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Determination of circadian rhythms in consumer-grade actigraphy devices
Thesis   Open access

Determination of circadian rhythms in consumer-grade actigraphy devices

Gregory William Yeutter
Master of Science (M.S.), Drexel University
Dec 2016
DOI:
https://doi.org/10.17918/etd-7128
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Abstract

Electrical engineering Cancer--Sleep disorders Circadian Rhythms
The recent growth in popularity of fitness-tracking watches is a major step in the right direction for individual health awareness. It turns out that these devices are very similar electronically to clinical-grade devices used to study sleep health. Those clinical devices are not available for sale to the general public, and cost in the range of $1,500-$2,000. If the output of the consumer fitness tracker compares well with the medical-grade device, individuals may soon be able to deeply understand and optimize their sleep. This experiment consisted of a test subject simultaneously wearing two clinical Respironincs Actiwatch 2 devices and a consumer device, the Fitbit Charge HR (suggested retail price $149.95 U.S.). The experiment was performed over twelve nights. The data output for each platform was compared following the conclusion of the study. The results demonstrate that the Fitbit Charge HR corresponds well with the Respironics Actiwatch 2 in terms of five sleep time metrics: bed time, wake up time, time spent in bed, time spent asleep, and total sleep time. Other measures do not correspond well or have statistically significant differences between the platforms. These include time spent awake in bed, number of awakenings, sleep efficiency, sleep onset latency, percent of time asleep, and percent of time awake. Time-series data and the wake after sleep onset metric have fair correspondence between the platforms, and the data may be used with knowledge of the limitations. This experiment shows that a consumer-grade fitness-tracking device may provide some dependable data points for individuals looking to understand their sleep hygiene. Based on these results, three case studies for health-aware systems are presented: adaptive environments, drug delivery systems, and health recommendation software. In addition, suggestions for future experiments and developments are provided.

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