Associations between depression symptom severity and individuals’ behaviors measured by smartphones and wearable devices

Student thesis: Doctoral ThesisDoctor of Philosophy


Depression is a prevalent and severe mental health disorder that is one of the leading causes of disability worldwide. It can cause various physical and psychological problems, leading to loss of productivity, increased social burden, and even suicide. The current diagnosis of depression relies on skilled clinicians and self-reported questionnaires, which have limitations including subjective recall bias and loss of day-to-day fluctuation information. As a result, the majority of individuals with depression did not receive timely and effective treatment. Therefore, there is a need for more effective auxiliary techniques for recognizing and monitoring depression.

With the development and widespread use of sensors, mobile technology provides a cost-effective and convenient means for gathering individuals’ behavioral data related to depression symptoms. Several past studies have attempted to monitor depression using mobile phones and wearable devices. However, the majority of these studies were conducted on relatively small and homogeneous cohorts with short follow-up periods, which may have limited the generalizability of their findings. Furthermore, the impact of participant attrition and engagement, the direction of relationships over time, and individual differences need further exploration.

To address these limitations, this thesis extracts a variety of behavioral features from multiple data streams of mobile phone and wearable data and explores their associations with depression symptom severity using a large, longitudinal, multi-center data set. Specifically, Chapter 1 provides an overview of the background of depression, motivations for using mobile technology for depression monitoring, and existing related studies.

Chapter 2 performs a novel investigation into long-term participant retention and engagement from a European longitudinal observational program, the RADAR-MDD study, which is used throughout the whole thesis. A significantly higher participant retention rate is found in the RADAR-MDD study than in previous remote digital health studies. According to the data-driven method, lower participant engagement is found to be associated with higher depression symptom severity, younger age, and longer questionnaire response/completion time in the study app. Finally, the strategies for increasing participant engagement in future digital health research are also discussed in this chapter.

Next, the associations between depression symptom severity and various categories of behaviors are explored separately in the following chapters: sleep (Chapter 3), sociability as measured by Bluetooth device counts (Chapter 4), mobility (Chapter 5), daily walking (Chapter 6), and circadian rhythms (Chapter 7). These associations are examined using multilevel models that incorporate demographics as between-participant covariates. A number of significant associations between behavioral characteristics and depression symptom severity are found in these chapters. For example, higher depression severity is significantly associated with worse sleep, lower sociability, lower mobility, slower cadence of daily walking, and weaker circadian rhythmicity. Notably, the longitudinal association between mobility and depression over time is assessed using dynamic structural equation models in Chapter 5. Changes in several mobility features are found to significantly affect subsequent changes in depression severity. Furthermore, daily-life gait patterns are found to provide extra information for recognizing depression relative to laboratory gait patterns in Chapter 6.

Taken together, the findings in this thesis demonstrate that depression is closely associated with individuals’ daily-life behaviors, which can be captured by mobile technology in real-world settings. Despite challenges of data quality and participant attrition, the evidence may provide support for the development of future clinical tools to passively monitor mental health status and trajectory with minimal burden on the participant.
Date of Award2023
Original languageEnglish
Awarding Institution
  • King's College London
SupervisorRichard Dobson (Supervisor)

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