Date Submitted: Nov 24, 2025. Open Peer Review Period: Nov 25, 2025 - Jan 20, 2026.

Date Submitted: Nov 24, 2025. Open Peer Review Period: Nov 25, 2025 - Jan 20, 2026.

Background: Globally, TB programs have enhanced efforts to improve case detection, treatment initiation and monitoring of treatment outcomes. However, there is less attention on reducing the number of presumptive TB patients that never get tested for TB or the confirmed ones that never start treatment in endemic areas, such as Uganda. These losses slow down progress towards attaining the 2035 End TB goals. The World Health Organization (WHO) recommends mobile health (#mHealth) interventions such as short message services (SMS), phone calls, mobile phone applications and #Digital monitoring devices to improve universal health coverage. To our knowledge, there is limited evidence on whether these #mHealth interventions can increase linkage to care for presumptive TB patients, particularly in sub-Saharan Africa. Objective: We propose a trial (MILEAGE4TB) whose aim is to assess the effect of SMS reminders, phone call reminders and transport incentives on improving linkage to care of presumptive TB patients in Uganda. Methods: This will be a five-arm individually randomised controlled trial among presumptive TB patients aged 18 years and above, who are referred for Xpert MTB/RIF testing. Participants will be randomized (2:2:2:1:1) to: (i) standard of care (ii) SMS only (iii) phone call only (iv) SMS and a transport incentive (v) phone call and a transport incentive. An estimated sample size of 2389 participants will be considered. Participants will be followed up for 30 days to see if they tested for TB and collected their results. Those who test positive for TB, will be followed for 14 days to measure treatment initiation. Analysis will be by intention to treat. Modified Poisson regression models will be used to estimate the effects of the interventions on completion of TB diagnosis and treatment initiation. Results: Results from this trial are not yet available. Recruitment of participants commenced on 14th August 2023. As of 28th December 2024, a total of 2355 participants (98.6%) were recruited. Results are expected once all #Study activities are complete in June 2025. Conclusions: This #RCT #ClinicalTrial will provide insights on use of #mHealth interventions to improve linkage to care of presumptive TB patients. Clinical Trial: The trial was registered at ClinicalTrials.gov, Number:NCT05964842.

Background: Subjective report of pain remains the gold standard for assessing symptoms in patients with chronic pain and their response to analgesics. This subjectivity underscores the importance of understanding patients’ personal narratives, as they offer an accurate representation of the illness experience. Objective: In this pilot study involving 20 patients with chronic low back pain (CLBP), we applied emerging tools from natural language processing (NLP) to derive quantitative measures that captured patients’ pain narratives. Methods: Patients’ narratives were collected during recorded semistructured interviews in which they spoke about their lives in general and their experiences with CLBP. Given that NLP is a novel approach in this field, our goal was to demonstrate its ability to extract measures that relate to commonly used tools, such as validated pain questionnaires and rating scales, including the numerical rating scale and visual analog scale. Results: First, we showed that patients’ utterances were significantly closer in semantic space to anchor sentences derived from validated pain questionnaires than to their antithetical counterparts. Furthermore, we found that the semantic distances between patients’ utterances and anchor sentences related to quality of life were strongly correlated with reported CLBP intensity on the numerical rating and visual analog scales. Consistently, we observed significant differences between individuals with low and high pain levels. Conclusions: Although our small sample size limits the generalizability of these findings, the results provide preliminary evidence that NLP can be used to quantify the subjective experience of chronic pain and may hold promise for clinical applications.

Background: Medical, psychiatric-psychosomatic facilities are confronted with a variety of daily challenges that affect working conditions, the mental health of employees and the quality of patient care. This project focuses on the work-related challenges faced by healthcare professionals in psychiatric-psychosomatic clinics in Germany. Objective: Therefore, the aim of the current #Research is to investigate the interactions between individuals and their social environment, identify psychological and organizational challenges and job demands to use these findings to inform the development of a participatory, evidence-based intervention. Methods: This two-phase #Research is grounded in the job demands-resources model (JD-R). #Study phase one (needs assessment) employs a cross-sectional online survey with healthcare professionals in German psychiatric-psychosomatic clinics to assess job demands, resources, and outcomes in a target sample of 600 participants (power analysis). #Study phase two (co-design of a training) involves co-creatively designing an intervention based on survey findings through participatory workshops with at least 20 participants. Analyses include regression and moderation tests (SPSS), and qualitative data analysis to co-design training. Results: The recruitment of participants is planned to be finished by October 2025. The co-designing of workshops (phase two) will be started in February 2026. As this is a #Study #Protocol, results are not available yet. Conclusions: This current #Research examines the work-related challenges faced by healthcare professionals in psychiatric-psychosomatic clinics. It is expected that burnout, engagement, and psychological safety will likely emerge as central mediating and moderating variables. As the findings of phase one serve as a basis for the development of an intervention this #Research seeks to improve the well-being of healthcare professionals in psychiatric-psychosomatic institutions sustainably. Clinical Trial: ClinicalTrials.gov NCT06867601; https://clinicaltrials.gov/#Study/NCT06867601

Background: Nonpharmacological interventions are important prevention strategies for mild cognitive impairment (MCI), but effects vary significantly between individuals based on personal characteristics, while current practice relies on experience-based approaches lacking personalized, adaptive intervention strategies. Objective: The objective of our study was to develop and evaluate evidence-based adaptive intervention strategies for optimizing cognitive function among older adults with MCI using a Sequential Multiple Assignment Randomized Trial (SMART) design, comparing the effectiveness of cognitive training (CT) combined with virtual reality Taichi (VRTC) versus offline Taichi (OffTC) versus control, and to identify baseline characteristics that predict treatment response for personalized intervention delivery. Methods: We recruited 92 community-dwelling adults aged ≥60 years diagnosed with MCI from 3 districts in Shanghai, China. A 24-week SMART was conducted between April and December 2023. During the first stage (weeks 1-12), participants were randomly assigned to control (n=26) or intervention groups receiving CT combined with either OffTC (n=33) or VRTC (n=34). Nonresponders at week 12 were rerandomized to alternative or intensified interventions during the second stage (weeks 13-24). The primary outcome was the Memory Guard score (MGs) at 24 weeks. Dynamic treatment regimen analysis assessed optimal adaptive strategies using regression models. Results: A total of 81 participants completed the trial. CT+VRTC demonstrated significantly superior cognitive improvement compared to control (5.10 MGs, 95% CI 2.93-7.27; Cohen d=1.425, 95% CI 0.785-2.060; P

Background: Background: The Symptom iNput Clinical (SYNC) system was developed to enhance the timely reporting of Head and Neck #Cancer (HNC) symptoms and to ensure that high-risk patients receive faster diagnoses. A key feature of the system is a #Digital questionnaire co-designed with patient representatives to accommodate varying levels of #Digital literacy. The system integrates a validated algorithm that assigns risk scores to categorise cases as low or high risk, and a dashboard that supports clinicians by providing them with patient reports. However, the development process encountered challenges that necessitates a systematic evaluation of the process, roles, and experiences of team members. Objective: Objective: This #Study aims to identify challenges faced during development, how these challenges were addressed, and the implications for future #Digital health innovations. Methods: Methods: A qualitative single-case #Study approach will be employed, focusing on individuals involved in the SYNC system’s development. Participants will be selected using a combination of purposive and snowball sampling to ensure diverse perspectives, using meeting minutes and recommendations from key stakeholders. A total of 8 to 12 participants will be interviewed, representing clinical, #Research, and IT roles. Data collection will involve semi-structured interviews which will be conducted through Microsoft Teams. The interviews are expected to last between 40–60 minutes each. These interviews will be audio-recorded, transcribed, and analysed using framework analysis in Dedoose. Actor-Network Theory (ANT) will guide the analysis by mapping interactions between human and non-human actors, such as developers, clinicians, and technological tools, to understand how they influenced the project’s outcomes. Participant confidentiality will be maintained through data encryption, de-identification, and secure storage. Results: Expected Results: The #Study anticipates identifying key barriers and facilitators in the SYNC system’s development, including technical, organisational, and collaboration-related challenges. The findings are expected to provide a detailed account of the challenges encountered, such as delays, security concerns, and coordination issues, an insight into how these challenges were mitigated and lessons learned and recommendations for improving #Digital health technology development, including best practices for co-design, technical integration, and stakeholder engagement. Conclusions: Conclusion: This case #Study will provide valuable insights into the complexities of developing #Digital health technologies, particularly in collaborative, multi-stakeholder environments. Documenting the challenges encountered in the SYNC system’s development will contribute to best practices in #Digital health innovation.

Background: #ehrs (EHR) store rich data involving #medical entities such as diagnoses, treatment procedures, and prescribed medications, offering valuable resources for developing automated systems for hypertension medication recom-mendations. The entities in EHR show significant synergies during treatment. How-ever, existing medication recommendation methods mainly focus on homogeneous graphs, overlooking the crucial synergistic relationships among heterogeneous #medical entities. Also, accurately modeling the progression of hypertension using EHR is essential for precise medication recommendations, but current approaches often lack comprehensive temporal modeling and don't fully meet clinical require-ments. Objective: To overcome the challenges in existing methods, introduce a novel model for hy-pertension medication recommendation that leverages the synergy and selectivity of heterogeneous #medical entities. Methods: First, use #patient EHR to construct both heterogeneous and homogeneous graphs. Then, capture the inter-entity synergies with a multi-head graph attention mecha-nism to enhance entity-level representations. Next, apply a dual-layer temporal se-lection mechanism to calculate selective coefficients between current and historical visit records and aggregate them to form refined visit-level representations. Finally, determine medication recommendation probabilities based on these comprehen-sive #patient representations. Results: Experimental evaluations on the real-world dataset MIMIC-IV v2.2 show that the model achieves a Jaccard similarity coefficient of 55.82%, a precision-recall AUC of 80.69%, and an F1 score of 64.83%, outperforming baseline models. Conclusions: The findings indicate the superior efficacy of the introduced model in medication recommendation, highlighting its potential to enhance clinical decision-making in the management of hypertension.

Background: The cerebrospinal fluid (CSF) drainage test is crucial for evaluating patients with idiopathic normal pressure hydrocephalus (iNPH) before shunt surgery, while traditional methods have low sensitivity. Objective: This study aimed to evaluate the improvement of cognitive and gait parameters after external lumbar drainage (ELD) through the application of digital tests; and investigate the predictive value of digital cognitive and gait assessments for shunt outcomes. Methods: Seventy probable iNPH patients were enrolled from the West China Hospital of Sichuan University. All patients underwent traditional and digital cognitive and gait assessments at baseline and 3-day after ELD. Thirty-nine patients received lumboperitoneal shunt, and were followed up at 3, 6, and 12 months postoperatively by modified Rankin scale (mRS) and the Japanese iNPH grading scales (iNPHGS). The Firth’s logistic regression models and receiver operating characteristic (ROC) analysis were used to assess the predictive value of digital tests for shunt response. Results: The performance of the digital tests including one-back test (P=0.01), Stroop color-word test (P=0.009), and gait parameters exhibited significant improvement 3-day post-ELD. Of thirty-nine shunted patients, 34 exhibited at least 1-point improvement in mRS or iNPHGS post-shunt at their last follow-up. Greater improvement rate in combined digital neuropsychological and gait tests after ELD is associated with a lower risk of unfavorable shunt outcome (adjusted odds ratio = 0.98, P=0.032). Combined digital neuropsychological and gait tests outperformed traditional tests in distinguishing shunt responders (Area under ROC curves=0.92 vs. 0.55, P=0.015). Conclusions: Our study has shown that digital neuropsychological and gait tests enhance predictive efficacy when compared to traditional testing methods. It could serve as objective evaluation tools for assessing patients with iNPH.

Background: The multi-disciplinary life course theory emphasizes the relation between a person’s choices and their socio-economic context and their capacity to make decisions within existing opportunities/constraints. Older age is particularly characterized by social and environment conditions that may impact people’s use of technology and eHealth applications. Objective: This research aims to present an overview of eHealth applications use among older Canadian adults and examine the relationship between eHealth use and social and health care (HC) system engagement determinants. Methods: We conducted a national cross-sectional survey of 2000 older adults in Canada assessing their technology (e.g., tablets, computers etc.) and eHealth applications (e.g., mApps, fall detection and telemonitoring technologies, Internet etc.) use, social determinants (e.g., socio-demographic characteristics, living conditions etc.) and aspects related to HC system engagement and use (e.g., home care, hospitalizations etc.). Results: Findings indicate technological readiness (85% owned computers, 74% used Internet daily/weekly, 90% used e-mail) among older Canadian adults, although it does not translate into eHealth applications use. Internet use to connect with HC professionals, access results/patient portals, or book medical appointment was limited. The use of wearables, telemonitoring, and fall detection technologies was low (11.9%, 9.4%, 4.2%, respectively). A digital divide exists within the older adults population that is underscored by significant associations between eHealth use and social determinants and HC system engagement. This raises concerns about whether those with higher needs and limited resources (e.g., cannot benefit from home care services, cannot acquire FDT, are unable to afford living in a retirement home) have access to and are capable of benefitting from eHealth applications. Conclusions: The results establish a baseline for ongoing eHealth monitoring that can be used for international comparisons and benchmarking. Evidence-informed eHealth policies should focus on the older adults population and eHealth programs that consider social determinants and engagement with the HC system to improve health equity, reach and access to care.

Background: Preventive strategies integrated with digital health and artificial intelligence (AI), have significant potential to mitigate the global burden of atherosclerotic cardiovascular disease (ASCVD). AI-enabled clinical decision support (CDS) systems increasingly provide patient-specific insights beyond traditional risk factors. Despite these advances, their capacity to enhance clinical decision-making in resource-constrained settings remains largely unexplored. Objective: We conducted a randomised controlled study to assess the effect of AI-based CDS on 10-year ASCVD risk assessment and management in primary prevention. Methods: In a three-way within-subject randomised design, doctors completed nine clinical vignettes representative of primary care presentations in a resource-constrained outpatient setting. For each vignette, participants assessed 10-year ASCVD risk and made management decisions using either a conceptual prototype of AI-based CDS, automated CDS, or no decision support. The conceptual prototype represented contemporary risk calculators based on traditional machine learning models (e.g., random forest, neural networks, logistic regression) that incorporate additional predictors alongside traditional risk factors. Primary outcomes were correct risk assessment and patient management (prescription of aspirin, statins, and anti-hypertensives; referral for advanced examinations). Decision-making time and perceptions about AI utility were also measured. Results: 102 doctors from all seven geographical regions of Indonesia participated. Most participants were 26–35 years (83%), 56% male, with a median of six years of clinical experience (IQR=4.75). AI-based CDS improved risk assessment by 27% (2(2, n=102) = 48.875, P

Background: We previously established the efficacy and cost-effectiveness of a web-based physical activity (PA) intervention for Latina adults, which increased PA, but few participants met PA guidelines, and long-term maintenance was not examined. A new version with enhanced intervention features was found to outperform the original intervention in long-term guideline adherence. Objective: This study aimed to determine the costs and cost-effectiveness of the enhanced multitechnology PA intervention compared to the original web-based intervention in increasing minutes of activity and adherence to guidelines. Methods: Latina adults (N=195) were randomly assigned to receive a Spanish-language, individually tailored web-based PA intervention (original) or the same intervention with additional phone calls and interactive SMS text messaging (enhanced). PA was measured at baseline, 12 months (end of active intervention), and 24 months (end of tapered maintenance) using self-report (7-day PA recall interview) and ActiGraph accelerometers. Costs were estimated from a payer perspective and included all features needed to deliver the intervention, including staff, materials, and technology. Cost-effectiveness was calculated as the cost per additional minute of PA added over the intervention and the incremental cost-effectiveness ratios of each additional person meeting guidelines. Results: At 12 months, the costs of delivering the interventions were US $16 per person per month in the enhanced arm and US $13 per person per month in the original arm. These costs decreased to US $14 and US $8 at 24 months, respectively. At 12 months, each additional minute of self-reported activity in the enhanced group cost US $0.09 compared to US $0.11 in the original group (US $0.19 vs US $0.16 for ActiGraph), with incremental costs of US $0.05 per additional minute in the enhanced group beyond the original group. At the end of the maintenance period (24 mo), costs per additional minute decreased to US $0.06 and US $0.05 (US $0.12 vs US $0.10 for ActiGraph), with incremental costs of US $0.08 per additional minute in the enhanced group (US $0.20 for ActiGraph). Costs of meeting PA guidelines at 12 months were US $705 in the enhanced group compared to US $503 in the original group and increased to US $812 and US $601 at 24 months, respectively. The incremental cost-effectiveness ratio for meeting guidelines at 24 months was US $1837 (95% CI US $730.89-US $2673.89) per additional person in the enhanced group compared to the original group. Conclusions: The enhanced intervention was more expensive but yielded better long-term maintenance of activity, costing US $1837 per extra person meeting guidelines beyond those in the original group. Both conditions were low cost relative to other medical interventions. The enhanced intervention may be preferable in populations at high risk, where more investment in meeting guidelines could yield more cost savings. Trial Registration: ClinicalTrials.gov NCT03491592; https://clinicaltrials.gov/study/NCT03491592

Background: While the positive effects of digital technology on cognitive function are established, the specific impacts of different technology activities on distinct cognitive domains remain underexplored. Objective: This study aimed to examine the associations between transitions in and out of various technology activities and cognitive domain trajectories among community-dwelling older adults without dementia. Methods: Data were drawn from 5,566 community-dwelling older adults without dementia who participated in the National Health and Aging Trends Study (NHATS) from 2015 to 2022. Technology activities assessed included online shopping, banking, medication refills, social media use, and checking health conditions online. The cognitive domains measured were episodic memory, executive function, and orientation. Asymmetric effect models were used to analyze the associations between technology activity transitions and cognitive outcomes, adjusting for demographic, socioeconomic, and health-related covariates. Lagged models were applied for sensitivity analysis. Results: From the asymmetric effect models, the onset of online shopping (β = 0.046**), medication refills (β = 0.073***), and social media (β = 0.065**) were associated with improved episodic memory. The cessation of online shopping was associated with faster episodic memory decline (β = -0.023*). In contrast, the cessation of online banking (β = -0.078**) and social media use (β = -0.066**) were associated with decreased episodic memory. The initiation of instrumental, social, and health-related technology activities was associated with slower cognitive decline in orientation. The lagged models further emphasized the effects of stopping online banking and starting online medication refills in relation to episodic memory, as well as the positive associations between online shopping and social media use on orientation. Noting that all the significances were of small magnitude. Conclusions: Combining findings from the main and sensitivity analyses, results suggest that interventions designed to support episodic memory in older adults should emphasize promoting the use of online medication refill services and sustaining engagement with online banking, particularly among those already established the habit. To support orientation, strategies should focus on facilitating the adoption of online shopping and social media use, helping older adults become comfortable navigating these platforms. Future trials are needed to assess the clinical relevance of targeted interventions on specific cognitive domains, with the goal of promoting the initiation and sustainment of digital activities to help mitigate domain-specific cognitive decline in aging populations.

Background: Digital inclusion has become increasingly important in promoting healthy aging, yet its impact on mental health among older adults appears complex and heterogeneous. While existing research has identified associations between digital technology use and depression risk in older adults, the role of cognitive function as a moderator and the underlying mechanisms remain understudied. Objective: Data from the 2020 wave of the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey of 18,673 Chinese adults aged 60 and above. Methods: We examined cognitive function's moderating role in the relationship between digital inclusion and depression risk. We constructed interaction effect models to test the moderation hypothesis and employed path analysis to investigate multiple pathways (direct effect, cognitive enhancement, and social participation) through which digital inclusion influences depression. Results: Cognitive function significantly moderated the relationship between digital inclusion and depression risk (β = -0.002, p < 0.05), with stronger protective effects observed among older adults with higher cognitive function. Path analysis revealed that digital inclusion affected depression through three main pathways: a direct pathway (accounting for 66.7% of the total effect), a cognitive function mediation pathway (8.3%), a social participation mediation pathway (8.0%), and a sequential mediation pathway through cognitive function and social participation (2.8%). The remaining 14.2% was attributed to unexplained mechanisms. Conclusions: Our findings reveal a "cognitive threshold effect," suggesting that older adults require a baseline level of cognitive function to derive mental health benefits from digital participation. This study advances understanding of digital inclusion's differential impacts and mechanisms of action, supporting the development of cognitive-informed digital inclusion programs and suggesting the integration of cognitive training and digital skills development in mental health interventions for older adults.

Background: Systematic symptom management is a crucial component in #Patient-centred #Cancer care. Despite advancements in symptom management and the development of numerous electronic #Patient-reported outcome tools (ePROMs), integrating these tools into clinical practice remains challenging. Engaging key stakeholders, including #Patients, in the development of ePROM tools is pivotal to fostering the adaption of such tools. As part of an innovation and implementation #Study aimed at enhancing efficiency and #Patient-centred care through the development of #digital #Patient-centred care pathways, we explored #Cancer #Patients’ perspectives on current clinical practice regarding symptom management and #Patient-centred care, as well as their needs and preferences related to ePROMs. Objective: To explore #Cancer #Patients’ perspectives on PCC and symptom management, including their experience with current clinical practice and their views on how ePROMs might enhance #Patient-centred follow-up. Methods: A two-stage qualitative design was employed. In Stage 1 semi-structured individual interviews were conducted to gain an in-depth understanding of #Patients’ experiences with current clinical practice, including perceived challenges and unmet needs. Stage 2 involved structured interviews to further explore #Patients’ perspectives on the potential role of ePROMs in enhancing #Patient-centred follow up. Results: A total of ten #Patients were included in the #Study, participating in either or both stages. Two main themes were developed through a reflexive thematic analysis process: 1) Symptom management in the shadow of disease-centred care, and 2) ePROMs: Bridging holistic care and disease management. Theme 1 highlighted how #Patients made sense of symptom management within a healthcare context primarily focused on disease treatment and progression. Their narratives revealed that bio#Medical concerns often dominated clinical encounters, while #Patients’ broader lived experiences and symptom-related needs were marginalised. #Patients shared an understanding that it was their own responsibility to redirect the focus of clinical consultations toward symptoms. While they generally expressed satisfaction with the care received, they also described a sense of unmet needs that remained unaddressed. The second theme explored how #Patients made sense of the potential role of an ePROM tool in supporting more #Patient-centred #Cancer care. Their accounts revealed both perceived barriers and facilitators to its use, shaped by the expectations and needs that contrasted with current clinical practices. Central to this was a belief, emerging through engagement with the conceptual tool’s functionalities, that it could enable a more holistic approach to care, extending beyond physical symptom to encompass the lived experience of #Cancer. Conclusions: #Patients often felt personally responsible for ensuring their symptoms were addressed, indicating shortcomings in follow-up and communication. ePROMs were identified as a promising tool to strengthen #Patient-centred care by amplifying #Patient voices and enabling more holistic and responsive follow-up. Integrating ePROMs into routine care may improve symptom visibility, foster shared understanding between #Patients and healthcare professionals, and support more equitable care delivery.

Background: Large Language Models (LLMs) have great potential to improve and make the work of clinicians more efficient. Previous studies have mainly focused on web-based services such as ChatGPT, often with simulated cases. For the processing of personalized patient data, web-based services have major data protection concerns. Ensuring compliance with data protection and medical device regulations therefore remains a critical challenge for adopting LLMs in clinical settings. Objective: This retrospective single-center study aimed to evaluate locally run LLMs (Gemma 2, Mistral Nemo and Llama 3) on providing diagnosis and treatment recommendation for real world outpatient cases in Otorhinolaryngology (ORL). Methods: Outpatient cases (n=30) from regular consultation hours and the emergency service at a university hospital ORL outpatient department were randomly selected. ORL doctors’ documentation including anamnesis and examination results were passed to locally run LLMs (Gemma 2, Mistral Nemo and Llama 3) which were asked to provide diagnostic and treatment strategies. Recommendations of the LLMs and the treating ORL doctors were rated by three experienced ORL consultants on a 6-point Likert scale for medical adequacy, conciseness, coherence and comprehensibility. Moreover, consultants were asked whether the answers pose a risk for the patient's safety. A modified Turing test was performed distinguishing LLMs from doctors' responses. Finally, the potential influence of the information generated by the LLMs on the raters’ own diagnosis and treatment opinions was evaluated. Results: Over all categories, ORL doctors achieved superior (p

Background: Peers are known to influence the health behaviours and attitudes of adolescents, yet recruitment of these networks is challenging. Previous studies have used web-based respondent-driven sampling (WebRDS) methods to recruit this population, yet none have experimentally investigated the impact of financial reimbursements. Objective: This study aimed to (1) compare the effectiveness of two financial reimbursement strategies for recruiting adolescents and their peer networks, and (2) explore factors associated with successfully recruiting peers. Methods: A parallel design randomised controlled trial was conducted in which participants (seeds) were randomly allocated to a fixed cash reimbursement (control) or scaled reimbursement (experimental) group as a strategy to be recruited into a web-based peer network study. Seeds aged 16-18 were recruited online through social media advertisements and through an online student panel. They completed a web-based survey which assessed eligibility and included questions about their friends (peers). Allocation occurred through the survey platform using a simple randomisation method. In the fixed group, all participants in a peer network received AUD $5 (USD $3.29); in the scaled group, all participants in a peer network received an additional AUD $5 (USD $3.29) per peer who successfully completed the survey (to a maximum of AUD $30 each; USD $19.72). Participants and researchers were not blinded to intervention groups. The intervention was automated and did not require direct researcher contact. The primary outcome was recruitment of peers to complete the web-based survey (proportion of nominated peers). The number of peers recruited was a secondary outcome. In secondary analyses we identified peer- relationship- and seed-level variables associated with successfully recruiting peers. Results: Of 463 seeds allocated to an intervention (scaled n=221; fixed n=242), 319 (68.9%) had complete data for analysis (scaled n=157 (71.0%); fixed n=162 (67.0%)). A total of 11.9% of seeds successfully referred peers (18.5% scaled group; 5.6% fixed group). Those in the scaled reimbursement intervention were 3.80 times more likely to successfully recruit their peers than those in the fixed reimbursement intervention (proportion ratio (PR): 3.80, 95% CI 1.78–8.09). Similarly, the average number of peers recruited differed by 0.19 per seed between the scaled and fixed intervention groups (95% CI = 0.11–0.28). Peer recruitment success was similar regardless of the gender, age, education level, and network size of seeds, or the gender, age and closeness of peers. Seeds recruited through social media were more likely to successfully recruit their nominated peers then those recruited through a research panel (PR: 2.20, 95% CI = 1.06–4.55). Conclusions: Scaled reimbursements resulted in significantly greater recruitment of peers than fixed reimbursements, however, the total number of peers recruited was low. Other than recruitment site, no demographic or relational factors were found to impact on peer recruitment success. Despite the relative effectiveness of scaled reimbursements, recruiting peers through WebRDS remained challenging. Greater-value incentives and stronger initial recruitment through social media may be needed to recruit large numbers of friend networks. Clinical Trial: International Registered Report Identifier (IRRID): DERR1-10.2196/44813

Background: The impact of smartphone use on sleep remains intensely debated. Most existing studies used self-reported smartphone use data. Moreover, few studies have simultaneously examined associations of both smartphone addiction and objectively measured smartphone use with sleep, and the dose-response relationship between smartphone use and sleep risk has been consistently overlooked, requiring systematic and further research on this topic. Objective: To examine the associations of smartphone addiction and objectively measured smartphone use with sleep quality and duration. Methods: This cross-sectional study enrolled 17,713 participants from a university in China. We assessed objective smartphone screen time and unlocks by collecting screenshots of use records, and measured smartphone addiction using a validated questionnaire. Sleep quality and duration were estimated via the Pittsburgh Sleep Quality Index. Binary logistic regression, linear regression, and restricted cubic splines (RCS) regression models were used for the analyses. Results: 2,533 participants (14.3%) met the criterion for poor sleep, with a mean (SD) sleep duration of 507.1(103.2) minutes/night. Notably, university students with smartphone addiction exhibited 184% higher risk of poor sleep(OR=2.84; 95%CI: 2.59 to 3.11) and 15.47 minutes shorter nighttime sleep duration (β=-15.47; 95%CI: -18.53 to -12.42) compared to those without smartphone addiction. Regarding objectively measured smartphone use, participants with ≥63 hour/week of smartphone screen time had 22% higher odds of poor sleep(OR=1.22; 95%CI: 1.08 to 1.37) and 6.66 minutes shorter nighttime sleep duration (β=-6.66; 95%CI: -10.19 to -3.13) compared to those with 0–21 hour/week, while those with 21~42 hour/week of smartphone screen time had 5.47 minutes longer nighttime sleep duration (β=5.47; 95%CI: 1.28 to 9.65). Similarly, compared to those with 0–50 times/week, participants with ≥400 times/week smartphone unlocks showed 61% higher odds of poor sleep(OR=1.61; 95%CI: 1.41 to 1.85) accompanied by 4.09 minutes shorter nighttime sleep duration (β=-4.09; 95%CI: -8.08 to -0.09), while those with 50~150 times/week of unlocks had 5.84 minutes longer sleep duration (β=5.84; 95%CI: 2.32 to 9.36). An inverted U-shaped association between objectively measured smartphone screen time and sleep duration was observed(P for non-linearity＜.001). Conclusions: Smartphone addiction, excessive objectively measured smartphone screen time, and unlocks are associated with both sleep quality and duration. RCS analyses revealed nuanced different dose-response relationships, with an inverted U-shaped association observed between smartphone screen time and sleep duration.

Background: Return-to-play (RTP) guidelines for concussion recommend a multimodal approach to assess recovery including symptoms, balance, exertion tolerance, and cognition. However, existing assessments do not reflect the speed or complexity of multidomain skill integration in sport. We developed R2Play, a dynamic multidomain RTP assessment tool, and previously established proof-of-concept by demonstrating alignment with design objectives. Objective: (1) Assess the feasibility of R2Play according to a priori criteria for usability, reliability, practicality, and safety; (2) Examine physical exertion levels during R2Play as a preliminary marker of face validity; (3) Understand clinician and youth perspectives on the feasibility, face validity, potential value, and challenges associated with R2Play. Methods: A convergent parallel mixed methods design was used. #Rehabilitation clinicians were paired with youth cleared to RTP post-concussion to complete R2Play together and provide feedback through individual semi-structured interviews. Feasibility was assessed based on pre-defined criteria for usability (clinician ratings on System Usability Scale, SUS), practicality (assessment duration), reliability (technical issues), and safety (adverse events). Face validity was evaluated with a target of youth achieving ≥80% of age-predicted maximal heart rate (HR) or rating of perceived exertion (RPE) ≥7/10. Interviews explored perspectives on feasibility domains and face validity, analyzed using content analysis. Quantitative and qualitative results were merged via joint display to identify areas of convergence, divergence, and complementarity. Results: Participants included 10 youth (ages 13-20 years) with a history of concussion and five clinicians (n= 2 physiotherapists, n= 2 occupational therapists, n= 1 kinesiologist). Success criteria were met or approached for all feasibility domains. Clinician-rated usability was good-to-excellent (SUS= 84.00± 6.02) and youth reported that instructions were easy to learn. There were no catastrophic technical or user errors interrupting assessments. Configuration was completed in 5.74± 1.09 minutes and assessments took 26.50± 6.02 minutes. There were no safety or symptom exacerbation incidents requiring assessment modification or cessation. R2Play elicited vigorous intensity physical exertion (peak HR= 90.10± 5.78% age-predicted maximal, peak RPE= 5.50± 1.72), with target exertion criteria met for 9/10 youth. Clinician and youth feedback confirmed that R2Play reflects key elements of sport across physical, cognitive, and perceptual domains, making it a potentially valuable tool for assessing readiness to RTP and informing #Rehabilitation treatment planning for unresolved issues. Mixed methods meta-inferences provided enhanced insights regarding how to improve the usability, practicality, safety and face validity of R2Play. Conclusions: Findings support the feasibility and face validity of R2Play, a new multidomain assessment tool for youth with concussion, demonstrating excellent usability, vigorous physical exertion demands, and promising feedback regarding its potential to fill gaps in the RTP process. Future work is underway to establish the cross-site feasibility of R2Play and evaluate its content validity by establishing the physical, cognitive, and perceptual loading of assessment levels.

Background: Hand motor dysfunctions significantly reduce the performance of stroke survivors. This affects their motor task abilities to perform effectively. Patients receive slow intervention due to interventional limitations in stroke rehabilitation, which pose challenges for sustaining enduring improvements. The immersive #VirtualReality #VR (VR) #Games in this study utilized an innovative approach to cognitive engagement within visual training feedback to attain long-lasting improvements. Objective: This study aimed to evaluate the effectiveness of fully immersive #VirtualReality #VR (VR) hand #Games compared to conventional physical therapy and to assess the correlation between electromyographic data and clinical outcome instruments in subacute stroke patients for improving hand motor functions. Methods: A randomized controlled study was conducted for 52 subacute stroke patients who met the inclusion criteria. These patients were equally allocated to the experimental group (n=26) and control group (n=26). The experimental group underwent both fully immersive VR hand #Games intervention and conventional physical therapy, whereas the control group only received conventional physical therapy. Due to the nature of the intervention, the study was unblinded, and both therapists and patients were aware of the interventions. Both groups underwent 24 intervention sessions, four days a week, for six weeks. Both groups also underwent two weeks of follow-up. Clinical outcome measures Fugl–Meyer assessment-upper extremity (FMA-UE), Action research arm test (ARAT), and Box and block test (BBT), were assessed for motor recovery and functional performance. Minimal clinically meaningful differences (MCID) were utilized to compare with clinical outcome measures to examine intervention changes perceived clinically meaningful improvements. Furthermore, the correlation between electromyography and clinical outcome measures and the weekly progression of movement performance were also evaluated for improvements in hand motor functions. Results: FMA-UE, ARAT, and BBT revealed significant differences (all, P

Background: While China's aging population and strained healthcare resources heighten the need for effective health promotion, traditional community health education faces barriers such as passive participation among older adults, short-term behavioural changes, and limited sustainability. Objective: This study aims to develop and examine the impact of an innovative community healthy self-help education model for older adults (CHSE-O) on healthy behavior and active health awareness among older people. Methods: A mixed-methods study was conducted enrolling a total of 80 older participants, including a 12-month pre-post controlled trial in five communities in Shanghai, China. Health behaviors, autonomy, and digital health literacy were assessed at baseline, 6 months, and 12 months using standardized scales (measuring Health-Promoting Lifestyle, Self-Rated Abilities for Health Practices, Healthy Self-Management Behaviors, Participation/Autonomy, and eHealth Literacy). Comparisons of scale scores at each time point were analysed using repeated measures ANOVA. Semi-structured interviews were conducted after the intervention (n=11), focusing on the dimensions of willingness to manage health, behavioral transformation, social role change and attend experiences, and themes were extracted through thematic analysis. Results: Intervention participants showed significant improvement in healthy self-help behavior (P

Date Submitted: Nov 24, 2025. Open Peer Review Period: Nov 25, 2025 - Jan 20, 2026.

Date Submitted: Nov 24, 2025. Open Peer Review Period: Nov 25, 2025 - Jan 20, 2026.

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