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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.
The World Health Organization ranks depression as the single largest contributor to global disability [
Recent advances in digital technologies, including smartphones and wearable devices, enable the collection of a variety of data streams that can be used to objectively characterize an individual’s daily activity and physical condition [
Several recent studies have demonstrated the association between MDD symptoms and mobility patterns derived from mobile devices. For example, individuals with greater severity of MDD symptoms were reported to make fewer transitions between locations of interest (ie, those frequently visited in the past) and spend more time at home [
Most studies used small and homogeneous cohorts of participants (eg, university students) and were conducted over a short period (eg, several weeks). In this study, we examined the association between the overall severity of MDD symptoms and a measure of daily mobility patterns using data from a larger and more diverse group of participants collected in the Remote Assessment of Disease and Relapse–Major Depressive Disorder (RADAR-MDD) study [
Participants were recruited for the RADAR-MDD study from November 2017 to November 2019. The recruited participants were aged ≥18 years and had experienced at least two episodes of MDD in their lifetime, with the most recent episode occurring within the last 2 years. The exclusion criteria included lifetime history of bipolar disorder; schizophrenia; MDD with psychotic features, schizoaffective disorders; history of moderate to severe drug or alcohol dependence within 6 months before enrollment; history of a major medical disease that could affect the participant’s ability to be involved in normal daily activities for >2 weeks; dementia; and pregnancy. No limitations were applied regarding any treatment that the participants were receiving over the course of the study. Written consent was obtained before the enrollment session, followed by collection of sociodemographic, social environment, and medical history and technology use questionnaires and the Lifetime Depression Assessment Self-Report [
Data set characteristics (N=164).
Characteristic | Clinical site | ||||
|
KCLa | CIBERb | VUMCc | All sites | |
Participants with both PHQ-8d and GPS data collected, n (%) | 232 (57.9) | 116 (28.9) | 53 (13.2) | 401 (100) | |
|
109 (66.4) | 38 (23.2) | 17 (10.4) | 164 (100) | |
|
Female, n (%) | 83 (76.1) | 26 (68.4) | 14 (82.4) | 123 (75) |
|
Age (years), median (range; SD) | 46 (18-73; 15.0) | 54 (27-71; 9.8) | 33 (19-69; 14.9) | 48 (18-73; 14.7) |
|
483 (62.8) | 222 (28.9) | 64 (8.3) | 769 (100) | |
|
For employed participants | 277 (72.5) | 64 (16.8) | 41 (10.7) | 382 (100) |
|
For unemployed participants | 204 (53) | 158 (41) | 23 (6) | 385 (100) |
aKCL: King’s College London.
bCIBER: Centro de Investigación Biomédica en Red.
cVUMC: Vrije Universiteit Medisch Centrum.
dPHQ-8: 8-item Patient Health Questionnaire.
The number of participants and biweekly segments collected at each site was normalized by the corresponding total obtained by pooling data across the 3 sites (column
We used the RADAR-based platform for data collection and storage [
The passive RMT apps ran in the background and required minimal input from the participants. The apps collected data on participants’ physical (eg, transitions in space) and socially relevant activity (eg, number and duration of phone calls) as well as on some ambient factors (eg, ambient noise and light). GPS location data were obfuscated by adding a fixed random number to the latitude and longitude of all GPS data points generated by a single participant (
Exemplar geolocation data which correspond to a biweekly segment of a study participant. The red dots denote individual’s home location, whereas longitude and latitude along the axes are expressed in decimal degrees.
A single completed PHQ-8 combined with the GPS data acquired over the 2 preceding weeks and obtained from the same participant is herein referred to as a
For each day in a biweekly segment, we computed the number of GPS data points collected for each of the 24 hours separately and over the entire day. Ideally, a GPS signal sampled uniformly over a period of 5 minutes would give 12 GPS data points per hour and a total of 288 GPS data points per day. We specified completeness of the daily data as a ratio between the actual number of GPS data points collected over a day and the expected number as determined by a sampling period (ie, 288 and 144 GPS data points for the sampling periods of 5 minutes and 10 minutes, respectively). The extreme completeness values of 0.0 and 1.0 correspond to an empty and a complete day of GPS recordings, respectively, with the values in-between corresponding to partial or interrupted GPS recordings throughout a day (
Home location was identified in a stepwise manner. Initially, the home location was approximated by the median longitude and latitude of all GPS data points in a biweekly segment acquired between 12 AM and 6 AM [
where
The home stay for a given day was specified as the ratio between the number of GPS data points separated by ≤60 meters from the home location and the total number of GPS data points acquired on that day. Home stay values of 0 (or 0%) and 1 (or 100%) correspond to an entire day spent outside versus at home, respectively. Median home stay, as computed across 14 days of a biweekly segment, was used to characterize the home stay of a study participant for that segment (
A linear regression model was selected to test the relationship between home stay and overall severity of MDD symptoms. Specifically, home stay was used as a dependent variable, with PHQ-8 total score being used as an independent variable. Participant age at enrollment, gender (men vs women), occupational status (employed vs unemployed), median completeness, and sampling constancy of the daily data in a biweekly segment were included in the model as additional explanatory variables:
We chose home stay as a dependent variable to test its relationship not only to the severity of MDD symptoms but also to participants’ demographics and quality characteristics of the collected geolocation data in a single model in a uniform manner. For each study participant, we randomly selected one of the biweekly segments generated by that participant. The model was fitted using data from biweekly segments pooled across the participants. To obtain a CI for each of the 6 regression coefficients, the procedure of random selection of a biweekly segment per participant followed by pooling data across the participants and fitting the model was repeated 100 times. A model variable was deemed to be significantly related to home stay if a 95% 2-sided CI obtained for the regression coefficient of that parameter did not include 0. The model was fitted using data from all 3 sites combined (
CIs and medians for the 6 regression coefficients of the linear regression model.
Analyzed time frame | Value, median (95% CI) | |||||
|
Age | Gender | PHQ-8a total score | Occupational status | Median completeness of the daily data | Median sampling constancy of the daily data |
Over the entire week |
|
–0.121 (–0.272 to 0.024) |
|
– |
–0.044 (–0.108 to 0.022) | –0.064 (–0.130 to 0.005) |
Weekdays only |
|
–0.061 (–0.220 to 0.058) |
|
– |
–0.024 (–0.097 to 0.034) | –0.041 (–0.110 to 0.041) |
Weekends only |
|
–0.036 (–0.265 to 0.174) | 0.052 (–0.079 to 0.149) | – |
0.023 (–0.074 to 0.101) | –0.075 (–0.144 to 0.006) |
aPHQ-8: 8-item Patient Health Questionnaire.
bCIs that do not include 0 are italicized. The regression coefficients obtained with standardized data for each clinical site separately are reported in
The model was fitted using standardized data pooled across the 3 sites for each analyzed time frame separately. The positive sign of the regression coefficients that correspond to the categorical variables (ie, gender and occupational status) indicates greater home stay for men and employed as compared with women and unemployed participants, respectively. All reported CIs are 95% 2-sided intervals.
To test whether the relationship between home stay and the independent variables differed between weekdays and weekends, a similar approach was followed. Specifically, home stay, median completeness, and sampling constancy of the daily data in a biweekly segment were estimated separately for weekdays and weekends. As a single biweekly segment included 10 weekdays and only 4 weekend days, we used 4 days to generate those estimates to equalize variance in the estimates of weekdays and weekends. For each analyzed biweekly segment, we randomly drew 4 weekdays 100 times. The medians of the estimates computed for each of these 100 draws were used to characterize the weekdays of that segment in the model.
To account for nonnormality of both dependent and independent variables as well as for differences in their variance, each variable (except for gender and occupational status) was standardized by applying the Yeo–Johnson transformation followed by the zero-mean, unit-variance normalization. All models and findings reported throughout the manuscript were obtained by using these
As of January 1, 2020, the total number of participants enrolled in the RADAR-MDD study across the 3 clinical sites was 432 (
Distributions of data set characteristics. (A) Number of biweekly segments available for analysis per study participant. (B) 8-item Patient Health Questionnaire total score. (C) Participant age. Data were pooled across the 3 clinical sites. PHQ-8: 8-item Patient Health Questionnaire.
The number of biweekly segments available for analysis varied considerably across the sites, with VUMC (64/769, 8.3% segments;
Over the course of the study, the participants spent most of their time at home. When no distinction between weekdays and weekends was made, median home stay across the sites was 89% (21.4 hours a day; 25th-75th percentiles 76%-96% or 18.2-23.0 hours a day;
Home stay computed (A) over the entire week, (B) for weekdays, and (C) weekends only. A grey horizontal bar and a cross in each boxplot indicate median and mean of the presented data. Data were pooled across the 3 clinical sites. Home stay corresponds to the percentage of time spent at home a day.
Similarly, home stay was affected by occupational status. The employed participants spent less time at home compared with their unemployed peers. Median home stay across the sites was 82% (19.7 hours a day; 25th-75th percentiles 67%-92% or 16.1-22.1 hours a day) and 94% (22.6 hours a day; 25th-75th percentiles 85%-98% or 20.4-23.5 hours a day) for the employed and unemployed participants, respectively, with the difference being more prominent during weekdays (79% vs 93% or 19.0 vs 22.3 hours a day) than during the weekends (88% vs 96% or 21.1 vs 23.0 hours a day). The same pattern of observations was seen across each clinical site (
When data were pooled across the sites and no distinction between weekdays and weekends was made, the linear regression model revealed a significant relationship between home stay and overall severity of the MDD symptoms as captured by the PHQ-8 total score (median 0.100, 2-sided 95% CI 0.015-0.184;
In addition, the model revealed a significant relationship between home stay and age. Specifically, the participants spent more time at home with age (median 0.241, 95% CI 0.161-0.325;
Neither gender nor median completeness and sampling constancy of the daily data in a biweekly segment had a significant impact on home stay and this held for all the analyzed time frames (
Relationship (A, B, and C) between home stay and participant age and (D, E, and F) between home stay and the 8-item Patient Health Questionnaire total score as assessed using data of (A and D) the entire week, (B and E) weekdays, and (C and F) weekends only. Each dot indicates a single biweekly segment. Data of all biweekly segments pooled across the 3 clinical sites are presented. A black line in each panel corresponds to the linear fit of the presented data. Home stay corresponds to the percentage of time spent at home a day. PHQ-8: 8-item Patient Health Questionnaire.
Multiple studies have demonstrated associations between patterns of daily movements of an individual in an area of the primary residence and an individual’s mood [
The participants in the RADAR-MDD study were recruited from a nonhomogeneous population (ie, clinical and community samples with a wide age range) across 3 clinical sites in different European countries. When we pooled the data from all sites and used the entire biweekly segment before PHQ-8 completion, we found that home stay was positively associated with the PHQ-8 total score and age (
A variety of features can be extracted from geolocation data generated by smartphones and wearable devices and used to characterize the mobility patterns of an individual. These include home stay [
Several previous studies have documented a positive relationship between home stay and the severity of MDD symptoms [
It has been pointed out [
Although this was a multicenter study and the estimates of home stay were similar across the 3 sites, most participants were recruited at KCL (
Apart from medical and mental conditions, social factors may have also influenced how much time participants spent at home. These include the number of people living under the same roof and engaging in outdoor or community activities. The participants who were expected to assist their elderly family members in daily routines or take care of their children likely spent more time at home than their peers without such responsibilities. In contrast, engagement in outdoor or community activities, such as playing bingo or going to church, likely resulted in reduced home stay. Furthermore, it is commonly assumed that employment implies the physical presence of an employee in a designated workplace outside of home. However, we cannot rule out that some employed participants worked from home. Home teleworking likely increased home stay for those participants. As employment was significantly associated with reduced home stay in our data set, most employed participants in the study still worked outside their home. The effect of medication and physical comorbidity, social factors, and home teleworking on the relationship between daily mobility patterns and severity of MDD symptoms was beyond the scope of this study, although further research is warranted.
The stringent selection criteria imposed on completeness and sampling constancy of the collected geolocation data considerably reduced the number of biweekly segments available for analysis. Several factors could have affected the quality of the collected geolocation data. Poor mobile network coverage or weak GPS signals, for example, was expected to result in a higher missing rate of geolocation recordings. This was likely the case for participants living or traveling in distant or rural areas. Smartphone battery capacity could have constrained the total duration of the geolocation recordings. Owing to a limited battery capacity, frequent user interaction with a smartphone could have accelerated the battery drain and further limited the total duration of geolocation recordings. In addition, a high number of apps running in the background could have also contributed to a more rapid battery drain. The RADAR-MDD study was designed to concurrently collect a variety of data streams (eg, from a GPS sensor, a gyroscope, an accelerometer, a microphone, and an ambient light sensor embedded in a smartphone) to characterize the individual’s behavior at full capacity [
We demonstrated that longer home stay can reflect greater symptom severity in individuals diagnosed with MDD. Although the relationship between home stay and MDD severity is modest, it can nonetheless improve remote monitoring of the individual’s mental well-being, especially when combined with other informative correlates of MDD severity. However, it remains unclear whether the findings represent behavioral manifestations of MDD or are associated with changes in depressive symptoms. Additional analyses are required to test whether changes in home stay over time can be predictive of relapses in MDD. We also demonstrated that the relationship between home stay and MDD severity can be modulated by age, occupational status, and changes in daily routine. This finding is of great importance for a proper interpretation of similar studies conducted in the past and for better planning of future studies. Furthermore, our findings illustrate that passive remote monitoring of mobility patterns in individuals with MDD is feasible. This demonstrates the utility of smartphones and wearable devices with a GPS sensor in the collection of clinically relevant information that can be used to monitor the course of the disorder in a remote, unobtrusive, and ubiquitous manner, thus reducing patient burden and improving treatment.
Tables and figures with information for each individual population site.
Centro de Investigación Biomédica en Red
King’s College London
major depressive disorder
8-item Patient Health Questionnaire
Remote Assessment of Disease and Relapse–Major Depressive Disorder
remote monitoring technology
Vrije Universiteit Medisch Centrum
The Remote Assessment of Disease and Relapse–Central Nervous System project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement number 115902. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and European Federation of Pharmaceutical Industries and Associations [
Participant recruitment in Amsterdam was partially accomplished through Hersenonderzoek.nl, a Dutch online registry that facilitates participant recruitment for neuroscience studies [
Participants in Spain were recruited through the following institutions: Parc Sanitari Sant Joan de Déu network of mental health services (Barcelona); Institut Català de la Salut primary care services (Barcelona); Institut Pere Mata-Mental Health Care (Tarrassa); Hospital Clínico San Carlos (Madrid).
This paper represents independent research part funded by the National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley National Health Service (NHS) Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care.
We thank all the members of the Remote Assessment of Disease and Relapse–Central Nervous System patient advisory board for their contribution to the device selection procedures, and their invaluable advice throughout the study protocol design.
This research was reviewed by a team with experience of mental health problems and their carers who have been specially trained to advise on research proposals and documentation through the Feasibility and Acceptability Support Team for Researchers: a free, confidential service in England provided by the National Institute for Health Research Maudsley Biomedical Research Centre via King’s College London and South London and Maudsley NHS Foundation Trust.
We thank all Genetic Links to Anxiety and Depression Study volunteers for their participation, and gratefully acknowledge the NIHR BioResource, NIHR BioResource centres, NHS Trusts and staff for their contribution. We also acknowledge NIHR BRC, King’s College London, South London and Maudsley NHS Trust and King's Health Partners. We thank the National Institute for Health Research, NHS Blood and Transplant, and Health Data Research UK as part of the Digital Innovation Hub Programme.
PL and DAK extracted and integrated the questionnaire and geolocation data for the analysis, planned and performed the analysis, and drafted the manuscript. MH and VAN gained funding and co-led the Remote Assessment of Disease and Relapse–Central Nervous System program. MH is the principal investigator for the Remote Assessment of Disease and Relapse–Major Depressive Disorder study. RJBD, AAF, YR, ZR, PC, and CS have contributed to the development of the Remote Assessment of Disease and Relapse–based platform used for data collection and management across sites, data protection, security, and storage. PL, DAK, AAF, S Sun, YZ, FM, FL, S Siddi, BWJHP, JMH, PA, NC, SV, NVM, VAN, RJBD, and MH contributed to the design of the study. FM, AI, GL, and S Siddi collected the data. All authors meet the International Committee of Medical Journal Editors criteria, and all those who fulfilled these criteria are listed as authors. All authors have been involved in reviewing the manuscript, had access to the study data, provided direction and comments on the manuscript, made the final decision about where to publish these data, and approved submission to this journal. All authors agree with the content and author list of this manuscript.
DAK and NVM are employees of the Janssen Pharmaceutica NV and may hold company equity. SV and VAN are employees of Janssen Research and Development, LLC, and may hold company equity. PA is an employee of H. Lundbeck A/S and may hold company equity. MH declares research grants and in-kind contributions from Janssen, Biogen, UCB, MSD, and H. Lundbeck A/S through the Remote Assessment of Disease and Relapse–Central Nervous System consortium.