Acceptability and feasibility of using actigraphy to assess habitual physical activity and sleep parameters in men and women living in rural communities in conflict-affected Eastern Democratic Republic of Congo

Abstract

Background The goals of this study were to (1) determine the feasibility and acceptability of using actigraphy to objectively measure sleep quality and habitual physical activity in rural Democratic Republic of Congo (DRC) and (2) examine the relationship between sleep parameters, self-report symptoms, daytime physical activity, and physical function, including the ability to work. Method Thirty individuals were asked to wear a wrist-worn accelerometer for 5 nights and 4 days. Nighttime sleep parameters derived were average and intra-individual variability (IIV) in total sleep time (TST), sleep onset latency (SOL), sleep efficiency (SE), and wake after sleep onset (WASO). Daytime habitual physical data derived were average and peak activity and daytime napping. Results Ninety-three percent (n = 28) of participants completed the study. All participants who wore the device marked sleep and wake cycles and periods of non-wear using the marker. Trauma-related symptoms were not associated with mean or IIV in TST, SE, SOL, or WASO (p > 0.01). Those with higher levels of bodily pain slept longer (β = 0.633, p = 0.003, adjusted R2 = 0.279), were more likely to report that their physical health limited their physical activities (β = 0.71, p < 0.001, adjusted R2 = 0.679) and had greater difficulty doing daily work (β = 0.84, p = 0.001, adjusted R2 = 0.665). Conclusion The use of actigraphy to collect objective measures of activity and sleep quality in rural post-conflict settings is feasible and acceptable. Our preliminary findings suggest that bodily pain and not trauma-related symptoms have a significant impact on sleep and functional outcomes in men and women survivors of prolonged conflict in the DRC.

Motionwatch8 Wrist Activity and Light Analysis: from ambulatory recording toward real life monitoring.

“…Then we compute over the full year the percent of time of light during the Sunrise/Sunset day (Figure 16 In red there are the Sunrise/Sunset days when the clock time changes to the solar time (one hour back, from UTC+2 to UTC+1). While the Author did not find the explicit description of a "behavior" data model in other articles, it seems somehow implicit in several interventional and non interventional field studies [17][18][19][20][21][22][23][24][25][26].…”