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.

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.

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.

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.

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 a Smartphone App (S-Check) on Actual and Intended Help-Seeking and Motivation to Change Methamphetamine Use Among Adult Consumers of Methamphetamine in Australia: Randomized Waitlist-Controlled Trial.

BACKGROUND: Interventions are required that address delays in treatment-seeking and low treatment coverage among people consuming methamphetamine. OBJECTIVE: We aim to determine whether a self-administered smartphone-based intervention, the "S-Check app" can increase help-seeking and motivation to change methamphetamine use, and determine factors associated with app engagement. METHODS: This study is a randomized, 28-day waitlist-controlled trial. Consenting adults residing in Australia who reported using methamphetamine at least once in the last month were eligible to download the app for free from Android or iOS app stores. Those randomized to the intervention group had immediate access to the S-Check app, the control group was wait-listed for 28 days before gaining access, and then all had access until day 56. Actual help-seeking and intention to seek help were assessed by the modified Actual Help Seeking Questionnaire (mAHSQ), modified General Help Seeking Questionnaire, and motivation to change methamphetamine use by the modified readiness ruler. χ2 comparisons of the proportion of positive responses to the mAHSQ, modified General Help Seeking Questionnaire, and modified readiness ruler were conducted between the 2 groups. Logistic regression models compared the odds of actual help-seeking, intention to seek help, and motivation to change at day 28 between the 2 groups. Secondary outcomes were the most commonly accessed features of the app, methamphetamine use, feasibility and acceptability of the app, and associations between S-Check app engagement and participant demographic and methamphetamine use characteristics. RESULTS: In total, 560 participants downloaded the app; 259 (46.3%) completed eConsent and baseline; and 84 (32.4%) provided data on day 28. Participants in the immediate access group were more likely to seek professional help (mAHSQ) at day 28 than those in the control group (n=15, 45.5% vs n=12, 23.5%; χ21=4.42, P=.04). There was no significant difference in the odds of actual help-seeking, intention to seek help, or motivation to change methamphetamine use between the 2 groups on the primary logistic regression analyses, while in the ancillary analyses, the imputed data set showed a significant difference in the odds of seeking professional help between participants in the immediate access group compared to the waitlist control group (adjusted odds ratio 2.64, 95% CI 1.19-5.83, P=.02). For participants not seeking help at baseline, each minute in the app increased the likelihood of seeking professional help by day 28 by 8% (ratio 1.08, 95% CI 1.02-1.22, P=.04). Among the intervention group, a 10-minute increase in app engagement time was associated with a decrease in days of methamphetamine use by 0.4 days (regression coefficient [ß] -0.04, P=.02). CONCLUSIONS: The S-Check app is a feasible low-resource self-administered intervention for adults in Australia who consume methamphetamine. Study attrition was high and, while common in mobile health interventions, warrants larger studies of the S-Check app. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12619000534189; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377288&isReview=true.

A Deep Learning-Based Rotten Food Recognition App for Older Adults: Development and Usability Study.

BACKGROUND: Older adults are at greater risk of eating rotten fruits and of getting food poisoning because cognitive function declines as they age, making it difficult to distinguish rotten fruits. To address this problem, researchers have developed and evaluated various tools to detect rotten food items in various ways. Nevertheless, little is known about how to create an app to detect rotten food items to support older adults at a risk of health problems from eating rotten food items. OBJECTIVE: This study aimed to (1) create a smartphone app that enables older adults to take a picture of food items with a camera and classifies the fruit as rotten or not rotten for older adults and (2) evaluate the usability of the app and the perceptions of older adults about the app. METHODS: We developed a smartphone app that supports older adults in determining whether the 3 fruits selected for this study (apple, banana, and orange) were fresh enough to eat. We used several residual deep networks to check whether the fruit photos collected were of fresh fruit. We recruited healthy older adults aged over 65 years (n=15, 57.7%, males and n=11, 42.3%, females) as participants. We evaluated the usability of the app and the participants' perceptions about the app through surveys and interviews. We analyzed the survey responses, including an after-scenario questionnaire, as evaluation indicators of the usability of the app and collected qualitative data from the interviewees for in-depth analysis of the survey responses. RESULTS: The participants were satisfied with using an app to determine whether a fruit is fresh by taking a picture of the fruit but are reluctant to use the paid version of the app. The survey results revealed that the participants tended to use the app efficiently to take pictures of fruits and determine their freshness. The qualitative data analysis on app usability and participants' perceptions about the app revealed that they found the app simple and easy to use, they had no difficulty taking pictures, and they found the app interface visually satisfactory. CONCLUSIONS: This study suggests the possibility of developing an app that supports older adults in identifying rotten food items effectively and efficiently. Future work to make the app distinguish the freshness of various food items other than the 3 fruits selected still remains.

Evaluating usability of and satisfaction with mHealth app in rural and remote areas-Germany GIZ collaboration in Bosnia-Herzegovina to optimize type 1 diabetes care.

Background: Type 1 diabetes mellitus (T1DM) management in children and adolescents requires intensive supervision and monitoring to prevent acute and late diabetes complications and to improve quality of life. Digital health interventions, in particular diabetes mobile health apps (mHealth apps) can facilitate specialized T1DM care in this population. This study evaluated the initial usability of and satisfaction with the m-Health intervention Diabetes: M app, and the ease of use of various app features in supporting T1DM care in rural and remote areas of Bosnia-Herzegovina with limited access to specialized diabetes care. Methods: This cross-sectional study, performed in February-March 2023, evaluated T1DM pediatric patients who used the Diabetes: M app in a 3-month mHealth-based T1DM management program, along with their parents and healthcare providers (HCPs). All participants completed self-administered online questionnaires at the end of the 3-month period. Data were analyzed by descriptive statistics. Results: The study population included 50 T1DM patients (children/parents and adolescents) and nine HCPs. The mean ± SD age of the T1DM patients was 14 ± 4.54 years, with 26 (52%) being female. The mean ± SD age of the HCPs was 43.4 ± 7.76 years; all (100%) were women, with a mean ± SD professional experience of 17.8 ± 8.81 years. The app was reported usable in the domains of ease-of-use and satisfaction by the T1DM children/parents (5.82/7.0), T1DM adolescents/young adults (5.68/7.0), and HCPs (5.22/7.0). Various app features, as well as the overall app experience, were rated positively by the participants. Conclusion: The results strongly support the usability of mHealth-based interventions in T1DM care, especially in overcoming care shortage and improving diabetes management and communications between HCPs and patients. Further studies are needed to compare the effectiveness of apps used to support T1DM management with routine care.

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.

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.

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.

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.

An Artificial Intelligence-Based App for Self-Management of Low Back and Neck Pain in Specialist Care: Process Evaluation From a Randomized Clinical Trial.

BACKGROUND: Self-management is endorsed in clinical practice guidelines for the care of musculoskeletal pain. In a randomized clinical trial, we tested the effectiveness of an artificial intelligence-based self-management app (selfBACK) as an adjunct to usual care for patients with low back and neck pain referred to specialist care. OBJECTIVE: This study is a process evaluation aiming to explore patients' engagement and experiences with the selfBACK app and specialist health care practitioners' views on adopting digital self-management tools in their clinical practice. METHODS: App usage analytics in the first 12 weeks were used to explore patients' engagement with the SELFBACK app. Among the 99 patients allocated to the SELFBACK interventions, a purposive sample of 11 patients (aged 27-75 years, 8 female) was selected for semistructured individual interviews based on app usage. Two focus group interviews were conducted with specialist health care practitioners (n=9). Interviews were analyzed using thematic analysis. RESULTS: Nearly one-third of patients never accessed the app, and one-third were low users. Three themes were identified from interviews with patients and health care practitioners: (1) overall impression of the app, where patients discussed the interface and content of the app, reported on usability issues, and described their app usage; (2) perceived value of the app, where patients and health care practitioners described the primary value of the app and its potential to supplement usual care; and (3) suggestions for future use, where patients and health care practitioners addressed aspects they believed would determine acceptance. CONCLUSIONS: Although the app's uptake was relatively low, both patients and health care practitioners had a positive opinion about adopting an app-based self-management intervention for low back and neck pain as an add-on to usual care. Both described that the app could reassure patients by providing trustworthy information, thus empowering them to take actions on their own. Factors influencing app acceptance and engagement, such as content relevance, tailoring, trust, and usability properties, were identified. TRIAL REGISTRATION: ClinicalTrials.gov NCT04463043; https://clinicaltrials.gov/study/NCT04463043.

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.

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.

Prepare Romania: study protocol for a randomized controlled trial of an intervention to promote pre-exposure prophylaxis adherence and persistence among gay, bisexual, and other men who have sex with men.

BACKGROUND: Gay, bisexual, and other men who have sex with men (GBMSM) represent a high-risk group for HIV transmission in Romania, yet they possess few resources for prevention. Despite having no formal access to pre-exposure prophylaxis (PrEP) through the health system, GBMSM in Romania demonstrate a high need for and interest in this medication. In anticipation of a national rollout of PrEP, this study tests the efficacy of a novel strategy, Prepare Romania, that combines two evidence-based PrEP promotion interventions for GBMSM living in Romania. METHODS: This study uses a randomized controlled trial design to examine whether GBMSM living in Romania receiving Prepare Romania, a culturally adapted counseling and mobile health intervention (expected n = 60), demonstrate greater PrEP adherence and persistence than those assigned to a PrEP education control arm (expected n = 60). Participants from two main cities in Romania are prescribed PrEP and followed-up at 3 and 6 months post-randomization. PrEP adherence data are obtained through weekly self-report surveys and dried blood spot testing at follow-up visits. Potential mediators (e.g., PrEP use motivation) of intervention efficacy are also assessed. Furthermore, Prepare Romania's implementation (e.g., proportion of enrolled participants attending medical visits, intervention experience) will be examined through interviews with participants, study implementers, and healthcare officials. DISCUSSION: The knowledge gained from this study will be utilized for further refinement and scale-up of Prepare Romania for a future multi-city effectiveness trial. By studying the efficacy of tools to support PrEP adherence and persistence, this research has the potential to lay the groundwork for PrEP rollout in Romania and similar contexts. Trial registration This study was registered on ClinicalTrials.gov, identifier NCT05323123 , on March 25, 2022.

A Novel mHealth App for Smokers Living With HIV Who Are Ambivalent About Quitting Smoking: Formative Research and Randomized Feasibility Study.

BACKGROUND: More people who smoke and are living with HIV now die from tobacco-related diseases than HIV itself. Most people are ambivalent about quitting smoking and want to quit someday but not yet. Scalable, effective interventions are needed to motivate and support smoking cessation among people ambivalent about quitting smoking (PAQS) who are living with HIV. OBJECTIVE: This study aims to develop an app-based intervention for PAQS who are living with HIV and assess its feasibility, acceptability, and potential impact. Results of this study will inform plans for future research and development. METHODS: In phase 1, PAQS living with HIV (n=8) participated in user-centered design interviews to inform the final intervention app design and recruitment plan for a subsequent randomized pilot study. In phase 2, PAQS living with HIV were randomized to either a standard care control app or a similar experimental app with additional content tailored for PAQS and those with HIV. Participants were followed for 3 months. Feasibility focused on recruitment, retention, and participants' willingness to install the app. The study was not powered for statistical significance. Indices of acceptability (satisfaction and use) and impact (smoking behavior change and treatment uptake) were assessed via automated data and self-report among those who installed and used the app (n=19). RESULTS: Recruitment for both study phases was a challenge, particularly via web-based and social media platforms. Enrollment success was greater among people living with HIV recruited from a health care provider and research registry. Once enrolled, retention for the phase 2 randomized study was good; 74% (14/19) of the participants completed the 3-month follow-up. Phase 1 findings suggested that PAQS living with HIV were receptive to using an app-based intervention to help them decide whether, when, and how to stop smoking, despite not being ready to quit smoking. Phase 2 findings further supported this conclusion based on feedback from people who agreed to use an app, but group differences were observed. Indices of acceptability favored the experimental arm, including a descriptively higher mean number of sessions and utilization badges. Similarly, indices of potential impact were descriptively higher in the experimental arm (proportion reducing smoking, making a quit attempt, or calling free tobacco quitline). No participants in either arm quit smoking at the 3-month follow-up. CONCLUSIONS: On the basis of this formative work, PAQS living with HIV may be receptive to using a mobile health-based app intervention to help them decide whether, when, or how to stop using tobacco. Indices of acceptability and impact indicate that additional research and development are warranted. TRIAL REGISTRATION: ClinicalTrials.gov NCT05339659; https://clinicaltrials.gov/study/NCT05339659.