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The purpose of this study was to investigate the combined impact of obesity and physical activity (PA) on the health of older adults. Pedometer-determined steps/d, body-mass index (BMI), resting blood pressure, and fasting glucose (FG) were assessed in 137 older adults (69.0 ± 8.9 yr). The active group (>4,227 steps/d) had lower systolic blood pressure (SBP; p = .001), diastolic blood pressure (DBP; p = .028), and FG (p < .001) than the inactive group (≤4,227 steps/d). The normal-BMI group (18.5-24.9 kg/m²) had lower SBP (p < .001) and DBP (p = .01) than the obese group (≥30 kg/m²). There were no differences in SBP (p = .963) or DBP (p = 1.0) between active obese and inactive normal-BMI groups. The active obese group, however, had a more favorable FG than the inactive normal-BMI group (χ² = 18.9, df = 3, p = .001). Efforts to increase PA of older adults should receive the same priority as reducing obesity to improve BP and FG levels. [ABSTRACT FROM AUTHOR]

Introduction: Physical activity is known to significantly impact cardiometabolic health. Accelerometer data, as a measure of physical activity, can be used to objectively identify a disparity in movement (movement discordance) between healthy and unhealthy adults. The purpose of this study was to examine the Movement Discordance between healthy and unhealthy adults in a large US population sample. Methods: Demographic, health and accelerometer data from the National Health and Nutrition Examination Study (NHANES) 2003–2004 and 2005–2006 cohorts were used for this study. Participants were classified as either having a “normal” or “abnormal” value for each cardiometabolic health parameter examined, based on published criteria. Linear regression analyses were performed to determine significance of each abnormal health parameter (risk factor) in its unique effect on the accelerometer counts, controlling for age and gender. Average accelerometer counts per minute (cpm) by gender and age categories were estimated separately for the groups of normal and abnormal cardiometabolic risk. Results: Average cpm for those with healthy levels of each individual cardiometabolic health parameter range from 296 cpm (for C reactive protein) to 337 cpm (for waist circumference), while average cpm for those with abnormal levels of each individual cardiometabolic health parameter range from 216 cpm (for insulin) to 291 cpm (for LDL-cholesterol). After controlling for age and gender, waist circumference, HbA1c, Insulin, Homocysteine, and HDL-Cholesterol were the cardiometabolic health parameters that showed significant, unique and independent effects on cpm. Overall, individuals who have abnormal values for all significant cardiometabolic health parameters (“unhealthy”) averaged 267 cpm (SE = 15 cpm), while the healthy sample of this study averaged 428 cpm (SE = 10 cpm). The difference in cpm between the unhealthy and healthy groups is similar between males and females. Further, for both males and females, the cpm gap between unhealthy and healthy is largest in the 30s (males: 183 cpm; females 144 cpm) and lessens as age increases, with the lowest gap seen in those 80+ years (males, 81 cpm; females, 85 cpm). Conclusion: This Movement Discordance between healthy and unhealthy adults represents a gap in movement that needs to be closed to improve the health of individuals with, or at risk for cardiometabolic disease. [ABSTRACT FROM AUTHOR]

Nowhere is improving understanding and accurate assessment of physical activity more important for disease prevention and health promotion than among health disparities populations such as those residing in rural and Appalachian regions. To enhance accurate assessment of physical activity and potentially improve intervention capacity, we conducted a mixed-methods study examining the acceptability and feasibility of self-report physical activity questionnaires, pedometers, and accelerometers among rural Appalachian children, adolescents, and adults. Most participants reported positive experiences with all three physical activity assessment tools. Several acceptability ratings differed by age group and by sex within each age group. With very few exceptions, no significant differences in acceptability were found by race, education, employment status, health status, BMI categories, income levels, or insurance status within age groups or overall. Several factors may impact the choice of the physical activity assessment method, including target population age, equipment cost, researcher burden, and potential influence on physical activity levels. Children and adolescents appear to have more constraints on when they can wear pedometers and accelerometers. While pedometers are inexpensive and convenient, they may influence physical activity levels, rather than simply measure them. Accelerometers, while less influential on behavior, consume extensive resources, including high purchase costs and researcher burden. [ABSTRACT FROM AUTHOR]

Introduction. It is unclear if community-based fitness resources (CBFR) translate to heightened activity levels within areas neighboring. The purpose of this study was to determine whether awareness and utilization of fitness resources and physical activity differed depending on residential distance from CBFR. Methods. Four hundred and seventeen older adults (72.9 ± 7.7 years) were randomly recruited from three spatial tiers (≤1.6, >1.6 to ≤3.2, and >3.2 to 8.0 km) surrounding seven senior centers, which housed CBFR. Participants completed questionnaires on health history, CBFR, and physical activity, gathering data on CBFR awareness, utilization, and barriers, overall levels, and predictors to engagement in moderate to vigorous physical activity (MVPA). Results. Across spatial tiers, there were no differences in positive awareness rates of CBFR or CBFR utilization. Engagement in MVPA differed across spatial tiers (P < 0.001), with the >3.2 to 8.0 km radius having the highest mean energy expenditure. Across all sites, age and income level (P < 0.05) were significant predictors of low and high amounts of MVPA, respectively, and current health status and lack of interest represented barriers to CBFR utilization (P < 0.05). Conclusion. Closer proximity to CBFR did not impact awareness or utilization rates and had an inverse relationship with physical activity. [ABSTRACT FROM AUTHOR]

Background: Long-term pedometer monitoring has not been attempted. Purpose: The purpose of this project was to collect 365 days of continuous self-monitored pedometer data to explore the natural variability of physical activity. Methods: Twenty-three participants (7 men, 16 women; M age = 38± 9.9 years; M body mass index = 27.7± 6.2 kg/m2) were recruited by word of mouth at two southern U.S. universities. Participants were asked to wear pedometers at their waist during waking hours and record steps per day and daily behaviors (e.g., sport/exercise, work or not) on a simple calendar. In total, participants wore pedometers and recorded 8,197 person-days of data (of a possible 8,395 person-days, or 98%) for a mean of 10,090± 3,389 steps/day. Missing values were estimated using the Missing Values Analysis EM function in SPSS, Version 11.0.1. Results: A mean of 10,082± 3,319 steps/day was computed. Using the corrected data, differences in steps/day were significant for season (summer>winter, F = 7.57, p = .001), day of the week (weekday>weekend, F = 3.97, p = .011), type of day (workday vs. nonworkday, F = 9.467, p = .008), and participation in sport/exercise (day with sport/exercise>day without sport/exercise, F = 102.5, p<.0001). Conclusions: These data suggest that surveillance should be conducted in the spring/fall or that an appropriate correction factor should be considered if the intent is to capture values resembling the year-round average. [ABSTRACT FROM AUTHOR]

Purpose: The prevalence of female homemakers (those who stay at home to care for the home or family) has increased to 29%. Homemakers may be more active than employed women (EW). Limited data are available for domestic-related activity; therefore, the assessment of the activity levels of homemakers has been sparse. This study compared objectively measured activity (total activity counts, counts per minute, and percent time in various activity intensity levels) of homemakers and EW. Methods: Women's (18-60 yr) accelerometer data from the 2003-2006 National Health and Nutrition Examination Survey were analyzed in 2014 (n = 1763). Daily (hour-by-hour) profiles of activity were compared between homemakers and EW. Results: Women spent most of their day in sedentary (~55%) and light (~32%) activity, with limited lifestyle (~11%) and moderate vigorous physical activity (MVPA; ~2%); and there were no differences between the homemakers and EW. Hour-by-hour analysis showed that the homemakers had more light and less sedentary activity than EW during the afternoon (P < 0.002), whereas EW had more MVPA at times corresponding to commuting to and from work and midday (P < 0.002). On weekdays, EW initiated activity earlier than homemakers but not on weekends. On weekends, both groups had less MVPA than weekdays. Employed women with child(ren) younger than 18 yr had greater counts per minute and lifestyle activity and less sedentary activity than EW without child(ren) younger than 18 yr. Conclusion: Our hourly analysis delineated important differences in activity between the groups. Homemakers accumulate enough light activity throughout the day to be as active as EW who are highly sedentary during the workday, but seem to acquire activity through commuting. Interventions to reduce sedentary behavior and increase activity are highly desirable and should take into consideration the temporality of homemakers and EW activity patterns. [ABSTRACT FROM AUTHOR]

Background: Metabolic syndrome (MetS) increases the risk of cardiovascular disease morbidity and mortality. Physical activity (PA) reduces the likelihood of MetS, but it is a complex behavior and is accumulated in multiple domains., Purpose: To cross-sectionally investigate the total and domain-specific relationships between PA and MetS in U.S. adults., Methods: Data from 3,408 adults participating in the National Health and Nutrition Examination Survey (2013-2018) were analyzed. Blood pressure (BP), waist circumference (WC), fasting blood glucose (GLU), triglycerides (TRIG), and high-density lipoprotein (HDL) were measured. MetS and its risk factors were the primary and secondary outcomes, respectively. Weekly minutes of total PA and domain-specific PA (i.e., leisure-time (LTPA), transportation (TPA), occupational & household (OHPA)), were self-reported. For each exposure, participants were grouped into weekly PA: (1) 0 min, (2) 1-149 min, (3) 150-299 min, (4) 300-599 min, and (5) 600 + minutes. Logistic regression estimated the odds of having MetS, and its risk factors from PA., Results: Total PA was associated with lower odds of most MetS risk factors. Compared to no LTPA, and independent of TPA and OHPA, engaging in 150-299 and 300-599 min/week of LTPA was associated with 30% (OR = 0.70 [95%CI: 0.50, 0.98]) and 43% (OR = 0.57 [95%CI: 0.35, 0.92]) lower odds of MetS, respectively. LTPA was also associated with lower odds of having high WC, GLU, TRIG, and low HDL (ORs = 0.52-0.68). Compared to no TPA, and independent of LTPA and OHPA, engaging in 300-599 min/week of TPA was associated with 54% lower odds of MetS (OR = 0.46 [95%CI: 0.25, 0.84]) and 40% lower odds of having a high WC (OR = 0.40 [95%CI: 0.21, 0.76]). Engaging in OHPA was not associated with MetS but was associated with greater odds of having a high WC (OR = 1.44 [95%CI: 1.03, 2.01]), and GLU (ORs = 1.52-1.83), independent of LTPA and TPA., Conclusion: Total PA, seemingly driven by LTPA, was inversely associated with cardiometabolic health. TPA also showcases some protective associations, while OHPA appears to not confer cardiometabolic health benefits. Longitudinal data should confirm these associations using more robust PA measurement tools., Competing Interests: Declarations. Ethics approval and consent to participate: NHANES 2013-2018 received NCHS Ethics Review Board approval under “Protocol #2011-17”. All participants provided their informed consent to participate in the study. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

The deleterious health consequences of physical inactivity are vast, and they are of paramount clinical and research importance. Risk identification, benchmarks, efficacy, and evaluation of physical activity behavior change initiatives for clinicians and researchers all require a clear understanding of how to assess physical activity. In the present report, we have provided a clear rationale for the importance of assessing physical activity levels, and we have documented key concepts in understanding the different dimensions, domains, and terminology associated with physical activity measurement. The assessment methods presented allow for a greater understanding of the vast number of options available to clinicians and researchers when trying to assess physical activity levels in their patients or participants. The primary outcome desired is the main determining factor in the choice of physical activity assessment method. In combination with issues of feasibility/practicality, the availability of resources, and administration considerations, the desired outcome guides the choice of an appropriate assessment tool. The decision matrix, along with the accompanying tables, provides a mechanism for this selection that takes all of these factors into account. Clearly, the assessment method adopted and implemented will vary depending on circumstances, because there is no single best instrument appropriate for every situation. In summary, physical activity assessment should be considered a vital health measure that is tracked regularly over time. All other major modifiable cardiovascular risk factors (diabetes mellitus, hypertension, hypercholesterolemia, obesity, and smoking) are assessed routinely. Physical activity status should also be assessed regularly. Multiple physical activity assessment methods provide reasonably accurate outcome measures, with choices dependent on setting-specific resources and constraints. The present scientific statement provides a guide to allow professionals to make a goal-specific selection of a meaningful physical activity assessment method.

Background: This study examined the independent association of objectively measured physical activity on insulin resistance while controlling for confounding variables including: cardiorespiratory fitness, adiposity, sex, age, and smoking status., Methods: Data were obtained from National Health and Nutrition Examination Survey 2003-2004, a cross-sectional observational study conducted by the National Center for Health Statistics of the Centers for Disease Control that uses a stratified, multistage probability design to obtain a nationally representative sample of the U.S. population. The analysis included 402 healthy U.S. adults with valid accelerometer, cardiorespiratory fitness, and fasting plasma glucose and insulin concentrations. After controlling for relevant confounding variables we performed a multiple linear regression to predict homeostatic model of insulin resistance (HOMA-IR) based on average daily minutes of moderate-to-vigorous physical activity (MVPA)., Results: In our bivariate models, MVPA, cardiorespiratory fitness and body fat percentage were all significantly correlated with log HOMA-IR. In the complete model including MVPA and relevant confounding variables, there were strong and significant associations between MVPA and log HOMA-IR (β= -0.1607, P=0.004). In contrast the association between cardiorespiratory fitness and log HOMA-IR was not significant., Conclusion: When using an objective measure of physical activity the amount of time engaged in daily physical activity was associated with lower insulin resistance, whereas higher cardiorespiratory fitness was not. These results suggest that the amount of time engaged in physical activity may be an important determinant for improving glucose metabolism.

Introduction: Little is known about the relation between duration of physical activity and obesity. The objective of this study was to compare the effects of physical activity in bouts (> or = 10 minutes) to the effects of physical activity in nonbouts (<10 minutes) on markers of obesity., Methods: We used data from the 2003-2004 National Health and Nutrition Examination Survey on body mass index, waist circumference, and objectively determined physical activity levels for 3,250 adults aged 18 years or older. After controlling for relevant confounding variables, we performed multiple linear regression analyses to predict body mass index and waist circumference for bout and nonbout minutes of moderate- to vigorous-intensity physical activity (MVPA) and for bout and nonbout accelerometer counts of physical activity., Results: MVPA bout minutes and MVPA nonbout minutes are independently associated with body mass index and waist circumference, after controlling for confounding variables. The strength of the association between lower body mass index and MVPA bout minutes (beta = -0.04, P <.001) was nearly 4 times greater than for MVPA nonbout minutes (beta = -0.01, P = .06). For smaller waist circumference the association was nearly 3 times greater for MVPA bout minutes (beta = -0.09, P <.001) than for MVPA nonbout minutes (beta = -0.03, P = .01). Bout minutes of physical activity were at a higher intensity of activity compared with nonbout minutes of physical activity., Conclusion: Accumulating MVPA in nonbouts may be a beneficial starting point for individuals to increase physical activity levels and decrease body mass index and waist circumference. However, bouts of physical activity lasting > or = 10 minutes may be a more time-efficient strategy to decrease body mass index and waist circumference.

Unlabelled: Current methods for validating physical activity (PA) questionnaires typically use accelerometers as objective measures for comparison with questionnaire responses and obtain low correlations or agreement., Purpose: We examined possible reasons for weak associations in validation studies by comparing published ActiGraph accelerometer intensity cut points with cut points based on intensity thresholds for heart rate response to PA., Methods: Twelve adults (five men, seven women; age 31.0 +/- 14.3 yr) wore an ActiGraph accelerometer and a Polar Vantage NV heart watch simultaneously for seven consecutive days during their waking hours. We identified PA bouts from the minute-by-minute ActiGraph data using published accelerometer thresholds for defining moderate- and vigorous-intensity PA. We then compared PA bout intensities identified with these criteria with intensity classifications of the PA bouts using mean percent heart rate reserve (HRR)., Results: Most of the moderate-intensity PA bouts identified by the Freedson (78.3%), Swartz (88.0%), and Hendelman (94.7%) ActiGraph cut points were associated with a mean %HRR < 45% (very light and light intensities). The estimated mean frequency with which study participants engaged in moderate-intensity PA varied with the cut points and type of bouts used and ranged from 1.1 d.wk(-1) (45-60%HRR) to 7.0 d.wk(-1) (Hendelman cut points). The mean total duration on active days ranged from 17.9 min.d(-1)(45-60%HRR) to 139.2 min.d(-1) (Hendelman cut points). Fewer bouts of vigorous PA were found in the accelerometer data, and most were in the vigorous-intensity category of > or = 60%HRR., Conclusions: The method used for analyzing ActiGraph activity data can result in large differences in the summary measure of minutes of moderate- to vigorous-intensity activity.