Comparing sleep measures in cancer survivors: Self-reported sleep diary versus objective wearable sleep tracker

Purpose

Cancer survivors are increasingly using wearable fitness trackers, but it's unclear if they match traditional self-reported sleep diaries. We aimed to compare sleep data from Fitbit and the Consensus Sleep Diary (CSD) in this group.

Methods

We analyzed data from two randomized clinical trials, using both CSD and Fitbit to collect sleep outcomes: total sleep time (TST), wake time after sleep onset (WASO), number of awakenings (NWAK), time in bed (TIB) and sleep efficiency (SE). Insomnia severity was measured by Insomnia Severity Index (ISI). We used the Wilcoxon Singed Ranks Test, Spearman's rank correlation coefficients, and the Mann-Whitney Test to compare sleep outcomes and assess their ability to distinguish insomnia severity levels between CSD and Fitbit data.

Results

Among 62 participants, compared to CSD, Fitbit recorded longer TST by an average of 14.6 (SD = 84.9) minutes, longer WASO by an average of 28.7 (SD = 40.5) minutes, more NWAK by an average of 16.7 (SD = 6.6) times per night, and higher SE by an average of 7.1% (SD = 14.4); but shorter TIB by an average of 24.4 (SD = 71.5) minutes. All the differences were statistically significant (all p < 0.05), except for TST (p = 0.38). Moderate correlations were found for TST (r = 0.41, p = 0.001) and TIB (r = 0.44, p < 0.001). Compared to no/mild insomnia group, participants with clinical insomnia reported more NWAK (p = 0.009) and lower SE (p = 0.029) as measured by CSD, but Fitbit outcomes didn't.

Conclusions

TST was the only similar outcome between Fitbit and CSD. Our study highlights the advantages, disadvantages, and clinical utilization of sleep trackers in oncology.

A 2022 Survey of Commercially Available Smartphone Apps for Sleep: Most Enhance Sleep

Commercially available smartphone apps represent an ever-evolving and fast-growing market. Our review systematically surveyed currently available commercial sleep smartphone apps to provide details to inform both providers and patients alike, in addition to the healthy consumer market. Most current sleep apps offer a free version and are designed to be used while awake, prior to sleep, and focus on the enhancement of sleep, rather than measurement, by targeting sleep latency using auditory stimuli. Sleep apps could be considered a possible strategy for patients and consumers to improve their sleep, although further validation of specific apps is recommended.

Wearable Device-Delivered Intensive Sleep Retraining as an Adjunctive Treatment to Kickstart Cognitive-Behavioral Therapy for Insomnia

Intensive Sleep Retraining is a behavioral treatment for sleep onset insomnia that produces substantial benefits in symptoms after a single treatment session. This technique involves falling asleep and waking up shortly afterward repeatedly: a process that is thought to retrain people to fall asleep quickly when attempting sleep. Although originally confined to the sleep laboratory, recent technological developments mean that this technique is feasible to self-administer at home. With multiple randomised controlled trials required to confirm its efficacy, Intensive Sleep Retraining may serve as an adjunctive treatment to cognitive-behavioral therapy for insomnia, improving short-term efficacy by kick-starting treatment gains.

Poor false sleep feedback does not affect pre-sleep cognitive arousal or subjective sleep continuity in healthy sleepers: a pilot study

Modern wearable devices calculate a numerical metric of sleep quality (sleep feedback), which are intended to allow users to monitor and, potentially, improve their sleep. This feedback may have a negative impact on pre-sleep cognitive arousal, and subjective sleep, even in healthy sleepers, but it is not known if this is the case. This pilot study examined the impact of poor false sleep feedback, upon pre-sleep arousal and subjective sleep continuity in healthy sleepers. A total of 54 healthy sleepers (Mage = 30.19 years; SDage = 12.94 years) were randomly allocated to receive good, or poor, false sleep feedback, in the form of a numerical sleep score. Participants were informed that this feedback was a true reflection of their habitual sleep. Pre-sleep cognitive and somatic arousal was measured at baseline, immediately after the presentation of the feedback, and one week afterwards. Subjective sleep continuity was measured using sleep diaries for one week before, and after, the presentation of the feedback. There were no significant differences between good and poor feedback groups in terms of pre-sleep cognitive arousal, or subjective sleep continuity, before or after the presentation of the sleep feedback. The presentation of false sleep feedback, irrespective of direction (good vs. poor) does not negatively affect pre-sleep cognitive arousal or subjective sleep continuity in healthy sleepers. Whilst the one-off presentation of sleep feedback does not negatively affect subjective sleep, the impact of more frequent sleep feedback on sleep should be examined.

Investigating the within-person relationships between activity levels and sleep duration using Fitbit data

The advancement of wearable technologies provides opportunities to continuously track individuals' daily activity levels and sleep patterns over extended periods of time. These data are useful in examining the reciprocal relationships between physical activity and sleep at the intrapersonal level. The purpose of this study is to test the bidirectional relationships between daily activity levels and sleep duration. The current study analyzed activity and sleep data collected from a Fitbit device as part of a 6 month employer-sponsored weight loss program. A total of 105 overweight/obese adults were included (92% female, 70% obese, and 44% Hispanic). Multilevel models were used to examine (a) whether daily active and sedentary minutes predicted that night's sleep duration and (b) whether sleep duration predicted active and sedentary minutes the following day. Potential extended effects were explored by using a 2 day average of the activity minutes/sleep duration as the predictor. No significant relationships between active minutes and sleep duration were found on a daily basis. However, having less sleep over two nights than one's usual level was associated with an increased likelihood of engaging in some physical activity the following day. There was a significant bidirectional negative association between sedentary minutes and sleep duration for both the daily and 2 day models. Data from wearable trackers, such as Fitbit, can be used to investigate the daily within-person relationship between activity levels and sleep duration. Future studies should investigate other sleep metrics that may be obtained from wearable trackers, as well as potential moderators and mediators of daily activity levels and sleep.

Validity of a commercial wearable sleep tracker in adult insomnia disorder patients and good sleepers

OBJECTIVES

To compare the accuracy of the commercial Fitbit Flex device (FF) with polysomnography (PSG; the gold-standard method) in insomnia disorder patients and good sleepers.

METHODS

Participants wore an FF and actigraph while undergoing overnight PSG. Primary outcomes were intraclass correlation coefficients (ICCs) of the total sleep time (TST) and sleep efficiency (SE), and the frequency of clinically acceptable agreement between the FF in normal mode (FFN) and PSG. The sensitivity, specificity, and accuracy of detecting sleep epochs were compared among FFN, actigraphy, and PSG.

RESULTS

The ICCs of the TST between FFN and PSG in the insomnia (ICC=0.886) and good-sleepers (ICC=0.974) groups were excellent, but the ICC of SE was only fair in both groups. The TST and SE were overestimated for FFN by 6.5min and 1.75%, respectively, in good sleepers, and by 32.9min and 7.9% in the insomnia group with respect to PSG. The frequency of acceptable agreement of FFN and PSG was significantly lower (p=0.006) for the insomnia group (39.4%) than for the good-sleepers group (82.4%). The sensitivity and accuracy of FFN in an epoch-by-epoch comparison with PSG was good and comparable to those of actigraphy, but the specificity was poor in both groups.

CONCLUSIONS

The ICC of TST in the FFN-PSG comparison was excellent in both groups, and the frequency of agreement was high in good sleepers but significantly lower in insomnia patients. These limitations need to be considered when applying commercial sleep trackers for clinical and research purposes in insomnia.