Associations Between Smartphone Keystroke Metadata and Mental Health Symptoms in Adolescents: Findings From the Future Proofing Study

Taylor A Braund; Bridianne O'Dea; Debopriyo Bal; Kate Maston; Mark Larsen; Aliza Werner-Seidler; Gabriel Tillman; Helen Christensen

doi:10.2196/44986

Associations Between Smartphone Keystroke Metadata and Mental Health Symptoms in Adolescents: Findings From the Future Proofing Study

JMIR Ment Health. 2023 May 15:10:e44986. doi: 10.2196/44986.

Authors

Taylor A Braund^{1

2}, Bridianne O'Dea^{1

2}, Debopriyo Bal², Kate Maston², Mark Larsen^{1

2}, Aliza Werner-Seidler^{1

2}, Gabriel Tillman³, Helen Christensen^{1

2}

Affiliations

¹ Faculty of Medicine and Health, University of New South Wales, Kensington, Australia.
² Black Dog Institute, University of New South Wales, Randwick, Australia.
³ Institute of Health and Wellbeing, Federation University, Ballarat, Australia.

PMID: 37184904
PMCID: PMC10227695
DOI: 10.2196/44986

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.

Trial registration: Australian and New Zealand Clinical Trial Registry, ACTRN12619000855123; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377664&isReview=true.

Keywords: adolescents; anxiety; depression; digital phenotype; keystroke dynamics; keystroke metadata; smartphone; students.

©Taylor A Braund, Bridianne O’Dea, Debopriyo Bal, Kate Maston, Mark Larsen, Aliza Werner-Seidler, Gabriel Tillman, Helen Christensen. Originally published in JMIR Mental Health (https://mental.jmir.org), 15.05.2023.