JMIR mHealth and uHealth

Behavioral Modification as a Putative Mediator of Digital Therapeutic Response in Temporomandibular Disorders: Secondary Analysis of a Multicenter Sham-Controlled Randomized Trial

2026-09-03T15:30:14-04:00

Background: Temporomandibular disorders (TMDs) are common chronic conditions involving orofacial pain and functional limitations. Digital therapeutics (DTx) have demonstrated efficacy in TMD management; yet, the behavioral and clinical mechanisms underlying treatment response remain poorly characterized, particularly whether behavioral modification or DTx engagement intensity drives therapeutic benefit. Objective: This study aimed to investigate the behavioral mechanisms, responder profiles, and moderators of clinical response to a DTx intervention for TMD through a post hoc analysis integrating self-reported, server-derived, and clinician-rated outcome measures. Methods: We performed a post hoc secondary analysis of a multicenter, double-blind, sham-controlled randomized superiority trial conducted at 2 tertiary care centers in South Korea. The per-protocol cohort comprised 93 participants (DTx: n=44; sham: n=49). Five complementary analyses were applied: responder logistic regression at ≥30%, ≥50%, and ≥70% Visual Analog Scale (VAS) pain-reduction thresholds; subgroup comparison by Oral Behaviors Checklist (OBC) modifier status; causal mediation analysis using the potential outcomes framework with bootstrap CIs; week-4 sensitivity analysis; and moderator analysis testing the treatment×Patient Health Questionnaire-4 (PHQ-4) interaction on VAS change. Results: DTx assignment was consistently associated with clinically meaningful pain reduction across all 3 responder thresholds (adjusted odds ratios [ORs] 5.39, 95% CI 1.72‐16.94 at ≥30%; 3.21, 95% CI 1.14‐8.99 at ≥50%; and 3.45, 95% CI 1.12‐10.63 at ≥70%; all <.05). Mediation analysis suggested that approximately 29.1% of the total VAS treatment effect may be transmitted via OBC-defined behavioral modification (natural indirect effect −6.91 mm; 95% CI −13.23 to −0.58; =.03), with the mediated proportion rising from 19.6% to 30.2% as responder thresholds became more stringent. Participants with OBC modifiers achieved substantially greater pain reduction than nonmodifiers (−45.71 vs −22.61 mm; difference −23.11; 95% CI −36.97 to −9.24; <.01) despite no significant differences in any objective DTx engagement metric. Treatment ORs were 33%‐42% higher at week 4 than at the 6-week end point (week-4 ORs 7.15, 95% CI 2.39‐21.32 at ≥30%; 4.48, 95% CI 1.68‐11.94 at ≥50%; and 4.91, 95% CI 1.74‐13.87 at ≥70%), suggesting that week 4 may be a candidate time point for future adaptive protocols. Baseline psychological distress (PHQ-4 ≥3) appeared to moderate the treatment response (interaction β=−19.63; 95% CI −37.86 to −1.39; =.04). Conclusions: Sham-controlled randomized trials in TMD that empirically differentiate behavioral realization from digital engagement volume remain scarce. Behavioral modification, rather than engagement volume, appears to be an important pathway associated with DTx efficacy and may mediate approximately 29% of the pain-reduction effect under exploratory causal assumptions. This advances mechanism-based evaluation of DTx beyond engagement-based surrogates. Week 4 may represent a promising candidate for future adaptive protocols, and baseline psychological distress may warrant investigation for precision patient selection. These preliminary findings should be corroborated by future prospective studies and further mechanistic investigations. Trial Registration: Clinical Research Information Service KCT0009493; https://tinyurl.com/w5huy4zn

Time to Cancer Screening Completion Following an SMS Reminder Intervention in a Large Federally Qualified Health Center Network: Secondary Data Analysis

2026-09-03T12:15:12-04:00

Background: Delays in completing cancer screening diminish the preventive benefits of early detection, particularly among women receiving care in Federally Qualified Health Centers (FQHCs). Although many patients receive SMS reminders and complete screening, less is known about how quickly they complete testing or which patient-level and structural factors are associated with delays. Objective: This study examined factors associated with time to cancer screening completion among women aged 50 years or older who received SMS reminders and completed screening across a large FQHC network in Texas. The study also compared time to completion across three cancer screening tests: human papillomavirus (HPV) or the Papanicolaou test (hereinafter “Pap test”), mammography, and the fecal immunochemical test (FIT) or Cologuard screening. Methods: We conducted a secondary data analysis using electronic health record (EHR) data from a 56-clinic FQHC network in Texas. The initial cohort included 1803 women aged 50 years or older who (1) were overdue for HPV or Pap testing, mammography, or FIT or Cologuard screening, and (2) received at least three SMS reminders. Of those, 551 completed the screening and constituted the analytic cohort for this study. The outcome was the number of days from the initial SMS reminder to documented completion of the overdue screening test in the EHR. Kaplan-Meier methods were used to estimate time to completion by screening modality. A multivariable Cox proportional hazards model assessed associations of screening modality, sociodemographic, and clinical characteristics, and self-reported health-related social needs with the rate of screening completion. Results: Overall, 40.8% (n=212) of patients overdue for HPV or Pap testing completed their screening, while 21.1% (n=138) of those overdue for mammography and 32.1% (n=201) of those overdue for FIT or Cologuard screening completed their respective screening. Median time to completion was 72.5 (95% CI 64‐86) days for HPV or Pap screening and 52.0 days for both mammography (95% CI 43‐64) and FIT or Cologuard screening (95% CI 52‐53). In the adjusted model, screening completion was faster for FIT or Cologuard screening (hazard ratio [HR] 1.65, 95% CI 1.34‐2.05) and mammography (HR 1.41, 95% CI 1.11‐1.78) than for HPV or Pap screening. Patient-reported transportation limitation was associated with slower screening completion (HR 0.74, 95% CI 0.55‐0.99). Conclusions: These findings demonstrate meaningful variation in both the completion and timeliness of overdue cancer screening across screening modalities. Although HPV or Pap testing had the highest overall completion rate, time to completion was significantly shorter for mammography and FIT or Cologuard screening. The association between transportation limitations and delayed screening further underscores the influence of access-related barriers on timely preventive care. This suggests that efforts to improve cancer screening should extend beyond patient outreach to incorporate modality-specific strategies and interventions that address structural barriers to screening completion.

User Engagement and Feature Preferences in an AI-Powered mHealth Intervention for Diabetes Prevention: Secondary Analysis of a Randomized Controlled Trial

2026-09-02T12:00:03-04:00

Background: Prediabetes is highly prevalent and increasing globally, yet lifestyle interventions remain underused. AI-driven mobile health (mHealth) tools can help scale diabetes prevention efforts, but the key factors driving their success are not well understood. Objective: This post hoc secondary analysis of a randomized controlled trial (RCT) aimed to characterize the most valued features and the role of user engagement in outcomes of a fully automated mHealth intervention for diabetes prevention. Methods: Data from 151 participants with prediabetes and overweight or obesity who were assigned to an AI-based diabetes prevention program (Sweetch) in a parent RCT (NCT05056376) were analyzed. Engagement (defined as the total number of days the app was used) was categorized into tertiles (low, medium, and high). Baseline characteristics were compared across engagement groups using ANOVA, Kruskal-Wallis, and chi-square tests, and regression models assessed the association between engagement and achievement of diabetes risk reduction outcomes (≥5% weight loss, ≥4% weight loss with ≥150 min/week of physical activity, or ≥0.2 percentage point reduction in hemoglobin A_1c [HbA_1c] at 12 months). Perceived usefulness of intervention features was surveyed at 12 months. Results: Median engagement was 98 (IQR 34-232) days. Older age (P<.001) and lower baseline BMI (P=.04) were significantly associated with higher engagement. Compared with low engagement, high engagement was associated with greater odds of achieving the composite diabetes risk reduction outcome (odds ratio [OR] 2.59, 95% CI 1.11-6.01; P=.03), ≥5% weight loss (OR 3.31, 95% CI 1.16-9.42; P=.03), and ≥0.2 percentage point reduction in HbA_1c (OR 3.57, 95% CI 1.19-10.75; P=.02). Participants most frequently rated weight tracking, physical activity tracking, and the digital body weight scale as the features that were most helpful for achieving their health goals. Conclusions: Higher engagement with an AI-driven intervention requiring no human intervention was associated with improved diabetes risk reduction. Contrary to concerns about lower digital literacy, older adults engaged with the intervention more than younger adults. Features related to weight and physical activity tracking were most valued by patients in the program. Trial Registration: ClinicalTrials.gov NCT05056376; https://clinicaltrials.gov/study/NCT05056376

Correction: Effects of Telehealth Interventions for People With Parkinson Disease: Systematic Review and Meta-Analysis of Randomized Controlled Trials

2026-09-01T16:00:03-04:00

Digital Structured Education With Behavioral Nudge Tools for Adults With Type 2 Diabetes: Multicenter Randomized Controlled Trial

2026-09-01T15:00:18-04:00

Background: Digital interventions offer scalable alternatives to traditional face-to-face diabetes education, but often face challenges related to inconsistent clinical effectiveness, and declining user engagement. However, whether a digital structured education program integrated with behavioral nudge tools can improve metabolic, behavioral, and psychological outcomes in adults with type 2 diabetes remains unclear. Objective: This study aimed to evaluate the effectiveness of a digital structured education program integrated with behavioral nudge tools in improving metabolic, behavioral, and psychological outcomes among adults with type 2 diabetes. Methods: This multicenter randomized controlled trial was conducted in the endocrinology departments of 4 hospitals in China. Adults with type 2 diabetes were randomly assigned to an intervention group receiving a digital structured education program integrated with behavioral nudge tools (n=146) or a control group receiving standard digital diabetes education (n=147). Assessments were conducted at baseline and 12-week follow-up. The primary outcome was hemoglobin A (HbA) at 12 weeks, adjusted for baseline HbA, and study center. Secondary outcomes included fasting blood glucose (FBG), weight, BMI, waist circumference, blood pressure, lipid profiles, self-management behaviors, self-efficacy, and habit strength. Results: Among 293 participants (mean age 49.19, SD 10.02 y), 287 (97.9%) completed follow-up. At 12 weeks, the intervention group demonstrated significantly greater improvements than the control group in HbA (adjusted mean difference −0.38%, 95% CI −0.68% to −0.09%; .01), FBG (adjusted mean difference −0.75, 95% CI −1.27 to −0.44 mmol/L; <.001), weight (adjusted mean difference −0.84, 95% CI −1.61 to −0.07 kg; .03), BMI (adjusted mean difference −0.38, 95% CI −0.65 to −0.11 kg/m²; .01), systolic blood pressure (adjusted mean difference −2.71, 95% CI −4.62 to −0.79 mm Hg; .01), diastolic blood pressure (adjusted mean difference −2.92, 95% CI −4.47 to −1.37 mm Hg; <.001), and total cholesterol (adjusted mean difference −0.27, 95% CI −0.48 to −0.05 mmol/L; .02). The intervention was also associated with significantly greater improvements in self-management behaviors, self-efficacy, and habit strength (all <.05). Conclusions: Digital structured education integrated with behavioral nudge tools improved metabolic outcomes and strengthened psychological and behavioral determinants of self-management among adults with type 2 diabetes over a 12-week period. These findings suggest that a digital structured education program integrated with behavioral nudge tools may enhance diabetes self-management beyond standard digital diabetes education. Further studies with longer follow-up and real-world implementation are warranted to evaluate the sustainability, generalizability, and long-term clinical impact of this integrated intervention. Trial Registration: Chinese Clinical Trial Registry ChiCTR2400082373; https://tinyurl.com/3djbnt36

Feasibility of a Stool State Check App Using AI During Bowel Preparation Before Colonoscopy: Multicenter Prospective Study (SCAN Study)

2026-09-01T14:00:17-04:00

Background: Optimal bowel preparation (BP) is crucial for a successful colonoscopy. Although multiple factors influence BP quality, including patient adherence to laxatives and dietary instructions, the stool state during BP should be properly evaluated to perform a colonoscopy of sufficient quality. Therefore, we developed a smartphone app to evaluate a patient’s stool state during BP and a viewer to enable real-time monitoring by medical staff. Objective: This study aimed to assess the feasibility of performing colonoscopies of appropriate quality using the app-and-viewer system. Methods: This prospective observational study was conducted between November 2022 and December 2023, involving patients scheduled for colonoscopy at 10 Japanese institutions, comprising 6 tertiary hospitals, 3 regional general hospitals, and 1 community-based clinic. Patients who (1) underwent a colonoscopy at participating institutions, (2) were aged between 20 and 70 years, and (3) owned smartphones compatible with Android or iOS were included in the study. The patients downloaded the app on their smartphones and captured images of their stools during BP, while the medical staff reviewed the evaluation of the stools by the app via the viewer system. The primary end point was defined as the proportion of patients with a Boston Bowel Preparation Scale (BBPS) score of ≥6 among those who successfully used the app. Secondary end points included mean BBPS score, rate of an excellent BBPS score (≥8), adenoma detection rate, cecal intubation rate, and withdrawal time in negative colonoscopy. Additionally, we evaluated the usability of the app, medical staff workload burden with the app, and viewer usage via questionnaire surveys. Results: A total of 343 patients were enrolled, and 326 were ultimately included in the analysis. Overall, 99.1% (323/326, 95% CI 97.3%-99.8%) of the patients achieved the primary end point. The mean BBPS score was 8.5 (SD 1.0), and the proportion of excellent BBPS scores was 87.4% (285/326). The adenoma detection rate, cecal intubation rate, and mean withdrawal time in negative colonoscopy were 46.9% (153/326, 95% CI 41.4%-52.5%), 99.7% (325/326, 95% CI 98.3%-99.9%), and 10.7 (SD 5.9) minutes, respectively. In the questionnaire survey, 98.5% (321/326) of the patients reported that the tutorial was easy to understand, 96.0% (313/326) found stool image capture easy, and 87.8% (286/326) reported reduced anxiety regarding BP. Furthermore, 90.5% (295/326) of the patients indicated that they would like to use the app again for future colonoscopies. Among medical staff, 92.5% (62/67) considered the viewer system necessary, 89.6% (60/67) found it easy to use, and 89.6% (60/67) reported a reduction in workload burden. Conclusions: AI-based stool state assessment using the app and the viewer during BP was feasible across diverse BP methods and clinical environments. Favorable BP outcomes and high usability among patients and medical staff support the potential use of this approach in real-world colonoscopy practice.

mHealth–Supported Perioperative Care for Family Caregivers of Children Undergoing Tonsillectomy and/or Adenoidectomy (TONAPP): Randomized Controlled Trial

2026-09-01T13:30:13-04:00

Background: Pediatric ear, nose, and throat (ENT) surgery is common, but generates perioperative anxiety for caregivers and distress in children. Limited time for perioperative education and reliance on unverified online information can reduce family preparedness and increase stress. Few studies have evaluated co-designed mobile health (mHealth) apps to support and engage families in the perioperative ENT journey. Objective: This study aimed to compare caregiver anxiety between an mHealth app-supported care pathway and standard supportive and educational care alone in the perioperative ENT context. Secondary objectives explored between-group differences in caregiver anxiety at follow-up, family preparation, child distress, and social-impact indicators. Methods: A 2-arm, parallel-group, open-label randomized controlled trial (RCT) enrolled caregivers of children undergoing ENT surgery (tonsillectomy, adenoidectomy, tympanostomy tube insertion). The intervention was an mHealth app co-designed through a user-centered participatory approach and developed following Schnall and colleagues’ Information Systems Research Framework, with content based on caregivers’ informational needs. RCT participants were recruited at the hospital during their presurgery visit, when a health care provider introduced the study and provided instructions on how to use the app. No additional human support was scheduled thereafter. A sample size of 180 participants (90 per group) was estimated to detect the expected between-group difference in caregiver anxiety. Participants were randomly assigned in a 1:1 ratio to app use or standard care alone. The primary outcome was the between-group difference in caregiver state anxiety (State-Trait Anxiety Inventory [STAI-Y]). Secondary outcomes included between-group differences in child distress (modified version of the Yale Preoperative Anxiety Scale [mYPAS]), child preparation for surgery, family preparation for hospital admission and surgery, and social impact indicators. Outcomes were assessed online through questionnaires, which included both self-reported measures and evaluations completed by a nurse on the day of surgery. App engagement metrics were also collected. Reporting followed the CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) guidelines. Results: The study enrolled 227 caregivers, with 111 allocated to the control group (CG) and 116 to the experimental group (EG), achieving the target sample size. No statistically significant differences were observed between the groups for the primary or secondary outcomes (all >.05). In the EG, 75% (n=87) of the participants accessed at least 1 item of in-app content. Higher baseline anxiety was linked to lower app use (ρ=–0.22, 95% CI –0.39 to –0.04; =.02), while greater use was linked to lower child distress (ρ=–0.23, 95% CI –0.40 to –0.04; =.02). Conclusions: Although the hypotheses were not confirmed, these findings provide valuable insights for future perioperative mHealth research. The lack of effectiveness may reflect limited exposure to the intervention, outcome selection and timing, and contextual factors such as caregivers’ independent information-seeking. These findings support a greater focus on implementation processes and on identifying the caregivers most likely to benefit from mHealth-supported education. Trial Registration: Clinicaltrials.gov NCT05460689; https://clinicaltrials.gov/study/NCT05460689 International Registered Report Identifier (IRRID): RR2-10.1186/s13063-023-07376-z

Self-Monitoring of Weight Loss Over Time: Secondary Data Analysis of a Randomized Controlled Trial

2026-08-31T14:15:07-04:00

Background: Behavior change support systems aim to shape, modify, or strengthen attitudes or behaviors without using coercion or deception. One of the main software features of persuasive system design is self-monitoring, which provides the means for users to continuously track their own performance or status, thereby facilitating goal attainment. Objective: The aim of this study is to examine whether self-input of weight (self-monitoring frequency) and its interaction with time influence weight loss in adults using a mobile health behavior change support system (mHBCSS). We hypothesized that higher self-monitoring frequency would be associated with greater weight loss, with effects varying across the intervention period. Methods: This secondary analysis used data from the intervention group of a randomized, open, waitlist-controlled trial in adults with obesity (BMI 30‐40 kg/m²). Participants used the mHBCSS for 12 months, and analyses included only participants who maintained self-monitoring for at least 6 months (N=75). Weight changes were analyzed across 9 time periods. Quantile regression (QR) was applied to examine effects on the 25th, 50th (median), and 75th percentiles of weight loss. The models included self-monitoring frequency, time periods, and their interaction. A sensitivity analysis using a multiple imputation procedure was performed to assess the robustness of the QR. Results: The time period variable was significant at the 25th weight loss percentile (QR coefficient: −1.164, 95% CI −1.453 to −0.756) and at the 50th weight loss percentile (QR coefficient: −0.603, 95% CI −0.733 to −0.493). The interaction variable was significant at the 50th (QR coefficient: −0.018, 95% CI −0.047 to −0.003) and 75th (QR coefficient: −0.036, 95% CI −0.051 to −0.027) weight loss percentiles. Self-monitoring frequency alone was not statistically significant. Conclusions: The study demonstrates that the effect of self-monitoring on weight loss is time-dependent. While a higher frequency of self-monitoring is associated with greater weight loss early in the intervention, its influence decreases as time progresses. These findings emphasize the importance of sustained engagement with self-monitoring rather than focusing solely on frequency, suggesting that interventions should incorporate strategies to maintain consistent self-monitoring use throughout the behavior change process. Trial Registration: ClinicalTrials.gov NCT04558801; https://clinicaltrials.gov/study/NCT04558801

The Representation of Different Populations in Studies Assessing the Validity of Consumer Wearable Photoplethysmography-Based Measurements: Scoping Review

2026-08-28T17:00:18-04:00

Background: Consumer wearables are increasingly being integrated into health research for data collection. Although they are attractive to use, the accuracy of their photoplethysmography (PPG)-based measurements can be influenced by user characteristics such as sex, age, BMI, and skin tone. However, our knowledge regarding the validity of these measurements in certain populations, such as those with darker skin tones, seems limited. This is concerning as uncorrected differences in measurement accuracy can lead to health disparities when consumer wearable measurements are used more frequently. A potential cause for the gap in our knowledge regarding consumer wearable validity is the underrepresentation of certain population groups in studies validating PPG-based consumer wearables. Objective: This scoping review aimed to map the representation of different sex, age, BMI, and skin tone groups in studies assessing the validity of PPG-based pulse rate, heart rate variability, blood pressure, peripheral blood oxygen saturation (SpO), and respiratory rate measurements of consumer wearables. Methods: A literature search was conducted in Scopus, PubMed, and IEEE Xplore in July 2025. Papers were eligible if they assessed the validity of consumer wearable PPG-based measurements, expressed as the agreement with a reference method. From the included papers, the study population distribution of sex, age, BMI, and Fitzpatrick scale was extracted. To evaluate the representation, percentages of people in specific age, BMI, and skin tone groups were estimated based on reported means and SDs. The median percentage of participants in each population group, as well as the total percentage, is reported. Results: After the removal of duplicates, 734 papers were screened for eligibility. Following title and abstract screening, 238 papers remained, of which 186 passed full-text screening and were included in the review. Most of the studies (n=160) focused on pulse rate. Sex, age, BMI, and Fitzpatrick scale were reported by 179 (96.0%), 178 (96.0%), 101 (54.0%), and 35 (19.0%) out of 186 studies, respectively. While the median representation was 0% (IQR 0%-8%) for both older adults (>65 y) and individuals with obesity (BMI>30 kg/m; IQR 0%-13%), aggregate participation across all studies was higher (1290/6367, 20.0% and 473/3428, 14.0%, respectively). Individuals with underweight (BMI<18.5 kg/m) remained rare (median 3%, IQR 0%-7%), and the aggregate was 7.0% (225/3428). The median percentage of people with darker skin tones (Fitzpatrick type V and VI) participating in a study was 0%. Conclusions: Based on our results, it can be concluded that older adults and people with underweight, obesity, or darker skin tones are generally underrepresented in studies assessing the validity of consumer wearable PPG-based measurements. Future validation studies should focus more on the representativeness of the study population. This can be achieved by setting a benchmark for representativeness and including study population representatives during the study design process.

Immediate and Sustained Improvements in Mood and Stress Associated With Yuna, an AI-Powered Digital Mental Health Intervention: Real-World Retrospective Study

2026-08-25T15:45:11-04:00

Background: AI-powered digital mental health interventions (DMHIs) are a promising approach to address barriers to traditional mental health care. However, real-world evidence of their immediate and sustained benefits remains limited. Objective: The purpose of this real-world, retrospective study is to explore patterns of perceived mood and stress change associated with the use of Yuna, an AI-powered DMHI. We aimed to (1) describe user demographics and session characteristics, (2) quantify the magnitude of mood and stress change within sessions and over time with continued Yuna use, and (3) identify session-level factors associated with changes in mood and stress. Methods: Adult Yuna users (aged ≥18 y) who initiated at least one session with the Yuna app were included in this study. Users self-reported mood and stress on Visual Analog Scales (VASs; range 0‐1) before and after sessions. Linear mixed effects models were used to explore the immediate, within-session improvements in mood and stress, and the sustained, between-session changes in symptoms of mood and stress. Linear mixed effects models were also used to examine session-level predictors of within-session improvements, including baseline symptom severity, session duration, total number of unique therapeutic approaches used, safety guardrail activation, and gender. Results: A total of 5549 real-world users were included (2901/5549, 52.3% female; mean sessions 3.44, SD 9.83). Users demonstrated significant, immediate within-session improvements in both mood (=0.55) and stress (=0.56), with sensitivity analyses yielding consistent results. Between-session analyses revealed gradual improvements in baseline mood (=−0.009) and stress (d=−0.011). Baseline symptom severity was the strongest predictor of immediate, within-session change (mood: β=.118, SE 0.003; <.001; stress: β=.131, SE 0.004; <.001), followed by session duration (mood: β=.027, SE 0.003; =.003; stress: β=.029, SE 0.003; <.001). A greater number of unique therapeutic approaches used was associated with smaller improvements in both outcomes (mood: β=−0.007, SE 0.003; =.043; stress: β=-0.011, SE 0.003; =.002). Conclusions: Use of Yuna, an AI-powered DMHI, was associated with perceived within-session improvements in mood and stress, with preliminary evidence of gradual improvements in mood and stress across repeated sessions. However, the absence of a control group and potential selection bias preclude causal conclusions. These findings offer promising, real-world evidence for AI-powered DMHIs as accessible, on-demand support tools. Prospective, controlled designs are needed to establish causal effects and evaluate the sustainability of observed improvements.