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. 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. 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. 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; P<.001). Smartphone health app use predicted greater BMI (β=.068; P=.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; P=.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. 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.
Sedentary employees face increased chronic health risks due to physical inactivity, immobility, and unhealthy eating behavior. Although mobile health (mHealth) interventions show promise in improving lifestyle behaviors, their effectiveness in occupational settings remains underexplored. Building on previous workplace interventions, this study developed and evaluated a mobile-enabled web app, SIMPLE HEALTH, developed with Din-J Design Co, Ltd, integrating activity tracking, healthy eating, and behavioral support for sedentary employees. This study evaluated the short-term effects of a 12-week mHealth intervention on physical activity, sedentary behavior, dietary habits, and cardiometabolic health indicators among sedentary employees in Taiwan. A 2-arm quasi-experimental study was conducted at 2 aerospace industrial workplaces. A total of 101 sedentary employees (mean age 46.9, SD 12.2 years; 52/101, 51.5% female) were enrolled from 2 worksites that were assigned by coin toss to either the intervention condition (n=50) or the control condition (n=51). The intervention group participated in the SIMPLE HEALTH program, an mHealth intervention grounded in Social Cognitive Theory and the Ecological Model, consisting of 8 components: activity tracking, goal setting, behavior logging, reminders, personalized advice, educational and motivational electronic booklets, and individual and team challenges. The control group received 6 print educational booklets. Cardiometabolic biomarkers, objectively measured physical activity (Fitbit Charge 3; Fitbit Inc), occupational sitting (occupational sitting and physical activity questionnaire), and dietary behavior (3-day photographic food records and the healthy eating behavior inventory) were assessed at baseline and 12 weeks. Data were analyzed using generalized estimating equations following the intention-to-treat principle. At 12 weeks, the intervention group showed a significant increase in step counts (adjusted mean difference, MD 1227.13, 95% CI 2.90-2451.36; P=.049), a more favorable between-group change in moderate physical activity (adjusted MD 0.17, 95% CI 0.01-0.33; P=.04), and favorable dietary behaviors, including reduced intake of calories (adjusted MD -144.59, 95% CI -276.57 to -12.60; P=.03), carbohydrates (adjusted MD -19.88, 95% CI -37.99 to -1.78; P=.03), fats (adjusted MD -6.99, 95% CI -13.69 to -0.29; P=.04), and grains (adjusted MD -1.46, 95% CI -2.43 to -0.50; P=.003), and increased vegetable intake (adjusted MD 0.47, 95% CI 0.06-0.88; P=.02), compared to the control group. Favorable trends were noted in diastolic blood pressure (adjusted MD -2.38, 95% CI -4.99 to 0.22; P=.07) and soft lean mass (adjusted MD 0.34, 95% CI -0.06 to 0.75; P=.10). Both groups showed significant within-group improvements in low-density lipoprotein cholesterol (intervention: P=.01; control: P=.03), body fat percentage (intervention: P<.001; control: P=.01), waist circumference (intervention: P=.001; control: P=.002), and occupational sitting (intervention: P<.001; control: P=.03), and occupational walking (intervention: P=.01; control: P=.046), but between-group differences were nonsignificant. The 12-week mHealth intervention improved physical activity and dietary behaviors and showed favorable trends in cardiometabolic indicators among sedentary employees. These findings support integrating mHealth programs into employee wellness initiatives to promote healthy behaviors, mitigate productivity loss, and reduce chronic disease burden. Further research should assess long-term sustainability, scalability, and cost-effectiveness in diverse occupational settings.
Anemia is a global health concern. It is disproportionately prevalent among pregnant women in low-resource regions, where iron deficiency is the leading cause. Given the multifactorial nature of anemia, a range of nutritional interventions is recommended. However, effective implementation is often hindered by limited health care access, poor adherence to supplementation, and gaps in nutrition knowledge and counseling. To address these challenges and optimize hemoglobin (Hb) levels among pregnant women, mobile health (mHealth)-based nutritional interventions offer a promising alternative. The aim of the study is to review available evidence on the effectiveness of mHealth-based nutritional interventions on iron status (Hb and/or serum ferritin concentration) among pregnant women. Searches were conducted in Embase, CINAHL, Cochrane Library, PubMed, Web of Science, and Scopus, and supplemented by snowballing to identify additional relevant studies from citation lists. The key search strings comprised 4 concepts: "mobile health," "nutritional intervention," "Hb, anemia or iron deficiency anemia," and "pregnant women." Predefined inclusion and exclusion criteria were applied during screening. The methodological quality of included studies was assessed using the Risk of Bias 2 tool. The primary end point was the change in mean Hb concentration or serum ferritin level. Effect sizes (ESs) were calculated as standardized mean differences, including Cohen d and Hedges g. Of the 14,284 studies identified, only 11 randomized controlled trials were included. These studies used various modes of delivery, including mobile phone calls (n=1), SMS text messaging (n=3), and mobile apps (n=4), with some using more than 2 modes (n=3). The effect of mHealth-based nutritional interventions on iron status varied significantly. In total, 4 studies demonstrated a large ES (>0.8), with 3 relying on WhatsApp Messenger as an mHealth delivery mode. Approximately 82% (9/11) of the included studies reported a positive effect (P values ranging from <.001 to .047) of the intervention on Hb level, whereas 2 studies reported no statistically significant association (P=.33 and P=.35, respectively). Notably, interventions with the largest ES achieved clinically significant improvements in Hb concentration, with within- and between-group differences exceeding 1 g/dL. However, including behavioral change theories and nutrition-sensitive components was not consistently associated with larger ESs. Due to high heterogeneity (I2>95%), attributed to variations in mHealth delivery modes, functions, and interactive features across the included studies, meta-analysis could not be performed. This review demonstrates that mHealth-supported nutritional interventions effectively optimize Hb concentration in pregnant women. While SMS text messaging was less effective in improving Hb concentration, combining it with another mHealth delivery mode, such as phone calls, improved intervention effectiveness. However, the variability in mHealth delivery modes, functions, and interactive features underscores the need for tailored strategies that account for context-specific challenges, digital literacy, and access to technology to enhance effectiveness.
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. 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. 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. 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). 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. ClinicalTrials.gov NCT05110508; https://clinicaltrials.gov/ct2/show/NCT05110508.
In the current digital landscape, ensuring optimal usability is one of the most crucial factors determining the success of any mobile app. Questionnaire-based usability evaluations represent a highly prevalent methodology for this purpose. To date, questionnaires have been developed to assess the general system usability; however, there are hardly any questionnaires specifically designed to assess the usability of mobile health (mHealth) apps. The most widespread, the mHealth App Usability Questionnaire (MAUQ), has been developed in 4 versions according to the type of app (interactive or standalone) and the target user (patient or provider). The objective of this study was to translate and validate the English version of the MAUQ (standalone, for patients) into a Spanish version (S-MAUQ). The methodology used here follows that proposed by Sousa and Rojjanasrirat, which comprises 4 stages. The initial stage of the process entails a translation, harmonization, and adaptation procedure. The second and third entailed content validation (by 10 experts) and face validation (by 12 target users), respectively, which were conducted to evaluate the relevance and clarity of the questionnaire items. The item-level content validity index, scale content validity index (S-CVI), item-level face validity index, and scale face validity index (S-FVI), as well as the modified kappa statistic (κ) were used to evaluate interrater agreement among the raters, considering the probability of agreement by chance (Pc). The fourth and final stage of the process involved the assessment of the questionnaire's reliability. A sample of 61 young adult participants installed an mHealth app (the Yazio app), used it, and responded to the S-MAUQ. The Cronbach α value for the entire questionnaire and its subscales were then calculated. For the second stage, the S-CVI was initially 0.778. We removed items #14 and #15 from the Spanish version as they were unclear and not relevant. The S-CVI changed to 0.881. The third stage had an S-FVI of 0.927, indicating that the items are clear and straightforward for the nonexpert target user to understand. Furthermore, with each κ value >0.74, the validity of the instrument is supported. The fourth stage demonstrated the reliability of the S-MAUQ with a Cronbach α value of 0.87. The final version of the S-MAUQ met the validation criteria, demonstrating reliability and validity that are comparable with those of the original version. Consequently, the S-MAUQ is suitable for evaluating the usability of mHealth apps for young Spanish adults. Further research involving larger and more diverse samples is recommended.
Frailty is highly prevalent in survivors of multiple myeloma (MM) after autologous hematopoietic cell transplantation and is associated with poor functional recovery and adverse clinical outcomes. Although exercise is known to improve physical function, traditional center-based rehabilitation models are often inaccessible to this population during early posttransplant recovery. Mobile health (mHealth)-supported exercise may offer a scalable alternative; however, evidence in hematologic malignancies remains limited. This study aimed to evaluate the effects of a 16-week mHealth-supported exercise rehabilitation program on frailty phenotype and physical function in survivors of MM within 180 days after autologous hematopoietic cell transplantation. In this single-center randomized controlled trial, participants who self-reported as prefrail or frail were randomized 1:1 to an mHealth-supported exercise group (n=16) or usual care control (n=16). Remote assessments were conducted at baseline (week 0), midpoint (week 9), and follow-up (week 17). The intervention consisted of 8 weeks of supervised tele-exercise (3 sessions/week, 50 minutes/session), followed by 8 weeks of independent home-based exercise using the same mHealth platform. Exercise intensity was prescribed using a repetitions-in-reserve-based rating of perceived exertion approach with symptom-guided progression. The primary outcome was change in the 5-component Fried frailty phenotype score (0-5). Secondary outcomes included Short Physical Performance Battery components, chair stand time, gait speed, and handgrip strength. Intention-to-treat analyses were conducted using generalized estimating equations to evaluate between-group differences over time. Participants had a mean age of 64.6 (SD 7.1) years and were enrolled a mean of 136 (SD 36.3) days posttransplant. At baseline, 94% (30/32) of participants were classified as frail. Adherence to the supervised sessions was 85% (326/384 sessions), and adherence during the unsupervised phase was 78% (298/384 sessions). The exercise group demonstrated a significantly greater reduction in frailty score compared with control from baseline to week 17 (P<.001). Between-group difference estimates showed a clinically meaningful improvement favoring exercise at both week 9 and week 17 (P<.001). Chair stand time improved significantly in the exercise group compared with control, with faster completion times observed at week 9 and sustained through week 17 (P=.002). Improvements in other Short Physical Performance Battery components and handgrip strength favored the exercise group but did not reach statistical significance. No serious adverse events occurred. A 16-week mHealth-supported, progressively prescribed exercise rehabilitation program was feasible, safe, and effective in reversing frailty phenotype and improving functional mobility in survivors of MM early after autologous transplantation. This approach provides a scalable model for delivering structured rehabilitation during a high-risk recovery window. Larger trials incorporating attention-matched controls and longer follow-up are warranted. ClinicalTrials.gov NCT05142371; https://clinicaltrials.gov/study/NCT05142371.
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. We aim to evaluate the effectiveness, acceptability, and use of WDYD in reducing alcohol consumption of adolescents and young adults at risk. 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. 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 β=-0.35, SE 0.15; 95% CI -0.64 to -0.05; binge drinking: standardized β=-0.36, SE 0.16; 95% CI -0.68 to -0.04; mood: standardized β=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 β=0.54, SE 0.24; 95% CI 0.06 to 1.02 for motivation; standardized β=0.72, SE 0.26; 95% CI 0.22 to 1.23 for self-confidence). Participants evaluated WDYD as acceptable and usable. 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.
Mobile health (mHealth) technologies are increasingly promoted as tools for chronic disease management and healthy aging, yet adoption remains persistently uneven across demographic groups. Japan, where 29.1% of the population is 65 years or older-the highest proportion globally-exemplifies the challenges of mHealth promotion in super-aging societies. Despite high smartphone penetration (90.1%) and active national digital transformation initiatives, only 21.6% of Japanese adults report regular mHealth app use, with marked disparities by age and sex. This study examined determinants of mHealth acceptance by extending the unified theory of acceptance and use of technology to incorporate eHealth literacy, self-efficacy, perceived risk, distrust, and health-related factors (health status and health interest). Age- and sex-specific differences in acceptance mechanisms were also investigated using multigroup structural equation modeling (SEM). We conducted a cross-sectional online survey in November 2023 with 960 Japanese adults sampled across 7 age strata (aged 18-27 years to aged ≥78 years). SEM tested hypothesized relationships among 9 constructs. Health status and health interest were included as observed covariates. Multigroup SEM with configural, metric, and structural invariance testing examined age- and sex-specific differences, and binary logistic regression identified predictors of current mHealth app use. The structural model demonstrated good fit (χ2/df=2.06; comparative fit index 0.953; Tucker-Lewis index 0.945; root mean square error of approximation 0.047) and explained 71.6% of the variance in behavioral intention. Effort expectancy (β=0.404), facilitating conditions (β=0.349), and performance expectancy (β=0.188) were the primary proximal predictors of behavioral intention. Social influence exerted strong upstream effects on effort expectancy (β=0.811), eHealth literacy (β=0.507), and self-efficacy (β=0.422). Health interest positively influenced performance expectancy (β=0.133), whereas neither health interest nor health status showed a significant direct effect on distrust. Distrust did not directly predict behavioral intention in the overall sample. Multigroup analyses identified 5 significant age differences and 5 sex differences. eHealth literacy increased distrust among young adults but reduced perceived risk among middle-aged and older adults. Self-efficacy negatively predicted performance expectancy among young adults yet positively predicted it among middle-aged and older adults. Distrust significantly reduced behavioral intention only among middle-aged adults. mHealth acceptance in Japan's aging society is characterized by stable proximal determinants of behavioral intention alongside heterogeneous upstream belief formation processes that vary systematically by age and sex. Health interest, rather than health status, emerged as the key contextual driver of perceived usefulness. At the theoretical level, this study clarifies how eHealth literacy, self-efficacy, and distrust function as age- and sex-contingent antecedents within an extended unified theory of acceptance and use of technology framework. At the practical level, these findings highlight the need for trust-centered, demographically tailored, and literacy-sensitive strategies to promote equitable mHealth adoption in rapidly aging societies.
Patients with head and neck cancer (HNC) frequently experience functional impairments and psychological distress following surgery or radiotherapy. While mobile health (mHealth) interventions are increasingly integrated into clinical care to support patient self-management and home-based recovery, evidence of their effectiveness in HNC remains inconsistent. This study aims to evaluate the effectiveness of mHealth interventions on quality of life (QoL), psychological symptoms, physical symptoms, and functional recovery among HNC survivors. This systematic review and meta-analysis followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Overall, 12 electronic databases were searched for randomized controlled trials published up to December 1, 2025. Two reviewers independently performed study selection, data extraction, and risk of bias assessment using the Cochrane Risk of Bias 2.0 tool. Certainty of evidence was evaluated using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) approach. Pooled effects were calculated as standardized mean differences (SMDs) with 95% CIs. The primary outcome was QoL; secondary outcomes included anxiety, depression, fatigue, pain, swallowing function, and maximal interincisal opening (MIO). A total of 26 randomized controlled trials involving 2385 participants were included. mHealth interventions significantly improved QoL (SMD 0.64, 95% CI 0.41-0.88; P<.001), anxiety (SMD -0.75, 95% CI -1.42 to -0.08; P=.03), depression (SMD -0.89, 95% CI -1.37 to -0.40; P<.001), and fatigue (SMD -0.85, 95% CI -1.19 to -0.51; P<.001). Pain showed a small reduction (SMD -0.37, 95% CI -0.49 to -0.24; P<.001). However, the improvement in swallowing function reached only borderline significance (SMD 0.66, 95% CI 0.28-1.04; P=.04), suggesting that current evidence for this outcome is fragile. No significant effect was found for MIO (P=.68). Subgroup analysis revealed that interventions featuring home practice support, self-monitoring, and shorter durations (<3 months) yielded stronger clinical effects. The overall certainty of evidence ranged from low to very low. mHealth interventions effectively enhance QoL and alleviate psychosocial distress in patients. However, evidence for improving swallowing function and MIO remains insufficient. Future research should prioritize standardized protocols and high-quality trials to validate long-term clinical value.
Cancer poses a significant threat to children's health, and mobile health (mHealth) is emerging as a key tool for remote disease management, health education, and follow-up. However, evidence of its effectiveness remains limited. This study aimed to summarize the effects of mHealth interventions for pediatric cancer compared with usual care, providing evidence-based support for optimizing intervention models and improving patient outcomes. A systematic search of 14 databases identified randomized controlled trials (RCTs) on mHealth apps for pediatric patients with cancer from inception to August 1, 2025. Two reviewers independently screened studies, extracted data, assessed bias risk, and graded evidence quality. The meta-analysis was conducted using RevMan 5.4 and Stata 15. A total of 24 RCTs involving 2645 patients were included. This review found that mHealth interventions significantly reduced infection rates (odds ratio [OR] 0.25, 95% CI 0.10-0.60; P=.002) and the overall incidence of peripherally inserted central catheter (PICC) complications (OR 0.16, 95% CI 0.10-0.24; P<.001), while improving quality of life (standardized mean difference [SMD] 1.34, 95% CI 0.13-2.55; P=.03), self-management ability (SMD 6.39, 95% CI 1.26-11.53; P=.01), and treatment adherence (OR 2.83, 95% CI 1.41-5.66; P=.003). However, mHealth interventions had no significant effect on PICC catheter displacement (OR 0.44, 95% CI 0.15-1.29; P=.13) or health knowledge (SMD 4.44, 95% CI -2.40 to 11.29; P=.20). Further high-quality studies are needed to verify their impact in these areas. The intervention components covered 9 behavior change techniques: goals and planning, feedback and monitoring, social support, shaping knowledge, repetition and substitution, reward and threat, comparison of outcomes, natural consequences, and regulation. This systematic review and meta-analysis synthesized evidence from RCTs. The findings support the use of mHealth to reduce infections and PICC-related complications among pediatric patients with cancer while improving quality of life, self-management capabilities, and treatment adherence. These results underscore the importance of incorporating mHealth strategies into pediatric cancer care and guide the development and enhancement of future mHealth interventions.
Continuous follow-up for patients with major depressive disorder (MDD) is essential for treatment decisions and a better prognosis. There remains limited evidence regarding the critical issue of depression variation trajectory prediction using mobile health (mHealth) measures. Moreover, the temporal dynamics of mHealth measures have not been fully modeled in previous studies, and the poor patient adherence to mHealth records poses great challenges to the dynamic feature modeling. This study aimed to examine the contribution of mHealth measures in predicting depression variation trajectory for patients with MDD, with full consideration of the temporal dynamics of mHealth measures. A total of 229 patients with MDD from a multiple-center, prospective cohort were included. A 12-week follow-up was conducted involving the collection of the Hamilton Depression Rating Scale (HAMD-17), along with patient-reported outcomes (Immediate Mood Scaler and Altman Self-Rating Mania Scale) via mobile devices and sleep duration through wearable wristbands. We used functional data analysis to extract dynamic features from the sparse mHealth records, rather than aggregating the data to a single scalar summary measure through collapsing over time. Subsequently, 3 machine learning models were applied to predict the depression variation trajectory classes based on the baseline characteristics and these extracted dynamic features. Based on the variation of HAMD-17 scores within 12 weeks, the participants were labeled into 4 classes through the k-means algorithm. The classes included stable decline (n=93), fluctuate decline (n=44), fast decline (n=60), and delayed and fluctuate (n=32), in light of the shape of depression trajectories. With both baseline features and dynamic features of the mHealth measures, accuracy rates for the overall data were 54.35%, 60.87%, and 56.52%, for the stable decline patients were 78.95%, 84.21%, and 73.68%, for the nonstable decline patients were 59.26%, 62.96%, and 70.37% based on the 3 machine learning models, respectively. The results were significantly superior to the prediction obtained without mHealth measures (with an overall accuracy below 50%) and only showed a marginal reduction in accuracy relative to the ideal prediction with assessment obtained from clinical visits. Moreover, in the construction of the most accurate prediction model, dynamic features of the Immediate Mood Scaler, the Altman Self-Rating Mania Scale, and sleep duration emerged as the most influential predictors, ranking first, third, and fourth, respectively, in terms of their relative importance. Longitudinal mHealth measures show potential in depression variation trajectory monitoring for patients with MDD even under poor patient adherence. Our work provides practical help in alleviating the follow-up burden for patients with MDD and validates the effectiveness of mHealth measures in clinical applications.
Healthy aging has emerged as a global priority. However, older adults' participation in health promotion programs remains low, and traditional health promotion models have achieved limited success in fostering sustained engagement among this population. Mobile health (mHealth)-based gamification interventions offer a promising way to address these challenges. However, no published reviews support or oppose the use of mHealth-based gamification interventions as health promotion strategies in older adults. The study aimed to identify mHealth interventions using gamification to promote health among older adults. Our scoping review was conducted following the Joanna Briggs Institute recommendations for scoping reviews and Arksey and O'Malley's framework. The process followed PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines and PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Literature Search Extension) checklist. A comprehensive literature search was conducted across 8 databases: PubMed, Scopus, Web of Science, Embase, Cochrane Library, CINAHL, PsycARTICLES, and IEEE Xplore Digital Library, from their inception to December 10, 2025. Two reviewers independently screened titles, abstracts, and full texts via Rayyan, with disagreements resolved by a third reviewer. This scoping review identified 11 studies. Only 1 article was published before 2022. The interventions were found to improve enjoyment and motivation (n=5), cognitive function (n=3), physical activity (n=2), and digital literacy (n=2). Individual studies also reported improvements in mental health (n=1) and adherence (n=1), a reduction in suicidal ideation (n=1), improvements in physical function (n=1), the promotion of social engagement (n=1), and the identification of mild cognitive impairment (n=1). Game elements used were ranked by frequency as progress, challenges, goals, levels, reward, sensation, storytelling or narration, leaderboard, surprise, and avatar. No research was found to use the game element of "social sharing." mHealth types included augmented and virtual reality-based training systems, wearable devices, mobile phones, tablets, and Windows platforms and devices. Notably, only 4 studies applied theoretical frameworks, and 3 omitted the concrete approach to gamification. As the first scoping review to identify and map mHealth-based gamification interventions for older adults, this study highlights their potential as an innovative approach to health promotion. By systematically synthesizing evidence regarding intervention designs, gamification strategies, and preliminary health outcomes, it establishes a foundation for future inquiry. However, this review is limited by the small number of included studies, precluding broad generalizations. Future research should assess long-term impacts, integrate theoretical frameworks, establish reporting guidelines, design personalized social-interactive interventions, and expand to broader health domains. Ultimately, these insights provide targeted guidance for developing age-appropriate digital health solutions, contributing to the realization of active aging.
There is growing recognition of the role of health apps in addressing health care system challenges, yet app quality and evidence vary widely, and consumers have little decision support at the point of download. This study aimed to explore which sources of recommendations European residents use and trust when choosing health apps and whether residents support government review and rating of health apps. We conducted a cross-sectional online survey (December 7, 2022, to February 16, 2023) in 26 languages targeting residents of the European Economic Area, the United Kingdom, and Ukraine. The survey contained 11 questions covering demographics, types of apps used, sources of advice used and trusted, and views on government review and rating. We included only fully completed responses (N=1228). Descriptive statistics are presented as counts and percentages. For subgroup analyses, we dichotomized trust responses ("I trust" vs other responses) and tested associations with gender (Fisher exact test), age group, and education level (chi-square test); an adjusted significance threshold was applied (P<.004) to account for multiple testing. We followed the CHERRIES (Checklist for Reporting Results of Internet E-Surveys) guidelines for reporting online surveys and STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) recommendations for observational studies. A total of 1228 respondents from 33 countries completed the survey; 1110 (90.4%) reported using one or more health apps. COVID-19 apps (763/1228, 62.1%) and activity apps (737/1228, 60.0%) were the most frequently used, whereas disease management (86/1228, 7.0%), diagnostic (77/1228, 6.3%), and treatment apps (70/1228, 5.7%) were the least used. Sources used to choose apps included family and friends (434/1228, 35.3%), health professionals (412/1228, 33.6%), and government or health authorities (358/1228, 29.2%). The most trusted sources were health professionals (987/1228, 80.4%), pharmacists (750/1228, 61.1%), and government or health authorities (736/1228, 59.9%). No statistically significant differences in trust by gender were observed (all P>.0038). Some differences by age and education were observed for select sources (eg, government or health authorities: χ21=8.546; P=.003; family and friends: χ2=19.133; P<.001). Overall, 1060 (86.3%) of 1228 respondents supported government review and rating of health apps (either directly or by commissioning another organization). In this large multilingual European survey, most respondents reported experience with health apps and placed greatest trust in health professionals, pharmacists, and government or health authorities, yet professional recommendations were used less often than informal sources. There is clear public support for government-led review and rating schemes to guide consumer choice. Efforts to make trustworthy, easy-to-find information available at the point of download and to support health care professionals in recommending high-quality apps could help bridge the gap between trusted and used sources.
The first 2000 days can profoundly influence long-term health. Healthy Beginnings for Hunter New England Kids (HB4HNEKids) is an SMS text messaging program delivered alongside routine Child and Family Health Nursing (CFHN) care, which provides families with evidence-based, age- and stage-related preventive health information across the first 2000 days. This pilot study aimed to explore the feasibility, engagement, and acceptability of the HB4HNEKids program. It also aimed to explore the potential effectiveness of the program at 6 and/or 12 months post partum on outcomes including breastfeeding, child diet, child movement, and parental mental well-being. During the pilot phase (October 2021 to July 2024), project records were used to assess the number of families enrolled, number of SMS text messages sent (feasibility), and the number of opt outs (engagement). Repeat cross-sectional surveys were conducted at 5-7 months post partum and again at 12-14 months post partum using validated survey instruments. Using convenience sampling methods, survey participants consisted of birthing parents who had received HB4HNEKids and a concurrent nonrandomized comparison group that did not receive the program. Surveys assessed parental self-reported engagement with the messages, program acceptability, breastfeeding status, child diet, child movement, and parental mental well-being. Mixed linear regression analyses were conducted to calculate mean differences and odds ratios. During the pilot phase, HB4HNEKids was delivered to 6243 families (73.4% of families contacted by CFHN). A total of 383 birthing parents completed the survey at 6 months (99/383, 26% receiving HB4HNEKids), and 283 completed the survey at 12 months (104/283, 37% receiving HB4HNEKids). Of the survey participants who received HB4HNEKids (n=200), between 76% and 83% reported that they always or very often read the SMS text messages, spending on average 5-7 minutes engaged with the content. At both survey time points, more than 90% of participants receiving HB4HNEKids agreed that the program was acceptable. Child daily intake of vegetables was significantly higher in the HB4HNEKids group (adjusted mean difference 0.23, 95% CI 0.07-0.40; P=.006) than in the comparison group at 12 months. Parents receiving HB4HNEKids also reported significantly better mental well-being scores (P=.005). While HB4HNEKids participants reported breastfeeding rates 5 percentage points greater than comparison participants at 6 and 12 months, this result was not statistically significant. There were no statistically significant differences between HB4HNEKids, and comparison participant responses related to child movement behaviors. The HB4HNEKids SMS text messaging program is feasible to deliver at scale alongside routine CFHN care and is highly acceptable and engaging to parents. This pragmatic evaluation of the pilot, embedded into usual care, indicates potential effectiveness of the program for improving child vegetable intakes and parental mental well-being. Further evaluation of this program using robust methodology is needed to determine the effectiveness of this innovative mHealth program across the first 2000 days.
In the management of gestational diabetes mellitus (GDM), the usual medical treatment requires frequent visits for glucose monitoring and insulin dose adjustment, and this imposes significant physical, psychological, and economic burdens on pregnant women. As mobile health platforms become increasingly integrated into diabetes care, telemedicine may help alleviate these burdens; however, evidence evaluating its effectiveness as a replacement for routine in-person GDM care remains limited. This study aims to evaluate the impact of telemedicine on the quality of life and costs for patients with GDM requiring insulin therapy. This single-center, 2-arm, randomized, open-label, parallel-group study included patients with GDM who started insulin injection therapy. Participants were randomized to either the telemedicine or standard face-to-face care groups for 10 (SD 2) weeks. The telemedicine intervention used a smartphone-linked platform that enabled the automatic transfer of glucose data from connected glucose meters and facilitated real-time video consultations. Primary end points included costs and patient satisfaction. Costs were assessed using claims data, transportation calculations, and wage-based productivity losses, while patient satisfaction was evaluated through changes in the Problem Areas in Diabetes Survey and Diabetes Therapy-Related Quality of Life questionnaire scores. Secondary outcomes included glycemic control and perinatal outcomes. In total, 38 participants were included, with 18 assigned to the telemedicine group and 20 to the standard care group. Total costs (32,712, 95% CI 15,412-50,013 vs 59,202, 95% CI 42,603-75,800 Japanese yen; $284, 95% CI 134-435 vs $515, 95% CI 370-659, purchasing power parity [PPP]-adjusted; P=.01), direct non-health care costs (922, 95% CI -240 to 2084 vs 2561, 95% CI 1447-3676 yen; $8, 95% CI -2 to 18 vs $22, 95% CI 13 to 32 PPP-adjusted; P=.02), and indirect costs (8981, 95% CI -7119 to 25,082 vs 32,832, 95% CI 17,384-48,279 yen; $78, 95% CI -62 to 218 vs $285, 95% CI 151-420 PPP-adjusted; P=.01) reduced significantly in the telemedicine group compared with the standard care group. The improvements in the Problem Areas in Diabetes Survey (-7.6, 95% CI -13.7 to -1.4; P=.02) and Diabetes Therapy-Related Quality of Life domain 1 (10.5, 95% CI 0.9-20.1; P=.03) scores from the baseline were significantly greater in the telemedicine group than that in the standard care group. Nonetheless, glycemic control and frequency of perinatal complications were comparable between the 2 groups. Consultation time was similar across groups, suggesting no added workload for clinicians. In this randomized trial, mobile health-enabled telemedicine safely replaced routine in-person visits for patients with GDM requiring insulin therapy. Telemedicine significantly reduced psychological and economic burdens without compromising glycemic or perinatal outcomes, demonstrating its value as a patient-centered and cost-efficient model of care. These findings support the broader implementation of mobile-based telemedicine approaches in GDM management.
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. 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. 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. 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. 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.
Access to oral health promotion for older adults is globally limited, especially in rural, low- and middle-income settings. Digital research often lacks theoretical foundation and focuses primarily on younger cohorts, yielding few randomized trials evaluating accessible tools for oral health education in older adults. This study aimed to develop a telehealth reinforcement strategy for oral health promotion to improve knowledge, attitudes, and self-efficacy in community-dwelling older adults. A single-center, parallel-group randomized controlled trial was conducted in 4 municipalities (2 urban and 2 rural) in La Araucanía, Chile. Eligible participants were functionally independent adults aged ≥60 years with smartphone and internet access; those with cognitive impairment, complete edentulism, or inability to use WhatsApp were excluded. Participants were recruited from regional databases and assessed using the Geriatric Dental Specialties Tele-platform, a teledentistry tool for older adults. Participants were randomized (1:1) to face-to-face instruction (comparator) or the same instruction plus 2 weeks of social cognitive theory-informed telehealth reinforcement (4 validated videos via WhatsApp). Clinicians and statistical advisors were blinded. Primary outcomes (oral health knowledge, attitudes, and self-efficacy) were measured via telephone-administered questionnaires at baseline and 6 weeks post intervention. Secondary outcomes included acceptability and self-reported behaviors. Analyses included hypothesis testing, multiple correspondence analysis, and k-means clustering. A total of 120 older adults were randomized (comparator: n=59; telehealth: n=61), with 103 analyzed (comparator: n=51; telehealth: n=52). Both groups showed substantial within-group improvements in oral health knowledge (comparator: Cohen d=0.93, 95% CI 0.52-1.34; P<.001; telehealth: Cohen d=1.07, 95% CI 0.66-1.48; P<.001) and self-efficacy (comparator: r=0.59, 95% CI 0.38-0.74; P<.001; telehealth: r=0.62, 95% CI 0.43-0.77; P<.001). In per-protocol analysis, telehealth improved dental caries knowledge (P=.03) and attitudes (P=.004), with no between-group differences in other domains (P>.05). In intention-to-treat analysis, telehealth showed a significant between-group difference for attitudes only (adjusted mean difference=0.91, 95% CI 0.34-1.48; P=.002), with no differences for overall oral health knowledge (P=.11) or self-efficacy (P=.59). Exploratory analyses indicated only the rural telehealth subgroup showed significant gains in attitudes (P=.003) and flossing (P<.001). Clustering suggested greater improvements among participants with higher baseline needs, predominantly rural, with fewer teeth. Telehealth demonstrated acceptability across multiple indicators (>80% for most measures) with no clinical adverse events; minor video-access issues occurred. Telehealth reinforcement provided significant advantages in oral health attitudes compared with face-to-face instruction. The intervention was acceptable and showed benefits among older adults with higher preventive needs, commonly seen in rural settings. By integrating theory-informed strategies into a familiar digital platform, this study adds evidence from rural and urban contexts, extending prior work on mobile oral health. It offers insights to address service gaps in underserved areas and highlights potential for feasible, context-aligned implementation. Future research should evaluate long-term effects, adaptability, and cost-effectiveness. ClinicalTrials.gov NCT05917548; https://clinicaltrials.gov/study/NCT05917548.
Bystander cardiopulmonary resuscitation (CPR) and automated external defibrillator (AED) use are critical for improving survival after out-of-hospital cardiac arrest. Although conventional training methods are initially effective, they are often hampered by rapid skill decay over time. Game-based mobile apps have emerged as a promising and scalable alternative for CPR and AED education; however, evidence of their long-term efficacy remains scarce. This study aimed to evaluate the integration of a game-based mobile app into traditional CPR and AED training. We assessed its impact on university students' theoretical knowledge, practical skills, and theoretical knowledge retention, as well as their willingness to perform CPR and their awareness of disseminating these skills. A nonrandomized controlled trial was conducted among university students in China from March 21 to September 21, 2024. Participants were assigned to either an experimental group, which received game-based mobile app training supplemented with traditional training, or a control group, which received traditional training only. The game-based app featured a simulated scenario that required users to execute the correct sequence of resuscitation procedures and operate a virtual AED under time constraints. The intervention period lasted for 6 months. Participants' theoretical knowledge and practical skills were assessed immediately after training (baseline) and at the 7-day follow-up. Long-term retention of knowledge, willingness to perform CPR, and dissemination awareness were evaluated at the 6-month follow-up. Data were analyzed using SPSS software (IBM Corp), employing the chi-square test, Mann-Whitney U test, and Wilcoxon signed-rank test. A total of 481 participants completed the entire survey (n=241 in the experimental group and n=240 in the control group). In the short-term (7-d) assessment, the experimental group demonstrated significantly higher scores in both theoretical knowledge (P=.02) and practical skills (P<.001) compared to the control group. This advantage was maintained in the long term, with the experimental group showing superior knowledge retention at the 6-month follow-up (median score: 9/10 vs 8/10; P<.001). Furthermore, a majority of all participants expressed willingness to perform CPR on strangers (70.9%, 341/481) and to disseminate first-aid knowledge (92.1%, 443/481). However, no significant intergroup differences were observed for these latter 2 outcomes (P=.85 and P=.97, respectively). Despite the methodological limitations inherent in this nonrandomized study, our findings indicate that supplementing traditional training with the game-based mobile app significantly enhanced short-term acquisition of theoretical knowledge and practical skills and promoted sustained knowledge retention. This supports the app's potential as an effective and promising complement to conventional CPR and AED training programs.
Mobile apps and wearable devices may help to facilitate early detection of mental health conditions by providing objective, real-time data to supplement other forms of feedback and diagnoses. Few studies have investigated the acceptability and feasibility of using a mobile app to track survey- and wearable-based data in mental health research in Sub-Saharan Africa. This pilot study evaluated the feasibility and acceptability of using a mobile app and wearables to capture mental health-based survey data and passively sensed data among Kenyan health care workers. A mixed methods study was conducted among health care workers employed at 4 hospitals in Nairobi, Kenya, over 30 days. A mobile app was used to collect and integrate active (baseline questionnaire and daily mood) and passive (wearable) data. The baseline questionnaire gathered information on sociodemographics, work environment, and mental health assessments on depression, anxiety, personality, early family environment, posttraumatic stress disorder, and substance use. A wearable device was used to gather data on steps, heart rate, and sleep. Qualitative interviews were conducted post trial to gain in-depth insights into participants' experiences during the study. Fifty-one participants enrolled in the pilot study. They were primarily nurses (47%) and female (70%), with a median (IQR) age of 32 (29-36) years. Attrition over 30 days was low, with only one participant dropping out due to device malfunction, which was a broken screen. Completeness of the baseline survey was high, with participants completing 96.1% of the questions. Further, 58% of the daily mood ratings were completed over the 30 days. For the wearable measures, participants submitted steps, heart rate, and sleep data on 93%, 73%, and 51% of study days, respectively. The proportion of days the wearable was worn for over 10 hours was 63%. Interviews revealed 2 primary themes. The first was intrinsic and extrinsic motivation; participants indicated that they liked having their health metrics tracked and receiving congratulatory messages from the app, encouraging increased step counts. The second theme was technical and usability challenges; 48% (10/21) of the participants reported discomfort wearing the watch while sleeping and challenges with synchronization of data due to the nonautomated nature of the process. Participants suggested additional prompts to remind them to complete the daily mood question. This pilot study demonstrates the feasibility of deploying mental health surveys, collecting data through wearable devices, and integrating such data within a single mobile platform under real-world infrastructure constraints. Health care workers in Kenya were willing to provide sensitive information through mental health assessments using a mobile app. To improve adherence, future studies should consider addressing some contextual factors such as daily prompts, enhanced data synchronization methods, and comfort concerns to improve adherence, especially during sleep.
Over the past 2 decades, global rates of cannabis use have risen significantly, especially among young adults. This has corresponded to an increase in cannabis-related problems and hospitalizations. Thus, there has been significant interest in developing new interventions that can help facilitate cannabis cessation and reduce hospitalization rates. Specifically, mobile apps have emerged as scalable and accessible stand-alone or adjunct interventions that can help individuals with cannabis use disorders. This study aimed to evaluate the quality of free cannabis cessation apps available on both the Apple App Store and Google Play Store, focusing on the analysis of their features, content, and adherence to evidence-based practices. A systematic search was conducted in April 2023 using a variety of keywords. The apps were deemed eligible if they were free, available in English, accessible on both the Apple App Store and the Google Play Store, and related to cannabis cessation. Eligible apps were used for at least 1 month and were rated on the Mobile App Rating Scale by 2 reviewers. Interrater reliability was excellent, with a weighted Cohen κ of 0.893 (95% CI 0.835-0.943). Four apps were included in the analysis, namely, "Grounded-Quit Weed," "Quit Weed," "Marijuana Addiction Calendar," and "Marijuana Anonymous." The mean overall quality score of the apps was 3.4 out of 5, indicating poor to acceptable quality. The apps scored the highest on the "functionality" section and the lowest on the "information" section. Of the 4 apps, 3 focused on tracking cannabis use and duration of abstinence, whereas 1 focused on peer support. A limited number of cannabis cessation apps were identified, and those that were available were of low quality due to a lack of evidence-based information. This study is the first to evaluate the current availability and quality of mobile apps designed for cannabis cessation. Unlike previous research that broadly assessed cannabis-related mobile apps, this study focuses on the limited number of free cannabis cessation tools, reflecting what is most available to the general population. The findings highlight a significant gap between the growing demand for virtual cessation tools and the quality of existing options. With the rising global prevalence of cannabis use disorders, there is an increasing need for robust, accessible, and evidence-based therapeutic options. While mobile health apps may be a viable option to support cannabis cessation, the current landscape is limited by poor quality apps and a lack of evidence-based information. From a real-world perspective, this study highlights the need for users to exercise caution when relying on current cannabis cessation apps and underscores the urgent need for the development and evaluation of new evidence-based digital interventions.