Summary: People with major depressive disorder who spent more time at home during a two-week period reported more severe symptoms of depression.
Source: King’s College London
Researchers from the RADAR-CNS project have used data from smartphones and wearables to investigate the association between time spent at home and the severity of symptoms in people with major depressive disorder (MDD).
The study, published in JMIR mHealth and uHealth, revealed that individuals diagnosed with MDD who spent more time at home also reported more severe symptoms.
The researchers collected data from 164 participants with MDD who were recruited for the RADAR-CNS project—a collaboration co-led by the King’s College London Institute of Psychiatry, Psychology & Neuroscience (IoPPN). Using geolocation data obtained from smartphones and wearables, the researchers were able to determine the amount of time each participant spent at home.
The findings showed that those who spent more time at home within the 2-week study period reported more severe symptoms of depression. Further analyses revealed that those who were older and more severely affected by depression had spent more time at home during the two weeks prior to symptom reports.
The association between time spent at home and symptom severity was stronger on weekdays than on weekends.
The study indicates that geolocation data obtained from smartphones and wearables could provide clinically relevant information to improve remote monitoring of an individual’s mental well-being.
“Our findings showed that age and whether a person is employed influence the strength of the association between the severity of MDD symptoms and home stay. This insight is of great importance for the interpretation of similar studies that were conducted in the past or will be planned in the future to ensure we consider the different factors at play in the relationship between time spent at home and depression.
“Future work will investigate whether changes in home stay can be used to predict relapses in MDD, which could ultimately provide an important approach to monitor severity of symptoms in a continuous and informative way,” noted Dr. Petroula Laiou, the study’s joint first author from the IoPPN.
RADAR-CNS is jointly led by Professor Matthew Hotopf, Vice Dean of Research at the IoPPN, and Vaibhav Narayan, Vice President of Research Technology at Janssen Pharmaceutica NV. The project brings together clinicians, researchers, engineers, computer scientists, and bioinformaticians from all over the world to investigate how wearable technologies and smartphones can help track and prevent symptoms of depression, epilepsy, and multiple sclerosis.
More than 264 million people suffer from depression around the world and the condition is commonly associated with other physical or psychological conditions, loss of employment, low quality of life, and suicide. Symptoms of MDD can fluctuate and the current evaluation of their severity relies heavily upon the information a patient provides during meetings with clinicians.
Advances in digital technologies are allowing researchers and clinicians to obtain continuous data about an individual’s daily activity and physical condition, which may be able to predict an individual’s well-being and enable timely and effective treatments.
Most smartphones and wearables are currently equipped with location sensing (using GPS and mobile network information), which enables continuous location tracking of their users. Several studies have reported that various mobility metrics, as well as home stay, that is, the amount of time an individual spends at home in a day, are associated with symptom severity in people with major depressive disorder (MDD). Owing to the use of small and homogeneous cohorts of participants, it is uncertain whether the findings reported in those studies generalize to a broader population of individuals with MDD symptoms.
The objective of this study is to examine the relationship between the overall severity of depressive symptoms, as assessed by the 8-item Patient Health Questionnaire, and median daily home stay over the 2 weeks preceding the completion of a questionnaire in individuals with MDD.
We used questionnaire and geolocation data of 164 participants with MDD collected in the observational Remote Assessment of Disease and Relapse–Major Depressive Disorder study. The participants were recruited from three study sites: King’s College London in the United Kingdom (109/164, 66.5%); Vrije Universiteit Medisch Centrum in Amsterdam, the Netherlands (17/164, 10.4%); and Centro de Investigación Biomédica en Red in Barcelona, Spain (38/164, 23.2%). We used a linear regression model and a resampling technique (n=100 draws) to investigate the relationship between home stay and the overall severity of MDD symptoms. Participant age at enrollment, gender, occupational status, and geolocation data quality metrics were included in the model as additional explanatory variables. The 95% 2-sided CIs were used to evaluate the significance of model variables.
Participant age and severity of MDD symptoms were found to be significantly related to home stay, with older (95% CI 0.161-0.325) and more severely affected individuals (95% CI 0.015-0.184) spending more time at home. The association between home stay and symptoms severity appeared to be stronger on weekdays (95% CI 0.023-0.178, median 0.098; home stay: 25th-75th percentiles 17.8-22.8, median 20.9 hours a day) than on weekends (95% CI −0.079 to 0.149, median 0.052; home stay: 25th-75th percentiles 19.7-23.5, median 22.3 hours a day). Furthermore, we found a significant modulation of home stay by occupational status, with employment reducing home stay (employed participants: 25th-75th percentiles 16.1-22.1, median 19.7 hours a day; unemployed participants: 25th-75th percentiles 20.4-23.5, median 22.6 hours a day).
Our findings suggest that home stay is associated with symptom severity in MDD and demonstrate the importance of accounting for confounding factors in future studies. In addition, they illustrate that passive sensing of individuals with depression is feasible and could provide clinically relevant information to monitor the course of illness in patients with MDD.