Actigraphy-based sleep estimation in adolescents and adults: a comparison with polysomnography using two scoring algorithms Mirja Quante,1–3 Emily R Kaplan,2 Michael Cailler,2 Michael Rueschman,2 Rui Wang,2–5 Jia Weng,2 Elsie M Taveras,3,5,6 Susan Redline2,3,7 1Department of Neonatology, University of Tuebingen, Tuebingen, Germany; 2Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Boston, MA, USA; 3Harvard Medical School, Boston, MA, USA; 4Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA; 5Department of Population Medicine, Harvard Medical School and The Harvard Pilgrim Health Care Institute, Boston, MA, USA; 6Division of General Academic Pediatrics, Department of Pediatrics, MassGeneral Hospital for Children, Boston, MA, USA; 7Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA Objectives: Actigraphy is widely used to estimate sleep–wake time, despite limited information regarding the comparability of different devices and algorithms. We compared estimates of sleep–wake times determined by two wrist actigraphs (GT3X+ versus Actiwatch Spectrum [AWS]) to in-home polysomnography (PSG), using two algorithms (Sadeh and Cole–Kripke) for the GT3X+ recordings.Subjects and methods: Participants included a sample of 35 healthy volunteers (13 school children and 22 adults, 46% male) from Boston, MA, USA. Twenty-two adults wore the GT3X+ and AWS simultaneously for at least five consecutive days and nights. In addition, actigraphy and PSG were concurrently measured in 12 of these adults and another 13 children over a single night. We used intraclass correlation coefficients (ICCs), epoch-by-epoch comparisons, paired t-tests, and Bland–Altman plots to determine the level of agreement between actigraphy and PSG, and differences between devices and algorithms.Results: Each actigraph showed comparable accuracy (0.81–0.86) for sleep–wake estimation compared to PSG. When analyzing data from the GT3X+, the Cole–Kripke algorithm was more sensitive (0.88–0.96) to detect sleep, but less specific (0.35–0.64) to detect wake than the Sadeh algorithm (sensitivity: 0.82–0.91, specificity: 0.47–0.68). Total sleep time measured using the GT3X+ with both algorithms was similar to that obtained by PSG (ICC=0.64–0.88). In contrast, agreement between the GT3X+ and PSG wake after sleep onset was poor (ICC=0.00–0.10). In adults, the GT3X+ using the Cole–Kripke algorithm provided data comparable to the AWS (mean bias=3.7±19.7 minutes for total sleep time and 8.0±14.2 minutes for wake after sleep onset).Conclusion: The two actigraphs provided comparable and accurate data compared to PSG, although both poorly identified wake episodes (i.e., had low specificity). Use of actigraphy scoring algorithm influenced the mean bias and level of agreement in sleep–wake times estimates. The GT3X+, when analyzed by the Cole–Kripke, but not the Sadeh algorithm, provided comparable data to the AWS. Keywords: validation, actigraphy, polysomnography, scoring algorithm

Rest-activity patterns provide an indication of circadian rhythmicity in the free-living setting. We aimed to describe the distributions of rest-activity patterns in a sample of adults and children across demographic variables. A sample of adults (N = 590) and children (N = 58) wore an actigraph on their nondominant wrist for 7 days and nights. We generated rest-activity patterns from cosinor analysis (MESOR, acrophase and magnitude) and nonparametric circadian rhythm analysis (IS: interdaily stability; IV: intradaily variability; L5: least active 5-hour period; M10: most active 10-hour period; and RA: relative amplitude). Demographic variables included age, sex, race, education, marital status, and income. Linear mixed-effects models were used to test for demographic differences in rest-activity patterns. Adolescents, compared to younger children, had (1) later M10 midpoints (β = 1.12 hours [95% CI: 0.43, 1.18] and lower M10 activity levels; (2) later L5 midpoints (β = 1.6 hours [95% CI: 0.9, 2.3]) and lower L5 activity levels; (3) less regular rest-activity patterns (lower IS and higher IV); and 4) lower magnitudes (β = −0.95 [95% CI: −1.28, −0.63]) and relative amplitudes (β = −0.1 [95% CI: −0.14, −0.06]). Mid-to-older adults, compared to younger adults (aged 18–29 years), had (1) earlier M10 midpoints (β = −1.0 hours [95% CI: −1.6, −0.4]; (2) earlier L5 midpoints (β = −0.7 hours [95% CI: −1.2, −0.2]); and (3) more regular rest-activity patterns (higher IS and lower IV). The magnitudes and relative amplitudes were similar across the adult age categories. Sex, race and education level rest-activity differences were also observed. Rest-activity patterns vary across the lifespan, and differ by race, sex and education. Understanding population variation in these patterns provides a foundation for further elucidating the health implications of rest-activity patterns across the lifespan.

Zeitgebers such as light, eating and physical activity provide input to the circadian clock. Chronic circadian misalignment is associated with significant adverse health effects. An improved understanding of the impact of the timing of zeitgebers on the stability of 24-hour rest-activity rhythm in free-living settings may identify behavioural and environmental intervention targets. A total of 133 healthy adults, aged 21–60 years, wore a wrist actigraph for 7 consecutive days. We applied a non-parametric analysis to activity counts to derive rest-activity patterns. We administered a questionnaire through a smartphone app to collect self-reported timing of light exposure, eating episodes and physical activity. To assess the relationship between timing exposures (first and last exposure to outdoor light, first exposure to indoor light, last eating episode, first eating episode, morning physical activity proportion, evening physical activity proportion) and rest-activity or sleep outcomes (bedtimes, total sleep time, inter-daily stability, intra-daily variability, L5 and M10 midpoint), we first calculated Spearman correlations, using the false discovery rate method to control for multiple comparisons. From those significant associations, we then fit regression models adjusting for age, sex, race, household income, education level, study site, body mass index, as well as physical activity. Finally, we tested for interaction between chronotype and each timing-related exposure and stratified the analysis by morning type. All zeitgebers, except for evening physical activity proportion, were correlated with at least four of the seven sleep and rest-activity outcomes. In adjusted analysis, later timing of first (after 6:30 to 7:45 AM versus earlier) and last exposure to indoor light (after 11:00 PM versus earlier) and first (after 7:45–9:45 AM versus earlier) and last eating episode (after 8:00–09:00 PM versus earlier) were associated with a shift of 0.60–1.39 hours to later bedtimes, M10 and L5 midpoints (i.e. timing of peak activities or inactivities). Later timing of first exposure to outdoor light (after 09:30 AM versus earlier) was also associated with 0.51 (95% CI: 0.19 to 0.83) hours longer total sleep time. Higher morning physical activity proportion (> 33%) was associated with 0.95 (95% CI: −1.38 to −0.53) hours earlier in-bed time and 0.69 (95% CI: −1.14 to −0.24) hours earlier out-of-bed time, 0.92 (95% CI: −1.41 to −0.42) hours earlier M10 and 0.96 (95% CI: −1.42 to −0.49) min earlier L5 midpoint. The results did not change substantially with further adjustment for total activity. There was a significant interaction between morning chronotype and first eating episode with rest-activity patterns (p < 0.05), with first eating episode associating with timing of activities only in non-morning type adults. Timing of zeitgebers was associated with sleep and rest-activity patterns, including bedtimes, L5 and M10 midpoint. Future research should evaluate the impact of manipulating zeitgebers on both circadian rhythms and health outcomes.

Background: Understanding variation in physical activity (PA) and sleep is necessary to develop novel intervention strategies targeting adolescents’ health behaviors. We examined the extent to which PA and sleep vary by aspects of the physical environment. Participants: We performed a cross-sectional analysis of 669 adolescents in the Project Viva cohort. Methods: We estimated total PA, sleep duration, sleep efficiency, and sleep midpoint timing from wrist accelerometers. We used multivariable linear regression models and generalized estimated equations to assess associations of PA and sleep with season and daily weather conditions obtained from the National Oceanic and Atmospheric Administration archive. Results: Mean age was 12.9 (SD 0.6) years; 51% were female and 68% were white. Mean sleep duration was 466 (SD 42) min per night and total PA was 1,652 (SD 431) counts per min per day. Sleep midpoint time was 41 (95% CI: 27 to 54) min later in summer, 28 (95% CI: −41 to −14) min earlier in spring, and 29 (95% CI: −43 to −15) min earlier in autumn compared to winter. Higher temperature and longer day length both were associated with small reductions of nightly sleep duration. Adolescents were less physically active during winter and on rainy and short sunlight days. There was an inverse U-shaped relationship between PA and mean temperature. Conclusions: Season was associated with large changes in sleep timing, and smaller changes in other sleep and PA measurements. Given the importance of sleep and circadian alignment, future health behavioral interventions may benefit by targeting “season-specific” interventions.

Aim of the present study is an additional validation of the Morningness-Eveningness-Stability Scale improved (MESSi). We screened a total of 97 German students using the reduced Morningness-Eveningness Questionnaire (rMEQ) to identify a subsample (N = 42) of definite morning and evening types (31% males, mean age: 24.8 ± 5.8 years). The participants provided information about their sleep-wake rhythm (diary), personality traits (questionnaire) and experienced actigraphic monitoring. Correlations of the MESSi components "Morning affect subscale" (MA) (r = 0.91, p < 0.01) and "Eveningness subscale" (r = -0.87, p < 0.01) with the rMEQ showed good convergent validity. MA was also significantly negatively correlated with the acrophase and the midpoint of sleep as measured by actigraphy.