %>%
  mutate(plantarimp=case_when(PLANTAR_IMPACT<=2~"Oftenalways",
                               PLANTAR_IMPACT>2~"other"))

table(often$plantarimp)
x=xtabs(~plantarimp+status, data=often)
x
result=chisq.test(x)
result
ci_cramersv(result)

b=data.frame(
num=c(8, 72, 45),
den=c(8+53, 72+185, 45+171),
stage=c("pre", "peri", "post")
)

pairwise.prop.test(x=b$num, n=b$den, p.adjust.method = "holm")

#never and rarely-combine responses for x2 test

never = data %>%
  mutate(plantarimp=case_when(PLANTAR_IMPACT>=4~"rarelynever",
                               PLANTAR_IMPACT<4~"other"))

table(never$plantarimp)
x=xtabs(~plantarimp+status, data=never)
x
result=chisq.test(x)
result
ci_cramersv(result)

```

# Question #85. How often do you alter your training program due to joint pain, frozen shoulder,  
*A chi-square goodness-of-fit test was conducted to examine who said "Often/Always" to Q85 were equally distributed across three stages. The results indicated that the observed frequencies differed significantly from the expected frequencies, χ²(2) = 7.4466, p = .02415. Cramer's V was trivial at V=0.037, 95% CI(0.0000, 0.0688). *

*Additionally, A chi-square goodness-of-fit test was conducted to examine who said "Rarely/Never" to Q84 were equally distributed across three stages. The results indicated that the observed frequencies did not differ significantly from the expected frequencies, χ²(2) = 3.4859, p= .175. *


```{r Q85, include=T}
first.data = first.data %>%
  mutate(aches_alter=case_when(ACHES_ALTER==1~"Always",
                          ACHES_ALTER==2~"Often", 
                          ACHES_ALTER==3~"Sometimes", 
                          ACHES_ALTER==4~"Rarely", 
                          ACHES_ALTER==5~"Never", TRUE~NA))
first.data$aches_alter <- factor(first.data$aches_alter, 
                                levels = c("Always", "Often", "Sometimes", "Rarely", "Never"))
table(first.data$aches_alter, useNA = "always")


#Remove missing N=853, skip logic
data = first.data %>%
  filter(ACHES_ALTER<6)
x=xtabs(~aches_alter+status, data=data)
prop.table(x,2)
x
table(data$status, useNA="always")

#often and always-combine responses for x2 test
often = data %>%
  mutate(achesimp=case_when(ACHES_ALTER<=2~"Oftenalways",
                               ACHES_ALTER>2~"other"))

table(often$achesimp)
x=xtabs(~achesimp+status, data=often)
x
result=chisq.test(x)
result
ci_cramersv(result)

b=data.frame(
num=c(47, 313, 300),
den=c(47+295, 313+1151, 300+1189),
stage=c("pre", "peri", "post")
)

pairwise.prop.test(x=b$num, n=b$den, p.adjust.method = "holm")

#never and rarely-combine responses for x2 test

never = data %>%
  mutate(achesimp=case_when(PLANTAR_IMPACT>=4~"rarelynever",
                               PLANTAR_IMPACT<4~"other"))

table(never$achesimp)
x=xtabs(~achesimp+status, data=never)
x
result=chisq.test(x)
result
ci_cramersv(result)
```


# Question #86. From one workout to the next, how often do you feel fully recovered?

*A chi-square goodness-of-fit test was conducted to examine who said "Often/Always" to Q86 were equally distributed across three stages. The results indicated that the observed frequencies did not differ significantly from the expected frequencies, χ²(2) = 44.517, p<.000001.*

*Additionally, A chi-square goodness-of-fit test was conducted to examine who said "Rarely/Never" to Q86 were equally distributed across three stages. The results indicated that the observed frequencies did not differ significantly from the expected frequencies, χ²(2) = 17.739, p= .00014. *

```{r Q86}

first.data = first.data %>%
  mutate(recover=case_when(RECOVERY==1~"Always",
                          RECOVERY==2~"Often", 
                          RECOVERY==3~"Sometimes", 
                          RECOVERY==4~"Rarely", 
                          RECOVERY==5~"Never", TRUE~NA))
first.data$recover <- factor(first.data$recover, 
                                levels = c("Always", "Often", "Sometimes", "Rarely", "Never"))
table(first.data$recover, useNA = "always")

#No missing values for this question
#data = first.data %>% filter(FROZEN_IMPACT<6)
x=xtabs(~recover+status, data=first.data)
prop.table(x,2)
x
table(first.data$status, useNA="always")


#often and always-combine responses for x2 test

often = first.data %>%
  mutate(rec=case_when(RECOVERY<=2~"Oftenalways",
                               RECOVERY>2~"other"))

table(often$rec)
x=xtabs(~rec+status, data=often)
x
result=chisq.test(x)
result
ci_cramersv(result)

b=data.frame(
num=c(318, 938, 1114),
den=c(318+178, 938+885, 1114+714),
stage=c("pre", "peri", "post")
)

pairwise.prop.test(x=b$num, n=b$den, p.adjust.method = "holm")

#never and rarely

never = first.data %>%
  mutate(rec=case_when(RECOVERY>=4~"rarelynever",
                               RECOVERY<4~"other"))

table(never$rec)
x=xtabs(~rec+status, data=never)
x
result=chisq.test(x)
result
ci_cramersv(result)

b=data.frame(
num=c(8, 72, 45),
den=c(8+53, 72+185, 45+171),
stage=c("pre", "peri", "post")
)

pairwise.prop.test(x=b$num, n=b$den, p.adjust.method = "holm")

```

# Question #87. Do you experience urinary incontinence?

*A Chi-square test of independence was conducted to assess the relationship between incontinence and menopausal status. A statistically significant association was not found, X2(df=2)=21.699, p=.000019. Cramer's V was trivial at V=0.072, 95% CI(0.0454, 0.1038). *

```{r Q87}
table(first.data$INCONTINENCE)
first.data = first.data %>%
  mutate(incont=case_when(INCONTINENCE==1~"Yes",
                          INCONTINENCE==2~"No", 
                          INCONTINENCE==3~"Prefer not to answer", TRUE~NA))
first.data$incont <- factor(first.data$incont, 
                                levels = c("Yes", "No", "Prefer Not to Answer"))
table(first.data$incont, useNA = "always")
#Remove missing N=8, IDK=0, PNTA=0
data = first.data %>%
  filter(incont<3)

first.data$incont <- as.character(first.data$incont)

x=xtabs(~incont+status, data=first.data)
x
prop.table(x,2)
result=chisq.test(x)
result
ci_cramersv(result)

b=data.frame(
num=c(142, 727, 657),
den=c(142+351, 727+1094, 657+1169),
stage=c("pre", "peri", "post")
)

pairwise.prop.test(x=b$num, n=b$den, p.adjust.method = "holm")


```


# Question #88. What do you believe was the cause of your urinary incontinence?


```{r Q88}
table(first.data$INCONT_CAUSE)
first.data = first.data %>%
  mutate(cause=case_when(INCONT_CAUSE==1~"Menopause",
                          INCONT_CAUSE==2~"Childbirth", 
                          INCONT_CAUSE==3~"Other", 
                          INCONT_CAUSE==4~"I don't know", TRUE~NA))
first.data$cause <- factor(first.data$cause, 
                                levels = c("Menopause", "Childbirth", "Other","I don't know"))
table(first.data$cause, useNA = "always")
#Remove missing N=2622, IDK=0, PNTA=0
data = first.data %>%
  filter(INCONT_CAUSE<5)

data$cause <- as.character(data$cause)

x=xtabs(~cause+status, data=data)
x
prop.table(x,2)
result=chisq.test(x)
result
ci_cramersv(result)

```


# Question #89. Please rate the extent to which urinary incontinence impacts your desire to engage in exercise. 

*A chi-square goodness-of-fit test was conducted to examine who said "Often/Always" to Q89 were equally distributed across three stages. The results indicated that the observed frequencies differed significantly from the expected frequencies, χ²(2) = 8.3636, p = .01527. Cramer's V was moderate at V=0.187, 95% CI(0.1535, 0.2232).*

*Additionally, A chi-square goodness-of-fit test was conducted to examine who said "Rarely/Never" to Q89 were equally distributed across three stages. The results indicated that the observed frequencies did not differ significantly from the expected frequencies, χ²(2) = 2.3826, p= .3038. *

```{r Q89}

first.data = first.data %>%
  mutate(in_impact=case_when(INCONT_IMPACT==1~"Always",
                          INCONT_IMPACT==2~"Often", 
                          INCONT_IMPACT==3~"Sometimes", 
                          INCONT_IMPACT==4~"Rarely", 
                          INCONT_IMPACT==5~"Never", TRUE~NA))
first.data$in_impact <- factor(first.data$in_impact, 
                                levels = c("Always", "Often", "Sometimes", "Rarely", "Never"))
table(first.data$in_impact, useNA = "always")


#Remove missing N=2622, skip logic
data = first.data %>%
  filter(INCONT_IMPACT<6)
x=xtabs(~in_impact+status, data=data)
prop.table(x,2)
x
table(data$status, useNA="always")


#often and always-combine responses for x2 test

often = data %>%
  mutate(incimp=case_when(INCONT_IMPACT<=2~"Oftenalways",
                               INCONT_IMPACT>2~"other"))

table(often$incimp)
x=xtabs(~incimp+status, data=often)
x
result=chisq.test(x)
result
ci_cramersv(result)


#never and rarely-combine responses for x2 test

never = data %>%
  mutate(incimp=case_when(INCONT_IMPACT>=4~"rarelynever",
                               INCONT_IMPACT<4~"other"))

table(never$incimp)
x=xtabs(~incimp+status, data=never)
x
result=chisq.test(x)
result
ci_cramersv(result)
```

