Development and Initial Evaluation of a Digital Phenotype Collection System for Adolescents: Proof-of-Concept Study.

adolescent mental health adolescents digital phenotyping ecological momentary assessment phenotype data phenotypes proof of concept qualitative research self-monitoring smartphone apps

Journal

JMIR formative research
ISSN: 2561-326X
Titre abrégé: JMIR Form Res
Pays: Canada
ID NLM: 101726394

Informations de publication

Date de publication:
24 Oct 2024
Historique:
received: 17 04 2024
accepted: 20 08 2024
revised: 31 07 2024
medline: 24 10 2024
pubmed: 24 10 2024
entrez: 24 10 2024
Statut: epublish

Résumé

The growing concern on adolescent mental health calls for proactive early detection and intervention strategies. There is a recognition of the link between digital phenotypes and mental health, drawing attention to their potential use. However, the process of collecting digital phenotype data presents challenges despite its promising prospects. This study aims to develop and validate system concepts for collecting adolescent digital phenotypes that effectively manage inherent challenges in the process. In a formative investigation (N=34), we observed adolescent self-recording behaviors and conducted interviews to develop design goals. These goals were then translated into system concepts, which included planners resembling interfaces, simplified data input with tags, visual reports on behaviors and moods, and supportive ecological momentary assessment (EMA) prompts. A proof-of-concept study was conducted over 2 weeks (n=16), using tools that simulated the concepts to record daily activities and complete EMA surveys. The effectiveness of the system was evaluated through semistructured interviews, supplemented by an analysis of the frequency of records and responses. The interview findings revealed overall satisfaction with the system concepts, emphasizing strong support for self-recording. Participants consistently maintained daily records throughout the study period, with no missing data. They particularly valued the recording procedures that aligned well with their self-recording goal of time management, facilitated by the interface design and simplified recording procedures. Visualizations during recording and subsequent report viewing further enhanced engagement by identifying missing data and encouraging deeper self-reflection. The average EMA compliance reached 72%, attributed to a design that faithfully reflected adolescents' lives, with surveys scheduled at convenient times and supportive messages tailored to their daily routines. The high compliance rates observed and positive feedback from participants underscore the potential of our approach in addressing the challenges of collecting digital phenotypes among adolescents. Integrating observations of adolescents' recording behavior into the design process proved to be beneficial for developing an effective and highly compliant digital phenotype collection system.

Sections du résumé

BACKGROUND BACKGROUND
The growing concern on adolescent mental health calls for proactive early detection and intervention strategies. There is a recognition of the link between digital phenotypes and mental health, drawing attention to their potential use. However, the process of collecting digital phenotype data presents challenges despite its promising prospects.
OBJECTIVE OBJECTIVE
This study aims to develop and validate system concepts for collecting adolescent digital phenotypes that effectively manage inherent challenges in the process.
METHODS METHODS
In a formative investigation (N=34), we observed adolescent self-recording behaviors and conducted interviews to develop design goals. These goals were then translated into system concepts, which included planners resembling interfaces, simplified data input with tags, visual reports on behaviors and moods, and supportive ecological momentary assessment (EMA) prompts. A proof-of-concept study was conducted over 2 weeks (n=16), using tools that simulated the concepts to record daily activities and complete EMA surveys. The effectiveness of the system was evaluated through semistructured interviews, supplemented by an analysis of the frequency of records and responses.
RESULTS RESULTS
The interview findings revealed overall satisfaction with the system concepts, emphasizing strong support for self-recording. Participants consistently maintained daily records throughout the study period, with no missing data. They particularly valued the recording procedures that aligned well with their self-recording goal of time management, facilitated by the interface design and simplified recording procedures. Visualizations during recording and subsequent report viewing further enhanced engagement by identifying missing data and encouraging deeper self-reflection. The average EMA compliance reached 72%, attributed to a design that faithfully reflected adolescents' lives, with surveys scheduled at convenient times and supportive messages tailored to their daily routines. The high compliance rates observed and positive feedback from participants underscore the potential of our approach in addressing the challenges of collecting digital phenotypes among adolescents.
CONCLUSIONS CONCLUSIONS
Integrating observations of adolescents' recording behavior into the design process proved to be beneficial for developing an effective and highly compliant digital phenotype collection system.

Identifiants

pubmed: 39446465
pii: v8i1e59623
doi: 10.2196/59623
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e59623

Informations de copyright

©Minseo Cho, Doeun Park, Myounglee Choo, Jinwoo Kim, Doug Hyun Han. Originally published in JMIR Formative Research (https://formative.jmir.org), 24.10.2024.

Auteurs

Minseo Cho (M)

Human Computer Interaction Lab, Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea.

Doeun Park (D)

Human Computer Interaction Lab, School of Business, Yonsei University, Seoul, Republic of Korea.

Myounglee Choo (M)

Human Computer Interaction Lab, Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea.
HAII Corp, Seoul, Republic of Korea.

Jinwoo Kim (J)

Human Computer Interaction Lab, School of Business, Yonsei University, Seoul, Republic of Korea.
HAII Corp, Seoul, Republic of Korea.

Doug Hyun Han (DH)

College of Medicine, Chung-Ang University, Seoul, Republic of Korea.

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