JMIR mHealth and uHealth

Automated Physical Activity Support for Adults and Youth From Low-Income Communities: Single-Arm Pilot Study

2026-06-05T16:00:47-04:00

Background: Mobile health (mHealth) interventions are growing in popularity, but less research has focused on low-income families, particularly interventions integrating wearable devices with automated personalized messages. Objective: We tested a preliminary wearable-integrated mHealth intervention with initial personalization elements among adults and youth from low-income urban communities, focusing on feasibility, acceptability, and preliminary evidence of physical activity behavior. Methods: Participants were 83 adults and 31 youth recruited through community health events held in low-income urban communities. Using a single-arm pre-post design, participants were enrolled into a 7-week beta-version mHealth intervention that integrated a Garmin activity monitor with automated text messages. Messages were sent 4 days/week and focused on increasing step counts using theory-based behavior change techniques related to goal setting, self-monitoring, reinforcement, contextual factors, and self-efficacy. Most messages were personalized by including calculations based on the step-count and step-goal data, using branching logic, and using 2-way question-and-response messages. Feasibility measures included enrollment, retention, fidelity of message delivery, and adherence to wearing the Garmin device. Acceptability measures included survey items and engagement with responding to 2-way messages. Changes in daily steps were explored using mixed-effects linear regression. Results: Enrollment and eligibility rates were 64% (84/132, adults) and 63% (31/49, youth), retention for physical activity measures was 84% (70/83) and 77% (24/31), and 99% (3910/3955) of the intended messages were delivered. Adults and youth adhered to wearing the Garmin on 82% (45/56) and 79% (44/56) of the study days, respectively. Overall acceptability ratings were 83% to 100%, with 97% (75/77) of adults and 100% (27/27) of youth indicating they would recommend the program. Adults and youth replied to a mean of 2.6 (SD 2.2) and 3.2 (SD 2.7) of the 7 text messages that asked for a reply, with higher engagement among adults who participated with their child. Pre-post changes in daily steps were β=240 (95% CI –387 to 866) for adults and β=413 (95% CI –877 to 1703) among youth, with larger changes observed among those in the highest tertile of engagement (adults: β=584, 95% CI –784 to 1952; n=19; youth: β=941, 95% CI –827 to 2709; n=11) and those who were meeting less than two-thirds of the physical activity guideline at baseline (adults: β=609, 95% CI –30 to 1247; n=47; youth: β=1406, 95% CI –94 to 2907; n=22). Conclusions: Personalized mHealth physical activity interventions integrating wearable step trackers with automated text messaging appear to be feasible and acceptable among adults and youth from low-income communities. Step-count findings show promise for the intervention’s ability to support individuals who are further from meeting physical activity guidelines and warrant more research among parent–child dyads. Overall, findings support additional research to optimize and evaluate similar interventions within this population group using fully powered randomized controlled trials. Trial Registration: ClinicalTrials.gov NCT05110508; https://clinicaltrials.gov/ct2/show/NCT05110508

The Impact of Sunlight and Artificial Light at Night on Sleep Stages: Evidence From a 7-Day Sensor-Based Observational Study

2026-06-05T16:00:03-04:00

Background: Exposure to circadian entrainers, such as sunlight, positively impacts sleep architecture, while exposure before bedtime to circadian disruptors, such as artificial light and smartphone use, can negatively affect sleep. However, real-world evidence from longitudinal observational studies that simultaneously capture these factors alongside electroencephalography-derived sleep stages remains limited. Objective: This study aimed to investigate the effects of specific environmental and behavioral factors on sleep metrics and architecture by using sensor-based measurements over 7 consecutive days. Specifically, it examined day-to-day associations between (1) daytime sunlight exposure and (2) prebedtime artificial light exposure and smartphone use with selected sleep outcomes on the following night. Methods: A total of 21 participants from the Jerusalem metropolitan area were monitored continuously using the Dreem wearable electroencephalography for sleep staging, HOBO data loggers for light exposure, the wGT3X+ triaxial accelerometer for physical activity, and a dedicated mobile app to record smartphone usage. Sleep outcomes included total sleep time (TST), sleep onset latency (SOL), and the proportions of light sleep (N1) and deep sleep (N3). Sunlight exposure was defined as the number of hours above 1000 lux during daytime, and artificial light and smartphone use before bedtime were quantified as the duration of exposure accumulated in the 2 hours preceding sleep onset. Linear mixed-effects models with a random intercept at the individual level estimated the associations between these exposures and next-night sleep outcomes, adjusting for step count and other individual covariates. Results: The average TST was 420 (SD 85) minutes, and SOL averaged 17.6 (SD 18) minutes. Light sleep (N1) represented 6.6% (SD 2.1%) of sleep, and deep sleep (N3) accounted for 20.1% (SD 7.6%). Each additional hour of daytime sunlight exposure was associated with an increase of 10.67 (95% CI 0.6-20.7) minutes in TST the following night and with a 0.3 (95% CI –0.6 to –0.0) percentage-point decrease in light sleep (N1) percentage. No associations were found between evening artificial light exposure and sleep outcomes, while each minute of smartphone use before bedtime was linked to an increase in SOL of 0.2 (95% CI 0.0-0.4) minutes. Conclusions: These findings emphasize the importance of daylight exposure for circadian alignment and the potential sleep-disruptive effects of evening digital engagement. This study demonstrates the feasibility and value of integrating wearable electroencephalography and environmental and behavioral sensors in naturalistic settings to uncover behavioral and environmental correlates of sleep architecture.

The Role of Videoconferencing Teleconsultation in Improving Transfer Efficiency and Functional Outcomes in Rural Stroke Care: Retrospective Cohort Study

2026-06-04T12:45:15-04:00

Background: Interhospital transfer delays remain a major barrier to timely reperfusion therapy and are associated with worse functional outcomes in acute ischemic stroke (AIS), particularly in rural regions. Objective: This study evaluated whether videoconferencing teleconsultation, compared with the standard referral process, was associated with improved transfer efficiency, treatment delivery, and functional outcomes for patients with AIS requiring interhospital transfer in a hub-and-spoke model. Methods: We conducted a retrospective cohort study of patients with AIS identified as potential candidates for endovascular thrombectomy (EVT) who were transferred from a primary stroke center (PSC) to a comprehensive stroke center (CSC) between January 2022 and December 2024. Patients were managed using either videoconferencing teleconsultation or the standard referral process, defined as telephone-based consultation between emergency physicians at the PSC and CSC, in which clinical evaluation and thrombolysis decisions were made primarily by the PSC emergency physicians. Group allocation was determined via institutional workflow. The primary outcome was door-in-door-out time, with additional analyses on its components. Secondary outcomes included intravenous thrombolysis rate at the PSC, EVT rates at the CSC, door-to-puncture time, reperfusion rates, and 90-day functional outcomes assessed via modified Rankin Scale shift analysis. Safety outcomes included all-cause mortality within 90 days and symptomatic intracranial hemorrhage after intravenous thrombolysis and/or EVT. Results: A total of 83 patients were included, with 41 (49.4%) in the teleconsultation group and 42 (50.6%) in the standard referral process group (mean age 73.3, SD 12.9 years), and baseline characteristics were comparable. Teleconsultation was associated with a significant reduction in door-in-door-out time (mean 95.2, SD 22.9 vs 132.3, SD 41.5 minutes; <.001) by shortening computed tomography angiography–to-ambulance notification time (mean 44.6, SD 17.4 vs 79.5, SD 37.6 minutes; <.001). The teleconsultation group had higher intravenous thrombolysis rates at the PSC (26/41, 63.4% in the teleconsultation group vs 17/42, 40.5% in the standard referral process group; =.04), higher EVT rates (14/41, 34.1% in the teleconsultation group vs 6/42, 14.3% in the standard referral process group; =.03), and shorter door-to-puncture time (mean 83.0, SD 35.5 vs 118.5, SD 25.9 minutes; =.04) at the CSC. Patients who received teleconsultation demonstrated a greater shift toward better functional outcomes at the 90th day (27/41, 65.9%; odds ratio 4.55, 95% CI 1.96-11.11; <.001) than patients who did not (13/42, 31.0%; odds ratio 1.35, 95% CI 0.63-2.94; =.07). Safety outcomes were comparable between groups. Conclusions: Videoconferencing teleconsultation was associated with improved transfer efficiency and higher use of reperfusion therapies and was potentially associated with better functional outcomes. This model may represent a feasible strategy for optimizing stroke care pathways in rural settings. Future studies are warranted to assess its applicability in broader stroke populations beyond conventional EVT eligibility criteria across multicenter networks.

Managing BMI and Emotional Distress Using mHealth: Nationally Representative Survey Study

2026-06-03T15:45:16-04:00

Background: Mobile health (mHealth) technologies, including smartphone health apps and wearable trackers, are increasingly used to promote health behaviors. However, their impact on physical and mental well-being remains complex, with both benefits and potential unintended negative consequences. Objective: This study aimed to examine the relationship between mHealth use (ie, health app and wearable tracker) and 2 health outcomes (BMI and emotional distress), as well as the mediating roles of healthy eating, sleep, and physical activity based on a representative sample. Methods: We analyzed data from a nationally representative sample of US adults aged 33 to 43 years (N=1931). Chi-square tests and 1-way ANOVA were used to compare demographic differences between mHealth users and nonusers. A path model examined the relationship between mHealth use (ie, smartphone health apps and wearable trackers) and health outcomes (ie, BMI and emotional distress), with lifestyle factors (ie, healthy eating, physical activity, and sleep) as mediators. Mediation analyses tested indirect effects through these lifestyle factors. Results: mHealth users were more likely to be female, married, have higher levels of education and income, and have health insurance. The primary use of mHealth was the management of physical activity. Smartphone health app use positively correlated with wearable tracker use (β=.394; <.001). Smartphone health app use predicted greater BMI (β=.068; =.006), whereas wearable tracker use did not significantly predict BMI. Smartphone health app use was unrelated to emotional distress, while wearable tracker use was associated with lower emotional distress (β=–.074; =.003). Mediation analyses showed that physical activity negatively mediated the relationships between both types of mHealth use and health outcomes, indicating that mHealth users were more physically active, which was linked to lower BMI and emotional distress. Sleep hours mediated only the association between wearable tracker use and health outcomes, such that greater tracker use was related to fewer sleep hours, predicting higher BMI and emotional distress. Finally, healthy eating mediated only the associations between mHealth use and emotional distress, suggesting that healthier dietary behaviors among mHealth users contributed to lower emotional distress. Conclusions: mHealth technologies can potentially promote healthier behaviors, but their effectiveness depends on users taking the initiative to sustain lifestyle changes. While wearable trackers may aid in mental well-being, their association with reduced sleep warrants further investigation.

Terminal Digit Preference and Threshold Avoidance in Digital Blood Pressure Measurements During Pregnancy: Secondary Analysis of Data From the CLIP and PRECISE Cohorts

2026-06-03T15:15:16-04:00

Background: Screening for, detecting, and managing pregnancy hypertension is a core function of antenatal care. To reduce both training requirements and the risks of measurement error in blood pressure (BP) values, automated and semiautomated BP devices have been validated in pregnant women with normal BP and pregnant women with hypertension and introduced for serial antenatal measurement of BP. Objectives: The study aimed to (1) determine whether or not repeated BP measurements reduced the presence of terminal digit preference and (2) discern whether or not there was evidence of threshold avoidance in the Community-Level Interventions for Preeclampsia (CLIP) trials compared with the purely observational Pregnancy Care Integrating Translational Science, Everywhere (PRECISE) cohorts. Methods: The BP 3AS1-2 and CRADLE Vital Signs Alert low-cost Microlife BP devices were used by trained research staff in the CLIP trials conducted in India, Mozambique, Nigeria (pilot trial only), and Pakistan and the PRECISE cohorts of unselected pregnant women and nonpregnant women of reproductive age recruited in the Gambia, Kenya, and Mozambique. Both devices algorithmically calculate systolic blood pressure and diastolic blood pressure values displayed on digital read-outs. All BP readings were entered manually into a digital platform, which averaged them as the BP for that visit; the first and second readings were averaged unless they were more than 10 mm Hg different, which triggered a third reading, and the second and third readings were averaged. Results: A total of 51,875 participants had their BP measured 438,404 times. Using raw BP values, there was terminal digit preference (129,539/911,500, 14.21% vs 10%; <.001 values ended in zero). A total of 28,929 out of 437,446 (6.61%) dBP values were 62 mm Hg, compared with 9310 of 195,349 (4.77%) from the averaged values (<.001); errors were obviated by averaging BP values. There was evidence of both threshold preference and avoidance in the CLIP trials and the PRECISE cohort. Conclusions: Given the excess of 62 mm Hg values, there is a shared inherent algorithmic error in the calculation of dBP in the BP 3AS1-2 and CRADLE Vital Signs Alert devices. Averaged BP measurements are important to reduce the impact of user errors in manually recording BP values. We recommend that automated and semiautomated BP devices should be connected wirelessly to automatically transfer readings to digital health records to further optimize care. Trial Registration: ClinicalTrials.gov NCT01911494; https://clinicaltrials.gov/study/NCT01911494

Digital Health Apps and Web-Based Platforms to Support the Prevention and Management of Snakebite Envenoming: Scoping Review

2026-06-02T16:30:04-04:00

Background: Neglected tropical diseases disproportionately affect underserved populations, with snakebite envenoming (SBE) remaining one of the most overlooked, despite its significant global burden. Digital health applications (DHAs) offer potential to improve prevention, care, and resource management, especially when integrated into digital health interventions. However, despite growing interest, evidence and structured evaluations are limited, making it difficult to assess their impact without a clear overview of existing tools. Objective: This scoping review aims to provide the first comprehensive mapping of DHAs for SBE, highlighting their potential to strengthen the World Health Organization (WHO) strategy while underscoring the urgent need for structured evaluation, improved quality, and strategic integration to enhance prevention, treatment, and coordination efforts. Methods: This review followed the Joanna Briggs Institute and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, with a protocol registered on the Open Science Framework. We searched the PubMed database, app stores, and Google for DHAs between September 24 and 26, 2024, addressing snakebite prevention or treatment. To be included, the DHA had to be accessible via the recorded link, contain a description with snakebite-related features (eg, identification, first aid, and treatment), and allow user interaction. Descriptions had to appear in abstracts, app store listings, or website text. Results were grouped by type (mobile- or web-based) and by WHO region. Furthermore, we examined the 2 most common features: first aid and snake identification. First aid content was benchmarked against global guidelines, while identification methods were categorized, and selected artificial intelligence (AI)–based identification apps were exploratively tested using images of medically significant snakes. Results: A total of 52 eligible results were included, of which 94.2% (49/52) were mobile apps and 5.8% (3/52) were web-based. Regional focus varied, with most apps targeting South-East Asia (n=11, 21.2%), the Americas (n=9, 17.3%), and the Western Pacific (n=5, 9.6%). However, these numbers largely reflect concentration in just a few countries, namely India (n=10, 19.2%), the United States (n=5, 9.6%), and Australia (n=5, 9.6%). The most frequent feature was snake identification support, for example, through photo upload and algorithm-based recognition. However, AI-driven identification often lacked clarity and performed inconsistently in testing. First aid guidance was also common, but only a handful of apps offered comprehensive, evidence-based advice, while others omitted key steps or recommended unsafe practices. Conclusions: This review provides the first structured evaluation of DHAs for SBE and offers a reproducible framework for assessing digital solutions across neglected tropical diseases. By highlighting key gaps and proposing a foundation for integration into national strategies, it supports the development of equitable, evidence-based digital health innovation in underserved areas. Trial Registration: OSF Registries osf.io/2zsfu; https://osf.io/2zsfu

Patient-Centered Lupus Erythematosus Mobile Apps: Systematic Search and Cross-Sectional Evaluation by Patients and Physicians

2026-05-29T15:45:16-04:00

Background: Lupus erythematosus (LE) is a chronic autoimmune disease that significantly impacts patients’ quality of life. Photosensitivity is a key impairment that severely limits the quality of life, especially in cutaneous lupus erythematosus (CLE), where exposure to sunlight can lead to rashes, exacerbations, and pain. In systemic lupus erythematosus (SLE), other manifestations such as joint pain, fatigue, and organ damage may contribute to decreased physical function and emotional distress. Mobile health apps (MHA) offer potential support for comprehensive disease management for the symptoms mentioned above. However, there is a lack of systematic analysis of available lupus management apps. Objective: This study aims to systematically identify publicly available German or English MHA for lupus management as well as to assess their quality by surveying both patients and physicians. Methods: A systematic search and assessment of German or English mobile apps for patients with lupus, available in the Google Play Store and Apple App Store, was conducted independently by two reviewers. The two apps that met all relevant criteria were then reviewed independently by seven physicians using the German Mobile Application Rating Scale (MARS) and the System Usability Scale (SUS). Subsequently, they were reviewed by five patients (three with SLE and two with CLE), using the user version of MARS (uMARS) and SUS. Additionally, the Affinity for Technology Interaction (ATI) scale was collected from both patients and physicians to evaluate the technical affinity in both groups. Results: In total, 29 apps were available on the Apple Store and 26 on the Google Store, with 18 apps being present and downloadable on both platforms. Of the 18 apps, 16 were excluded because they did not meet the inclusion and exclusion criteria. Only two apps, and met all the required criteria and were included in the study. The mean MARS scores varied from 2.61/5 to 4.17/5 and mean SUS from 17.5/100 to 100/100 between physicians. The app with the highest mean overall MARS score was , which was rated with 3.91/5 on average by the physicians. Patients evaluated the app with a comparably mean uMARS score (3.95/5). Technical affinity, objectified by ATI, was higher in patients than physicians (3.9 vs 3.68). Conclusions: Systematic identification and evaluation showed high-quality apps for patient-centered lupus MHA as indicated by MARS and uMARS scores greater than 3 for both and .

Acceptability, Feasibility, and Outcome Responsiveness of the Joint Effort Mobile App for Promoting Lower-Risk Cannabis Use Among Young Adults: Pilot Randomized Controlled Trial

2026-05-28T16:30:16-04:00

Background: Cannabis use (CU) among young adults continues to be an important public health issue. Interventions to support lower-risk CU during young adulthood can improve health outcomes. Mobile applications constitute a promising mode of service delivery. However, there is a lack of evidence-based apps specifically developed for young adult cannabis users. Objective: This study aimed to evaluate the acceptability of a novel mobile app intervention (Joint Effort) and to assess the feasibility and outcome responsiveness of the study procedures used. Methods: A pilot study with a parallel-group randomized trial design was conducted with Canadian-based university students aged 18‐30 years reporting using cannabis ≥1 day in the past month. Participants were randomly assigned on a 1:1 ratio to either an experimental group (EG) involving the use of the Joint Effort mobile app or to a control group (CG) involving a web-based brief normative feedback message. The Joint Effort mobile app was designed to support CU self-management. This theory-informed behavior change intervention aims to reinforce the use of protective behavioral strategies by targeting intention, attitude, social norms, and self-efficacy. The app’s acceptability was assessed via uptake, engagement, and appreciation. The feasibility of study procedures was assessed via recruitment time, recruitment rate, and attrition rate. Outcome responsiveness was informed by participant-reported outcomes: CU frequency, intention to take action on CU, protective behavioral strategies use, severity of dependence, and psychological distress. All data were collected using a web-based survey at baseline, one-month (T1), and 2-month (T2) postbaseline. Descriptive analyses were carried out on all outcomes. Results: The recruitment period lasted 124 days, and the recruitment rate was 56% (99/178). The final dataset analyzed included 80 participants (39 in EG and 41 in CG). Mean age was 23.4 (SD 2.6) years, and 66% (53/80) self-identified as women. Study attrition was 18% (14/80). User uptake of the Joint Effort app (ie, proportion of participants in the EG who downloaded the app) was estimated at 59% (23/39), and the average time spent on it per participant was 8.2 minutes (SD 7.3; median 7.5, IQR 5.7). The app obtained a mean total score on the User Engagement Scale-Short Form of 3.8/5 (SD 0.5) and a mean app quality total score of 4.2/5 (SD 0.5) on the end user version of the Mobile App Rating Scale. The proportion of participants who reported daily CU in the past month decreased from 13% (5/39) at baseline to 4% (1/24) at T2 in the EG and from 7% (3/41) to 6% (2/36) in the CG. Conclusions: Joint Effort appears to be a promising, acceptable, and scalable mobile app to help young adult cannabis users who wish to better manage their CU. Findings should inform future randomized controlled trials to assess the efficacy of this mobile-based intervention for cannabis users. Trial Registration: ClinicalTrials.gov NCT05099016; https://clinicaltrials.gov/study/NCT05099016

Effects, Acceptability, and Use of a Dynamically Tailored Mobile What Do You Drink Intervention to Reduce Excessive Drinking Among Adolescents and Young Adults in the Netherlands: Randomized Controlled Trial

2026-05-26T18:15:14-04:00

Background: Excessive alcohol consumption among adolescents and young adults is a serious health problem. Dynamically tailored interventions could reduce their excessive drinking. We therefore developed “What Do You Drink” (WDYD), a 17-week dynamically tailored mHealth (mobile health) intervention providing personalized support on alcohol consumption. Objective: We aim to evaluate the effectiveness, acceptability, and use of WDYD in reducing alcohol consumption of adolescents and young adults at risk. Methods: We conducted a 2-arm, parallel-group randomized controlled trial using ecological momentary assessments. Recruitment was via an educational alcohol program, an online lifestyle monitor, social media advertisements, or news items on websites. Participants downloaded the standalone WDYD app, and when having given active informed consent, were randomized to the intervention or control group. Participants in the intervention group received dynamically tailored feedback sessions on alcohol consumption (wk 0‐5, 7, 9, 13, and 17) and goal-monitoring reminders. Both groups completed an online baseline survey, 2 follow-up surveys (wk 9 and 33), and various ecological momentary assessments (7 daily assessments during wk 1, 7, 13, 19, 25, 31, and 33). Participants provided consent before randomization, in which they were informed that 2 study groups existed. After randomization, no disclosure of group assignment was provided, although participants could potentially infer it from receiving tailored sessions vs no tailored sessions. Primary outcomes were excessive drinking, binge drinking, and weekly alcohol consumption. Secondary outcomes were intrinsic motivation, self-confidence, and mood. Acceptability of WDYD was measured by survey questions; use was tracked via app data logs. Results: Analyses were based on data from 1767 participants; 720 in the intervention group and 1047 participants in the control group. Almost half of them were female (2276/4795, 47.5%), and most (3471/4595, 72.4%) participants were aged 18‐24 (median 19.40, IQR 2.92) years. The dropout rate was high, up to 96% (4603/4795) in the final 33rd week. No significant effect of WDYD was found on primary outcomes and mood, except for week 1 (excessive drinking: standardized =−0.35, SE 0.15; 95% CI −0.64 to −0.05; binge drinking: standardized =−0.36, SE 0.16; 95% CI −0.68 to −0.04; mood: standardized =0.20, SE 0.06, 95% CI 0.08 to 0.32). Both groups reduced their alcohol consumption. Significant positive effects were found for intrinsic motivation and self-confidence up to 25 weeks (wk 25: standardized =0.54, SE 0.24; 95% CI 0.06 to 1.02 for motivation; standardized =0.72, SE 0.26; 95% CI 0.22 to 1.23 for self-confidence). Participants evaluated WDYD as acceptable and usable. Conclusions: WDYD did not significantly reduce excessive drinking compared to control, but improved motivation and self-confidence. High dropout rates highlight challenges in sustaining engagement in long-term mHealth interventions. Future research should explore strategies to enhance retention and optimize dynamic tailoring. Trial Registration: ICTRP NL-OMON28135; https://trialsearch.who.int/Trial2.aspx?TrialID=NL-OMON28135

Reducing Sedentary Time After Knee Replacement Using a Multicomponent mHealth Intervention: Randomized Controlled Trial

2026-05-26T16:31:00-04:00

Background: Total knee replacement (TKR) is a common surgery for end-stage knee osteoarthritis. Although reductions in pain and improvements in mobility occur after surgery, physical activity levels often do not change. Given the challenges of increasing physical activity in this population, targeting reductions in sedentary behavior may be a first step; however, no prior studies have examined the feasibility and effects of a sedentary reduction intervention after TKR. Objective: This study examined the effects of a 2-month multicomponent mobile health sedentary reduction intervention (NEAT!2) on sedentary time in adults with TKR. Methods: Adults (N=83; mean age 65.3, SD 9.4 years; mean BMI 32.7, SD 6.9 kg/m²; 62/83, 74.7% female; 64/83, 77.1% White) with a TKR ≤1 year ago were randomized to the NEAT!2 group (n=42, 50.6%) or the attention-matched control group (n=41, 49.4%). The NEAT!2 intervention focused on reducing sedentary time via a smartphone app designed to interrupt prolonged bouts (≥30 minutes) of sedentary behavior and through 5 coaching calls emphasizing goal setting and problem solving. The control group focused on surgery recovery via an app or website and 5 educational calls. Sedentary time, total physical activity, physical function, and pain were measured at 2 and 5 months. Linear mixed-effects models examined intervention effects and differences between groups at each time point. Results: Retention was 96% and 95% at 2 and 5 months, respectively, with no differences between groups. Participants in the NEAT!2 group completed an average of 4.95 (SD 0.2) calls, used the app on an average of 40.3 (SD 13.8) days (out of 56 days), and received an average of 9.6 (SD 6.0) notifications per day. At 5 months, there was a significant increase in sit-to-stand transitions in the NEAT!2 group and a substantial decrease in the control group, resulting in a significant difference between groups (mean difference 4.06, 95% CI 0.13-7.99; P=.04); however, the NEAT!2 intervention did not result in significant effects on any of the other study outcomes at 2 or 5 months. Additionally, more days of app use were associated with greater increases in moderate-to-vigorous intensity physical activity (r=0.335; 95% CI 0.017-0.585; P=.04). Conclusions: This study highlights the challenges of reducing sitting time in adults with TKR. Future studies should explore alternative behavior change techniques across different levels of influence (eg, environmental and social) to support interventions implemented within the first year after TKR. Trial Registration: ClinicalTrials.gov NCT04482400; https://clinicaltrials.gov/ct2/show/NCT04482400