ArticleJournal of the American Medical Informatics Association : JAMIA2020
Effects of mood and aging on keystroke dynamics metadata and their diurnal patterns in a large open-science sample: A BiAffect iOS study.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
46 citing papers in PubMed, 3 syntheses or guidelines pooled it, 82 citations in OpenAlex.
- Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches.Sensors (Basel, Switzerland) · 2024Pooled it
- Depressive symptom screening in elderly by passive sensing data of smartphones or smartwatches: A systematic review.PloS one · 2024Pooled it
- Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis.Scientific reports · 2022Pooled it
- The Use of Smartphone Keystroke Dynamics to Passively Monitor Upper Limb and Cognitive Function in Multiple Sclerosis: Longitudinal Analysis.Journal of medical Internet research · 2022Trial
- Did you miss me? Making the most of digital phenotyping data by imputing missingness with point process models: observational study.BMJ health & care informatics · 2026Observational
- Smartphone Keystroke Biomarkers as Predictors of Adverse Neuropsychiatric Sequelae After Trauma in Trauma Survivors: Prospective Observational Cohort Study.Journal of medical Internet research · 2026Observational
- A multilevel meta-analysis of passive smartphone sensing of adolescent mental health.NPJ digital medicine · 2026Article
- Quantifying sleep wake rhythms in the hospital environment with digital technologies.NPJ digital medicine · 2026Article
- Keying Into Cognition: Temporal Smoothing of Smartphone Typing Behaviors for Passive Assessment of Processing Speed and Executive Function in Individuals With Mood Disorders.Cognitive computation · 2026Article
- Unobtrusive inference of diurnal rhythms from smartphone data.NPJ digital medicine · 2025Review
- Exploring Age-Related Patterns in Smartphone Keystroke Dynamics Considering Temporal Variability: Cross-Sectional Study With AI-Based Analysis.JMIR mHealth and uHealth · 2025Article
- Quantifying Sleep-Wake Rhythms in the Hospital Environment with Digital Technologies.medRxiv : the preprint server for health sciences · 2025Article
- Digital Measurement of Subjective Experiences in Alzheimer Disease and Related Dementias (AD/ADRD).JMIR aging · 2025Article
- Predicting cognitive functioning in mood disorders through smartphone typing dynamics.Journal of psychopathology and clinical science · 2025Article
- Uncovering Social States in Healthy and Clinical Populations Using Digital Phenotyping and Hidden Markov Models: Observational Study.Journal of medical Internet research · 2025Observational
- Unobtrusive inference of circadian rhythms from smartphone data.medRxiv : the preprint server for health sciences · 2025Article
- Revolutionizing Medicine: Chatbots as Catalysts for Improved Diagnosis, Treatment, and Patient Support.Cureus · 2025Review
- Tapping into truth: an exploratory cross-sectional analysis of psychomotor symptoms and typing behaviour in an adolescent observational cohort.NPP - digital psychiatry and neuroscience · 2025Article
- Feasibility, reliability and validity of smartphone administered cognitive ecological momentary assessments in breast cancer survivors.Frontiers in digital health · 2025Article
- The implementation of digital biomarkers in the diagnosis, treatment and monitoring of mood disorders: a narrative review.Frontiers in digital health · 2025Review
Corrections and comments
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Authors and funding
20 authors at 5 institutions in 2 countries.
Funding
Abstract
objectiveUbiquitous 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
methodsBiAffect, 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.
resultsWe 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.
conclusionsKeystroke 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.