Quantification of Differences in Sleep Measurement by a Wrist-Worn Consumer Wearable Compared to Research-Grade Accelerometry and Sleep Diaries of Female Adults in Free-Living Conditions

Cindy R Hu,1 Caitlin Delaney,2 Jorge E Chavarro,2– 4 Francine Laden,1– 3 Rachel Librett,4 Laura Katuska,4 Emily R Kaplan,5 Li Yi,4,6 Michael Rueschman,5 Joe Kossowsky,7 Jukka-Pekka Onnela,8 Brent A Coull,8 Susan Redline,3,5 Peter James,1,6,9 Jaime E Hart1,2 1Department of Environmental Health, Harvard T.H. Chan School of Public Health; Boston, Boston, MA, USA; 2Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School; Boston, Boston, MA, USA; 3Department of Epidemiology, Harvard T.H. Chan School of Public Health; Boston, Boston, MA, USA; 4Department of Nutrition, Harvard T.H. Chan School of Public Health; Boston, Boston, MA, USA; 5Division of Sleep of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital and Harvard Medical School; Boston, Boston, MA, USA; 6Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute; Boston, Boston, MA, USA; 7Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children’s Hospital and Harvard Medical School; Boston, Boston, MA, USA; 8Department of Biostatistics, Harvard T.H. Chan School of Public Health; Boston, Boston, MA, USA; 9Department of Public Health Sciences, University of California, Davis School of Medicine; Davis, Davis, CA, USACorrespondence: Cindy R Hu, Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, 181 Longwood Avenue, Boston, MA, 02115, USA, Email [email protected]: The objective of this study is to compare sleep measurements by a consumer-wearable with research-standard actigraphy coupled with sleep diaries in free-living female adults.Methods: Forty-seven females in the Nurses’ Health Study 3 (NHS3) participated in the Sleep and Physical Activity Validation Substudy (SPAVS), where they were asked to concurrently wear a consumer wearable (Fitbit Charge, Models 3 or 5) and a research-grade accelerometer (Actigraph, GT3X+ or Actisleep) on the same wrist and fill out a smartphone-based sleep diary for fourteen consecutive days. We compared measures of total sleep time (TST), time in bed (TIB), and sleep efficiency (SE) from the consumer wearable with actigraphy measures as our research-standard reference for TST and SE and self-reported sleep diary as our reference for TIB. We calculated mean absolute percent error (MAPE) and intra-class correlations (ICC), as well as Bland-Altman analyses to compute mean difference and limits of agreement.Results: For all three measures, the consumer wearable underestimated sleep parameters relative to research-standard actigraphy, with a mean bias of − 16.0 minutes and − 11.2 minutes for TST and TIB, respectively, and − 1.0% for SE. In terms of agreement, TST (MAPE = 11.18%; ICC = 0.79) and TIB (MAPE = 10.45%; ICC = 0.74) had similar MAPES and ICCs, while and SE (MAPE = 5.09%; ICC = 0.39) had a lower ICC.Conclusion: In the NHS3 SPAVS, the wearable sleep measurements modestly underestimated wrist actigraphy measures of TST, TIB, and SE from sleep over multiple days; within sleep measures assessed, TST and TIB had greater agreement with research-grade accelerometry than SE.Keywords: wearables, fitbit, sleep, actigraphy, accelerometer, wome

Neighborhood and weight-related health behaviors in the Look AHEAD (Action for Health in Diabetes) Study

Previous studies have shown that neighborhood factors are associated with obesity, but few studies have evaluated the association with weight control behaviors. This study aims to conduct a multi-level analysis to examine the relationship between neighborhood SES and weight-related health behaviors. In this ancillary study to Look AHEAD (Action for Health in Diabetes) a trial of long-term weight loss among individuals with type 2 diabetes, individual-level data on 1219 participants from 4 clinic sites at baseline were linked to neighborhood-level data at the tract level from the 2000 US Census and other databases. Neighborhood variables included SES (% living below the federal poverty level) and the availability of food stores, convenience stores, and restaurants. Dependent variables included BMI, eating patterns, weight control behaviors and resource use related to food and physical activity. Multi-level models were used to account for individual-level SES and potential confounders. The availability of restaurants was related to several eating and weight control behaviors. Compared to their counterparts in neighborhoods with fewer restaurants, participants in neighborhoods with more restaurants were more likely to eat breakfast (prevalence Ratio [PR] 1.29 95% CI: 1.01-1.62) and lunch (PR = 1.19, 1.04-1.36) at non-fast food restaurants. They were less likely to be attempting weight loss (OR = 0.93, 0.89-0.97) but more likely to engage in weight control behaviors for food and physical activity, respectively, than those who lived in neighborhoods with fewer restaurants. In contrast, neighborhood SES had little association with weight control behaviors. In this selected group of weight loss trial participants, restaurant availability was associated with some weight control practices, but neighborhood SES was not. Future studies should give attention to other populations and to evaluating various aspects of the physical and social environment with weight control practices