FFMS Manuscript 2 Statistical Analysis
1 Question #10 Body Weight by status
- N size, % of respondents, mean, SD, median for all respondents and
across all menopausal stage.
- Please conduct an analysis of statistical significance between the three groups (Pre; Peri; Post) for reported value of Body Weight.
- Between-Group Comparison: Is there a statistically significant difference in the Body Weight value reported between women who are premenopausal, perimenopausal, and postmenopausal?
The weight variable was skewed for all three groups (Shapiro-Wilk p-values all less than .0001). As a result, a Kruskal-Wallis test was used to find differences among the menopausal status groups for the variable WTKG. A statistically significant difference was found among the status groups, X2(df=2)=17.908, p=.00013. The Dunn test was used to evaluate pairwise comparisons, and only perimenopause and postmenopause were found to be statistically significant (adjusted p=.00007).
| mean | sd | median | n |
|---|---|---|---|
| 66.18129 | 10.88626 | 65 | 4148 |
| status | mean | sd | median | n |
|---|---|---|---|---|
| pre-menopause | 66.54032 | 12.39984 | 65 | 496 |
| peri-menopause | 66.85299 | 10.59572 | 65 | 1823 |
| post-menopause | 65.41443 | 10.68849 | 64 | 1829 |
| status | statistic | p_value |
|---|---|---|
| pre-menopause | 0.8534337 | 0 |
| peri-menopause | 0.9432977 | 0 |
| post-menopause | 0.9336935 | 0 |
Kruskal-Wallis rank sum test
data: WTKG by status
Kruskal-Wallis chi-squared = 17.908, df = 2, p-value = 0.0001292 Comparison Z P.unadj P.adj
1 peri-menopause - post-menopause 4.229002 0.00002347304 0.00007041912
2 peri-menopause - pre-menopause 1.239827 0.21503921122 0.21503921122
3 post-menopause - pre-menopause -1.524382 0.12741336926 0.254826738512 Question #20 Are you taking any prescription anti-depressant medications?
- Analysis of statistical significance between the three groups (Pre; Peri; Post) for the answer ‘Yes’.
- Between-Group Comparison: Is there a statistically significant difference in the number of ‘Yes’ responses between women who are premenopausal, perimenopausal, and postmenopausal?
A Chi-square test of independence was conducted to assess the relationship between antidepressant use and menopausal status. No statistically significant association was found, X2(df=2)=1.328, p=.5148. Cramer’s V was trivial at V=0.018, 95% CI(0.00, 0.049).
Yes No I don't know
462 3678 0
Prefer Not to Answer <NA>
7 1 status
dep pre-menopause peri-menopause post-menopause
Yes 48 209 205
No 448 1607 1623 status
dep pre-menopause peri-menopause post-menopause
Yes 0.09677419 0.11508811 0.11214442
No 0.90322581 0.88491189 0.88785558
Pearson's Chi-squared test
data: x
X-squared = 1.328, df = 2, p-value = 0.5148 status
dep pre-menopause peri-menopause post-menopause
Yes 55.35072 202.6551 203.9942
No 440.64928 1613.3449 1624.0058
1-sample proportions test with continuity correction
data: 48 out of 48 + 448, null probability 0.5
X-squared = 320.97, df = 1, p-value < 0.00000000000000022
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.07288888 0.12709489
sample estimates:
p
0.09677419
1-sample proportions test with continuity correction
data: 209 out of 209 + 1607, null probability 0.5
X-squared = 1074.7, df = 1, p-value < 0.00000000000000022
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.1009565 0.1308752
sample estimates:
p
0.1150881
1-sample proportions test with continuity correction
data: 205 out of 205 + 1623, null probability 0.5
X-squared = 1098.4, df = 1, p-value < 0.00000000000000022
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.09822743 0.12771880
sample estimates:
p
0.1121444
Pairwise comparisons using Pairwise comparison of proportions
data: b$num out of b$den
1 2
2 0.85 -
3 0.85 0.85
P value adjustment method: holm
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.01791018
Confidence interval:
2.5% 97.5%
0.00000000 0.04910733 3 Question #21 Are you taking any prescription sleep medications?
- Analysis of statistical significance between the three groups (Pre; Peri; Post) for the answer ‘Yes’.
- Between-Group Comparison: Is there a statistically significant difference in the number of ‘Yes’ responses between women who are premenopausal, perimenopausal, and postmenopausal?
A Chi-square test of independence was conducted to assess the relationship between and menopausal status. A statistically significant association was found, X2(df=2)=6.3055, p=.0427. Cramer’s V was trivial at V=0.039, 95% CI(0.00, 0.071).
Yes No I don't know
192 3950 4
Prefer Not to Answer <NA>
0 2 status
sleep pre-menopause peri-menopause post-menopause
Yes 12 88 92
No 483 1733 1734 status
sleep pre-menopause peri-menopause post-menopause
Yes 0.02424242 0.04832510 0.05038335
No 0.97575758 0.95167490 0.94961665
Pearson's Chi-squared test
data: x
X-squared = 6.3055, df = 2, p-value = 0.04274 status
sleep pre-menopause peri-menopause post-menopause
Yes 22.94544 84.4114 84.64317
No 472.05456 1736.5886 1741.35683
1-sample proportions test with continuity correction
data: 12 out of 12 + 483, null probability 0.5
X-squared = 446.26, df = 1, p-value < 0.00000000000000022
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.01318411 0.04315340
sample estimates:
p
0.02424242
1-sample proportions test with continuity correction
data: 88 out of 88 + 1733, null probability 0.5
X-squared = 1484.2, df = 1, p-value < 0.00000000000000022
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.03914314 0.05946140
sample estimates:
p
0.0483251
1-sample proportions test with continuity correction
data: 92 out of 92 + 1734, null probability 0.5
X-squared = 1474.7, df = 1, p-value < 0.00000000000000022
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.04101321 0.06169254
sample estimates:
p
0.05038335
Pairwise comparisons using Pairwise comparison of proportions
data: b$num out of b$den
1 2
2 0.054 -
3 0.053 0.833
P value adjustment method: holm
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.03901698
Confidence interval:
2.5% 97.5%
0.00000000 0.07060679 4 Question #22 Are you taking any prescription weight loss medications?
- Please conduct an analysis of statistical significance between the three groups (Pre; Peri; Post) for the answer ‘Yes’.
- Between-Group Comparison: Is there a statistically significant difference in the number of ‘Yes’ responses between women who are premenopausal, perimenopausal, and postmenopausal?
A Chi-square test of independence was conducted to assess the relationship between taking weight loss medication and menopausal status. No statistically significant association was found, X2(df=2)=0.316, p=.8539. Cramer’s V was trivial at V=0.01, 95% CI(0.00, 0.037).
Yes No I don't know
203 3940 1
Prefer Not to Answer <NA>
0 4 status
WLMED pre-menopause peri-menopause post-menopause
Yes 23 93 87
No 471 1727 1742 status
WLMED pre-menopause peri-menopause post-menopause
Yes 0.04655870 0.05109890 0.04756698
No 0.95344130 0.94890110 0.95243302
Pearson's Chi-squared test
data: x
X-squared = 0.31585, df = 2, p-value = 0.8539 status
WLMED pre-menopause peri-menopause post-menopause
Yes 24.20517 89.17692 89.61791
No 469.79483 1730.82308 1739.38209
1-sample proportions test with continuity correction
data: 23 out of 23 + 471, null probability 0.5
X-squared = 404.47, df = 1, p-value < 0.00000000000000022
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.03040551 0.07008276
sample estimates:
p
0.0465587
1-sample proportions test with continuity correction
data: 93 out of 93 + 1727, null probability 0.5
X-squared = 1465.2, df = 1, p-value < 0.00000000000000022
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.04164589 0.06249398
sample estimates:
p
0.0510989
1-sample proportions test with continuity correction
data: 87 out of 87 + 1742, null probability 0.5
X-squared = 1495.7, df = 1, p-value < 0.00000000000000022
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.03847846 0.05860485
sample estimates:
p
0.04756698
Pairwise comparisons using Pairwise comparison of proportions
data: b$num out of b$den
1 2
2 1 -
3 1 1
P value adjustment method: holm
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.008731422
Confidence interval:
2.5% 97.5%
0.00000000 0.03747234 5 Question #31 Body fat percentage by status
- Analysis of statistical significance between the three groups (Pre; Peri; Post) for self-reported value of Body Fat %.
-Between-Group Comparison: Is there a statistically significant difference in the Body Fat % value reported between women who are premenopausal, perimenopausal, and postmenopausal?
The body fat percentage variable was skewed for all three groups (Shapiro-Wilk p-values all less than .0001). A Kruskal-Wallis test was used to find differences among the menopausal status groups for the variable WTKG. A statistically significant difference was found among the status groups, X2(df=2)=18.167, p=.00011. The Dunn test was used to evaluate pairwise comparisons; perimenopause and postmenopause were found to be statistically significantly different (adjusted p=.0012), as were premenopause and postmenopause (adjusted p=0014).
| status | statistic | p_value |
|---|---|---|
| pre-menopause | 0.9857073 | 0.0087803 |
| peri-menopause | 0.9903387 | 0.0000022 |
| post-menopause | 0.9914790 | 0.0000124 |
Kruskal-Wallis rank sum test
data: BFPCT by status
Kruskal-Wallis chi-squared = 18.167, df = 2, p-value = 0.0001135 Comparison Z P.unadj P.adj
1 peri-menopause - post-menopause -3.550098 0.0003850884 0.001155265
2 peri-menopause - pre-menopause 1.110993 0.2665715714 0.266571571
3 post-menopause - pre-menopause 3.384328 0.0007135281 0.0014270566 Question #32 Measure frequencies of body composition measurement tools.
- Please provide N size and % of respondents for each body composition measurement tool
- Total values should add up to 100%
DEXA Other High-quality BIA device
374 322 659
Skinfolds Home bathroom scale Ultrasound
106 862 4
BodPod <NA>
51 1770
DEXA Other High-quality BIA device
0.090 0.078 0.159
Skinfolds Home bathroom scale Ultrasound
0.026 0.208 0.001
BodPod <NA>
0.012 0.427 7 Question #33 Body Composition Satisfaction
- Please provide N size, % of respondents who replied yes or no for each menopause stage.
- Please conduct an analysis of statistical significance between the three groups (Pre; Peri; Post) for Body Composition Satisfaction (‘No’ Responses)
- Between-Group Comparison: Is there a statistically significant difference in the number of ‘No’ responses between women who are premenopausal, perimenopausal, and postmenopausal?
A Chi-square test of independence was conducted to assess the relationship between BC satisfaction and menopausal status. A statistically significant association was found, X2(df=2)=40.8, p=.000000001. Cramer’s V was small at V=0.1, 95% CI(0.07, 0.13).
Yes No <NA>
1158 2990 0 status
bcsatis pre-menopause peri-menopause post-menopause
Yes 192 521 445
No 304 1302 1384 status
bcsatis pre-menopause peri-menopause post-menopause
Yes 0.3870968 0.2857926 0.2433024
No 0.6129032 0.7142074 0.7566976
Pearson's Chi-squared test
data: x
X-squared = 40.8, df = 2, p-value = 0.000000001381 status
bcsatis pre-menopause peri-menopause post-menopause
Yes 138.4687 508.9282 510.6032
No 357.5313 1314.0718 1318.3968
1-sample proportions test with continuity correction
data: 304 out of 304 + 192, null probability 0.5
X-squared = 24.841, df = 1, p-value = 0.0000006227
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.5683052 0.6557252
sample estimates:
p
0.6129032
1-sample proportions test with continuity correction
data: 1302 out of 1302 + 521, null probability 0.5
X-squared = 333.74, df = 1, p-value < 0.00000000000000022
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.692755 0.734747
sample estimates:
p
0.7142074
1-sample proportions test with continuity correction
data: 1384 out of 1384 + 445, null probability 0.5
X-squared = 481.05, df = 1, p-value < 0.00000000000000022
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.7362285 0.7760759
sample estimates:
p
0.7566976
Pairwise comparisons using Pairwise comparison of proportions
data: b$num out of b$den
1 2
2 0.00003734913 -
3 0.00000000083 0.0041
P value adjustment method: holm
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.09917743
Confidence interval:
2.5% 97.5%
0.07076358 0.13040325 8 Question #34 Perceived Recent Body Fat Gain
- Please provide N size, % of respondents who replied Yes’ or ‘No’ or “I do not know”
- Please conduct an analysis of statistical significance between the three groups (Pre; Peri; Post) for recent body fat gain
- Between-Group Comparison: Is there a statistically significant difference in the number of ‘Yes’ responses between women who are premenopausal, perimenopausal, and postmenopausal?
A Chi-square test of independence was conducted to assess the relationship between perceived weight gain and menopausal status. A statistically significant association was found, X2(df=4)=60.9, p=.00000000000006. Cramer’s V was small at V=0.12, 95% CI(0.09, 0.15).
Yes No
3012 1019 status
wtgain pre-menopause peri-menopause post-menopause
Yes 287 1353 1372
No 190 419 410 status
wtgain pre-menopause peri-menopause post-menopause
Yes 0.6016771 0.7635440 0.7699214
No 0.3983229 0.2364560 0.2300786
Pearson's Chi-squared test
data: x
X-squared = 60.855, df = 2, p-value = 0.00000000000006103 status
wtgain pre-menopause peri-menopause post-menopause
Yes 356.4188 1324.0546 1331.5267
No 120.5812 447.9454 450.4733
1-sample proportions test with continuity correction
data: 287 out of 287 + 19 + 190, null probability 0.5
X-squared = 11.954, df = 1, p-value = 0.0005454
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.5337186 0.6223029
sample estimates:
p
0.578629
1-sample proportions test with continuity correction
data: 1353 out of 1353 + 51 + 419, null probability 0.5
X-squared = 426.73, df = 1, p-value < 0.00000000000000022
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.7213279 0.7620061
sample estimates:
p
0.7421832
1-sample proportions test with continuity correction
data: 1372 out of 1372 + 47 + 410, null probability 0.5
X-squared = 456.75, df = 1, p-value < 0.00000000000000022
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.7295053 0.7697051
sample estimates:
p
0.7501367
Pairwise comparisons using Pairwise comparison of proportions
data: b$num out of b$den
1 2
2 0.00000000000380 -
3 0.00000000000031 0.61
P value adjustment method: holm
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.1228686
Confidence interval:
2.5% 97.5%
0.09351581 0.15444816 9 Question #35a-g Factors Contributing to Body Fat Gain
- For each cell in the table, please provide the n size and the % of respondents
9.1 35a Increased calorie Intake
Very responsible Somewhat responsible Not at all responsible
379 1473 1160
<NA>
1136 status
cal pre-menopause peri-menopause post-menopause
Very responsible 53 158 168
Somewhat responsible 177 649 647
Not at all responsible 57 546 557 status
cal pre-menopause peri-menopause post-menopause
Very responsible 0.185 0.117 0.122
Somewhat responsible 0.617 0.480 0.472
Not at all responsible 0.199 0.404 0.4069.2 35b Changes in Workout Routine
Very responsible Somewhat responsible Not at all responsible
245 988 1779
<NA>
1136 status
change pre-menopause peri-menopause post-menopause
Very responsible 35 88 122
Somewhat responsible 100 412 476
Not at all responsible 152 853 774 status
change pre-menopause peri-menopause post-menopause
Very responsible 0.122 0.065 0.089
Somewhat responsible 0.348 0.305 0.347
Not at all responsible 0.530 0.630 0.5649.3 35c Sleep Depravation
Somewhat responsible Very responsible Not at all responsible
1454 597 961
<NA>
1136 status
sleep pre-menopause peri-menopause post-menopause
Somewhat responsible 141 627 686
Very responsible 45 273 279
Not at all responsible 101 453 407 status
sleep pre-menopause peri-menopause post-menopause
Somewhat responsible 0.491 0.463 0.500
Very responsible 0.157 0.202 0.203
Not at all responsible 0.352 0.335 0.2979.4 35d Stress
Somewhat responsible Very responsible Not at all responsible
1542 731 739
<NA>
1136 status
stress pre-menopause peri-menopause post-menopause
Somewhat responsible 149 711 682
Very responsible 77 345 309
Not at all responsible 61 297 381 status
stress pre-menopause peri-menopause post-menopause
Somewhat responsible 0.519 0.525 0.497
Very responsible 0.268 0.255 0.225
Not at all responsible 0.213 0.220 0.2789.5 35e Fatigue or Lack of Energy
Somewhat responsible Very responsible Not at all responsible
1460 677 875
<NA>
1136 status
low pre-menopause peri-menopause post-menopause
Somewhat responsible 157 684 619
Very responsible 59 320 298
Not at all responsible 71 349 455 status
low pre-menopause peri-menopause post-menopause
Somewhat responsible 0.547 0.506 0.451
Very responsible 0.206 0.237 0.217
Not at all responsible 0.247 0.258 0.3329.6 35f Hormonal Changes
Very responsible Somewhat responsible Not at all responsible
1717 1108 187
<NA>
1136 status
hormone pre-menopause peri-menopause post-menopause
Very responsible 83 843 791
Somewhat responsible 168 465 475
Not at all responsible 36 45 106 status
hormone pre-menopause peri-menopause post-menopause
Very responsible 0.289 0.623 0.577
Somewhat responsible 0.585 0.344 0.346
Not at all responsible 0.125 0.033 0.0779.7 35g Reduced Daily Activities/Movement
Very responsible Somewhat responsible Not at all responsible
298 1041 1673
<NA>
1136 status
move pre-menopause peri-menopause post-menopause
Very responsible 32 124 142
Somewhat responsible 105 442 494
Not at all responsible 150 787 736 status
move pre-menopause peri-menopause post-menopause
Very responsible 0.111 0.092 0.103
Somewhat responsible 0.366 0.327 0.360
Not at all responsible 0.523 0.582 0.536| stage | Pre | Pre | Pre | Peri | Peri | Peri | Post | Post | Post | |
| Response | Not at all | somewhat | very | Not at all | somewhat | very | Not at all | somewhat | very | |
| Item | Increased calorie intake | 57 | 177 | 53 | 546 | 649 | 158 | 557 | 647 | 168 |
| Ppn | Increased calorie intake | 0.199 | 0.617 | 0.185 | 0.404 | 0.48 | 0.117 | 0.406 | 0.472 | 0.122 |
| Item | Changes in Workout | 152 | 100 | 35 | 853 | 412 | 88 | 774 | 476 | 122 |
| Ppn | Changes in Workout | 0.53 | 0.348 | 0.122 | 0.63 | 0.305 | 0.065 | 0.564 | 0.347 | 0.089 |
| Item | Sleep | 101 | 45 | 141 | 453 | 273 | 627 | 407 | 279 | 686 |
| Ppn | Sleep | 0.352 | 0.157 | 0.491 | 0.335 | 0.202 | 0.463 | 0.297 | 0.203 | 0.5 |
| Item | Stress | 61 | 77 | 149 | 297 | 345 | 711 | 381 | 309 | 682 |
| Ppn | Stress | 0.213 | 0.268 | 0.519 | 0.22 | 0.255 | 0.525 | 0.278 | 0.225 | 0.497 |
| Item | Fatigue | 71 | 59 | 157 | 349 | 320 | 684 | 455 | 298 | 619 |
| Ppn | Fatigue | 0.247 | 0.206 | 0.547 | 0.258 | 0.237 | 0.506 | 0.332 | 0.217 | 0.451 |
| Item | Hormonal changes | 36 | 168 | 83 | 45 | 465 | 843 | 106 | 475 | 791 |
| Ppn | Hormonal changes | 0.125 | 0.585 | 0.289 | 0.033 | 0.344 | 0.623 | 0.077 | 0.346 | 0.577 |
| Item | Reduced activities | 150 | 105 | 32 | 787 | 442 | 124 | 736 | 494 | 142 |
| Ppn | Reduced activities | 0.523 | 0.366 | 0.111 | 0.582 | 0.327 | 0.092 | 0.536 | 0.36 | 0.103 |
10 Question #36 Weight loss resistance
- Please conduct an analysis of statistical significance between the three groups (Pre; Peri; Post) currently experiencing weight loss resistance Between-Group Comparison:
- Is there a statistically significant difference in the number of ‘Yes’ responses between women who are premenopausal, perimenopausal, and postmenopausal?
A Chi-square test of independence was conducted to assess the relationship between perceived weight gain and menopausal status. A statistically significant association was found, X2(df=6)=60.554, p=.00000000003. Cramer’s V was small at V=0.09, 95% CI(0.07, 0.11).
Yes No I don't know Does Not Apply <NA>
2361 804 362 621 0 status
wlr pre-menopause peri-menopause post-menopause
Yes 212 1032 1117
No 132 366 306
I don't know 67 151 144
Does Not Apply 85 274 262 status
wlr pre-menopause peri-menopause post-menopause
Yes 0.42741935 0.56609984 0.61071624
No 0.26612903 0.20076796 0.16730454
I don't know 0.13508065 0.08283050 0.07873155
Does Not Apply 0.17137097 0.15030170 0.14324768
Pearson's Chi-squared test
data: x
X-squared = 60.554, df = 6, p-value = 0.00000000003472 status
wlr pre-menopause peri-menopause post-menopause
Yes 282.31823 1037.6333 1041.0485
No 96.13886 353.3491 354.5121
I don't know 43.28640 159.0950 159.6186
Does Not Apply 74.25651 272.9226 273.8209
1-sample proportions test with continuity correction
data: 212 out of 212 + 132 + 67 + 85, null probability 0.5
X-squared = 10.163, df = 1, p-value = 0.001433
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.3836174 0.4723626
sample estimates:
p
0.4274194
1-sample proportions test with continuity correction
data: 1032 out of 1032 + 274 + 151 + 366, null probability 0.5
X-squared = 31.596, df = 1, p-value = 0.00000001898
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.5429582 0.5889601
sample estimates:
p
0.5660998
1-sample proportions test with continuity correction
data: 1117 out of 1117 + 262 + 144 + 306, null probability 0.5
X-squared = 89.238, df = 1, p-value < 0.00000000000000022
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.5878851 0.6330776
sample estimates:
p
0.6107162
Pairwise comparisons using Pairwise comparison of proportions
data: b$num out of b$den
1 2
2 0.0000001061716 -
3 0.0000000000011 0.0068
P value adjustment method: holm
Two-sided 95% chi-squared confidence interval for the population
Cramer's V
Sample estimate: 0.08543557
Confidence interval:
2.5% 97.5%
0.0654849 0.1071792 11 Question #37a-f Factors contributing to WLR
11.1 37a Changes in Workout Routine
- For each cell in the table, please provide the n size and the % of respondents
Somewhat responsible Not at all responsible Very responsible
729 1502 130
<NA>
1787 status
change pre-menopause peri-menopause post-menopause
Somewhat responsible 69 282 378
Not at all responsible 132 693 677
Very responsible 11 57 62 status
change pre-menopause peri-menopause post-menopause
Somewhat responsible 0.325 0.273 0.338
Not at all responsible 0.623 0.672 0.606
Very responsible 0.052 0.055 0.05611.2 37b Sleep Depravation
Not at all responsible Somewhat responsible Very responsible
796 1101 464
<NA>
1787 status
sleep pre-menopause peri-menopause post-menopause
Not at all responsible 76 362 358
Somewhat responsible 102 460 539
Very responsible 34 210 220 status
sleep pre-menopause peri-menopause post-menopause
Not at all responsible 0.358 0.351 0.321
Somewhat responsible 0.481 0.446 0.483
Very responsible 0.160 0.203 0.19711.3 37c Stress
Somewhat responsible Very responsible Not at all responsible
1173 570 618
<NA>
1787 status
stress pre-menopause peri-menopause post-menopause
Somewhat responsible 103 527 543
Very responsible 67 254 249
Not at all responsible 42 251 325 status
stress pre-menopause peri-menopause post-menopause
Somewhat responsible 0.486 0.511 0.486
Very responsible 0.316 0.246 0.223
Not at all responsible 0.198 0.243 0.29111.4 37d Fatigue or Lack of Energy
Somewhat responsible Not at all responsible Very responsible
1130 757 474
<NA>
1787 status
low pre-menopause peri-menopause post-menopause
Somewhat responsible 100 504 526
Not at all responsible 68 304 385
Very responsible 44 224 206 status
low pre-menopause peri-menopause post-menopause
Somewhat responsible 0.472 0.488 0.471
Not at all responsible 0.321 0.295 0.345
Very responsible 0.208 0.217 0.18411.5 37e Hormonal Changes
Very responsible Somewhat responsible Not at all responsible
1475 765 121
<NA>
1787 status
hormone pre-menopause peri-menopause post-menopause
Very responsible 69 717 689
Somewhat responsible 130 289 346
Not at all responsible 13 26 82 status
hormone pre-menopause peri-menopause post-menopause
Very responsible 0.325 0.695 0.617
Somewhat responsible 0.613 0.280 0.310
Not at all responsible 0.061 0.025 0.07311.6 37f Reduced Daily Activities/Movement
Somewhat responsible Not at all responsible Very responsible
772 1396 193
<NA>
1787 status
move pre-menopause peri-menopause post-menopause
Somewhat responsible 72 297 403
Not at all responsible 123 646 627
Very responsible 17 89 87 status
move pre-menopause peri-menopause post-menopause
Somewhat responsible 0.340 0.288 0.361
Not at all responsible 0.580 0.626 0.561
Very responsible 0.080 0.086 0.078| stage | Pre | Pre | Pre | Peri | Peri | Peri | Post | Post | Post | |
| Response | Not at all | somewhat | very | Not at all | somewhat | very | Not at all | somewhat | very | |
| Item | Workoutchanges | 11 | 132 | 69 | 57 | 693 | 282 | 62 | 677 | 378 |
| Ppn | Workoutchanges | 0.052 | 0.623 | 0.325 | 0.055 | 0.672 | 0.273 | 0.056 | 0.606 | 0.338 |
| Item | Sleep | 34 | 102 | 76 | 210 | 460 | 362 | 220 | 539 | 358 |
| Ppn | Sleep | 0.16 | 0.481 | 0.358 | 0.203 | 0.446 | 0.351 | 0.197 | 0.483 | 0.321 |
| Item | Stress | 42 | 67 | 103 | 251 | 254 | 527 | 325 | 249 | 543 |
| Ppn | Stress | 0.198 | 0.316 | 0.486 | 0.243 | 0.246 | 0.511 | 0.291 | 0.223 | 0.486 |
| Item | Fatigue | 44 | 68 | 100 | 224 | 304 | 504 | 206 | 385 | 526 |
| Ppn | Fatigue | 0.208 | 0.321 | 0.472 | 0.217 | 0.295 | 0.488 | 0.184 | 0.345 | 0.471 |
| Item | Hormonal changes | 13 | 130 | 69 | 26 | 289 | 717 | 82 | 346 | 689 |
| Ppn | Hormonal changes | 0.061 | 0.613 | 0.325 | 0.025 | 0.28 | 0.695 | 0.073 | 0.31 | 0.617 |
| Item | Reduced activites | 17 | 123 | 72 | 89 | 646 | 297 | 87 | 627 | 403 |
| Ppn | Reduced activities | 0.08 | 0.58 | 0.34 | 0.086 | 0.626 | 0.288 | 0.078 | 0.561 | 0.361 |
---
title: "FFMS Manuscript 2 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 #10 Body Weight by status

-   N size, % of respondents, mean, SD, median for all respondents and across all menopausal stage.\
-   Please conduct an analysis of statistical significance between the three groups (Pre; Peri; Post) for reported value of Body Weight.
-   Between-Group Comparison: Is there a statistically significant difference in the Body Weight value reported between women who are premenopausal, perimenopausal, and postmenopausal?

*The weight variable was skewed for all three groups (Shapiro-Wilk p-values all less than .0001). As a result, a Kruskal-Wallis test was used to find differences among the menopausal status groups for the variable WTKG. A statistically significant difference was found among the status groups, X2(df=2)=17.908, p=.00013. The Dunn test was used to evaluate pairwise comparisons, and only perimenopause and postmenopause were found to be statistically significant (adjusted p=.00007).*

```{r Q10, include=T}

summary_overall = first.data %>%
  summarise(mean=mean(WTKG), sd=sd(WTKG), median=median(WTKG), n=n())

summary_bystatus = first.data %>%
  group_by(status) %>%
  summarise(mean=mean(WTKG), sd=sd(WTKG), median=median(WTKG), n=n())

kable(summary_overall, format="html", caption="Descriptive statsitcs of WTKG") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)

kable(summary_bystatus, format="html", caption="Descriptive statsitcs of WTKG by status") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)

#model=lm(WTKG~status, data=first.data)
 #summary(model)
 
#epsilon_squared(model)
 
results <- first.data %>%
  group_by(status) %>%
  summarise(shapiro_test = list(shapiro.test(WTKG))) %>%
  mutate(statistic = sapply(shapiro_test, function(x) x$statistic),
         p_value = sapply(shapiro_test, function(x) x$p.value)) %>%
  select(status, statistic, p_value)


kable(results, format="html", caption="Normality assessment by status") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)

ggplot(first.data, aes(x = WTKG)) +
  geom_histogram() +
  facet_wrap(~ status) + 
  labs(title = "Weight by menopause status",
       x = "kilgrams",
       y = "Frequency")

kruskal.test(WTKG~status, data=first.data)
dunnTest(WTKG~status, data=first.data)

```

# Question #20 Are you taking any prescription anti-depressant medications?

-   Analysis of statistical significance between the three groups (Pre; Peri; Post) for the answer 'Yes'.
-   Between-Group Comparison: Is there a statistically significant difference in the number of 'Yes' responses between women who are premenopausal, perimenopausal, and postmenopausal?

*A Chi-square test of independence was conducted to assess the relationship between antidepressant use and menopausal status. No statistically significant association was found, X2(df=2)=1.328, p=.5148. Cramer's V was trivial at V=0.018, 95% CI(0.00, 0.049).*

```{r Q20, include=T}

first.data = first.data %>%
    mutate(depression=case_when(ANTDEP==1~"Yes",
                          ANTDEP==2~"No", 
                          ANTDEP==3~"I don't know",
                          ANTDEP==4~"Prefer Not to Answer", TRUE~NA))
first.data$depression <- factor(first.data$depression, 
                                levels = c("Yes", "No", "I don't know", "Prefer Not to Answer"))
table(first.data$depression, useNA = "always")

#Remove everyone except yes or no and missing, only 8
data = first.data %>%
  filter(ANTDEP<3)

data = data %>%
  mutate(dep=case_when(ANTDEP==1~"Yes",
                          ANTDEP==2~"No", TRUE~NA))
data$dep <- factor(data$dep, levels = c("Yes", "No"))

x=xtabs(~dep+status, data=data)
x
prop.table(x,2)
result=chisq.test(x)
result
result$expected
#pre
prop.test(48, 48+448)
#peri
prop.test(209, 209+1607)
#post
prop.test(205, 205+1623)

b=data.frame(
num=c(48, 209, 205),
den=c(48+448, 209+1607, 205+1623),
stage=c("pre", "peri", "post")
)

pairwise.prop.test(x=b$num, n=b$den, p.adjust.method = "holm")

ci_cramersv(result)


df <- data.frame(
  x = c("Pre", "Peri", "Post"),
  y = c(.097, .115, .112),
  ymin = c(.073, .101, .098),
  ymax = c(.127, .131, .128)
)

df$x=fct_inorder(df$x)

#ggplot(df, aes(x = x, y = y)) +
 # geom_bar(stat = "identity", position = position_dodge(width = 0.9), fill = "blue") +
 # geom_errorbar(aes(ymin = ymin, ymax = ymax),
 #               width = 0.10,  # Width of the error bar caps
#                position = position_dodge(width = 0.9)) +
#  scale_fill_grey() +
#  labs(title = "Yes to Antidepressant Use w/95% CI",
 #      x = "Menopausal Status",
 #      y = "Proportion") +
 # theme_classic()
```

# Question #21 Are you taking any prescription sleep medications?

-   Analysis of statistical significance between the three groups (Pre; Peri; Post) for the answer 'Yes'.
-   Between-Group Comparison: Is there a statistically significant difference in the number of 'Yes' responses between women who are premenopausal, perimenopausal, and postmenopausal?

*A Chi-square test of independence was conducted to assess the relationship between and menopausal status. A statistically significant association was found, X2(df=2)=6.3055, p=.0427. Cramer's V was trivial at V=0.039, 95% CI(0.00, 0.071).*

```{r Q21, include=T}

first.data = first.data %>%
  mutate(slp=case_when(SLEEPMED==1~"Yes",
                          SLEEPMED==2~"No", 
                          SLEEPMED==3~"I don't know", 
                          SLEEPMED==4~"Prefer not to answer", TRUE~NA))
first.data$slp <- factor(first.data$slp, 
                                levels = c("Yes", "No", "I don't know", "Prefer Not to Answer"))
table(first.data$slp, useNA = "always")
#Remove everyone except yes or no and missing, only 6
data = first.data %>%
  filter(SLEEPMED<3)

data = first.data %>%
  mutate(sleep=case_when(SLEEPMED==1~"Yes",
                          SLEEPMED==2~"No", TRUE~NA))

data$sleep <- factor(data$sleep, levels = c("Yes", "No"))

x=xtabs(~sleep+status, data=data)
x
prop.table(x,2)
result=chisq.test(x)
result
result$expected
#pre
prop.test(12, 12+483)
#peri
prop.test(88, 88+1733)
#post
prop.test(92, 92+1734)
          
b=data.frame(
num=c(12, 88, 92),
den=c(12+483, 88+1733, 92+1734),
stage=c("pre", "peri", "post")
)

pairwise.prop.test(x=b$num, n=b$den, p.adjust.method = "holm")

ci_cramersv(result)

df <- data.frame(
  x = c("Pre", "Peri", "Post"),
  y = c(.024, .048, .050),
  ymin = c(.013, .039, .041),
  ymax = c(.043, .059, .062)
)

df$x=fct_inorder(df$x)

#ggplot(df, aes(x = x, y = y)) +
#  geom_bar(stat = "identity", position = position_dodge(width = 0.9), fill = "blue") +
#  geom_errorbar(aes(ymin = ymin, ymax = ymax),
#                width = 0.10,  # Width of the error bar caps
#                position = position_dodge(width = 0.9)) +
#  scale_fill_grey() +
#  labs(title = "Yes to Sleep Meds w/95% CI",
#       x = "Menopausal Status",
#       y = "Proportion") +
#  theme_classic()
```

# Question #22 Are you taking any prescription weight loss medications?

-   Please conduct an analysis of statistical significance between the three groups (Pre; Peri; Post) for the answer 'Yes'.
-   Between-Group Comparison: Is there a statistically significant difference in the number of 'Yes' responses between women who are premenopausal, perimenopausal, and postmenopausal?

*A Chi-square test of independence was conducted to assess the relationship between taking weight loss medication and menopausal status. No statistically significant association was found, X2(df=2)=0.316, p=.8539. Cramer's V was trivial at V=0.01, 95% CI(0.00, 0.037).*

```{r Q22, include=T}

first.data = first.data %>%
  mutate(wlm=case_when(WTLOSSMED==1~"Yes",
                          WTLOSSMED==2~"No", 
                          WTLOSSMED==3~"I don't know", 
                          WTLOSSMED==4~"Prefer not to answer", TRUE~NA))
first.data$wlm <- factor(first.data$wlm, 
                                levels = c("Yes", "No", "I don't know", "Prefer Not to Answer"))
table(first.data$wlm, useNA = "always")

#Remove everyone except yes or no and missing, only 5
data = first.data %>%
  filter(WTLOSSMED<3)

data = data %>%
  mutate(WLMED=case_when(WTLOSSMED==1~"Yes",
                          WTLOSSMED==2~"No", TRUE~NA))

data$WLMED <- factor(data$WLMED, 
                                levels = c("Yes", "No"))
x=xtabs(~WLMED+status, data=data)
x
prop.table(x,2)
result=chisq.test(x)
result
result$expected
#pre
prop.test(23, 23+471)
#peri
prop.test(93, 93+1727)
#post
prop.test(87, 87+1742)
          
b=data.frame(
num=c(23, 93, 87),
den=c(23+471, 93+1727, 87+1742),
stage=c("pre", "peri", "post")
)

pairwise.prop.test(x=b$num, n=b$den, p.adjust.method = "holm")

ci_cramersv(result)

df <- data.frame(
  x = c("Pre", "Peri", "Post"),
  y = c(.047, .051, .048),
  ymin = c(.030, .042, .038),
  ymax = c(.070, .062, .059)
)

df$x=fct_inorder(df$x)

#ggplot(df, aes(x = x, y = y)) +
#  geom_bar(stat = "identity", position = position_dodge(width = 0.9), fill = "blue") +
 # geom_errorbar(aes(ymin = ymin, ymax = ymax),
  #              width = 0.10,  # Width of the error bar caps
   #             position = position_dodge(width = 0.9)) +
  #scale_fill_grey() +
  #labs(title = "Yes to Prescription Wt Loss Meds w/5% CI",
   #    x = "Menopausal Status",
    #   y = "Proportion") +
#  theme_classic()
```

# Question #31 Body fat percentage by status

-   Analysis of statistical significance between the three groups (Pre; Peri; Post) for self-reported value of Body Fat %.

-Between-Group Comparison: Is there a statistically significant difference in the Body Fat % value reported between women who are premenopausal, perimenopausal, and postmenopausal?

*The body fat percentage variable was skewed for all three groups (Shapiro-Wilk p-values all less than .0001). A Kruskal-Wallis test was used to find differences among the menopausal status groups for the variable WTKG. A statistically significant difference was found among the status groups, X2(df=2)=18.167, p=.00011. The Dunn test was used to evaluate pairwise comparisons; perimenopause and postmenopause were found to be statistically significantly different (adjusted p=.0012), as were premenopause and postmenopause (adjusted p=0014).*

```{r Q31}

#filter values of 1, 2, and 99
data=first.data %>%
  filter(BFPCT>2 & BFPCT<99)


#model=lm(BFPCT~status, data=data)
# summary(model)

 
ggplot(data, aes(x = BFPCT)) +
  geom_histogram() +
  facet_wrap(~ status) + 
  labs(title = "Weight by menopause status",
       x = "kilgrams",
       y = "Frequency")

results <- data %>%
  group_by(status) %>%
  summarise(shapiro_test = list(shapiro.test(BFPCT))) %>%
  mutate(statistic = sapply(shapiro_test, function(x) x$statistic),
         p_value = sapply(shapiro_test, function(x) x$p.value)) %>%
  select(status, statistic, p_value)

kable(results, format="html", caption="Normality assessment by status") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)

kruskal.test(BFPCT~status, data=data)
dunnTest(BFPCT~status, data=data)


```

# Question #32 Measure frequencies of body composition measurement tools.

-   Please provide N size and % of respondents for each body composition measurement tool
-   Total values should add up to 100%

```{r Q32, include=T}

data = first.data %>%
  mutate(inst=case_when(BFINSTR==1~"DEXA",
                           BFINSTR==2~"BodPod",
                           BFINSTR==3~"Skinfolds", 
                           BFINSTR==4~"Ultrasound",
                           BFINSTR==5~"Home bathroom scale",
                           BFINSTR==6~"High-quality BIA device", 
                           BFINSTR==7~"Other", TRUE~NA))

data$inst=fct_inorder(data$inst)
x=table(data$inst, useNA = "always")
x
round(prop.table(table(data$inst, useNA = "always")), 3)

```

# Question #33 Body Composition Satisfaction

-   Please provide N size, % of respondents who replied yes or no for each menopause stage.
-   Please conduct an analysis of statistical significance between the three groups (Pre; Peri; Post) for Body Composition Satisfaction ('No' Responses)
-   Between-Group Comparison: Is there a statistically significant difference in the number of 'No' responses between women who are premenopausal, perimenopausal, and postmenopausal?

*A Chi-square test of independence was conducted to assess the relationship between BC satisfaction and menopausal status. A statistically significant association was found, X2(df=2)=40.8, p=.000000001. Cramer's V was small at V=0.1, 95% CI(0.07, 0.13).*

```{r Q33, include=T}

data = first.data %>%
  mutate(bcsatis=case_when(BCSATIS==1~"Yes",
                            BCSATIS==2~"No", TRUE~NA))

data$bcsatis <- factor(data$bcsatis, 
                                levels = c("Yes", "No"))
table(data$bcsatis, useNA = "always")
x=xtabs(~bcsatis+status, data=data)
x
prop.table(x,2)
result=chisq.test(x)
result
result$expected
#pre
prop.test(304, 304+192)
#peri
prop.test(1302, 1302+521)
#post
prop.test(1384, 1384+445)
          
b=data.frame(
num=c(304, 1302, 1384),
den=c(304+192, 1302+521, 1384+445),
stage=c("pre", "peri", "post")
)

pairwise.prop.test(x=b$num, n=b$den, p.adjust.method = "holm")

ci_cramersv(result)
# Create a sample data frame 
# Ensure your data frame has columns for the x-axis variable, the grouping variable, 
# the mean value, and the standard deviation/error.
df <- data.frame(
  x = c("Pre", "Peri", "Post"),
  y = c(.613, .714, .757),
  ymin = c(.568, .693, .736),
  ymax = c(.656, .735, .776)
)

df$x=fct_inorder(df$x)

#ggplot(df, aes(x = x, y = y)) +
 # geom_bar(stat = "identity", position = position_dodge(width = 0.9), fill = "blue") +
  #geom_errorbar(aes(ymin = ymin, ymax = ymax),
   #             width = 0.10,  # Width of the error bar caps
    #            position = position_dodge(width = 0.9)) +
#  scale_fill_manual(values = c("Depression" = "black", "Non-depression" = "black"))+
#  scale_fill_grey() +
 # labs(title = "No to Body Composition Satisfaction w/95% CI",
  #     x = "Menopausal Status",
   #    y = "Proportion") +
#  theme_classic()
```

# Question #34 Perceived Recent Body Fat Gain

-   Please provide N size, % of respondents who replied Yes' or 'No' or "I do not know"
-   Please conduct an analysis of statistical significance between the three groups (Pre; Peri; Post) for recent body fat gain
-   Between-Group Comparison: Is there a statistically significant difference in the number of 'Yes' responses between women who are premenopausal, perimenopausal, and postmenopausal?

*A Chi-square test of independence was conducted to assess the relationship between perceived weight gain and menopausal status. A statistically significant association was found, X2(df=4)=60.9, p=.00000000000006. Cramer's V was small at V=0.12, 95% CI(0.09, 0.15).*

```{r Q34, include=T}

data = first.data %>%
  mutate(wtgain=case_when(WTGAIN==1~"Yes",
                          WTGAIN==2~"No", TRUE~NA))

data$wtgain <- factor(data$wtgain, 
                                levels = c("Yes", "No"))
table(data$wtgain)
x=xtabs(~wtgain+status, data=data)
x
prop.table(x,2)
result=chisq.test(x)
result
result$expected
#pre
prop.test(287, 287+19+190)
#peri
prop.test(1353, 1353+51+419)
#post
prop.test(1372, 1372+47+410)
          
b=data.frame(
num=c(287, 1353, 1372),
den=c(287+19+190,1353+51+419,1372+47+410),
stage=c("pre", "peri", "post")
)

pairwise.prop.test(x=b$num, n=b$den, p.adjust.method = "holm")

ci_cramersv(result)

# Create a sample data frame 
# Ensure your data frame has columns for the x-axis variable, the grouping variable, 
# the mean value, and the standard deviation/error.
df <- data.frame(
  x = c("Pre", "Peri", "Post"),
  y = c(.579, .742, .750),
  ymin = c(.534, .721, .730),
  ymax = c(.622, .762, .770)
)

df$x=fct_inorder(df$x)

#ggplot(df, aes(x = x, y = y)) +
 # geom_bar(stat = "identity", position = position_dodge(width = 0.9), fill = "blue") +
  #geom_errorbar(aes(ymin = ymin, ymax = ymax),
   #             width = 0.10,  # Width of the error bar caps
    #            position = position_dodge(width = 0.9)) +
#  scale_fill_manual(values = c("Depression" = "black", "Non-depression" = "black"))+
  #scale_fill_grey() +
  #labs(title = "Yes to Perceived Recenet Body Fat Gain w/95% CI",
   #    x = "Menopausal Status",
    #   y = "Proportion") +
  #theme_classic()
```

# Question #35a-g Factors Contributing to Body Fat Gain

-   For each cell in the table, please provide the n size and the % of respondents

## 35a Increased calorie Intake

```{r Q35a}

data = first.data %>%
  mutate(cal=case_when(CALINC==1~"Not at all responsible",
                          CALINC==2~"Somewhat responsible", 
                          CALINC==3~"Very responsible", TRUE~NA))
data$cal=fct_inorder(data$cal)
table(data$cal, useNA = "always")

x=xtabs(~cal+status, data=data)
x
y=round(prop.table(x,2), 3)
y
result=chisq.test(x)

not=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[3,1], x[3,2], x[3,3]),
  ppn=c(y[3,1], y[3,2], y[3,3])
)
not$extent=c("Not at all")

somewhat=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[2,1], x[2,2], x[2,3]),
  ppn=c(y[2,1], y[2,2], y[2,3])
)
somewhat$extent=c("Somewhat")


very=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[1,1], x[1,2], x[1,3]),
  ppn=c(y[1,1], y[1,2], y[1,3])
)
very$extent=c("Very")

changes_table=as.data.frame(rbind(not, somewhat, very))

stage=c("   ", "Pre", "Pre", "Pre", "Peri", "Peri","Peri","Post","Post","Post")
Response=c("  ", "Not at all", "somewhat", "very", "Not at all", "somewhat", "very", "Not at all", "somewhat", "very" )
Item=c("Increased calorie intake", x[3,1], x[2,1],x[1,1], x[3,2],x[2,2],x[1,2],x[3,3],x[2,3],x[1,3])
Ppn=c("Increased calorie intake", y[3,1], y[2,1], y[1,1], y[3,2],y[2,2],y[1,2],y[3,3],y[2,3],y[1,3])
Q35a=rbind(stage, Response, Item, Ppn)
```

## 35b Changes in Workout Routine

```{r Q35b}

data = first.data %>%
  mutate(change=case_when(BF_WKTCHANGE==1~"Not at all responsible",
                          BF_WKTCHANGE==2~"Somewhat responsible", 
                          BF_WKTCHANGE==3~"Very responsible", TRUE~NA))
data$change=fct_inorder(data$change)
table(data$change, useNA = "always")

x=xtabs(~change+status, data=data)
x
y=round(prop.table(x,2), 3)
y
result=chisq.test(x)

not=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[3,1], x[3,2], x[3,3]),
  ppn=c(y[3,1], y[3,2], y[3,3])
)
not$extent=c("Not at all")

somewhat=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[2,1], x[2,2], x[2,3]),
  ppn=c(y[2,1], y[2,2], y[2,3])
)
somewhat$extent=c("Somewhat")


very=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[1,1], x[1,2], x[1,3]),
  ppn=c(y[1,1], y[1,2], y[1,3])
)
very$extent=c("Very")

changes_table=as.data.frame(rbind(not, somewhat, very))

#kable(changes_table, format="html", caption="Changes in workout routine") %>%
 # kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)

Item=c("Changes in Workout", x[3,1], x[2,1],x[1,1], x[3,2],x[2,2],x[1,2],x[3,3],x[2,3],x[1,3])
Ppn=c("Changes in Workout", y[3,1], y[2,1], y[1,1], y[3,2],y[2,2],y[1,2],y[3,3],y[2,3],y[1,3])
Q35b=rbind(Item, Ppn)
```

## 35c Sleep Depravation

```{r Q35c}
data = first.data %>%
  mutate(sleep=case_when(BF_SLEEPDEP==1~"Not at all responsible",
                          BF_SLEEPDEP==2~"Somewhat responsible", 
                          BF_SLEEPDEP==3~"Very responsible", TRUE~NA))
data$sleep=fct_inorder(data$sleep)
table(data$sleep, useNA = "always")

x=xtabs(~sleep+status, data=data)
x
y=round(prop.table(x,2), 3)
y
result=chisq.test(x)

not=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[3,1], x[3,2], x[3,3]),
  ppn=c(y[3,1], y[3,2], y[3,3])
)
not$extent=c("Not at all")

somewhat=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[2,1], x[2,2], x[2,3]),
  ppn=c(y[2,1], y[2,2], y[2,3])
)
somewhat$extent=c("Somewhat")


very=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[1,1], x[1,2], x[1,3]),
  ppn=c(y[1,1], y[1,2], y[1,3])
)
very$extent=c("Very")

changes_table=as.data.frame(rbind(not, somewhat, very))

#kable(changes_table, format="html", caption="Sleep Depravation") %>%
 # kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)

Item=c("Sleep", x[3,1], x[2,1],x[1,1], x[3,2],x[2,2],x[1,2],x[3,3],x[2,3],x[1,3])
Ppn=c("Sleep", y[3,1], y[2,1], y[1,1], y[3,2],y[2,2],y[1,2],y[3,3],y[2,3],y[1,3])
Q35c=rbind(Item, Ppn)
```

## 35d Stress

```{r Q35d}

data = first.data %>%
  mutate(stress=case_when(BF_STRESS==1~"Not at all responsible",
                          BF_STRESS==2~"Somewhat responsible", 
                          BF_STRESS==3~"Very responsible", TRUE~NA))
data$stress=fct_inorder(data$stress)
table(data$stress, useNA = "always")

x=xtabs(~stress+status, data=data)
x
y=round(prop.table(x,2), 3)
y
result=chisq.test(x)

not=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[3,1], x[3,2], x[3,3]),
  ppn=c(y[3,1], y[3,2], y[3,3])
)
not$extent=c("Not at all")

somewhat=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[2,1], x[2,2], x[2,3]),
  ppn=c(y[2,1], y[2,2], y[2,3])
)
somewhat$extent=c("Somewhat")


very=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[1,1], x[1,2], x[1,3]),
  ppn=c(y[1,1], y[1,2], y[1,3])
)
very$extent=c("Very")

changes_table=as.data.frame(rbind(not, somewhat, very))

#kable(changes_table, format="html", caption="Changes in workout routine") %>%
 # kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)

Item=c("Stress", x[3,1], x[2,1],x[1,1], x[3,2],x[2,2],x[1,2],x[3,3],x[2,3],x[1,3])
Ppn=c("Stress", y[3,1], y[2,1], y[1,1], y[3,2],y[2,2],y[1,2],y[3,3],y[2,3],y[1,3])
Q35d=rbind(Item, Ppn)
```

## 35e Fatigue or Lack of Energy

```{r Q35e}

data = first.data %>%
  mutate(low=case_when(BF_LOWENERG==1~"Not at all responsible",
                          BF_LOWENERG==2~"Somewhat responsible", 
                          BF_LOWENERG==3~"Very responsible", TRUE~NA))
data$low=fct_inorder(data$low)
table(data$low, useNA = "always")

x=xtabs(~low+status, data=data)
x
y=round(prop.table(x,2), 3)
y
result=chisq.test(x)

not=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[3,1], x[3,2], x[3,3]),
  ppn=c(y[3,1], y[3,2], y[3,3])
)
not$extent=c("Not at all")

somewhat=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[2,1], x[2,2], x[2,3]),
  ppn=c(y[2,1], y[2,2], y[2,3])
)
somewhat$extent=c("Somewhat")


very=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[1,1], x[1,2], x[1,3]),
  ppn=c(y[1,1], y[1,2], y[1,3])
)
very$extent=c("Very")

changes_table=as.data.frame(rbind(not, somewhat, very))

#kable(changes_table, format="html", caption="CFatique or Low Energy") %>%
 # kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)

Item=c("Fatigue", x[3,1], x[2,1],x[1,1], x[3,2],x[2,2],x[1,2],x[3,3],x[2,3],x[1,3])
Ppn=c("Fatigue", y[3,1], y[2,1], y[1,1], y[3,2],y[2,2],y[1,2],y[3,3],y[2,3],y[1,3])
Q35e=rbind(Item, Ppn)
```

## 35f Hormonal Changes

```{r Q35f}

data = first.data %>%
  mutate(hormone=case_when(BF_HRMCHANGE==1~"Not at all responsible",
                          BF_HRMCHANGE==2~"Somewhat responsible", 
                          BF_HRMCHANGE==3~"Very responsible", TRUE~NA))
data$hormone=fct_inorder(data$hormone)
table(data$hormone, useNA = "always")

x=xtabs(~hormone+status, data=data)
x
y=round(prop.table(x,2), 3)
y
result=chisq.test(x)

not=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[3,1], x[3,2], x[3,3]),
  ppn=c(y[3,1], y[3,2], y[3,3])
)
not$extent=c("Not at all")

somewhat=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[2,1], x[2,2], x[2,3]),
  ppn=c(y[2,1], y[2,2], y[2,3])
)
somewhat$extent=c("Somewhat")


very=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[1,1], x[1,2], x[1,3]),
  ppn=c(y[1,1], y[1,2], y[1,3])
)
very$extent=c("Very")

changes_table=as.data.frame(rbind(not, somewhat, very))

#kable(changes_table, format="html", caption="Hormonal Changes") %>%
 # kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)

Item=c("Hormonal changes", x[3,1], x[2,1],x[1,1], x[3,2],x[2,2],x[1,2],x[3,3],x[2,3],x[1,3])
Ppn=c("Hormonal changes", y[3,1], y[2,1], y[1,1], y[3,2],y[2,2],y[1,2],y[3,3],y[2,3],y[1,3])
Q35f=rbind(Item, Ppn)
```

## 35g Reduced Daily Activities/Movement

```{r Q35g}

data = first.data %>%
  mutate(move=case_when(BF_MOVELESS==1~"Not at all responsible",
                          BF_MOVELESS==2~"Somewhat responsible", 
                          BF_MOVELESS==3~"Very responsible", TRUE~NA))
data$move=fct_inorder(data$move)
table(data$move, useNA = "always")

x=xtabs(~move+status, data=data)
x
y=round(prop.table(x,2), 3)
y
result=chisq.test(x)

not=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[3,1], x[3,2], x[3,3]),
  ppn=c(y[3,1], y[3,2], y[3,3])
)
not$extent=c("Not at all")

somewhat=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[2,1], x[2,2], x[2,3]),
  ppn=c(y[2,1], y[2,2], y[2,3])
)
somewhat$extent=c("Somewhat")


very=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[1,1], x[1,2], x[1,3]),
  ppn=c(y[1,1], y[1,2], y[1,3])
)
very$extent=c("Very")

changes_table=as.data.frame(rbind(not, somewhat, very))

#kable(changes_table, format="html", caption="Reduced Daily Activities/Movement") %>%
 # kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)

Item=c("Reduced activities", x[3,1], x[2,1],x[1,1], x[3,2],x[2,2],x[1,2],x[3,3],x[2,3],x[1,3])
Ppn=c("Reduced activities", y[3,1], y[2,1], y[1,1], y[3,2],y[2,2],y[1,2],y[3,3],y[2,3],y[1,3])
Q35g=rbind(Item, Ppn)
```

``` {r}
tab=rbind(Q35a, Q35b, Q35c, Q35d, Q35e, Q35f, Q35g)

kable(tab)
```




# Question #36 Weight loss resistance
- Please conduct an analysis of statistical significance between the three groups (Pre; Peri; Post) currently experiencing weight loss resistance Between-Group Comparison: 
- Is there a statistically significant difference in the number of ‘Yes’ responses between women who are premenopausal, perimenopausal, and postmenopausal? 

*A Chi-square test of independence was conducted to assess the relationship between perceived weight gain and menopausal status. A statistically significant association was found, X2(df=6)=60.554, p=.00000000003. Cramer's V was small at V=0.09, 95% CI(0.07, 0.11). *

```{r Q36, include=T}



data = first.data %>%
  mutate(wlr=case_when(WLR==1~"Yes",
                            WLR==2~"No", 
                           WLR==3~"I don't know",
                           WLR==4~"Does Not Apply", TRUE~NA))

data$wlr <- factor(data$wlr, levels = c("Yes", "No", "I don't know", "Does Not Apply"))
table(data$wlr, useNA = "always")
x=xtabs(~wlr+status, data=data)
x
prop.table(x,2)
result=chisq.test(x)
result
result$expected
#pre
prop.test(212, 212+132+67+85)
#peri
prop.test(1032, 1032+274+151+366)
#post
prop.test(1117, 1117+262+144+306)
          
b=data.frame(
num=c(212, 1032, 1117),
den=c(212+132+67+85, 1032+274+151+366, 1117+262+144+306),
stage=c("pre", "peri", "post")
)
pairwise.prop.test(x=b$num, n=b$den, p.adjust.method = "holm")
ci_cramersv(result)
# Create a sample data frame 
# Ensure your data frame has columns for the x-axis variable, the grouping variable, 
# the mean value, and the standard deviation/error.
df <- data.frame(
  x = c("Pre", "Peri", "Post"),
  y = c(.427, .566, .611),
  ymin = c(.384, .523, .588),
  ymax = c(.472, .589, .633)
)

df$x=fct_inorder(df$x)
#ggplot(df, aes(x = x, y = y)) +
 # geom_bar(stat = "identity", position = position_dodge(width = 0.9), fill = "blue") +
  #geom_errorbar(aes(ymin = ymin, ymax = ymax),
   #             width = 0.10,  # Width of the error bar caps
    #            position = position_dodge(width = 0.9)) +
#  scale_fill_grey() +
 # labs(title = "Weight Loss resistance",
  #     x = "Menopausal Status",
   #    y = "Proportion") +
#  theme_classic()
```

# Question #37a-f Factors contributing to WLR

## 37a Changes in Workout Routine

-   For each cell in the table, please provide the n size and the % of respondents

```{r Q37a}

data = first.data %>%
  mutate(change=case_when(WLR_WKTCHANGE==1~"Not at all responsible",
                          WLR_WKTCHANGE==2~"Somewhat responsible", 
                          WLR_WKTCHANGE==3~"Very responsible", TRUE~NA))
data$change=fct_inorder(data$change)
table(data$change, useNA = "always")

x=xtabs(~change+status, data=data)
x
y=round(prop.table(x,2), 3)
y
result=chisq.test(x)

not=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[3,1], x[3,2], x[3,3]),
  ppn=c(y[3,1], y[3,2], y[3,3])
)
not$extent=c("Not at all")

somewhat=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[2,1], x[2,2], x[2,3]),
  ppn=c(y[2,1], y[2,2], y[2,3])
)
somewhat$extent=c("Somewhat")


very=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[1,1], x[1,2], x[1,3]),
  ppn=c(y[1,1], y[1,2], y[1,3])
)
very$extent=c("Very")

changes_table=as.data.frame(rbind(not, somewhat, very))

#kable(changes_table, format="html", caption="Changes in workout routine") %>%
 # kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)

stage=c("   ", "Pre", "Pre", "Pre", "Peri", "Peri","Peri","Post","Post","Post")
Response=c("  ", "Not at all", "somewhat", "very", "Not at all", "somewhat", "very", "Not at all", "somewhat", "very" )
Item=c("Workoutchanges", x[3,1], x[2,1],x[1,1], x[3,2],x[2,2],x[1,2],x[3,3],x[2,3],x[1,3])
Ppn=c("Workoutchanges", y[3,1], y[2,1], y[1,1], y[3,2],y[2,2],y[1,2],y[3,3],y[2,3],y[1,3])
Q37a=rbind(stage, Response, Item, Ppn)
```

## 37b Sleep Depravation

```{r Q37b}
data = first.data %>%
  mutate(sleep=case_when(WLR_SLP==1~"Not at all responsible",
                          WLR_SLP==2~"Somewhat responsible", 
                          WLR_SLP==3~"Very responsible", TRUE~NA))
data$sleep=fct_inorder(data$sleep)
table(data$sleep, useNA = "always")

x=xtabs(~sleep+status, data=data)
x
y=round(prop.table(x,2), 3)
y
result=chisq.test(x)

not=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[3,1], x[3,2], x[3,3]),
  ppn=c(y[3,1], y[3,2], y[3,3])
)
not$extent=c("Not at all")

somewhat=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[2,1], x[2,2], x[2,3]),
  ppn=c(y[2,1], y[2,2], y[2,3])
)
somewhat$extent=c("Somewhat")


very=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[1,1], x[1,2], x[1,3]),
  ppn=c(y[1,1], y[1,2], y[1,3])
)
very$extent=c("Very")

changes_table=as.data.frame(rbind(not, somewhat, very))

#kable(changes_table, format="html", caption="Sleep Depravation") %>%
 # kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)


Item=c("Sleep", x[3,1], x[2,1],x[1,1],x[3,2],x[2,2],x[1,2],x[3,3],x[2,3],x[1,3])
Ppn=c("Sleep", y[3,1], y[2,1], y[1,1], y[3,2],y[2,2],y[1,2],y[3,3],y[2,3],y[1,3])
Q37b=rbind(Item, Ppn)
```

## 37c Stress

```{r Q37c}

data = first.data %>%
  mutate(stress=case_when(WLR_STRESS==1~"Not at all responsible",
                          WLR_STRESS==2~"Somewhat responsible", 
                          WLR_STRESS==3~"Very responsible", TRUE~NA))
data$stress=fct_inorder(data$stress)
table(data$stress, useNA = "always")

x=xtabs(~stress+status, data=data)
x
y=round(prop.table(x,2), 3)
y
result=chisq.test(x)

not=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[3,1], x[3,2], x[3,3]),
  ppn=c(y[3,1], y[3,2], y[3,3])
)
not$extent=c("Not at all")

somewhat=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[2,1], x[2,2], x[2,3]),
  ppn=c(y[2,1], y[2,2], y[2,3])
)
somewhat$extent=c("Somewhat")


very=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[1,1], x[1,2], x[1,3]),
  ppn=c(y[1,1], y[1,2], y[1,3])
)
very$extent=c("Very")

changes_table=as.data.frame(rbind(not, somewhat, very))

#kable(changes_table, format="html", caption="Changes in workout routine") %>%
 # kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)

Item=c("Stress", x[3,1], x[2,1],x[1,1],x[3,2],x[2,2],x[1,2],x[3,3],x[2,3],x[1,3])
Ppn=c("Stress", y[3,1], y[2,1], y[1,1], y[3,2],y[2,2],y[1,2],y[3,3],y[2,3],y[1,3])
Q37c=rbind(Item, Ppn)
```

## 37d Fatigue or Lack of Energy

```{r Q37d}

data = first.data %>%
  mutate(low=case_when(WLR_LOWENERG==1~"Not at all responsible",
                          WLR_LOWENERG==2~"Somewhat responsible", 
                          WLR_LOWENERG==3~"Very responsible", TRUE~NA))
data$low=fct_inorder(data$low)
table(data$low, useNA = "always")

x=xtabs(~low+status, data=data)
x
y=round(prop.table(x,2), 3)
y
result=chisq.test(x)

not=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[3,1], x[3,2], x[3,3]),
  ppn=c(y[3,1], y[3,2], y[3,3])
)
not$extent=c("Not at all")

somewhat=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[2,1], x[2,2], x[2,3]),
  ppn=c(y[2,1], y[2,2], y[2,3])
)
somewhat$extent=c("Somewhat")


very=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[1,1], x[1,2], x[1,3]),
  ppn=c(y[1,1], y[1,2], y[1,3])
)
very$extent=c("Very")

changes_table=as.data.frame(rbind(not, somewhat, very))

#kable(changes_table, format="html", caption="CFatique or Low Energy") %>%
 # kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)

Item=c("Fatigue", x[3,1], x[2,1],x[1,1],x[3,2],x[2,2],x[1,2],x[3,3],x[2,3],x[1,3])
Ppn=c("Fatigue", y[3,1], y[2,1], y[1,1], y[3,2],y[2,2],y[1,2],y[3,3],y[2,3],y[1,3])
Q37d=rbind(Item, Ppn)
```

## 37e Hormonal Changes

```{r Q37e}

data = first.data %>%
  mutate(hormone=case_when(WLR_HRMCHANGE==1~"Not at all responsible",
                          WLR_HRMCHANGE==2~"Somewhat responsible", 
                          WLR_HRMCHANGE==3~"Very responsible", TRUE~NA))
data$hormone=fct_inorder(data$hormone)
table(data$hormone, useNA = "always")

x=xtabs(~hormone+status, data=data)
x
y=round(prop.table(x,2), 3)
y
result=chisq.test(x)

not=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[3,1], x[3,2], x[3,3]),
  ppn=c(y[3,1], y[3,2], y[3,3])
)
not$extent=c("Not at all")

somewhat=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[2,1], x[2,2], x[2,3]),
  ppn=c(y[2,1], y[2,2], y[2,3])
)
somewhat$extent=c("Somewhat")


very=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[1,1], x[1,2], x[1,3]),
  ppn=c(y[1,1], y[1,2], y[1,3])
)
very$extent=c("Very")

changes_table=as.data.frame(rbind(not, somewhat, very))

#kable(changes_table, format="html", caption="Hormonal Changes") %>%
 # kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)


Item=c("Hormonal changes", x[3,1], x[2,1],x[1,1],x[3,2],x[2,2],x[1,2],x[3,3],x[2,3],x[1,3])
Ppn=c("Hormonal changes", y[3,1], y[2,1], y[1,1], y[3,2],y[2,2],y[1,2],y[3,3],y[2,3],y[1,3])
Q37e=rbind(Item, Ppn)
```

## 37f Reduced Daily Activities/Movement

```{r Q37f}

data = first.data %>%
  mutate(move=case_when(WLR_MOVELESS==1~"Not at all responsible",
                          WLR_MOVELESS==2~"Somewhat responsible", 
                          WLR_MOVELESS==3~"Very responsible", TRUE~NA))
data$move=fct_inorder(data$move)
table(data$move, useNA = "always")

x=xtabs(~move+status, data=data)
x
y=round(prop.table(x,2), 3)
y
result=chisq.test(x)

not=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[3,1], x[3,2], x[3,3]),
  ppn=c(y[3,1], y[3,2], y[3,3])
)
not$extent=c("Not at all")

somewhat=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[2,1], x[2,2], x[2,3]),
  ppn=c(y[2,1], y[2,2], y[2,3])
)
somewhat$extent=c("Somewhat")


very=data.frame(
  stage=c("pre", "peri", "post"),
  count=c(x[1,1], x[1,2], x[1,3]),
  ppn=c(y[1,1], y[1,2], y[1,3])
)
very$extent=c("Very")

changes_table=as.data.frame(rbind(not, somewhat, very))

#kable(changes_table, format="html", caption="Reduced Daily Activities/Movement") %>%
  #kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)


Item=c("Reduced activites", x[3,1], x[2,1],x[1,1],x[3,2],x[2,2],x[1,2],x[3,3],x[2,3],x[1,3])
Ppn=c("Reduced activities", y[3,1], y[2,1], y[1,1], y[3,2],y[2,2],y[1,2],y[3,3],y[2,3],y[1,3])
Q37f=rbind(Item, Ppn)
```


``` {r}
tab=rbind(Q37a, Q37b, Q37c, Q37d, Q37e, Q37f)

kable(tab)
```

``` {r barplots, include=F}