The purpose of this study is to determine the feasibility of three widely used wearable sensors in research settings for 24 h monitoring of sleep, sedentary, and active behaviors in middle-aged women.
Methods
Participants were 21 inactive, overweight (M Body Mass Index (BMI) = 29.27 ± 7.43) women, 30 to 64 years (M = 45.31 ± 9.67). Women were instructed to wear each sensor on the non-dominant hip (ActiGraph GT3X+), wrist (GENEActiv), or upper arm (BodyMedia SenseWear Mini) for 24 h/day and record daily wake and bed times for one week over the course of three consecutive weeks. Women received feedback about their daily physical activity and sleep behaviors. Feasibility (i.e., acceptability and demand) was measured using surveys, interviews, and wear time.
Results
Women felt the GENEActiv (94.7 %) and SenseWear Mini (90.0 %) were easier to wear and preferred the placement (68.4, 80 % respectively) as compared to the ActiGraph (42.9, 47.6 % respectively). Mean wear time on valid days was similar across sensors (ActiGraph: M = 918.8 ± 115.0 min; GENEActiv: M = 949.3 ± 86.6; SenseWear: M = 928.0 ± 101.8) and well above other studies using wake time only protocols. Informational feedback was the biggest motivator, while appearance, comfort, and inconvenience were the biggest barriers to wearing sensors. Wear time was valid on 93.9 % (ActiGraph), 100 % (GENEActiv), and 95.2 % (SenseWear) of eligible days. 61.9, 95.2, and 71.4 % of participants had seven valid days of data for the ActiGraph, GENEActiv, and SenseWear, respectively.
Conclusion
Twenty-four hour monitoring over seven consecutive days is a feasible approach in middle-aged women. Researchers should consider participant acceptability and demand, in addition to validity and reliability, when choosing a wearable sensor. More research is needed across populations and study designs.
Validity of the Rapid Eating Assessment for Patients for assessing dietary patterns in NCAA athletes
Athletes may be at risk for developing adverse health outcomes due to poor eating behaviors during college. Due to the complex nature of the diet, it is difficult to include or exclude individual food items and specific food groups from the diet. Eating behaviors may better characterize the complex interactions between individual food items and specific food groups. The purpose was to examine the Rapid Eating Assessment for Patients survey (REAP) as a valid tool for analyzing eating behaviors of NCAA Division-I male and female athletes using pattern identification. Also, to investigate the relationships between derived eating behavior patterns and body mass index (BMI) and waist circumference (WC) while stratifying by sex and aesthetic nature of the sport.
Methods
Two independent samples of male (n = 86; n = 139) and female (n = 64; n = 102) collegiate athletes completed the REAP in June-August 2011 (n = 150) and June-August 2012 (n = 241). Principal component analysis (PCA) determined possible factors using wave-1 athletes. Exploratory (EFA) and confirmatory factor analyses (CFA) determined factors accounting for error and confirmed model fit in wave-2 athletes. Wave-2 athletes' BMI and WC were recorded during a physical exam and sport participation determined classification in aesthetic and non-aesthetic sport. Mean differences in eating behavior pattern score were explored. Regression models examined interactions between pattern scores, participation in aesthetic or non-aesthetic sport, and BMI and waist circumference controlling for age and race.
Results
A 5-factor PCA solution accounting for 60.3% of sample variance determined fourteen questions for EFA and CFA. A confirmed solution revealed patterns of Desserts, Healthy food, Meats, High-fat food, and Dairy. Pattern score (mean ± SE) differences were found, as non-aesthetic sport males had a higher (better) Dessert score than aesthetic sport males (2.16 ± 0.07 vs. 1.93 ± 0.11). Female aesthetic athletes had a higher score compared to non-aesthetic female athletes for the Dessert (2.11 ± 0.11 vs. 1.88 ± 0.08), Meat (1.95 ± 0.10 vs. 1.72 ± 0.07), High-fat food (1.70 ± 0.08 vs. 1.46 ± 0.06), and Dairy (1.70 ± 0.11 vs. 1.43 ± 0.07) patterns.
Conclusions
REAP is a construct valid tool to assess dietary patterns in college athletes. In light of varying dietary patterns, college athletes should be evaluated for healthful and unhealthful eating behaviors.
protocols, including within sleep-focused studies. This study seeks to address accuracy of
accelerometer data in detection of the beginnings and ends of sleep bouts in young adults with
polysomnography (PSG) corroboration. An existing algorithm used to differentiate valid/invalid wear
time and detect bouts of sleep has been modified with the goal of maximizing accuracy of sleep bout
detection. Methods: Three key decisions and thresholds of the algorithm have been modified with three
experimental values each being tested. The main experimental variable Sleepwindow controls the
amount of time before and after a determined bout of sleep that is searched for additional sedentary
time to incorporate and consider part of the same sleep bout. Results were compared to PSG and sleep
diary data for absolute agreement of sleep bout start time (START), end time (END) and time in bed
(TIB). Adjustments were made for outliers as well as sleep latency, snooze time, and the sum of both.
Results: Only adjustments made to a sleep window variable yielded altered results. Between a 5-, 15-,
and 30-minute window, a 15-minute window incurred the least error and most agreement to
comparisons for START, while a 5-minute window was best for END and TIB. Discussion: Contrary
to expectation, corrections for snooze, latency, and both did not substantially improve agreement to
PSG. Algorithm-derived estimates of START and END always fell after sleep diary and PSG both,
suggesting either participants’ sedentary behavior beginning and ends were at a delay from sleep and
wake times, or the algorithm estimates consistently later times than appropriate. The inclusion of a
sleep window variable yields substantial variety in results. A 15-minute window appears best at
determining START while a 5-minute window appears best for END and TIB. Further investigation on
the optimal window length per demographic and condition is required.
In March 2020, the COVID-19 pandemic triggered a sudden and severe economic downturn. Between February and May 2020, the number of unemployed individuals rose by more than 14 million, resulting in an unprecedented increase in the unemployment rate, which went from 3.8% in February to 14.4% in April. Even though unemployment has declined in recent months, with some individuals returning to work, the rate is still much higher than it was one year ago (7.9% in September 2020 vs. 3.5% in September 2019). Further, as of September 2020, there are 19.4 million persons unable to work due to the pandemic, as well as 6.3 million persons working only part time even though they would prefer to work more.
In the face of the coronavirus (COVID-19) pandemic, food assistance programs adapted quickly and in unprecedented ways to meet the challenges of high unemployment, disruptions in the food supply, and school closures. Supported by US Department of Agriculture’s COVID-19 program-specific waivers, some programs relaxed their eligibility criteria, while others improvised on delivery modalities or temporarily increased benefits.1 To examine food assistance program participation and participant experiences during the first few months of the pandemic, we collected online survey data in July 2020 from a sample of over 1,500 U.S. households, representative of the US population. This brief summarizes participation in key food assistance programs, namely, the Supplemental Nutrition Assistance Program (SNAP), the Special Supplemental Program for Women Infants and Children (WIC), School Food Programs, as well as emergency food assistance provided through Food Pantries.
The coronavirus (COVID-19) pandemic has affected employment and food security globally and in the United States. To understand the impacts of COVID-19 on food security in Arizona, a representative survey of Arizona households was launched online from July 1 to August 10, 2020. This brief provides an overview of changes in food security rate, perceived worries and challenges about food security, as well as behavioral changes and strategies adopted since the pandemic. Additional briefs from the Arizona survey covering topics on economic consequences, food access, and participations in food assistance programs during the pandemic are also available.
The coronavirus (COVID-19) pandemic led to disruptions in the food supply and high rates of unemployment and under-employment, both in Arizona and nationally. These emergencies required food assistance programs to adapt quickly and in unprecedented ways by relaxing eligibility criteria, improvising on delivery modalities, and increasing benefits. To examine food assistance program participation during the pandemic, we collected data from a representative sample of 620 Arizona households. The sample was drawn from across Arizona in July-August 2020 using an online survey. This brief provides the summary for participation in key food assistance programs, namely, the Supplementary Nutrition Assistance Program (SNAP), the Special Supplemental Program for Women Infants and Children (WIC), School Food Programs, and the emergency food assistance provided through food pantries.
With more than 19 million confirmed COVID-19 cases across the United States1 and over 500,000 in Arizona as of December 2020, the ongoing pandemic has had devastating impacts on local, national, and global economies. Prior to the pandemic (February 2020), based on U.S. Bureau of Labor Statistics data, the unemployment rate in Arizona was 6.5%, compared to 4.9% at the national level.3 Since the beginning of the COVID-19 pandemic (March 2020), the United States has experienced striking increases in the unemployment rate, reaching 13.2% in April. Similarly, in Arizona, the unemployment rate jumped to over 13.5% in April. The unemployment rates have since declined both nationally and in Arizona but remain higher compared to February 2020. In November 2020 (the most recent data available), the national unemployment rate was 6.7%, while in Arizona the rate was 7.8%—the 10th highest unemployment rate among all U.S. states.
As of May 2022, there have been more than 80 million confirmed cases of COVID-19 across the United States, and over two million cases in Arizona. The pandemic has had a devastating impact on local, national, and global economies. This brief features the findings from data collected from a survey administered to Arizona residents in April of 2021, as well as national statistics, to understand some of the economic consequences of COVID-19 and its impacts on Arizona households.