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. 2024 Sep-Oct;22(5):725-738.
doi: 10.1080/15402002.2024.2359413. Epub 2024 Jun 12.

Integration of Sensor-Based and Self-Reported Metrics in a Sleep Diary: A Pilot Exploration

Sarah Conklin  1   2 , Jessica R Dietch  3 , Golshan Kargosha  1   2 , Faith Luyster  4 , Molly Atwood  5 , Matthew S Tenan  6 , Gary Zammit  7 , Nilanjan Banerjee  1   2 , Justin Brooks  1   2
Affiliations

Integration of Sensor-Based and Self-Reported Metrics in a Sleep Diary: A Pilot Exploration

Sarah Conklin et al. Behav Sleep Med. 2024 Sep-Oct.

Abstract

Objectives: Discrepancies between sleep diaries and sensor-based sleep parameters are widely recognized. This study examined the effect of showing sensor-based sleep parameters while completing a daily diary. The provision of sensor-based data was expected to reduce variance but not change the mean of self-reported sleep parameters, which would in turn align better with sensor-based data compared to a control diary.

Method: In a crossover study, 24 volunteers completed week-long periods of control diary (digital sleep diary without sensor-based data feedback) or integrated diary (diary with device feedback), washout, and then the other diary condition.

Results: The integrated diary reduced self-reported total sleep time (TST) by <10 minutes and reduced variance in TST. The integrated diary did not impact mean sleep onset latency (SOL) and, unexpectedly, the variance in SOL increased. The integrated diary improved both bias and limits of agreement for SOL and TST.

Conclusions: Integration of wearable, sensor-based device data in a sleep diary has little impact on means, mixed evidence for less variance, and better agreement with sensor-based data than a traditional diary. How the diary impacts reporting and sensor-based sleep measurements should be explored.

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Conflict of interest statement

Disclosure statement: Justin Brooks, MD, Ph.D., holds a provisional U.S. Patent, “Method and system to improve the accuracy of patient-reported outcomes in clinical trials” that protects the diary method described in this manuscript.

Figures

Figure 1
Figure 1
Data Distributions for Sleep Parameters Note.Panels showA) self-reported SOL, B) sensor-based SOL, C) self-reported TST, and D) sensor-based TST where the X-Axis shows log-transformed value and the Y-Axis indicates the density of those values.SOL = sleep onset latency; TST = total sleep time.
Figure 2
Figure 2
Bayesian Probability of Direction and Possible Parameter Values Note. Probability of direction obtained from Bayesian Distributional Analysis on the effect of the integrated diary on the self-reported and sensor-based sleep parameters; TST = total sleep time; SOL = sleep onset latency.
Figure 3
Figure 3
Bayesian Probability of Direction and Possible Parameter Values for Confidence and Quality Appraisals Note.Bayesian probability of direction and possible parameter values for confidence in each sleep parameter and sleep quality appraisal. TST Confidence = the participants self-assessed appraisal in the confidence of their sleep diary entry. SOL Confidence reflects participant self-assessed appraisal in the confidence of their diary entry. Sleep Quality = self-rated sleep quality entered into the diary. TST = total sleep time; SOL = sleep onset latency.
Figure 4
Figure 4
Bland Altman Agreement Analysis for Sleep Onset Latency Note. The panels show the control condition (left) and the experimental condition (right) non-parametric Bland-Altman plot for Sleep Onset Latency over 7 nights. On both plots, the y-axis represents the difference between the two measurements (control-experimental) and the x-axis shows the average of both methods. The blue dashed vertical line in both plots at 30 minutes served as a point of comparison of the limits of agreement. On the left panel, dotted lines show the span of the binomial proportions test showing 71.4% of the data points were within the acceptable 30-minute bounds in the control condition. In the panel on the right, the binomial proportions test shows that 92% of the data points were with the acceptable 30-minute bounds in the experimental condition.
Figure 5
Figure 5
Bland Altman Agreement Analysis for Total Sleep Time Note. The panels show the control condition (A; left) and the experimental condition (B; right) non-parametric Bland-Altman plot for Total Sleep Time over 7 nights. On both plots, the y-axis represents the difference between the two measurements (control-experimental) and the x-axis shows the average of both methods. The blue dashed vertical line in both plots at 350 minutes served as a point of comparison of the limits of agreement. On the left panel, dotted lines show the span of the binomial proportions test showing 54% of the data points were within the acceptable 60-minute bounds in the control condition. In the panel on the right, the binomial proportions test shows that 79% of the data points were with the acceptable 60-minute bounds in the experimental condition.
Figure 6
Figure 6
Binomial Proportions Test for Sleep Parameters Note. Each panel shows the proportion of data expressed as a percentage falling within 30-minute bounds for SOL (Top panel) and 60 minutes for TST (Lower panel) for each diary.
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References

    1. Ancoli-Israel S, Martin JL, Blackwell T, Buenaver L, Liu L, Meltzer LJ, Sadeh A, Spira AP, & Taylor DJ (2015). The SBSM guide to actigraphy monitoring: clinical and research applications. Behavioral Sleep Medicine, 13(sup1), S4–S38. 10.1080/15402002.2015.1046356 - DOI - PubMed
    1. Bensen-Boakes D-B, Lovato N, Meaklim H, Bei B, & Scott H (2022). Sleep-wake state discrepancy: toward a common understanding and standardized nomenclature. Sleep, 45(10). 10.1093/sleep/zsac187 - DOI - PubMed
    1. Bland JM, & Altman DG (1999). Measuring agreement in method comparison studies. Statistical Methods in Medical Research, 8(2), 135–160. 10.1177/096228029900800204 - DOI - PubMed
    1. Campanini MZ, Lopez-Garcia E, Rodríguez-Artalejo F, González AD, Andrade SM, & Mesas AE (2017). Agreement between sleep diary and actigraphy in a highly educated Brazilian population. Sleep Medicine, 35, 27–34. 10.1016/j.sleep.2017.04.004 - DOI - PubMed
    1. Cella D, Riley W, Stone A, Rothrock N, Reeve B, Yount S, Amtmann D, Bode R, Buysse D, Choi S, Cook K, DeVellis R, DeWalt D, Fries JF, Gershon R, Hahn EA, Lai J-S, Pilkonis P, Revicki D, … Hays R (2010). The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. Journal of Clinical Epidemiology, 63(11), 1179–1194. 10.1016/j.jclinepi.2010.04.011 - DOI - PMC - PubMed

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