Connecting Neighborhoods and Sleep Health

2019 ◽  
pp. 409-430 ◽  
Author(s):  
Dayna A. Johnson ◽  
Yazan A. Al-Ajlouni ◽  
Dustin T. Duncan, ScD
Keyword(s):  
Physical Activity ◽  
Health Behaviors ◽  
Sleep Disorders ◽  
Health Research ◽  
Methodological Issues ◽  
Neighborhood Safety ◽  
Sleep Health ◽  
Physical Context ◽  
Health Field ◽  
Neighborhood Level

This chapter is a meta-commentary on the field of neighborhood health research. Neighborhood research has hitherto focused on a variety of health behaviors and outcomes—such as diet, smoking, drinking, physical activity—but not so much sleep. This chapter focuses on the neighborhood social and physical context (e.g., neighborhood safety, neighborhood stigma, neighborhood noise, neighborhood light, and neighborhood crowding) and how it can impact sleep health and sleep disorders across populations and geographies. In addition to discussing a range of neighborhood exposures, the chapter discusses the designs of studies as well general methodological issues in the neighborhood health field. The chapter also provides a discussion of neighborhood-level interventions that can be utilized in the sleep field. Lastly, this chapter highlights the advancement of neighborhood research extending to sleep health.

scholarly journals Neighborhood-Level Predictors of Obesity Among African American Children in California

2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Uche Onyeka
Keyword(s):  
Physical Activity ◽  
Childhood Obesity ◽  
Neighborhood Safety ◽  
Open Spaces ◽  
Neighborhood Walkability ◽  
Parental Educational Level ◽  
American Children ◽  
And Gender ◽  
The Relationship ◽  
Neighborhood Level

The increased prevalence of childhood obesity is a major public health concern nationally and globally. Childhood obesity is primarily caused by the imbalance between caloric intake and caloric expenditure; however, its increase over the past decades may be due to environmental and behavioral factors. The purpose of the current study was to examine if any relationships existed between childhood obesity, level of physical activity, and neighborhood-level risk factors. This study used the California Health Interview Survey 2009–2014 data sets for African American children aged 5–11 years (<em>n </em>= 1,049). The dependent variable was body mass index (BMI) while the predictors included physical activity, neighborhood, walkability, support, safety, and the presence of parks. Potential confounds were gender and parental education level. Chi-square tests were used to evaluate the associations between BMI and age, educational attainment, neighborhood walkability, physical activity, built environment, neighborhood support, and neighborhood safety. Multivariate logistic regression was used to assess the relationship between BMI and physical activity; parental educational level; presence of parks, playground, or open spaces; neighborhood walkability; neighborhood safety; neighborhood support; and gender while adjusting for other known risk factors. Low physical activity levels were a significant risk factor for increased obesity. No associations were discovered between childhood obesity and neighborhood safety; parental educational level; presence of parks, playgrounds, or open spaces; neighborhood walkability; neighborhood safety; neighborhood support; and gender. This study reinforces the relationship between environmental policy and physical activity.

scholarly journals 0850 Self-report And Polysomnography Sleep And Mortality In Adults: A Machine Learning Replication Analysis

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A323-A324
Author(s):  
M L Wallace ◽  
P Peppard ◽  
T L Coleman ◽  
L Mentch ◽  
D J Buysse ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Health Behaviors ◽  
Physical Health ◽  
Sleep Disorders ◽  
Signs And Symptoms ◽  
Self Report ◽  
Health Study ◽  
Apnea Hypopnea Index ◽  
Sleep Health

Abstract Introduction Individual sleep health characteristics (e.g. efficiency, timing, duration, architecture) and signs and symptoms of sleep disorders (e.g., difficulty falling and staying asleep, apnea hypopnea index, measures of oxygen desaturation) predict mortality in adults using traditional regression methods. However, it is important to examine and compare their predictive abilities in context of other established non-sleep predictors using high-dimensional methods that better reflect the complexity of the data. Therefore, we applied a novel random forest machine learning (RFML) hypothesis-testing framework to data from the Sleep Heart Health Study (SHHS) and the Wisconsin Sleep Cohort (WSC) to determine which risk factor domains (sleep, physical health, sociodemographic factors, medications, health behaviors, mental health) and sleep subdomains (self-report and polysomnography sleep health characteristics and signs and symptoms of sleep disorders) predict time to mortality in adults. Methods We harmonized 82 predictors across SHHS and WSC (32 sleep, 24 physical health, 8 sociodemographic, 9 medications, 4 mental health, 5 health behaviors) and fit sociodemographic-adjusted and fully-adjusted RFML models in each cohort to test the overall predictive importance of each domain and sleep subdomain. Permutation-based p-values and unbiased variable importance metrics (change in Harrell’s C *100, ΔC) were computed and summarized with medians across 20 independent subsampled testing sets in each cohort. Results In the fully-adjusted SHHS and WSC models, the most predictive domains were physical health (SHHS p&lt;0.001, ΔC=1.48; WSC p=0.002, ΔC=2.68) and sleep (SHHS p=0.008, ΔC=0.71; WSC p=0.044, ΔC=1.65). Sleep subdomains were not significant in the fully adjusted model. However, the sociodemographic-adjusted models indicated that the predictive importance of sleep may be driven by polysomnography sleep health characteristics in SHHS (p=0.026, ΔC =0.77) and polysomnography signs of sleep apnea in WSC (p&lt;0.001, ΔC=3.20). Conclusion Sleep is a strong predictor of mortality in adults that should be considered among other more routinely used predictors. Future research should examine differences and similarities between SHHS and WSC that may explain the finding that different aspects of sleep were important in each cohort. Support NIA grant AG056331, NHLBI grant HL114473, NHLBI grant R01HL62252, NIA grant R01AG036838, NIA grant R01AG058680.

Regular consumption of red meat and health behaviors in the Brazilian population

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
D de Assumpção ◽  
S M Álvares Domene ◽  
A M Pita Ruiz ◽  
P M Stolses Bergamo Francisco
Keyword(s):  
Physical Activity ◽  
Health Behaviors ◽  
Meat Consumption ◽  
Cross Sectional Study ◽  
National Health Survey ◽  
Red Meat ◽  
Alcoholic Beverages ◽  
Cross Sectional ◽  
Unhealthy Eating ◽  
Regular Consumption

Abstract Background The consumption of red meat should not surpass 500 g of cooked weight per week. Regular consumption can exceed this recommendation, increasing the risk of chronic diseases. Objective Estimate the prevalence of the regular consumption of red meat according to health behaviors in Brazilian adults (≥18 years). Methods A cross-sectional study was conducted with data from the 2013 National Health Survey, which is a household inquiry with a representative sample of the population ≥18 years of age. The regular consumption of red meat (beef, pork, goat) was defined as ≥ 5 days/week. Prevalence ratios (PR) and 95% confidence intervals (CI) were estimated according to health behaviors (healthy and unhealthy eating patterns, smoking, practice of physical activity during leisure and alcohol intake). Results A total of 60,202 adults were interviewed, 52.9% of whom were women and mean age was 42.9 years (95%CI: 42.6-43.2). The prevalence of regular red meat consumption was 36.7% (95%CI: 36.0-37.5) and was higher among those who ingested soft drinks/artificial juice (PR = 1.08) and sweets (PR = 1.05) ≥3 days/week, ingested beans (PR = 1.07) and raw vegetables (PR = 1.03) ≥5 days/week, ingested fatty meat (PR = 1.09), smokers (PR = 1.05), individuals who were inactive during leisure (PR = 1.04) and those who consumed alcohol ≥2 times/week (PR = 1.06). The prevalence was lower among those who ate fruit (PR = 0.99) and chicken (PR = 0.95) ≥5 days/week, those who ate fish (PR = 0.90) at least 1 day/week and those who drank no fat/low fat milk rather than whole milk. Conclusions The regular consumption of red meat was higher among individuals who ingested unhealthy foods more often, those who ingested fatty meat, smokers, those who ingested alcoholic beverages and those who did not practice physical activity. Actions are needed to reduce the frequency of red meat consumption. Key messages Regular consumption of red meat can exceed the recommendation of 500 g of cooked weight per week. The regular consumption of red meat was associated with the more frequent ingestion of unhealthy foods and fatty meat.

scholarly journals Effect of a physical activity and sleep m-health intervention on a composite activity-sleep behaviour score and mental health: a mediation analysis of two randomised controlled trials

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Mitch J. Duncan ◽  
Anna T. Rayward ◽  
Elizabeth G. Holliday ◽  
Wendy J. Brown ◽  
Corneel Vandelanotte ◽  
...  
Keyword(s):  
Mental Health ◽  
Physical Activity ◽  
Trial Registration ◽  
Poor Sleep ◽  
Poor Sleep Quality ◽  
Sleep Health ◽  
Intervention Effects ◽  
Symptoms Of Depression ◽  
Randomised Controlled

Abstract Background To examine if a composite activity-sleep behaviour index (ASI) mediates the effects of a combined physical activity and sleep intervention on symptoms of depression, anxiety, or stress, quality of life (QOL), energy and fatigue in adults. Methods This analysis used data pooled from two studies: Synergy and Refresh. Synergy: Physically inactive adults (18–65 years) who reported poor sleep quality were recruited for a two-arm Randomised Controlled Trial (RCT) (Physical Activity and Sleep Health (PAS; n = 80), or Wait-list Control (CON; n = 80) groups). Refresh: Physically inactive adults (40–65 years) who reported poor sleep quality were recruited for a three-arm RCT (PAS (n = 110), Sleep Health-Only (SO; n = 110) or CON (n = 55) groups). The SO group was omitted from this study. The PAS groups received a pedometer, and accessed a smartphone/tablet “app” using behaviour change strategies (e.g., self-monitoring, goal setting, action planning), with additional email/SMS support. The ASI score comprised self-reported moderate-to-vigorous-intensity physical activity, resistance training, sitting time, sleep duration, efficiency, quality and timing. Outcomes were assessed using DASS-21 (depression, anxiety, stress), SF-12 (QOL-physical, QOL-mental) and SF-36 (Energy & Fatigue). Assessments were conducted at baseline, 3 months (primary time-point), and 6 months. Mediation effects were examined using Structural Equation Modelling and the product of coefficients approach (AB), with significance set at 0.05. Results At 3 months there were no direct intervention effects on mental health, QOL or energy and fatigue (all p > 0.05), and the intervention significantly improved the ASI (all p < 0.05). A more favourable ASI score was associated with improved symptoms of depression, anxiety, stress, QOL-mental and of energy and fatigue (all p < 0.05). The intervention effects on symptoms of depression ([AB; 95%CI] -0.31; − 0.60,-0.11), anxiety (− 0.11; − 0.27,-0.01), stress (− 0.37; − 0.65,-0.174), QOL-mental (0.53; 0.22, 1.01) and ratings of energy and fatigue (0.85; 0.33, 1.63) were mediated by ASI. At 6 months the magnitude of association was larger although the overall pattern of results remained similar. Conclusions Improvements in the overall physical activity and sleep behaviours of adults partially mediated the intervention effects on mental health and quality of life outcomes. This highlights the potential benefit of improving the overall pattern of physical activity and sleep on these outcomes. Trial registration Australian New Zealand Clinical Trial Registry: ACTRN12617000680369; ACTRN12617000376347. Universal Trial number: U1111–1194-2680; U1111–1186-6588. Human Research Ethics Committee Approval: H-2016-0267; H-2016–0181.

scholarly journals Can Health-Promoting Schools Contribute to Better Health Behaviors? Physical Activity, Sedentary Behavior, and Dietary Habits among Israeli Adolescents

2021 ◽  
Vol 18 (3) ◽  
pp. 1183
Author(s):  
Hila Beck ◽  
Riki Tesler ◽  
Sharon Barak ◽  
Daniel Sender Moran ◽  
Adilson Marques ◽  
...  
Keyword(s):  
Physical Activity ◽  
Health Behavior ◽  
Confidence Interval ◽  
Health Behaviors ◽  
Sedentary Behavior ◽  
Dietary Habits ◽  
Group Differences ◽  
Time Behavior ◽  
Health Promoting ◽  
The Mean

Schools with health-promoting school (HPS) frameworks are actively committed to enhancing healthy lifestyles. This study explored the contribution of school participation in HPS on students’ health behaviors, namely, physical activity (PA), sedentary behavior, and dieting. Data from the 2018/2019 Health Behavior in School-aged Children study on Israeli adolescents aged 11–17 years were used. Schools were selected from a sample of HPSs and non-HPSs. Between-group differences and predictions of health behavior were analyzed. No between-group differences were observed in mean number of days/week with at least 60 min of PA (HPS: 3.84 ± 2.19 days/week, 95% confidence interval of the mean = 3.02–3.34; non-HPS: 3.93 ± 2.17 days/week, 95% confidence interval of the mean = 3.13–3.38). Most children engaged in screen time behavior for >2 h/day (HPS: 60.83%; non-HPS: 63.91%). The odds of being on a diet were higher among more active children (odds ratio [OR] = 1.20), higher socio-economic status (OR = 1.23), and female (OR = 2.29). HPS did not predict any health behavior. These findings suggest that HPSs did not contribute to health behaviors more than non-HPSs. Therefore, health-promoting activities in HPSs need to be improved in order to justify their recognition as members of the HPS network and to fulfill their mission.

scholarly journals Utilizing Participatory Research to Engage Underserved Populations to Improve Health-Related Outcomes in Delaware

Nutrients ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 2353
Author(s):  
Shannon M. Robson ◽  
Samantha M. Rex ◽  
Katie Greenawalt ◽  
P. Michael Peterson ◽  
Elizabeth Orsega-Smith
Keyword(s):  
Physical Activity ◽  
Health Behavior ◽  
Health Behaviors ◽  
Participatory Research ◽  
Cooperative Extension ◽  
Underserved Populations ◽  
Community Outreach ◽  
Outreach Program ◽  
Health Related ◽  
Improve Health

Cooperative Extension is a community outreach program. Despite its large reach, there is a need for the evaluation of changes in health-related outcomes for individuals engaged with Cooperative Extension. A team-based challenge was developed using community-engaged participatory research integrated with Cooperative Extension to encourage healthy eating and physical activity behaviors through Cooperative Extension programming. Thus, the primary purpose of this secondary analysis was to (1) evaluate changes in anthropometric outcomes and (2) evaluate changes in health behavior outcomes. Associations of anthropometric changes and health behavior changes with engagement in the three-month team-based challenge were explored. Anthropometrics were measured using standard procedures, and intake of fruits and vegetables and physical activity were self-reported. Of the 145 participants in the community-engaged participatory research portion of the study, 52.4% (n = 76) had complete anthropometrics before and after the team-based challenge and were included in this study. At 3 months, there was a significant reduction in body mass index (−0.3 kg/m2, p = 0.024) and no significant change in waist circumference (p = 0.781). Fruit and vegetable intake significantly increased (+0.44 servings/day, p = 0.018). Physical activity did not significantly change based on (1) the number of days 30 or more minutes of physical activity was conducted (p = 0.765) and (2) Godin Leisure-Time Exercise Questionnaire scores (p = 0.612). Changes in anthropometrics and health behaviors were not associated with engagement in the team-based challenge. Using community-engaged participatory research with community outreach programs, such as Cooperative Extension, can improve health-related outcomes in underserved populations. However, despite a participatory approach, changes in anthropometrics and health behaviors were not associated with engagement in the developed team-based challenge.

scholarly journals The Impact of a Workplace Wellness Program on Provider Health in Early Childhood Education Settings

2019 ◽  
Vol 68 (2) ◽  
pp. 65-72 ◽  
Author(s):  
Jamie N. Powers ◽  
Charlotte V. Farewell ◽  
Emily Maiurro ◽  
Jini Puma
Keyword(s):  
Physical Activity ◽  
Early Childhood ◽  
Logistic Regression ◽  
Early Childhood Education ◽  
Health Behaviors ◽  
Healthy Eating ◽  
Fruits And Vegetables ◽  
Workplace Wellness ◽  
Physically Active ◽  
Childhood Education

Background: Early childhood education (ECE) working environments often contribute to poor health outcomes. The purpose of this study was to describe healthy eating–related and physical activity–related awareness and adoption of behavior change of ECE providers after participating in a workplace wellness (WW) program and to explore facilitators and barriers to ECE provider participation in WW program. Methods: The WW program offered healthy eating and physical activity challenges to promote ECE provider health and well-being. Approximately 1,000 ECE providers in Colorado from 35 ECE settings were invited to participate. After the intervention, ECE providers completed two surveys: (a) a provider postsurvey and (b) a WW challenge survey. Multivariable logistic regression modeling was used to examine factors associated with percent agreement that participation in the WW program increased awareness and adoption of health behaviors. Findings: A total of 250 (25%) ECE providers participated in WW program from 2015 to 2017. After participation, approximately 84% of respondents agreed they were more aware of the importance of eating fruits and vegetables and of being physically active, while 81% reported eating more fruits and vegetables, and 80% reported being more physically active in the workplace. Logistic regression models found that the length of time teaching in ECE settings was positively and significantly associated (odds ratio [OR] = 1.10, 95% confidence interval [CI] = [1.00, 1.21]) with the odds of providers agreeing that participation in the WW program increased their awareness of health behaviors. Conclusion/Application to Practice: The design and implementation of WW programs that emphasize facilitators, such as intrinsic and extrinsic motivation, as well as reduce barriers, such as time constraints and unachievable goal setting, may increase the awareness and adoptions of healthy eating–related and physical activity–related behaviors among ECE work settings.

scholarly journals 660 Chronotype Associations with Insomnia, Depressive Symptoms, and Changes in Sleep and Health Behaviors during the COVID-19 Pandemic

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A258-A258
Author(s):  
Megan Petrov ◽  
Matthew Buman ◽  
Dana Epstein ◽  
Shawn Youngstedt ◽  
Nicole Hoffmann ◽  
...  
Keyword(s):  
Physical Activity ◽  
Depressive Symptoms ◽  
Health Behaviors ◽  
Screen Time ◽  
Online Survey ◽  
Light Exposure ◽  
Multinomial Logistic Regression ◽  
Depression Scale ◽  
Time In Bed ◽  
Significant Difference

Abstract Introduction Evening chronotype (i.e., night owl preference) is associated with worse insomnia and depressive symptoms, and poorer health behaviors. The aim of this study was to examine the association between chronotype and these symptoms and health behaviors during COVID-19 pandemic quarantine. Methods An online survey, distributed internationally via social media from 5/21/2020–7/1/2020, asked adults to report sociodemographic/economic information, changes in sleep (midpoint, total sleep time, sleep efficiency, time-in-bed), and health behaviors (i.e., physical activity, sedentary screen time, and outdoor light exposure patterns) from prior to during the pandemic, chronotype preference (definitely morning [DM], rather more morning [RM], rather more evening [RE], or definitely evening [DE]), and complete the Insomnia Severity Index (ISI) and the 10-item Center for Epidemiologic Studies Depression scale (CES-D-10). Multinomial logistic regression and ANCOVA models, adjusting for age and sex, examined associations of chronotype with COVID-19 pandemic related impacts on sleep, depressive symptoms, and health behaviors. Results A subsample of 579 participants (M age: 39y, range: 18–80; 73.6% female), currently under quarantine and neither pregnant nor performing shift work, represented each chronotype evenly (~25%). Participants delayed their sleep midpoint by 72.0min (SD=111.5) during the pandemic. DE chronotypes had a greater delay than morning types (M±SD DE: 91.0±9.0 vs. RM: 55.9±9.2 & DM: 66.1±9.3; p=0.046) with no significant change in other sleep patterns relative to other chronotypes. However, DE and RE chronotypes had greater odds of reporting that their new sleep/wake schedule was still not consistent with their “body clock” preference relative to morning types (Χ2[15]=54.8, p&lt;0.001), reported greater ISI (F[3,503]=5.3, p=.001) and CES-D-10 scores (F[3,492]=7.9, p&lt;.001), and had greater odds for increased or consistently moderate-to-high sedentary screen time (Χ2[12]=22.7, p=0.03) and decreased physical activity (Χ2[12]=22.5, p=0.03) than DM chronotype. There was no significant difference in change in outdoor light exposure by chronotype (Χ2[12]=12.1, p=0.43). Conclusion In an international online sample of adults under COVID-19 pandemic quarantine, evening chronotypes, despite taking the opportunity to delay sleep to match biological clock preference, reported their sleep/wake schedules were still inconsistent with personal preference, and reported greater insomnia and depressive symptoms, and odds of engaging in poorer health behaviors than morning chronotypes. Support (if any):

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