Current advances in wearable devices and their sensors in patients with depression
Open accessAbstract
In this study, a literature survey was conducted of research into the development and use of wearable devices and sensors in patients with depression. We collected 18 studies that had investigated wearable devices for assessment, monitoring, or prediction of depression. In this report, we examine the sensors of the various types of wearable devices (e.g., actigraphy units, wristbands, fitness trackers, and smartwatches) and parameters measured through sensors in people with depression. In addition, we discuss future trends, referring to research in other areas employing wearable devices, and suggest the challenges of using wearable devices in the field of depression. Real-time objective monitoring of symptoms and novel approaches for diagnosis and treatment using wearable devices will lead to changes in management of patients with depression. During the process, it is necessary to overcome several issues, including limited types of collected data, reliability, user adherence, and privacy concerns.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Digital technologies offer promising tools for detecting MDD and depression-related symptoms objectively and precisely.
“Digital technologies offer promising tools for detecting MDD and depression-related symptoms objectively and precisely (18, 19).”
From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [19]
Multiple studies show that digital biomarkers including movement intensity, light exposure, and smartphone usage patterns can forecast the diagnostic status of individuals with mood disorders.
“Multiple studies have showcased the promise of digital biomarkers, encompassing factors like movement intensity, light exposure, and smartphone usage patterns, in forecasting the diagnostic status of individuals grappling with mood disorders (19, 22, 38).”
From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [19]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 55 · Cited by 117
- Authors
- Lee, S., Kim, H., Park, M. J., & Jeon, H. J.
- Affiliations
- Sungkyunkwan University (KR)
- Hanyang University Seoul Hospital (KR)
- Samsung Medical Center (KR)
- Corresponding author
- Jeon, Hong Jin
- Venue
- Frontiers in Psychiatry
- Publisher
- Frontiers Media SA
- Published
- 2021-06-17
- Volume
- 12
- Pages
- 672347-672347
- ISSN
- 1664-0640 (ISSN-L)
- PMC
- PMC8245757
- OpenAlex
- W3176919247
- Cited by
- 117
- References
- 55
- Citations by year
- 2026: 11 · 2025: 50 · 2024: 26 · 2023: 18 · 2022: 12
- FWCI
- 2.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Psychiatry (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Research Foundation of Korea
- National IT Industry Promotion Agency
- Institute for Information and Communications Technology Promotion
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- wearable devices, depression, wearable sensors, actigraphy, smartwatches, wearable activity trackers, wristband, depression monitoring, depression diagnosis, depression prediction, user adherence, data privacy
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
