Objective characterization of activity, sleep, and circadian rhythm patterns using a wrist-worn actigraphy sensor: insights into post-traumatic stress disorder

Thanasis Tsanas, Elizabeth Woodward, Anke Ehlers

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Background Wearables have been gaining increasing momentum and have enormous potential to provide insights into daily life behaviors and longitudinal health monitoring. However, to date there is still lack of a principled algorithmic framework to facilitate the analysis of actigraphy and objectively characterize day-by-day data patterns, particularly in cohorts with sleep problems. Objective This study proposes a principled algorithmic framework for the assessment of activity, sleep, and circadian rhythm patterns in Post-Traumatic Stress Disorder (PTSD), a mental disorder with long-lasting distressing symptoms such as intrusive memories, avoidance behaviors and sleep disturbance. In clinical practice, these symptoms are typically assessed using retrospective self-reports which are prone to recall bias. The aim of this study was to develop objective measures from patients’ everyday lives which could potentially considerably enhance the understanding of symptoms, behaviors, and treatment effects. Methods Using a wrist-worn sensor we recorded actigraphy, light, and temperature data over seven consecutive days from three groups: 42 people diagnosed with PTSD, 43 traumatized controls, and 30 non-traumatized controls. The participants also completed a daily sleep diary over seven days and the standardized Pittsburgh Sleep Quality Index (PSQI) questionnaire. We develop a novel approach to automatically determine sleep onset and offset, which can also capture awakenings that are crucial for assessing sleep quality. Moreover, we introduce a new intuitive methodology facilitating actigraphy exploration and characterize day-by-day data across 49 activity, sleep, and circadian rhythm patterns. Results We demonstrate that the new algorithm matches closer the sleep onset and offset against the participants’ sleep diaries compared to an existing open-access widely 3 used approach. PTSD participants exhibited considerably more fragmented sleep patterns (as indicated by greater nocturnal activity, including awakenings), greater intra-day variability compared to traumatized and non-traumatized control groups exhibiting statistically significant (p<0.05) and statistically strong associations (|푅|>0.3). Conclusions This study lays the foundations for objective assessment of activity, sleep, and circadian rhythm patterns using passively collected data from a wrist-worn sensor, facilitating large community studies to monitor longitudinally healthy and pathological cohorts under free-living conditions. These findings may be useful in clinical PTSD assessment and could inform therapy and monitoring of treatment effects.
Original languageEnglish
Pages (from-to)e14306
JournalJMIR mHealth and uHealth
Volume8
Issue number4
DOIs
Publication statusPublished - 20 Apr 2020

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