Remote Assessment of Depression Using Digital Biomarkers From Cognitive Tasks
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We describe the design and evaluation of a sub-clinical digital assessment tool that integrates digital biomarkers of depression. Based on three standard cognitive tasks (D2 Test of Attention, Delayed Matching to Sample Task, Spatial Working Memory Task) on which people with depression have been known to perform differently than a control group, we iteratively designed a digital assessment tool that could be deployed outside of laboratory contexts, in uncontrolled home environments on computer systems with widely varying system characteristics (e.g., displays resolution, input devices). We conducted two online studies, in which participants used the assessment tool in their own homes, and completed subjective questionnaires including the Patient Health Questionnaire (PHQ-9)—a standard self-report tool for assessing depression in clinical contexts. In a first study (n = 269), we demonstrate that each task can be used in isolation to significantly predict PHQ-9 scores. In a second study (n = 90), we replicate these results and further demonstrate that when used in combination, behavioral metrics from the three tasks significantly predicted PHQ-9 scores, even when taking into account demographic factors known to influence depression such as age and gender. A multiple regression model explained 34.4% of variance in PHQ-9 scores with behavioral metrics from each task providing unique and significant contributions to the prediction.
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Nonadherence to ADTs is associated with increased severity of depressive symptoms, risk of relapse and recurrence, and healthcare resource utilization, as well as significantly higher medical costs, inpatient admissions, and visits to the emergency department.
“Nonadherence to ADTs is associated with increased severity of depressive symptoms, risk of relapse (ie, early return of symptoms within the expected duration of a current episode) and recurrence (ie, a new major depressive episode following a full-episode remission), and healthcare resource utilization, as well as significantly higher”
From Digital health technologies and major depressive disorder (2023) · cites this paper as [44]
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- Authors
- Mandryk, R. L., Birk, M. V., Vedress, S., Wiley, K., Reid, E., Berger, P., & Frommel, J.
- Affiliations
- University of Saskatchewan (CA)
- Eindhoven University of Technology (NL)
- Corresponding author
- Mandryk, Regan L.
- ORCID
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2021-12-15
- Volume
- 12
- Pages
- 767507-767507
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC8714741
- OpenAlex
- W4200557082
- Cited by
- 23
- References
- 82
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 3 · 2023: 5 · 2022: 3
- FWCI
- 2.43
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Psychology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- TU/e Research Portal (publishedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- TU/e Research Portal (submittedVersion, other-oa)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (VI.Veni.202.171)
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Keywords
- depression, digital biomarkers, remote assessment, cognitive tasks, D2 attention test, delayed matching-to-sample, spatial working memory, PHQ-9, home-based assessment, behavioral metrics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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