Daily Consistency Over Timing: Routine Formation and Population-Specific Opportunities in mHealth User Adherence.

Analyses included 13,997 person-level days (i.e., 2659 weeks) of data from 203 participants (131 with a CPPD diagnosis, 72 healthy controls) across 90 days (14 weeks) of tracking. Cases had a confirmed CPPD diagnosis and controls were healthy volunteers with no history of CPPDs matched on age, race/ethnicity, education and employment status. Participants used the ehive research mHealth app [ 15 ] to self-track pain, QoL, sleep, and mood daily, with weekly-administered PROMIS Pain Interference Questionnaire (PIQ). Surveys were self-initiated and could be completed any time during the day. All participants provided informed consent, and the study protocol was approved by the Institutional Review Board. We calculated: (1) Dominant Time-of-Day, defined as the person-level mode of tracking period/week (Morning: until 11:59; Afternoon: 12:00–17:59; Evening: 18:00 and later); and (2) Temporal Variability, defined as the standard deviation (SD) of the tracking hour within each week. Consistent responders were defined as having a weekly SD < 4 hours. This 4-hour threshold was selected based on preliminary sensitivity analysis showing similar person-level SDs for cases (M=4.68h) and controls (M=4.48h), rounded down for a conservative boundary distinguishing consistent vs variable behavior. Overall adherence was calculated as the percentage of enrolled days on which participants self-tracked at least one daily symptom: (days with ≥1 response / total days enrolled) × 100, where total days enrolled = (end-of-study date - informed consent date) + 1. To avoid ecological fallacy and multicollinearity in models, we decomposed weekly PIQ T-scores into between-person mean (chronic burden) and within-person centered (acute weekly fluctuation) [ 1 ]. We used mixed-effects models (via lmerTest Library [ 18 ]) on weekly aggregated data (e.g., number of days tracked per week) to account for repeated-measures data structure. For RQ1, we used a generalized linear mixed model (GLMM) to estimate weekly adherence as a binomial outcome (days tracked | days missed per week). For RQ2, we used a linear mixed model (LMM) to estimate temporal consistency (within-week SD) from predictors: dominant time-of-day category (person-level mode), CPPD status, and PIQ scores. To assess disease specificity, we included CPPD × Time-of-Day and CPPD × Pain interactions. Both models adjusted for study week and included participant as a random intercept. All analyses were conducted in R[ 23 ] with α = .05. To corroborate our a priori time-of-day categorization and identify emergent temporal engagement phenotypes, we conducted unsupervised clustering via functional mixture models (FMM) [ 5 , 6 ]. Following published guidelines and other similar studies using this method [ 5 , 28 ], we converted each participant’s 90 days into functional trajectories (“curves”) by smoothing via Fourier basis expansion. The FMM clusters these smooth functional curves, which represent each participant’s temporal engagement pattern over time, and selects the optimal number of clusters using the Bayesian Information Criterion (BIC). We then evaluated cluster-level summary statistics.

Participant demographics are provided in Appendix Table 2 . There were no significant demographic differences between participants with versus without CPPDs. The median weekly adherence was 73.7%, consistent with digital engagement trajectories in other similar studies[ 17 , 29 ]. Daily tracking times showed a bimodal distribution (See Appendix 3a ) with morning (around 10am) and evening peaks (around 10pm). Evening (vs afternoon) responders had higher adherence (71.9% vs 59.1%; Tukey HSD p = 0.010; Figure 1a ). Morning completion showed the largest case-control adherence gap (See Figure 1b ). Timing variability was comparable between CPPDs and controls (Mean=4.86h vs 4.48h; t=1.55, p=.123), and 69.4% exhibited variable timing (i.e., weekly SD≥4h). Full GLMER results are provided in Table 1 . There was a significant CPPD × Time Window interaction where, evening engagement yielded a unique adherence amplification for CPPD cases (OR = 1.32) vs controls ( Figure 4b ). Temporal variability independently predicted lower adherence after controlling for PIQ and study duration (OR = 0.96 per 1 SD increase, p = .025). Neither chronic nor weekly PIQ scores were significant predictors (See Table 1 ), suggesting temporal consistency and symptom burden operate as separable influences on engagement. Results of the LMM are provided in Table 1 and Figure 4a . The LMM indicated that for every 1-hour shift toward evening, within-week variability decreased by 11 minutes ( β =−0.191, P < .001). This corresponds to about 2.3 hours of reduced variability for a participant who tracks at 8:00 versus 20:00. The interaction between evening engagement and CPPD status was significant (OR = 1.32, 95% CI [1.05, 1.66], p = .019), indicating that CPPD patients who tracked predominantly in the evening had substantially higher adherence odds compared to the additive effects of CPPD status and time-of-day alone (See Figure 4b ). The FMMs indicated a 4-cluster solution as optimal (BIC=−8769.1; See Appendix Table 3 ), with cluster sizes of: 18.3% (n=37), 38.1% (n=77), 25.9% (n=53), and 17.5% (n=36). These four temporal engagement patterns are depicted in Figure 2 . Clusters 1 and 2 (high SD: 2.9–4.1h) had 41–60% adherence, while Clusters 3 and 4 (low SD: 1.7–2.0h) had 85–93% adherence. This pattern held across CPPD status, corroborating the GLMER finding that consistency benefits both populations. Within Cluster 1, CPPD cases had 18-point higher pain interference (i.e., PIQ scores) than controls (60.7 vs 42.6), but higher adherence (53.3% vs 40.7%), supporting the LMM null finding on pain interference and temporal variability. Cluster-level summary statistics are provided in Appendix Table 4 .

These findings support reframing mHealth accessibility in research to accommodate those with varying temporal accessibility or agency to reduce systematic bias. Personalization for mHealth design can support routine formation at personally sustainable times rather than prescribing “optimal” windows derived from general populations. Such “chronoergonomic” designs can reduce the burden of self-tracking and improve the inclusivity of mHealth research [ 10 , 16 ]. As digital health tools become integral to clinical care and research, attention to temporal accessibility represents a critical dimension of inclusive design.

This work contributes novel evidence on how temporal stability and time-of-day patterns relate to adherence in a chronic pain mHealth context, and articulate corresponding mHealth design principles. We identify three key insights emerging that point to self-tracking consistency as a behavioral marker: Routine Stability Predicts Engagement . The first key finding is that how consistently a user engages matters more than how much pain they are in. Contrary to the common assumption that high symptom burden drives attrition, our models indicated that within-week temporal consistency, but not pain, was a significant predictor of adherence. That is, users with established stable timing habits (lower SD) maintained higher adherence regardless of their symptom status. This is in line with previous reports of user consistency as a stronger predictor of sustained compliance better than demographic or clinical characteristics [ 17 , 29 ]. Further, routine consistency improved ( β =−0.031, P = .006) while adherence declined ( β =−0.085, P < .001) over the 14 weeks, likely reflecting attrition bias where less motivated participants dropped out, leaving more consistent responders. It is possible that mHealth fatigue (“habituation decay”) outweighs the benefits of routine learning over time and thus incentive structures and engagement refreshers are needed to sustain adherence beyond the initial weeks. Chrono-Ergonomics and Evening Anchoring . The LMM results suggest a possible routine anchoring effect of later-day tracking that reduced within-week variability (See Table 1 ). However, this stability translated into adherence differently for those with vs without a CPPD, with a unique adherence amplification for the former. Unsupervised clustering analysis independently identified an “Evening-Stable” cluster where adherence was similar between participants with vs without a CPPD (See Appendix 4 ), in contrast to the “Morning-Moderate” cluster that showed a 12.6% group difference. This convergent evidence from a method blind to group status and our hypotheses supports a flexible chrono-ergonomic framework to accommodate those with symptom-related constraints [ 14 , 21 ]. Population-Specific Temporal Accessibility . Third, pain interference did not significantly predict either adherence or routine consistency. Morning periods may be challenging for individuals with chronic pain [ 21 ], explaining why general population principles fail to transfer, and this could be due to various possible mechanisms. The relationship between pain and adherence could be indirect and mediated by temporal factors, e.g., adherence is hindered because patient symptoms disrupt the consistent daily routines that enable habit formation. These findings suggest separability of pain management from adherence management, and that targeting routine stability may be more effective than simply addressing pain intensity in interventions. In sum, optimal timing could be population-specific, though future work can investigate whether this framework applies to other conditions with similar symptomology [ 9 , 13 , 14 , 26 ]. Routine Stability Predicts Engagement . The first key finding is that how consistently a user engages matters more than how much pain they are in. Contrary to the common assumption that high symptom burden drives attrition, our models indicated that within-week temporal consistency, but not pain, was a significant predictor of adherence. That is, users with established stable timing habits (lower SD) maintained higher adherence regardless of their symptom status. This is in line with previous reports of user consistency as a stronger predictor of sustained compliance better than demographic or clinical characteristics [ 17 , 29 ]. Further, routine consistency improved ( β =−0.031, P = .006) while adherence declined ( β =−0.085, P < .001) over the 14 weeks, likely reflecting attrition bias where less motivated participants dropped out, leaving more consistent responders. It is possible that mHealth fatigue (“habituation decay”) outweighs the benefits of routine learning over time and thus incentive structures and engagement refreshers are needed to sustain adherence beyond the initial weeks. Chrono-Ergonomics and Evening Anchoring . The LMM results suggest a possible routine anchoring effect of later-day tracking that reduced within-week variability (See Table 1 ). However, this stability translated into adherence differently for those with vs without a CPPD, with a unique adherence amplification for the former. Unsupervised clustering analysis independently identified an “Evening-Stable” cluster where adherence was similar between participants with vs without a CPPD (See Appendix 4 ), in contrast to the “Morning-Moderate” cluster that showed a 12.6% group difference. This convergent evidence from a method blind to group status and our hypotheses supports a flexible chrono-ergonomic framework to accommodate those with symptom-related constraints [ 14 , 21 ]. Population-Specific Temporal Accessibility . Third, pain interference did not significantly predict either adherence or routine consistency. Morning periods may be challenging for individuals with chronic pain [ 21 ], explaining why general population principles fail to transfer, and this could be due to various possible mechanisms. The relationship between pain and adherence could be indirect and mediated by temporal factors, e.g., adherence is hindered because patient symptoms disrupt the consistent daily routines that enable habit formation. These findings suggest separability of pain management from adherence management, and that targeting routine stability may be more effective than simply addressing pain intensity in interventions. In sum, optimal timing could be population-specific, though future work can investigate whether this framework applies to other conditions with similar symptomology [ 9 , 13 , 14 , 26 ]. Design Implications . These findings provide actionable levers for: designers of mHealth apps (timing of prompts, onboarding), researchers setting mHealth-based tracking protocols (flexible vs fixed times), and clinicians using self-tracking as part of chronic symptom management. First, rather than prescribing times, on-boarding algorithms can help users identify their natural “stability window”—the time of day when they can most consistently engage, even on their worst symptom days. Second , systems could provide consistency-based feedback (e.g., visualizing timing precision [ 22 , 24 ]) rather than completion rates alone, reinforcing habit loops and enhancing self-efficacy through individualized strength-focused messages [ 7 ]. Feedback messages could be individualized to highlight each participant’s specific strengths to support intrinsic motivation [ 7 ]. Third , adaptive quiet periods could be implemented to determine participants’ optimal engagement window based on contextual factors [ 4 ]. For example, just-in-time adaptive interventions could use temporal pattern recognition to detect a user’s emerging stability window and provide increasingly targeted support during that window. Several measurement limitations warrant consideration. First, observational data precludes causal claims about consistency- adherence directionality. Future work can examine mechanisms underlying consistency benefits and experimentally manipulate notification timing to test causal effects. The 14-week duration limits assessment of whether consistency patterns persist long-term or reflect initial habit formation phases. Third, the 4-hour consistency threshold requires validation as a meaningful boundary versus a context-specific threshold. Generalizability may be limited to female populations and those with chronic pain conditions, thus whether findings extend to other symptomatic populations should be tested.

Mobile health applications (mHealth Apps) have emerged as promising tools for longitudinal health and disease symptom monitoring, especially for conditions that are poorly understood and under-documented in traditional data sources such as electronic health records (EHRs) [ 8 ]. Adherence remains a challenge, with engagement rates often declining substantially over time in multi-week studies [ 17 , 29 ], as well as from initial uptake to longer term follow-up (e.g., 73.6% to 28% after 7 months among individuals with chronic pain [ 3 ]). This attrition threatens both data quality and clinical benefit, as engagement frequency has been linked to improved pain outcomes [ 27 ]. Existing work on mHealth self-tracking has focused on frequency, duration, or completion, leaving the timing of engagement largely unexplored. Unlike fixed-timing ecological momentary assessment (EMA) studies, self-initiated tracking allows flexible time windows, raising questions about whether temporal patterns predict adherence and whether fixed schedules create systematic barriers for clinical populations [ 25 , 29 ]. Accordingly, it remains unknown whether these temporal patterns carry predictive information about adherence and whether fixed timing creates systematic barriers for clinical populations. This aligns with recent calls to design technology that accommodates the variable ability and temporal experiences of chronically ill users [ 20 ]. We herein investigate this gap in women with conditions commonly associated with chronic pelvic pain (e.g., endometriosis, adenomyosis, fibroids) due to their high symptom burden. CPPDs affect approximately 20% of the global female population and account for 40% of laparoscopies and 12% of hysterectomies in the US annually [ 19 ]. Yet, they remain poorly understood due to the difficulty capturing symptom fluctuations over time. Temporal patterns in engagement offer a novel lens for understanding adherence facilitators and barriers, and can guide personalized management strategies, thus representing a high-value intervention target. Habit formation literature suggests morning routines may achieve automaticity faster than evening routines in general populations [ 2 , 11 , 12 ], with morning practices reaching habit strength approximately 45% sooner in experimental studies [ 11 ]. However, these findings primarily rely on general population samples and may not account for symptom burden in clinical populations (e.g., CPPDs), thus limiting our understanding of mHealth feasibility and benefit. In contrast, user-centered mHealth designs that tailor recommendations and messages based on motivation level could enhance adherence and self-efficacy in chronic pain populations [ 7 ]. Similarly, patient response patterns can be strongly influenced by the environment they are in at the time of alert, which can be leveraged to optimize when a participant is most likely to complete tasks [ 4 ]. We used data from a 14-week observational study that involved daily App-based self-Stracking of health symptoms and behaviors with participants with CPPDs and matched healthy controls to investigate: RQ1: Temporal Consistency Effects: Is self-tracking timing consistency associated to adherence beyond chronic symptom burden? We hypothesized that lower variability in response times would predict higher adherence, reflecting successful habit formation. RQ2: Population-Specific Temporal Accessibility: Do the daily engagement patterns over time and their relationships with adherence differ by CPPD diagnosis? We hypothesized that CPPD-related pain may disrupt daily routines and impose variable symptom burden, thus acting as a differential barrier.