Study on Wearable Activity Trackers and Depression Detection

Introduction to Depression and Its Impact

A recent study explored the potential of wearable activity trackers in detecting biomarkers linked to depression, aiming to assess their effectiveness as a screening tool for this prevalent mental health condition. Depression affects approximately five percent of adults worldwide, presenting various psychological, physical, and social challenges. If untreated, it can lead to additional complications. Alarmingly, around half of those suffering from depression remain undiagnosed, which hinders their access to necessary treatment.

Importance of Diagnosis and Treatment

Receiving an accurate diagnosis is crucial for individuals battling depression, as it initiates the treatment process. Research indicates a correlation between early diagnosis, timely treatment, and improved outcomes. However, diagnosing depression is complex due to numerous associated risk factors. To address this challenge, researchers are increasingly investigating biological risk factors, known as biomarkers, which may facilitate effective screening for depression.

Current Research on Biomarkers

Recent findings suggest that lower physical activity levels and sleep disruptions are linked to an increased risk of depression. A study conducted in Singapore aimed to further examine these biomarkers and assess the efficacy of wearable activity trackers in detecting them.

Study Details and Methodology

The study was conducted from August to October 2019 and involved 290 healthy adults over the age of 20 who were employed full-time at Nanyang Technological University. Participants wore a Fitbit Charge 2 activity tracker for at least 14 days to monitor various biomarkers related to physical activity, sleep, and circadian rhythms. To assess depression levels, participants completed the 9-item Patient Health Questionnaire (PHQ-9), a widely used self-reported survey for screening depressive disorders.

Analysis and Findings

PHQ-9 scores range from zero to 27, with higher scores indicating greater severity of depressive symptoms. Participants completed the questionnaire before and after the study, and their average scores were analyzed. On average, participants wore the device for 17 days and 20 hours, achieving an 85% compliance rate, with complete tracking data collected from 267 participants.

After adjusting for confounding variables, three specific biomarkers were found to correlate with PHQ-9 scores:
– Lower steps taken on weekdays
– Decreased circadian rhythm stability
– Increased heart rate variability between 4:00 am and 6:00 am

These biomarkers were associated with higher PHQ-9 scores, suggesting that the data collected by wearable technology could be valuable in future depression screening research.

Conclusion and Future Research Directions

The study’s results indicate that these biomarkers may serve as promising areas for further investigation in the context of depression screening. Additional research is necessary to validate these findings and determine their applicability across larger populations and diverse demographics.

Related Topics of Interest

– Can wearable activity monitors help monitor cancer development?
– Do wearable defibrillators save lives?
– New wearable device tracks UV exposure.
– Wearable sensors for early detection of anxiety in children are in development.
– Smartphones versus wearables in remote patient monitoring.
– Wearable devices and their role in detecting COVID-19.
– Key benefits of wearable technology in home-based healthcare.

References

1. World Health Organization (2021 September 13). Depression. Accessed 2022 January 27, from https://www.who.int/news-room/fact-sheets/detail/depression
2. Rykov, Y., Thach, T., Bojic, I., et al (2021). Digital biomarkers for depression screening with wearable devices: Cross-sectional study with machine learning modeling. JMIR Mhealth Uhealth 2021;9(10):e24872. Doi: 10.2196/24872
3. Farid, D., Li, P., Da Costa, D., et al (2020, August 14). Undiagnosed depression, persistent depressive symptoms, and seeking mental health care: analysis of immigrant and non-immigrant participants of the Canadian Longitudinal Study of Aging. Epidemiology and Psychiatric Sciences 29:E158. Doi: 10.1017/S2045796020000670
4. Williams, S.Z., Chung, G.S., Muennig, P.A. (2017, July 28). Undiagnosed depression: A community diagnosis. SSM – Population Health 3: 633-638. Doi: 10.1016/j.ssmph.2017.07.012
5. Vallance, J.K., Winkler, E.A.H., Gardiner, P.A., et al (2011, October). Associations of objectively-assessed physical activity and sedentary time with depression: NHANES (2005-2006). Prev Med 53(4-55): 284-288. Doi: 10.1016/j.ypmed.2011.07.013
6. Baglioni, C., Nanovska, S., Regen, W. (2016, July 14). Sleep and mental disorders: A meta-analysis of polysomnographic research. Psychological Bulletin 142(9): 969-990. Doi: 10.1037/bul0000053
7. Negeri, Z.F., Levis, B., Sun, Y., et al (2021, October 5). Accuracy of the Patient Health Questionnaire-9 for screening to detect major depression: updated systematic review and individual participant data meta-analysis. BMJ 2021(375):n2183. Doi: 10.1136/bmj.n2183
8. Sun, Y., Fu, Z., Bo, Q., et al (2020, September 29). The reliability and validity of PHQ-9 in patients with major depressive disorder in a psychiatric hospital. BMC Psychiatry 20:474. Doi: 10.1186/s12888-020-02885-6