The promise of digital mood tracking technologies: are we heading on the right track?
Open accessAbstract
The growing understanding that mood disorders are dynamic in nature and fluctuate over variable epochs of time has compelled researchers to develop innovative methods of monitoring mood. Technological advancement now allows for the detection of minute-to-minute changes while also capturing a longitudinal perspective of an individual’s illness. Traditionally, assessments of mood have been conducted by means of clinical interviews and paper surveys. However, these methods are often inaccurate due to recall bias and compliance issues, and are limited in their capacity to collect and process data over long periods of time. The increased capability, availability and affordability of digital technologies in recent decades has offered a novel, non-invasive alternative to monitoring mood and emotion in daily life. This paper reviews the emerging literature addressing the use of digital mood tracking technologies, primarily focusing on the strengths and inherent limitations of using these new methods including electronic self-report, behavioural data collection and wearable physiological biosensors. This developing field holds great promise in generating novel insights into the mechanistic processes of mood disorders and improving personalised clinical care. However, further research is needed to validate many of these novel approaches to ensure that these devices are indeed achieving their purpose of capturing changes in mood.
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Heart rate variability has been found to be consistently lower in patients with psychiatric disorders.
“Other promising biomarkers from wearable devices are heart rate variability, which has been found to be consistently lower in patients with psychiatric disorders, electrodermal activity, and skin conductance [36,37].”
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Mayer, G., Hummel, S., Oetjen, N., Gronewold, N., Bubolz, S., Slawik, M., Blankenhagel, K. J., Zarnekow, R., Hilbel, T., & Schultz, J. (2021) · doi:10.2196/preprints.32151
Luca, V. D., Marston, H. R., Angelini, L., Militeva, N., Klimczuk, A., Fabian, C., Papitto, P., Bernardo, J., Ventura, F. I. Q. S., da Silva, R. C. G., et al. (2024) · IntechOpen eBooks · doi:10.5772/intechopen.1004109
Luca, V. D., Marston, H. R., Angelini, L., Militeva, N., Klimczuk, A., Fabian, C., Papitto, P., Bernardo, J., Ventura, F. I. Q. S., da Silva, R. C. G., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4867317
Stroke Imprint: Knitting Reassurance into Anxious Moments
Liu, Y., Kim, R., & Kim, K. (2025) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/3721250.3743045
Silman, D., Middag, M. E., Cleare, A. J., Young, A. H., & Strawbridge, R. (2025) · BJPsych Open · doi:10.1192/bjo.2025.10932
AI-Driven Support for Mental Wellness
Sharma, K., Mittal, H., Tripathi, S., & Gaur, K. (2025) · International Conference for Advancement in Technology (ICONAT) · doi:10.1109/iconat66879.2025.11362624
Wright, L. A., Meyerhoff, J., Zhang, Y., Evans, J., Guo, B., & Morriss, R. K. (2026) · Wellcome Open Research · doi:10.12688/wellcomeopenres.26220.1
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- Authors
- Malhi, G. S., Hamilton, A., Morris, G., Mannie, Z., Das, P., & Outhred, T.
- Affiliations
- The University of Sydney (AU)
- Corresponding author
- Malhi, Gin S
- Venue
- Evidence Based Mental Health
- Publisher
- BMJ
- Published
- 2017-08-30
- Volume
- 20
- Issue
- 4
- Pages
- 102-107
- ISSN
- 1362-0347 (ISSN-L)
- 1468-960X
- PMC
- PMC10516397
- OpenAlex
- W2752663532
- Cited by
- 62
- References
- 41
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 8 · 2023: 4 · 2022: 7 · 2021: 9 · 2020: 10 · 2019: 8 · 2018: 2 · 2017: 1
- FWCI
- 0.67
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Evidence-Based Mental Health (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- mood disorders, mood fluctuations, wearable biosensors, recall bias
- MeSH terms
- Bipolar Disorder, Humans, Monitoring, Physiologic, Telemedicine, Mood Disorders, Mobile Applications
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 ›
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