Adverse childhood experiences, low self-esteem, and salient stimulus response in eating disorders.

BACKGROUND: Adverse childhood experiences (ACEs) are elevated in individuals with eating disorders (EDs), but how the neurobiology of EDs and ACEs interact is unclear. METHODS: Women 18-45 years old with anorexia nervosa (AN, n = 38), bulimia nervosa (BN, n = 32), or healthy controls (n = 60) were assessed for ACEs and ED behaviours and performed a taste-conditioning task during brain imaging. Mediation analyses tested relationships between ACE score, self-esteem, and ED behaviours. RESULTS: ACE scores were elevated in EDs and correlated positively with body mass index (p = 0.001), drive for thinness (p = 0.001), and body dissatisfaction (p = 0.032); low self-esteem mediated the relationship between ACEs and body dissatisfaction, drive for thinness, and bulimia severity. ACE scores correlated negatively (FDR-corrected) with unexpected, salient stimulus receipt in AN (substantia nigra) and BN (anterior cingulate, frontal and insular cortex, ventral striatum, and substantia nigra). When ACE scores were included in the model, unexpected stimulus receipt brain response was elevated in EDs in the anterior cingulate and ventral striatum. CONCLUSIONS: ACEs attenuate unexpected salient stimulus receipt response, which may be a biological marker for altered valence or hedonic tone perception in EDs. Low self-esteem mediates the relationships between ACEs and ED behaviours. Adverse childhood experiences should be assessed in biological studies, and their effects targeted in treatment.

Digital phenotyping of student mental health during COVID-19: an observational study of 100 college students.

Objective: This study assessed the feasibility of capturing smartphone based digital phenotyping data in college students during the COVID-19 pandemic with the goal of understanding how digital biomarkers of behavior correlate with mental health. Participants: Participants were 100 students enrolled in 4-year universities. Methods: Each participant attended a virtual visit to complete a series of gold-standard mental health assessments, and then used a mobile app for 28 days to complete mood assessments and allow for passive collection of GPS, accelerometer, phone call, and screen time data. Students completed another virtual visit at the end of the study to collect a second round of mental health assessments. Results: In-app daily mood assessments were strongly correlated with their corresponding gold standard clinical assessment. Sleep variance among students was correlated to depression scores (ρ = .28) and stress scores (ρ = .27). Conclusions: Digital Phenotyping among college students is feasible on both an individual and a sample level. Studies with larger sample sizes are necessary to understand population trends, but there are practical applications of the data today.

College student engagement with mental health apps: analysis of barriers to sustained use.

Objective: College students'demand for mental health resources is straining the services offered by colleges. While mobile apps demonstrate potential to help, students' engagement with these apps remains low. This study examines why college students show poor engagement with mental health apps and how apps may be adapted to suit this population. Participants: Participants were a convenience sample of 100 college students. Methods: Qualitative data was gathered through individual online interviews concerningattitudes toward mental health apps, and quantitative data was gathered through a survey about phone and app use. Results: Students were interested in mental health apps. 53% haddownloaded an app at one point, but only 19% currently used a mental health app. Stress and costdrove mental health app choices. Responses around engagement centered on: Data privacy, user interface, credibility, and customization. Conclusions: Students have specific wants for mental health apps including safety, simplicity, credibility, and customizability.

Needs and Experiences of Users of Digital Navigation Tools for Mental Health Treatment and Supportive Services: Survey Study.

BACKGROUND: Despite a recent proliferation in web-based and digital resources that are designed to assist users in finding appropriate mental health treatment and supportive services, it can be overwhelming, confusing, and difficult for an individual or family member to access and use an appropriate navigation tool. As digital resources are increasingly sought after, there is an urgent need for a clearer understanding of digital navigation tools in order to help link individuals with the tool that is best suited to their needs. OBJECTIVE: The objective of this study was to determine the needs of individuals seeking mental health treatment and supportive services and to quantify their experiences and satisfaction with available digital navigation tools. METHODS: A survey was offered via an email newsletter and social media posting throughout the extended membership of the National Alliance on Mental Illness, which includes both individuals with a mental health condition and their family members and support networks. A 13-item anonymous survey, which consisted of multiple-choice and open response options, was developed to measure participants' past use of and experiences with web-based, mobile, and phone-based navigation tools. The survey was available from April 9 through May 21, 2020. RESULTS: A total of 478 respondents completed the survey; the majority of respondents were female (397/478, 83.1%) and aged ≥35 years (411/478, 86%). Younger respondents were more likely to report seeking mental health services for themselves, while older respondents were more likely to be searching for such services on behalf of a family member. The majority of respondents seeking such services on behalf of a family member (113/194, 58.2%) required a combination of mental health treatment and supportive services. Furthermore, two-thirds of respondents (322/478, 67.4%) used a navigation tool to find treatment or services. The majority of respondents who provided feedback about their experiences with navigation tools (224/280, 80%) reported difficulties, with data availability and accuracy being the most commonly reported issues. CONCLUSIONS: The survey results suggest that issues with data availability and accuracy in available navigation tools remain a major barrier for locating timely and appropriate mental health treatment and supportive services within the population of individuals seeking such services. Particularly for individuals seeking care on behalf of a family member, improving the accuracy of and users' experiences with navigation tools could have a major impact on effectively connecting people to treatment and support services.

Mental Health App Evaluation: Updating the American Psychiatric Association's Framework Through a Stakeholder-Engaged Workshop.

The app evaluation framework of the American Psychiatric Association (APA) has emerged as an adaptable tool for those seeking to navigate the ever-growing space of mental health apps. The authors describe a meeting convened in December 2019 to refine the APA framework. The expert panel comprised 16 individuals across health care fields, with representation from psychiatry, psychology, social work, nursing, clinical informatics, peer support specialists, and individuals with lived mental health experience. This meeting resulted in an update to the APA framework with the addition of clearer question descriptions and the release of an eight-question screener that may be useful in clinical settings.

Assessing mental health apps marketplaces with objective metrics from 29,190 data points from 278 apps.

OBJECTIVE: Utilizing a standard framework that may help clinicians and patients to identify relevant mental health apps, we sought to gain a comprehensive picture of the space by searching for, downloading, and reviewing 278 mental health apps from both the iOS and Android stores. METHODS: 278 mental health apps from the Apple iOS store and Google Play store were downloaded and reviewed in a standardized manner by trained app raters using a validated framework. Apps were evaluated with this framework comprising 105 questions and covering app origin and accessibility, privacy and security, inputs and outputs, clinical foundation, features and engagement style, and interoperability. RESULTS: Our results confirm that app stars and downloads-even for the most popular apps by these metrics-did not correlate with more clinically relevant metrics related to privacy/security, effectiveness, and engagement. Most mental health apps offer similar functionality, with 16.5% offering both mood tracking and journaling and 7% offering psychoeducation, deep breathing, mindfulness, journaling, and mood tracking. Only 36.4% of apps were updated with a 100-day window, and 7.5% of apps had not been updated in four years. CONCLUSION: Current app marketplace metrics commonly used to evaluate apps do not offer an accurate representation of individual apps or a comprehensive overview of the entire space. The majority of apps overlap in terms of features offered, with many domains and other features not well represented. Selecting an appropriate app continues to require personal matching given no clear trends or guidance offered by quantitative metrics alone.

Evaluating evaluation frameworks: a scoping review of frameworks for assessing health apps.

OBJECTIVES: Despite an estimated 300 000 mobile health apps on the market, there remains no consensus around helping patients and clinicians select safe and effective apps. In 2018, our team drew on existing evaluation frameworks to identify salient categories and create a new framework endorsed by the American Psychiatric Association (APA). We have since created a more expanded and operational framework Mhealth Index and Navigation Database (MIND) that aligns with the APA categories but includes objective and auditable questions (105). We sought to survey the existing space, conducting a review of all mobile health app evaluation frameworks published since 2018, and demonstrate the comprehensiveness of this new model by comparing it to existing and emerging frameworks. DESIGN: We conducted a scoping review of mobile health app evaluation frameworks. DATA SOURCES: References were identified through searches of PubMed, EMBASE and PsychINFO with publication date between January 2018 and October 2020. ELIGIBILITY CRITERIA: Papers were selected for inclusion if they meet the predetermined eligibility criteria-presenting an evaluation framework for mobile health apps with patient, clinician or end user-facing questions. DATA EXTRACTION AND SYNTHESIS: Two reviewers screened the literature separately and applied the inclusion criteria. The data extracted from the papers included: author and dates of publication, source affiliation, country of origin, name of framework, study design, description of framework, intended audience/user and framework scoring system. We then compiled a collection of more than 1701 questions across 79 frameworks. We compared and grouped these questions using the MIND framework as a reference. We sought to identify the most common domains of evaluation while assessing the comprehensiveness and flexibility-as well as any potential gaps-of MIND. RESULTS: New app evaluation frameworks continue to emerge and expand. Since our 2019 review of the app evaluation framework space, more frameworks include questions around privacy (43) and clinical foundation (57), reflecting an increased focus on issues of app security and evidence base. The majority of mapped frameworks overlapped with at least half of the MIND categories. The results of this search have informed a database (apps.digitalpsych.org) that users can access today. CONCLUSION: As the number of app evaluation frameworks continues to rise, it is becoming difficult for users to select both an appropriate evaluation tool and to find an appropriate health app. This review provides a comparison of what different app evaluation frameworks are offering, where the field is converging and new priorities for improving clinical guidance.

Digital health developments and drawbacks: a review and analysis of top-returned apps for bipolar disorder.

BACKGROUND: Although a growing body of literature highlights the potential benefit of smartphone-based mobile apps to aid in self-management and treatment of bipolar disorder, it is unclear whether such evidence-based apps are readily available and accessible to a user of the app store. RESULTS: Using our systematic framework for the evaluation of mental health apps, we analyzed the accessibility, privacy, clinical foundation, features, and interoperability of the top-returned 100 apps for bipolar disorder. Only 56% of the apps mentioned bipolar disorder specifically in their title, description, or content. Only one app's efficacy was supported in a peer-reviewed study, and 32 apps lacked privacy policies. The most common features provided were mood tracking, journaling, and psychoeducation. CONCLUSIONS: Our analysis reveals substantial limitations in the current digital environment for individuals seeking an evidence-based, clinically usable app for bipolar disorder. Although there have been academic advances in development of digital interventions for bipolar disorder, this work has yet to be translated to the publicly available app marketplace. This unmet need of digital mood management underscores the need for a comprehensive evaluation system of mental health apps, which we have endeavored to provide through our framework and accompanying database (apps.digitalpsych.org).

Actionable health app evaluation: translating expert frameworks into objective metrics.

As use and availability of mobile health apps have increased, so too has the need for a thorough, accessible framework for app evaluation. The American Psychiatric Association's app evaluation model has emerged as a way to critically assess an app by considering accessibility, privacy and security, clinical foundation, engagement, and interoperability; however, there is no centralized database where users can view how various health apps perform when assessed via the APA model. In this perspective, we propose and outline our effort to translate the APA's model for the evaluation of health apps into a set of objective metrics that can be published online, making the framework actionable and accessible to a broad audience. The questions from the APA model were operationalized into 105 objective questions that are either binary or numeric. These questions serve as the foundation of an online database, where app evaluation consists of answering these 105 questions and can be crowdsourced. While the database has yet to be published and crowdsourced, initial internal testing demonstrated excellent interrater reliability. The database proposed here introduces a public and interactive approach to data collection that is guided by the APA model. The published product enables users to sort through the many mobile health apps and filter them according to individual preferences and priorities, making the ever-growing health app market more navigable.

Technology Evaluation and Assessment Criteria for Health Apps (TEACH-Apps): Pilot Study.

BACKGROUND: Despite the emergence of app evaluation tools, there remains no well-defined process receptive to diverse local needs, rigorous standards, and current content. The need for such a process to assist in the implementation of app evaluation across all medical fields is evident. Such a process has the potential to increase stakeholder engagement and catalyze interest and engagement with present-day app evaluation models. OBJECTIVE: This study aimed to develop and pilot test the Technology Evaluation and Assessment Criteria for Health apps (TEACH-apps). METHODS: Tailoring a well-known implementation framework, Replicating Effective Programs, we present a new process to approach the challenges faced in implementing app evaluation tools today. As a culmination of our experience implementing this process and feedback from stakeholders, we present the four-part process to aid the implementation of mobile health technology. This paper outlines the theory, evidence, and initial versions of the process. RESULTS: The TEACH-apps process is designed to be broadly usable and widely applicable across all fields of health. The process comprises four parts: (1) preconditions (eg, gathering apps and considering local needs), (2) preimplementation (eg, customizing criteria and offering digital skills training), (3) implementation (eg, evaluating apps and creating educational handouts), and (4) maintenance and evolution (eg, repeating the process every 90 days and updating content). TEACH-apps has been tested internally at our hospital, and there is growing interest in partnering health care facilities to test the system at their sites. CONCLUSIONS: This implementation framework introduces a process that equips stakeholders, clinicians, and users with the foundational tools to make informed decisions around app use and increase app evaluation engagement. The application of this process may lead to the selection of more culturally appropriate and clinically relevant tools in health care.

Longitudinal trends in the quality, effectiveness and attributes of highly rated smartphone health apps.

BACKGROUND: While there are numerous mental health apps on the market today, less is known about their safety and quality. This study aims to offer a longitudinal perspective on the nature of high visibility apps for common mental health and physical health conditions. METHODS: In July 2019, we selected the 10 top search-returned apps in the Apple App Store and Android Google Play Store using six keyword terms: depression, anxiety, schizophrenia, addiction, high blood pressure and diabetes. Each app was downloaded by two authors and reviewed by a clinician, and the app was coded for features, functionality, claims, app store properties, and other properties. RESULTS: Compared with 1 year prior, there were few statistically significant changes in app privacy policies, evidence and features. However, there was a high rate of turnover with only 34 (57%) of the apps from the Apple's App Store and 28 (47%) from the Google Play Store remaining in the 2019 top 10 search compared with the 2018 search. DISCUSSION: Although there was a high turnover of top search-returned apps between 2018 and 2019, we found that there were few significant changes in features, privacy, medical claims and other properties. This suggests that, although the highly visible and available apps are changing, there were no significant improvements in app quality or safety.