Cross cultural and global uses of a digital mental health app: results of focus groups with clinicians, patients and family members in India and the United States

Efficacy of digital mental health interventions for PTSD symptoms: A systematic review of meta-analyses

BACKGROUND

The present systematic review aimed to synthesize the results of meta-analyses which examine the effects of digital mental health interventions (DMHIs) on post-traumatic stress disorder (PTSD) symptoms, and investigate whether intervention characteristics (i.e., technique, timeframe, and therapeutic guidance) and methodological characteristics including outcome measures and sample inclusion criteria (age, gender, socioeconomic status, country, comorbidity) moderate the efficacy of digital interventions.

METHODS

A systematic search of various sources (ECSCOhost PsycInfo, PubMed, Web of Science, Scopus, EBSCOhost ERIC, Google Scholar, ProQuest Dissertations & Theses) including five peer-reviewed journals was conducted to identify relevant meta-analyses up to December 2023, and 11 meta-analyses were included in the final review.

RESULTS

Overall, our review elucidates that DMHIs are appropriate for alleviating PTSD symptoms in adults, with more consistent evidence supporting the efficacy of cognitive behavioral therapy (CBT)-based, compared to non-CBT-based, interventions when compared to control conditions. However, we found inconclusive evidence that the efficacy of DMHIs varied according to intervention timeframe, therapeutic guidance, or sample characteristics.

LIMITATIONS

A relatively limited number of different populations was sampled across meta-analyses. Further, while our review focused on PTSD symptoms to indicate the efficacy of digital interventions, other indices of effectiveness were not examined.

CONCLUSION

Our findings indicate the clinical utility of DMHIs for managing PTSD symptoms particularly when CBT-based intervention techniques are employed.

Digital Mental Health for Schizophrenia and Other Severe Mental Illnesses: An International Consensus on Current Challenges and Potential Solutions

BACKGROUND

Digital approaches may be helpful in augmenting care to address unmet mental health needs, particularly for schizophrenia and severe mental illness (SMI).

OBJECTIVE

An international multidisciplinary group was convened to reach a consensus on the challenges and potential solutions regarding collecting data, delivering treatment, and the ethical challenges in digital mental health approaches for schizophrenia and SMI.

METHODS

The consensus development panel method was used, with an in-person meeting of 2 groups: the expert group and the panel. Membership was multidisciplinary including those with lived experience, with equal participation at all stages and coproduction of the consensus outputs and summary. Relevant literature was shared in advance of the meeting, and a systematic search of the recent literature on digital mental health interventions for schizophrenia and psychosis was completed to ensure that the panel was informed before the meeting with the expert group.

RESULTS

Four broad areas of challenge and proposed solutions were identified: (1) user involvement for real coproduction; (2) new approaches to methodology in digital mental health, including agreed standards, data sharing, measuring harms, prevention strategies, and mechanistic research; (3) regulation and funding issues; and (4) implementation in real-world settings (including multidisciplinary collaboration, training, augmenting existing service provision, and social and population-focused approaches). Examples are provided with more detail on human-centered research design, lived experience perspectives, and biomedical ethics in digital mental health approaches for SMI.

CONCLUSIONS

The group agreed by consensus on a number of recommendations: (1) a new and improved approach to digital mental health research (with agreed reporting standards, data sharing, and shared protocols), (2) equal emphasis on social and population research as well as biological and psychological approaches, (3) meaningful collaborations across varied disciplines that have previously not worked closely together, (4) increased focus on the business model and product with planning and new funding structures across the whole development pathway, (5) increased focus and reporting on ethical issues and potential harms, and (6) organizational changes to allow for true communication and coproduction with those with lived experience of SMI. This study approach, combining an international expert meeting with patient and public involvement and engagement throughout the process, consensus methodology, discussion, and publication, is a helpful way to identify directions for future research and clinical implementation in rapidly evolving areas and can be combined with measurements of real-world clinical impact over time. Similar initiatives will be helpful in other areas of digital mental health and similarly fast-evolving fields to focus research and organizational change and effect improved real-world clinical implementation.

Health Care Professionals' Views on the Use of Passive Sensing, AI, and Machine Learning in Mental Health Care: Systematic Review With Meta-Synthesis

BACKGROUND

Mental health difficulties are highly prevalent worldwide. Passive sensing technologies and applied artificial intelligence (AI) methods can provide an innovative means of supporting the management of mental health problems and enhancing the quality of care. However, the views of stakeholders are important in understanding the potential barriers to and facilitators of their implementation.

OBJECTIVE

This study aims to review, critically appraise, and synthesize qualitative findings relating to the views of mental health care professionals on the use of passive sensing and AI in mental health care.

METHODS

A systematic search of qualitative studies was performed using 4 databases. A meta-synthesis approach was used, whereby studies were analyzed using an inductive thematic analysis approach within a critical realist epistemological framework.

RESULTS

Overall, 10 studies met the eligibility criteria. The 3 main themes were uses of passive sensing and AI in clinical practice, barriers to and facilitators of use in practice, and consequences for service users. A total of 5 subthemes were identified: barriers, facilitators, empowerment, risk to well-being, and data privacy and protection issues.

CONCLUSIONS

Although clinicians are open-minded about the use of passive sensing and AI in mental health care, important factors to consider are service user well-being, clinician workloads, and therapeutic relationships. Service users and clinicians must be involved in the development of digital technologies and systems to ensure ease of use. The development of, and training in, clear policies and guidelines on the use of passive sensing and AI in mental health care, including risk management and data security procedures, will also be key to facilitating clinician engagement. The means for clinicians and service users to provide feedback on how the use of passive sensing and AI in practice is being received should also be considered.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42022331698; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=331698.

Mental health service users' experiences of telehealth interventions facilitated during the COVID-19 pandemic and their relevance to nursing: An integrative review

WHAT THIS PAPER ADDS TO EXISTING KNOWLEDGE?: Service users report telehealth interventions to be useful in terms of access and convenience however, a preference for face-to-face interventions remains. Nurses are using telehealth interventions in clinical practice however, further research is necessary in this area as the evidence of their involvement is limited. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: This paper highlights the use of telehealth interventions should augment rather than replace face-to-face care provision.

ABSTRACT

INTRODUCTION: The Covid-19 pandemic saw the swift implementation of physical and social distancing that impacted the way in which mental health services were facilitated. Consequently, telehealth/e-health interventions are increasing in use.

AIM

This integrative review aims to explore existing literature regarding mental health service users' experiences of telehealth interventions facilitated through the COVID-19 pandemic, to determine the visibility of nursing involvement in the facilitation of telehealth interventions and to use these experiences to inform nursing practice.

METHOD

A methodical search of eight (n = 8) academic databases was undertaken using CINAHL, SCOPUS, EMBASE, PsycINFO, Web of Science, Cochrane, MEDLINE and Academic Search Complete between January 2020 and January 2022.

RESULTS

A total of 5133 papers were screened by title and abstract of which (n = 77) progressed for full-text screening. Five (n = 5) papers met the inclusion criteria for this review and results were mapped onto the four meta-paradigms of nursing: person; where the findings discuss the acceptability of telehealth interventions; environment; where the findings highlight barriers and facilitators to the use of telehealth interventions; health; where the findings discuss staff time and logistical issues relating to telehealth interventions and nursing; where the findings centre around the therapeutic relationship.

DISCUSSION

This review highlights there is a paucity of direct evidence relating to nursing involvement in the facilitation of telehealth interventions. However, there are benefits to the use of telehealth interventions that include increased access to services, less perceived stigma and increased engagement which are relevant to nursing practice. A lack of individual contact and concerns relating to infrastructure indicates a fondness for face-to-face interventions remains high.

IMPLICATIONS FOR PRACTICE

There is a need for further research on the role of the nurse in the facilitation of telehealth interventions, specific interventions used and outcomes of such interventions.

Assessment of App Store Description and Privacy Policy to Explore Ethical and Safety Concerns Associated with the Use of Mental Health Apps for Depression

Background

Mental Health apps (MH apps) could help address the huge unmet mental health care need of developing countries. This study aimed to explore potential ethical, data safety, and privacy issues associated with using MH apps for depression.

Methods

A cross-sectional assessment of the top 50 MH apps (by Google Play store search result ordering) for depression available in India was conducted in November 2021.

Results

Most apps were listed under the category of health and fitness (54%). The median number of total and dangerous permissions requested at the time of download was nine and three, respectively. Privacy policy in English was available for 76%. The average length of the privacy policy was 2171 words, and the mean Flesch-Kincaid reading grade level was 12 (much higher than the recommended cut-off of eight). Important features relevant to safeguarding consumer confidentiality, including names of third parties with which user data could be shared (42%), explicit consent before sharing data with third parties (16%), and assurance regarding the collection of de-identifiable data (11%), were missing from the majority of privacy policies.

Conclusion

There is an urgent need to improve the accessibility and usability of privacy policies by app developers, with the active involvement of other stakeholders.

Relapse prediction in schizophrenia with smartphone digital phenotyping during COVID-19: a prospective, three-site, two-country, longitudinal study

Smartphone technology provides us with a more convenient and less intrusive method of detecting changes in behavior and symptoms that typically precede schizophrenia relapse. To take advantage of the aforementioned, this study examines the feasibility of predicting schizophrenia relapse by identifying statistically significant anomalies in patient data gathered through mindLAMP, an open-source smartphone app. Participants, recruited in Boston, MA in the United States, and Bangalore and Bhopal in India, were invited to use mindLAMP for up to a year. The passive data (geolocation, accelerometer, and screen state), active data (surveys), and data quality metrics collected by the app were then retroactively fed into a relapse prediction model that utilizes anomaly detection. Overall, anomalies were 2.12 times more frequent in the month preceding a relapse and 2.78 times more frequent in the month preceding and following a relapse compared to intervals without relapses. The anomaly detection model incorporating passive data proved a better predictor of relapse than a naive model utilizing only survey data. These results demonstrate that relapse prediction models utilizing patient data gathered by a smartphone app can warn the clinician and patient of a potential schizophrenia relapse.

A qualitative exploration of perceived needs and barriers of individuals with schizophrenia, caregivers and clinicians in using mental health applications in Madhya Pradesh, India

Introduction

About 3.5 million people are living with schizophrenia in India, with most failing to receive minimally adequate care. Digital mental health applications could potentially decrease this treatment gap; however, these applications should be tailored to meet the needs and overcoming barriers of its end-users to ensure their adoption and sustained usage. Few studies in India have explored the perspectives of target stakeholders to understand how digital tools could be viable for supporting care. Therefore, this study explores the perceived needs and barriers of patients with schizophrenia, caregivers and clinicians in using digital mental health applications.

Methods

Focus group discussions (FGDs) were conducted with patients having schizophrenia attending outpatient clinics at a government tertiary hospital, and their caregivers, and mental health clinicians in Bhopal, Madhya Pradesh, India. FGDs were audio-recorded and coded. Framework analysis was employed to guide the analysis, involving deductive and inductive generation of themes, data triangulation and comparison of perspectives between participant groups.

Results

Six FGDs were conducted with individuals with schizophrenia (n ​= ​11), their caregivers (n ​= ​14), and mental health clinicians (n ​= ​19). Four a priori themes were established: a) Prior experiences with health applications; b) Content of a mental health application; c) Involvement of caregivers in mental health application usage and d) Supporting doctors' work through mental health applications. Additionally, two themes were generated inductively: a) Qualities of a mental health application and b) Data privacy and confidentiality.

Conclusion

Exploration of stakeholder perspectives on the content, features, and uses of mental health applications is crucial to yield initial insights about the use of these digital programs in India. This study generated a multitude of suggestions on app functionality and components, which can guide ongoing efforts to develop and deliver digital mental health applications for patients living with schizophrenia in low-resource settings, with limited access to mental health services.

Schizophrenia Assessment, Referral and Awareness Training for Health Auxiliaries (SARATHA): Protocol for a Mixed-Methods Pilot Study in Rural India

BACKGROUND

Workforce shortages pose major obstacles to the timely detection and treatment of schizophrenia, particularly in low-income and middle-income countries. The SARATHA (Schizophrenia Assessment, Referral, and Awareness Training for Health Auxiliaries) project involves the systematic development, iterative refinement, and pilot testing of a digital program for training community health workers in the early detection and referral of schizophrenia in primary care settings in rural India.

METHODS

SARATHA is a three-phase study. Phase 1 involves consulting with experts and clinicians, and drawing from existing evidence to inform the development of a curriculum for training community health workers. Phase 2 consists of designing and digitizing the training content for delivery on a smartphone app. Design workshops and focus group discussions will be conducted to seek input from community health workers and service users living with schizophrenia to guide revisions and refinements to the program content. Lastly, Phase 3 entails piloting the training program with a target sample of 20 community health workers to assess feasibility and acceptability. Preliminary effectiveness will be explored, as measured by community health workers' changes in knowledge about schizophrenia and the program content after completing the training.

DISCUSSION

If successful, this digital training program will offer a potentially scalable approach for building capacity of frontline community health workers towards reducing delays in early detection of schizophrenia in primary care settings in rural India. This study can inform efforts to improve treatment outcomes for persons living with schizophrenia in low-resource settings.

Smartphone digital phenotyping, surveys, and cognitive assessments for global mental health: Initial data and clinical correlations from an international first episode psychosis study

Objective

To examine feasibility and acceptability of smartphone mental health app use for symptom, cognitive, and digital phenotyping monitoring among people with schizophrenia in India and the United States.

Methods

Participants in Boston, USA and Bhopal and Bangalore, India used a smartphone app to monitor symptoms, play cognitive games, access relaxation and psychoeducation resources and for one month, with an initial clinical and cognitive assessment and a one-month follow-up clinical assessment. Engagement with the app was compared between study sites, by clinical symptom severity and by cognitive functioning. Digital phenotyping data collection was also compared between three sites.

Results

By Kruskal-Wallis rank-sum test, we found no difference between app activities completed or digital phenotyping data collected across the three study sites. App use also did not correlate to clinical or cognitive assessment scores. When using the app for symptom monitoring, preliminary findings suggest app-based assessment correlate with standard cognitive and clinical assessments.

Conclusions

Smartphone app for symptom monitoring and digital phenotyping for individuals with schizophrenia appears feasible and acceptable in a global context. Clinical utility of this app for real-time assessments is promising, but further research is necessary to determine the long-term efficacy and generalizability for serious mental illness.