{"paper_id":"ef6a0a1a-ef90-4279-bf8e-fe311fa24c1d","body_text":"Trajectories of mHealth-tracked mental health symptoms and  1 \ntheir predictors in chronic pelvic pain 2 \nEmily L. Leventhal, B.A.,1,2 Nivedita Nukavarapu, Ph.D.,1,2 Noemie Elhadad, Ph.D.,3 Suzanne 3 \nBakken, Ph.D., RN, FAAN, FACMI, FIAHSI,4  Michal Elovitz, M.D.,6 Robert Hirten, M.D.,7 4 \nJovita Rodrigues, M.S.,1,2 Matteo Danieletto, Ph.D.,1,2 Kyle Landell, B.A.,1,2 and Ipek Ensari, 5 \nPh.D.1,2 6 \n 7 \n1Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount 8 \nSinai, New York, NY, USA 9 \n2Hasso Plattner Institute for Digital Health Mount Sinai, New York, NY, USA 10 \n3Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, 11 \nNY, USA 12 \n4Columbia University School of Nursing, Columbia University Irving Medical Center, New York, 13 \nNY, USA 14 \n6Department of Obstetrics, Gynecology and Reproductive Sciences, Icahn School of Medicine at 15 \nMount Sinai, New York, NY, USA 16 \n7The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at The 17 \nMount Sinai Hospital, New York, NY, USA  18 \n 19 \n  20 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\n \n2 \nAbstract 21 \nBackground. Female chronic pelvic pain disorders (CPPDs) affect 1 in 7 women 22 \nworldwide and are characterized by psychosocial comorbidities, including reduced quality of life 23 \nand 2-10 fold increased risk of depression and anxiety. Despite its prevalence and morbidity, 24 \nCPPDs are often inadequately managed with few patients experiencing relief from any medical 25 \nintervention. Characterizing mental health symptom trajectories and lifestyle predictors of 26 \nmental health is a starting point to enhancing patient self-efficacy in managing symptoms. Here, 27 \nwe investigate the association between mental health, pain, and physical activity (PA) in females 28 \nwith CPPD and demonstrate a method for handling multi-modal mobile health (mHealth) data. 29 \nMethod. The study sample included 4,270 person-level days and 799 person-level weeks of data 30 \nfrom CPPD participants (N=76). Participants recorded PROMIS global mental health (GMH) 31 \nand physical functioning, and pain weekly for 14 weeks using a research mHealth app, and 32 \nmoderate-to-vigorous PA (MVPA) was passively collected via activity trackers. Data analysis. 33 \nWe used penalized functional regression (PFR) to regress weekly GMH-T (GMH-T) on MVPA 34 \nand weekly pain outcomes, while adjusting for baseline measures, time in study, and the random 35 \nintercept of the individual. We converted 7-day MVPA data into a single smooth using spline 36 \nbasis functions to model the potential non-linear relationship. Results: MVPA was a significant, 37 \ncurvilinear predictor of GMH-T (p<0.001), independent of pain measures and prior psychiatric 38 \ndiagnosis. Physical functioning was positively associated with GMH-T, while pain was 39 \nnegatively associated with GMH-T (β =2.24, β =-1.16, respectively; p<0.05). Conclusion: These 40 \nfindings suggest that engaging in MVPA is beneficial to the mental health of females with 41 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n3 \nCPPD. Additionally, this study demonstrates the potential of ambulatory mHealth-based data 42 \ncombined with functional models for delineating inter-individual and temporal variability. 43 \nKeywords 44 \nChronic pelvic pain; digital health; functional data modeling; global mental health. 45 \nCorresponding author 46 \nEmily Leventhal, B.A., 3 E 101st Street, New York, NY 10029, 47 \nemily.leventhal@icahn.mssm.edu 48 \nIntroduction 49 \nDescribed as a “neglected reproductive health morbidity,” chronic pelvic pain (CPP) is a 50 \nhighly debilitating condition that affects between 5.7% and 26.6% of women worldwide.1–3 CPP, 51 \nwhich encompasses complex CPP disorders (CPPDs) such as endometriosis, adenomyosis, and 52 \nfibroids, is characterized by non-cyclic pain in the pelvis or abdomen that lasts for at least 6 53 \nmonths and leads to functional disability or the necessity for medical intervention.3–5 Its severity 54 \nis underscored by its associated physical, psychological, and emotional, and social 55 \nconsequences.4,5  56 \nThe strong psychosocial impact of CPPDs contributes to their morbidity. For example, 57 \nindividuals with CPPDs are more likely to experience reduced quality of life, emotional well-58 \nbeing, productivity, and sexual function compared to the general population.4 Additionally, 59 \nCPPD patients have a significantly higher risk of comorbid psychiatric disorders.4,6,7. For 60 \nexample, individuals with CPPDs have been reported to experience depressive disorders at a 61 \nprevalence of 2 to 10 times that of the general population and anxiety disorders 3 to 6 times that 62 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n4 \nof the general population.4 Because chronic pain is tightly linked to mental health problems, 63 \ninvestigation of potentially modifiable predictors of mental well-being in individuals with 64 \nCPPDs may be a starting point for comprehensively managing and treating CPPD patients.8  65 \nDespite its prevalence and burden, CPP is often inadequately managed, with less than 66 \nhalf of patients experiencing pain relief from any medical treatment.6,9 Patient self-management, 67 \nwhich encompasses active efforts to manage pain and its effects on physical and emotional 68 \nfunction, is a common chronic pain care model intervention, and it has been associated with 69 \nsignificant improvement in symptoms.10,11 Further, Center for Disease Control (CDC) guidelines 70 \nstate that non-opioid and non-pharmacologic therapies should be prioritized for chronic pain 71 \nmanagement.12,13 Non-pharmacological self-management strategies, especially those that target 72 \nmental health outcomes of CPPD patients, are needed for effective personalized treatment of 73 \nCPPD.  74 \nPhysical activity (PA), and exercise, defined as planned, structured, and repetitive PA 75 \nwith the goal of improved health or fitness, have been demonstrated to be effective pain self-76 \nmanagement for both reducing pain severity and improving psychological function in chronic 77 \npain patients.11,14 Experts recommend that chronic pain patients exercise on a regular schedule on 78 \nthe premise that avoiding activity during pain and increasing intensity later may lead to pain 79 \nflares.4 Importantly, exercise is a modifiable behavior that can also improve pain self-efficacy, 80 \ndefined as the confidence in one’s ability to function effectively while in pain, which is 81 \nassociated with improved quality of life.15,16 Further, for chronic pain patients with comorbid 82 \npsychiatric conditions, exercise may improve mood, depression, and anxiety symptoms.4 A 83 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n5 \nprevious study with individuals with endometriosis estimated a small but statistically significant 84 \nfavorable effect of exercise on pain severity.17 However, this study relied on self-reported 85 \nexercise, which is limited in its ability to capture more granular PA parameters (eg, step counts, 86 \nintensity-level).17 While most of the evidence connecting PA to psychosocial improvement has 87 \nbeen from other chronic pain conditions, yoga has previously been demonstrated to be 88 \nefficacious for improving pain and quality of life for patients with endometriosis.4 The impact of 89 \nbroader PA on mental health in patients with CPP specifically remains to be investigated, with a 90 \nfocus on using longitudinal data to capture potentially meaningful trends over time.  91 \nCPPDs and their symptomatic patterns are notably heterogeneous in clinical presentation 92 \nboth between patients and within-individuals over time.18 Capturing these fluctuations under 93 \necologically valid circumstances can help improve our understanding of the dynamic unfolding 94 \nof these symptoms and their potential predictors. In the context of health behaviors such as PA, 95 \ndata from mobile health (mHealth) technologies (eg, smartphone apps, trackers) combined with 96 \nlongitudinal analytic techniques can help elucidate symptom associations with psychosocial 97 \noutcomes in CPP.7,18 For example, there may be non-linear associations and cumulative effects 98 \nin these longitudinal data that are not possible to capture via linear modeling approaches. In sum, 99 \nflexible techniques can be particularly useful when considering variables that differ in sampling 100 \nfrequencies, missingness patterns, modality, and temporal complexity, which is often the case 101 \nwith mHealth data.  102 \nFunctional regression models, which are a part of the family of generalized additive 103 \nmodels (GAMs), constitute one such approach.19 In a functional regression framework, the entire 104 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n6 \ndata curve is considered as the unit of analysis, instead of discrete data points in a set of 105 \nlongitudinal data. This is particularly useful for handling PA data from wearables, rather than 106 \naggregating multiple data points per individual,20 as they allow investigating the associations 107 \nbetween scalar and functional variables with different time intervals. One example of a scenario 108 \nrelevant to this study is consideration of continuous or daily PA data with weekly self-reported 109 \nsurvey data, in a repeated-measures design. This results in a data structure where each weekly 110 \nquestionnaire corresponds to 7 days of PA data leading up to the survey data. A functional 111 \nregression model considers the PA data as a weekly data curve rather than aggregating the entire 112 \nweek into a summary score and thus preserves the temporal pattern within the data. This can 113 \nreveal important information that may be lost otherwise, such as periods of inactivity or bursts of 114 \nactivity, which could be related to mental health.4,21  115 \nAccordingly, this study aims to characterize the patterns of association between self-116 \nreported mental health symptoms and their predictors in CPPDs, with a focus on modifiable 117 \nlifestyle factors. Specifically, this overall aim includes investigation of 1) between- and within-118 \nindividual fluctuations in weekly self-reported mental health, and 2) possible modifiable and trait 119 \npredictors of weekly mental health. We hypothesized that there would be significant variability 120 \nin the mental health both between and within individuals and that PA would be a positive non-121 \nlinear predictor of mental health.  122 \nMethods 123 \nStudy Design and Procedures 124 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n7 \nThe study design and procedures were approved by the IRB of the Icahn School of 125 \nMedicine at Mount Sinai (ISMMS; IRB# STUDY-22-01002). This is a secondary analysis of the 126 \ndata from an ongoing larger study that aims to design, develop, and evaluate CPPD-specific 127 \nmHealth measures from patient generated health data with high complexity and temporality 128 \nusing non-linear distributed lag and functional data modeling (NIH/NICHD: R01HD108263). It 129 \nuses an observational study design to collect 90 days of data on patient self-tracked symptoms 130 \nvia a research mHealth app (ehive22) and passively collected activity data using activity trackers 131 \nfrom participants. All participants used the ehive research study app for providing the baseline 132 \nand weekly data on overall health, symptoms, well-being and health behaviors, as well as for 133 \nreceiving prompts and reminders about the study.22 Participants were instructed to wear a Fitbit 134 \nfor the duration of the study.  135 \nStudy Sample 136 \nThe study sample included individuals who met the following eligibility criteria for the 137 \nparent study: 1) females who menstruate currently, between the ages of 18 and 64, 2) self-138 \nreported CPPD based on clinician diagnosis, 3) experience of CPP for at least 6 months, and 4) 139 \nability to read and write in English. Exclusion criteria include: 1) current pregnancy, a birth in 140 \nthe past 6 months, or planning pregnancy during the months of the study and 2) major diseases or 141 \ncomorbidities (eg, active cancer, acute coronary syndrome within the past 3 months) that might 142 \nconfound the outcomes of the primary pelvic pain-related condition. Participants were recruited 143 \nfrom all campuses of the Mount Sinai Health System (MSHS) and Columbia University Irving 144 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n8 \nMedical Center (CUIMC) via email advertisements and on the myChart by EPIC mobile app for 145 \nMSHS patients.  146 \nEnrollment 147 \nInterested patients reached out to the study coordinator at Mount Sinai for screening and 148 \nenrollment, after which they were onboarded and oriented to the study app and data collection 149 \nprotocols. All participants were mailed a Fitbit Inspire 2 device and instructed to use for the 150 \nduration of the study (90 days). Participants were remunerated $15 for every 2 weeks of data 151 \ncollection and $20 for the final week (ie, up to $120 in total for completing 90 days of data 152 \ncollection). All participants provided informed consent prior to enrolling in the study.  153 \nStudy Measures 154 \nPrimary Outcomes 155 \nSelf-reported mental health was assessed every week using the PROMIS Global Mental 156 \nHealth Questionnaire (GMH; 2a, v1.2).23 The GMH includes 2 questions: 1) “In general, how 157 \nwould you rate your mental health, including your mood and your ability to think?” 2) “In 158 \ngeneral, how would you rate your satisfaction with your social activities and relationships?” Both 159 \nquestions have a 5-point multiple choice response scale (1-not at all, 5-very much) and the 160 \nresponses are added to compute the total score on the GMH (range 2-10). Higher scores 161 \nrepresent better mental health.23 The two-item GMH survey provides a brief measure of mental 162 \nhealth that has been found to be both reliable and have construct validity.23 Scores from the 163 \nGMH survey have been positively associated with other self-reported outcomes including overall 164 \nquality of life and physical functioning, and negatively correlated with fatigue, anxiety, anger, 165 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n9 \ndepressive symptoms, and chronic conditions (e.g,. liver disease, kidney disease, hypertension, 166 \netc.).23 We converted raw GMH scores to population-standardized GMH scores (T-scores) 167 \naccording to the PROMIS Global Health scoring manual by standardizing the raw total score to a 168 \nmean of 50 and a standard deviation (SD) of 10.24 GMH T-scores (GMH-T) are further 169 \ncategorized as excellent (>55), very good (48-55), good (40-47), fair (29-39), and poor (<29).25  170 \nPredictors 171 \nPhysical activity. Daily minutes of moderate-to-vigorous intensity PA (MVPA) and step 172 \ncounts were obtained from the wrist-worn Fitbit devices. Participants were instructed to wear 173 \ntheir devices continuously for the study duration. The study app (ehive) allows the user to link 174 \ntheir account with their Fitbit device,22 which enables regular daily data synching on the backend 175 \nof the app. Fitbit uses its proprietary algorithms for detection of step counts and activity 176 \nintensities. We collected 6,341 days of physical activity data for 78 participants. For wear time 177 \nvalidation, we relied on the commonly used standard “10-hour minimum wear” rule, in which a 178 \nvalid day is defined as at least 10 hours of non-zero activity counts.26–28 Ten hours of wear has 179 \nbeen shown to be sufficient to estimate total daily physical activity during non-sleep time.29 180 \nThere were 4,301 valid days of Fitbit data for 76 participants. Days with unrealistically low 181 \nactivity counts (eg, <500 steps in a day; n=14) were removed in accordance with similar cutoffs 182 \nthat have been used in the past to define a valid day, although we used a more conservative 183 \ncutoff.26,27  This resulted in 4,287 days of physical activity data for 76 participants. If there were 184 \nmore than 7 days of Fitbit data in between survey responses (ie, if a participant waited more than 185 \n7 days before completing the next survey), we only considered the first 7 days of Fitbit activity 186 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n10 \ndata to avoid sparsity in the penalized functional regression (PFR) model (described below). 77 187 \ndays of activity data measured more than 7 days after a survey response were removed for this 188 \nreason. The final dataset had 4,270 days of data for 76 participants.  189 \nPhysical functioning. Weekly physical functioning scores were measured using the 190 \nPROMIS physical function survey (4a, v1.0).30 Physical functioning is the self-reported 191 \ncapability of performing everyday physical activities. The score evaluates functioning of upper 192 \nextremities, lower extremities, central regions, and activities of daily living. The 4-item PROMIS 193 \nsurvey assesses the extent to which individuals find difficulty with physical tasks (5-without any 194 \ndifficulty to 1-unable to do). Scores range from 4 to 20, with higher scores indicating better 195 \nphysical functioning. We used the physical functioning T-scores in the analyses, which are 196 \nstandardized to a mean of 50 and a SD of 10 based on a representative population distribution.30  197 \nPain. We measured weekly pain levels using the VAS pain intensity item from the short-198 \nform McGill Pain Questionnaire (MPQ-VAS).31 The MPQ-VAS asks participants to rate the 199 \nintensity of their present pain intensity on a scale of 0 (no pain) to 100 (worst imaginable pain).32  200 \nThis type of VAS-based pain assessment is commonly used as a standard practice in clinical 201 \nsettings to evaluate patient pain status and treatment outcomes.33,34 202 \nOther covariates. Data on personal demographics and general health were collected via 203 \na baseline questionnaire on the ehive app. We collected age, marital status, ethnicity, and 204 \nemployment status from the demographics survey. In addition, we used prior psychiatric 205 \ndiagnosis (“Have you ever been diagnosed with a psychiatric diagnosis by a provider?”) as a 206 \ncovariate from the general health survey. 207 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n11 \nData analysis 208 \nDescriptive and bivariate analyses 209 \nFirst, we performed descriptive analyses and investigated bivariate associations between 210 \nthe weekly-measured survey items. Given the repeated-measures design, we use both person-211 \nlevel means (ie, a participant’s mean score across the 14 weeks) and overall sample means (ie, 212 \nmean of means) where necessary to report the overall study average scores from the daily (ie, 213 \nsteps, MVPA) and weekly (ie, pain, physical functioning T-score, GMH-T) measures. To 214 \nanalyze the GMH-T, we converted the mean GMH-T for each participant to its corresponding 215 \nGMH category (eg, fair, good, excellent, etc.), and computed the percent of participants in each 216 \ncategory.25 To evaluate sample GMH-T and physical functioning T-scores against known 217 \npopulation means, we used a one-sample T-test to compare the sample means to the population 218 \nmeans. We then computed repeated-measures correlations between GMH-T, physical 219 \nfunctioning, MPQ-VAS, and the sum of MVPA over 7 days using the rmcorr R package, which 220 \nevaluates the within-individual association of paired measurements taken two or more times 221 \nlongitudinally.35  222 \nMultivariable regression analysis of GMH predictors  223 \nTo investigate the potential predictors of GMH-T scores at the week level, we 224 \nimplemented PFR modeling using the R refund library.20 PFR models are flexible in numerous 225 \nways that are particularly useful for the data in this study. First, they allow for entire data curves 226 \nto be units of analysis as opposed to individual data points. Next, they accommodate different 227 \nsampling intervals in the outcomes vs predictors, ie, week-level outcome (eg, GMH-T) and 228 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n12 \nweek-level (eg, pain, physical functioning) and day-level (eg, MVPA) predictors. Instead of 229 \naggregating multiple day-level MVPA values for each week, this feature of the PFR allows for 230 \nthe preservation of temporal variability in MVPA over a week. Third, it allows specification of 231 \nrandom intercepts (ie, individual participants), which is useful for both accommodating a 232 \nrepeated measures design and for investigation of potential between- vs within-individual 233 \nvariability in the outcome of interest (ie, GMH-T scores). 234 \nWe regressed GMH-T on MVPA while considering MPQ-VAS, PROMIS physical 235 \nfunctioning, age, marital status, employment status, and prior psychiatric diagnosis. We further 236 \nadjusted for time in study using month-level cyclical encoding, in which each date is mapped 237 \ninto a cyclic coordinate system using sine-cosine waves and allows the models to infer the 238 \ndistance between dates based on their sine-cosine coordinates. We converted 7-day MVPA data 239 \ninto smooths with up to 7 knots using the tensor product basis function36 to model the potential 240 \nnon-linear relationship between GMH-T and daily PA. We similarly included the time covariate 241 \nas a functional smooth with up to 7 knots.20 We scaled MPQ-VAS, PROMIS physical 242 \nfunctioning, and age by mean-centering each variable and dividing by its standard deviation. We 243 \nincluded participant and week in study as random effects. Finally, other categorical variables (ie, 244 \npsychiatric diagnosis, employment status, and marital status) were included as person-level 245 \nlinear covariates.20 We used a generalized additive model as the fitter to estimate the model and 246 \nrestricted maximum likelihood as the smoothing parameter estimation method, which are the 247 \ndefault recommended methods for the function.20  248 \nResults  249 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n13 \nStudy sample 250 \nParticipants (n=76) provided 799 weeks of survey and 4,270 days of activity data in total 251 \nfor analysis. Participants had a mean age of 35 years and were mostly employed (76%). Most 252 \nparticipants identified as White (42%) or Hispanic or Latino (17%). In our sample, 28% had at 253 \nleast one prior diagnosis of a psychiatric condition, including anxiety and mood disorders (Table 254 \n1). The CPPD diagnoses included endometriosis (N=51), adenomyosis (N=1), uterine fibroids 255 \n(N=2), interstitial cystitis (N=1), inflammatory bowel syndrome (3), and inflammatory pelvic 256 \ndysfunction (N=1). 257 \nDescriptive and bivariate analyses  258 \nThe overall sample means of the scores from the daily and weekly measures are reported 259 \nin Table 2. Thirty-nine percent of the participants, on average, reported scores that corresponded 260 \nto “fair” mental health, with another 39% of the participants on average reporting “good” mental 261 \nhealth (Table 2). The mean GMH-T was 42.166 (95% CI: 40.363-43.969), which is 7.83 SDs 262 \nbelow the population mean (ie, M=50, “very good”)23 and significantly different (t=-8.658, p < 263 \n.001; Figure 1).  The mean physical functioning T-score was 45.19 (95% CI: 43.52-46.853), 264 \nwhich is 0.48 SDs below the population mean (ie, M=50; Figure 1; t = -5.758, p < .001).  265 \nTo characterize the PA patterns in the sample, we compared participants’ activity levels 266 \nto the published recommendations and CDC/HHS PA guidelines for adults with respect to steps 267 \nand MVPA.37,37–39 On average, participants accumulated 8,313 steps and 38 minutes of MVPA 268 \nper day (Table 2). Forty-three percent of the sample engaged in fewer than 7,500 daily steps, 269 \nwhich is the lower threshold recommended for being considered “sufficiently active” (Figure 270 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n14 \n2a). 38,39 Similarly, 40.9% accumulated fewer than 150 minutes of weekly MVPA recommended 271 \nby the PA Guidelines (Figure 2b).40 272 \nTo inspect the bivariate associations between weekly measures, we computed repeated 273 \nmeasures correlations between GMH-T and the other variables. GMH-T were positively 274 \ncorrelated with weekly MVPA (p<.05), and physical function T-score (p<.01), while they were 275 \nnegatively correlated with MPQ-VAS (p<.001; Figure 3). Weekly MVPA was additionally 276 \npositively correlated with physical functioning T-score (p<.05) but was not significantly 277 \ncorrelated with MPQ-VAS. 278 \nPFR model  279 \nWe fitted a PFR model to the data to investigate cumulative and non-linear effects of 280 \nMVPA on the weekly GMH-T. The best fitting final model explained 72.6% of the variance in 281 \nGMH-T (R2=0.65). The smooth of MVPA and time on GMH-T indicated a significant non-linear 282 \nrelationship (Table S1; Table S2; edf=2.23, F=18.99, p<.001). Predicted GMH-T increased with 283 \nincreasing daily MVPA minutes (Figure 4a). Over time, the largest positive effect of MVPA on 284 \npredicted GMH-T as reported at the end of the week was a few days prior (~day 4). The positive 285 \neffect of MVPA on GMH-T reported at the end of the week diminished after day 4, suggesting 286 \nthe positive effects of MVPA lagged by a couple of days. Weekly MPQ-VAS was a significant 287 \nnegative predictor of GMH-T (β =-1.16, SE=0.50, t=-2.34, p<.05), while physical functioning T-288 \nscore was a significant positive predictor of GMH-T (Figure 4b; Table S3; β =2.24, SE=0.598, 289 \nt=3.75, p<.001). For demographic factors, age was negatively associated with GMH-T (β =-1.20, 290 \nSE=0.46, t=-2.58, p<.05), while being employed and married were positively associated with 291 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n15 \nGMH-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 \ndiagnosis was not a significant predictor of weekly GMH. The random effect of participant was 293 \nsignificant (Figure 4c; edf=33.43, F=2.76, p<0.001). The random effect of week and the 294 \ncyclically encoded sine and cosine functions of month were not significant.  295 \nDiscussion 296 \n In this study, we leveraged ambulatory mHealth-tracked mental health, pain, and physical 297 \nactivity data to characterize longitudinal self-reported mental health patterns of individuals with 298 \nCPPDs. Our results indicate a positive, non-linear relationship between PA and mental health, 299 \nindependent of prior psychiatric diagnosis or other pain-related factors, with considerable 300 \nvariability both between and within participants over time. To our knowledge, this study 301 \nprovides the first line of evidence on the positive effect of PA on mental health in females with 302 \nCPPDs using repeated measures data collected in real time. We further report lower scores of 303 \nmental health and physical functioning compared to the general population, as well as lower PA 304 \nlevels than those recommended by the PA guidelines. 305 \n Our cohort had a 28% incidence of prior psychiatric conditions and lower average global 306 \nmental health compared to the general population. Chronic pain, and specifically CPPDs, has 307 \nbeen established as a strong predisposing factor for psychiatric conditions, due to both the 308 \npsychosocial impact of chronic pain and common neurobiological vulnerabilities and genetic 309 \nfactors between chronic pain and mood.4,6,41,42 CPPD patients with comorbid psychiatric 310 \nconditions are more likely to incur higher health care costs, experience lower quality of life, 311 \nendure increased disability, and are more likely to be prescribed opioids.4 Additionally, our 312 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n16 \nfindings add to the literature documenting the worsened mental health of CPPD patients as a 313 \nwhole compared to the general population.4,23,42 In the 2019 National Health Interview Survey, 314 \nthose with chronic pain had a 23.9% prevalence of co-occurring anxiety and/or depression 315 \nsymptoms, whereas the population without chronic pain had a prevalence of 4.9%.42 Given the 316 \nhigh incidence of psychiatric co-morbidities and the generally low mental health among CPPD 317 \npatients, it is important to treat mental health as part of comprehensive chronic pain management 318 \nand continue to determine ways to aid patients to manage their symptoms. As such, here, we 319 \ninvestigated how lifestyle factors may modify the association of CPP with poorer mental health 320 \noutcomes.  321 \nOur findings suggest that many females with CPPDs do not reach nationally 322 \nrecommended activity levels, and moreover, that engaging in MVPA is beneficial for the mental 323 \nhealth of CPPD patients. The PA levels found in this sample are consistent with previous studies 324 \nindicating that individuals with CPPDs have lower PA levels,43 though data on CPPDs are 325 \nscarce. One longitudinal study using accelerometers indicated that MVPA negatively mediated 326 \nthe relationship between chronic pain and risk of mental disorders, although this study did not 327 \nfocus on CPP.44 Increased MVPA in individuals with chronic pain was associated with decreased 328 \nanxiety and depression symptoms, whereas light intensity PA did not have this effect.42 While 329 \nprevious studies have established the connection between MVPA and mental health in chronic 330 \npain, this is the first study to establish the relationship between PA and mental health in the 331 \ncontext of CPP by using passively-obtained data from activity trackers.17   332 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n17 \nOur findings further indicate that increased pain is associated with worsened GMH, while 333 \nincreased physical functioning was associated with improved GMH. Though pain and depression 334 \nor anxiety have been noted to have a bidirectional relationship, there is more evidence that pain 335 \nis a risk factor for mental health problems than the inverse.4 Additionally, a longitudinal study 336 \nfocused on musculoskeletal conditions found that improvements in physical functioning were 337 \nassociated with improved anxiety symptoms, although it was not associated with improved 338 \ndepression symptoms.45 The relationship between physical functioning and mental health in CPP 339 \nhas not been well defined to this point, however, one previous longitudinal study on 340 \nendometriosis reported that functional pain disability did not predict later emotional distress.46   341 \n With respect to demographic factors as potential predictors, increased age was associated 342 \nwith worsened GMH, while prior psychiatric diagnosis was not a significant predictor. Age may 343 \nbe a proxy for years of experience with the chronic pain condition or severity of the condition. In 344 \nthis study, we did not have a survey item assessing time of initial diagnosis, although this may be 345 \npossible in the future by linking mobile health studies with electronic health records (EHRs). 346 \nOver time, chronic pain may become more difficult to treat due to structural and functional 347 \nneuroplastic changes that eventually become irreversible and insensitive to treatment.41 From a 348 \npsychosocial standpoint, the economic consequences of health care costs and loss of productivity 349 \nmay accumulate over time.41 It will be important to assess how length of time of living with 350 \nchronic pain impacts mental health in the future. Interestingly, diagnosis with a prior psychiatric 351 \ncondition, including mood and anxiety disorders, was not a significant predictor of GMH. This 352 \nmay suggest that some individuals with prior psychiatric diagnoses may not be actively 353 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n18 \nexperiencing symptoms, or alternatively, that this sample has a large number of participants with 354 \nundiagnosed psychiatric conditions that are actively experiencing symptoms.  355 \nWe observed substantial between- and within-individual variability in mental health 356 \nscores in the sample, underscoring the importance of personalized approaches to care. Predicted 357 \naverage GMH-T varied greatly between individuals as shown by the random intercepts. CPPDs 358 \nare notoriously heterogeneous in pain symptomatology, and it follows that mental health would 359 \nexhibit similar variability among and within participants.7 As such, it is important to use 360 \nindividualized approaches, such as that which may be achieved with mHealth, to 361 \ncomprehensively understand the complexity of CPP. Due to their heterogeneous clinical 362 \npresentation and differing etiologies, CPPDs are often non-responsive to treatment, and a 363 \npersonalized approach is necessary for the successful management of CPPD. To better 364 \nunderstand how to manage the mental health of CPPD patients, we should continue to study 365 \nmodifiable lifestyle factors, as was done here with PA, that may alter the poor mental health 366 \noutcomes associated with CPP. This study demonstrates the potential of using ambulatory 367 \nmHealth-based data combined with functional data methods to delineate inter-individual and 368 \ntemporal variability in symptoms of chronic conditions. 369 \nThere are numerous strengths of this work. First, we focus on a patient population that 370 \nhas been under-studied (ie, CPPDs) and currently still not well-understood as a cluster of 371 \ndisorders with overlapping symptomatology. While endometriosis, the most common underlying 372 \nprimary diagnosis for a CPPD, has been receiving more attention recently, our sample also 373 \nincluded those less-studied CPP conditions (eg, adenomyosis, fibroids, inflammatory pelvic 374 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n19 \ndisease). Next, implementation of functional data methods and generalized additive modeling 375 \nusing smooths provide robust, flexible approaches for handling the complex patient-generated 376 \nhealth data from mHealth technologies. The PFR models in this context facilitate the evaluation 377 \nof complex relationships between outcomes and their predictors in instances where data 378 \nsampling frequency differs between the outcomes and predictors, or between different predictors. 379 \nAs mHealth use is becoming more ubiquitous for conducting research, expanding upon the 380 \navailable methods will enable fully harnessing the information from these data. Third, our 381 \nanalyses were based on frequently-sampled prospective data of up to 14 weeks from the study 382 \nparticipants. This is a strength of the data design as most studies to date are limited to 383 \nconvenience samples of retrospective data with much less frequency of data points.  384 \nNevertheless, we acknowledge the limitations of this study. Although we had 799 person-385 \nlevel weeks for analysis, 76 participants is a relatively modest sample size in comparison to 386 \nlarge, nationally-representative cohort studies. Similarly, the sample was somewhat 387 \nhomogeneous with respect to demographic factors including employment status and education 388 \nlevels. Third, despite our careful inspection of the missing data and implementing cautious 389 \nfiltering criteria to prevent potentially erroneous inference from the data, Fitbit’s proprietary 390 \nalgorithms do not always enable as informed decisions regarding the missing data as do some 391 \nother devices, such as research grade trackers that allow access to the raw acceleration data. To 392 \ncircumvent these issues, we conducted a series of sensitivity analyses to assess the pattern of 393 \nmissingness in the data, as well as the possible influence of missingness on the model results. 394 \nResults (not reported herein) indicated no significant bias, suggesting a missing-at-random 395 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n20 \n(MAR) pattern, or change in model point estimates. Finally, most of the participants had 396 \nendometriosis as their primary CPPD, therefore we are not able to delineate differences in mental 397 \nhealth trajectories among different disorders within CPPD.  398 \nConclusions 399 \nmHealth-enabled direct patient input and passive tracking via wearables enables the 400 \ncapturing of real-world data to improve our understanding of inter-individual and temporal 401 \nvariability in mental health symptoms and factors that may improve mental health. By leveraging 402 \npatient-tracked mental health and pain outcomes combined with passively-obtained activity data 403 \nfrom CPPD patients, we demonstrate a positive, non-linear relationship between PA and mental 404 \nhealth in CPP.  405 \nEthics approval and informed consent  406 \nThe study was approved by the Institutional Review Board (IRB) of the Icahn School of 407 \nMedicine at Mount Sinai (IRB# STUDY-22-01002) and all participants provided informed 408 \nconsent.  409 \nData availability 410 \nThe data collection for the parent grant is currently ongoing. After completion of the 411 \nactive grant period, the data produced in the present study will be made available upon 412 \nreasonable request to the corresponding author. 413 \nFunding 414 \nThis study was supported by a grant award from the Eunice Kennedy Shriver National 415 \nInstitute Of Child Health & Human Development of the National Institutes of Health (Award 416 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n21 \nNumber: R01HD108263, PI=Ensari). The content does not necessarily represent the official 417 \nviews of the National Institutes of Health. Additionally, this research was supported by the T32 418 \ngrant 1T32GM146636.   419 \nAuthors’ contributions 420 \nAll authors contributed significantly to the work presented in this manuscript, including 421 \nthe conception, study design, execution, acquisition of data, analysis and interpretation. Each 422 \nauthor reviewed this article and agree to take responsibility for the contents of this article.  423 \nReferences 424 \n1. Latthe P, Latthe M, Say L, Gülmezoglu M, Khan KS. 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(which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n28 \nTable 1. Study sample demographics.  554 \n 555 \nVariable N \nMean or \n% \nAge (years) 72 35 \nSex 76   \n… female 76 100% \n… male  0 0% \nEmployment status 75  \n... employed 57 76% \n... other 7 9% \n... unemployed 11 15% \nMarital status 76  \n... divorce 6 8% \n... married 35 46% \n... single 35 46% \nPsychiatric \ndiagnosis 76   \n... None 55 72% \n... At least 1 21 28% \nRace/Ethnicity 76   \n... asian 8 11% \n... black 11 14% \n... hispanic or latino 13  17% \n... mixed 7 9% \n... unknown 5 7% \n... white 32 42% \n 556 \n  557 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n29 \nTable 2. Average weekly and daily measures across the study. The average was taken of the 558 \nparticipant means for each repeated measure.  559 \nVariable N \nMean or \n% \nMean MVPA 76 38 \nMean steps 76 8313 \nMean MPQ-VAS 75 34 \nMean phys. func. T-score 73 45 \nMean GMH T-score 75 42 \nMean GMH T Category 75  \n... Poor 3 4% \n... Fair 29 39% \n... Good 29 39% \n... Very Good 8 11% \n... Excellent 6 8% \n  560 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n30\n 561 \nFigure 1 --562 \n--563 \n- =42.166, 95% CI: 40.363-43.969, M=50, t=-8.658, p < .001) and physical564 \n- =45.19, 95% CI: 43.52-46.853, M=50, t = -5.758, p < .001) means were 565 \nsignificantly different than the general population. 566 \n 567 \n 568 \n  569 \n30 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n31 \nA. 570 \n 571 \nB. 572 \n 573 \nFigure 2. Mean participant A) daily step count and B) mean weekly MVPA minutes compared 574 \nto nationally recommended activity levels. The y-axis represents the number of participants. 575 \nDashed lines represent the recommended levels (7500 steps, 150 MVPA minutes). The values 576 \nrepresent the number of individuals who fell above and below these nationally recommended 577 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n32 \nvalues.  578 \n 579 \nFigure 3. Repeated measures correlations for weekly measures. MVPA=moderate-to-vigorous 580 \nphysical activity; Phys. Func. T = physical functioning T-score; MPQ-VAS=McGill Pain 581 \nQuestionnaire-VAS; Global Mental Health T=GMH-T  582 \n  583 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n33 \nA .         C .   584 \n 585 \n 586 \n 587 \n 588 \n 589 \n 590 \n 591 \n 592 \n 593 \n 594 \n 595 \n 596 \n 597 \n 598 \n 599 \n 600 \nB. 601 \n 602 \n 603 \n 604 \n 605 \n 606 \n 607 \n 608 \n 609 \n 610 \n 611 \n 612 \n 613 \n 614 \n 615 \n 616 \n 617 \n 618 \n 619 \n 620 \n 621 \nFigure 4. Results from the PFR model. A) The smooth effect of MVPA on GMH-T over time. 622 \nThe MVPA axis is scaled. The y-axis represents predicted GMH-T and is scaled according to 623 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n34 \npredicted sample GMH-T mean. B) Coefficients and confidence intervals for scalar predictors of 624 \nthe model. C) Random effect of participant, with each dot representing predicted mean GMH-T 625 \nfor that participant.  626 \n  627 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n35 \nSupplemental Tables 628 \n 629 \nTable S1. Smooth predictors of the PFR model.  630 \n 631 \nPredictor edf Ref.df F p-value \nt2(MVPA.tmat,MVPA.omat):L.MVPA 2.23222202 2.40665028 18.9885386 4.0841E-06 \ns(month_cos.tmat):L.month_cos 2.00001858 2.00003559 0.48641865 0.61566209 \ns(month_sin.tmat):L.month_sin 2.5591852 2.8159533 0.63612865 0.59048914 \ns(Participant) 33.4252443 57 2.75714802 1.5399E-06 \ns(Week) 5.2859E-05 1 5.0264E-06 0.96005532 \n 632 \nTable S2. Point estimates for smooth terms.  633 \n 634 \nPredictor Estimate  SE \nscale(pfr_age) -1.1946 0.4629 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.1 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.2 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.3 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.4 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.5 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.6 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.7 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.8 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.9 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.10 0 0.0012 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n36 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.11 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.12 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.13 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.14 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.15 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.16 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.17 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.18 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.19 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.20 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.21 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.22 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.23 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.24 0 0.0013 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.25 0 0.001 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.26 0 0.001 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.27 0 0.001 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.28 0 0.001 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.29 0 0.0012 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.30 0 0.001 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.31 0 0.001 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.32 0 0.001 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n37 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.33 0 0.001 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.34 0 0.001 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.35 1.0687 0.0317 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.36 0.0002 0.1151 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.37 -0.123 0.0037 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.38 0.0028 0.115 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.39 -1.3998 0.0415 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.40 0.0332 0.1141 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.41 -0.2206 0.0066 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.42 -0.0057 0.1145 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.43 10.0619 0.2981 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.44 0.0442 0.1032 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.45 0.2974 0.0781 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.46 0.5677 0.2809 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.47 -0.5547 0.0165 \nt2(MVPA.tmat,MVPA.omat):L.MVPA.48 0.6484 0.387 \ns(pfr_month_cos.tmat):L.pfr_month_cos.1 -0.0001 0.2867 \ns(pfr_month_cos.tmat):L.pfr_month_cos.2 -0.0001 0.2627 \ns(pfr_month_cos.tmat):L.pfr_month_cos.3 -0.0002 0.623 \ns(pfr_month_cos.tmat):L.pfr_month_cos.4 0.0003 0.7492 \ns(pfr_month_cos.tmat):L.pfr_month_cos.5 0.0002 0.5422 \ns(pfr_month_cos.tmat):L.pfr_month_cos.6 -1.6094 1.8354 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n38 \ns(pfr_month_cos.tmat):L.pfr_month_cos.7 -3.1231 5.9218 \ns(pfr_month_sin.tmat):L.pfr_month_sin.1 -8.7097 71.675 \ns(pfr_month_sin.tmat):L.pfr_month_sin.2 -1.0972 65.6637 \ns(pfr_month_sin.tmat):L.pfr_month_sin.3 0.0284 156.1398  \ns(pfr_month_sin.tmat):L.pfr_month_sin.4 12.0161 187.3235  \ns(pfr_month_sin.tmat):L.pfr_month_sin.5 7.3255 135.9212  \ns(pfr_month_sin.tmat):L.pfr_month_sin.6 19.7849 30.3586 \ns(pfr_month_sin.tmat):L.pfr_month_sin.7 6.1957 25.4692 \ns(pfr_participant).1 -0.8195 3.2219 \ns(pfr_participant).2 -3.3994 2.33 \ns(pfr_participant).3 -0.3429 3.0436 \ns(pfr_participant).4 -1.9507 1.8907 \ns(pfr_participant).5 -3.1004 1.8541 \ns(pfr_participant).6 2.7715 1.7634 \ns(pfr_participant).7 -4.7251 2.5942 \ns(pfr_participant).8 6.4256 2.302 \ns(pfr_participant).9 -2.2581 1.7001 \ns(pfr_participant).10 2.5425 2.4007 \ns(pfr_participant).11 -5.2992 2.2013 \ns(pfr_participant).12 7.0277 3.3273 \ns(pfr_participant).13 2.9095 2.0834 \ns(pfr_participant).14 1.1844 1.7011 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n39 \ns(pfr_participant).15 -0.7041 3.3855 \ns(pfr_participant).16 3.6399 1.6518 \ns(pfr_participant).17 1.327 3.2448 \ns(pfr_participant).18 0.2492 1.9396 \ns(pfr_participant).19 -0.6143 1.7548 \ns(pfr_participant).20 -3.9002 1.8232 \ns(pfr_participant).21 -3.2474 1.81 \ns(pfr_participant).22 3.3082 2.3462 \ns(pfr_participant).23 -1.9553 2.7352 \ns(pfr_participant).24 0.4892 3.3464 \ns(pfr_participant).25 -1.0614 2.0089 \ns(pfr_participant).26 7.3399 1.7229 \ns(pfr_participant).27 -2.2067 3.3497 \ns(pfr_participant).28 -2.5714 2.4798 \ns(pfr_participant).29 4.0968 2.4943 \ns(pfr_participant).30 3.2534 2.0713 \ns(pfr_participant).31 -1.0354 2.924 \ns(pfr_participant).32 1.6512 2.4759 \ns(pfr_participant).33 0.0849 1.7427 \ns(pfr_participant).34 0.9219 3.3273 \ns(pfr_participant).35 -1.5758 1.8535 \ns(pfr_participant).36 -1.1452 2.757 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n40 \ns(pfr_participant).37 7.3676 2.7764 \ns(pfr_participant).38 0.1432 3.0061 \ns(pfr_participant).39 -0.5169 2.8012 \ns(pfr_participant).40 -2.5837 3.4357 \ns(pfr_participant).41 -2.4264 1.8566 \ns(pfr_participant).42 7.4204 2.7515 \ns(pfr_participant).43 3.701 3.2246 \ns(pfr_participant).44 -2.3142 2.7271 \ns(pfr_participant).45 -1.4783 1.8362 \ns(pfr_participant).46 -0.8119 3.4236 \ns(pfr_participant).47 -2.91 2.2243 \ns(pfr_participant).48 -2.3047 2.7677 \ns(pfr_participant).49 -1.9364 2.7055 \ns(pfr_participant).50 -0.8793 3.229 \ns(pfr_participant).51 -1.9784 3.0708 \ns(pfr_participant).52 -8.8145 1.9939 \ns(pfr_participant).53 -0.1424 2.7878 \ns(pfr_participant).54 -4.7399 2.6715 \ns(pfr_participant).55 11.8348 1.8121 \ns(pfr_participant).56 -0.0268 3.1258 \ns(pfr_participant).57 -0.9704 1.8023 \ns(week).1 0 0.0008 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint \n\n \n41 \n 635 \n 636 \nTable S3. Linear predictors for the PFR model.  637 \n 638 \nPredictor Estimate SE T.value P.value \n(Intercept) 2.9429 0.0872 33.752 0 \nMPQ-VAS -1.16 0.4964 -2.3368 0.0206 \nPhysical Functioning  2.2409 0.598 3.7475 0.0002 \nPsychiatric Diagnosis 0.2181 0.8874 0.2458 0.8061 \nEmployed 4.0117 1.0939 3.6674 0.0003 \nEmployed - Other 6.0273 1.2929 4.6617 0 \nDivorced 0.3939 1.8963 0.2077 0.8357 \nMarried 3.5996 0.8582 4.1946 0 \nAge -1.1946 0.4629 -2.5804 0.0107 \n 639 \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted November 7, 2024. ; https://doi.org/10.1101/2024.09.25.24314368doi: medRxiv preprint","source_license":"CC0","license_restricted":false}