Mobile App Tools for Identifying and Managing Mental Health Disorders in Primary Care

PRimary carE digital Support ToOl in mental health (PRESTO): Design, development and study protocols

BACKGROUND

About 30-50% of Primary Care (PC) users in Spain suffer mental health problems, mostly mild to moderate anxious and depressive symptoms, which account for 2% of Spain's total Gross domestic product and 50% of the costs associated to all mental disorders. Mobile health tools have demonstrated to cost-effectively reduce anxious and depressive symptoms while machine learning (ML) techniques have shown to accurately detect severe cases. The main aim of this project is to develop a comprehensive ML digital support platform (PRESTO) to cost-effectively screen, assess, triage, and provide personalized treatments for anxious and depressive symptoms in PC.

METHODS

The project will be carried out in 3 complementary phases: First, a ML predictive severity model will be built based on all the cases referred to the PC mental health support programme during the last 5 years in Catalonia. Simultaneously, a smartphone app to monitor and deliver psychological interventions for anxiety and depressive symptoms will be developed and tested in a clinical trial. Finally, the ML models and the app will be integrated in a comprehensive decision-support platform (PRESTO) which will triage and assign to each patient a specific intervention based on individual personal and clinical characteristics. The effectiveness of PRESTO to reduce waiting times in receiving mental healthcare will be tested in a stepped-wedge cluster randomized controlled trial in 5 PC centres.

DISCUSSION

PRESTO will offer timely and personalized cost-effective mental health treatment to people with mild to moderate anxious and depressive symptoms. This will result in a reduction of the burden of mental health problems in PC and on society as a whole.

TRIAL REGISTRATION

The project and their clinical trials were registered in Clinical Trials.gov: NCT04559360 (September 2020).

Design, development and evaluation of a mobile-based self-care application for patients with COVID-19 not requiring hospitalization; a study of Northwest of Iran

BACKGROUND

Given the effective role of a mobile applications in disease management, disease monitoring, and self-care in patients with COVID-19 disease, we aimed to design, development and evaluation of a self-care Mobile app for COVID-19 patients not requiring hospitalization.

METHODS

The design, development and evaluation the usability of the self-care and education mobile app for patients with COVID-19 disease were conducted in two main phases at 2021 in Northwest of IRAN; (1) Determine the features and capabilities and (2) Design, development and evaluation of self-care mobile App. JAVA programming languages and Android Operating System were used and selected to design and development of a mobile app. There were 25 participants who conducted evaluations of the mobile app's usability and impact using the mobile health app usability a Questionnaire of User Interface Satisfaction was administered to assess the usability of the developed application. The results were analyzed via Excel 2013.

RESULTS

The model of developing a mobile app as an Information System was the Waterfall model. The smartphone application based on a set of capabilities and features was designed and consists of two main parts: the login screen for user registration, and the main home menu. The user interface includes three main pages or activities; (a) Main Menu for quick access to all of the pages, (b) Symptom management and monitoring to monitor the signs and symptoms during the illness, and (c) Set Reminders and Alarms to notify patients. The users' mean score of the application usability was calculated as 7.91 out of 9 indicating a good level of satisfaction.

CONCLUSION

This app can be a guideline and a useful tools for managing and monitoring symptoms, reminding medications, and implementing self-care instructions in outpatients. The authors suggest evaluating the efficacy and functionality test of mobile-based applications for COVID-19 in clinical trial studies.

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.

A brief web-based screening plus emotional-disorder health education was associated with improvement of mental health awareness and service-seeking attitudes among patients seeking nonpsychiatric clinical services in China

OBJECTIVES

Low recognition and intervention rates of emotional disorders among nonpsychiatric clinical patients are primarily attributable to poor mental health awareness of patients and a paucity of mental health care resources. This study aims to investigate the association of a resource-saving brief web-based emotional-disorder self-screening plus a health self-education program (BWBED-SS + HSE) with improved mental health awareness and service-seeking attitudes among nonpsychiatric clinical patients.

METHOD

A sample of 2065 patients seeking health services in nonpsychiatric clinical settings underwent BWBED-SS + HSE using mobile terminals. Participants were defined as being at high risk of anxiety and/or depression according to the optimal cut-off point of ≥11 on the Huaxi emotional-distress index (HEI).

RESULTS

The rate of participants at high risk of anxiety and/or depression was 6.63%. Following participation in the BWBED-SS + HSE, after controlling for demographics, type of hospital, and test time, the rates of participants considering themselves as having an emotional disorder and willing to seek mental health services among those at high risk of anxiety and/or depression increased from 29.93% to 47.45% (adjusted odds ratio [aOR] = 2.28, p = .002) and from 11.68% to 29.93% (aOR = 3.65, p < .001), respectively.

CONCLUSIONS

The BWBED-SS + HSE were associated with improved mental health awareness and service-seeking attitudes among patients seeking nonpsychiatric clinical services in China.

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.

Safety concerns with consumer-facing mobile health applications and their consequences: a scoping review

Objective

To summarize the research literature about safety concerns with consumer-facing health apps and their consequences.

Materials and Methods

We searched bibliographic databases including PubMed, Web of Science, Scopus, and Cochrane libraries from January 2013 to May 2019 for articles about health apps. Descriptive information about safety concerns and consequences were extracted and classified into natural categories. The review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) statement.

Results

Of the 74 studies identified, the majority were reviews of a single or a group of similar apps (n = 66, 89%), nearly half related to disease management (n = 34, 46%). A total of 80 safety concerns were identified, 67 related to the quality of information presented including incorrect or incomplete information, variation in content, and incorrect or inappropriate response to consumer needs. The remaining 13 related to app functionality including gaps in features, lack of validation for user input, delayed processing, failure to respond to health dangers, and faulty alarms. Of the 52 reports of actual or potential consequences, 5 had potential for patient harm. We also identified 66 reports about gaps in app development, including the lack of expert involvement, poor evidence base, and poor validation.

Conclusions

Safety of apps is an emerging public health issue. The available evidence shows that apps pose clinical risks to consumers. Involvement of consumers, regulators, and healthcare professionals in development and testing can improve quality. Additionally, mandatory reporting of safety concerns is needed to improve outcomes.

Evaluation of Treatment Descriptions and Alignment With Clinical Guidance of Apps for Depression on App Stores: Systematic Search and Content Analysis

BACKGROUND

The use of apps for the treatment of depression shows great promise. However, there is uncertainty regarding the alignment of publicly available apps for depression with clinical guidance, their treatment fidelity and evidence base, and their overall safety.

OBJECTIVE

Built on previous analyses and reviews, this study aims to explore the treatment and safety issues of publicly available apps for depression.

METHODS

We conducted a content analysis of apps for depression in the 2 main UK app stores (Google Play and Apple App Store). App store listings were analyzed for intervention content, treatment fidelity, and fit with the National Institute for Health and Care Excellence (NICE) guidelines for the treatment of depression in adults.

RESULTS

A total of 353 apps for depression were included in the review. App descriptions reported the use of 20 treatment approaches and 37 treatment strategies. Many apps used transdiagnostic (155/353, 43.9%) and multitheoretical interventions to treat multiple disorders including depression. Although many interventions appeared to be evidence-informed, there were issues with treatment fidelity, research evidence, and fit with clinical guidelines. None of the apps fully aligned with the NICE guidelines for depression.

CONCLUSIONS

App developers have adopted many evidence-informed treatments in their interventions; however, more work is needed to improve clinical validity, treatment fidelity, and the safety of apps. We urge developers to consult relevant guidelines and standards, and to engage in reflective questioning on treatment and safety to address these issues and to improve treatment content and intervention design.

Smartphone-Delivered Progressive Muscle Relaxation for the Treatment of Migraine in Primary Care: A Randomized Controlled Trial

OBJECTIVE

Scalable, accessible forms of behavioral therapy for migraine prevention are needed. We assessed the feasibility and acceptability of progressive muscle relaxation (PMR) delivered by a smartphone application (app) in the Primary Care setting.

METHODS

This pilot study was a non-blinded, randomized, parallel-arm controlled trial of adults with migraine and 4+ headache days/month. Eligible participants spoke English and owned a smartphone. All participants were given the RELAXaHEAD app which includes an electronic headache diary. Participants were randomized to receive 1 of the 2 versions of the app-one with PMR and the other without PMR. The primary outcomes were measures of feasibility (adherence to the intervention and diary entries during the 90-day interval) and acceptability (satisfaction levels). We conducted exploratory analyses to determine whether there was a change in Migraine Disability Assessment Scale (MIDAS) scores or a change in headache days.

RESULTS

Of 139 participants (77 PMR, 62 control), 116 (83%) were female, mean age was 41.7 ± 12.8 years. Most patients 108/139 (78%) had moderate-severe disability. Using a 1-5 Likert scale, participants found the app easy to use (mean 4.2 ± 0.7) and stated that they would be happy to engage in the PMR intervention again (mean 4.3 ± 0.6). For the first 6 weeks, participants practiced PMR 2-4 days/week. Mean per session duration was 11.1 ± 8.3 minutes. Relative to the diary-only group, the PMR group showed a greater non-significant decline in mean MIDAS scores (-8.7 vs -22.7, P = .100) corresponding to a small-moderate mean effect size (Cohen's d = 0.38).

CONCLUSION

Smartphone-delivered PMR may be an acceptable, accessible form of therapy for migraine. Mean effects show a small-moderate mean effect size in disability scores.

Connected Mental Health: Systematic Mapping Study

BACKGROUND

Although mental health issues constitute an increasing global burden affecting a large number of people, the mental health care industry is still facing several care delivery barriers such as stigma, education, and cost. Connected mental health (CMH), which refers to the use of information and communication technologies in mental health care, can assist in overcoming these barriers.

OBJECTIVE

The aim of this systematic mapping study is to provide an overview and a structured understanding of CMH literature available in the Scopus database.

METHODS

A total of 289 selected publications were analyzed based on 8 classification criteria: publication year, publication source, research type, contribution type, empirical type, mental health issues, targeted cohort groups, and countries where the empirically evaluated studies were conducted.

RESULTS

The results showed that there was an increasing interest in CMH publications; journals were the main publication channels of the selected papers; exploratory research was the dominant research type; advantages and challenges of the use of technology for mental health care were the most investigated subjects; most of the selected studies had not been evaluated empirically; depression and anxiety were the most addressed mental disorders; young people were the most targeted cohort groups in the selected publications; and Australia, followed by the United States, was the country where most empirically evaluated studies were conducted.

CONCLUSIONS

CMH is a promising research field to present novel approaches to assist in the management, treatment, and diagnosis of mental health issues that can help overcome existing mental health care delivery barriers. Future research should be shifted toward providing evidence-based studies to examine the effectiveness of CMH solutions and identify related issues.

The Model of Gamification Principles for Digital Health Interventions: Evaluation of Validity and Potential Utility

BACKGROUND

Although gamification continues to be a popular approach to increase engagement, motivation, and adherence to behavioral interventions, empirical studies have rarely focused on this topic. There is a need to empirically evaluate gamification models to increase the understanding of how to integrate gamification into interventions.

OBJECTIVE

The model of gamification principles for digital health interventions proposes a set of five independent yet interrelated gamification principles. This study aimed to examine the validity and reliability of this model to inform its use in Web- and mobile-based apps.

METHODS

A total of 17 digital health interventions were selected from a curated website of mobile- and Web-based apps (PsyberGuide), which makes independent and unbiased ratings on various metrics. A total of 133 independent raters trained in gamification evaluation techniques were instructed to evaluate the apps and rate the degree to which gamification principles are present. Multiple ratings (n≥20) were collected for each of the five gamification principles within each app. Existing measures, including the PsyberGuide credibility score, mobile app rating scale (MARS), and the app store rating of each app were collected, and their relationship with the gamification principle scores was investigated.

RESULTS

Apps varied widely in the degree of gamification implemented (ie, the mean gamification rating ranged from 0.17≤m≤4.65 out of 5). Inter-rater reliability of gamification scores for each app was acceptable (κ≥0.5). There was no significant correlation between any of the five gamification principles and the PsyberGuide credibility score (P≥.49 in all cases). Three gamification principles (supporting player archetypes, feedback, and visibility) were significantly correlated with the MARS score, whereas three principles (meaningful purpose, meaningful choice, and supporting player archetypes) were significantly correlated with the app store rating. One gamification principle was statistically significant with both the MARS and the app store rating (supporting player archetypes).

CONCLUSIONS

Overall, the results support the validity and potential utility of the model of gamification principles for digital health interventions. As expected, there was some overlap between several gamification principles and existing app measures (eg, MARS). However, the results indicate that the gamification principles are not redundant with existing measures and highlight the potential utility of a 5-factor gamification model structure in digital behavioral health interventions. These gamification principles may be used to improve user experience and enhance engagement with digital health programs.

Accuracy of a Chatbot (Ada) in the Diagnosis of Mental Disorders: Comparative Case Study With Lay and Expert Users

Background

Health apps for the screening and diagnosis of mental disorders have emerged in recent years on various levels (eg, patients, practitioners, and public health system). However, the diagnostic quality of these apps has not been (sufficiently) tested so far.

Objective

The objective of this pilot study was to investigate the diagnostic quality of a health app for a broad spectrum of mental disorders and its dependency on expert knowledge.

Methods

Two psychotherapists, two psychology students, and two laypersons each read 20 case vignettes with a broad spectrum of mental disorders. They used a health app (Ada—Your Health Guide) to get a diagnosis by entering the symptoms. Interrater reliabilities were computed between the diagnoses of the case vignettes and the results of the app for each user group.

Results

Overall, there was a moderate diagnostic agreement (kappa=0.64) between the results of the app and the case vignettes for mental disorders in adulthood and a low diagnostic agreement (kappa=0.40) for mental disorders in childhood and adolescence. When psychotherapists applied the app, there was a good diagnostic agreement (kappa=0.78) regarding mental disorders in adulthood. The diagnostic agreement was moderate (kappa=0.55/0.60) for students and laypersons. For mental disorders in childhood and adolescence, a moderate diagnostic quality was found when psychotherapists (kappa=0.53) and students (kappa=0.41) used the app, whereas the quality was low for laypersons (kappa=0.29). On average, the app required 34 questions to be answered and 7 min to complete.

Conclusions

The health app investigated here can represent an efficient diagnostic screening or help function for mental disorders in adulthood and has the potential to support especially diagnosticians in their work in various ways. The results of this pilot study provide a first indication that the diagnostic accuracy is user dependent and improvements in the app are needed especially for mental disorders in childhood and adolescence.

Psychiatry Outpatients' Willingness to Share Social Media Posts and Smartphone Data for Research and Clinical Purposes: Survey Study

BACKGROUND

Psychiatry research has begun to leverage data collected from patients' social media and smartphone use. However, information regarding the feasibility of utilizing such data in an outpatient setting and the acceptability of such data in research and practice is limited.

OBJECTIVE

This study aimed at understanding the outpatients' willingness to have information from their social media posts and their smartphones used for clinical or research purposes.

METHODS

In this survey study, we surveyed patients (N=238) in an outpatient clinic waiting room. Willingness to share social media and passive smartphone data was summarized for the sample as a whole and broken down by sex, age, and race.

RESULTS

Most patients who had a social media account and who were receiving talk therapy treatment (74.4%, 99/133) indicated that they would be willing to share their social media posts with their therapists. The percentage of patients willing to share passive smartphone data with researchers varied from 40.8% (82/201) to 60.7% (122/201) depending on the parameter, with sleep duration being the parameter with the highest percentage of patients willing to share. A total of 30.4% of patients indicated that media stories of social media privacy breaches made them more hesitant about sharing passive smartphone data with researchers. Sex and race were associated with willingness to share smartphone data, with men and whites being the most willing to share.

CONCLUSIONS

Our results indicate that most patients in a psychiatric outpatient setting would share social media and passive smartphone data and that further research elucidating patterns of willingness to share passive data is needed.

Understanding the quality, effectiveness and attributes of top-rated smartphone health apps

Objective

This study aimed to understand the attributes of popular apps for mental health and comorbid medical conditions, and how these qualities relate to consumer ratings, app quality and classification by the WHO health app classification framework.

Methods

We selected the 10 apps from the Apple iTunes store and the US Android Google Play store on 20 July 2018 from six disease states: depression, anxiety, schizophrenia, addiction, diabetes and hypertension. Each app was downloaded by two authors who provided information on the apps’ attributes, functionality, interventions, popularity, scientific backing and WHO app classification rating.

Results

A total of 120 apps were examined. Although none of these apps had Food and Drug Administration marketing approval, nearly 50% made claims that appeared medical. Most apps offered a similar type of services with 87.5% assigned WHO classification 1.4.2 ‘self-monitoring of health or diagnostic data by a client’ or 1.6.1 ‘client look-up of health information’. The ‘last updated’ attribute was highly correlated with a quality rating of the app although no apps features (eg, uses Global Positioning System, reminders and so on) were.

Conclusion

Due to the heterogeneity of the apps, we were unable to define a core set of features that would accurately assess app quality. The number of apps making unsupported claims combined with the number of apps offering questionable content warrants a cautious approach by both patients and clinicians in selecting safe and effective ones.

Clinical Implications

‘Days since last updated’ offers a useful and easy clinical screening test for health apps, regardless of the condition being examined.