BiAffect3

BackgroundCognitive dysfunction in bipolar disorder persists in the euthymic state and has been shown to be associated with a number of negative sequelae including treatment resistance and increased risk of relapse. There has been recent attention on digital phenotyping and passive sensing through smart, connected devices to probe cognition in real-world settings. BiAffect is a custom-built smartphone keyboard that captures keystroke metadata ('how you type, not what you type'). In previous studies, our group has demonstrated that BiAffect-derived keystroke metadata is associated with cognitive domains like processing speed. For the present study, we hypothesized that typing metadata would be significantly associated with executive function and planning.Methods18 participants with bipolar disorder and 12 healthy comparison participants from the Prechter Longitudinal Study of Bipolar Disorder at the University of Michigan were provided a mobile phone with a customized keyboard that passively collected keystroke metadata. Participants also completed a neuropsychological battery including the Tower of London task. Irregularities in typing and times to make a move on the Tower of London task were compared using sample and Shannon entropy, respectively.ResultsParticipants with bipolar disorder had significant increases in entropy in typing (p = .005, d = -1.28) and entropy of Tower of London move times (p = .029, d = -.84). Furthermore, typing entropy was significantly associated with irregularity in Tower of London moves in participants (r = .59, p = .006), as well as variability of clinician-rated depressive symptoms and self-rated impulsive actions and feelings.ConclusionsThis pilot study demonstrates that passive, unobtrusive smartphone keystroke metadata can be used to probe cognitive function and dysfunction in bipolar disorder, revealing multi-scalar behavioral features accessible through digital assays

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.