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.

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.

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.

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.

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.

Evaluating the Quality of Virtual Urgent Care: Barriers, Motivations, and Implementation of Quality Measures.

BACKGROUND: Experts estimate virtual urgent care programs could replace approximately 20% of current emergency department visits. In the absence of widespread quality guidance to programs or quality reporting from these programs, little is known about the state of virtual urgent care quality monitoring initiatives. OBJECTIVE: We sought to characterize ongoing quality monitoring initiatives among virtual urgent care programs. APPROACH: Semi-structured interviews of virtual health and health system leaders were conducted using a pilot-tested interview guide to assess quality metrics captured related to care effectiveness and equity as well as programs' motivations for and barriers to quality measurement. We classified quality metrics according to the National Quality Forum Telehealth Measurement Framework. We developed a codebook from interview transcripts for qualitative analysis to classify motivations for and barriers to quality measurement. KEY RESULTS: We contacted 13 individuals, and ultimately interviewed eight (response rate, 61.5%), representing eight unique virtual urgent care programs at primarily academic (6/8) and urban institutions (5/8). Most programs used quality metrics related to clinical and operational effectiveness (7/8). Only one program reported measuring a metric related to equity. Limited resources were most commonly cited by participants (6/8) as a barrier to quality monitoring. CONCLUSIONS: We identified variation in quality measurement use and content by virtual urgent care programs. With the rapid growth in this approach to care delivery, more work is needed to identify optimal quality metrics. A standardized approach to quality measurement will be key to identifying variation in care and help focus quality improvement by virtual urgent care programs.

Evaluation of telehealth administration of MMPI symptom validity scales.

INTRODUCTION: Telehealth assessment (TA) is a quickly emerging practice, offered with increasing frequency across many different clinical contexts. TA is also well-received by most patients, and there are numerous guidelines and training opportunities which can support effective telehealth practice. Although there are extensive recommended practices, these guidelines have rarely been evaluated empirically, particularly on personality measures. While existing research is limited, it does generally support the idea that TA and in-person assessment (IA) produce fairly equitable test scores. The MMPI-3, a recently released and highly popular personality and psychopathology measure has been the subject of several of those experimental or student (non-client) based studies; however, no study to date has evaluated these trends within a clinical sample. This study empirically tests for differences in TA and IA test scores on the MMPI-3 validity scores when following recommended administration procedures. METHOD: Data were from a retrospective chart review. Veterans (n = 550) who underwent psychological assessment in a Veterans Affairs Medical Center ADHD evaluation clinic were contrasted between in person and telehealth assessment modalities on the MMPI-2-RF and MMPI-3. Groups were compared using t tests, chi square, and base rates. RESULTS: Results suggest that there were minimal differences in elevation rates or mean scores across modality, supporting the use of TA. CONCLUSIONS: This study's findings support the use of the MMPI via TA with ADHD evaluations, Veterans, and in neuro/psychological evaluation settings more generally. Observed elevation rates and mean scores of this study were notably different from those seen in other VA service clinics sampled nationally, which is an area of future investigation.

Adherence to Emergency Department Referral Criteria in a Direct-to-Consumer Telemedicine Center.

Background: The safety of direct-to-consumer telemedicine (TM) is closely related to red flag detection and correct referrals. The adherence to referral criteria from current guidelines is not well quantified. Objective: To analyze the emergency department (ED) referral rate and adherence to referral guidelines in TM encounters of acutely ill patients calling a center that adopts stewardship protocols. Methods: This is a retrospective observational unicentric study, between March 2020 and March 2022, with patients who spontaneously sought direct-to-consumer urgent virtual medical assistance. A video-based teleconsultation was provided immediately after connection. Physicians managed situations according to their clinical judgment. Current guidelines, containing specific guidance for referral if red flags were identified, were available for consultation. Physicians' semiannual performance feedback was carried out. We analyzed the patterns for referral to immediate face-to-face medical evaluation and the agreement degree with the institutional guidelines. Results: A total of 232,197 patients were available, and 14,051 (6.05%) patients were referred to ED. A total of 8,829 (68.4%) referrals were based in specific guidelines according to the International Classification of Diseases hypothesis, and 8,708 (98.6%) were justified according to guidelines. Diarrhea had the highest guidelines' adherence to referral (97.6%), followed by COVID-19 (90%), headache (84.2%), and conjunctivitis (78.8%). Policies did not support 5,222 (31.6%) referrals, though 5,100 (97.6%) of these were justified according to the doctor's clinical judgment. Conclusion: TM doctors' assessment of acutely ill patients has high rates of adherence to guidelines regarding referral. Stewardship protocol adoption provides high rates of red flag description, even in the referral of nonpolicy diseases.

Healthcare professionals' and patients' assessments of listed mobile health apps in China: a qualitative study.

Background: In recent years, mobile health (mHealth) has gradually developed in China, and intelligent medicine has become an important research topic. However, there are still significant problems in mHealth applications (apps). Although healthcare professionals and patients are the main users, few studies have focused on their perceptions of the quality of mHealth apps. Objective: This study aimed to (1) understand the respective perceptions of healthcare professionals and patients regarding mHealth apps, (2) assess what barriers exist that influence the user experience, and (3) explore how to improve the quality of mHealth apps and the development of the mHealth market in China. The study aims to promote the standardization of mHealth apps and provide effective information for the improvement and development of mHealth apps in the future. Methods: Semistructured interviews with 9 patients and 14 healthcare professionals were conducted from January 2022 to April 2022 in the Affiliated Hospital of Xuzhou Medical University. The participants used mHealth apps for more than 3 months, including the "Good Mood" and "Peace and Safe Doctors" apps and apps developed by the hospital that were popular in China. Interview transcripts were analysed using thematic analysis. Results: The following five themes were extracted: different concerns, hidden medical dangers, distance and insecurity, barriers for older people, and having positive perceptions of mHealth apps. Healthcare professionals prioritized simplicity in regard to mHealth apps, whereas patients rated effectiveness as the most crucial factor. The study also revealed several problems with mHealth apps, including insufficient information about physician qualifications, inaccurate medical content, nonstandard treatment processes, and unclear accountability, which led to a sense of distance and insecurity among participants. Older individuals faced additional obstacles when using mHealth apps. Despite these issues, the participants remained optimistic about the future of mHealth app development. Conclusion: The utilization, advantages, and obstacles of mHealth applications for healthcare professionals and patients were explored through semistructured interviews. Despite the promising prospects for mHealth apps in China, numerous issues still need to be addressed. Enhancing the safety monitoring system and developing user-friendly mHealth apps for older adult patients are essential steps to bridge the gap between healthcare providers and patients.

Online Physician-Patient Interaction and Patient Satisfaction: Empirical Study of the Internet Hospital Service.

BACKGROUND: In China, a form of online health service called the internet hospital became a prominent means of patient care when face-to-face visits were not possible during the COVID-19 pandemic to minimize transmission of the SARS-CoV-2 virus. Patients' internet hospital experiences largely depend on online physician-patient interaction. Yet, little is known about how physicians can improve patient satisfaction by using specific communication strategies online. OBJECTIVE: This study aimed to identify specific communication strategies to help physicians deliver better quality internet hospital services. We also outline recommendations for hospitals to operate internet hospital platforms more effectively. METHODS: A longitudinal data set was collected from an internet hospital platform operated by a top hospital in China. By extracting communication patterns from approximately 20,000 records of online health care services and by controlling the features of service requests, we tested the impacts of response load, more detailed style, and emotional comfort on patient satisfaction. We further explored the effects of these communication patterns in different service contexts. RESULTS: Physicians with a low response load, a more detailed style, and expressions of emotional comfort received more positive patient feedback. Response load did not affect patient satisfaction with free online health service, whereas a more detailed style and emotional comfort enhanced satisfaction with free service. Response load significantly reduced patient satisfaction with paid online health service, while a more detailed style had no effect. Compared with free service, emotional comfort more strongly promoted patient satisfaction with paid service. CONCLUSIONS: The communication strategies identified can help physicians provide patients with a better internet hospital experience. These strategies require hospitals to schedule each physician's online service period more appropriately. In addition, tailoring the strategies to service situations can facilitate more targeted and effective internet hospital service for patients.

COVID-19 y Telemedicina: Preparados, listos, click

El Departamento de Evidencia e Inteligencia para la Acción en Salud de la Organización Panamericana de la Salud (OPS/OMS) en colaboración con la División de Salud y Protección Social del Banco Interamericano de Desarrollo (BID) tienen el agrado de invitarlo al Segundo Seminario Virtual “COVID-19 y Telemedicina: Preparados, listos, click” de la Serie Compartir-Escuchar-Actuar, COVID 19: El potencial de la Salud Digital y los Sistemas de Información para la Salud (IS4H) en la lucha contra la pandemia.

Implementation Science Perspectives on Implementing Telemedicine Interventions for Hypertension or Diabetes Management: Scoping Review.

BACKGROUND: Hypertension and diabetes are becoming increasingly prevalent worldwide. Telemedicine is an accessible and cost-effective means of supporting hypertension and diabetes management, especially as the COVID-19 pandemic has accelerated the adoption of technological solutions for care. However, to date, no review has examined the contextual factors that influence the implementation of telemedicine interventions for hypertension or diabetes worldwide. OBJECTIVE: We adopted a comprehensive implementation research perspective to synthesize the barriers to and facilitators of implementing telemedicine interventions for the management of hypertension, diabetes, or both. METHODS: We performed a scoping review involving searches in Ovid MEDLINE, Embase, CINAHL, Cochrane Library, Web of Science, and Google Scholar to identify studies published in English from 2017 to 2022 describing barriers and facilitators related to the implementation of telemedicine interventions for hypertension and diabetes management. The coding and synthesis of barriers and facilitators were guided by the Consolidated Framework for Implementation Research. RESULTS: Of the 17,687 records identified, 35 (0.2%) studies were included in our scoping review. We found that facilitators of and barriers to implementation were dispersed across the constructs of the Consolidated Framework for Implementation Research. Barriers related to cost, patient needs and resources (eg, lack of consideration of language needs, culture, and rural residency), and personal attributes of patients (eg, demographics and priorities) were the most common. Facilitators related to the design and packaging of the intervention (eg, user-friendliness), patient needs and resources (eg, personalized information that leveraged existing strengths), implementation climate (eg, intervention embedded into existing infrastructure), knowledge of and beliefs about the intervention (eg, convenience of telemedicine), and other personal attributes (eg, technical literacy) were the most common. CONCLUSIONS: Our findings suggest that the successful implementation of telemedicine interventions for hypertension and diabetes requires comprehensive efforts at the planning, execution, engagement, and reflection and evaluation stages of intervention implementation to address challenges at the individual, interpersonal, organizational, and environmental levels.

Resumen ejecutivo sobre el uso de la telemedicina en el ámbito hospitalario: recomendaciones del Foro Internacional de Medicina Interna / Executive summary on the use of telemedicine in the hospital setting: recommendations from the International Forum on Internal Medicine

La implantación de la telemedicina como una herramienta más en la atención a pacientes en el ámbito hospitalario es un reto para cualquier sistema sanitario. Dadas las dificultades y limitaciones, el Foro Internacional de Medicina Interna (FIMI) ha promovido este Consenso que incluye a 20 sociedades científicas de 17 países de Europa y América. El objetivo fue realizar una propuesta, a modo de marco general, que permitiese el desarrollo e implantación de la telemedicina en la atención clínica hospitalaria y que fuese útil para los diferentes países integrantes del FIMI. El documento que presentamos recoge el resumen ejecutivo de las recomendaciones de la FIMI que pretenden garantizar intervenciones sanitarias efectivas, seguras, eficientes, sostenibles y proporcionadas y basadas en la mejor evidencia científica disponible. Los autores consideran que este documento debe actualizarse en el plazo máximo de dos años (AU)

The implementation of telemedicine as another tool for patient care in the hospital setting is a challenge for any healthcare system. Given the difficulties and limitations, the International Forum on Internal Medicine (FIMI, for its initials in Spanish) has sponsored this consensus document with 20 scientific societies from 17 countries in Europe and the Americas. The aim was to propose a general framework that allows for the development and implementation of telemedicine in hospital clinical care that would be useful to FIMI member countries. The document we present includes recommendations from the FIMI in its executive summary that intend to guarantee effective, safe, efficient, sustainable, and proportional healthcare interventions based on the best scientific evidence available. The authors believe that this document must be updated within a maximum period of two years (AU)

Protocol for an intervention development and pilot implementation evaluation study of an e-health solution to improve newborn care quality and survival in two low-resource settings, Malawi and Zimbabwe: Neotree.

INTRODUCTION: Every year 2.4 million deaths occur worldwide in babies younger than 28 days. Approximately 70% of these deaths occur in low-resource settings because of failure to implement evidence-based interventions. Digital health technologies may offer an implementation solution. Since 2014, we have worked in Bangladesh, Malawi, Zimbabwe and the UK to develop and pilot Neotree: an android app with accompanying data visualisation, linkage and export. Its low-cost hardware and state-of-the-art software are used to improve bedside postnatal care and to provide insights into population health trends, to impact wider policy and practice. METHODS AND ANALYSIS: This is a mixed methods (1) intervention codevelopment and optimisation and (2) pilot implementation evaluation (including economic evaluation) study. Neotree will be implemented in two hospitals in Zimbabwe, and one in Malawi. Over the 2-year study period clinical and demographic newborn data will be collected via Neotree, in addition to behavioural science informed qualitative and quantitative implementation evaluation and measures of cost, newborn care quality and usability. Neotree clinical decision support algorithms will be optimised according to best available evidence and clinical validation studies. ETHICS AND DISSEMINATION: This is a Wellcome Trust funded project (215742_Z_19_Z). Research ethics approvals have been obtained: Malawi College of Medicine Research and Ethics Committee (P.01/20/2909; P.02/19/2613); UCL (17123/001, 6681/001, 5019/004); Medical Research Council Zimbabwe (MRCZ/A/2570), BRTI and JREC institutional review boards (AP155/2020; JREC/327/19), Sally Mugabe Hospital Ethics Committee (071119/64; 250418/48). Results will be disseminated via academic publications and public and policy engagement activities. In this study, the care for an estimated 15 000 babies across three sites will be impacted. TRIAL REGISTRATION NUMBER: NCT0512707; Pre-results.

Assessing a New Prescreening Score for the Simplified Evaluation of the Clinical Quality and Relevance of eHealth Apps: Instrument Validation Study.

BACKGROUND: In 2020, more than 250 eHealth solutions were added to app stores each day, or 90,000 in the year; however, the vast majority of these solutions have not undergone clinical validation, their quality is unknown, and the user does not know if they are effective and safe. We sought to develop a simple prescreening scoring method that would assess the quality and clinical relevance of each app. We designed this tool with 3 health care stakeholder groups in mind: eHealth solution designers seeking to evaluate a potential competitor or their own tool, investors considering a fundraising candidate, and a hospital clinician or IT department wishing to evaluate a current or potential eHealth solution. OBJECTIVE: We built and tested a novel prescreening scoring tool (the Medical Digital Solution scoring tool). The tool, which consists of 26 questions that enable the quick assessment and comparison of the clinical relevance and quality of eHealth apps, was tested on 68 eHealth solutions. METHODS: The Medical Digital Solution scoring tool is based on the 2021 evaluation criteria of the French National Health Authority, the 2022 European Society of Medical Oncology recommendations, and other provided scores. We built the scoring tool with patient association and eHealth experts and submitted it to eHealth app creators, who evaluated their apps via the web-based form in January 2022. After completing the evaluation criteria, their apps obtained an overall score and 4 categories of subscores. These criteria evaluated the type of solution and domain, the solution's targeted population size, the level of clinical assessment, and information about the provider. RESULTS: In total, 68 eHealth solutions were evaluated with the scoring tool. Oncology apps (22%, 20/90) and general health solutions (23%, 21/90) were the most represented. Of the 68 apps, 32 (47%) were involved in remote monitoring by health professionals. Regarding clinical outcomes, 5% (9/169) of the apps assessed overall survival. Randomized studies had been conducted for 21% (23/110) of the apps to assess their benefit. Of the 68 providers, 38 (56%) declared the objective of obtaining reimbursement, and 7 (18%) out of the 38 solutions seeking reimbursement were assessed as having a high probability of reimbursement. The median global score was 11.2 (range 4.7-17.4) out of 20 and the distribution of the scores followed a normal distribution pattern (Shapiro-Wilk test: P=.33). CONCLUSIONS: This multidomain prescreening scoring tool is simple, fast, and can be deployed on a large scale to initiate an assessment of the clinical relevance and quality of a clinical eHealth app. This simple tool can help a decision-maker determine which aspects of the app require further analysis and improvement.