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

State and federal revenues from tobacco consumed by minors

OBJECTIVES: The purpose of this study was to estimate the value of cigarettes consumed in 1997 by youths younger than 18 years. METHODS: Price, population, and consumption data were used to compute conservative and comprehensive estimates, which were then averaged. RESULTS: An estimated 3.76 million daily smokers aged 12 through 17 years consume an estimated 924 million packs of cigarettes per year, generating $222 million in federal tax revenues,$293 million in state tax revenues, and $480 million in tobacco company profits, and producing a retail value of$1.86 billion. CONCLUSIONS: The revenues from cigarettes smoked by youths could be used to enforce laws prohibiting the sale of tobacco to minors

Recreation, Parks, and the Public Health Agenda: Developing Collaborative Surveillance Frameworks to Measure Leisure Time Activity and Active Park Use

Background:The purposes of this study were to review surveillance of recreation and park use to determine adaptations for tracking leisure time physical activity and increasing collaboration to achieve public health goals.Methods:Surveillance in public health and parks and recreation and discussions at the 2006 Cooper Institute conference were reviewed.Results:This review suggested four actions to improve collaborative surveillance of leisure time physical activity and active park use. The proposals are to incorporate more detailed measures of leisure time physical activity and active park visits into park surveillance; include key park, recreation, and leisure items in public health surveillance; assess active park visits and leisure time physical activity more frequently; and establish public health physical activity objectives for parks and recreation and outdoor recreation participation.Conclusions:These proposals can facilitate collaboration between public health and parks and recreation and exploration of active park use and outdoor recreation in relation to health.</jats:sec