Artificial intelligence-informed mobile mental health apps for young people: a mixed-methods approach on users’ and stakeholders’ perspectives

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Abstract

Background

Novel approaches in mobile mental health (mHealth) apps that make use of Artificial Intelligence (AI), Ecological Momentary Assessments, and Ecological Momentary Interventions have the potential to support young people in the achievement of mental health and wellbeing goals. However, little is known on the perspectives of young people and mental health experts on this rapidly advancing technology. This study aims to investigate the subjective needs, attitudes, and preferences of key stakeholders towards an AI–informed mHealth app, including young people and experts on mHealth promotion and prevention in youth.

Methods

We used a convergent parallel mixed–method study design. Two semi–structured online focus groups (n = 8) and expert interviews (n = 5) to explore users and stakeholders perspectives were conducted. Furthermore a representative online survey was completed by young people (n = 666) to investigate attitudes, current use and preferences towards apps for mental health promotion and prevention.

Results

Survey results show that more than two-thirds of young people have experience with mHealth apps, and 60% make regular use of 1–2 apps. A minority (17%) reported to feel negative about the application of AI in general, and 19% were negative about the embedding of AI in mHealth apps. This is in line with qualitative findings, where young people displayed rather positive attitudes towards AI and its integration into mHealth apps. Participants reported pragmatic attitudes towards data sharing and safety practices, implying openness to share data if it adds value for users and if the data request is not too intimate, however demanded transparency of data usage and control over personalization. Experts perceived AI-informed mHealth apps as a complementary solution to on–site delivered interventions in future health promotion among young people. Experts emphasized opportunities in regard with low-threshold access through the use of smartphones, and the chance to reach young people in risk situations.

Conclusions

The findings of this exploratory study highlight the importance of further participatory development of training components prior to implementation of a digital mHealth training in routine practice of mental health promotion and prevention. Our results may help to guide developments based on stakeholders’ first recommendations for an AI-informed mHealth app.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13034-022-00522-6.

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Most cited references 57

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Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups.

A Tong, P SAINSBURY, J. Craig (2007)

Qualitative research explores complex phenomena encountered by clinicians, health care providers, policy makers and consumers. Although partial checklists are available, no consolidated reporting framework exists for any type of qualitative design. To develop a checklist for explicit and comprehensive reporting of qualitative studies (in depth interviews and focus groups). We performed a comprehensive search in Cochrane and Campbell Protocols, Medline, CINAHL, systematic reviews of qualitative studies, author or reviewer guidelines of major medical journals and reference lists of relevant publications for existing checklists used to assess qualitative studies. Seventy-six items from 22 checklists were compiled into a comprehensive list. All items were grouped into three domains: (i) research team and reflexivity, (ii) study design and (iii) data analysis and reporting. Duplicate items and those that were ambiguous, too broadly defined and impractical to assess were removed. Items most frequently included in the checklists related to sampling method, setting for data collection, method of data collection, respondent validation of findings, method of recording data, description of the derivation of themes and inclusion of supporting quotations. We grouped all items into three domains: (i) research team and reflexivity, (ii) study design and (iii) data analysis and reporting. The criteria included in COREQ, a 32-item checklist, can help researchers to report important aspects of the research team, study methods, context of the study, findings, analysis and interpretations.

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Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication.

Ronald C Kessler, Patricia Berglund, Olga Demler … (2005)

Little is known about lifetime prevalence or age of onset of DSM-IV disorders. To estimate lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the recently completed National Comorbidity Survey Replication. Nationally representative face-to-face household survey conducted between February 2001 and April 2003 using the fully structured World Health Organization World Mental Health Survey version of the Composite International Diagnostic Interview. Nine thousand two hundred eighty-two English-speaking respondents aged 18 years and older. Lifetime DSM-IV anxiety, mood, impulse-control, and substance use disorders. Lifetime prevalence estimates are as follows: anxiety disorders, 28.8%; mood disorders, 20.8%; impulse-control disorders, 24.8%; substance use disorders, 14.6%; any disorder, 46.4%. Median age of onset is much earlier for anxiety (11 years) and impulse-control (11 years) disorders than for substance use (20 years) and mood (30 years) disorders. Half of all lifetime cases start by age 14 years and three fourths by age 24 years. Later onsets are mostly of comorbid conditions, with estimated lifetime risk of any disorder at age 75 years (50.8%) only slightly higher than observed lifetime prevalence (46.4%). Lifetime prevalence estimates are higher in recent cohorts than in earlier cohorts and have fairly stable intercohort differences across the life course that vary in substantively plausible ways among sociodemographic subgroups. About half of Americans will meet the criteria for a DSM-IV disorder sometime in their life, with first onset usually in childhood or adolescence. Interventions aimed at prevention or early treatment need to focus on youth.

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Mental health before and during the COVID-19 pandemic: a longitudinal probability sample survey of the UK population

Matthias Pierce, Holly F Hope, Tamsin Ford … (2020)

Summary Background The potential impact of the COVID-19 pandemic on population mental health is of increasing global concern. We examine changes in adult mental health in the UK population before and during the lockdown. Methods In this secondary analysis of a national, longitudinal cohort study, households that took part in Waves 8 or 9 of the UK Household Longitudinal Study (UKHLS) panel, including all members aged 16 or older in April, 2020, were invited to complete the COVID-19 web survey on April 23–30, 2020. Participants who were unable to make an informed decision as a result of incapacity, or who had unknown postal addresses or addresses abroad were excluded. Mental health was assessed using the 12-item General Health Questionnaire (GHQ-12). Repeated cross-sectional analyses were done to examine temporal trends. Fixed-effects regression models were fitted to identify within-person change compared with preceding trends. Findings Waves 6–9 of the UKHLS had 53 351 participants. Eligible participants for the COVID-19 web survey were from households that took part in Waves 8 or 9, and 17 452 (41·2%) of 42 330 eligible people participated in the web survey. Population prevalence of clinically significant levels of mental distress rose from 18·9% (95% CI 17·8–20·0) in 2018–19 to 27·3% (26·3–28·2) in April, 2020, one month into UK lockdown. Mean GHQ-12 score also increased over this time, from 11·5 (95% CI 11·3–11·6) in 2018–19, to 12·6 (12·5–12·8) in April, 2020. This was 0·48 (95% CI 0·07–0·90) points higher than expected when accounting for previous upward trends between 2014 and 2018. Comparing GHQ-12 scores within individuals, adjusting for time trends and significant predictors of change, increases were greatest in 18–24-year-olds (2·69 points, 95% CI 1·89–3·48), 25–34-year-olds (1·57, 0·96–2·18), women (0·92, 0·50–1·35), and people living with young children (1·45, 0·79–2·12). People employed before the pandemic also averaged a notable increase in GHQ-12 score (0·63, 95% CI 0·20–1·06). Interpretation By late April, 2020, mental health in the UK had deteriorated compared with pre-COVID-19 trends. Policies emphasising the needs of women, young people, and those with preschool aged children are likely to play an important part in preventing future mental illness. Funding None.

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Author and article information

Contributors

Christian Götzl: christian.goetzl@uni-ulm.de

Journal

Journal ID (nlm-ta): Child Adolesc Psychiatry Ment Health

Journal ID (iso-abbrev): Child Adolesc Psychiatry Ment Health

Title: Child and Adolescent Psychiatry and Mental Health

Publisher: BioMed Central (London )

ISSN (Electronic): 1753-2000

Publication date (Electronic): 17 November 2022

Publication date PMC-release: 17 November 2022

Publication date Collection: 2022

Volume: 16

Electronic Location Identifier: 86

Affiliations

[1 ]GRID grid.6582.9, ISNI 0000 0004 1936 9748, Department of Psychiatry II, , University of Ulm and BKH Guenzburg, ; Lindenallee 2, Guenzburg, 89312 Ulm, Germany

[2 ]GRID grid.6582.9, ISNI 0000 0004 1936 9748, Department of Forensic Psychiatry and Psychotherapy, , University of Ulm and BKH Guenzburg, ; Ulm, Germany

[3 ]GRID grid.7700.0, ISNI 0000 0001 2190 4373, Department of Public Mental Health, Central Institute of Mental Health, Medical Faculty Mannheim, , Heidelberg University, ; Mannheim, Germany

[4 ]GRID grid.7700.0, ISNI 0000 0001 2190 4373, Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, , Heidelberg University, ; Mannheim, Germany

[5 ]GRID grid.7700.0, ISNI 0000 0001 2190 4373, Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, , Heidelberg University, ; Mannheim, Germany

[6 ]GRID grid.13097.3c, ISNI 0000 0001 2322 6764, Centre for Epidemiology and Public Health, Health Service and Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, , King’s College London, ; London, UK

[7 ]GRID grid.13097.3c, ISNI 0000 0001 2322 6764, ESRC Centre for Society and Mental Health, King’s College London, ; London, UK

Article

Publisher ID: 522

DOI: 10.1186/s13034-022-00522-6

PMC ID: 9672578

PubMed ID: 36397097

SO-VID: 0f97395c-e544-42b8-bff5-9ae7f2f7ff98

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 18 February 2022

Date accepted : 5 November 2022

Funding

Funded by: Federal Ministry of Science, Education, and Culture of Baden–Wuerttemberg, Germany

Award ID: 31–7547.223–7/3/2

Funded by: DFG Heisenberg professorship

Award ID: 389624707

Award Recipient : Ulrich Reininghaus

Funded by: Universität Ulm (1055)

Open Access :

Open Access funding enabled and organized by Projekt DEAL.

Custom metadata

ScienceOpen disciplines: Clinical Psychology & Psychiatry

Keywords: youth mental health,mental health promotion and prevention,mobile health (mhealth),public mental health,ecological momentary assessment,ecological momentary interventions,artificial intelligence

Data availability:

ScienceOpen disciplines: Clinical Psychology & Psychiatry

Keywords: youth mental health, mental health promotion and prevention, mobile health (mhealth), public mental health, ecological momentary assessment, ecological momentary interventions, artificial intelligence

Artificial intelligence-informed mobile mental health apps for young people: a mixed-methods approach on users’ and stakeholders’ perspectives

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Abstract

Background

Methods

Results

Conclusions

Supplementary Information

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Artificial Intelligence in Medicine

Most cited references 57

Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups.

Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication.

Mental health before and during the COVID-19 pandemic: a longitudinal probability sample survey of the UK population

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