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Objective Measures of Activity in the Elderly: Distribution and Associations With Demographic and Health Factors.

ABSTRACT: BACKGROUND:Little is known about the distribution of activity over the full 24-hour spectrum in late old age and its association with demographic and health factors. Therefore, we aimed to evaluate the distribution of physical activity (PA), sedentary behavior, and sleep, and associated factors in the elderly population. METHODS:Our study included 1210 participants (51.9% women) aged 70-94 years [mean age 77.5 years, standard deviation (SD) 5.0] from the population-based Rotterdam Study. Participants wore a triaxial accelerometer (GENEActiv) around the wrist for 7 days between July 2014 and June 2016. We examined if PA, sedentary behavior, and sleep differed by age, sex, body mass index (BMI), smoking status, alcohol consumption, education, season, functional capacity, marital status, presence of chronic disease, and use of sleep medication. RESULTS:Mean total PA, expressed in milli-gravity (mg) units, was slightly higher for women (20.3, SD 5.6) than for men (19.3, SD 5.2, P < .01). Mean (SD) daily duration spent in sedentary behavior and light and moderate-to-vigorous PA was 13.3 (1.5) h/d, 147.5 (31.5) min/d, and 75.0 (25.5) min/d, respectively, among women; and 13.8 (1.6) h/d, 140.5 (31.1) min/d, and 71.5 (24.5) min/d, respectively, among men. Women spent on average 6.7 (SD 1.1) h/d sleeping and men 6.6 (1.4) h/d. Across increasing categories of age and BMI and in participants with chronic disease and disability, time spent in light and moderate-to-vigorous PA was decreased. Higher age and BMI were associated with more sedentary time. In addition, obese men spent slightly more time sleeping than their normal weight counterparts and women spent slightly less time sleeping in the summer than in spring. CONCLUSIONS:PA and sedentary behavior in the elderly differed by sex, age, BMI, prevalence of chronic disease, and disability, whereas there were no clear patterns for sleep. On average, our participants spent up to 79.5% of their time awake being sedentary and 7%-8% in moderate-to-vigorous PA. Replacing sedentary behavior with light PA would be a good starting point for those with the lowest level of PA. Older adults, those with high BMI and worse health could benefit from targeted interventions to increase PA.

SUBMITTER: Koolhaas CM

PROVIDER: S-EPMC6276982 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Objective Measures of Activity in the Elderly: Distribution and Associations With Demographic and Health Factors.

Koolhaas Chantal M CM van Rooij Frank J A FJA Schoufour Josje D JD Cepeda Magda M Tiemeier Henning H Brage Soren S Franco Oscar H OH

Journal of the American Medical Directors Association 20171001 10

<h4>Background</h4>Little is known about the distribution of activity over the full 24-hour spectrum in late old age and its association with demographic and health factors. Therefore, we aimed to evaluate the distribution of physical activity (PA), sedentary behavior, and sleep, and associated factors in the elderly population.<h4>Methods</h4>Our study included 1210 participants (51.9% women) aged 70-94 years [mean age 77.5 years, standard deviation (SD) 5.0] from the population-based Rotterdam ...[more]

PMID: 28602617

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Project description:BackgroundCortisol is often used as a biological marker for stress. When measured in urine or serum, representing a short-term measurement of the hormone, it has been associated with unfavorable sleep characteristics and both low and high physical activity levels. However, cortisol in hair represents a long-term stress measure and has been suggested as a promising new marker for chronic stress. Therefore, we aimed to examine the association between objectively measured sleep, physical activity and hair cortisol levels in preschool children.MethodsIn order to obtain objective measures of physical activity and sleep habits, 54 children aged 2-6 years wore an ActiGraph for 5 consecutive days and nights. For chronic stress measurements of each child, hair was cut from the back of the head close to the scalp for analysis of cortisol levels. Associations between measured sleep quality and quantity and level of physical activity and hair cortisol levels were estimated using linear regression analysis, presented as β. Results were adjusted for sex, age and BMI z-score.ResultsWe found no significant association between log-transformed cortisol (pg/mg) and sleep duration (hours) (β = - 0.0016, p = 0.99), sleep efficiency (β = - 3.1, p = 0.18), sleep latency (β = 0.015, p = 0.16) or physical activity level (100 counts per min) (β = 0.014, p = 0.22). However, sleep latency (min) was directly associated with physical activity (counts per min) levels (β = 35.2, p = 0.02), while sleep duration (hours) (β = - 142.1, p = 0.55) and sleep efficiency (%) (β = - 4087, p = 0.26) showed no significant associations.ConclusionsIn our study, a high physical activity level was associated with poorer sleep habits. Neither sleep quality nor physical activity were related to long term cortisol exposure. These results are among the first to study associations between objectively measured sleep, physical activity and chronic cortisol levels among preschool children. More and larger studies are therefore needed.

Project description:The purpose of this study was to examine associations between the Walk Score and physical activity in young, overweight/obese urban women. Project Health included 45 White or African American women (BMI 31.5±3.9 kg/m2; age 26.5±4.6 years; 62% African American) living in the Boston area. An accelerometer estimated steps/day and mins/day in light physical activity (100-2019 counts-per-minute) and moderate-to-vigorous-physical activity (≥2020 cpm). Walk Score was used to estimate the walkability of home address by analyzing proximity to nearby amenities. General linear regression models estimated associations between total Walk Score and physical activity (light physical activity, moderate-to-vigorous-physical activity, steps, total activity counts, METs), adjusting for body mass index, age, race/ethnicity, seasonality, wear time, employment and student status. For physical activity variables that had significant associations with Walk Score (steps/day and steps/min), regression models were estimated for Walk Score sub-scores (parks, grocery, errands, shopping, dining/drinking, culture/entertainment and schools). Logistic regression models estimated the odds of meeting the guidelines for steps (≥10,000/day) and moderate-to-vigorous-physical activity (≥150mins MVPA/week) based on Walk Score. Participants had a Walk Score of 63.9±26.4, took 14,143±3,934 steps/day, and spent 206.2±66.0 mins/day in light physical activity and 46.7±17.5 mins/day in moderate-to-vigorous- physical activity. Walk Score was significantly and positively associated with steps/day (β = 51.4, p = 0.01) and steps/min (β = 0.06, p = 0.009) but was not associated with mins/day of light physical activity, moderate-to-vigorous-physical activity, total activity counts or METs. Parks, grocery, errands, shopping, dining/drinking, and culture/entertainment Walk Score sub-scores were significantly associated with steps and steps/min (all p<0.05), but not significantly associated for schools. Participants who lived in higher Walk Score neighborhoods were more likely to meet the step guidelines (OR, 95% CI: 1.59, 1.04-2.99) and moderate-to-vigorous-physical activity guidelines (1.63, 1.06-3.02), respectively, per 10-unit increase in Walk Score. These results indicate that living in a more walkable neighborhood may support walking behavior in young, urban-dwelling overweight/obese women and provide further evidence for the expanded use of urban planning and transportation policies to improve the walkability of urban neighborhoods.

Project description:PURPOSE:Although physical activity is an established risk factor for chronic disease prevention, the specific mechanisms underlying these relationships are poorly understood. We examined the associations between total activity counts and moderate-vigorous physical activity (MVPA) measured by accelerometer, and physical activity energy expenditure measured by doubly labeled water, with plasma levels of proinsulin, insulin, c-peptide, insulin growth factor binding protein-3, insulin growth factor-1, adiponectin, leptin, and leptin-sR. METHODS:We conducted a cross-sectional analysis of 526 healthy US women in the Women's Lifestyle Validation Study, 2010 to 2012. We performed multiple linear regression models adjusting for potential lifestyle and health-related confounders to assess the associations between physical activity, measured in quartiles (Q) and biomarkers. RESULTS:Participants in Q4 versus Q1 of total activity counts had lower proinsulin (-20%), c-peptide (-7%), insulin (-31%), and leptin (-46%) levels, and higher adiponectin (55%), leptin-sR (25%), and insulin growth factor-1 (9.6%) levels (all P trend ? 0.05). Participants in Q4 versus Q1 of MVPA had lower proinsulin (-26%), c-peptide (-7%), insulin (-32%), and leptin (-40%) levels, and higher adiponectin (31%) and leptin-sR (22%) levels (all P trend ? 0.05). Further adjustment for body mass index (BMI) attenuated these associations, but the associations with adipokines remained significant. Those in Q4 versus Q1 of physical activity energy expenditure had lower leptin (-21%) and higher leptin-sR (10%) levels (all P trend ? 0.05), after additional adjustment for BMI. In the sensitivity analysis, the associations were similar but attenuated when physical activity was measured using the subjective physical activity questionnaire. CONCLUSIONS:Our data suggest that greater physical activity is modestly associated with favorable levels of cardiometabolic and endocrine biomarkers, where the strongest associations were found with accelerometer-measured physical activity. These associations may be only partially mediated through BMI, further supporting the role of physical activity in the reduction of cardiometabolic and endocrine disease risk, independent of adiposity.

Project description:BackgroundSedentary behavior has been reported to be associated with metabolic and vascular health independent of moderate-to-vigorous physical activity (MVPA). In order to select appropriate options to measure sedentary behavior in practice and research settings, it is worthwhile to characterize the extent to which objective and subjective measures of sedentary behavior quantify adverse health risks in the same population. This cross-sectional analysis compared accelerometer-derived and self-reported sedentary time to identify their association with cardio-metabolic risk factors.MethodsCross-sectional analysis was conducted using data from 661 Japanese workers (145 women) aged 20-64 years. Participants wore a tri-axial accelerometer device for 10 consecutive days and completed the Japan Atherosclerosis Longitudinal Study Physical Activity Questionnaire. Data on body mass index, waist circumference, resting blood pressure, triglycerides, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol, total:HDL cholesterol ratio, blood glucose, and glycosylated hemoglobin (HbA1c) were obtained from annual health examinations.ResultsBoth accelerometer-derived and self-reported sedentary time were deleteriously associated with triglycerides, HDL-cholesterol, total:HDL ratio, and HbA1c after adjustment for potential confounders including MVPA. There were no significant differences in regression coefficients between the two measures. Thus, the magnitude of the associations of both measures with cardio-metabolic risk factors was similar, despite poor agreement between them. Occupational sedentary time was correlated with both measures of total sedentary time, and more consistently associated with cardio-metabolic risk factors than sedentary leisure time.ConclusionsBoth accelerometer and self-report measurements are similarly associated with cardio-metabolic risk factors in a Japanese working adult population. Subjective and objective measures of sedentary behaviors appear to capture different aspects of behaviors. Further efforts to establish data processing methods integrating objective and subjective measures are needed to more effectively assess sedentary time's relationship to health outcomes.

Project description:Background:Health insurance is one of the instruments to achieve universal health coverage. However, in Ethiopia, the coverage of health insurance is very low and varies from place to place as well. Therefore, exploring the spatial distribution of health insurance is important to prioritize and design targeted intervention programs in the country. Methods:A total of 16,583 reproductive age group women (15-49?years) were included in this study. The Bernoulli model was used by applying Kulldorff methods using the SaTScan software to analyse the purely spatial clusters of health insurance coverage. ArcGIS version 10.3 was used to visualize the distribution of health insurance coverage across the country. Mixed-effect logistic regression analysis was also used to identify predictors of health insurance coverage. Results:Health insurance coverage among women aged 15-49?years had spatial variations across the country (Moran's I: 0.115, p?<?0.001). Health insurance coverage in Amhara (p?<?0.001) and Tigray (p?<?0.001) National Regional States clustered spatially. Reading newspapers at least once a week (Adjusted Odds Ratio (AOR)?=?1.78, 95% CI: (1.18-2.68))), 40-44?years of age (AOR?=?2.14, 95% CI: (1.37-3.35)), clerical working mothers (AOR?=?4.33, 95% CI: (2.50-7.49)), mothers' with secondary school education (AOR?=?1.77; 95% CI: (1.21-2.58)), mothers' with higher school education (AOR?=?2.62; 95% CI: (1.63-4.23)), having more than 5 family members (AOR?=?1.25; 95% CI: (1.01-1.55)) and richest wealth quantile (AOR?=?3.43, 95% CI: (1.96-6.01)) were predictors of health insurance coverage among reproductive age group women in Ethiopia. Conclusion:Health insurance coverage was very low in Ethiopia and had spatial variations across the country. The hot spot areas with low health insurance coverage need more coherent and harmonized action such as strengthening financial protection through national health packages, sharing experience from regions which have better health insurance coverage and using mass media to increase awareness and confidence of potentials in the systems, which may encourage them to enrol.

Dataset Information

Objective Measures of Activity in the Elderly: Distribution and Associations With Demographic and Health Factors.

Publications

Objective Measures of Activity in the Elderly: Distribution and Associations With Demographic and Health Factors.

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