mHealth最新文献

{"title":"Development and validation of an e-health literacy scale for pregnant women.","authors":"Yafei Zhao, Ruting Gu, Yueshuai Pan, Jingyuan Wang, Qianqian Li, Yalin Tang, Lili Wei","doi":"10.21037/mhealth-25-48","DOIUrl":"10.21037/mhealth-25-48","url":null,"abstract":"<p><strong>Background: </strong>Existing e-health literacy assessment tools fail to accurately assess the e-health literacy status of pregnant women. We aimed to develop an e-health literacy scale for pregnant women, and evaluate its psychometric properties.</p><p><strong>Methods: </strong>The scale may provide a scientific tool for evaluating the e-health literacy of pregnant women and developing targeted intervention plans. The initial constructs and items of the scale were developed through a literature review, qualitative analysis, and Delphi expert consultation based on the e-health literacy interactive model. Item analysis used a sample (n=220) of pregnant women in China to develop the formal scale. Additional participants (n=230) completed a survey to assess the internal consistency and test-retest reliability, and the content, construct, convergence and discrimination validity, of the scale.</p><p><strong>Results: </strong>The e-health literacy scale for pregnant women consisted of 22 items, and four dimensions including e-health information acquisition ability, e-health information evaluation ability, e-health information interaction ability, and e-health information application ability. The Cronbach's alpha coefficient was 0.937, test-retest reliability was 0.772, and the content validity index was 0.962. The cumulative variance contribution rate of the four common factors was 64.159%, the Kendall harmony coefficients were 0.386 and 0.439 (P<0.001), and the confirmatory factor analysis model had acceptable goodness-of-fit indices [χ²/df =2.250, root mean squared error of approximation (RMSEA) =0.075]. The average variance extracted (AVE) of each dimension were all above 0.500, and the composite reliability (CR) were all >0.700.</p><p><strong>Conclusions: </strong>The e-health literacy scale for pregnant women showed satisfactory psychometric properties and practice implications.</p>","PeriodicalId":74181,"journal":{"name":"mHealth","volume":"12 ","pages":"16"},"PeriodicalIF":3.3,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13187564/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147990173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of AI-generated versus registered dietitian-authored nutrition responses: a cross-sectional study.","authors":"Kathryn Ayres, Maryam Nadery, Pia Henfridsson","doi":"10.21037/mhealth-2025-70","DOIUrl":"10.21037/mhealth-2025-70","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) has emerged as a potential tool in nutrition counseling, but its performance compared with registered dietitians (RDs) remains unclear. This study aimed to compare the clinical quality, empathy, and readability of nutrition responses by a large language model (LLM, ChatGPT-4o) with those provided by licensed RDs, as assessed by RD evaluators.</p><p><strong>Methods: </strong>In this cross-sectional study, 100 nutrition-related questions were selected from public online forums where RDs had provided answers. Each question was paired with an AI-generated response. Licensed RDs (n=8), blinded to the source, rated responses for quality and empathy (5-point Likert scales) and overall performance (0-100). Readability was assessed using the Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), syllables per word, and words per sentence. Statistical analyses included independent two-tailed t-tests, z-tests for proportions meeting a threshold for \"acceptable\" (≥4), Pearson correlations, and sensitivity analyses by response length.</p><p><strong>Results: </strong>AI-generated responses scored higher than RD-authored responses for quality (4.48±0.31 <i>vs.</i> 2.56±0.76; P<0.001), empathy (4.62±0.37 <i>vs.</i> 3.21±0.62; P<0.001), and overall performance (91.10±5.38 <i>vs.</i> 66.83±14.71; P<0.001). AI scores clustered at the upper end, while RD scores were more variable. Quality and empathy were not correlated for AI (r=-0.10, P=0.32) but showed a moderate positive correlation for RDs (r=0.37, P<0.001). Nearly all AI responses met the ≥4 threshold for quality (96%) and empathy (97%), compared with few RD responses (3% and 14%; P<0.001). Word count did not differ, and longer RD responses were not associated with higher ratings. RDs' responses were more readable, with higher FRES (53.5±13.5 <i>vs.</i> 46.2±12.5; P<0.001), and simpler vocabulary (1.60±0.1 <i>vs.</i> 1.73±0.1 syllables/word, P<0.001), though both groups averaged a 10th-grade level on the FKGL, exceeding Centers for Disease Control and Prevention (CDC) and National Institutes of Health (NIH) recommendations.</p><p><strong>Conclusions: </strong>AI-generated nutrition responses demonstrated consistently high perceived quality and empathy, independent of length, while RD-authored responses display greater variability but higher readability. These findings highlight both the promise and the limitations of LLMs in nutrition counseling, suggesting that AI may complement, but not replace, human expertise, provided that accuracy, transparency, and professional oversight are maintained.</p>","PeriodicalId":74181,"journal":{"name":"mHealth","volume":"12 ","pages":"17"},"PeriodicalIF":3.3,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13187554/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147990230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart home Internet of Things-based behavioural analysis for early detection of cognitive decline: toward Saudi future vision.","authors":"Arij Alfaidi, Shadi Majed Alshraah, Loubna Hussain Rashid Alajmi, Alhanof Almutairi, Mohamed Ibrahim Alsaid Hassan, Anwer Mustafa Hilal","doi":"10.21037/mhealth-2025-74","DOIUrl":"10.21037/mhealth-2025-74","url":null,"abstract":"<p><strong>Background: </strong>The increasing integration of Internet of Things (IoT) technologies in smart-home environments has enabled continuous collection of behavioural data that can support cognitive health monitoring. Early identification of behavioural deviations associated with cognitive decline is critical for timely intervention and quality-of-life improvement among older adults. However, conventional clinical assessments are often episodic, subjective, and resource-intensive. The objective of this study is to develop a non-invasive, data-driven framework for analysing daily behavioural patterns from smart-home IoT data to support early cognitive-risk screening rather than clinical diagnosis.</p><p><strong>Methods: </strong>This study proposes HEALNET (Home Environment Assisted Learning Network), a hybrid deep learning (DL) framework that integrates convolutional neural networks (CNNs) for spatial feature extraction, long short-term memory (LSTM) networks for temporal sequence modelling, and ensemble machine learning (ML) classifiers including Random Forest (RF) and support vector machine (SVM). The framework analyses longitudinal behavioural data collected from smart-home IoT sensors. Experimental evaluation was conducted using publicly available Centre for Advanced Studies in Adaptive Systems (CASAS) smart-home datasets.</p><p><strong>Results: </strong>The proposed HEALNET framework achieved a classification accuracy of 94.2%, outperforming baseline ML and DL models. Results demonstrate that the integration of spatial, temporal, and statistical behavioural representations improves the detection of behavioural patterns associated with elevated cognitive-risk indicators.</p><p><strong>Conclusions: </strong>The findings indicate that continuous, unobtrusive behavioural monitoring using smart-home IoT data can provide reliable indicators for cognitive-risk screening. HEALNET serves as a research-stage framework supporting data-driven behavioural analysis rather than clinical diagnosis and aligns with Saudi Vision objectives for digital health innovation and quality-of-life enhancement.</p>","PeriodicalId":74181,"journal":{"name":"mHealth","volume":"12 ","pages":"18"},"PeriodicalIF":3.3,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13187557/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147990243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accessibility and equity considerations in the clinical validation of prescription digital therapeutics: a scoping review.","authors":"Shreya Divatia, Tina Foster, Sunny Cui, Vedant Tapiavala, Karen L Fortuna","doi":"10.21037/mhealth-25-12","DOIUrl":"10.21037/mhealth-25-12","url":null,"abstract":"<p><strong>Background: </strong>Prescription digital therapeutics (DTx) are software-based medical interventions regulated by the Food and Drug Administration (FDA). Ensuring accessibility and equity is crucial, as effectiveness depends on patient engagement. This scoping review examines whether clinical validation studies adequately assess health equity, cultural competence, and digital accessibility. This study aimed to evaluate the extent to which FDA-mandated clinical trials of DTx products report diverse participant characteristics, cultural adaptations, and digital literacy considerations.</p><p><strong>Methods: </strong>A systematic search of PubMed, Google Scholar, and ClinicalTrials.gov identified clinical validation studies of FDA-approved DTx products. Studies were assessed for health equity factors using the PROGRESS-Plus framework and categorized based on reporting of cultural competence, linguistic accessibility, and digital literacy.</p><p><strong>Results: </strong>Thirty-two studies covering six FDA-approved DTx products were included. Over 60% reported participant demographics (gender, race/ethnicity, education level), but fewer than 15% addressed other health equity factors. Fifteen studies excluded participants who were not fluent in English. No studies incorporated cultural adaptation frameworks or linguistic translations. Two studies found digital literacy significantly affected intervention effectiveness.</p><p><strong>Conclusions: </strong>Clinical validation studies of DTx lack sufficient assessment of health equity, cultural competence, and digital accessibility. Addressing these gaps is essential to ensure equitable access and effectiveness for diverse populations.</p>","PeriodicalId":74181,"journal":{"name":"mHealth","volume":"12 ","pages":"10"},"PeriodicalIF":2.2,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12885779/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146168304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tailoring mobile health apps for lifestyle management: a discrete choice experiment.","authors":"Chiara Seghieri, Tallys Feldens, Costanza Tortù, Natalya Usheva, Florian Toti, Ditila Doracaj, Natalia Giménez-Legarre, Eva Karaglani, Yannis Manios","doi":"10.21037/mhealth-25-30","DOIUrl":"10.21037/mhealth-25-30","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Health apps designed to monitor, motivate, and educate people towards their health goals are getting more users and features each time. These apps offer valuable support for self-managing health behaviors and achieving long-term objectives. However, there is limited understanding of user preferences regarding essential app features. The aim of the study is to get insights about potential users' preferences, in order to tailor better apps for lifestyle management.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a three-part web survey with 389 respondents from four countries as part of the DigiCare4You European Union (EU) project. In the first part, we collected the socioeconomic characteristics and health status of each respondent. In the following stage, we asked five questions on a Likert scale to ascertain the individual level of usage and general attitude towards technology. Finally, we performed a discrete choice experiment (DCE) using an unlabeled design and estimated the odds ratio for each feature using conditional logit analysis. We also ran alternative estimations stratifying by non-communicable disease (NCD) patients and non-NCD patients, and explored latent profile analysis (LPA) to understand whether the general attitude towards technology impacts the preference pattern between users.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The DCE revealed that respondents showed a clear preference for monitoring physical health over emotional status. They favored receiving lifestyle achievement notifications weekly rather than daily, and daily rather than more frequently. Similarly, respondents preferred uploading body weight measurements on a weekly or monthly basis rather than daily. Users expressed a preference for collaborating with their doctors to set exercise and diet goals, rather than either deciding independently or delegating entirely to their doctors. End-users also show a pattern of preferring notifications for goals instead of challenging other users. Preferences regarding the subjects of health content between workout routines, food recipes, and new scientific evidence were not significant; also, no statistical significance was found for the decision between follow-up visits with their doctor in person or remotely. LPA returned two groups regarding their general attitude towards technology: a lower, an intermediate, and a higher usage in their private life based on their responses to the questionnaire. Stratified DCEs have shown heterogeneity of users' preferences according to their specific attitude towards technology.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our study indicates that potential mobile health (mHealth) app users managing chronic conditions prefer platforms that enable shared responsibility with their doctors in defining health goals while having an intermediate level of interaction frequency with the app. These findings are key to tailoring mHealth apps that can optimize motivation triggers, support he","PeriodicalId":74181,"journal":{"name":"mHealth","volume":"12 ","pages":"3"},"PeriodicalIF":2.2,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12885856/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146167664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent wearable-enhanced WeChat platform with multidisciplinary tele-rehabilitation reduces caregiver burden and psychological distress in congenital pseudarthrosis of the tibia: a randomized controlled trial.","authors":"Yuyuan Xiong, Cuilan Dong, Haibo Mei, Jianhui Xie","doi":"10.21037/mhealth-25-42","DOIUrl":"10.21037/mhealth-25-42","url":null,"abstract":"<p><strong>Background: </strong>Congenital pseudarthrosis of the tibia (CPT) is a rare and refractory congenital malformation with a prolonged and complex treatment course, imposing substantial caregiving burdens and psychological distress on the parents of affected children. Conventional postoperative management, limited by time and space, fails to provide individualized and dynamic rehabilitation and psychological support. Remote management via a smart wearable-enhanced WeChat platform offers a promising alternative. This study aimed to evaluate the effects of this remote management approach on the mental health, sleep, and overall well-being of parents of children with CPT.</p><p><strong>Methods: </strong>A double-blind randomized controlled design was adopted in this study. A total of 78 primary caregivers of 78 CPT patients were assigned to the standard education and care (SEC) group (n=39) and the WeChat-platform intelligent management (WPIM) group (n=39) using block randomization. Both groups received standard peri-operative education and outpatient follow-up after discharge. The WPIM group additionally implemented an intelligent intervention system based on the WeChat platform: (I) real-time monitoring of postoperative rehabilitation exercise data through smartwatches (Huawei GT3), including biomechanical parameters such as daily gait characteristics, weight-bearing time, activity intensity, and heart rate variability; (II) establishment of personalized exercise prescriptions based on machine-learning algorithms, with automatic warnings triggered when the monitored data deviated from the preset rehabilitation trajectory; (III) formation of a multidisciplinary team (orthopedic surgeons, rehabilitation therapists, and psychologists) for online guidance twice a week, and dynamic adjustment of the training program in combination with the data from wearable devices; (IV) development of a dedicated WeChat mini-program for visual tracking of exercise compliance, and embedding of a cognitive-behavioral therapy module to improve the psychological adaptation of parents. The Family Caregiver Task Inventory (FCTI), Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), and Pittsburgh Sleep Quality Index (PSQI) were used for evaluation.</p><p><strong>Results: </strong>There were no statistically significant differences in the demographic characteristics of the caregivers of CPT patients between the two groups. After the intervention, the FCTI, SAS, SDS, and PSQI scores of the caregivers in the WPIM group were significantly lower than those in the SEC group (P<0.05).</p><p><strong>Conclusions: </strong>WPIM is a feasible and effective intervention measure that can reduce the family care burden and anxiety of parents of CPT patients.</p><p><strong>Trial registration: </strong>This study was registered at Chinese Clinical Trial Registry (ChiCTR2500066877).</p>","PeriodicalId":74181,"journal":{"name":"mHealth","volume":"12 ","pages":"7"},"PeriodicalIF":2.2,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12885845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146168340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the effects of mobile health interventions in the emergency department to improve patient health behaviors: a literature review.","authors":"Mohammad Moe Alghawi, Heather Herman, Jason Zucker, Vinay Saggar, Lauren S Chernick","doi":"10.21037/mhealth-25-63","DOIUrl":"10.21037/mhealth-25-63","url":null,"abstract":"<p><strong>Background and objective: </strong>Mobile health (mHealth) has emerged as an innovative and cost-effective tool for enhancing patient education, reinforcing user engagement, and improving continuity of care. While the utility is well documented in outpatient settings, its implementation within emergency departments (ED) remains an emerging field that may help alleviate unique acute care challenges. This is particularly important because EDs often serve as a safety net and the only point of healthcare access for underserved populations, meaning that successful digital integration could significantly bridge gaps by which ED-based interventions aid in preventative care. This review aimed to evaluate the effect of digital health interventions on the health of ED patients.</p><p><strong>Methods: </strong>This study was a narrative review of randomized controlled trials (RCTs) evaluating mHealth interventions in the ED and their effects on health behaviors. Three primary databases were used to scan the literature: PubMed, Scopus, and The Cochrane Database of Systematic Reviews. Studies were limited to a 10-year span between January 2014-2024, set in the United States, and focused on health outcomes.</p><p><strong>Key content and findings: </strong>Twenty-one studies that met the inclusion criteria were assessed and categorized across five domains: (I) substance use, (II) sexual health, (III) chronic disease and medication adherence, (IV) linkage to care and (V) injury prevention and safety. Text and app-based interventions were common (86%). They showed moderate effects on behaviors such as reducing binge drinking and improving medication adherence, though results for hemoglobin A1c and asthma morbidity were mixed. Interventions that were bidirectional, culturally tailored, or rooted in behavioral change theory presented greater success in affecting outcomes.</p><p><strong>Conclusions: </strong>mHealth in the ED offers an innovative strategy to promote health equity by enhancing patient education and improving scientific and patient-centered health outcomes. Current literature mainly focuses on interventions with shorter-term follow-up; future directives should focus on long-term outcomes, cost-effectiveness and how to efficiently implement digital interventions into the unique ED setting.</p>","PeriodicalId":74181,"journal":{"name":"mHealth","volume":"12 ","pages":"13"},"PeriodicalIF":2.2,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12885844/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146168287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mobile applications in enhanced recovery after surgery: a systematic review of protocol adherence and outcomes.","authors":"Paula Duarte D'Ambrosio, Felipe S Passos, Rachid Eduardo Noleto da Nobrega Oliveira, Vanessa Alvarenga-Bezerra, Paula Hirayama, Andressa Hiromi Amaral Kawano, Ricardo Mingarini Terra, Renato Moretti-Marques","doi":"10.21037/mhealth-25-37","DOIUrl":"10.21037/mhealth-25-37","url":null,"abstract":"<p><strong>Background: </strong>Enhanced recovery after surgery (ERAS) programs are designed to reduce postoperative complications and accelerate recovery. Despite their proven benefits, maintaining patient adherence to these protocols remains a significant challenge. Mobile health (mHealth) applications have been introduced as potential tools to support adherence and improve perioperative outcomes; however, their effectiveness within ERAS pathways has not been clearly established. This review aims to evaluate the impact of mobile applications on protocol compliance, recovery, and clinical outcomes among surgical patients enrolled in ERAS programs.</p><p><strong>Methods: </strong>PubMed, EMBASE, and the Cochrane Library were systematically searched through February 2025 to identify studies comparing ERAS care with and without mobile application support. Due to heterogeneity in study design, outcomes, and app functionalities, a meta-analysis was not performed. The primary outcome was adherence to ERAS protocols. Secondary outcomes included quality of recovery, patient and clinician satisfaction, postoperative complication rates, hospital length of stay (LOS), and readmissions.</p><p><strong>Results: </strong>Eight studies encompassing 1,623 patients were included. Mobile app use was associated with improved adherence to ERAS components, particularly early mobilization and oral intake. Some studies reported higher Quality of Recovery-15 (QoR-15) scores and greater patient satisfaction, although these differences were not always statistically significant. The clinician reported reductions in unnecessary visits and improved communication. No study reported an increased rate of complications. One study found that non-adherent patients had a significantly higher risk of 30-day readmission; another reported reduced infection rates and better pain control. The risk of bias was moderate in most studies.</p><p><strong>Conclusions: </strong>mHealth applications integrated into ERAS protocols may improve adherence, recovery quality, and patient engagement without increasing adverse outcomes. However, the overall quality of evidence remains limited due to heterogeneity and a predominance of non-randomized studies.</p>","PeriodicalId":74181,"journal":{"name":"mHealth","volume":"12 ","pages":"9"},"PeriodicalIF":2.2,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12885806/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146168316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SolanoConnex: developing a web-app for accessing local, up-to-date mental health services in a diverse county.","authors":"Carinne Brody, Alaina Star, Valentina Kelly, Teresa Hoskins","doi":"10.21037/mhealth-25-56","DOIUrl":"10.21037/mhealth-25-56","url":null,"abstract":"<p><strong>Background: </strong>There is a discrepancy between the number of people who report a mental health need and the number of people who use mental health services. The SolanoConnex web-app was developed to address this disparity in a diverse county by enhancing access to existing mental health services, with this paper describing the participatory and iterative development process.</p><p><strong>Methods: </strong>The web-app was developed in a five-stage process beginning with (I) secondary data analysis and landscape data collection, followed by a participatory approach to (II) design the product using fictitious, representative patient profiles developed by an advisory board to test the algorithm, (III) develop the web-app. The web-app was (IV) beta-tested via one-on-one interviews during which participants had access to beta versions of the app and were asked to respond to a structured feedback questionnaire. Finally, the web-app was (V) launched with continued assessment of the product.</p><p><strong>Results: </strong>The advisory board patient profiles and beta-testing feedback led to changes in how priority information such as insurance and cost details, services in a preferred language, and services tailored to specific marginalized groups appeared, and the wording used to describe mental health in the dials. The activity of matching fictitious, representative patient profile to services with early versions of the app led to a reduction in how many clicks it took to get to a services page. These changes resulted in an easy-to-use, jargon-free and intuitive interface providing the necessary information to access mental health services that was tailored to the specific needs and attitudes of the local community. Modifications continue to be made as necessary.</p><p><strong>Conclusions: </strong>The rigorous multi-stage process with participation and oversight from numerous local stakeholders ensured the development of an end-product that addressed the county-specific gaps and barriers in accessing mental and emotional health services. The lessons derived from this process can benefit those attempting to develop a similar tool to address public health disparities.</p>","PeriodicalId":74181,"journal":{"name":"mHealth","volume":"12 ","pages":"2"},"PeriodicalIF":2.2,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12885820/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146168333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum: Transforming daily support with multidisciplinary teleassistance: impact on health parameters in older adults-a randomized controlled trial.","authors":"","doi":"10.21037/mhealth-2025-01","DOIUrl":"10.21037/mhealth-2025-01","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.21037/mhealth-24-72.].</p>","PeriodicalId":74181,"journal":{"name":"mHealth","volume":"12 ","pages":"14"},"PeriodicalIF":2.2,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12885796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146168296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}