Assessing actigraphy performance for daytime sleep detection following stroke: insights from inpatient monitoring in a rehabilitation hospital

Abstract

Study objectives: Stroke can result in or exacerbate various sleep disorders. The presence of behaviors such as daytime sleepiness poststroke can indicate underlying sleep disorders which can significantly impact functional recovery and thus require prompt detection and monitoring for improved care. Actigraphy, a quantitative measurement technology, has been primarily validated for nighttime sleep in healthy adults; however, its validity for daytime sleep monitoring is currently unknown. Therefore this study aims to identify the best-performing actigraphy sensor and algorithm for detecting daytime sleep in poststroke individuals.

Methods: Participants wore Actiwatch Spectrum and ActiGraph wGT3X-BT on their less-affected wrist, while trained observers recorded daytime sleep occurrences and activity levels (active, sedentary, and asleep) during non-therapy times. Algorithms, Actiwatch (Autoscore AMRI) and ActiGraph (Cole-Kripke, Sadeh), were compared with on-site observations and assessed using F2 scores, emphasizing sensitivity to detect daytime sleep.

Results: Twenty-seven participants from an inpatient stroke rehabilitation unit contributed 173.5 hours of data. The ActiGraph Cole-Kripke algorithm (minute sleep time = 15 minutes, bedtime = 10 minutes, and wake time = 10 minutes) achieved the highest F2 score (0.59). Notably, when participants were in bed, the ActiGraph Cole-Kripke algorithm continued to outperform Sadeh and Actiwatch AMRI, with an F2 score of 0.69.

Conclusions: The study demonstrates both Actiwatch and ActiGraph's ability to detect daytime sleep, particularly during bed rest. ActiGraph (Cole-Kripke) algorithm exhibited a more balanced sleep detection profile and higher F2 scores compared to Actiwatch, offering valuable insights for optimizing daytime sleep monitoring with actigraphy in stroke patients.

Keywords: Inpatient Rehabilitation Hospital; actigraphy; napping; stroke.

Figure 1.

Top: Total time spent in each location, bed or wheelchair, and the associated times spent in each activity, active, sedentary, or sleep. Bottom: Total sleep by participant in bed and in wheelchair.

Figure 2.

Total recorded hours by specified subgroup, displaying percent spent in each activity: active, sedentary, and sleep. Top left: low DS group, top right: high DS group, bottom left: low function group, bottom right: high function group.

Figure 3.

Estimated versus observed sleep time during daytime observations from the best-performing algorithm for detecting daytime sleep. Left: ActiGraph, Sadeh (minimum time in sleep = 15, minimum time in bed = 5) (F2 score = 0.57, Specificity = 0.77). Middle: ActiGraph, Cole-Kripke (minimum time in sleep = 15, minimum time in bed = 10) (F2 score = 0.59, Specificity = 0.69). Right: Actiwatch, Medium threshold (minimum time in sleep = 15) (F2 score = 0.52, Specificity = 0.80).