Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) induce clinically meaningful weight loss, but their real-world impact is constrained by poor medication persistence, treatment-limiting gastrointestinal adverse effects, and rapid weight regain after cessation. These limitations may be structural rather than incidental: GLP-1 RAs powerfully address appetite biology but do not build the behavioral skills, environmental supports, and routines needed to sustain outcomes when biological pressures revert to baseline. This viewpoint argues that theory-based digital health companion programs are best understood as structural complements to glucagon-like peptide-1 (GLP-1) therapy rather than optional adjuncts, and it articulates a testable mechanistic hypothesis: a pharmacologically enabled “habit window.” We map theoretical determinants from the social cognitive theory (SCT) and behavioral economics (BE) to classes of digital intervention across 3 problems (medication persistence, tolerability, and postcessation durability) and grade the supporting evidence as established, observational, or hypothesized. For each problem, we link candidate SCT- and BE-informed mechanisms (such as self-efficacy and enactive mastery, present bias, defaults, and loss aversion) to specific digital intervention classes and to the studies needed to test them. Because reduced appetitive drive may free cognitive resources and lower the need for food-related self-control, GLP-1 therapy may open a privileged window in which habit formation is easier. Supporting evidence is drawn from adjacent behavioral trials, combined pharmacological and lifestyle trials, and observational engagement data; the observational data are hypothesis generating and subject to selection effects. Digital behavioral infrastructure may help translate the biological effects of GLP-1 therapy into durable behavioral and environmental change, but the “habit window” remains a hypothesis requiring prospective and randomized testing. We outline a research agenda prioritizing randomized trials with postcessation follow-up and mechanistic mediation studies.
Background:Personalized dietary counseling is central to recurrence prevention in patients with urolithiasis, particularly after a 24-hour urine metabolic evaluation. However, translating quantitative metabolic abnormalities into patient-facing, guideline-concordant, and safe dietary recommendations can be challenging in routine clinical practice. Large language models (LLMs) may assist with this task, but unguided responses may overlook key metabolic priorities or case-specific safety constraints. Objective:This study evaluated whether a guideline-integrated, safety-aware, LLM-based clinical interaction framework (StoneAgent) could generate higher-quality, personalized dietary recommendations than a standard LLM configuration for recurrent urolithiasis. We also assessed whether any performance advantage persisted when the same clinical scenarios were presented as patient query-style inputs. Methods:We conducted an in silico comparative study using 30 synthetic clinical vignettes representing common, mixed, and safety-relevant metabolic stone scenarios. For the primary experiment, StoneAgent and a standard LLM configuration were compared using structured vignette inputs. For the robustness experiment, each vignette was reformulated into 2 patient query-style variants (query A and query B), preserving the same clinical content in more natural conversational language. A vignette-specific expert reference standard was developed from guideline-informed specialist consensus. Three independent reviewers blindly rated outputs on a 5-point Likert scale for metabolic specificity, guideline adherence, and actionability; safety was assessed as a binary outcome. For the patient query-style experiment, query A and query B were aggregated at the vignette level for paired comparison. Results:In the structured-input experiment, StoneAgent achieved higher performance than the standard LLM across metabolic specificity, guideline adherence, and actionability, with median case-level scores of 5.00 (IQR 5.00-5.00) vs 3.00 (IQR 2.75-3.92) for metabolic specificity, 5.00 (IQR 5.00-5.00) vs 3.67 (IQR 3.08-4.00) for guideline adherence, and 5.00 (IQR 5.00-5.00) vs 3.00 (IQR 2.67-3.33) for actionability (all P<.001). Safety pass rates were 100% (30/30) for StoneAgent and 83.3% (25/30) for the standard LLM (exact McNemar P=.06). In the patient query-style robustness experiment, StoneAgent retained a directional advantage after case-level aggregation, with mean scores of 4.44 vs 3.62 for metabolic specificity, 4.51 vs 3.63 for guideline adherence, and 4.11 vs 3.40 for actionability. Safety pass rates were 100% (30/30) for StoneAgent and 90% (27/30) for the standard LLM (exact McNemar P=.25). The performance gap was more conservative under patient query-style inputs compared with structured inputs, but the overall pattern remained consistent across domains. Conclusions:In this in silico study, a guideline-integrated, safety-aware clinical interaction framework generated higher-quality dietary recommendations for recurrent urolithiasis than a standard LLM condition with structured vignette inputs. This advantage was retained with patient query-style inputs. These findings suggest that explicit clinical framing, guideline grounding, and safety-oriented response scaffolding may improve the reliability of specialty counseling tasks involving metabolic stone prevention. Further validation is needed using real patient-authored queries and prospective clinical workflows.
Background:Social media is increasingly used for research recruitment and knowledge mobilization. However, the general use of social media has not been documented among individuals living with chronic pain. Objective:Our study aimed to describe social media use in this population and identify associated factors. Methods:This cross-sectional study analyzed data from 1549 participants of the CEMPUS (Cohort as Part of Undergraduate Medical Studies at the University of Sherbrooke) cohort (Quebec, Canada) who reported living with chronic pain and completed a health questionnaire either online or by phone in 2024. Multivariable logistic regression was conducted to examine factors associated with social media use. Results:Overall, 86.4% (n=1308) of participants (95% CI 84.7-88.1) reported using social media. Use was higher among females (n=853, 88.9%) than males (n=446, 81.7%). The most frequently used platforms were Facebook, Messenger, and YouTube. Factors associated with higher odds of reporting social media use included being a female (adjusted odds ratio [aOR] 1.96, 95% CI 1.39-2.77), having a postsecondary education (aOR 1.57, 95% CI 1.11-2.23), drinking alcohol (occasional vs never: aOR 1.64, 95% CI 1.04-2.57), and reporting difficulties accessing health care (aOR 1.50, 95% CI 1.01-2.22). Lower odds of reporting social media use were associated with older age (aOR 0.94, 95% CI 0.92-0.96), having chronic pain for 10 years or more (aOR ≥10 vs <1 y=0.55, 95% CI 0.31-0.96), and having higher levels of depression (aOR 0.92, 95% CI 0.87-0.99). Conclusions:Most participants with chronic pain use social media; this points to the potential of social media for research recruitment and knowledge mobilization. However, as exclusive reliance on these platforms may overlook certain groups, there is a clear need for diverse outreach strategies.
Background:Digital health applications generate rich behavioral data; yet, how users transition between behavioral states remains poorly understood. Existing approaches show limited capacity to capture and represent the evolving and nuanced nature of real-world behavioral dynamics, which are essential for informing personalized behavior change interventions. Objective:This study aimed to understand how users' behaviors evolve within digital health applications by developing a data-driven approach that captures transition dynamics across behavioral features. We aimed to validate modeled transitions against observed user data, analyze transition pathways, and derive interpretable insights. Methods:We analyzed 32 weeks of data from 36,574 users from a population health program run by the Health Promotion Board (HPB) in Singapore. We developed a graph-based behavioral trajectory model (GraphBeTraM) that models behavioral transitions as shortest paths through user similarity graphs. By aggregating paths over time, the model captures users' progression toward target behavioral states and quantifies the direction, magnitude, and timing of change. We validated modeled transitions against real-world data by comparing (1) transition matrices and (2) feature-level pathways, using Spearman correlation, Fisher z-transformed means, and cosine distance. We focused on high-variance features and used heatmaps to visualize patterns of change. Finally, characterized transition pathways between behavioral states and distinguished generalizable and context-specific features. Results:We observed strong alignment between observed and modeled transition matrices with strong correlation (Spearman ρ=0.82; P<.001) and low cosine distance (0.05), suggesting that real-world behavioral transitions can be effectively represented through shortest paths with GraphBeTraM. Feature-level pathway validation showed consistent patterns of change across both personalized and real-world pathways. Physical activity features like weekly moderate to vigorous physical activity (MVPA) emerged as stable, generalizable signals of change at the population level, with strong mean correlation (ρ_personalized=0.97; ρ_real-world=0.79) and low mean cosine distance (cosine_personalized=0.24; cosine_real-world=0.41) across both personalized and real-world transitions. In contrast, features related to personal preferences, including time-related activity patterns (eg, proportion of weekday to weekend MVPA), purchase preferences for healthy foods and drinks, and engagement indicators (eg, last contact with the program or app), exhibited context-specific relevance and reflected change at an individual level. An in-depth transition analysis from an active state with prolonged sedentary periods to an active state with healthy eating habits revealed interpretable patterns in onset, magnitude, and rate of change. Specifically, features such as preferences for healthy food and drink purchases emerged later in the trajectory but then showed sharper and faster transitions once these behaviors began to shift. These insights highlight how GraphBeTraM can guide nudging strategies in alignment with natural behavior dynamics, including both stable and later-onset patterns. Conclusions:GraphBeTraM provides an interpretable framework for modeling behavioral transitions in digital health applications and captures key dynamics that support personalized intervention design.
Background:In burn care, one of the most debated topics is the optimal treatment of patients with deep partial-thickness burns. With these patients, the decision must be made to perform early surgery or to wait and potentially limit, or even avoid, surgery. Both options are available in Dutch burn care, and the best treatment option is decided on clinical outcomes as well as patients' preferences. This complexity highlights the need for shared decision-making (SDM) and a decision aid (DA) to facilitate this process. Objective:This study aimed to support patients and health care professionals (HPs) in the process of SDM regarding the treatment decision for deep partial-thickness burns by developing and implementing a DA. Methods:This multimethod design was conducted in a Dutch burn care setting between September 2023 and October 2025 and included 3 phases. Phase 1 (needs assessment) included semistructured interviews with patients, analyzed using a qualitative descriptive approach with deductive and inductive coding, and an online survey of HPs with a descriptive analysis of closed-ended questions and inductive content analysis of open-ended questions by 2 researchers. Phase 2 (development) involved 5 co-design sessions with patients, HPs, and researchers. The think-aloud method was used for usability testing with patients and HPs. Additionally, HPs participated in acceptability testing using interviews guided by the Consolidated Framework for Implementation Research, and analyzed using deductive thematic analysis. Phase 3 (implementation) consisted of a 6-month pilot period, including patient interviews, 3 focus groups with HPs, usage data, and the Normalization Measure Development questionnaire to assess the level of normalization. Results:Eight patient interviews revealed 2 distinct information needs: patients seeking detailed treatment information and those preferring to defer decisions to clinicians. The HPs survey (response rate 36%) showed that burn physicians typically make the final treatment decisions, although SDM was considered preferable, and 91.2% (31/34) supported the use of a DA. Usability testing with 4 patients and 7 HPs showed overall satisfaction, with minor revisions suggested, such as clarifying text and illustrations. Key implementation facilitators included professional engagement and local support, while infrastructure was the main barrier. The final DA comprised a paper handout sheet, an interactive website, and a summary sheet capturing patient preferences. During the pilot, the DA was distributed 42 times and used by 28 patients (67% participation rate). Both patients and HPs reported positive experiences, and the tool was considered feasible to integrate into routine care. Conclusions:A DA for the treatment of deep partial-thickness burns was successfully developed and implemented using a comprehensive, user-centered approach. It supports SDM and patient-centered care by providing tailored information and helping patients participate more actively in treatment decisions.
Background:Federal and state school nutrition policies over the past 20 years have improved school meal nutritional quality, children's diet quality, and childhood obesity prevalence in the United States. However, increasing use of mobile food delivery apps during school hours may introduce new dietary risks among adolescents. Objective:This study aimed to assess adolescents' usage patterns of, and perceptions toward, mobile food delivery during school hours in the United States. Methods:We administered a national online survey in July to August 2025 using the AmeriSpeak Teen Omnibus platform operated by NORC at the University of Chicago. The respondents were 1027 adolescents aged 13 to 17 years. We estimated and statistically compared survey-weighted distributions of self-reported types and frequencies of mobile food app usage characteristics during and after school by age, sex, race/ethnicity, household income levels, and regional strata using χ2 tests. We also conducted thematic analyses of responses to an open-ended question asking why participants supported or opposed the use of mobile food delivery services during school hours. Results:Nearly 1 in 4 adolescents have used mobile food delivery services during school hours, and nearly half used them after school. Large proportions of adolescents used these services to order fast food (58.8%) and sugar-sweetened beverages (34.1%), while grain bowls, fruit, non-deep-fried vegetables, and unsweetened beverages were less popular. About 34.0% of adolescents in the western United States attended schools that allowed mobile food delivery during school hours, a substantially higher proportion than other national regions; however, adolescents in the West more frequently cited the perceived high costs as the reason for not using those services. Nearly half of the respondents (47.2%) supported the idea of mobile food delivery during school hours. However, distraction from their learning environment was a major concern regardless of their support or opposition to mobile food delivery at school. Conclusions:Mobile food delivery during school hours is a relatively new method of food acquisition for adolescent students. School nutrition policy should consider students' access to and usage patterns of both physical and digital food environments to help ensure the development of lifelong healthy eating habits among adolescents.
Background:Differentiating among liver disease entities such as autoimmune liver disease (AILD), drug-induced liver injury (DILI), and chronic hepatitis B (CHB) remains clinically challenging due to overlapping clinical manifestations and nonspecific laboratory findings. Conventional machine learning (ML) approaches rely mainly on structured laboratory data, whereas free-text clinical reports and other heterogeneous electronic medical record data are often underused. Large language models (LLMs) may provide a strategy for encoding heterogeneous clinical information, yet their usefulness for liver disease classification remains insufficiently evaluated. Objective:This study aimed to evaluate the usefulness of LLM-derived embeddings for clinical data mining in liver disease and to determine whether integrating these embeddings with laboratory variables improves classification across broad disease categories and closely related subtypes. Methods:We retrospectively analyzed electronic medical record data from 7543 patients with nonoverlapping liver disease etiologies treated at Beijing Youan Hospital, Capital Medical University, between 2010 and 2025. Three LLMs (Qwen3, Huatuo-o1, and II-Medical) generated semantic embeddings from standardized clinical text, combining free-text examination reports, and structured clinical observations. Performance was assessed in a 3-class etiological task (AILD, DILI, and CHB) and a 4-class task further subclassifying AILD into autoimmune hepatitis and primary biliary cholangitis. We compared embedding-only models, LLM-integrated ML models, and an ML-only baseline using the same structured variable set and preprocessing pipeline, with lightweight natural language processing encoders and zero-shot LLM reasoning as additional comparators. Models were developed using 5-fold cross-validation and evaluated on an internal holdout set using accuracy, macroaveraged precision, recall, and F1-score. Results:In the 3-class task, the LLM-integrated ML models achieved macro F1-scores of 0.835-0.837, compared with 0.791 for the ML-only baseline, with corresponding accuracies of 0.925-0.929 versus 0.893. In the 4-class task, the LLM-integrated ML models achieved macro F1-scores of 0.717-0.734, compared with 0.665 for the ML-only baseline, with corresponding accuracies of 0.920-0.922 versus 0.874. A temporal split sensitivity analysis using cases from 2010 to 2019 for training and cases from 2020 to 2025 for testing showed that the relative advantage of LLM-integrated ML models over the ML-only baseline was preserved. Direct zero-shot LLM reasoning and lightweight natural language processing encoders performed below the embedding-based integrated models. Conclusions:In this single-center retrospective cohort of patients with clear-cut, nonoverlapping liver disease etiologies, LLM-derived embeddings provided complementary information to structured laboratory variables for multiclass liver disease classification. The integrated framework showed improved internal validation performance compared with the ML-only model, particularly for non-CHB categories and fine-grained subtype discrimination. Because patients with overlapping liver disease etiologies were excluded, the reported performance may overestimate diagnostic accuracy in broader real-world clinical settings where overlapping syndromes are common. Multicenter external validation and prospective evaluation in more heterogeneous patient populations are needed before clinical implementation.
Background:Systemic lupus erythematosus (SLE) is a multifactorial autoimmune disease influenced by genetic, epigenetic, ecological, and environmental factors, with a global prevalence of 7.7 to 13 per 100,000, and standardized mortality rates of 2.4% to 5.9%. Between 14% and 75% of patients experience psychiatric comorbidities such as anxiety and depression, which impair treatment adherence and health-related quality of life. Social media has become an important channel for patients to express health concerns and seek support. Reddit and Weibo, as mainstream platforms globally and in China, respectively, host large volumes of user-generated content; however, no prior study has examined SLE-related discourse across both cultural and platform contexts. Objective:This study aimed to characterize SLE-related discussions on Reddit and Weibo. Specifically, we sought to identify and hierarchically categorize discussion topics, compare platform-specific differences in patient concerns, assess sentiment polarity across topics and thematic groups, and characterize public misconceptions related to SLE. Methods:This observational, computational content analysis examined all SLE-related discussions on Reddit (r/lupus) and Weibo up to November 30, 2024. An AI-driven pipeline first embedded the discussions using the Multilingual-E5-base BERT (bidirectional encoder representations from transformers) model, then applied uniform manifold approximation and projection (UMAP) for dimensionality reduction and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) for density-based clustering to identify fine-grained topics, with keywords extracted via class-based term frequency-inverse document frequency (c-TF-IDF). Topics were further grouped into higher-level thematic domains through spectral clustering, with labels and definitions generated using GPT-5 Thinking via prompt engineering. Sentiment polarity (positive, neutral, and negative) was classified using a fine-tuned multilingual BERT model. Two clinical pharmacists independently validated the topic modeling and sentiment results. Results:We analyzed 11,318 discussions from 3649 unique authors on Reddit and 33,628 discussions from 21,509 authors on Weibo. We identified 99 fine-grained topics on Reddit and 189 on Weibo, grouped into 6 thematic domains per platform. Reddit discussions centered on diagnostic journeys, treatment experiences, and symptom management, whereas Weibo discussions emphasized social news, charitable activities, and traditional Chinese medicine (eg, artemisinin). The overall sentiment on Reddit was positive (mean 0.42, SD 0.86; 95% CI 0.40-0.43), whereas that on Weibo was neutral (mean 0.01, SD 0.91; 95% CI 0.00-0.02). Sentiment was positive in 7493, neutral in 1032, and negative in 2793 Reddit discussions, whereas on Weibo, it was positive, neutral, and negative in 14,026, 5768, and 13,834 discussions, respectively. Treatment-related misconceptions and unverified remedies recurred on both platforms. Conclusions:This cross-platform analysis reveals both shared and platform-specific concerns among people with SLE, highlighting distinct informational and emotional needs across cultural and platform contexts. Recurring treatment-related misconceptions and the substantial volume of negative-sentiment discussions warrant targeted public health communication and psychological support. The findings may help clinicians, public health authorities, and patient support organizations identify unmet needs, while the AI-driven approach offers a scalable, real-time tool for monitoring patient perspectives.
Unlabelled:Is electricity the next frontier in monitoring and treating brain tumors and other cancers? In this News and Perspectives article, JMIR Correspondent Simon Spichak reports on advances in the emerging cancer neuroscience field.
Unlabelled:A recent participatory co-design study for a social media-based mental health intervention targeting perinatal women in regional, rural, and remote communities identified five core intervention needs: peer support and connection, personalized care, access to information that is trustworthy and eases uncertainty, place and culturally specific support, and a digital platform that is accessible and easy to use. This commentary argues that digital platform selection should be addressed as an active intervention component. A hybrid model that uses the built-in user base and accessibility of social media alongside the increased privacy and content control afforded by a customized mobile app could maximize the benefits of both platforms to more fully address all components of the proposed intervention.
Background:Pediatric rare diseases often cause a prolonged diagnostic odyssey. AI, including machine learning, deep learning, large language models (LLMs), and multimodal systems, may support diagnosis, but these applications in children have not been systematically mapped. Objective:The aim of the study is to map diagnostic applications, data modalities, validation strategies, and evidence maturity of AI methods for pediatric rare diseases. Methods:We conducted a scoping review following Joanna Briggs Institute methodology and reported it according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). On June 26, 2026, we searched PubMed, Scopus, Web of Science Core Collection, Embase, China National Knowledge Infrastructure (CNKI), Wanfang Data, and the Cochrane Library for records published from January 1, 2015, through June 1, 2026. We additionally searched medRxiv and arXiv and hand-searched the reference lists of included studies and relevant reviews. Eligibility was defined using the population-concept-context framework: pediatric rare diseases, diagnostic AI, and any clinical or research setting. JZ and JL independently screened titles and abstracts and assessed potentially eligible full-text reports. JZ charted the data, and JL verified every field. Findings were synthesized descriptively according to disease focus, AI technology, input modality, diagnostic task, validation strategy, and evidence maturity. Results:Database searches identified 2557 records; 2063 remained after deduplication. Of 106 full-text reports assessed, 77 database studies and 4 studies from hand searching and preprint servers were included, yielding 81 studies. Studies were published from 2016 through 2026, with 55 of 81 (67.9%) published from 2024 through 2026. Using a mutually exclusive primary technology classification, classical machine learning accounted for 38 (46.9%) studies, facial AI for 18 (22.2%), deep learning for 15 (18.5%), LLMs for 6 (7.4%), and multimodal AI for 4 (4.9%). Electronic health records, claims, clinical text, or structured clinical vignettes were used in 25 (30.9%) studies, facial images in 17 (21%), and other medical imaging in 13 (16%). Evidence remained mainly retrospective and internally validated: 62 (76.5%) studies included a retrospective component and 76 (93.8%) reported internal validation, whereas 21 (25.9%) included external validation and 12 (14.8%) included a prospective component. Conclusions:Research on AI-assisted diagnosis of pediatric rare diseases has expanded rapidly, but evidence maturity has not kept pace. Most studies established technical feasibility rather than generalizable clinical benefit, and performance should be interpreted by task, inputs, reference standard, and validation design rather than used to rank technologies. Evidence for LLMs and multimodal AI remains limited. Future research should prioritize multicenter validation, reproducible task-specific benchmarks, prospective evaluation, and assessment of incremental clinical value.
Unlabelled:Online appointment platforms are often viewed as tools of convenience, but they may reproduce and potentially amplify inequities rooted in underlying insurance and reimbursement structures. In this commentary, we highlight implications from a study using simulated profiles of patients with statutory health insurance (SHI) and private health insurance (PHI) to conduct an internet-based audit of online appointments. Not only was the time to the first available appointment longer for SHI than PHI patients, but also the PHI profile had access to more listings with appointments, and listings offering earlier PHI than SHI appointments appeared higher in search results than listings offering the same appointment timing to both. Accounting for strengths, limitations, and opportunities for future research, the study suggests several policy implications. First, leaders should view online scheduling platforms as access infrastructure, not merely convenience tools, and evaluate them with respect to access and disparities, not only adoption and satisfaction. Second, future evaluation and work should treat access as multidimensional, assessing multiple measures of access and comparing online with other scheduling channels. Third, insurance and delivery reform should accompany actions to govern digital platforms, which can organize and display, but cannot alone eliminate, disparities rooted in reimbursement and care structures. Ultimately, the relevant question about online scheduling platforms is not only whether such systems make booking easier but also whether they narrow, preserve, or widen broader health system inequalities. Digital front doors should be judged both by how easily they open and whether patients have equitable opportunities to pass through them.
Unlabelled:As AI rapidly makes its way into elementary classrooms, understanding the potential impact on children's learning and development has become more pressing. In this News and Perspectives article, JMIR Correspondent Simon Spichak reports on what experts are saying and doing about the potential benefits and risks.
Background:Digital health technologies are increasingly being used in scoliosis rehabilitation, but current evidence remains fragmented, and the effectiveness of these technologies across different categories, and their implementation in the real world remains unclear. Objective:We systematically evaluated the efficacy, implementation factors, and potential mechanisms of action of digital health technologies across various categories for scoliosis rehabilitation. Methods:We searched PubMed, IEEE Xplore, Embase, and Web of Science from inception to November 7, 2025, with an updated search on May 12, 2026. We included English-language controlled trials, cohort studies, and feasibility studies of digital interventions for any type of scoliosis that reported at least one quantitative outcome; nonoriginal publications, purely surgical studies, and those without extractable data were excluded. Risk of bias was assessed using the Cochrane Risk of Bias 2 tool (RoB 2) and the Risk of Bias in Nonrandomized Studies of Interventions (ROBINS-I), and intervention reporting completeness was assessed using the Template for Intervention Description and Replication (TIDieR). Interventions were categorized into 5 technology types and further stratified by evidence maturity into 3 tiers. Evidence was synthesized using vote counting based on the direction of effect, with prespecified subgroup and sensitivity analyses, and certainty of evidence assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE). Results:Thirteen studies (574 patients) from 5 countries were included. Among the randomized trials, 14.3% (n=1) were at high risk of bias, and among the nonrandomized studies, 33.3% (n=2) were at serious risk of bias. Across the 4 outcome domains, clinical outcomes showed a generally favorable direction for Cobb angle and flexibility but inconsistent evidence for the angle of trunk rotation (ATR); functional outcomes were consistently favorable for respiratory function and postural control; patient-reported quality-of-life outcomes were predominantly favorable, whereas evidence for pain and body image was inconsistent; implementation outcomes showed the least favorable overall direction of effect, with adherence and dropout more closely tied to supervision intensity than technology category. The main findings remained robust in sensitivity analyses; no serious adverse events were reported, and the overall certainty of evidence was low. Conclusions:Digital health interventions showed a generally favorable direction of effect for spinal alignment, function, and quality of life; however, evidence certainty was low, and outcomes should be interpreted with caution. To our knowledge, this is the first review to integrate diverse digital interventions into a single framework of 5 technology categories and 3 maturity tiers and to synthesize effectiveness across technologies, unlike prior single-technology or single-function reviews. Its main contribution is to shift the focus from technology effectiveness toward sustained use and to identify human supervision as a key factor influencing implementation. Clinically, technologies may be selected by maturity tier and paired with an appropriate supervision strategy to extend access to rehabilitation. Larger, long-term multicenter trials are needed for confirmation.
Background:Limited public understanding of randomized controlled trials (RCTs) hinders recruitment, retention, and confidence in research. Early exposure to trial concepts may strengthen health literacy and research engagement. The Kid's Trial was a global, decentralized, child-led study that cocreated and conducted an RCT to help children understand trials and their importance and to improve critical thinking. Objective:This paper evaluated the feasibility and methodological implications of engaging children in the cocreation and conduct of a fully online RCT. Methods:The Kid's Trial ed a dedicated website guiding children through each step of designing and conducting an RCT. Materials were codeveloped with 2 patient and public involvement groups of children and parents. Any child aged 7-12 years could take part in as many steps as desired. Recruitment combined online and offline strategies, and engagement and self-reported learning were descriptively analyzed. The cocreated Randomized Evaluation of Sleeping With a Toy or Comfort Item (REST) trial was a 2-arm, pragmatic RCT comparing one week of sleeping with a comfort item versus without a comfort item. The primary outcome was sleep-related impairment, and the secondary outcome was overall sleep quality. Analyses followed an intention-to-treat (ITT) approach using mixed effects models adjusted for baseline measures. Results:Overall, 224 children participated in at least one step of The Kid's Trial. Participation varied: 37% (n=82) completed one step, and 21% (n=48) completed 6 surveys. The REST trial randomized 139 children, with 73% (n=101) completing outcome surveys. Adjusted mean differences (intervention - control) were -0.53 (95% CI -3.40 to 2.34) for sleep-related impairment (P=.71) and 0.28 (95% CI 0.01-0.55) for sleep quality (P=.04). The difference was small and was not supported by sensitivity analyses. Poststudy responses (n=20) suggested improved self-reported trial understanding among respondents but were limited by low response rate and potential selection bias. Conclusions:The Kid's Trial demonstrates the feasibility of a decentralized, child-led RCT cocreated through participatory citizen-science methods. Children can meaningfully contribute to trial design and conduct, and experiential participation may support engagement with trial concepts. Future studies should enhance engagement through community partnerships, shorter intervals between steps, and embedded learning assessments to improve inclusivity and retention.
Background:Problematic internet use (PIU) in adolescents often co-occurs with psychopathological symptoms. Social connectedness (SC) is a potential protective factor for both PIU and psychopathological symptoms, but how SC interacts with the PIU-psychopathology system remains unclear. Objective:This study aimed to characterize the factor-level comorbidity network linking PIU with psychopathological symptoms among adolescents at high risk of PIU and to clarify how offline and online SC function in this system. We also applied Bayesian network structure learning to explore algorithm-preferred edge orientations. Methods:This multicenter study included 9407 adolescents aged 12 to 18 years from 4 provinces in China. PIU, depression, anxiety, irritability, repetitive thoughts and behaviors, anger, aggression, and SC were assessed. We estimated an undirected factor-level symptom network and a directed acyclic graph derived from a Bayesian network structure to characterize associations and conditional dependency structures among SC, PIU, and 8 psychopathological symptoms. Results:The integrative symptom network encompassing PIU, internalizing and externalizing symptoms, and SC showed great stability and accuracy. Anxiety, anger, and depression were identified as central bridging symptoms that reinforce the interplay between PIU and other psychopathological symptoms. Offline SC exhibited the highest number of negative edges and the highest bridge expected influence value, followed by online SC. In the exploratory directed acyclic graph, offline SC showed algorithm-selected outgoing arrows to PIU salience and to several internalizing and externalizing symptoms, including depression, anxiety, verbal aggression, and hostility. Conclusions:PIU and psychopathological symptoms formed a closely interconnected factor-level network among adolescents in the high-risk PIU group. Offline SC occupied a prominent negative bridge position, whereas online SC showed a secondary negative network position and was positively connected with offline SC. These findings support further evaluation of offline SC as a candidate prevention and intervention target.
