Trajectories of mHealth-tracked mental health symptoms and their predictors in chronic pelvic pain

21 Background. Female chronic pelvic pain disorders (CPPDs) affect 1 in 7 women 22 worldwide and are characterized by psychosocial comorbidities, including reduced quality of life 23 and 2-10 fold increased risk of depression and anxiety. Despite its prevalence and morbidity, 24 CPPDs are often inadequately managed with few patients experiencing relief from any medical 25 intervention. Characterizing mental health symptom trajectories and lifestyle predictors of 26 mental health is a starting point to enhancing patient self-efficacy in managing symptoms. Here, 27 we investigate the association between mental health, pain, and physical activity (PA) in females 28 with CPPD and demonstrate a method for handling multi-modal mobile health (mHealth) data. 29 Method. The study sample included 4,270 person-level days and 799 person-level weeks of data 30 from CPPD participants (N=76). Participants recorded PROMIS global mental health (GMH) 31 and physical functioning, and pain weekly for 14 weeks using a research mHealth app, and 32 moderate-to-vigorous PA (MVPA) was passively collected via activity trackers. Data analysis. 33 We used penalized functional regression (PFR) to regress weekly GMH-T (GMH-T) on MVPA 34 and weekly pain outcomes, while adjusting for baseline measures, time in study, and the random 35 intercept of the individual. We converted 7-day MVPA data into a single smooth using spline 36 basis functions to model the potential non-linear relationship. Results: MVPA was a significant, 37 curvilinear predictor of GMH-T (p<0.001), independent of pain measures and prior psychiatric 38 diagnosis. Physical functioning was positively associated with GMH-T, while pain was 39 negatively associated with GMH-T (β =2.24, β =-1.16, respectively; p<0.05). Conclusion: These 40 findings suggest that engaging in MVPA is beneficial to the mental health of females with 41 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 3 CPPD. Additionally, this study demonstrates the potential of ambulatory mHealth-based data 42 combined with functional models for delineating inter-individual and temporal variability. 43

44 Chronic pelvic pain; digital health; functional data modeling; global mental health. 45 Corresponding author 46 Emily Leventhal, B.A., 3 E 101st Street, New York, NY 10029, 47 [email protected] 48

49 Described as a “neglected reproductive health morbidity,” chronic pelvic pain (CPP) is a 50 highly debilitating condition that affects between 5.7% and 26.6% of women worldwide.1–3 CPP, 51 which encompasses complex CPP disorders (CPPDs) such as endometriosis, adenomyosis, and 52 fibroids, is characterized by non-cyclic pain in the pelvis or abdomen that lasts for at least 6 53 months and leads to functional disability or the necessity for medical intervention.3–5 Its severity 54 is underscored by its associated physical, psychological, and emotional, and social 55 consequences.4,5 56 The strong psychosocial impact of CPPDs contributes to their morbidity. For example, 57 individuals with CPPDs are more likely to experience reduced quality of life, emotional well-58 being, productivity, and sexual function compared to the general population.4 Additionally, 59 CPPD patients have a significantly higher risk of comorbid psychiatric disorders.4,6,7. For 60 example, individuals with CPPDs have been reported to experience depressive disorders at a 61 prevalence of 2 to 10 times that of the general population and anxiety disorders 3 to 6 times that 62 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 4 of the general population.4 Because chronic pain is tightly linked to mental health problems, 63 investigation of potentially modifiable predictors of mental well-being in individuals with 64 CPPDs may be a starting point for comprehensively managing and treating CPPD patients.8 65 Despite its prevalence and burden, CPP is often inadequately managed, with less than 66 half of patients experiencing pain relief from any medical treatment.6,9 Patient self-management, 67 which encompasses active efforts to manage pain and its effects on physical and emotional 68 function, is a common chronic pain care model intervention, and it has been associated with 69 significant improvement in symptoms.10,11 Further, Center for Disease Control (CDC) guidelines 70 state that non-opioid and non-pharmacologic therapies should be prioritized for chronic pain 71 management.12,13 Non-pharmacological self-management strategies, especially those that target 72 mental health outcomes of CPPD patients, are needed for effective personalized treatment of 73 CPPD. 74 Physical activity (PA), and exercise, defined as planned, structured, and repetitive PA 75 with the goal of improved health or fitness, have been demonstrated to be effective pain self-76 management for both reducing pain severity and improving psychological function in chronic 77 pain patients.11,14 Experts recommend that chronic pain patients exercise on a regular schedule on 78 the premise that avoiding activity during pain and increasing intensity later may lead to pain 79 flares.4 Importantly, exercise is a modifiable behavior that can also improve pain self-efficacy, 80 defined as the confidence in one’s ability to function effectively while in pain, which is 81 associated with improved quality of life.15,16 Further, for chronic pain patients with comorbid 82 psychiatric conditions, exercise may improve mood, depression, and anxiety symptoms.4 A 83 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 5 previous study with individuals with endometriosis estimated a small but statistically significant 84 favorable effect of exercise on pain severity.17 However, this study relied on self-reported 85 exercise, which is limited in its ability to capture more granular PA parameters (eg, step counts, 86 intensity-level).17 While most of the evidence connecting PA to psychosocial improvement has 87 been from other chronic pain conditions, yoga has previously been demonstrated to be 88 efficacious for improving pain and quality of life for patients with endometriosis.4 The impact of 89 broader PA on mental health in patients with CPP specifically remains to be investigated, with a 90 focus on using longitudinal data to capture potentially meaningful trends over time. 91 CPPDs and their symptomatic patterns are notably heterogeneous in clinical presentation 92 both between patients and within-individuals over time.18 Capturing these fluctuations under 93 ecologically valid circumstances can help improve our understanding of the dynamic unfolding 94 of these symptoms and their potential predictors. In the context of health behaviors such as PA, 95 data from mobile health (mHealth) technologies (eg, smartphone apps, trackers) combined with 96 longitudinal analytic techniques can help elucidate symptom associations with psychosocial 97 outcomes in CPP.7,18 For example, there may be non-linear associations and cumulative effects 98 in these longitudinal data that are not possible to capture via linear modeling approaches. In sum, 99 flexible techniques can be particularly useful when considering variables that differ in sampling 100 frequencies, missingness patterns, modality, and temporal complexity, which is often the case 101 with mHealth data. 102 Functional regression models, which are a part of the family of generalized additive 103 models (GAMs), constitute one such approach.19 In a functional regression framework, the entire 104 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 6 data curve is considered as the unit of analysis, instead of discrete data points in a set of 105 longitudinal data. This is particularly useful for handling PA data from wearables, rather than 106 aggregating multiple data points per individual,20 as they allow investigating the associations 107 between scalar and functional variables with different time intervals. One example of a scenario 108 relevant to this study is consideration of continuous or daily PA data with weekly self-reported 109 survey data, in a repeated-measures design. This results in a data structure where each weekly 110 questionnaire corresponds to 7 days of PA data leading up to the survey data. A functional 111 regression model considers the PA data as a weekly data curve rather than aggregating the entire 112 week into a summary score and thus preserves the temporal pattern within the data. This can 113 reveal important information that may be lost otherwise, such as periods of inactivity or bursts of 114 activity, which could be related to mental health.4,21 115 Accordingly, this study aims to characterize the patterns of association between self-116 reported mental health symptoms and their predictors in CPPDs, with a focus on modifiable 117 lifestyle factors. Specifically, this overall aim includes investigation of 1) between- and within-118 individual fluctuations in weekly self-reported mental health, and 2) possible modifiable and trait 119 predictors of weekly mental health. We hypothesized that there would be significant variability 120 in the mental health both between and within individuals and that PA would be a positive non-121 linear predictor of mental health. 122

123 Study Design and Procedures 124 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 7 The study design and procedures were approved by the IRB of the Icahn School of 125 Medicine at Mount Sinai (ISMMS; IRB# STUDY-22-01002). This is a secondary analysis of the 126 data from an ongoing larger study that aims to design, develop, and evaluate CPPD-specific 127 mHealth measures from patient generated health data with high complexity and temporality 128 using non-linear distributed lag and functional data modeling (NIH/NICHD: R01HD108263). It 129 uses an observational study design to collect 90 days of data on patient self-tracked symptoms 130 via a research mHealth app (ehive22) and passively collected activity data using activity trackers 131 from participants. All participants used the ehive research study app for providing the baseline 132 and weekly data on overall health, symptoms, well-being and health behaviors, as well as for 133 receiving prompts and reminders about the study.22 Participants were instructed to wear a Fitbit 134 for the duration of the study. 135 Study Sample 136 The study sample included individuals who met the following eligibility criteria for the 137 parent study: 1) females who menstruate currently, between the ages of 18 and 64, 2) self-138 reported CPPD based on clinician diagnosis, 3) experience of CPP for at least 6 months, and 4) 139 ability to read and write in English. Exclusion criteria include: 1) current pregnancy, a birth in 140 the past 6 months, or planning pregnancy during the months of the study and 2) major diseases or 141 comorbidities (eg, active cancer, acute coronary syndrome within the past 3 months) that might 142 confound the outcomes of the primary pelvic pain-related condition. Participants were recruited 143 from all campuses of the Mount Sinai Health System (MSHS) and Columbia University Irving 144 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 8 Medical Center (CUIMC) via email advertisements and on the myChart by EPIC mobile app for 145 MSHS patients. 146 Enrollment 147 Interested patients reached out to the study coordinator at Mount Sinai for screening and 148 enrollment, after which they were onboarded and oriented to the study app and data collection 149 protocols. All participants were mailed a Fitbit Inspire 2 device and instructed to use for the 150 duration of the study (90 days). Participants were remunerated $15 for every 2 weeks of data 151 collection and $20 for the final week (ie, up to $120 in total for completing 90 days of data 152 collection). All participants provided informed consent prior to enrolling in the study. 153 Study Measures 154 Primary Outcomes 155 Self-reported mental health was assessed every week using the PROMIS Global Mental 156 Health Questionnaire (GMH; 2a, v1.2).23 The GMH includes 2 questions: 1) “In general, how 157 would you rate your mental health, including your mood and your ability to think?” 2) “In 158 general, how would you rate your satisfaction with your social activities and relationships?” Both 159 questions have a 5-point multiple choice response scale (1-not at all, 5-very much) and the 160 responses are added to compute the total score on the GMH (range 2-10). Higher scores 161 represent better mental health.23 The two-item GMH survey provides a brief measure of mental 162 health that has been found to be both reliable and have construct validity.23 Scores from the 163 GMH survey have been positively associated with other self-reported outcomes including overall 164 quality of life and physical functioning, and negatively correlated with fatigue, anxiety, anger, 165 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 9 depressive symptoms, and chronic conditions (e.g,. liver disease, kidney disease, hypertension, 166 etc.).23 We converted raw GMH scores to population-standardized GMH scores (T-scores) 167 according to the PROMIS Global Health scoring manual by standardizing the raw total score to a 168 mean of 50 and a standard deviation (SD) of 10.24 GMH T-scores (GMH-T) are further 169 categorized as excellent (>55), very good (48-55), good (40-47), fair (29-39), and poor (<29).25 170 Predictors 171 Physical activity. Daily minutes of moderate-to-vigorous intensity PA (MVPA) and step 172 counts were obtained from the wrist-worn Fitbit devices. Participants were instructed to wear 173 their devices continuously for the study duration. The study app (ehive) allows the user to link 174 their account with their Fitbit device,22 which enables regular daily data synching on the backend 175 of the app. Fitbit uses its proprietary algorithms for detection of step counts and activity 176 intensities. We collected 6,341 days of physical activity data for 78 participants. For wear time 177 validation, we relied on the commonly used standard “10-hour minimum wear” rule, in which a 178 valid day is defined as at least 10 hours of non-zero activity counts.26–28 Ten hours of wear has 179 been shown to be sufficient to estimate total daily physical activity during non-sleep time.29 180 There were 4,301 valid days of Fitbit data for 76 participants. Days with unrealistically low 181 activity counts (eg, <500 steps in a day; n=14) were removed in accordance with similar cutoffs 182 that have been used in the past to define a valid day, although we used a more conservative 183 cutoff.26,27 This resulted in 4,287 days of physical activity data for 76 participants. If there were 184 more than 7 days of Fitbit data in between survey responses (ie, if a participant waited more than 185 7 days before completing the next survey), we only considered the first 7 days of Fitbit activity 186 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 10 data to avoid sparsity in the penalized functional regression (PFR) model (described below). 77 187 days of activity data measured more than 7 days after a survey response were removed for this 188 reason. The final dataset had 4,270 days of data for 76 participants. 189 Physical functioning. Weekly physical functioning scores were measured using the 190 PROMIS physical function survey (4a, v1.0).30 Physical functioning is the self-reported 191 capability of performing everyday physical activities. The score evaluates functioning of upper 192 extremities, lower extremities, central regions, and activities of daily living. The 4-item PROMIS 193 survey assesses the extent to which individuals find difficulty with physical tasks (5-without any 194 difficulty to 1-unable to do). Scores range from 4 to 20, with higher scores indicating better 195 physical functioning. We used the physical functioning T-scores in the analyses, which are 196 standardized to a mean of 50 and a SD of 10 based on a representative population distribution.30 197 Pain. We measured weekly pain levels using the VAS pain intensity item from the short-198 form McGill Pain Questionnaire (MPQ-VAS).31 The MPQ-VAS asks participants to rate the 199 intensity of their present pain intensity on a scale of 0 (no pain) to 100 (worst imaginable pain).32 200 This type of VAS-based pain assessment is commonly used as a standard practice in clinical 201 settings to evaluate patient pain status and treatment outcomes.33,34 202 Other covariates. Data on personal demographics and general health were collected via 203 a baseline questionnaire on the ehive app. We collected age, marital status, ethnicity, and 204 employment status from the demographics survey. In addition, we used prior psychiatric 205 diagnosis (“Have you ever been diagnosed with a psychiatric diagnosis by a provider?”) as a 206 covariate from the general health survey. 207 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 11 Data analysis 208 Descriptive and bivariate analyses 209 First, we performed descriptive analyses and investigated bivariate associations between 210 the weekly-measured survey items. Given the repeated-measures design, we use both person-211 level means (ie, a participant’s mean score across the 14 weeks) and overall sample means (ie, 212 mean of means) where necessary to report the overall study average scores from the daily (ie, 213 steps, MVPA) and weekly (ie, pain, physical functioning T-score, GMH-T) measures. To 214 analyze the GMH-T, we converted the mean GMH-T for each participant to its corresponding 215 GMH category (eg, fair, good, excellent, etc.), and computed the percent of participants in each 216 category.25 To evaluate sample GMH-T and physical functioning T-scores against known 217 population means, we used a one-sample T-test to compare the sample means to the population 218 means. We then computed repeated-measures correlations between GMH-T, physical 219 functioning, MPQ-VAS, and the sum of MVPA over 7 days using the rmcorr R package, which 220 evaluates the within-individual association of paired measurements taken two or more times 221 longitudinally.35 222 Multivariable regression analysis of GMH predictors 223 To investigate the potential predictors of GMH-T scores at the week level, we 224 implemented PFR modeling using the R refund library.20 PFR models are flexible in numerous 225 ways that are particularly useful for the data in this study. First, they allow for entire data curves 226 to be units of analysis as opposed to individual data points. Next, they accommodate different 227 sampling intervals in the outcomes vs predictors, ie, week-level outcome (eg, GMH-T) and 228 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 12 week-level (eg, pain, physical functioning) and day-level (eg, MVPA) predictors. Instead of 229 aggregating multiple day-level MVPA values for each week, this feature of the PFR allows for 230 the preservation of temporal variability in MVPA over a week. Third, it allows specification of 231 random intercepts (ie, individual participants), which is useful for both accommodating a 232 repeated measures design and for investigation of potential between- vs within-individual 233 variability in the outcome of interest (ie, GMH-T scores). 234 We regressed GMH-T on MVPA while considering MPQ-VAS, PROMIS physical 235 functioning, age, marital status, employment status, and prior psychiatric diagnosis. We further 236 adjusted for time in study using month-level cyclical encoding, in which each date is mapped 237 into a cyclic coordinate system using sine-cosine waves and allows the models to infer the 238 distance between dates based on their sine-cosine coordinates. We converted 7-day MVPA data 239 into smooths with up to 7 knots using the tensor product basis function36 to model the potential 240 non-linear relationship between GMH-T and daily PA. We similarly included the time covariate 241 as a functional smooth with up to 7 knots.20 We scaled MPQ-VAS, PROMIS physical 242 functioning, and age by mean-centering each variable and dividing by its standard deviation. We 243 included participant and week in study as random effects. Finally, other categorical variables (ie, 244 psychiatric diagnosis, employment status, and marital status) were included as person-level 245 linear covariates.20 We used a generalized additive model as the fitter to estimate the model and 246 restricted maximum likelihood as the smoothing parameter estimation method, which are the 247 default recommended methods for the function.20 248

249 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 13 Study sample 250 Participants (n=76) provided 799 weeks of survey and 4,270 days of activity data in total 251 for analysis. Participants had a mean age of 35 years and were mostly employed (76%). Most 252 participants identified as White (42%) or Hispanic or Latino (17%). In our sample, 28% had at 253 least one prior diagnosis of a psychiatric condition, including anxiety and mood disorders (Table 254 1). The CPPD diagnoses included endometriosis (N=51), adenomyosis (N=1), uterine fibroids 255 (N=2), interstitial cystitis (N=1), inflammatory bowel syndrome (3), and inflammatory pelvic 256 dysfunction (N=1). 257 Descriptive and bivariate analyses 258 The overall sample means of the scores from the daily and weekly measures are reported 259 in Table 2. Thirty-nine percent of the participants, on average, reported scores that corresponded 260 to “fair” mental health, with another 39% of the participants on average reporting “good” mental 261 health (Table 2). The mean GMH-T was 42.166 (95% CI: 40.363-43.969), which is 7.83 SDs 262 below the population mean (ie, M=50, “very good”)23 and significantly different (t=-8.658, p < 263 .001; Figure 1). The mean physical functioning T-score was 45.19 (95% CI: 43.52-46.853), 264 which is 0.48 SDs below the population mean (ie, M=50; Figure 1; t = -5.758, p < .001). 265 To characterize the PA patterns in the sample, we compared participants’ activity levels 266 to the published recommendations and CDC/HHS PA guidelines for adults with respect to steps 267 and MVPA.37,37–39 On average, participants accumulated 8,313 steps and 38 minutes of MVPA 268 per day (Table 2). Forty-three percent of the sample engaged in fewer than 7,500 daily steps, 269 which is the lower threshold recommended for being considered “sufficiently active” (Figure 270 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 14 2a). 38,39 Similarly, 40.9% accumulated fewer than 150 minutes of weekly MVPA recommended 271 by the PA Guidelines (Figure 2b).40 272 To inspect the bivariate associations between weekly measures, we computed repeated 273 measures correlations between GMH-T and the other variables. GMH-T were positively 274 correlated with weekly MVPA (p<.05), and physical function T-score (p<.01), while they were 275 negatively correlated with MPQ-VAS (p<.001; Figure 3). Weekly MVPA was additionally 276 positively correlated with physical functioning T-score (p<.05) but was not significantly 277 correlated with MPQ-VAS. 278 PFR model 279 We fitted a PFR model to the data to investigate cumulative and non-linear effects of 280 MVPA on the weekly GMH-T. The best fitting final model explained 72.6% of the variance in 281 GMH-T (R2=0.65). The smooth of MVPA and time on GMH-T indicated a significant non-linear 282 relationship (Table S1; Table S2; edf=2.23, F=18.99, p<.001). Predicted GMH-T increased with 283 increasing daily MVPA minutes (Figure 4a). Over time, the largest positive effect of MVPA on 284 predicted GMH-T as reported at the end of the week was a few days prior (~day 4). The positive 285 effect of MVPA on GMH-T reported at the end of the week diminished after day 4, suggesting 286 the positive effects of MVPA lagged by a couple of days. Weekly MPQ-VAS was a significant 287 negative predictor of GMH-T (β =-1.16, SE=0.50, t=-2.34, p<.05), while physical functioning T-288 score was a significant positive predictor of GMH-T (Figure 4b; Table S3; β =2.24, SE=0.598, 289 t=3.75, p<.001). For demographic factors, age was negatively associated with GMH-T (β =-1.20, 290 SE=0.46, t=-2.58, p<.05), while being employed and married were positively associated with 291 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 15 GMH-T (β =4.01, SE=1.09, t=3.67, p<.001; β =3.60, SE=0.86, t=4.20, p<.001). Prior psychiatric 292 diagnosis was not a significant predictor of weekly GMH. The random effect of participant was 293 significant (Figure 4c; edf=33.43, F=2.76, p<0.001). The random effect of week and the 294 cyclically encoded sine and cosine functions of month were not significant. 295

296 In this study, we leveraged ambulatory mHealth-tracked mental health, pain, and physical 297 activity data to characterize longitudinal self-reported mental health patterns of individuals with 298 CPPDs. Our results indicate a positive, non-linear relationship between PA and mental health, 299 independent of prior psychiatric diagnosis or other pain-related factors, with considerable 300 variability both between and within participants over time. To our knowledge, this study 301 provides the first line of evidence on the positive effect of PA on mental health in females with 302 CPPDs using repeated measures data collected in real time. We further report lower scores of 303 mental health and physical functioning compared to the general population, as well as lower PA 304 levels than those recommended by the PA guidelines. 305 Our cohort had a 28% incidence of prior psychiatric conditions and lower average global 306 mental health compared to the general population. Chronic pain, and specifically CPPDs, has 307 been established as a strong predisposing factor for psychiatric conditions, due to both the 308 psychosocial impact of chronic pain and common neurobiological vulnerabilities and genetic 309 factors between chronic pain and mood.4,6,41,42 CPPD patients with comorbid psychiatric 310 conditions are more likely to incur higher health care costs, experience lower quality of life, 311 endure increased disability, and are more likely to be prescribed opioids.4 Additionally, our 312 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 16 findings add to the literature documenting the worsened mental health of CPPD patients as a 313 whole compared to the general population.4,23,42 In the 2019 National Health Interview Survey, 314 those with chronic pain had a 23.9% prevalence of co-occurring anxiety and/or depression 315 symptoms, whereas the population without chronic pain had a prevalence of 4.9%.42 Given the 316 high incidence of psychiatric co-morbidities and the generally low mental health among CPPD 317 patients, it is important to treat mental health as part of comprehensive chronic pain management 318 and continue to determine ways to aid patients to manage their symptoms. As such, here, we 319 investigated how lifestyle factors may modify the association of CPP with poorer mental health 320 outcomes. 321 Our findings suggest that many females with CPPDs do not reach nationally 322 recommended activity levels, and moreover, that engaging in MVPA is beneficial for the mental 323 health of CPPD patients. The PA levels found in this sample are consistent with previous studies 324 indicating that individuals with CPPDs have lower PA levels,43 though data on CPPDs are 325 scarce. One longitudinal study using accelerometers indicated that MVPA negatively mediated 326 the relationship between chronic pain and risk of mental disorders, although this study did not 327 focus on CPP.44 Increased MVPA in individuals with chronic pain was associated with decreased 328 anxiety and depression symptoms, whereas light intensity PA did not have this effect.42 While 329 previous studies have established the connection between MVPA and mental health in chronic 330 pain, this is the first study to establish the relationship between PA and mental health in the 331 context of CPP by using passively-obtained data from activity trackers.17 332 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 17 Our findings further indicate that increased pain is associated with worsened GMH, while 333 increased physical functioning was associated with improved GMH. Though pain and depression 334 or anxiety have been noted to have a bidirectional relationship, there is more evidence that pain 335 is a risk factor for mental health problems than the inverse.4 Additionally, a longitudinal study 336 focused on musculoskeletal conditions found that improvements in physical functioning were 337 associated with improved anxiety symptoms, although it was not associated with improved 338 depression symptoms.45 The relationship between physical functioning and mental health in CPP 339 has not been well defined to this point, however, one previous longitudinal study on 340 endometriosis reported that functional pain disability did not predict later emotional distress.46 341 With respect to demographic factors as potential predictors, increased age was associated 342 with worsened GMH, while prior psychiatric diagnosis was not a significant predictor. Age may 343 be a proxy for years of experience with the chronic pain condition or severity of the condition. In 344 this study, we did not have a survey item assessing time of initial diagnosis, although this may be 345 possible in the future by linking mobile health studies with electronic health records (EHRs). 346 Over time, chronic pain may become more difficult to treat due to structural and functional 347 neuroplastic changes that eventually become irreversible and insensitive to treatment.41 From a 348 psychosocial standpoint, the economic consequences of health care costs and loss of productivity 349 may accumulate over time.41 It will be important to assess how length of time of living with 350 chronic pain impacts mental health in the future. Interestingly, diagnosis with a prior psychiatric 351 condition, including mood and anxiety disorders, was not a significant predictor of GMH. This 352 may suggest that some individuals with prior psychiatric diagnoses may not be actively 353 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 18 experiencing symptoms, or alternatively, that this sample has a large number of participants with 354 undiagnosed psychiatric conditions that are actively experiencing symptoms. 355 We observed substantial between- and within-individual variability in mental health 356 scores in the sample, underscoring the importance of personalized approaches to care. Predicted 357 average GMH-T varied greatly between individuals as shown by the random intercepts. CPPDs 358 are notoriously heterogeneous in pain symptomatology, and it follows that mental health would 359 exhibit similar variability among and within participants.7 As such, it is important to use 360 individualized approaches, such as that which may be achieved with mHealth, to 361 comprehensively understand the complexity of CPP. Due to their heterogeneous clinical 362 presentation and differing etiologies, CPPDs are often non-responsive to treatment, and a 363 personalized approach is necessary for the successful management of CPPD. To better 364 understand how to manage the mental health of CPPD patients, we should continue to study 365 modifiable lifestyle factors, as was done here with PA, that may alter the poor mental health 366 outcomes associated with CPP. This study demonstrates the potential of using ambulatory 367 mHealth-based data combined with functional data methods to delineate inter-individual and 368 temporal variability in symptoms of chronic conditions. 369 There are numerous strengths of this work. First, we focus on a patient population that 370 has been under-studied (ie, CPPDs) and currently still not well-understood as a cluster of 371 disorders with overlapping symptomatology. While endometriosis, the most common underlying 372 primary diagnosis for a CPPD, has been receiving more attention recently, our sample also 373 included those less-studied CPP conditions (eg, adenomyosis, fibroids, inflammatory pelvic 374 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 19 disease). Next, implementation of functional data methods and generalized additive modeling 375 using smooths provide robust, flexible approaches for handling the complex patient-generated 376 health data from mHealth technologies. The PFR models in this context facilitate the evaluation 377 of complex relationships between outcomes and their predictors in instances where data 378 sampling frequency differs between the outcomes and predictors, or between different predictors. 379 As mHealth use is becoming more ubiquitous for conducting research, expanding upon the 380 available methods will enable fully harnessing the information from these data. Third, our 381 analyses were based on frequently-sampled prospective data of up to 14 weeks from the study 382 participants. This is a strength of the data design as most studies to date are limited to 383 convenience samples of retrospective data with much less frequency of data points. 384 Nevertheless, we acknowledge the limitations of this study. Although we had 799 person-385 level weeks for analysis, 76 participants is a relatively modest sample size in comparison to 386 large, nationally-representative cohort studies. Similarly, the sample was somewhat 387 homogeneous with respect to demographic factors including employment status and education 388 levels. Third, despite our careful inspection of the missing data and implementing cautious 389 filtering criteria to prevent potentially erroneous inference from the data, Fitbit’s proprietary 390 algorithms do not always enable as informed decisions regarding the missing data as do some 391 other devices, such as research grade trackers that allow access to the raw acceleration data. To 392 circumvent these issues, we conducted a series of sensitivity analyses to assess the pattern of 393 missingness in the data, as well as the possible influence of missingness on the model results. 394

(not reported herein) indicated no significant bias, suggesting a missing-at-random 395 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 20 (MAR) pattern, or change in model point estimates. Finally, most of the participants had 396 endometriosis as their primary CPPD, therefore we are not able to delineate differences in mental 397 health trajectories among different disorders within CPPD. 398

399 mHealth-enabled direct patient input and passive tracking via wearables enables the 400 capturing of real-world data to improve our understanding of inter-individual and temporal 401 variability in mental health symptoms and factors that may improve mental health. By leveraging 402 patient-tracked mental health and pain outcomes combined with passively-obtained activity data 403 from CPPD patients, we demonstrate a positive, non-linear relationship between PA and mental 404 health in CPP. 405 Ethics approval and informed consent 406 The study was approved by the Institutional Review Board (IRB) of the Icahn School of 407 Medicine at Mount Sinai (IRB# STUDY-22-01002) and all participants provided informed 408 consent. 409 Data availability 410 The data collection for the parent grant is currently ongoing. After completion of the 411 active grant period, the data produced in the present study will be made available upon 412 reasonable request to the corresponding author. 413 Funding 414 This study was supported by a grant award from the Eunice Kennedy Shriver National 415 Institute Of Child Health & Human Development of the National Institutes of Health (Award 416 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 21 Number: R01HD108263, PI=Ensari). The content does not necessarily represent the official 417 views of the National Institutes of Health. Additionally, this research was supported by the T32 418 grant 1T32GM146636. 419 Authors’ contributions 420 All authors contributed significantly to the work presented in this manuscript, including 421 the conception, study design, execution, acquisition of data, analysis and interpretation. Each 422 author reviewed this article and agree to take responsibility for the contents of this article. 423

424 1. Latthe P, Latthe M, Say L, Gülmezoglu M, Khan KS. WHO systematic review of prevalence 425 of chronic pelvic pain: a neglected reproductive health morbidity. BMC Public Health. 426 2006;6(1):177. doi:10.1186/1471-2458-6-177 427 2. Ahangari A. Prevalence of Chronic Pelvic Pain AmongWomen: An Updated Review. Pain 428 Physician. 2014;2;17(2;3):E141-E147. doi:10.36076/ppj.2014/17/E141 429 3. Hutton D, Mustafa A, Patil S, et al. The burden of Chronic Pelvic Pain (CPP): Costs and 430 quality of life of women and men with CPP treated in outpatient referral centers. Raimondo 431 D, ed. PLOS ONE. 2023;18(2):e0269828. doi:10.1371/journal.pone.0269828 432 4. Till SR, As-Sanie S, Schrepf A. Psychology of Chronic Pelvic Pain: Prevalence, 433 Neurobiological Vulnerabilities, and Treatment. Clin Obstet Gynecol. 2019;62(1):22-36. 434 doi:10.1097/GRF.0000000000000412 435 5. Ayorinde AA, Macfarlane GJ, Saraswat L, Bhattacharya S. Chronic Pelvic Pain in Women: 436 An Epidemiological Perspective. Womens Health. 2015;11(6):851-864. 437 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 22 doi:10.2217/whe.15.30 438 6. Meltzer-Brody S, Leserman J. Psychiatric Comorbidity in Women with Chronic Pelvic Pain. 439 CNS Spectr. 2011;16(2):29-35. doi:10.1017/S1092852912000156 440 7. Till SR, Nakamura R, Schrepf A, As-Sanie S. Approach to Diagnosis and Management of 441 Chronic Pelvic Pain in Women. Obstet Gynecol Clin North Am. 2022;49(2):219-239. 442 doi:10.1016/j.ogc.2022.02.006 443 8. Sheng J, Liu S, Wang Y, Cui R, Zhang X. The Link between Depression and Chronic Pain: 444 Neural Mechanisms in the Brain. Neural Plast. 2017;2017:1-10. doi:10.1155/2017/9724371 445 9. The Initial Management of Chronic Pelvic Pain. Published online 2012. 446 10. Kerns RD, Burgess DJ, Coleman BC, et al. Self-Management of Chronic Pain: 447 Psychologically Guided Core Competencies for Providers. Pain Med. 2022;23(11):1815-448 1819. doi:10.1093/pm/pnac083 449 11. Leonardi M, Horne AW, Vincent K, et al. Self-management strategies to consider to combat 450 endometriosis symptoms during the COVID-19 pandemic. Hum Reprod Open. 451 2020;2020(2):hoaa028. doi:10.1093/hropen/hoaa028 452 12. Nonopioid Therapies for Pain Management | Overdose Prevention | CDC. 453 13. CDC Clinical Practice Guideline for Prescribing Opioids for Pain — United States, 2022 | 454 MMWR. 455 14. Geneen LJ, Moore RA, Clarke C, Martin D, Colvin LA, Smith BH. Physical activity and 456 exercise for chronic pain in adults: an overview of Cochrane Reviews. Cochrane Pain, 457 Palliative and Supportive Care Group, ed. Cochrane Database Syst Rev. 2017;2020(2). 458 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 23 doi:10.1002/14651858.CD011279.pub3 459 15. Gilanyi YL, Wewege MA, Shah B, et al. Exercise Increases Pain Self-efficacy in Adults 460 With Nonspecific Chronic Low Back Pain: A Systematic Review and Meta-analysis. J 461 Orthop Sports Phys Ther. 2023;53(6):335-342. doi:10.2519/jospt.2023.11622 462 16. Kalapurakkel S, A. Carpino E, Lebel A, E. Simons L. “Pain Can’t Stop Me”: Examining 463 Pain Self-Efficacy and Acceptance as Resilience Processes Among Youth With Chronic 464 Headache. J Pediatr Psychol. 2015;40(9):926-933. doi:10.1093/jpepsy/jsu091 465 17. Ensari I, Lipsky-Gorman S, Horan EN, Bakken S, Elhadad N. Associations between physical 466 exercise patterns and pain symptoms in individuals with endometriosis: a cross-sectional 467 mHealth-based investigation. BMJ Open. 2022;12(7):e059280. doi:10.1136/bmjopen-2021-468 059280 469 18. Ensari I, Pichon A, Lipsky-Gorman S, Bakken S, Elhadad N. Augmenting the Clinical Data 470 Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical 471 Documentation in Endometriosis. Appl Clin Inform. 2020;11(05):769-784. doi:10.1055/s-472 0040-1718755 473 19. Muller HG, Wu Y, Yao F. Continuously additive models for nonlinear functional regression. 474 Biometrika. 2013;100(3):607-622. doi:10.1093/biomet/ast004 475 20. Goldsmith J, Bobb J, Crainiceanu CM, Caffo B, Reich D. Penalized Functional Regression. J 476 Comput Graph Stat. 2011;20(4):830-851. doi:10.1198/jcgs.2010.10007 477 21. Manning JR, Notaro GM, Chen E, Fitzpatrick PC. Fitness tracking reveals task-specific 478 associations between memory, mental health, and physical activity. Sci Rep. 479 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 24 2022;12(1):13822. doi:10.1038/s41598-022-17781-0 480 22. Hirten RP, Danieletto M, Landell K, et al. Development of the ehive Digital Health App: 481 Protocol for a Centralized Research Platform. JMIR Res Protoc. 2023;12:e49204. 482 doi:10.2196/49204 483 23. Hays RD, Schalet BD, Spritzer KL, Cella D. Two-item PROMIS® global physical and 484 mental health scales. J Patient-Rep Outcomes. 2017;1(1):2. doi:10.1186/s41687-017-0003-8 485 24. Global Health Scoring Manual. Published online August 15, 2021. 486 25. Elsman EBM, Roorda LD, Crins MHP, Boers M, Terwee CB. Dutch reference values for the 487 Patient-Reported Outcomes Measurement Information System Scale v1.2 - Global Health 488 (PROMIS-GH). J Patient-Rep Outcomes. 2021;5(1):38. doi:10.1186/s41687-021-00314-0 489 26. Master H, Annis J, Huang S, et al. Association of step counts over time with the risk of 490 chronic disease in the All of Us Research Program. Nat Med. 2022;28(11):2301-2308. 491 doi:10.1038/s41591-022-02012-w 492 27. Perry AS, Annis JS, Master H, et al. Association of Longitudinal Activity Measures and 493 Diabetes Risk: An Analysis From the National Institutes of Health All of Us Research 494 Program. J Clin Endocrinol Metab. 2023;108(5):1101-1109. doi:10.1210/clinem/dgac695 495 28. Beagle AJ, Tison GH, Aschbacher K, Olgin JE, Marcus GM, Pletcher MJ. Comparison of 496 the Physical Activity Measured by a Consumer Wearable Activity Tracker and That 497 Measured by Self-Report: Cross-Sectional Analysis of the Health eHeart Study. JMIR 498 MHealth UHealth. 2020;8(12):e22090. doi:10.2196/22090 499 29. Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, Mcdowell M. Physical Activity in 500 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 25 the United States Measured by Accelerometer. Med Sci Sports Exerc. 2008;40(1):181-188. 501 doi:10.1249/mss.0b013e31815a51b3 502 30. Rose M, Bjorner JB, Gandek B, Bruce B, Fries JF, Ware JE. The PROMIS Physical 503 Function item bank was calibrated to a standardized metric and shown to improve 504 measurement efficiency. J Clin Epidemiol. 2014;67(5):516-526. 505 doi:10.1016/j.jclinepi.2013.10.024 506 31. Melzack R. The short-form McGill pain questionnaire. Pain. 1987;30(2):191-197. 507 doi:10.1016/0304-3959(87)91074-8 508 32. Hawker GA, Mian S, Kendzerska T, French M. Measures of adult pain: Visual Analog Scale 509 for Pain (VAS Pain), Numeric Rating Scale for Pain (NRS Pain), McGill Pain Questionnaire 510 (MPQ), Short/i2 Form McGill Pain Questionnaire (SF/i2 MPQ), Chronic Pain Grade Scale 511 (CPGS), Short Form/i2 36 Bodily Pain Scale (SF/i2 36 BPS), and Measure of Intermittent and 512 Constant Osteoarthritis Pain (ICOAP). Arthritis Care Res. 2011;63(S11). 513 doi:10.1002/acr.20543 514 33. Lukacz ES, Lawrence JM, Burchette RJ, Luber KM, Nager CW, Galen Buckwalter J. The 515 use of Visual Analog Scale in urogynecologic research: A psychometric evaluation. Am J 516 Obstet Gynecol. 2004;191(1):165-170. doi:10.1016/j.ajog.2004.04.047 517 34. Creinin MD. Pain Associated With Cervical Priming for First-Trimester Surgical Abortion: 518 A Randomized Controlled Trial. Obstet Gynecol. 2021;138(4):680-680. 519 doi:10.1097/AOG.0000000000004552 520 35. Bakdash JZ, Marusich LR. Repeated Measures Correlation. Front Psychol. 2017;8:456. 521 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 26 doi:10.3389/fpsyg.2017.00456 522 36. Simon Wood . mgcv: Mixed GAM Computation Vehicle with 523 Automatic Smoothness Estimation. Published online October 4, 2000:1.9-1. 524 doi:10.32614/CRAN.package.mgcv 525 37. Leavitt MO. 2008 Physical Activity Guidelines for Americans. 526 38. Tudor-Locke C, Craig CL, Thyfault JP, Spence JC. A step-defined sedentary lifestyle index: 527 <5000 steps/day. Appl Physiol Nutr Metab. 2013;38(2):100-114. doi:10.1139/apnm-2012-528 0235 529 39. Tudor-Locke C, Hatano Y, Pangrazi RP, Kang M. Revisiting “How Many Steps Are 530 Enough?” Med Sci Sports Exerc. 2008;40(7):S537-S543. 531 doi:10.1249/MSS.0b013e31817c7133 532 40. Physical Activity Guidelines for Americans, 2nd Edition. US Department of Health and 533 Human Services; 2018. 534 41. Fine PG. Long-Term Consequences of Chronic Pain: Mounting Evidence for Pain as a 535 Neurological Disease and Parallels with Other Chronic Disease States. Pain Med. 536 2011;12(7):996-1004. doi:10.1111/j.1526-4637.2011.01187.x 537 42. De La Rosa JS, Brady BR, Ibrahim MM, et al. Co-occurrence of chronic pain and 538 anxiety/depression symptoms in U.S. adults: prevalence, functional impacts, and 539 opportunities. Pain. 2024;165(3):666-673. doi:10.1097/j.pain.0000000000003056 540 43. Sachs MK, Dedes I, El-Hadad S, et al. Physical Activity in Women with Endometriosis: Less 541 or More Compared with a Healthy Control? Int J Environ Res Public Health. 542 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 27 2023;20(17):6659. doi:10.3390/ijerph20176659 543 44. Chen J. The associations of chronic pain and 24-h movement behaviors with incident mental 544 disorders: evidence from a large-scale cohort study. Published online 2024. 545 45. Zhang W, Singh SP, Clement A, Calfee RP, Bijsterbosch JD, Cheng AL. Improvements in 546 Physical Function and Pain Interference and Changes in Mental Health Among Patients 547 Seeking Musculoskeletal Care. JAMA Netw Open. 2023;6(6):e2320520. 548 doi:10.1001/jamanetworkopen.2023.20520 549 46. Dowding C, Mikocka/i2 Walus A, Skvarc D, et al. The temporal effect of emotional distress 550 on psychological and physical functioning in endometriosis: A 12/i2 month prospective study. 551 Appl Psychol Health Well-Being. 2023;15(3):901-918. doi:10.1111/aphw.12415 552 553 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 28 Table 1. Study sample demographics. 554 555 Variable N Mean or % Age (years) 72 35 Sex 76 … female 76 100% … male 0 0% Employment status 75 ... employed 57 76% ... other 7 9% ... unemployed 11 15% Marital status 76 ... divorce 6 8% ... married 35 46% ... single 35 46% Psychiatric diagnosis 76 ... None 55 72% ... At least 1 21 28% Race/Ethnicity 76 ... asian 8 11% ... black 11 14% ... hispanic or latino 13 17% ... mixed 7 9% ... unknown 5 7% ... white 32 42% 556 557 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 29 Table 2. Average weekly and daily measures across the study. The average was taken of the 558 participant means for each repeated measure. 559 Variable N Mean or % Mean MVPA 76 38 Mean steps 76 8313 Mean MPQ-VAS 75 34 Mean phys. func. T-score 73 45 Mean GMH T-score 75 42 Mean GMH T Category 75 ... Poor 3 4% ... Fair 29 39% ... Good 29 39% ... Very Good 8 11% ... Excellent 6 8% 560 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 30 561 Figure 1 --562 --563 - =42.166, 95% CI: 40.363-43.969, M=50, t=-8.658, p < .001) and physical564 - =45.19, 95% CI: 43.52-46.853, M=50, t = -5.758, p < .001) means were 565 significantly different than the general population. 566 567 568 569 30 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 31 A. 570 571 B. 572 573 Figure 2. Mean participant A) daily step count and B) mean weekly MVPA minutes compared 574 to nationally recommended activity levels. The y-axis represents the number of participants. 575 Dashed lines represent the recommended levels (7500 steps, 150 MVPA minutes). The values 576 represent the number of individuals who fell above and below these nationally recommended 577 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 32 values. 578 579 Figure 3. Repeated measures correlations for weekly measures. MVPA=moderate-to-vigorous 580 physical activity; Phys. Func. T = physical functioning T-score; MPQ-VAS=McGill Pain 581 Questionnaire-VAS; Global Mental Health T=GMH-T 582 583 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 33 A . C . 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 B. 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 Figure 4. Results from the PFR model. A) The smooth effect of MVPA on GMH-T over time. 622 The MVPA axis is scaled. The y-axis represents predicted GMH-T and is scaled according to 623 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 34 predicted sample GMH-T mean. B) Coefficients and confidence intervals for scalar predictors of 624 the model. C) Random effect of participant, with each dot representing predicted mean GMH-T 625 for that participant. 626 627 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 35 Supplemental Tables 628 629 Table S1. Smooth predictors of the PFR model. 630 631 Predictor edf Ref.df F p-value t2(MVPA.tmat,MVPA.omat):L.MVPA 2.23222202 2.40665028 18.9885386 4.0841E-06 s(month_cos.tmat):L.month_cos 2.00001858 2.00003559 0.48641865 0.61566209 s(month_sin.tmat):L.month_sin 2.5591852 2.8159533 0.63612865 0.59048914 s(Participant) 33.4252443 57 2.75714802 1.5399E-06 s(Week) 5.2859E-05 1 5.0264E-06 0.96005532 632 Table S2. Point estimates for smooth terms. 633 634 Predictor Estimate SE scale(pfr_age) -1.1946 0.4629 t2(MVPA.tmat,MVPA.omat):L.MVPA.1 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.2 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.3 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.4 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.5 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.6 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.7 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.8 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.9 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.10 0 0.0012 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 36 t2(MVPA.tmat,MVPA.omat):L.MVPA.11 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.12 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.13 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.14 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.15 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.16 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.17 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.18 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.19 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.20 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.21 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.22 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.23 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.24 0 0.0013 t2(MVPA.tmat,MVPA.omat):L.MVPA.25 0 0.001 t2(MVPA.tmat,MVPA.omat):L.MVPA.26 0 0.001 t2(MVPA.tmat,MVPA.omat):L.MVPA.27 0 0.001 t2(MVPA.tmat,MVPA.omat):L.MVPA.28 0 0.001 t2(MVPA.tmat,MVPA.omat):L.MVPA.29 0 0.0012 t2(MVPA.tmat,MVPA.omat):L.MVPA.30 0 0.001 t2(MVPA.tmat,MVPA.omat):L.MVPA.31 0 0.001 t2(MVPA.tmat,MVPA.omat):L.MVPA.32 0 0.001 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 37 t2(MVPA.tmat,MVPA.omat):L.MVPA.33 0 0.001 t2(MVPA.tmat,MVPA.omat):L.MVPA.34 0 0.001 t2(MVPA.tmat,MVPA.omat):L.MVPA.35 1.0687 0.0317 t2(MVPA.tmat,MVPA.omat):L.MVPA.36 0.0002 0.1151 t2(MVPA.tmat,MVPA.omat):L.MVPA.37 -0.123 0.0037 t2(MVPA.tmat,MVPA.omat):L.MVPA.38 0.0028 0.115 t2(MVPA.tmat,MVPA.omat):L.MVPA.39 -1.3998 0.0415 t2(MVPA.tmat,MVPA.omat):L.MVPA.40 0.0332 0.1141 t2(MVPA.tmat,MVPA.omat):L.MVPA.41 -0.2206 0.0066 t2(MVPA.tmat,MVPA.omat):L.MVPA.42 -0.0057 0.1145 t2(MVPA.tmat,MVPA.omat):L.MVPA.43 10.0619 0.2981 t2(MVPA.tmat,MVPA.omat):L.MVPA.44 0.0442 0.1032 t2(MVPA.tmat,MVPA.omat):L.MVPA.45 0.2974 0.0781 t2(MVPA.tmat,MVPA.omat):L.MVPA.46 0.5677 0.2809 t2(MVPA.tmat,MVPA.omat):L.MVPA.47 -0.5547 0.0165 t2(MVPA.tmat,MVPA.omat):L.MVPA.48 0.6484 0.387 s(pfr_month_cos.tmat):L.pfr_month_cos.1 -0.0001 0.2867 s(pfr_month_cos.tmat):L.pfr_month_cos.2 -0.0001 0.2627 s(pfr_month_cos.tmat):L.pfr_month_cos.3 -0.0002 0.623 s(pfr_month_cos.tmat):L.pfr_month_cos.4 0.0003 0.7492 s(pfr_month_cos.tmat):L.pfr_month_cos.5 0.0002 0.5422 s(pfr_month_cos.tmat):L.pfr_month_cos.6 -1.6094 1.8354 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 38 s(pfr_month_cos.tmat):L.pfr_month_cos.7 -3.1231 5.9218 s(pfr_month_sin.tmat):L.pfr_month_sin.1 -8.7097 71.675 s(pfr_month_sin.tmat):L.pfr_month_sin.2 -1.0972 65.6637 s(pfr_month_sin.tmat):L.pfr_month_sin.3 0.0284 156.1398 s(pfr_month_sin.tmat):L.pfr_month_sin.4 12.0161 187.3235 s(pfr_month_sin.tmat):L.pfr_month_sin.5 7.3255 135.9212 s(pfr_month_sin.tmat):L.pfr_month_sin.6 19.7849 30.3586 s(pfr_month_sin.tmat):L.pfr_month_sin.7 6.1957 25.4692 s(pfr_participant).1 -0.8195 3.2219 s(pfr_participant).2 -3.3994 2.33 s(pfr_participant).3 -0.3429 3.0436 s(pfr_participant).4 -1.9507 1.8907 s(pfr_participant).5 -3.1004 1.8541 s(pfr_participant).6 2.7715 1.7634 s(pfr_participant).7 -4.7251 2.5942 s(pfr_participant).8 6.4256 2.302 s(pfr_participant).9 -2.2581 1.7001 s(pfr_participant).10 2.5425 2.4007 s(pfr_participant).11 -5.2992 2.2013 s(pfr_participant).12 7.0277 3.3273 s(pfr_participant).13 2.9095 2.0834 s(pfr_participant).14 1.1844 1.7011 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 39 s(pfr_participant).15 -0.7041 3.3855 s(pfr_participant).16 3.6399 1.6518 s(pfr_participant).17 1.327 3.2448 s(pfr_participant).18 0.2492 1.9396 s(pfr_participant).19 -0.6143 1.7548 s(pfr_participant).20 -3.9002 1.8232 s(pfr_participant).21 -3.2474 1.81 s(pfr_participant).22 3.3082 2.3462 s(pfr_participant).23 -1.9553 2.7352 s(pfr_participant).24 0.4892 3.3464 s(pfr_participant).25 -1.0614 2.0089 s(pfr_participant).26 7.3399 1.7229 s(pfr_participant).27 -2.2067 3.3497 s(pfr_participant).28 -2.5714 2.4798 s(pfr_participant).29 4.0968 2.4943 s(pfr_participant).30 3.2534 2.0713 s(pfr_participant).31 -1.0354 2.924 s(pfr_participant).32 1.6512 2.4759 s(pfr_participant).33 0.0849 1.7427 s(pfr_participant).34 0.9219 3.3273 s(pfr_participant).35 -1.5758 1.8535 s(pfr_participant).36 -1.1452 2.757 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 40 s(pfr_participant).37 7.3676 2.7764 s(pfr_participant).38 0.1432 3.0061 s(pfr_participant).39 -0.5169 2.8012 s(pfr_participant).40 -2.5837 3.4357 s(pfr_participant).41 -2.4264 1.8566 s(pfr_participant).42 7.4204 2.7515 s(pfr_participant).43 3.701 3.2246 s(pfr_participant).44 -2.3142 2.7271 s(pfr_participant).45 -1.4783 1.8362 s(pfr_participant).46 -0.8119 3.4236 s(pfr_participant).47 -2.91 2.2243 s(pfr_participant).48 -2.3047 2.7677 s(pfr_participant).49 -1.9364 2.7055 s(pfr_participant).50 -0.8793 3.229 s(pfr_participant).51 -1.9784 3.0708 s(pfr_participant).52 -8.8145 1.9939 s(pfr_participant).53 -0.1424 2.7878 s(pfr_participant).54 -4.7399 2.6715 s(pfr_participant).55 11.8348 1.8121 s(pfr_participant).56 -0.0268 3.1258 s(pfr_participant).57 -0.9704 1.8023 s(week).1 0 0.0008 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint 41 635 636 Table S3. Linear predictors for the PFR model. 637 638 Predictor Estimate SE T.value P.value (Intercept) 2.9429 0.0872 33.752 0 MPQ-VAS -1.16 0.4964 -2.3368 0.0206 Physical Functioning 2.2409 0.598 3.7475 0.0002 Psychiatric Diagnosis 0.2181 0.8874 0.2458 0.8061 Employed 4.0117 1.0939 3.6674 0.0003 Employed - Other 6.0273 1.2929 4.6617 0 Divorced 0.3939 1.8963 0.2077 0.8357 Married 3.5996 0.8582 4.1946 0 Age -1.1946 0.4629 -2.5804 0.0107 639 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint