Claudia Vesel1, Homa Rashidisabet1, John Zulueta2, Jonathan P Stange2, Jennifer Duffecy2, Faraz Hussain2, Andrea Piscitello3, John Bark2, Scott A Langenecker4, Shannon Young5, Erin Mounts5, Larsson Omberg5, Peter C Nelson3, Raeanne C Moore6, Dave Koziol7, Keith Bourne7, Casey C Bennett8,9, Olusola Ajilore2, Alexander P Demos10, Alex Leow1,2,3. 1. Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA. 2. Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois, USA. 3. Department of Computer Science, University of Illinois at Chicago, Chicago, Illinois, USA. 4. Department of Psychiatry, University of Utah, Salt Lake City, Utah, USA. 5. Sage Bionetworks, Seattle, Washington, USA. 6. Department of Psychiatry, University of California, San Diego, San Diego, California, USA. 7. Arbormoon Software, Inc, Ann Arbor, Michigan, USA. 8. College of Computing and Digital Media, DePaul University, Chicago, Illinois, USA. 9. School of Intelligence, Hanyang University, Seoul, Korea. 10. Department of Psychology, University of Illinois at Chicago, Chicago, Illinois, USA.
Abstract
OBJECTIVE: Ubiquitous technologies can be leveraged to construct ecologically relevant metrics that complement traditional psychological assessments. This study aims to determine the feasibility of smartphone-derived real-world keyboard metadata to serve as digital biomarkers of mood. MATERIALS AND METHODS: BiAffect, a real-world observation study based on a freely available iPhone app, allowed the unobtrusive collection of typing metadata through a custom virtual keyboard that replaces the default keyboard. User demographics and self-reports for depression severity (Patient Health Questionnaire-8) were also collected. Using >14 million keypresses from 250 users who reported demographic information and a subset of 147 users who additionally completed at least 1 Patient Health Questionnaire, we employed hierarchical growth curve mixed-effects models to capture the effects of mood, demographics, and time of day on keyboard metadata. RESULTS: We analyzed 86 541 typing sessions associated with a total of 543 Patient Health Questionnaires. Results showed that more severe depression relates to more variable typing speed (P < .001), shorter session duration (P < .001), and lower accuracy (P < .05). Additionally, typing speed and variability exhibit a diurnal pattern, being fastest and least variable at midday. Older users exhibit slower and more variable typing, as well as more pronounced slowing in the evening. The effects of aging and time of day did not impact the relationship of mood to typing variables and were recapitulated in the 250-user group. CONCLUSIONS: Keystroke dynamics, unobtrusively collected in the real world, are significantly associated with mood despite diurnal patterns and effects of age, and thus could serve as a foundation for constructing digital biomarkers.
OBJECTIVE: Ubiquitous technologies can be leveraged to construct ecologically relevant metrics that complement traditional psychological assessments. This study aims to determine the feasibility of smartphone-derived real-world keyboard metadata to serve as digital biomarkers of mood. MATERIALS AND METHODS: BiAffect, a real-world observation study based on a freely available iPhone app, allowed the unobtrusive collection of typing metadata through a custom virtual keyboard that replaces the default keyboard. User demographics and self-reports for depression severity (Patient Health Questionnaire-8) were also collected. Using >14 million keypresses from 250 users who reported demographic information and a subset of 147 users who additionally completed at least 1 Patient Health Questionnaire, we employed hierarchical growth curve mixed-effects models to capture the effects of mood, demographics, and time of day on keyboard metadata. RESULTS: We analyzed 86 541 typing sessions associated with a total of 543 Patient Health Questionnaires. Results showed that more severe depression relates to more variable typing speed (P < .001), shorter session duration (P < .001), and lower accuracy (P < .05). Additionally, typing speed and variability exhibit a diurnal pattern, being fastest and least variable at midday. Older users exhibit slower and more variable typing, as well as more pronounced slowing in the evening. The effects of aging and time of day did not impact the relationship of mood to typing variables and were recapitulated in the 250-user group. CONCLUSIONS: Keystroke dynamics, unobtrusively collected in the real world, are significantly associated with mood despite diurnal patterns and effects of age, and thus could serve as a foundation for constructing digital biomarkers.
Authors: Jonathan P Stange; John Zulueta; Scott A Langenecker; Kelly A Ryan; Andrea Piscitello; Jenna Duffecy; Melvin G McInnis; Pete Nelson; Olusola Ajilore; Alex Leow Journal: Bipolar Disord Date: 2018-03-08 Impact factor: 6.744
Authors: G Stringer; S Couth; L J E Brown; D Montaldi; A Gledson; J Mellor; A Sutcliffe; P Sawyer; J Keane; C Bull; X Zeng; P Rayson; I Leroi Journal: Int J Geriatr Psychiatry Date: 2018-02-09 Impact factor: 3.485
Authors: John Zulueta; Andrea Piscitello; Mladen Rasic; Rebecca Easter; Pallavi Babu; Scott A Langenecker; Melvin McInnis; Olusola Ajilore; Peter C Nelson; Kelly Ryan; Alex Leow Journal: J Med Internet Res Date: 2018-07-20 Impact factor: 5.428
Authors: Erin Smith; Eric A Storch; Ipsit Vahia; Stephen T C Wong; Helen Lavretsky; Jeffrey L Cummings; Harris A Eyre Journal: Front Psychiatry Date: 2021-12-23 Impact factor: 4.157
Authors: John Zulueta; Alexander Pantelis Demos; Claudia Vesel; Mindy Ross; Andrea Piscitello; Faraz Hussain; Scott A Langenecker; Melvin McInnis; Peter Nelson; Kelly Ryan; Alex Leow; Olusola Ajilore Journal: Front Psychiatry Date: 2021-12-22 Impact factor: 4.157
Authors: Mindy K Ross; Alexander P Demos; John Zulueta; Andrea Piscitello; Scott A Langenecker; Melvin McInnis; Olusola Ajilore; Peter C Nelson; Kelly A Ryan; Alex Leow Journal: Brain Behav Date: 2021-10-06 Impact factor: 2.708