Development and usability evaluation of a culturally adapted stroke prevention educational programme on WeChat apps.

Background: The persistently high incidence of stroke in many nations is suggestive of an area for further improvement on existing strategies of primary stroke prevention. Although the era of digitalisation has led to the increasing use of mobile applications (apps) in healthcare, more studies are needed to determine the efficacy of apps in producing the desired health outcomes across different nations and cultures. Objective: To describe the development and evaluate the usability of a mobile app in delivering a culturally adapted stroke prevention educational programme for middle-aged adults in the Republic of China. Methods: The educational programme was developed in three phases. In Phase 1, the process involved analysing requirements and designing structured modules. Phase 2 concentrated on expert consultation and technical development to deliver the educational programme. Phase 3 included a usability trial and refinement of the educational program based on trial results. Results: Educational content was derived from the Chinese Guidelines for the Prevention and Treatment of Stroke and the Dietary Guidelines for Residents. The WeChat platform was used to deliver the educational programme. Participants expressed satisfaction with the content, interface, and functions of the apps, indicating that the apps have good usability. Conclusions: The development process of the Educational Programme was designed to maximise the culturally appropriate, and impact of lifestyle changes and stroke prevention. An app-based educational programme that has demonstrated good usability is a vital factor prior to deploying it in an intervention to evaluate its effects on health outcomes.

Patient Engagement Platforms: How Technology Is Impacting Perioperative Communication and Engagement in Total Hip and Knee Arthroplasty.

Orthopedic clinics are becoming strained with clinical volume outpacing resources and personnel. Patient engagement platforms can help bridge the communication and engagement gaps between patients and their healthcare teams as total hip and knee arthroplasty transitions to the outpatient setting. These platforms provide a digital infrastructure that allows patients to participate in their healthcare journey while alleviating the burdens on clinic staff. Multiple forms of patient engagement platforms exist but typically fall into one of 3 groups: patient portals, mobile health applications, and chatbots. They all play an important role in enhancing postoperative rehabilitation, patient engagement, and patient care overall. This article explores the spectrum of available patient engagement platforms and examines their advantages, limitations, and documented benefits on clinical outcomes.

Facilitators and barriers in acceptance of telemedicine among healthcare providers in Pakistan: A cross-sectional survey.

Objectives: This study aimed to assess healthcare professionals' awareness and acceptance of telemedicine residing in Pakistan. Materials and Methods: A cross-sectional study was conducted across Pakistan from November 2021 to March 2022. Data entry was done through a self-administered questionnaire. In this survey, the non-probability purposive sampling method was adopted. Medical doctors from all specialties that fall into the age category of 20-60 years were included in this study. Statistical Package for Social Sciences (SPSS) version 22.0 was used for univariate data analysis. Results: Among the 860 healthcare professionals who received the survey, 198 responded (23.0% response rate). The mean ± SD age of the participants was 31.16 ± 8.56. The results were found to be statistically significant for knowledge about telemedicine among healthcare professionals workplace, that is, healthcare professionals working in the private sector indicated a high level of awareness regarding telemedicine as compared to those in the public sector (p = 0.03). A significant difference (p-value ⩽ 0.05) in knowledge was seen among doctors working in the public sector and those working in a private setup. Results indicate significant positive associations between telemedicine utilization and perceived benefits over disadvantages (ß = 1.03, p = 0.017), reliance among healthcare professionals (ß = 1.22, p = 0.008), and enhancement in healthcare quality (ß = 1.42, p = 0.001). Moreover, familiarity with telemedicine correlates strongly with its usage (ß = 2.56, p < 0.000). Conclusion: Overall, healthcare professionals exhibited satisfactory knowledge regarding telemedicine and showed an accepting attitude toward telemedicine. Our study recommends enhancing the importance of Telemedicine training at the Healthcare professionals' level to produce quality service delivery in healthcare organizations.

Facilitators and barriers in acceptance of telemedicine among healthcare providers in Pakistan: A Cross-sectional survey: The aim of this study, was to evaluate healthcare professionals' knowledge and acceptance of telemedicine in Karachi, Pakistan. A cross-sectional survey was carried out between November 2021 and March 2022, and 198 out of 860 healthcare professionals responded (representing a 23.0% response rate). The participants' average age was 31.16 years. The results showed that healthcare professionals in the public and private sectors differed significantly in their awareness of telemedicine, with the former group having higher awareness levels. Most of the medical professionals showed adequate understanding and acceptance of telemedicine. However, in order to improve service delivery in healthcare organizations, the study emphasizes the necessity for improved telemedicine awareness and training among healthcare providers, especially in the public sector. The findings highlighted how crucial it is to integrate telemedicine within medical education.

The impact of mobile health interventions on service users' health outcomes and the role of health professions: a systematic review of systematic reviews-protocol.

BACKGROUND: Mobile health tools have gained prominence in global health care in recent years. Mobile health (mHealth) interventions have demonstrated their impact on managing healthcare service users' health. A pilot search revealed many systematic reviews on the effectiveness of mobile health tools on service users' health outcomes. However, how the role of healthcare professionals in promoting the adoption of mobile health may lead to improved outcomes needs to be clarified. Therefore, this systematic review aims to synthesise existing systematic reviews that examine both the impact of mobile health interventions on service users' outcomes and the role of healthcare professionals in facilitating the adoption of mobile health solutions. METHODS: Five electronic databases will be searched: EMBASE, CINHAL Plus, MEDLINE, Web of Science, and the Cochrane Library for systematic reviews exploring the impact of mobile health interventions on service users' outcomes and the role of healthcare professionals in facilitating the adoption of mobile health solutions. Systematic reviews published in English dated from January 2015 to June 2024 will be included. Screening and selection of the reviews against inclusion and exclusion criteria will be performed by three independent reviewers, as well as data extraction and quality assessment. DISCUSSION: Current systematic reviews in mHealth have primarily focused on assessing the effectiveness of mHealth interventions for managing a range of conditions. While these reviews provide valuable input into the outcomes for mHealth, more is needed to know about the impact of the involvement of health professions on service users' outcomes when adopting mHealth. This systematic review of systematic reviews aims to bridge this critical gap in the literature by critically appraising and synthesising the evidence of mHealth interventions' impact on service user outcomes and the level of involvement of health professionals. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD 42023414435.

Construction and Content Validation of Mobile Devices' Application Messages about Food and Nutrition for DM2 Older Adults.

Older adults face a decline in the quality of their diet, which affects their health. The prevalence of DM2 is increasing, as are the associated complications. Effective nutrition education and mobile health (mHealth) interventions offer a viable solution in the scenario of the widespread use of mobile devices. This study aimed to develop and validate messages for a mobile application aimed at older adult Brazilians with DM2 who receive care at the Brazilian Unified Health System (SUS). The educational messages on healthy eating for older adults with DM2 were created from 189 excerpts selected from Brazilian official documents. A total of 37 messages were created, categorized into 20 educational, 12 motivational, and 5 congratulatory, all up to 120 characters. Twenty-one experts validated the messages for clarity and relevance, and 11 messages had to be revised to meet the criteria. Subsequently, the 36 messages approved by the experts were tested on a sample of 57 older adults, guaranteeing clarity rates of over 80%. This study developed and validated 36 messages for a mobile health app aimed at older adults with type 2 diabetes mellitus in Brazil. Expert evaluation ensured clarity and relevance, confirmed by older adult participants who evaluated clarity. This research highlights the potential of mHealth to overcome barriers to accessing healthcare in the SUS, emphasizing personalized interventions for the effective management of older adults' health.

Deconstructing Fitbit to Specify the Effective Features in Promoting Physical Activity Among Inactive Adults: Pilot Randomized Controlled Trial.

BACKGROUND: Wearable activity trackers have become key players in mobile health practice as they offer various behavior change techniques (BCTs) to help improve physical activity (PA). Typically, multiple BCTs are implemented simultaneously in a device, making it difficult to identify which BCTs specifically improve PA. OBJECTIVE: We investigated the effects of BCTs implemented on a smartwatch, the Fitbit, to determine how each technique promoted PA. METHODS: This study was a single-blind, pilot randomized controlled trial, in which 70 adults (n=44, 63% women; mean age 40.5, SD 12.56 years; closed user group) were allocated to 1 of 3 BCT conditions: self-monitoring (feedback on participants' own steps), goal setting (providing daily step goals), and social comparison (displaying daily steps achieved by peers). Each intervention lasted for 4 weeks (fully automated), during which participants wore a Fitbit and responded to day-to-day questionnaires regarding motivation. At pre- and postintervention time points (in-person sessions), levels and readiness for PA as well as different aspects of motivation were assessed. RESULTS: Participants showed excellent adherence (mean valid-wear time of Fitbit=26.43/28 days, 94%), and no dropout was recorded. No significant changes were found in self-reported total PA (dz<0.28, P=.40 for the self-monitoring group, P=.58 for the goal setting group, and P=.19 for the social comparison group). Fitbit-assessed step count during the intervention period was slightly higher in the goal setting and social comparison groups than in the self-monitoring group, although the effects did not reach statistical significance (P=.052 and P=.06). However, more than half (27/46, 59%) of the participants in the precontemplation stage reported progress to a higher stage across the 3 conditions. Additionally, significant increases were detected for several aspects of motivation (ie, integrated and external regulation), and significant group differences were identified for the day-to-day changes in external regulation; that is, the self-monitoring group showed a significantly larger increase in the sense of pressure and tension (as part of external regulation) than the goal setting group (P=.04). CONCLUSIONS: Fitbit-implemented BCTs promote readiness and motivation for PA, although their effects on PA levels are marginal. The BCT-specific effects were unclear, but preliminary evidence showed that self-monitoring alone may be perceived demanding. Combining self-monitoring with another BCT (or goal setting, at least) may be important for enhancing continuous engagement in PA. TRIAL REGISTRATION: Open Science Framework; https://osf.io/87qnb/?view_only=f7b72d48bb5044eca4b8ce729f6b403b.

Usability of a Mobile Health Technology for HIV Testing Among Sexual Minorities.

In the United States, young men who have sex with men (YMSM) and young transgender women (YTGW) are disproportionality affected by HIV. To overcome this public health problem, we created and tested the mLab application (app), a novel mobile health (mHealth) that offers HIV prevention information and an imaging algorithm for interpreting the at-home HIV test. This study assessed the mLab app usability for HIV testing and its relation to users' education and health literacy. The results showed high user satisfaction and perceived usability of mLab to provide accessible HIV testing solutions for YMSM and YTGW. Findings suggest that the rigorous user-centered design of the mLab app supported a usable app independent of education and health literacy levels.

Mobile addiction treatment and harm reduction services as tools to address health inequities: a community case study of the Brockton Neighborhood Health Center mobile unit.

Opioid overdose deaths continue to increase in the US. Recent data show disproportionately high and increasing overdose death rates among Black, Latine, and Indigenous individuals, and people experiencing homelessness. Medications for opioid use disorder (MOUD) can be lifesaving; however, only a fraction of eligible individuals receive them. Our goal was to describe our experience promoting equitable MOUD access using a mobile delivery model. We implemented a mobile MOUD unit aiming to improve equitable access in Brockton, a racially diverse, medium-sized city in Massachusetts. Brockton has a relatively high opioid overdose death rate with increasingly disproportionate death rates among Black residents. Brockton Neighborhood Health Center (BNHC), a community health center, provides brick-and-mortar MOUD access. Through the Communities That HEAL intervention as part of the HEALing Communities Study (HCS), Brockton convened a community coalition with the aim of selecting evidence-based practices to decrease overdose deaths. BNHC leadership and coalition members recognized that traditional brick-and-mortar treatment locations were inaccessible to marginalized populations, and that a mobile program could increase MOUD access. In September 2021, with support from the HCS coalition, BNHC launched its mobile initiative - Community Care-in-Reach® - to bring low-threshold buprenorphine, harm reduction, and preventive care to high-risk populations. During implementation, the team encountered several challenges including: securing local buy-in; navigating a complex licensure process; maintaining operations throughout the COVID-19 pandemic; and finally, planning for sustainability. In two years of operation, the mobile team cared for 297 unique patients during 1,286 total visits. More than one-third (36%) of patients received buprenorphine prescriptions. In contrast to BNHC's brick-and-mortar clinics, patients with OUD seen on the mobile unit were more representative of historically marginalized racial and ethnic groups, and people experiencing homelessness, evidencing improved, equitable addiction care access for these historically disadvantaged populations. Offering varied services on the mobile unit, such as wound care, syringe and safer smoking supplies, naloxone, and other basic medical care, was a key engagement strategy. This on-demand mobile model helped redress systemic disadvantages in access to addiction treatment and harm reduction services, reaching diverse individuals to offer lifesaving MOUD at a time of inequitable increases in overdose deaths.

Use of mobile hospitals to improve access to health services and promote primary health care: lessons from Zambia (2011-2018).

Background: Mobile hospitals play a critical role in serving difficult to access populations. In 2011, they were introduced by the Zambian government to improve access to health care. However, little is known about and/or documented about their use in Zambia, and other similar settings where people rely on them to access critical health care, or have to travel long distances to the nearest health centre. Objective: To understand the use of mobile hospitals in Zambia and share lessons on their implementation that may be useful for similar settings. It describes their design, implementation, and challenges. Methods: The qualitative research employed document review, key informant interviews with 15 respondents, and observation of the operations of the mobile hospitals in the field. Results: The research finds that while they help to reduce inequities associated with accessing health services, there needs to be careful resource planning and addressing of the major issues in health care such as human resources, infrastructure, and disease prevention before long term use. Conclusion: The research not only highlights conditions that must be considered for the effective implementation of mobile hospitals, but also the need for engagement of various key stakeholders during agenda setting in order to build trust and buy in, which contribute to smoother implementation.

Developing a mobile exercise program for individuals with Spinal Cord Injury: Stakeholder perceptions of app features and implementation determinants.

BACKGROUND: Individuals with Spinal Cord Injury (SCI) often experience physical deconditioning, leading to long-term health challenges. While regular exercise can offer substantial health benefits, adherence to exercise guidelines among individuals with SCI is hindered by barriers such as inaccessibility. Exercise programs using the mobile application (App) tailored to individual needs present a promising solution for promoting exercise adherence among individuals with SCI. OBJECTIVE: This study aimed to identify factors contributing to the successful implementation of an app-based home exercise program for individuals with SCI and gather user feedback on app preferences, functionality, and features. METHODS: Guided by the Consolidated Framework for Implementation Research (CFIR), twenty-six clinicians completed an expert panel survey to rank factors influencing the implementation of an app-based intervention for increasing exercise adherence for individuals with SCI. CFIR-selected factors and app quality features obtained from the Mobile Application Rating Scale (MARS) framework were discussed in seven focus groups with 23 individuals with SCI, 6 caregivers, and 6 clinicians. RESULTS: The expert survey identified adaptability, complexity, evidence strength/quality, relative advantage, knowledge/beliefs about the initiative, and execution as the key CFIR factors that affected the intervention's success. Major themes emerging from focus groups with individuals with SCI and caregivers included usability, instruction and guidelines, user-friendly interface, and clinician interaction. In contrast, clinicians mentioned themes such as the representation of the SCI population, time commitment, accessibility, and equipment. CONCLUSIONS: The study highlights the significance of incorporating these determinants into future designs to develop app-based home exercise interventions for individuals with SCI.

Effect of m-Health-Based Interventions on Blood Pressure: An Updated Systematic Review with Meta-Analysis.

Background: In recent years, the integration of mobile health (m-Health) interventions has garnered increasing attention as a potential means to improve blood pressure (BP) management in adults. This updated systematic review with meta-analysis aimed to identify the effect of m-Health-based interventions on BP in adults and to evaluate the effect of m-Health on BP according to the characteristics of subjects, interventions, and countries. Methods: The search was carried out in PubMed, Embase, ResearchGate, and Cochrane databases in January 2022. Study selection and data extraction were performed by two independent reviewers. For analysis, random effects models were used with a confidence interval (CI) of 95% and p < 0.05. Results: Fifty studies were included in this review and in the meta-analysis. Interventions with m-Health reduced systolic BP in 3.5 mmHg (95% CI -4.3; -2.7; p < 0.001; I2 = 85.8%) and diastolic BP in 1.8 mmHg (95% CI -2.3; -1.4; p < 0.001; I2 = 78.9%) compared to usual care. The effects of m-Health interventions on BP were more evident in men and in older adults, in interventions lasting 6-8 weeks, with medication reminders, with the possibility of insertion of BP values (p < 0.05). Conclusion: The results of this study support the effectiveness of m-Health in reducing BP when compared to standard care. However, these effects are dependent on the characteristics of the subjects and interventions. Given the substantial heterogeneity among the results of this systematic review with meta-analysis, its interpretation should be cautious. Future research on this topic is warranted.

Bayesian Networks for Prescreening in Depression: Algorithm Development and Validation.

BACKGROUND: Identifying individuals with depressive symptomatology (DS) promptly and effectively is of paramount importance for providing timely treatment. Machine learning models have shown promise in this area; however, studies often fall short in demonstrating the practical benefits of using these models and fail to provide tangible real-world applications. OBJECTIVE: This study aims to establish a novel methodology for identifying individuals likely to exhibit DS, identify the most influential features in a more explainable way via probabilistic measures, and propose tools that can be used in real-world applications. METHODS: The study used 3 data sets: PROACTIVE, the Brazilian National Health Survey (Pesquisa Nacional de Saúde [PNS]) 2013, and PNS 2019, comprising sociodemographic and health-related features. A Bayesian network was used for feature selection. Selected features were then used to train machine learning models to predict DS, operationalized as a score of ≥10 on the 9-item Patient Health Questionnaire. The study also analyzed the impact of varying sensitivity rates on the reduction of screening interviews compared to a random approach. RESULTS: The methodology allows the users to make an informed trade-off among sensitivity, specificity, and a reduction in the number of interviews. At the thresholds of 0.444, 0.412, and 0.472, determined by maximizing the Youden index, the models achieved sensitivities of 0.717, 0.741, and 0.718, and specificities of 0.644, 0.737, and 0.766 for PROACTIVE, PNS 2013, and PNS 2019, respectively. The area under the receiver operating characteristic curve was 0.736, 0.801, and 0.809 for these 3 data sets, respectively. For the PROACTIVE data set, the most influential features identified were postural balance, shortness of breath, and how old people feel they are. In the PNS 2013 data set, the features were the ability to do usual activities, chest pain, sleep problems, and chronic back problems. The PNS 2019 data set shared 3 of the most influential features with the PNS 2013 data set. However, the difference was the replacement of chronic back problems with verbal abuse. It is important to note that the features contained in the PNS data sets differ from those found in the PROACTIVE data set. An empirical analysis demonstrated that using the proposed model led to a potential reduction in screening interviews of up to 52% while maintaining a sensitivity of 0.80. CONCLUSIONS: This study developed a novel methodology for identifying individuals with DS, demonstrating the utility of using Bayesian networks to identify the most significant features. Moreover, this approach has the potential to substantially reduce the number of screening interviews while maintaining high sensitivity, thereby facilitating improved early identification and intervention strategies for individuals experiencing DS.

Incentivized collaborative care to disseminate and enhance contingency-management services.

Contingency management is especially effective in supporting medication adherence and drug abstinence among people with opioid use disorder. However, the incorporation of contingency management into clinical practice has been slow. The present study was designed to evaluate the feasibility, acceptability, and usability of incentives for providers as a means of accelerating collaborative care with contingency management. Thirteen buprenorphine prescribers served as participants in a nonexperimental study. The prescribers who referred patients to a contingency-management service received monetary incentives for reviewing patient performance data and describing their use of these data in treatment decisions. The results show that this approach is feasible, acceptable, and easy to use for everyone involved. Self-reports indicate improved prescriber-patient relationships and more informed care. However, prescriber-focused incentives did not appear to greatly enhance access to contingency-management services for patients. Thus, provider incentives may be beneficial but further research is needed to advance adoption of contingency management.

Optimized efficient attention-based network for facial expressions analysis in neurological health care.

Facial Expression Analysis (FEA) plays a vital role in diagnosing and treating early-stage neurological disorders (NDs) like Alzheimer's and Parkinson's. Manual FEA is hindered by expertise, time, and training requirements, while automatic methods confront difficulties with real patient data unavailability, high computations, and irrelevant feature extraction. To address these challenges, this paper proposes a novel approach: an efficient, lightweight convolutional block attention module (CBAM) based deep learning network (DLN) to aid doctors in diagnosing ND patients. The method comprises two stages: data collection of real ND patients, and pre-processing, involving face detection and an attention-enhanced DLN for feature extraction and refinement. Extensive experiments with validation on real patient data showcase compelling performance, achieving an accuracy of up to 73.2%. Despite its efficacy, the proposed model is lightweight, occupying only 3MB, making it suitable for deployment on resource-constrained mobile healthcare devices. Moreover, the method exhibits significant advancements over existing FEA approaches, holding tremendous promise in effectively diagnosing and treating ND patients. By accurately recognizing emotions and extracting relevant features, this approach empowers medical professionals in early ND detection and management, overcoming the challenges of manual analysis and heavy models. In conclusion, this research presents a significant leap in FEA, promising to enhance ND diagnosis and care.The code and data used in this work are available at: https://github.com/munsif200/Neurological-Health-Care.

Improving Parental Health Literacy in Primary Caregivers of 0- to 3-Year-Old Children Through a WeChat Official Account: Cluster Randomized Controlled Trial.

Background: Parental health literacy is important to children's health and development, especially in the first 3 years. However, few studies have explored effective intervention strategies to improve parental literacy. Objective: This study aimed to determine the effects of a WeChat official account (WOA)-based intervention on parental health literacy of primary caregivers of children aged 0-3 years. Methods: This cluster randomized controlled trial enrolled 1332 caregiver-child dyads from all 13 community health centers (CHCs) in Minhang District, Shanghai, China, between April 2020 and April 2021. Participants in intervention CHCs received purposefully designed videos via a WOA, which automatically recorded the times of watching for each participant, supplemented with reading materials from other trusted web-based sources. The contents of the videos were constructed in accordance with the comprehensive parental health literacy model of WHO (World Health Organization)/Europe (WHO/Europe). Participants in control CHCs received printed materials similar to the intervention group. All the participants were followed up for 9 months. Both groups could access routine child health services as usual during follow-up. The primary outcome was parental health literacy measured by a validated instrument, the Chinese Parental Health Literacy Questionnaire (CPHLQ) of children aged 0-3 years. Secondary outcomes included parenting behaviors and children's health outcomes. We used the generalized linear mixed model (GLMM) for data analyses and performed different subgroup analyses. The ß coefficient, risk ratio (RR), and their 95% CI were used to assess the intervention's effect. Results: After the 9-month intervention, 69.4% (518/746) of caregivers had watched at least 1 video. Participants in the intervention group had higher CPHLQ total scores (ß=2.51, 95% CI 0.12-4.91) and higher psychological scores (ß=1.63, 95% CI 0.16-3.10) than those in the control group. The intervention group also reported a higher rate of exclusive breastfeeding (EBF) at 6 months (38.9% vs 23.44%; RR 1.90, 95% CI 1.07-3.38) and a higher awareness rate of vitamin D supplementation for infants younger than 6 months (76.7% vs 70.5%; RR 1.39, 95% CI 1.06-1.82). No significant effects were detected for the physical score on the CPHLQ, breastfeeding rate, routine checkup rate, and children's health outcomes. Furthermore, despite slight subgroup differences in the intervention's effects on the total CPHLQ score and EBF rate, no interaction effect was observed between these subgroup factors and intervention factors. Conclusions: Using a WHO literacy model-based health intervention through a WOA has the potential of improving parental health literacy and EBF rates at 6 months. However, innovative strategies and evidence-based content are required to engage more participants and achieve better intervention outcomes.

Requirements for the Acceptance of an App for Voice Therapy-A Usability Report Based on the "Oldenburger Logopädie App" (OLA).

OBJECTIVE: The prototype "Oldenburger Logopädie App" (OLA) was designed to support voice therapy for patients with recurrent paresis, such as to accompany homework or as a short-term substitute for regular therapy due to dropouts, such as during the COVID-19 pandemic. The treating speech and language pathologists (SLPs) unlocks videos individually applicable to the respective patients, in which the SLPs instruct the individual exercises. The app can be used without information technology knowledge or detailed instructions. MATERIALS AND METHODS: The prototype's usability was evaluated through a usability test battery (AttrakDiff questionnaire, System Usability Scale, Visual Aesthetics of Websites Inventory questionnaire) and informal interviews from the perspective of patients and SLPs. RESULTS: The acceptance, usability, user experience, self-descriptiveness, and user behavior of OLA were consistently given and mostly rated as positive. Both user groups rated OLA as practical and easy to use (eg, System Usability Scale: "practical" (agree: ∅ 49.5%), "cumbersome to use" (total: strongly disagree: ∅ 60.0%). However, the monotonous layout of the app and the instructional and exercise videos should be modified in the next editing step. An overview of relevant criteria for a voice therapy app, regarding design and functions, was derived from the results. CONCLUSION: This user-oriented feedback on the usability of the voice app provides the proof of concept and the basis for the further development of the Artificial intelligence-based innovative follow-up app LAOLA. In the future, it should be possible to support the treatment of all voice disorders with such an app. For the further development of the voice app, the therapeutic content and the effectiveness of the training should also be investigated.

Factors influencing the continuance intention of the women's health WeChat public account: an integrated model of UTAUT2 and HBM.

Background: Women's health WeChat public accounts play a crucial role in enhancing health literacy and fostering the development of healthy behaviors among women by disseminating women's health knowledge. Improving users' continuous usage behavior and retention rates for the women's health WeChat public account is vital for influencing the overall effectiveness of health communication on WeChat. Objective: This study aimed to construct a comprehensive model, delving into the key factors influencing women's continuance intention of the women's health public accounts from the perspectives of perceived health threats, individual abilities, and technological perceptions. The goal is to provide valuable insights for enhancing user stickiness and the effectiveness of health communication on WeChat public accounts. Method: An online survey was conducted among women receiving gynecological care at a certain hospital to gage their willingness for sustained use of the women's health WeChat public accounts. Through structural equation modeling, the study investigated the influencing factors on women's sustained intention to use the women's health WeChat public accounts. Results: The study included a total of 853 adult women. Among them, 241 (28.3%) women had followed women's health official accounts in the past but do not currently follow them, 240 (28.1%) women had followed women's health official accounts in the past and are still following them, and 372 (43.6%) women had never followed women's health official accounts. Currently, 240 women are still browsing women's health public accounts, 52 of whom read women's health public accounts every day, and most of them read women's health public accounts for 10-20 min at a time (100, 11.7%). The results of the structural equation model revealed that performance expectancy, social influence, hedonic motivation, habit, and e-health literacy had significantly positive effects on women's sustained intention to use public accounts (performance expectancy: ß = 0.341, p < 0.001; social influence: ß = 0.087, p = 0.047; hedonic motivation: ß = 0.119, p = 0.048; habit: ß = 0.102, p < 0.001; e-health literacy: ß = 0.158, p < 0.001). E-health literacy and self-efficacy indirectly influence sustained intention by affecting performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit. The effect sizes of e-health literacy on performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit were 0.244 (p < 0.001), 0.316 (p < 0.001), 0.188 (p < 0.001), 0.226(p < 0.001), 0.154 (p < 0.001), and 0.073 (p = 0.046). The effect sizes of self-efficacy on performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit were 0.502 (p < 0.001), 0.559 (p < 0.001), 0.454 (p < 0.001), 0.662 (p < 0.001), 0.707 (p < 0.001), and 0.682 (p < 0.001). Additionally, perceived severity and perceived susceptibility indirectly affected sustained intention by influencing performance expectancy and social influence. The effect sizes of perceived severity on performance expectancy and social influence were 0.223 (p < 0.001) and 0.146 (p < 0.001). The effect size of perceived susceptibility to social influence was 0.069 (p = 0.042). Conclusion: Users' e-health literacy, self-efficacy, perception of disease threat, and users' technological perceptions of the WeChat public accounts are critical factors influencing women's continuance intention of using the WeChat public accounts. Therefore, for female users, attention should be given to improving user experience and enhancing the professionalism and credibility of health information in public account design and promotion. Simultaneously, efforts should be made to strengthen users' health awareness and cultivate e-health literacy, ultimately promoting sustained attention and usage behavior among women toward health-focused public accounts.

Prescription digital therapeutics: An emerging treatment option for negative symptoms in schizophrenia.

Digital therapeutics-web-based programs, smartphone applications, and wearable devices designed to prevent, treat, or manage clinical conditions through software-driven, evidence-based intervention-can provide accessible alternatives and/or may supplement standard care for patients with serious mental illnesses (SMI), including schizophrenia. In this paper we provide a targeted summary of the rapidly growing field of digital therapeutics for schizophrenia and related SMI. We first define digital therapeutics. We then provide a brief summary of the emerging evidence of efficacy of digital therapeutics for improving clinical outcomes, focusing on potential mechanisms of action for addressing some of the most challenging problems, including negative symptoms of psychosis. Our focus on these promising targets for digital therapeutics, including the latest in prescription models in the commercial space, highlights future directions for research and practice in this exciting field.

mHealth Apps for Dementia, Alzheimer Disease, and Other Neurocognitive Disorders: Systematic Search and Environmental Scan.

BACKGROUND: Lifestyle behaviors including exercise, sleep, diet, stress, mental stimulation, and social interaction significantly impact the likelihood of developing dementia. Mobile health (mHealth) apps have been valuable tools in addressing these lifestyle behaviors for general health and well-being, and there is growing recognition of their potential use for brain health and dementia prevention. Effective apps must be evidence-based and safeguard user data, addressing gaps in the current state of dementia-related mHealth apps. OBJECTIVE: This study aims to describe the scope of available apps for dementia prevention and risk factors, highlighting gaps and suggesting a path forward for future development. METHODS: A systematic search of mobile app stores, peer-reviewed literature, dementia and Alzheimer association websites, and browser searches was conducted from October 19, 2022, to November 2, 2022. A total of 1044 mHealth apps were retrieved. After screening, 152 apps met the inclusion criteria and were coded by paired, independent reviewers using an extraction framework. The framework was adapted from the Silberg scale, other scoping reviews of mHealth apps for similar populations, and background research on modifiable dementia risk factors. Coded elements included evidence-based and expert credibility, app features, lifestyle elements of focus, and privacy and security. RESULTS: Of the 152 apps that met the final selection criteria, 88 (57.9%) addressed modifiable lifestyle behaviors associated with reducing dementia risk. However, many of these apps (59/152, 38.8%) only addressed one lifestyle behavior, with mental stimulation being the most frequently addressed. More than half (84/152, 55.2%) scored 2 points out of 9 on the Silberg scale, with a mean score of 2.4 (SD 1.0) points. Most of the 152 apps did not disclose essential information: 120 (78.9%) did not disclose expert consultation, 125 (82.2%) did not disclose evidence-based information, 146 (96.1%) did not disclose author credentials, and 134 (88.2%) did not disclose their information sources. In addition, 105 (69.2%) apps did not disclose adherence to data privacy and security practices. CONCLUSIONS: There is an opportunity for mHealth apps to support individuals in engaging in behaviors linked to reducing dementia risk. While there is a market for these products, there is a lack of dementia-related apps focused on multiple lifestyle behaviors. Gaps in the rigor of app development regarding evidence base, credibility, and adherence to data privacy and security standards must be addressed. Following established and validated guidelines will be necessary for dementia-related apps to be effective and advance successfully.

Human Factors in AI-Driven Digital Solutions for Increasing Physical Activity: Scoping Review.

BACKGROUND: Artificial intelligence (AI) has the potential to enhance physical activity (PA) interventions. However, human factors (HFs) play a pivotal role in the successful integration of AI into mobile health (mHealth) solutions for promoting PA. Understanding and optimizing the interaction between individuals and AI-driven mHealth apps is essential for achieving the desired outcomes. OBJECTIVE: This study aims to review and describe the current evidence on the HFs in AI-driven digital solutions for increasing PA. METHODS: We conducted a scoping review by searching for publications containing terms related to PA, HFs, and AI in the titles and abstracts across 3 databases-PubMed, Embase, and IEEE Xplore-and Google Scholar. Studies were included if they were primary studies describing an AI-based solution aimed at increasing PA, and results from testing the solution were reported. Studies that did not meet these criteria were excluded. Additionally, we searched the references in the included articles for relevant research. The following data were extracted from included studies and incorporated into a qualitative synthesis: bibliographic information, study characteristics, population, intervention, comparison, outcomes, and AI-related information. The certainty of the evidence in the included studies was evaluated using GRADE (Grading of Recommendations Assessment, Development, and Evaluation). RESULTS: A total of 15 studies published between 2015 and 2023 involving 899 participants aged approximately between 19 and 84 years, 60.7% (546/899) of whom were female participants, were included in this review. The interventions lasted between 2 and 26 weeks in the included studies. Recommender systems were the most commonly used AI technology in digital solutions for PA (10/15 studies), followed by conversational agents (4/15 studies). User acceptability and satisfaction were the HFs most frequently evaluated (5/15 studies each), followed by usability (4/15 studies). Regarding automated data collection for personalization and recommendation, most systems involved fitness trackers (5/15 studies). The certainty of the evidence analysis indicates moderate certainty of the effectiveness of AI-driven digital technologies in increasing PA (eg, number of steps, distance walked, or time spent on PA). Furthermore, AI-driven technology, particularly recommender systems, seems to positively influence changes in PA behavior, although with very low certainty evidence. CONCLUSIONS: Current research highlights the potential of AI-driven technologies to enhance PA, though the evidence remains limited. Longer-term studies are necessary to assess the sustained impact of AI-driven technologies on behavior change and habit formation. While AI-driven digital solutions for PA hold significant promise, further exploration into optimizing AI's impact on PA and effectively integrating AI and HFs is crucial for broader benefits. Thus, the implications for innovation management involve conducting long-term studies, prioritizing diversity, ensuring research quality, focusing on user experience, and understanding the evolving role of AI in PA promotion.