Apps and fitness trackers that measure sleep: Are they useful?

Meghna P. Mansukhani; Bhanu Prakash Kolla

doi:10.3949/ccjm.84a.15173

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TABLE 1

Fitness tracking devices vs conventional methods of evaluating sleep

Device	Comparator	N	Study population	Results
Fitbit¹³	Polysomnography and actigraphy	24	Healthy adults with no history of symptoms of sleep disorders Mean age 26.1	Sensitivity for sleep 97.8% Specificity for wakefulness 19.8% Overestimated total sleep time and sleep efficiency and underestimated wake time after sleep onset
Fitbit Ultra¹⁴	Polysomnography and actigraphy	63	Children and adolescents undergoing overnight clinical polysomnography Mean age 9.7	‘Normal’ mode overestimated total sleep time and sleep efficiency and underestimated wake time after sleep onset ‘Sensitive’ mode underestimated total sleep time and sleep efficiency and overestimated wake time after sleep onset
Jawbone UP¹⁵	Polysomnography	65	Healthy adolescents Mean age 15.8	Overestimated total sleep time and sleep efficiency and underestimated wake time after sleep onset, no difference in sleep onset latency No clear correlation between ‘light’ and ‘deep’ sleep and conventional polysomnographic sleep stages
Jawbone UP¹⁶	Polysomnography	28	Midlife women Mean age 50.1	Sensitivity for sleep 96% Specificity for wakefulness 37% Overestimated total sleep time and sleep onset latency and underestimated wake time after sleep onset
Jawbone UP¹⁷	Polysomnography and actigraphy	64	Children and adolescents with suspected sleep-disordered breathing Mean age 8.4	Sensitivity for sleep 92% Specificity for wakefulness 66% No difference from polysomnography on total sleep time, sleep efficiency, sleep onset latency, and wake time after sleep onset Compared with actinography, overestimated sleep onset latency and underestimated wake time after sleep onset

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TABLE 2

Smartphone sleep-tracking applications vs conventional methods of evaluating sleep

App	Comparator	N	Study population	Results
Sleep Time¹⁸	Polysomnography	20	Volunteers with no sleep disorders Mean age 39.5	Sensitivity for sleep 89.9% Specificity for wakefulness 50% No clear correlation between Sleep Time and polysomnography for sleep onset latency and sleep stages Did not awaken subjects exclusively from light sleep
Motion X 24/7¹⁷	Polysomnography and actigraphy	64	Children, adolescents with suspected obstructive sleep apnea Mean age 8.4	Overestimated total sleep time and sleep efficiency Underestimated sleep onset latency and wake time after sleep onset
Toss ‘N’ Turn¹⁹	Pittsburgh Sleep Quality Index (sleep diary)	27	Volunteers Mean age 34	Reasonable accuracy for classifying good and poor sleepers per diary No clear correlation between app and diary for bedtime, wake time, and sleep duration

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TABLE 3

Assessing data from sleep apps and fitness trackers

If the data from a device or app indicates…	The patient may have…
Substantially different daily bedtimes and wake times Significant variations in total sleep time Long naps Long awake periods in the middle of the night	Poor sleep hygiene
Substantial difference from what patient reports	Misperception of sleep
Consistent delay or early time to bed	Circadian rhythm sleep-wake disorder