- Title
- Associations between smartphone keystroke metadata and mental health symptoms in adolescents: findings from the future proofing study
- Creator
- Braund, Taylor; O'Dea, Bridianne; Bal, Debopriyo; Maston, Kate; Larsen, Mark; Werner-Seidler, Aliza; Tillman, Gabriel; Christensen, Helen
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/193174
- Identifier
- vital:18127
- Identifier
-
https://doi.org/10.2196/44986
- Identifier
- ISSN:2368-7959 (ISSN)
- Abstract
- Background: Mental disorders are prevalent during adolescence. Among the digital phenotypes currently being developed to monitor mental health symptoms, typing behavior is one promising candidate. However, few studies have directly assessed associations between typing behavior and mental health symptom severity, and whether these relationships differs between genders. Objective: In a cross-sectional analysis of a large cohort, we tested whether various features of typing behavior derived from keystroke metadata were associated with mental health symptoms and whether these relationships differed between genders. Methods: A total of 934 adolescents from the Future Proofing study undertook 2 typing tasks on their smartphones through the Future Proofing app. Common keystroke timing and frequency features were extracted across tasks. Mental health symptoms were assessed using the Patient Health Questionnaire-Adolescent version, the Children's Anxiety Scale-Short Form, the Distress Questionnaire 5, and the Insomnia Severity Index. Bivariate correlations were used to test whether keystroke features were associated with mental health symptoms. The false discovery rates of P values were adjusted to q values. Machine learning models were trained and tested using independent samples (ie, 80% train 20% test) to identify whether keystroke features could be combined to predict mental health symptoms. Results: Keystroke timing features showed a weak negative association with mental health symptoms across participants. When split by gender, females showed weak negative relationships between keystroke timing features and mental health symptoms, and weak positive relationships between keystroke frequency features and mental health symptoms. The opposite relationships were found for males (except for dwell). Machine learning models using keystroke features alone did not predict mental health symptoms. Conclusions: Increased mental health symptoms are weakly associated with faster typing, with important gender differences. Keystroke metadata should be collected longitudinally and combined with other digital phenotypes to enhance their clinical relevance. ©Taylor A Braund, Bridianne O'Dea, Debopriyo Bal, Kate Maston, Mark Larsen, Aliza Werner-Seidler, Gabriel Tillman, Helen Christensen.
- Publisher
- JMIR Publications Inc.
- Relation
- JMIR Mental Health Vol. 10, no. (2023), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © Taylor A Braund, Bridianne O'Dea, Debopriyo Bal, Kate Maston, Mark Larsen, Aliza Werner-Seidler, Gabriel Tillman, Helen Christensen
- Rights
- Open Access
- Subject
- 4203 Health services and systems; 5203 Clinical and health psychology; Adolescents; Anxiety; Depression; Digital phenotype; Keystroke dynamics; Keystroke metadata; Smartphone; Students
- Full Text
- Reviewed
- Funder
- This project was funded by a NHMRC project grant awarded to Helen Christensen (GNT1138405), a Ramsay Health Philanthropic Grant awarded to Helen Christensen and Aliza Werner-Seidler, a donation made by General Pants Co, a NHMRC Emerging Leader Fellowship awarded to Aliza Werner-Seidler (GNT1197074), and a SPRF NHMRC Fellowship to Helen Christensen (GNT 1155614).
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