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.

Reporting quality of published reviews of commercial and publicly available mobile health apps (mHealth app reviews): a scoping review protocol.

INTRODUCTION: Reviews of commercial and publicly available smartphone (mobile) health applications (mHealth app reviews) are being undertaken and published. However, there is variation in the conduct and reporting of mHealth app reviews, with no existing reporting guidelines. Building on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we aim to develop the Consensus for APP Review Reporting Items (CAPPRRI) guidance, to support the conduct and reporting of mHealth app reviews. This scoping review of published mHealth app reviews will explore their alignment, deviation, and modification to the PRISMA 2020 items for systematic reviews and identify a list of possible items to include in CAPPRRI. METHOD AND ANALYSIS: We are following the Joanna Briggs Institute approach and Arksey and O'Malley's five-step process. Patient and public contributors, mHealth app review, digital health research and evidence synthesis experts, healthcare professionals and a specialist librarian gave feedback on the methods. We will search SCOPUS, CINAHL Plus, AMED, EMBASE, Medline, APA PsycINFO and the ACM Digital Library for articles reporting mHealth app reviews and use a two-step screening process to identify eligible articles. Information on whether the authors have reported, or how they have modified the PRISMA 2020 items in their reporting, will be extracted. Data extraction will also include the article characteristics, protocol and registration information, review question frameworks used, information about the search and screening process, how apps have been evaluated and evidence of stakeholder engagement. This will be analysed using a content synthesis approach and presented using descriptive statistics and summaries. This protocol is registered on OSF (https://osf.io/5ahjx). ETHICS AND DISSEMINATION: Ethical approval is not required. The findings will be disseminated through peer-reviewed journal publications (shared on our project website and on the EQUATOR Network website where the CAPPRRI guidance has been registered as under development), conference presentations and blog and social media posts in lay language.

Effectiveness of a Multifaceted Mobile Health Intervention (Multi-Aid-Package) in Medication Adherence and Treatment Outcomes Among Patients With Hypertension in a Low- to Middle-Income Country: Randomized Controlled Trial.

BACKGROUND: The high prevalence of uncontrolled hypertension in Pakistan is predominantly attributed to poor medication adherence. As more than 137 million people in Pakistan use cell phones, a suitable mobile health (mHealth) intervention can be an effective tool to overcome poor medication adherence. OBJECTIVE: We sought to determine whether a novel mHealth intervention is useful in enhancing antihypertensive therapy adherence and treatment outcomes among patients with hypertension in a low- to middle-income country. METHODS: A 6-month parallel, single-blinded, superiority randomized controlled trial recruited 439 patients with hypertension with poor adherence to antihypertensive therapy and access to smartphones. An innovative, multifaceted mHealth intervention (Multi-Aid-Package), based on the Health Belief Model and containing reminders (written, audio, visual), infographics, video clips, educational content, and 24/7 individual support, was developed for the intervention group; the control group received standard care. The primary outcome was self-reported medication adherence measured using the Self-Efficacy for Appropriate Medication Adherence Scale (SEAMS) and pill counting; the secondary outcome was systolic blood pressure (SBP) change. Both outcomes were evaluated at baseline and 6 months. Technology acceptance feedback was also assessed at the end of the study. A generalized estimating equation was used to control the covariates associated with the probability of affecting adherence to antihypertensive medication. RESULTS: Of 439 participants, 423 (96.4%) completed the study. At 6 months post intervention, the median SEAMS score was statistically significantly higher in the intervention group compared to the controls (median 32, IQR 11 vs median 21, IQR 6; U=10,490, P<.001). Within the intervention group, there was an increase in the median SEAMS score by 12.5 points between baseline and 6 months (median 19.5, IQR 5 vs median 32, IQR 11; P<.001). Results of the pill-counting method showed an increase in adherent patients in the intervention group compared to the controls (83/220, 37.2% vs 2/219, 0.9%; P<.001), as well as within the intervention group (difference of n=83, 37.2% of patients, baseline vs 6 months; P<.001). There was a statistically significant difference in the SBP of 7 mmHg between the intervention and control groups (P<.001) at 6 months, a 4 mmHg reduction (P<.001) within the intervention group, and a 3 mmHg increase (P=.314) within the controls. Overall, the number of patients with uncontrolled hypertension decreased by 46 in the intervention group (baseline vs 6 months), but the control group remained unchanged. The variables groups (adjusted odds ratio [AOR] 1.714, 95% CI 2.387-3.825), time (AOR 1.837, 95% CI 1.625-2.754), and age (AOR 1.618, 95% CI 0.225-1.699) significantly contributed (P<.001) to medication adherence. Multi-Aid-Package received a 94.8% acceptability score. CONCLUSIONS: The novel Multi-Aid-Package is an effective mHealth intervention for enhancing medication adherence and treatment outcomes among patients with hypertension in a low- to middle-income country. TRIAL REGISTRATION: ClinicalTrials.gov NCT04577157; https://clinicaltrials.gov/study/NCT04577157.

Engagement in mHealth-Prompted Self-Measured Blood Pressure Monitoring Among Participants Recruited From a Safety-Net Emergency Department: Secondary Analysis of the Reach Out Trial.

Background: Hypertension, a key modifiable risk factor for cardiovascular disease, is more prevalent among Black and low-income individuals. To address this health disparity, leveraging safety-net emergency departments for scalable mobile health (mHealth) interventions, specifically using text messaging for self-measured blood pressure (SMBP) monitoring, presents a promising strategy. This study investigates patterns of engagement, associated factors, and the impact of engagement on lowering blood pressure (BP) in an underserved population. Objective: We aimed to identify patterns of engagement with prompted SMBP monitoring with feedback, factors associated with engagement, and the association of engagement with lowered BP. Methods: This is a secondary analysis of data from Reach Out, an mHealth, factorial trial among 488 hypertensive patients recruited from a safety-net emergency department in Flint, Michigan. Reach Out participants were randomized to weekly or daily text message prompts to measure their BP and text in their responses. Engagement was defined as a BP response to the prompt. The k-means clustering algorithm and visualization were used to determine the pattern of SMBP engagement by SMBP prompt frequency-weekly or daily. BP was remotely measured at 12 months. For each prompt frequency group, logistic regression models were used to assess the univariate association of demographics, access to care, and comorbidities with high engagement. We then used linear mixed-effects models to explore the association between engagement and systolic BP at 12 months, estimated using average marginal effects. Results: For both SMBP prompt groups, the optimal number of engagement clusters was 2, which we defined as high and low engagement. Of the 241 weekly participants, 189 (78.4%) were low (response rate: mean 20%, SD 23.4) engagers, and 52 (21.6%) were high (response rate: mean 86%, SD 14.7) engagers. Of the 247 daily participants, 221 (89.5%) were low engagers (response rate: mean 9%, SD 12.2), and 26 (10.5%) were high (response rate: mean 67%, SD 8.7) engagers. Among weekly participants, those who were older (>65 years of age), attended some college (vs no college), married or lived with someone, had Medicare (vs Medicaid), were under the care of a primary care doctor, and took antihypertensive medication in the last 6 months had higher odds of high engagement. Participants who lacked transportation to appointments had lower odds of high engagement. In both prompt frequency groups, participants who were high engagers had a greater decline in BP compared to low engagers. Conclusions: Participants randomized to weekly SMBP monitoring prompts responded more frequently overall and were more likely to be classed as high engagers compared to participants who received daily prompts. High engagement was associated with a larger decrease in BP. New strategies to encourage engagement are needed for participants with lower access to care.

Digital home care interventions and quality of primary care for older adults: a scoping review.

BACKGROUND: Population aging is forcing the transformation of health care. Long-term care in the home is complex and involves complex communication with primary care services. In this scenario, the expansion of digital health has the potential to improve access to home-based primary care; however, the use of technologies can increase inequalities in access to health for an important part of the population. The aim of this study was to identify and map the uses and types of digital health interventions and their impacts on the quality of home-based primary care for older adults. METHODS: This is a broad and systematized scoping review with rigorous synthesis of knowledge directed by the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The quantitative data were analyzed through descriptive statistics, and the qualitative data were analyzed through basic qualitative content analysis, considering the organizational, relational, interpersonal and technical dimensions of care. The preliminary results were subjected to consultation with stakeholders to identify strengths and limitations, as well as potential forms of socialization. RESULTS: The mapping showed the distribution of publications in 18 countries and in the Sub-Saharan Africa region. Older adults have benefited from the use of different digital health strategies; however, this review also addresses limitations and challenges, such as the need for digital literacy and technological infrastructure. In addition to the impacts of technologies on the quality of health care. CONCLUSIONS: The review gathered priority themes for the equitable implementation of digital health, such as access to home caregivers and digital tools, importance of digital literacy and involvement of patients and their caregivers in health decisions and design of technologies, which must be prioritized to overcome limitations and challenges, focusing on improving quality of life, shorter hospitalization time and autonomy of older adults.

Cross-Cutting mHealth Behavior Change Techniques to Support Treatment Adherence and Self-Management of Complex Medical Conditions: Systematic Review.

Background: Mobile health (mHealth) interventions have immense potential to support disease self-management for people with complex medical conditions following treatment regimens that involve taking medicine and other self-management activities. However, there is no consensus on what discrete behavior change techniques (BCTs) should be used in an effective adherence and self-management-promoting mHealth solution for any chronic illness. Reviewing the extant literature to identify effective, cross-cutting BCTs in mHealth interventions for adherence and self-management promotion could help accelerate the development, evaluation, and dissemination of behavior change interventions with potential generalizability across complex medical conditions. Objective: This study aimed to identify cross-cutting, mHealth-based BCTs to incorporate into effective mHealth adherence and self-management interventions for people with complex medical conditions, by systematically reviewing the literature across chronic medical conditions with similar adherence and self-management demands. Methods: A registered systematic review was conducted to identify published evaluations of mHealth adherence and self-management interventions for chronic medical conditions with complex adherence and self-management demands. The methodological characteristics and BCTs in each study were extracted using a standard data collection form. Results: A total of 122 studies were reviewed; the majority involved people with type 2 diabetes (28/122, 23%), asthma (27/122, 22%), and type 1 diabetes (19/122, 16%). mHealth interventions rated as having a positive outcome on adherence and self-management used more BCTs (mean 4.95, SD 2.56) than interventions with no impact on outcomes (mean 3.57, SD 1.95) or those that used >1 outcome measure or analytic approach (mean 3.90, SD 1.93; P=.02). The following BCTs were associated with positive outcomes: self-monitoring outcomes of behavior (39/59, 66%), feedback on outcomes of behavior (34/59, 58%), self-monitoring of behavior (34/59, 58%), feedback on behavior (29/59, 49%), credible source (24/59, 41%), and goal setting (behavior; 14/59, 24%). In adult-only samples, prompts and cues were associated with positive outcomes (34/45, 76%). In adolescent and young adult samples, information about health consequences (1/4, 25%), problem-solving (1/4, 25%), and material reward (behavior; 2/4, 50%) were associated with positive outcomes. In interventions explicitly targeting medicine taking, prompts and cues (25/33, 76%) and credible source (13/33, 39%) were associated with positive outcomes. In interventions focused on self-management and other adherence targets, instruction on how to perform the behavior (8/26, 31%), goal setting (behavior; 8/26, 31%), and action planning (5/26, 19%) were associated with positive outcomes. Conclusions: To support adherence and self-management in people with complex medical conditions, mHealth tools should purposefully incorporate effective and developmentally appropriate BCTs. A cross-cutting approach to BCT selection could accelerate the development of much-needed mHealth interventions for target populations, although mHealth intervention developers should continue to consider the unique needs of the target population when designing these tools.

Evaluating adherence to American Diabetes Association standards of care in diabetes and impacts of social determinants of health on patients at two nurse practitioner-owned clinics.

BACKGROUND: The COVID-19 pandemic created barriers in the management of type 2 diabetes mellitus (T2DM) and worsened social determinants of health (SDOH). A New Hampshire primary care office worked to adhere to T2DM standards of care and began screening for SDOH. This project assessed adherence to quality metrics, hemoglobin A1C, and SDOH screening as telehealth utilization decreased. LOCAL PROBLEM: A1C values have increased at the practice, especially since COVID-19. The practice also began screening for SDOH at every visit, but there was need to assess how needs were being documented and if/how they were addressed. METHODS: A retrospective chart review of patients with T2DM was performed. Demographic data and T2DM metrics were collected and compared with previous years and compared new versus established patients. Charts were reviewed to evaluate documentation of SDOH and appropriate referral. INTERVENTIONS: The practice transitioned from an increased utliization of telehealth back to prioritizing in-office visits. The practice also began routinely screening for SDOH in 2020; however, this process had not been standardized or evaluated. RESULTS: Adherence to nearly all quality metrics improved. Glycemic control improved after a year of nurse practitioner (NP) care, especially in new patients. All patients were screened for SDOH, but documentation varied, and affected patients had higher A1Cs, despite receiving comparable care. CONCLUSION: Nurse practitioners at this practice are adhering to American Diabetes Association guidelines, and A1C values improve under their care. Social determinants of health continue to act as unique barriers that keep patients from improving glycemic control, highlighting the need for individualized treatment of SDOH in T2DM care.

From diagnosis to intervention: a review of telemedicine's role in skin cancer care.

Skin cancer treatment is a core aspect of dermatology that relies on accurate diagnosis and timely interventions. Teledermatology has emerged as a valuable asset across various stages of skin cancer care including triage, diagnosis, management, and surgical consultation. With the integration of traditional dermoscopy and store-and-forward technology, teledermatology facilitates the swift sharing of high-resolution images of suspicious skin lesions with consulting dermatologists all-over. Both live video conference and store-and-forward formats have played a pivotal role in bridging the care access gap between geographically isolated patients and dermatology providers. Notably, teledermatology demonstrates diagnostic accuracy rates that are often comparable to those achieved through traditional face-to-face consultations, underscoring its robust clinical utility. Technological advancements like artificial intelligence and reflectance confocal microscopy continue to enhance image quality and hold potential for increasing the diagnostic accuracy of virtual dermatologic care. While teledermatology serves as a valuable clinical tool for all patient populations including pediatric patients, it is not intended to fully replace in-person procedures like Mohs surgery and other necessary interventions. Nevertheless, its role in facilitating the evaluation of skin malignancies is gaining recognition within the dermatologic community and fostering high approval rates from patients due to its practicality and ability to provide timely access to specialized care.

Understanding Patient Perspectives on the Use of Gamification and Incentives in mHealth Apps to Improve Medication Adherence: Qualitative Study.

BACKGROUND: Medication nonadherence remains a significant health and economic burden in many high-income countries. Emerging smartphone interventions have started to use features such as gamification and financial incentives with varying degrees of effectiveness on medication adherence and health outcomes. A more consistent approach to applying these features, informed by patient perspectives, may result in more predictable and beneficial results from this type of intervention. OBJECTIVE: This qualitative study aims to identify patient perspectives on the use of gamification and financial incentives in mobile health (mHealth) apps for medication adherence in Australian patients taking medication for chronic conditions. METHODS: A total of 19 participants were included in iterative semistructured web-based focus groups conducted between May and December 2022. The facilitator used exploratory prompts relating to mHealth apps, gamification, and financial incentives, along with concepts raised from previous focus groups. Transcriptions were independently coded to develop a set of themes. RESULTS: Three themes were identified: purpose-driven design, trust-based standards, and personal choice. All participants acknowledged gamification and financial incentives as potentially effective features in mHealth apps for medication adherence. However, they also indicated that the effectiveness heavily depended on implementation and execution. Major concerns relating to gamification and financial incentives were perceived trivialization and potential for medication abuse, respectively. CONCLUSIONS: The study's findings provide a foundation for developers seeking to apply these novel features in an app intervention for a general cohort of patients. However, the study highlights the need for standards for mHealth apps for medication adherence, with particular attention to the use of gamification and financial incentives. Future research with patients and stakeholders across the mHealth app ecosystem should be explored to formalize and validate a set of standards or framework.

Assessing the Efficacy of ChatGPT Versus Human Researchers in Identifying Relevant Studies on mHealth Interventions for Improving Medication Adherence in Patients With Ischemic Stroke When Conducting Systematic Reviews: Comparative Analysis.

BACKGROUND: ChatGPT by OpenAI emerged as a potential tool for researchers, aiding in various aspects of research. One such application was the identification of relevant studies in systematic reviews. However, a comprehensive comparison of the efficacy of relevant study identification between human researchers and ChatGPT has not been conducted. OBJECTIVE: This study aims to compare the efficacy of ChatGPT and human researchers in identifying relevant studies on medication adherence improvement using mobile health interventions in patients with ischemic stroke during systematic reviews. METHODS: This study used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Four electronic databases, including CINAHL Plus with Full Text, Web of Science, PubMed, and MEDLINE, were searched to identify articles published from inception until 2023 using search terms based on MeSH (Medical Subject Headings) terms generated by human researchers versus ChatGPT. The authors independently screened the titles, abstracts, and full text of the studies identified through separate searches conducted by human researchers and ChatGPT. The comparison encompassed several aspects, including the ability to retrieve relevant studies, accuracy, efficiency, limitations, and challenges associated with each method. RESULTS: A total of 6 articles identified through search terms generated by human researchers were included in the final analysis, of which 4 (67%) reported improvements in medication adherence after the intervention. However, 33% (2/6) of the included studies did not clearly state whether medication adherence improved after the intervention. A total of 10 studies were included based on search terms generated by ChatGPT, of which 6 (60%) overlapped with studies identified by human researchers. Regarding the impact of mobile health interventions on medication adherence, most included studies (8/10, 80%) based on search terms generated by ChatGPT reported improvements in medication adherence after the intervention. However, 20% (2/10) of the studies did not clearly state whether medication adherence improved after the intervention. The precision in accurately identifying relevant studies was higher in human researchers (0.86) than in ChatGPT (0.77). This is consistent with the percentage of relevance, where human researchers (9.8%) demonstrated a higher percentage of relevance than ChatGPT (3%). However, when considering the time required for both humans and ChatGPT to identify relevant studies, ChatGPT substantially outperformed human researchers as it took less time to identify relevant studies. CONCLUSIONS: Our comparative analysis highlighted the strengths and limitations of both approaches. Ultimately, the choice between human researchers and ChatGPT depends on the specific requirements and objectives of each review, but the collaborative synergy of both approaches holds the potential to advance evidence-based research and decision-making in the health care field.

Evaluation of usability and user feedback to guide telepharmacy application development in Indonesia: a mixed-methods study.

BACKGROUND: In Indonesia, the adoption of telepharmacy was propelled by the COVID-19 pandemic, prompting the need for a user-friendly application to support both the general population and pharmacists in accessing healthcare services. Therefore, this study aimed to evaluate usability and user feedback of a pioneering telepharmacy application known as Tanya Obat (translating to "Ask about Medications") in Indonesia, from the perspectives of the general population and pharmacists. METHODS: A mixed-methods sequential study was conducted with the early-stage Tanya Obat application in Bandung City. Participants, including the general population and pharmacists, were instructed to use the application for a week. Questionnaires for the general population and pharmacists were distributed from March to May and February to June 2023, respectively. The System Usability Scale questionnaire was adopted to describe usability of the developed application. Further exploration of the quantitative results required collecting open-ended feedback to assess the impressions of the participants, difficulties encountered, and desired features for enhanced user-friendliness. The collected statements were summarized and clustered using thematic analysis. Subsequently, the association between the characteristics of participants and perceived usability was determined with the Chi-square test. RESULT: A total of 176 participants, comprising 100 individuals from the general population and 76 pharmacists, engaged in this study. In terms of usability, the questionnaire showed that Tanya Obat application was on the borderline of acceptability, with mean scores of 63.4 and 64.1 from the general population and pharmacists, respectively. Additionally, open-ended feedback targeted at achieving a more compelling user experience was categorized into two themes, including concerns regarding the functionality of certain features and recommendations for improved visual aesthetics and bug fixes. No significant associations were observed between the characteristics of participants and perceived usability (p-value > 0.05). CONCLUSION: The results showed that the perceived usability of Tanya Obat developed for telepharmacy was below average. Therefore, feature optimizations should be performed to facilitate usability of this application in Indonesia.

Effectiveness of mHealth App-Based Interventions for Increasing Physical Activity and Improving Physical Fitness in Children and Adolescents: Systematic Review and Meta-Analysis.

BACKGROUND: The COVID-19 pandemic has significantly reduced physical activity (PA) levels and increased sedentary behavior (SB), which can lead to worsening physical fitness (PF). Children and adolescents may benefit from mobile health (mHealth) apps to increase PA and improve PF. However, the effectiveness of mHealth app-based interventions and potential moderators in this population are not yet fully understood. OBJECTIVE: This study aims to review and analyze the effectiveness of mHealth app-based interventions in promoting PA and improving PF and identify potential moderators of the efficacy of mHealth app-based interventions in children and adolescents. METHODS: We searched for randomized controlled trials (RCTs) published in the PubMed, Web of Science, EBSCO, and Cochrane Library databases until December 25, 2023, to conduct this meta-analysis. We included articles with intervention groups that investigated the effects of mHealth-based apps on PA and PF among children and adolescents. Due to high heterogeneity, a meta-analysis was conducted using a random effects model. The Cochrane Risk of Bias Assessment Tool was used to evaluate the risk of bias. Subgroup analysis and meta-regression analyses were performed to identify potential influences impacting effect sizes. RESULTS: We included 28 RCTs with a total of 5643 participants. In general, the risk of bias of included studies was low. Our findings showed that mHealth app-based interventions significantly increased total PA (TPA; standardized mean difference [SMD] 0.29, 95% CI 0.13-0.45; P<.001), reduced SB (SMD -0.97, 95% CI -1.67 to -0.28; P=.006) and BMI (weighted mean difference -0.31 kg/m2, 95% CI -0.60 to -0.01 kg/m2; P=.12), and improved muscle strength (SMD 1.97, 95% CI 0.09-3.86; P=.04) and agility (SMD -0.35, 95% CI -0.61 to -0.10; P=.006). However, mHealth app-based interventions insignificantly affected moderate to vigorous PA (MVPA; SMD 0.11, 95% CI -0.04 to 0.25; P<.001), waist circumference (weighted mean difference 0.38 cm, 95% CI -1.28 to 2.04 cm; P=.65), muscular power (SMD 0.01, 95% CI -0.08 to 0.10; P=.81), cardiorespiratory fitness (SMD -0.20, 95% CI -0.45 to 0.05; P=.11), muscular endurance (SMD 0.47, 95% CI -0.08 to 1.02; P=.10), and flexibility (SMD 0.09, 95% CI -0.23 to 0.41; P=.58). Subgroup analyses and meta-regression showed that intervention duration was associated with TPA and MVPA, and age and types of intervention was associated with BMI. CONCLUSIONS: Our meta-analysis suggests that mHealth app-based interventions may yield small-to-large beneficial effects on TPA, SB, BMI, agility, and muscle strength in children and adolescents. Furthermore, age and intervention duration may correlate with the higher effectiveness of mHealth app-based interventions. However, due to the limited number and quality of included studies, the aforementioned conclusions require validation through additional high-quality research. TRIAL REGISTRATION: PROSPERO CRD42023426532; https://tinyurl.com/25jm4kmf.

An mHealth Intervention Promoting Physical Activity and Healthy Eating in a Family Setting (SMARTFAMILY): Randomized Controlled Trial.

BACKGROUND: Numerous smartphone apps are targeting physical activity (PA) and healthy eating (HE), but empirical evidence on their effectiveness for the initialization and maintenance of behavior change, especially in children and adolescents, is still limited. Social settings influence individual behavior; therefore, core settings such as the family need to be considered when designing mobile health (mHealth) apps. OBJECTIVE: The purpose of this study was to evaluate the effectiveness of a theory- and evidence-based mHealth intervention (called SMARTFAMILY [SF]) targeting PA and HE in a collective family-based setting. METHODS: A smartphone app based on behavior change theories and techniques was developed, implemented, and evaluated with a cluster randomized controlled trial in a collective family setting. Baseline (t0) and postintervention (t1) measurements included PA (self-reported and accelerometry) and HE measurements (self-reported fruit and vegetable intake) as primary outcomes. Secondary outcomes (self-reported) were intrinsic motivation, behavior-specific self-efficacy, and the family health climate. Between t0 and t1, families of the intervention group (IG) used the SF app individually and collaboratively for 3 consecutive weeks, whereas families in the control group (CG) received no treatment. Four weeks following t1, a follow-up assessment (t2) was completed by participants, consisting of all questionnaire items to assess the stability of the intervention effects. Multilevel analyses were implemented in R (R Foundation for Statistical Computing) to acknowledge the hierarchical structure of persons (level 1) clustered in families (level 2). RESULTS: Overall, 48 families (CG: n=22, 46%, with 68 participants and IG: n=26, 54%, with 88 participants) were recruited for the study. Two families (CG: n=1, 2%, with 4 participants and IG: n=1, 2%, with 4 participants) chose to drop out of the study owing to personal reasons before t0. Overall, no evidence for meaningful and statistically significant increases in PA and HE levels of the intervention were observed in our physically active study participants (all P>.30). CONCLUSIONS: Despite incorporating behavior change techniques rooted in family life and psychological theories, the SF intervention did not yield significant increases in PA and HE levels among the participants. The results of the study were mainly limited by the physically active participants and the large age range of children and adolescents. Enhancing intervention effectiveness may involve incorporating health literacy, just-in-time adaptive interventions, and more advanced features in future app development. Further research is needed to better understand intervention engagement and tailor mHealth interventions to individuals for enhanced effectiveness in primary prevention efforts. TRIAL REGISTRATION: German Clinical Trials Register DRKS00010415; https://drks.de/search/en/trial/DRKS00010415. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/20534.

Patient and Staff Experience of Remote Patient Monitoring-What to Measure and How: Systematic Review.

BACKGROUND: Patient and staff experience is a vital factor to consider in the evaluation of remote patient monitoring (RPM) interventions. However, no comprehensive overview of available RPM patient and staff experience-measuring methods and tools exists. OBJECTIVE: This review aimed at obtaining a comprehensive set of experience constructs and corresponding measuring instruments used in contemporary RPM research and at proposing an initial set of guidelines for improving methodological standardization in this domain. METHODS: Full-text papers reporting on instances of patient or staff experience measuring in RPM interventions, written in English, and published after January 1, 2011, were considered for eligibility. By "RPM interventions," we referred to interventions including sensor-based patient monitoring used for clinical decision-making; papers reporting on other kinds of interventions were therefore excluded. Papers describing primary care interventions, involving participants under 18 years of age, or focusing on attitudes or technologies rather than specific interventions were also excluded. We searched 2 electronic databases, Medline (PubMed) and EMBASE, on February 12, 2021.We explored and structured the obtained corpus of data through correspondence analysis, a multivariate statistical technique. RESULTS: In total, 158 papers were included, covering RPM interventions in a variety of domains. From these studies, we reported 546 experience-measuring instances in RPM, covering the use of 160 unique experience-measuring instruments to measure 120 unique experience constructs. We found that the research landscape has seen a sizeable growth in the past decade, that it is affected by a relative lack of focus on the experience of staff, and that the overall corpus of collected experience measures can be organized in 4 main categories (service system related, care related, usage and adherence related, and health outcome related). In the light of the collected findings, we provided a set of 6 actionable recommendations to RPM patient and staff experience evaluators, in terms of both what to measure and how to measure it. Overall, we suggested that RPM researchers and practitioners include experience measuring as part of integrated, interdisciplinary data strategies for continuous RPM evaluation. CONCLUSIONS: At present, there is a lack of consensus and standardization in the methods used to measure patient and staff experience in RPM, leading to a critical knowledge gap in our understanding of the impact of RPM interventions. This review offers targeted support for RPM experience evaluators by providing a structured, comprehensive overview of contemporary patient and staff experience measures and a set of practical guidelines for improving research quality and standardization in this domain.

Effectiveness of an Interactive mHealth App (EVITE) in Improving Lifestyle After a Coronary Event: Randomized Controlled Trial.

BACKGROUND: Coronary heart disease is one of the leading causes of mortality worldwide. Secondary prevention is essential, as it reduces the risk of further coronary events. Mobile health (mHealth) technology could become a useful tool to improve lifestyles. OBJECTIVE: This study aimed to evaluate the effect of an mHealth intervention on people with coronary heart disease who received percutaneous coronary intervention. Improvements in lifestyle regarding diet, physical activity, and smoking; level of knowledge of a healthy lifestyle and the control of cardiovascular risk factors (CVRFs); and therapeutic adherence and quality of life were analyzed. METHODS: This was a randomized controlled trial with a parallel group design assigned 1:1 to either an intervention involving a smartphone app (mHealth group) or to standard health care (control group). The app was used for setting aims, the self-monitoring of lifestyle and CVRFs using measurements and records, educating people with access to information on their screens about healthy lifestyles and adhering to treatment, and giving motivation through feedback about achievements and aspects to improve. Both groups were assessed after 9 months. The primary outcome variables were adherence to the Mediterranean diet, frequency of food consumed, patient-reported physical activity, smoking, knowledge of healthy lifestyles and the control of CVRFs, adherence to treatment, quality of life, well-being, and satisfaction. RESULTS: The study analyzed 128 patients, 67 in the mHealth group and 61 in the control group; most were male (92/128, 71.9%), with a mean age of 59.49 (SD 8.97) years. Significant improvements were observed in the mHealth group compared with the control group regarding adherence to the Mediterranean diet (mean 11.83, SD 1.74 points vs mean 10.14, SD 2.02 points; P<.001), frequency of food consumption, patient-reported physical activity (mean 619.14, SD 318.21 min/week vs mean 471.70, SD 261.43 min/week; P=.007), giving up smoking (25/67, 75% vs 11/61, 42%; P=.01), level of knowledge of healthy lifestyles and the control of CVRFs (mean 118.70, SD 2.65 points vs mean 111.25, SD 9.05 points; P<.001), and the physical component of the quality of life 12-item Short Form survey (SF-12; mean 45.80, SD 10.79 points vs mean 41.40, SD 10.78 points; P=.02). Overall satisfaction was higher in the mHealth group (mean 48.22, SD 3.89 vs mean 46.00, SD 4.82 points; P=.002) and app satisfaction and usability were high (mean 44.38, SD 6.18 out of 50 points and mean 95.22, SD 7.37 out of 100). CONCLUSIONS: The EVITE app was effective in improving the lifestyle of patients in terms of adherence to the Mediterranean diet, frequency of healthy food consumption, physical activity, giving up smoking, knowledge of healthy lifestyles and controlling CVRFs, quality of life, and overall satisfaction. The app satisfaction and usability were excellent. TRIAL REGISTRATION: Clinicaltrials.gov NCT04118504; https://clinicaltrials.gov/study/NCT04118504.

Preliminary Investigation Into the Effectiveness of Group webSTAIR for Trauma-exposed Veterans in Primary Care.

INTRODUCTION: A substantial number of trauma-exposed veterans seen in primary care report significant symptoms of PTSD and depression. While primary care mental health integration (PCMHI) providers have been successful in delivering brief mental health treatments in primary care, few studies have evaluated interventions that combine mobile health resources with PCMHI groups. This pilot study assessed the potential benefits of webSTAIR, a 10-module transdiagnostic treatment for trauma-exposed individuals, supported by 5 biweekly group sessions delivered via telehealth. The transdiagnostic and mobile health nature of the treatment, as well as the therapist and peer support provided through group sessions, may offer an innovative approach to increasing access to patient-centered and trauma-informed treatment in primary care settings. MATERIALS AND METHODS: Thirty-nine male and female veterans with trauma-related symptoms (i.e., PTSD and/or depression) participated in group webSTAIR. Mixed effects analyses were conducted to assess changes in PTSD and depression at pre-, mid-, and post-treatment. Baseline symptom severity was assessed as a predictor of module completion and group attendance. The project was part of a VHA quality improvement project, and IRB approval was waived by the affiliated university. RESULTS: Analyses revealed significant pre-to-post improvement in both PTSD and depression outcomes with a large effect size for PTSD (Hedges' gav = 0.88) and medium to large for depression (Hedges' gav = 0.73). Of participants who completed the baseline assessment, 90% began webSTAIR; of those, 71% completed the program. Baseline symptoms of PTSD and depression did not predict group attendance or module completion. CONCLUSIONS: Good outcomes and a satisfactory retention rate suggest that group webSTAIR may provide easily accessible, high-quality, and effective treatment for patients presenting with trauma-related problems without increasing therapist or system burdens. The results suggest the value of conducting a randomized controlled trial to test the effectiveness of group webSTAIR relative to PCMHI usual care or other evidence-based, disorder-specific (e.g., PTSD) treatments for trauma-exposed individuals in PCMHI.

EmpRess: an eHealth implementation readiness checklist for dementia developed through an interview study of stakeholder needs.

OBJECTIVE: This study aimed to create a tool to assess eHealth interventions for dementia by adapting an existing implementation readiness (ImpRess) checklist that assessed manualised interventions. METHODS: In Part 1, online semi-structured interviews with individual stakeholders (N = 9) with expertise in eHealth and dementia were conducted (response rate 83%). The Nonadoption, Abandonment, and challenges to the Scale-Up, Spread, and Sustainability of Health and care technologies (NASSS) framework was applied, both to guide the construction of the interview guide, as well as to use its subdomains as codes in the deductive qualitative thematic analysis. Respondents were industry professionals (n = 3), researchers (n = 3), policy officers (n = 2), and a clinician (n = 1). In Part 2, the items of the original ImpRess checklist were supplemented by items that covered determinants discussed in the interviews, that were not included in the original checklist. RESULTS: The main findings from the interviews included: Participants' preference for a non-dementia-specific, more general approach to the checklist; the importance of searching for shared values with implementers; and the need for more systematic monitoring of implementation. CONCLUSIONS: The EmpRess checklist applies an inclusive design approach. The checklist will help evaluate the implementation determinants of eHealth interventions for dementia and provide up-to-date information on what is, and is not, working in eHealth for dementia care.

Can dermatologists reach their full potential in teledermatology? A validation study of diagnostic performance of skin diseases in live video conferencing teledermatology.

The diagnostic accuracy rate of live videoconferencing (LVC) teledermatology, by board-certified dermatologists compared to non-dermatologists has not yet been fully investigated. The aim of this study was to compare the diagnostic accuracy of board-certified dermatologists, dermatology specialty trainees, and board-certified internists in LVC teledermatology. We examined the diagnostic accuracy of clinicians from different specialties in diagnosing the same group of patients. The clinicians were isolated from each other during the diagnosis process. We enrolled 18 volunteer physicians (six board-certified dermatologists, six dermatology specialty trainees, and six board-certified internists) who reviewed the skin conditions of 18 patients via LVC teledermatology. The diagnostic accuracy of the participating physicians was evaluated using the final diagnosis as the reference standard. The diagnostic accuracy averages were compared according to the physicians' specialties and disease categories. The mean ± standard deviation diagnostic accuracy of the most detailed level diagnosis was 83.3% ± 3.5% (range, 77.8%-89.0%) for board-certified dermatologists, 53.7 ± 20.7% (range 27.8%-77.8%) for dermatology specialty trainees, and 27.8 ± 5.0% (range, 22.2%-33.3%) for board-certified internists. Board-certified dermatologists showed significantly higher diagnostic accuracy, not only against board-certified internists (p < 0.0001) but also against dermatology specialty trainees (p < 0.05). Disease categories with high accuracy rates (≥80%) only by board-certified dermatologists were inflammatory papulosquamous dermatoses (87.5%), compared to 58.3%, and 20.8% for dermatology specialty trainees and board-certified internists respectively). For inflammatory erythemas and other reactive inflammatory dermatoses the accuracy rates for board-certified dermatologists, dermatology specialty trainees, and board-certified internists were 83.3%, 33.3%, 8.3% respectively; for melanoma in situ neoplasms, 83.3%, 50.0%, 66.7% respectively), and for genetic disorders of keratinization 83.3%, 33.3%, and 0% respectively). Our findings showed that board-certified dermatologists may have high diagnostic accuracy with practical safety and effectiveness in LVC teledermatology.

Development and Validation of Telehealth Competency Questionnaire-Provider.

Background: Given the rapid increase in telehealth utilization, health care providers are being increasingly trained to deliver services virtually. However, there are limited measures available to assess the extent to which structured trainings influence competency domains associated with telehealth delivery. Methods: The authors developed the Telehealth Competency Questionnaire-Provider (TCQ-P) using a multistep process, including a literature review and expert reviewers. Using two datasets, we used exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to validate and refine the tool, respectively. The final version contained 17 items. Model fit was evaluated using the comparative fit index (CFI) (>0.90), Tucker-Lewis index (TLI) (>0.80), standardized root mean square residual (SRMR) (<0.08) and root mean square of error of approximation (RMSEA) (<0.08). Results: Participants included n = 701 in the exploratory study and n = 721 in the confirmatory study. Two items were revised, and one item was deleted as a result of the EFA, and the CFA of 17 number of items supported a 3-factor model (i.e., Evaluation, Rapport, Troubleshooting). Model fit was good, with CFI = 0.984, TLI = 0.978, RMSEA = 0.051, and SRMR = 0.035. Discussion: The TCQ-P measures three essential domains of telehealth competency, which is essential for future health care providers. The measure may be used to assess telehealth training outcomes.