Methods and Measures Used to Evaluate Patient-Operated Mobile Health Interventions: Scoping Literature Review

Use of Patient-Generated Health Data From Consumer-Grade Devices by Health Care Professionals in the Clinic: Systematic Review

BACKGROUND

Mobile health (mHealth) uses mobile technologies to promote wellness and help disease management. Although mHealth solutions used in the clinical setting have typically been medical-grade devices, passive and active sensing capabilities of consumer-grade devices like smartphones and activity trackers have the potential to bridge information gaps regarding patients' behaviors, environment, lifestyle, and other ubiquitous data. Individuals are increasingly adopting mHealth solutions, which facilitate the collection of patient-generated health data (PGHD). Health care professionals (HCPs) could potentially use these data to support care of chronic conditions. However, there is limited research on real-life experiences of HPCs using PGHD from consumer-grade mHealth solutions in the clinical context.

OBJECTIVE

This systematic review aims to analyze existing literature to identify how HCPs have used PGHD from consumer-grade mobile devices in the clinical setting. The objectives are to determine the types of PGHD used by HCPs, in which health conditions they use them, and to understand the motivations behind their willingness to use them.

METHODS

A systematic literature review was the main research method to synthesize prior research. Eligible studies were identified through comprehensive searches in health, biomedicine, and computer science databases, and a complementary hand search was performed. The search strategy was constructed iteratively based on key topics related to PGHD, HCPs, and mobile technologies. The screening process involved 2 stages. Data extraction was performed using a predefined form. The extracted data were summarized using a combination of descriptive and narrative syntheses.

RESULTS

The review included 16 studies. The studies spanned from 2015 to 2021, with a majority published in 2019 or later. Studies showed that HCPs have been reviewing PGHD through various channels, including solutions portals and patients' devices. PGHD about patients' behavior seem particularly useful for HCPs. Our findings suggest that PGHD are more commonly used by HCPs to treat conditions related to lifestyle, such as diabetes and obesity. Physicians were the most frequently reported users of PGHD, participating in more than 80% of the studies.

CONCLUSIONS

PGHD collection through mHealth solutions has proven beneficial for patients and can also support HCPs. PGHD have been particularly useful to treat conditions related to lifestyle, such as diabetes, cardiovascular diseases, and obesity, or in domains with high levels of uncertainty, such as infertility. Integrating PGHD into clinical care poses challenges related to privacy and accessibility. Some HCPs have identified that though PGHD from consumer devices might not be perfect or completely accurate, their perceived clinical value outweighs the alternative of having no data. Despite their perceived value, our findings reveal their use in clinical practice is still scarce.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/39389.

Outcome After Self-Triage App Referral in Urgent Direct-to-Consumer Telemedicine Encounter

Background: The quantification of self-triage effectiveness, guided by mobile applications, in urgent direct-to-consumer telemedicine (TM) encounters requires further investigation. The objective of this study was to evaluate the outcomes of referral guidance provided by a symptom-based self-management mobile application decision algorithm in the context of remote urgent care assessments. Methods: An observational retrospective single-center study was conducted from May 2022 to December 2023. The inclusion criteria encompassed individuals aged >18 years old, and those spontaneously seeking virtual emergency care through the EINSTEIN CONECTA application. Patients experiencing connectivity issues, preventing completion of the encounter, were excluded. The primary outcomes included the rate of patient concurrence with the algorithm's recommendation for seeking in-person emergency care and the referral rate to face-to-face assessment among cases evaluated through TM. The application's algorithm employs scientific evidence based on symptoms to recommend referrals to emergency departments (EDs). Results: Out of 88,834 patients connected to the TM Center, self-triage obviated the need for virtual physician assessment in 53,302 (60%) encounters. A total of 35,532 patients were remotely evaluated by 316 on-duty physicians, resulting in 1,125 ICD-coded diagnoses. Among these, 21,722 (61.1%) were initially advised by self-triage to visit the ED, with subsequent medical assessment leading to in-person referrals in 6,354 (29.3%) of the evaluations. Of the 13,810 patients recommended to continue with virtual care post-self-triage, 157 (1.1%) were referred for in-person assessment. Conclusions: Self-triage effectively reduced the need for physician encounters in approximately three-fifths of TM consultations. Despite being based on scientific evidence, symptom-based referral algorithms demonstrated high sensitivity but poor correlation with physician decision-making.

Digital health intervention on patient safety for children and parents: A scoping review

AIM

To explore digital health interventions on patient safety for children and their parents.

DESIGN

A scoping review.

METHODS

The PCC 'Participants, Concepts, and Contexts' guided the selection of studies that focused on children under 19 years of age or their parents, patient safety interventions for children, and digital health technology for patient safety interventions. This study was conducted using the Arksey and O'Malley framework's five steps. We reported the review according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews Checklist.

DATA SOURCES

PubMed, CINAHL, Embase, Web of Science, and Cochrane were searched for articles published up to November 2022.

RESULTS

A total of 13 articles were included and categorized according to the following criteria to describe the results: intervention characteristics, type of digital technology, and outcome characteristics. Regarding intervention characteristics, we identified two categories, prevention and risk management. Additionally, we identified four types of digital technology, mobile applications, web-based technologies, computer kiosks and electronic health records. Finally, in studies focussing on child safety, parental safety behaviours were used to assess injury risk or detect changes related to prevention.

CONCLUSION

Patient safety interventions provided through appropriate digital technologies should be developed to enhance continuum of care for children from hospitalization to home after discharge.

IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE

Digital health interventions can bolster the role of healthcare providers in patient safety in and out of hospitals, thus improving children's safety and quality of care.

IMPACT

What problem did the study address? Although the various advantages of digital health technology have been demonstrated, the potential role of digital technology in patient safety interventions for children has not been explored. What were the main finding? Preventive patient safety interventions and risk management for children have been developed. Where and on whom will the research have an impact? Digital health interventions on patient safety can improve children's safety and quality of care by promoting non-face-to-face engagement of children and parents after discharge and expanding healthcare providers' roles.

TRIAL AND PROTOCOL REGISTRATION

Registered on the Open Science Framework (https://osf.io/dkvst).

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

An Exploratory Study on the Utility of Patient-Generated Health Data as a Tool for Health Care Professionals in Multiple Sclerosis Care

BACKGROUND

Patient-generated health data (PGHD) are data collected through technologies such as mobile devices and health apps. The integration of PGHD into health care workflows can support the care of chronic conditions such as multiple sclerosis (MS). Patients are often willing to share data with health care professionals (HCPs) in their care team; however, the benefits of PGHD can be limited if HCPs do not find it useful, leading patients to discontinue data tracking and sharing eventually. Therefore, understanding the usefulness of mobile health (mHealth) solutions, which provide PGHD and serve as enablers of the HCPs' involvement in participatory care, could motivate them to continue using these technologies.

OBJECTIVE

The objective of this study is to explore the perceived utility of different types of PGHD from mHealth solutions which could serve as tools for HCPs to support participatory care in MS.

METHOD

A mixed-methods approach was used, combining qualitative research and participatory design. This study includes three sequential phases: data collection, assessment of PGHD utility, and design of data visualizations. In the first phase, 16 HCPs were interviewed. The second and third phases were carried out through participatory workshops, where PGHD types were conceptualized in terms of utility.

RESULTS

The study found that HCPs are optimistic about PGHD in MS care. The most useful types of PGHD for HCPs in MS care are patients' habits, lifestyles, and fatigue-inducing activities. Although these subjective data seem more useful for HCPs, it is more challenging to visualize them in a useful and actionable way.

CONCLUSION

HCPs are optimistic about mHealth and PGHD as tools to further understand their patients' needs and support care in MS. HCPs from different disciplines have different perceptions of what types of PGHD are useful; however, subjective types of PGHD seem potentially more useful for MS care.

Designing personalised mHealth solutions: An overview

INTRODUCTION

Mobile health, or mHealth, is based on mobile information and communication technologies and provides solutions for empowering individuals to participate in healthcare. Personalisation techniques have been used to increase user engagement and adherence to interventions delivered as mHealth solutions. This study aims to explore the current state of personalisation in mHealth, including its current trends and implementation.

MATERIALS AND METHODS

We conducted a review following PRISMA guidelines. Four databases (PubMed, ACM Digital Library, IEEE Xplore, and APA PsycInfo) were searched for studies on mHealth solutions that integrate personalisation. The retrieved papers were assessed for eligibility and useful information regarding integrated personalisation techniques.

RESULTS

Out of the 1,139 retrieved studies, 62 were included in the narrative synthesis. Research interest in the personalisation of mHealth solutions has increased since 2020. mHealth solutions were mainly applied to endocrine, nutritional, and metabolic diseases; mental, behavioural, or neurodevelopmental diseases; or the promotion of healthy lifestyle behaviours. Its main purposes are to support disease self-management and promote healthy lifestyle behaviours. Mobile applications are the most prevalent technological solution. Although several design models, such as user-centred and patient-centred designs, were used, no specific frameworks or models for personalisation were followed. These solutions rely on behaviour change theories, use gamification or motivational messages, and personalise the content rather than functionality. A broad range of data is used for personalisation purposes. There is a lack of studies assessing the efficacy of these solutions; therefore, further evidence is needed.

DISCUSSION

Personalisation in mHealth has not been well researched. Although several techniques have been integrated, the effects of using a combination of personalisation techniques remain unclear. Although personalisation is considered a persuasive strategy, many mHealth solutions do not employ it.

CONCLUSIONS

Open research questions concern guidelines for successful personalisation techniques in mHealth, design frameworks, and comprehensive studies on the effects and interactions among multiple personalisation techniques.

The impact of guided versus supportive coaching on mental health app engagement and clinical outcomes

Although mobile mental health apps have the unique potential to increase access to care, evidence reveals engagement is low unless coupled with coaching. However, most coaching protocols are limited in their scalability. This study assesses how human support and guidance from a Digital Navigator (DN), a scalable coach, can impact mental health app engagement and effectiveness on anxiety and depressive symptoms. This study aims to detach components of coaching, specifically personalized recommendations versus general support, to inform scalability of coaching models for mental health apps. 156 participants were split into the DN Guide versus DN Support groups for the 6-week study. Both groups utilized the mindLAMP app for the duration of the study and had equal time with the DN, but the Guide group received personalized app recommendations. The Guide group completed significantly more activities than the Support group. 34% (49/139) of all participants saw a 25% decrease in PHQ-9 scores and 38% (53/141) saw a 25% decrease in GAD-7 scores. These findings show mental health apps, especially when supported by DNs, can reduce depression and anxiety symptoms when coupled with coaching, suggesting a feasible path for large-scale deployment.

Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review

Digital data play an increasingly important role in advancing health research and care. However, most digital data in healthcare are in an unstructured and often not readily accessible format for research. Unstructured data are often found in a format that lacks standardization and needs significant preprocessing and feature extraction efforts. This poses challenges when combining such data with other data sources to enhance the existing knowledge base, which we refer to as digital unstructured data enrichment. Overcoming these methodological challenges requires significant resources and may limit the ability to fully leverage their potential for advancing health research and, ultimately, prevention, and patient care delivery. While prevalent challenges associated with unstructured data use in health research are widely reported across literature, a comprehensive interdisciplinary summary of such challenges and possible solutions to facilitate their use in combination with structured data sources is missing. In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health, along with possible solutions to address these challenges. Based on these findings, we developed a checklist that follows the standard data flow in health research studies. This checklist aims to provide initial systematic guidance to inform early planning and feasibility assessments for health research studies aiming combining unstructured data with existing data sources. Overall, the generality of reported unstructured data enrichment methods in the studies included in this review call for more systematic reporting of such methods to achieve greater reproducibility in future studies.

Improving Children's Sleep Habits Using an Interactive Smartphone App: Community-Based Intervention Study

BACKGROUND

Sleep problems are quite common among young children and are often a challenge for parents and a hinderance to children's development. Although behavioral therapy has proven effective in reducing sleep problems in children, a lack of access to professionals who can provide effective support is a major barrier for many caregivers. Therefore, pediatric sleep experts have begun developing apps and web-based services for caregivers. Despite the substantial influence of cultural and familial factors on children's sleep, little effort has gone into developing cultural or family-tailored interventions.

OBJECTIVE

This study aimed to examine the effectiveness of the interactive smartphone app "Nenne Navi," which provides culturally and family-tailored suggestions for improving sleep habits in young Japanese children through community-based long-term trials. The study also aimed to investigate the association between app-driven improvements in sleep and mental development in children.

METHODS

This study adopted a community-based approach to recruit individuals from the Higashi-Osaka city (Japan) who met ≥1 of the following eligibility criteria for sleep problems: sleeping after 10 PM, getting <9 hours of nighttime sleep, and experiencing frequent nighttime awakenings. A total of 87 Japanese caregivers with young children (mean 19.50, SD 0.70 months) were recruited and assigned to the app use group (intervention group) or the video-only group (control group). Both groups received educational video content regarding sleep health literacy. The caregivers in the intervention group used the app, which provides family-tailored suggestions, once per month for 1 year.

RESULTS

A total of 92% (33/36) of the caregivers in the app use group completed 1 year of the intervention. The participants' overall evaluation of the app was positive. The wake-up time was advanced (base mean 8:06 AM; post mean 7:48 AM; F_1,65=6.769; P=.01 and sleep onset latency was decreased (base mean 34.45 minutes; post mean 20.05 minutes; F_1,65=23.219; P<.001) significantly in the app use group at the 13th month compared with the video-only group. Moreover, multiple regression analysis showed that decreased social jetlag (β=-0.302; P=.03) and increased sleep onset latency SD (β=.426; P=.02) in children predicted a significant enhancement in the development of social relationships with adults. At 6 months after the completion of the app use, all the caregivers reported continuation of the new lifestyle.

CONCLUSIONS

The present findings suggest that the app "Nenne Navi" has high continuity in community use and can improve sleep habits in young Japanese children and that interventions for sleep habits of young children may lead to the enhancement of children's social development. Future studies must focus on the effectiveness of the app in other regions with different regional characteristics and neuroscientific investigations on how changes in sleep impact brain development.

mHealth program for patients with advanced cancer receiving treatment in a public health hospital in Brazil

OBJECTIVE

Previous studies have suggested the benefit of routine screening for biopsychosocial symptoms among patients with cancer. In recognition of the lack of data from low- and middle-income countries, this study sought to test and determine the effect of a mHealth program to screen biopsychological symptoms among patients with advanced breast or gynecological cancer.

METHODS

This was a quasi-experimental pre-post study conducted in a public hospital located in central western Brazil. Patients diagnosed with advanced breast of gynecological cancer who were about to initiate chemotherapy treatment at this institution and had access to Internet by smartphone, computer or tablet were invited to participate. Patients received training on using the app Comfort, a program developed to rate their physical and emotional symptoms during the 6-month of the proposed study. Patients were also asked to complete the EuroQOL 5D (EQ-5D-3L) every month. Mann-Whitney U test was used to determine differences among groups of patients (engaged and non-engaged). RM-ANOVA was used to determine the effect of time on mean visual analog scale (VAS) score.

RESULTS

A total of 125 patients were recruited (median age = 46.6 years old, 41.6% married). Mostly, patients possessed lower levels of education and had relatively low monthly incomes. Notably, 67.2% of patients engaged with the Comfort program, and few patients (4%) withdrew due to lack of engagement with the program or issues with internet connection. In general, patients who engaged with the program reported improvement in physical and emotional symptoms (p < 0.01), as well as in their overall quality of life (VAS; p = 0.009), compared with patients who did not engage with the program.

CONCLUSIONS

This is the first mHealth program developed in Brazil for patients in a low resource setting. Our findings suggest that Comfort could be an effective resource to assist patients and health care providers track symptoms and improve patients' quality of life.

A framework for design and usability testing of telerehabilitation system for adults with chronic diseases: A panoramic scoping review

Objective

This scoping review aimed to identify the design and usability testing of a telerehabilitation (TR) system, and its characteristics and functionalities that are best-suited for rehabilitating adults with chronic diseases.

Methods

Searches were conducted in PubMed, EBSCO, Web of Science, and Cochrane library for studies published between January 2017 and December 2022. We followed the Joanna Briggs Institute guidelines and the framework by Arksey and O'Malley. Screening was undertaken by two reviewers, and data extraction was undertaken by the first author. Then, the data were further reviewed and discussed thoroughly with the team members.

Results

A total of 31 results were identified, with the core criteria of developing and testing a telerehabilitation system, including a mobile app for cardiovascular diseases, cancer, diabetes, and chronic respiratory disorders. All developed systems resulted from multidisciplinary teams and employed mixed-methods research. We proposed the "input-process-output" framework that identified phases of both system design and usability testing. Through system design, we reported the use of user-centered design, iterative design, users' needs and characteristics, theory underpinning development, and the expert panel in 64%, 75%, 86%, 82%, and 71% of the studies, respectively. We recorded the application of moderated usability testing, unmoderated testing (1), and unmoderated testing (2) in 74%, 63%, and 15% of the studies, respectively. The identified design and testing activities produced a matured system, a high-fidelity prototype, and a released system in 81.5%, 15%, and 3.5%, respectively.

Conclusion

This review provides a framework for TR system design and testing for a wide range of chronic diseases that require prolonged management through remote monitoring using a mobile app. The identified "input-process-output" framework highlights the inputs, design, development, and improvement as components of the system design. It also identifies the "moderated-unmoderated" model for conducting usability testing. This review illustrates characteristics and functionalities of the TR systems and healthcare professional roles.

mHealth Technology Design and Evaluation for Early Childhood Health Promotion: Systematic Literature Review

BACKGROUND

Recent increases in smartphone ownership among underserved populations have inspired researchers in medicine, computing, and health informatics to design and evaluate mobile health (mHealth) interventions, specifically for those supporting child development and growth. Although these interventions demonstrate possible effectiveness at larger scales, few of these interventions are evaluated to address racial disparities and health equity, which are known factors that affect relevance, uptake, and adherence in target populations.

OBJECTIVE

In this study, we aimed to identify and document the current design and evaluation practices of mHealth technologies that promote early childhood health, with a specific focus on opportunities for those processes to address health disparities and health equity.

METHODS

We completed a systematic literature review of studies that design and evaluate mHealth interventions for early childhood health promotion. We then analyzed these studies to identify opportunities to address racial disparities in early- and late-stage processes and to understand the potential efficacy of these interventions.

RESULTS

Across the literature from medical, computing, and health informatics fields, we identified 15 articles that presented a design or evaluation of a parent-facing health intervention. We found that using mobile-based systems to deliver health interventions was generally well accepted by parents of children aged <5 years. We also found that, when measured, parenting knowledge of early childhood health topics and confidence to engage in health-promoting behaviors improved. Design and evaluation methods held internal consistency within disciplines (eg, experimental study designs were the most prevalent in medical literature, while computing researchers used user-centered design methods in computing fields). However, there is little consistency in design or evaluation methods across fields.

CONCLUSIONS

To support more interventions with a comprehensive design and evaluation process, we recommend attention to design at the intervention (eg, reporting content sources) and system level; interdisciplinary collaboration in early childhood health intervention development can lead to large-scale deployment and success among populations.

TRIAL REGISTRATION

PROSPERO CRD42022359797; https://tinyurl.com/586nx9a2.

Electronic Health Record-Based Recruitment and Retention and Mobile Health App Usage: Multisite Cohort Study

BACKGROUND

To address the obesity epidemic, there is a need for novel paradigms, including those that address the timing of eating and sleep in relation to circadian rhythms. Electronic health records (EHRs) are an efficient way to identify potentially eligible participants for health research studies. Mobile health (mHealth) apps offer available and convenient data collection of health behaviors, such as timing of eating and sleep.

OBJECTIVE

The aim of this descriptive analysis was to report on recruitment, retention, and app use from a 6-month cohort study using a mobile app called Daily24.

METHODS

Using an EHR query, adult patients from three health care systems in the PaTH clinical research network were identified as potentially eligible, invited electronically to participate, and instructed to download and use the Daily24 mobile app, which focuses on eating and sleep timing. Online surveys were completed at baseline and 4 months. We described app use and identified predictors of app use, defined as 1 or more days of use, versus nonuse and usage categories (ie, immediate, consistent, and sustained) using multivariate regression analyses.

RESULTS

Of 70,661 patients who were sent research invitations, 1021 (1.44%) completed electronic consent forms and online baseline surveys; 4 withdrew, leaving a total of 1017 participants in the analytic sample. A total of 53.79% (n=547) of the participants were app users and, of those, 75.3% (n=412), 50.1% (n=274), and 25.4% (n=139) were immediate, consistent, and sustained users, respectively. Median app use was 28 (IQR 7-75) days over 6 months. Younger age, White race, higher educational level, higher income, having no children younger than 18 years, and having used 1 to 5 health apps significantly predicted app use (vs nonuse) in adjusted models. Older age and lower BMI predicted early, consistent, and sustained use. About half (532/1017, 52.31%) of the participants completed the 4-month online surveys. A total of 33.5% (183/547), 29.3% (157/536), and 27.1% (143/527) of app users were still using the app for at least 2 days per month during months 4, 5, and 6 of the study, respectively.

CONCLUSIONS

EHR recruitment offers an efficient (ie, high reach, low touch, and minimal participant burden) approach to recruiting participants from health care settings into mHealth research. Efforts to recruit and retain less engaged subgroups are needed to collect more generalizable data. Additionally, future app iterations should include more evidence-based features to increase participant use.

Deploying the Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C) smart health system to support patients and family caregivers in managing pain: A feasibility and acceptability study. (Preprint)

Background

Distressing cancer pain remains a serious symptom management issue for patients and family caregivers, particularly within home settings. Technology can support home-based cancer symptom management but must consider the experience of patients and family caregivers, as well as the broader environmental context.

Objective

This study aimed to test the feasibility and acceptability of a smart health sensing system—Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C)—that was designed to support the monitoring and management of cancer pain in the home setting.

Methods

Dyads of patients with cancer and their primary family caregivers were recruited from an outpatient palliative care clinic at an academic medical center. BESI-C was deployed in each dyad home for approximately 2 weeks. Data were collected via environmental sensors to assess the home context (eg, light and temperature); Bluetooth beacons to help localize dyad positions; and smart watches worn by both patients and caregivers, equipped with heart rate monitors, accelerometers, and a custom app to deliver ecological momentary assessments (EMAs). EMAs enabled dyads to record and characterize pain events from both their own and their partners’ perspectives. Sensor data streams were integrated to describe and explore the context of cancer pain events. Feasibility was assessed both technically and procedurally. Acceptability was assessed using postdeployment surveys and structured interviews with participants.

Results

Overall, 5 deployments (n=10 participants; 5 patient and family caregiver dyads) were completed, and 283 unique pain events were recorded. Using our “BESI-C Performance Scoring Instrument,” the overall technical feasibility score for deployments was 86.4 out of 100. Procedural feasibility challenges included the rurality of dyads, smart watch battery life and EMA reliability, and the length of time required for deployment installation. Postdeployment acceptability Likert surveys (1=strongly disagree; 5=strongly agree) found that dyads disagreed that BESI-C was a burden (1.7 out of 5) or compromised their privacy (1.9 out of 5) and agreed that the system collected helpful information to better manage cancer pain (4.6 out of 5). Participants also expressed an interest in seeing their own individual data (4.4 out of 5) and strongly agreed that it is important that data collected by BESI-C are shared with their respective partners (4.8 out of 5) and health care providers (4.8 out of 5). Qualitative feedback from participants suggested that BESI-C positively improved patient-caregiver communication regarding pain management. Importantly, we demonstrated proof of concept that seriously ill patients with cancer and their caregivers will mark pain events in real time using a smart watch.

Conclusions

It is feasible to deploy BESI-C, and dyads find the system acceptable. By leveraging human-centered design and the integration of heterogenous environmental, physiological, and behavioral data, the BESI-C system offers an innovative approach to monitor cancer pain, mitigate the escalation of pain and distress, and improve symptom management self-efficacy.

International Registered Report Identifier (IRRID)

RR2-10.2196/16178

Considerations of diversity, equity, and inclusion in mental health apps: A scoping review of evaluation frameworks

Mental health applications or apps are a promising approach to reduce the pervasive unmet mental health need observed among marginalized groups. However, these groups face unique challenges to engage in and benefit from these interventions, and apps that consider issues of diversity, equity, and inclusion (DEI) may be better positioned to reach them. App evaluation frameworks emerge as crucial tools for researchers, clinicians, and users to select evidence-based apps. Nevertheless, it is unclear the extent to which existing assessment tools capture DEI factors. The present scoping review identified 68 studies that employed one or more assessment tools to evaluate a mental health app, leading to 44 unique app evaluation frameworks. Results showed that most frameworks were developed after 2015, and only 58% of them considered at least one DEI criterion. Frameworks that performed exceptionally well were those that combined multiple standardized and validated measures. This lack of consideration for DEI variables may limit the ability of app-based interventions to serve marginalized communities, or even worse, create new disparities. Therefore, we provide recommendations to improve current app evaluation frameworks' cultural robustness and clinical utility, maximizing their effectiveness when working with individuals from marginalized communities.

The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality

As the COVID-19 pandemic has largely increased the utilization of telehealth, mobile mental health technologies - such as smartphone apps, vir-tual reality, chatbots, and social media - have also gained attention. These digital health technologies offer the potential of accessible and scalable interventions that can augment traditional care. In this paper, we provide a comprehensive update on the overall field of digital psychiatry, covering three areas. First, we outline the relevance of recent technological advances to mental health research and care, by detailing how smartphones, social media, artificial intelligence and virtual reality present new opportunities for "digital phenotyping" and remote intervention. Second, we review the current evidence for the use of these new technological approaches across different mental health contexts, covering their emerging efficacy in self-management of psychological well-being and early intervention, along with more nascent research supporting their use in clinical management of long-term psychiatric conditions - including major depression; anxiety, bipolar and psychotic disorders; and eating and substance use disorders - as well as in child and adolescent mental health care. Third, we discuss the most pressing challenges and opportunities towards real-world implementation, using the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework to explain how the innovations themselves, the recipients of these innovations, and the context surrounding innovations all must be considered to facilitate their adoption and use in mental health care systems. We conclude that the new technological capabilities of smartphones, artificial intelligence, social media and virtual reality are already changing mental health care in unforeseen and exciting ways, each accompanied by an early but promising evidence base. We point out that further efforts towards strengthening implementation are needed, and detail the key issues at the patient, provider and policy levels which must now be addressed for digital health technologies to truly improve mental health research and treatment in the future.

[Adherence to digital health interventions: definitions, methods, and open questions]

Many digital interventions rely on the participation of their users to have a positive impact. In various areas it can be observed that the use of digital interventions is often reduced or fully discontinued by the users after a short period of time. This is seen as one of the main factors that can limit the effectiveness of digital interventions. In this context, the concept of adherence to digital interventions is becoming increasingly important. Adherence to digital interventions is roughly defined as "the degree to which the user followed the program as it was designed," which can also be paraphrased as "intended use" or "use as it is designed." However, both the theoretical-conceptual and practical discussions regarding adherence to digital interventions still receive too little attention.The aim of this narrative review article is to shed more light on the concept of adherence to digital interventions and to distinguish it from related concepts. It also discusses the methods and metrics that can be used to operationalize adherence and the predictors that positively influence adherence. Finally, needs for action to better address adherence are considered critically.

Experience Sampling and Programmed Intervention Method and System for Planning, Authoring, and Deploying Mobile Health Interventions: Design and Case Reports

BACKGROUND

Health professionals initiating mobile health (mHealth) interventions may choose to adapt apps designed for other activities (eg, peer-to-peer communication) or to employ purpose-built apps specialized in the required intervention, or to exploit apps based on methods such as the experience sampling method (ESM). An alternative approach for professionals would be to create their own apps. While ESM-based methods offer important guidance, current systems do not expose their design at a level that promotes replicating, specializing, or extending their contributions. Thus, a twofold solution is required: a method that directs specialists in planning intervention programs themselves, and a model that guides specialists in adopting existing solutions and advises software developers on building new ones.

OBJECTIVE

The main objectives of this study are to design the Experience Sampling and Programmed Intervention Method (ESPIM), formulated toward supporting specialists in deploying mHealth interventions, and the ESPIM model, which guides health specialists in adopting existing solutions and advises software developers on how to build new ones. Another goal is to conceive and implement a software platform allowing specialists to be users who actually plan, create, and deploy interventions (ESPIM system).

METHODS

We conducted the design and evaluation of the ESPIM method and model alongside a software system comprising integrated web and mobile apps. A participatory design approach with stakeholders included early software prototype, predesign interviews with 12 health specialists, iterative design sustained by the software as an instance of the method's conceptual model, support to 8 real case studies, and postdesign interviews.

RESULTS

The ESPIM comprises (1) a list of requirements for mHealth experience sampling and intervention-based methods and systems, (2) a 4-dimension planning framework, (3) a 7-step-based process, and (4) an ontology-based conceptual model. The ESPIM system encompasses web and mobile apps. Eight long-term case studies, involving professionals in psychology, gerontology, computer science, speech therapy, and occupational therapy, show that the method allowed specialists to be actual users who plan, create, and deploy interventions via the associated system. Specialists' target users were parents of children diagnosed with autism spectrum disorder, older persons, graduate and undergraduate students, children (age 8-12), and caregivers of older persons. The specialists reported being able to create and conduct their own studies without modifying their original design. A qualitative evaluation of the ontology-based conceptual model showed its compliance to the functional requirements elicited.

CONCLUSIONS

The ESPIM method succeeds in supporting specialists in planning, authoring, and deploying mobile-based intervention programs when employed via a software system designed and implemented according to its conceptual model. The ESPIM ontology-based conceptual model exposes the design of systems involving active or passive sampling interventions. Such exposure supports the evaluation, implementation, adaptation, or extension of new or existing systems.

Enabling Guidelines for the Adoption of eHealth Solutions: Scoping Review

BACKGROUND

Globally, public health care is under increasing pressure, an economic burden currently amplified by the COVID-19 outbreak. With the recognition that universal health coverage improves the health of a population and reduces health inequalities, universal health coverage has been acknowledged as a priority goal. To meet the global needs in a population with increased chronic illness and longer life expectancy, the health care system is in dire need of new, emerging technologies. eHealth solutions as a method of delivery may have an impact on quality of care and health care costs. As such, it is important to study methods previously used to avoid suboptimal implementation and promote general guidelines to further develop eHealth solutions.

OBJECTIVE

This study aims to explore and thematically categorize a selected representation of early phase studies on eHealth technologies, focusing on papers that are under development or undergoing testing. Further, we want to assess enablers and barriers in terms of usability, scaling, and data management of eHealth implementation. The aim of this study to explore early development phase and feasibility studies was an intentional effort to provide applicable guidelines for evaluation at different stages of implementation.

METHODS

A structured search was performed in PubMed, MEDLINE, and Cochrane to identify and provide insight in current eHealth technology and methodology under development and gain insight in the future potential of eHealth technologies.

RESULTS

In total, 27 articles were included in this review. The clinical studies were categorized thematically by illness comparing 4 technology types deemed relevant: apps/web-based technology, sensor technology, virtual reality, and television. All eHealth assessment and implementation studies were categorized by their focus point: usability, scaling, or data management. Studies assessing the effect of eHealth were divided into feasibility studies, qualitative studies, and heuristic assessments. Studies focusing on usability (16/27) mainly addressed user involvement and learning curve in the adoption of eHealth, while the majority of scaling studies (6/27) focused on strategic and organizational aspects of upscaling eHealth solutions. Studies focusing on data management (5/27) addressed data processing and data sensitivity in adoption and diffusion of eHealth. Efficient processing of data in a secure manner, as well as user involvement and feedback, both throughout small studies and during upscaling, were the important enablers considered for successful implementation of eHealth.

CONCLUSIONS

eHealth interventions have considerable potential to improve lifestyle changes and adherence to treatment recommendations. To promote efficient implementation and scaling, user involvement to promote user-friendliness, secure and adaptable data management, and strategical considerations needs to be addressed early in the development process. eHealth should be assessed during its development into health services. The wide variation in interventions and methodology makes comparison of the results challenging and calls for standardization of methods.

Review of Popularity and Quality Standards of Opioid-Related Smartphone Apps

Purpose of review

Opioid misuse, addiction, and related harm is a global crisis that affects public health and social and economic welfare. Many of the strategies being used to combat the opioid crisis could benefit from improved access and dissemination, such as that afforded by smartphone apps. The goal of this study was to characterize the purpose, audience, quality and popularity of opioid-related smartphone apps. Using web scraping, available information from 619 opioid-related apps (e.g., popularity metrics) was downloaded from Google Play, and 59 apps met criteria for review. The apps were additionally coded for quality by two raters using an 8-item screener for the American Psychiatric Association App Evaluation Model.

Findings

Sixty one percent of apps targeted patients, 29% providers, 8% the general community, and 2% healthcare trainees. Regarding app purpose, 49% addressed treatment, 27% prevention, and 24% overdose. Only one app met all criteria on the screener for quality, and there was no association between a total score calculated for the screener and measures of app popularity (e.g., star ratings; R²=0.10, p=0.19).

Summary

Opioid-related apps available for consumers addressed key stakeholders (patients, providers, community) and were consistent with strategies to address the opioid crisis (prevention, treatment, overdose). However, there was little evidence that available opioid-related apps meet basic quality standards, and no relationship was found between app quality and popularity. This review was conducted at the level of consumer decision-making (i.e., the app store), where only a handful of opioid-related apps met quality standards enough to warrant a more detailed evaluation of the app before recommendation for use. Because smartphone apps could be a critical tool to increase access to and utilization of opioid prevention, treatment, and recovery services, further development and testing is sorely needed.