Sedentary Minutes Research Articles

Degree of implementation of a play-based curricular intervention affects children’s movement in afterschool settings

Programs implemented in afterschool settings can support children’s health; however, their effectiveness may depend on the degree of implementation which can vary by school. In this cluster-randomized controlled trial, we assessed the effect of a play-based curricular intervention on physical activity (PA) levels among children (N=133) attending seven intervention and seven comparison afterschool programs in Arizona (U.S.) using general linear mixed models, and examined how degree of intervention implementation impacted children’s PA using linear regression models. PA was measured using wrist-worn accelerometers and degree of implementation was measured using a researcher-developed a 100-point index with data from surveys and training attendance from each school. After the intervention, sedentary time among children receiving the curriculum was reduced by an average of 10.2 minutes daily. When degree of implementation was considered, we found variability between schools in children’s sedentary time and moderate-to-vigorous PA (MVPA) after the intervention. Based on the average time spent in the afterschool programs (2.9 hours), children averaged 5.2 more MVPA minutes and 8.7 less sedentary minutes for every 10-point increase in implementation index score. Considering the 30-point variability in scores between schools, this translated to children spending up to 16 minutes more in MVPA and 26 minutes less being sedentary in afterschool programs with the highest level of implementation compared to the lowest. Degree of implementation matters when integrating curricula in afterschool settings that target children’s movement. Stronger implementation may reduce children’s sedentary behavior and increase MVPA.

Characterizing daily physical activity patterns with unsupervised learning via functional mixture models.

Physical inactivity is a significant public health concern. Consideration of inter-individual variations in physical activity (PA) trends can provide additional information about the groups under study to aid intervention design. This study aims to identify latent profiles ("phenotypes") based on daily PA trends among adults living in. This was a secondary analysis of 724 person-level days of accelerometry data from 133 urban-dwelling adults (89% Latinx, age = 19-77 years). We used Actigraph accelerometers and the Actilife software to collect and process 24-hour PA data. We implemented a probabilistic clustering technique based on functional mixture models. Multiple days of data per person were averaged for entry into the models. We evaluated step counts, moderate-intensity PA (MOD), total activity and sedentary minutes as potential model variables. Bayesian Information Criterion (BIC) index was used to select the model that provided the best fit for the data. A 4-cluster resolution provided the best fit for the data (i.e., BIC=-3257, improvements of Δ = 13 and Δ = 7 from 3- and 5-cluster models, respectively). MOD provided the greatest between-cluster discrimination. Phenotype 1 (N = 61) was characterized by a morning peak in PA that declined until bedtime. Later bedtimes and the highest daily PA volume were distinct for phenotype 2 (N = 18), along with a similar peak pattern. Phenotype 3 (N = 29) membership was associated with the lowest PA levels throughout the day. Phenotype 4 was characterized by a more evenly distributed PA during the day, and later waking/bedtimes. Our findings point to distinct, interpretable PA phenotypes based on temporal patterns. Functional clustering of PA data could provide additional actionable points for tailoring behavioral interventions.

Do Children's Health Behaviors Buffer the Impact of Cumulative Environmental Stress on Emerging Cardiometabolic Risk?

Previous studies have found that exposure to childhood environmental stress is associated with cardiometabolic risk. However, it is not known whether individual health behaviors disrupt this relationship. This study prospectively evaluated the relationship between cumulative environmental stress in a low-income sample and cardiometabolic risk in middle childhood and examined whether child health behaviors attenuated this relationship. In a cohort of children (n=338; 57% Hispanic children; 25% Black children), environmental stressors (family and neighborhood factors representing disadvantage/deprivation) and child health behaviors (accelerometry measured physical activity; parent-reported screen time and diet recalls) were measured over 5 time points beginning when children were aged 2 to 4 years and ending when they were aged 7 to 11 years. Children's cardiometabolic risk factors (body mass index, blood pressure, triglyceride/high-density lipoprotein ratio, glucose, hemoglobin A1c, C-reactive protein) were measured at 7 to 11 years. Emerging cardiometabolic risk was defined as having ≥1 elevations that exceeded clinical thresholds. In adjusted path analyses, greater cumulative environmental stress was associated with higher likelihood of emerging cardiometabolic risk in middle childhood (P<0.001). Higher levels of moderate to vigorous physical activity and fewer sedentary minutes attenuated the positive relationship between stress and cardiometabolic risk (P<0.05). Children with >2 hours of average daily screen time had a higher likelihood of elevated cardiometabolic risk (P<0.01), but screen time did not moderate the stress-cardiometabolic risk relationship. Dietary intake was not related to cardiometabolic risk. Interventions that promote moderate to vigorous physical activity and limit sedentary behavior may have particular importance for the cardiometabolic health of children exposed to high levels of cumulative environmental stress.

Changes in sedentary behavior in the chronic phase following stroke

BackgroundSedentary behavior increases risk for cardiovascular diseases. Little is known about sedentary behavior through the chronic phase after stroke. We aimed to describe how long and short bouts of sedentary behavior changed over the first three years after stroke and if cognition at baseline was an independent risk factor for sedentary behavior. MethodsThis is a sub-study of the Norwegian cognitive impairment after stroke (Nor-COAST) study, a multicenter study recruiting patients with acute stroke. Sedentary behavior was monitored with a thigh-worn sensor (ActivPal3®), at three-, 18- and 36-months post stroke. Stroke severity was assessed by National Institutes of Health Stroke Scale (NIHSS) and cognition by Montreal cognitive assessment (MoCA). Mixed model analysis with mean number of sedentary minutes accumulated daily as the dependent variable was repeated for all four zones (<30min, 30-60min, 60-90min, >90min) and for total sedentary time. ResultsThe number of included participants was 528 (mean age 71.4, NIHSS on day 1, 2.7). The total amount of sedentary time accumulated between 08.00-22.00 increased significantly from about 9.8 hours at three months to 10.1 hours at 36 months post stroke (p=0.002). Patient characteristics associated with prolonged duration of the sedentary bouts and sedentary time were age, high BMI, comorbidities, and impaired physical function. No significant associations between MoCA score and sedentary time were found. ConclusionThe participants became increasingly sedentary and had fewer breaks in sedentary time from three to 36 months after stroke. Baseline cognition was not related to later sedentary behavior.

Feasibility of wearable activity tracking devices to measure physical activity and sleep change among adolescents with chronic pain—a pilot nonrandomized treatment study

PurposePersonal informatics devices are being used to measure engagement in health behaviors in adults with chronic pain and may be appropriate for adolescent use. The aim of this study was to evaluate the utilization of a wearable activity tracking device to measure physical activity and sleep among adolescents attending a three-week, intensive interdisciplinary pain treatment (IIPT) program. We also assessed changes in physical activity and sleep from baseline to the treatment phase.MethodsParticipants (57.1% female, average age 15.88, SD = 1.27) wore an activity tracking device three weeks prior to starting and during the treatment program.ResultsOf 129 participants contacted, 47 (36.4%) agreed to participate. However, only 30 (64%) complied with the instructions for using the device prior to programming and during program participation. Preliminary analyses comparing averages from 3-weeks pre-treatment to 3-weeks during treatment indicated increases in daily overall activity minutes, daily step counts, and minutes of moderate to vigorous physical activity (by 353%), as well as a corresponding decrease in sedentary minutes. There was more missing data for sleep than anticipated.ConclusionsWearable activity tracking devices can be successfully used to measure adolescent physical activity in-person, with more difficulty obtaining this information remotely. Adolescents with chronic pain experience improvements in objective measurements of physical activity over the course of a 3-week IIPT program. Future studies may want to spend more time working with pediatric patients on their understanding of how to use trackers for sleep and physical activity.

Testing the Effect of a Smartphone App on Hospital Admissions and Sedentary Behavior in Cardiac Rehabilitation Participants: ToDo-CR Randomized Controlled Trial.

People with coronary heart disease are at an increased risk of morbidity and mortality even if they attend cardiac rehabilitation. High sedentary behavior levels potentially contribute to this morbidity. Smartphone apps may be feasible to facilitate sedentary behavior reductions and lead to reduced health care use. We aimed to test the effect of a sedentary behavior change smartphone app (Vire app and ToDo-CR program) as an adjunct to cardiac rehabilitation on hospital admissions and emergency department (ED) presentations over 12 months. A multicenter, randomized controlled trial was conducted with 120 participants recruited from 3 cardiac rehabilitation programs. Participants were randomized 1:1 to cardiac rehabilitation plus the fully automated 6-month Vire app and ToDo-CR program (intervention) or usual care (control). The primary outcome was nonelective hospital admissions and ED presentations over 12 months. Secondary outcomes including accelerometer-measured sedentary behavior, BMI, waist circumference, and quality of life were recorded at baseline and 6 and 12 months. Logistic regression models were used to analyze the primary outcome, and linear mixed-effects models were used to analyze secondary outcomes. Data on intervention and hospital admission costs were collected, and the incremental cost-effectiveness ratios (ICERs) were calculated. Participants were, on average, aged 62 (SD 10) years, and the majority were male (93/120, 77.5%). The intervention group were more likely to experience all-cause (odds ratio [OR] 1.54, 95% CI 0.58-4.10; P=.39) and cardiac-related (OR 3.26, 95% CI 0.84-12.55; P=.09) hospital admissions and ED presentations (OR 2.07, 95% CI 0.89-4.77; P=.09) than the control group. Despite this, cardiac-related hospital admission costs were lower in the intervention group over 12 months (Aus $252.40 vs Aus $859.38; P=.24; a currency exchange rate of Aus $1=US $0.69 is applicable). There were no significant between-group differences in sedentary behavior minutes per day over 12 months, although the intervention group completed 22 minutes less than the control group (95% CI -22.80 to 66.69; P=.33; Cohen d=0.21). The intervention group had a lower BMI (β=1.62; P=.05), waist circumference (β=5.81; P=.01), waist-to-hip ratio (β=.03, P=.03), and quality of life (β=3.30; P=.05) than the control group. The intervention was more effective but more costly in reducing sedentary behavior (ICER Aus $351.77) and anxiety (ICER Aus $10,987.71) at 12 months. The intervention was also more effective yet costly in increasing quality of life (ICER Aus $93,395.50) at 12 months. The Vire app and ToDo-CR program was not an outcome-effective or cost-effective solution to reduce all-cause hospital admissions or ED presentations in cardiac rehabilitation compared with usual care. Smartphone apps that target sedentary behavior alone may not be an effective solution for cardiac rehabilitation participants to reduce hospital admissions and sedentary behavior. Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12619001223123; https://australianclinicaltrials.gov.au/anzctr/trial/ACTRN12619001223123. RR2-10.1136/bmjopen-2020-040479.

ASSOCIATION OF LIFESTYLE FACTORS AND CARDIOMETABOLIC HEALTH AMONG PREADOLESCENT CHILDREN

PURPOSE: Cardiometabolic disease (CMD) risk begins early in life, with lifestyle behaviors (e.g. activity, diet) playing a prominent role. However, it is unclear how these lifestyle behaviors associate with each other in preadolescents and whether certain behaviors are more or less important to CMD risk than others. Further, prior studies have mostly examined singular CMD risk factors, combined risk factors as a linear sum, or investigated lifestyle behaviors in isolation. Thus, the purpose of this cross-sectional study was to simultaneously examine the associations among lifestyle behaviors (sedentary minutes, social jetlag, handgrip strength, cardiorespiratory fitness, processed food pattern, fruit/veg pattern) with CMD risk in preadolescent children. METHODS: A data-driven approach (factor analysis), using 11 variables, identified four cardiometabolic risk factors: blood pressure, cholesterol, vascular, and carbohydrate-metabolic. Multivariable analyses, adjusted for age, sex, ethnicity, and socioeconomic status identified associations between exposure (lifestyle behaviors) and outcome (cardiometabolic factors) variables. RESULTS: Sedentary minutes, social jetlag, and fruit/veg pattern associated with the cholesterol factor (β = 0.001, -0.20 and -0.08, respectively; all p < 0.05); sedentary minutes and processed food pattern associated with the vascular factor (β = 0.001 and 0.08, respectively; both p < 0.05); and cardiorespiratory fitness and handgrip strength associated with the carbohydrate-metabolic factor (β = -0.08 and 0.07, respectively; both p < 0.001). No exposure variables associated with the blood pressure factor. CONCLUSIONS: With respect to cardiovascular risk, sedentary minutes may be an especially critical behavior, followed by diet quality. In terms of metabolic control, physical fitness may be key. This study was a necessary starting point to gain an initial understanding of multiple lifestyle behaviors and their effects on CMD risk in preadolescents. Future longitudinal studies are now needed to identify optimal CMD risk prevention strategies in this population.

Longitudinal Path Analysis Of Child Behavior Difficulties And Physical Activity Behaviors From Early Childhood To Adolescence.

Extracurricular physical activity (PA) are a major contributor to child development and social wellbeing. However, disparities exist in both experience and opportunity for participation by individual characteristics such as race, sex, and behavior difficulties. PURPOSE: To test effects and associations of individual characteristics, primary school PA experiences, and secondary school psychosocial outcomes. METHODS: We tested relationships in a structural equation model using longitudinal data from the NICHD Study of Early Child Care and Youth development (SECCYD) (N=1,364, 52% female, 24% non-White, 21% families at twice poverty level). Independent variables were attained at grades 3. These included measures of total problems, inattention, social skills, and enjoyment of physical activity. Dependent variable were collected at 3 time points (grade 5, grade 6, age 15). These included repeated measures grade 5 and age 15 inattention and total problems, social skills at grade 5, and impulsivity and social problems at age 15. Grade 6 mediating PA variables included minutes of PA and sedentary time after school, device-assessed MVPA, played a sport, and enjoyment of PA. RESULTS: Grade 5 inattention predicted minutes of MVPA in grade 6 (β=0.117, p=0.01). Grade 5 total problems predicted grade 6 sedentary time after school (β=0.095, p=0.05). Grade 5 social skills predicted minutes of PA after school (β=0.094, p=0.018) and enjoyment of PA in grade 6 (β=0.094, p=0.032). The only grade 6 PA related behavior to show direct effects on age 15 social health outcomes was sedentary minutes after school predicting social problem scores (β=0.128, p=0.012). Boys demonstrated higher grade 3 inattention, grade 6 PA after school, grade 6 total MVPA, and age 15 impulsivity; and lower sedentary minutes after school (all ps<.05). Children from families with low income to needs ratio at baseline evidenced higher grade 6 PA after school, age 15 social problems, and age 15 total problems. CONCLUSIONS: This longitudinal view of the influence of social characteristics on PA experiences and mental health suggests reciprocal effects of these constructs throughout childhood. These findings indicate the importance of PA opportunities and experiences for all developing children.

Physical Activity and Sedentary Time Among Mothers of School-Aged Children: Differences in Accelerometer-Derived Pattern Metrics by Demographic, Employment, and Household Factors.

Dynamic patterns of how physical activity and sedentary time are accumulated across the day are associated with health outcomes, independent of total activity levels. Individual factors may influence activity patterns in mothers, but these associations are unknown. This study examined multivariable associations between demographic, employment, and household factors and day-level pattern metrics. Mothers (N=200) of school-aged children (ages 8-12years) participated in 6 semi-annual 7-day assessments. Waist-worn Actigraph GT3X accelerometers assessed daily moderate-to-vigorous physical activity (MVPA; minutes, number of short bouts [<10minutes], proportion of long bouts [≥20minutes]) and sedentary time (minutes, number of breaks, proportion of long bouts [≥60minutes], temporal dispersion). Multilevel models examined associations between individual characteristics and activity metrics. There were 4,930 day-level observations. Having a college degree was associated with fewer short MVPA bouts (B=-2.67), more sedentary minutes (B=21.66), greater long sedentary bouts (odds ratio=1.50), and having sedentary time less evenly distributed across the day (B=0.01). Working full-time was associated with more short MVPA bouts (B=1.39) and breaks in sedentary time (B=2.08). Having at least 1 infant (<6months old) in the same household was associated with fewer MVPA minutes (B=-0.11) and short MVPA bouts (B=-4.46), whereas having at least 1 young child (6 months-5 years old) in the same household was associated with fewer sedentary minutes (B=-11.85) and fewer long sedentary bouts (odds ratio=0.70). Day-level pattern metrics show differences not captured when examining total volume alone. Results provide more nuanced information as to how activity is accumulated in terms of bouts and breaks, which can inform programs to increase MVPA and reduce sedentary time by elucidating subpopulations that should be targeted by health behavior interventions.

‘Tracking Together’—Simultaneous Use Of Human And Dog Activity Trackers: A Factorial Randomized Controlled Trial

PURPOSE: Human activity trackers have been shown to increase daily steps and physical activity intensity. Dog activity trackers may increase the owner’s awareness of their dog’s well-being, resulting in increased dog walking behavior, which may increase the owner’s physical activity. This study examines the impact of the simultaneous use of activity trackers by humans and their dogs on the physical activity of humans. METHODS: Fifty dog owners were randomized into one of four groups. Each group consisted of dog-human duos, in which both, human-only, dog-only, or neither were given an activity tracker to use for eight weeks. All human subjects wore a waist mounted accelerometer that quantified physical activity at two timepoints: one week prior to randomization and eight weeks after randomization. Commercial activity trackers were used during the eight-week session for tracking human and dog activity remotely. A factorial analysis of covariance was used to compare daily average sedentary, low, light, moderate, and vigorous physical activity across the four groups from pre to post. RESULTS: Change in daily average sedentary, low, light, moderate, and vigorous physical activity from fifty dog owners were analyzed (Control n = 13, Human Activity Tracker (HAT) n = 14, Dog Activity Tracker (DAT) n = 12, HAT + DAT n = 11). There was a significant main effect of HAT on change in average daily light physical activity (Avg Light PA) minutes. No HAT compared to HAT significantly increased Avg Light PA (30.83 ± 14.87 minutes/day, p = 0.04). There was a marginally significant effect of HAT on change in average daily sedentary physical activity minutes (Avg Sed PA). No HAT compared to HAT decreased Avg Sed PA (-43.8 ± 23.76 minutes/day, p = 0.07). There were no other significant changes in average daily physical activity categories. CONCLUSIONS: Simultaneous use of activity trackers did not change physical activity for this small, self-selected sample. The pandemic may have already increased dog walking for this sample, they may already have well-established dog-walking routines, or they may not have continuously worn trackers. Future studies should also explore behavioral interventions that utilize DAT for improving dog-walking behavior and other dog-engaged activities among dog-owners that positively impact health of both species.

Daily and hourly patterns of physical activity and sedentary behavior of older adults: Atherosclerosis risk in communities (ARIC) study

This cross-sectional study of older adults ≥ 65 years describes daily and hourly patterns of accelerometer-derived steps, sedentary, and physical activity behaviors and examines differences by day of the week and sociodemographic and health-related factors to identify time-use patterns. Data were from 459 Atherosclerosis Risk in Communities (ARIC) study participants (60% female; mean ± SD age = 78.3 ± 4.6 years; 20% Black) who wore a hip accelerometer ≥ 4 of 7 days, for ≥ 10 h/day in 2016. We used linear mixed models to examine daily patterns of steps, sedentary, low light, high light, and moderate-to-vigorous intensity physical activity (MVPA). Differences by sex, median age (</≥ 78 years), body mass index, self-rated health, depressive symptoms, and performance in a two-minute walk test were explored. Men (vs women), and those with overweight and obesity (vs normal weight), had significantly higher sedentary minutes and lower minutes of low light per day. For each additional meter walked during the two-minute walk test, sedentary behavior was lower while high light, MVPA, and daily steps were higher. No significant differences in time-use behaviors were found by self-reported race, age, education, self-rated health, or depressive symptoms. Participants were least active (22.5 min MVPA, 95% CI: 11.5, 33.5) and most sedentary (453.9 min, 95% CI: 417.7, 490.2) on Sunday. Most activity was accrued in the morning (before 12 PM) while the evening hours (3–11 PM) were spent ≥ 50% sedentary. Movement patterns suggest opportunities for promotion of activity and reduction in sedentary time on Sundays, in the evening hours, and for those with overweight or obesity.

541-P: Patterns of Physical Activity and Cardiometabolic Risk in Hispanic/Latino Adults with or Vulnerable to Type 2 Diabetes

While physical activity (PA) improves physical health, and wearable accelerometers clarify PA behavior, patterns of PA and cardiometabolic risk have not been well studied in ethnic minorities. In the Mil Familias observational cohort study of Hispanic/Latino adults with or at risk for type 2 diabetes (T2D) , PA was measured in 118 women and 59 men (age 56.9±10.6 years, mean±SD, HbA1c 7.1 [6.3, 9.2] %, median [IQR]) ; 150 had T2D (duration 12.2±8.2 years) . An ActiGraph wGT3X-BT was worn on the right hip 24 hours/day for 7 consecutive days (mean 4.5±1.8 days) with ≥hours waking wear time as a valid day. We found that study participants in the low HbA1c (&amp;lt;7.0%) had significantly less waking sedentary minutes (SED) per day than those with high (≥7.0%) HbA1c (see table) . For men, there were significant differences for time standing and sitting, levels of moderate to vigorous physical activity (MVPA) , and LDL cholesterol. In women with diabetes (n=103) , individual fasting HDL (50.7±11.7 mg/dL) was positively correlated with light physical activity minutes (266.0±66.8, r=0.3, p&amp;lt;0.05) . Also, sitting minutes (328±83.2) and standing minutes (352±97.4) correlated with waist circumference (100±12.9 cm) , r=0.2 &amp; r=-0.2, p≤0.respectively. In these Hispanic/Latino adults, PA (from work and/or exercise) and SED time appear to have important cardiometabolic health implications. Disclosure W.C.Bevier: Research Support; Abbott Diabetes. S.Lamunion: None. M.Roach: None. N.M.Glantz: Research Support; Abbott. A.Larez: Research Support; Abbott. K.N.Castorino: Consultant; Lilly Diabetes, Research Support; Abbott Diabetes, Dexcom, Inc., Drawbridge Health, Inc., Eyenuk, Inc., Laxmi Therapeutic Devices, Medtronic, National Institute of Diabetes and Digestive and Kidney Diseases, Novo Nordisk, Speaker's Bureau; Dexcom, Inc. K.Y.Chen: None. D.Kerr: Advisory Panel; Abbott Diabetes, Novo Nordisk A/S, Sanofi, Consultant; Evidation Health, Research Support; Novo Nordisk A/S, Stock/Shareholder; Glooko, Inc., Hi.Health.

0018 Sleep Difficulty and Weekly Sedentary Minutes

Abstract Introduction Decreased energy and activity may be a mechanism linking poor sleep health and cardiometabolic risk. This study aimed to examine, at the national level, whether poor sleep quality was associated with more sedentary time (as opposed to less exercise, which has been difficult to establish). Methods Data from the 2017 - March 2020 National Health and Nutrition Examination Survey was used. A linear regression analysis was completed to assess the relationship between sedentary minutes and self-reported sleep difficulties. These were assessed by self-report of difficulty “falling asleep, staying asleep, or sleeping too much” over the past 2 weeks, with options for “never,” “less than half the days,” “more than half the days” and “almost always.” Covariates included sex, age, race/ethnicity, education level, and relationship status. Results are unweighted; weighted results are forthcoming. Results All groups experiencing self-reported sleep difficulties demonstrated increased sedentary minutes when compared to those that do not experience sleep difficulties. More specifically, unadjusted results show 21.5 more sedentary minutes (B: 21.5, [95%CI:10.8,32.2], p&amp;lt;0.001) for those that have sleep problems several days a week, 21.8 more sedentary minutes (B:21.8, [95%CI:4.90,38.7], p&amp;lt;0.001) for those that have sleep difficulties more than half the days in a week, and 42.3 minutes (B: 42.3, [95%CI:26.8,57.8], p&amp;lt;0.001) for those that have sleep difficulties nearly every day during the week. Once adjusted for covariates, results showed 17.6 more sedentary minutes (B:17.6, [95%CI:6.76,28.3], p&amp;lt;0.001) for those that have sleep difficulties several days a week, 18.3 more minutes (B:18.3, [95%CI:1.45,35.1], p&amp;lt;0.001) for those that have sleep difficulties more than half the days in a week, and 48.4 more sedentary minutes (B:48.4, [95%CI:32.8,63.9], p&amp;lt;0.001) for those that have sleep difficulties nearly every day during the week. Conclusion Those with general sleep difficulties were more likely to report more sedentary minutes per day. Although previous efforts have focused on improving physical activity directly, perhaps future efforts could target sleep health as a way to reduce sedentary behavior. Support (If Any)

0714 Population-Level Snoring and Probable Sleep-Disordered Breathing Associated with Greater Sedentary Activity

Abstract Introduction Increased frequency of snoring can be an indicator of sleep-disordered breathing, which is associated with a myriad of comorbidities, including increased cardiovascular disease risk. Previous studies have shown that sleep-disordered breathing is associated with less physical activity, but few studies examined this at the population lavel, or relative to primary snoring. Methods This analysis used a linear regression analysis on the 2017- March 2020 data collected from the National Health and Nutrition Examination Survey (NHANES) to explore the relationship between the minutes of sedentary activity and the frequency of snoring. Participants were asked how often they snored in the last 12 months. Responses were categorized as “Never”, “Rarely-- 1-2 nights/week”, “Occasionally-- 3-4 nights/week”, or “Frequently-- &amp;gt;=5 nights/week.” Self-reported sedentary activity was measured in minutes during a typical day. A modified STOP-BANG score was created based on NHANES measures of snoring, daytime tiredness, snorting/gasping during sleep, hypertension, body mass index, age, and gender (no measure of neck circumference). Reported results were unweighted; weighted results forthcoming. Results Significant unadjusted results indicate that those who reported snoring frequently had 19.2 minutes more sedentary time ([7.98,30.4], p&amp;lt;0.0001); and those with estimated sleep apnea had 16.2 more minutes of sedentary time than those without sleep apnea ([7.19,25.2], p&amp;lt;0.001). When adjusted for sex, age, race, education level, and marital status, the estimated difference between frequent snorers and those that reported never snoring increased to 35.9 minutes of more sedentary activity ([24.4,47.3], p&amp;lt;0.0001) a day. After adjusting for covariates, those with probable sleep apnea showed 43.9 more minutes of sedentary activity compared to those without sleep apnea ([34.1,53.6], p&amp;lt;0.001). Conclusion Overall, those who snore frequently (5 or more nights a week) or have a high risk of sleep apnea show a larger number of sedentary minutes per day then those that don’t snore or have probable sleep apnea. These relationships may be bidirectional, and directionality should be addressed in future studies. Support (If Any)

Increased sedentary time and decreased physical activity increases lipoprotein associated phospholipase A2 in obese individuals

Background and aimsLipoprotein-associated Phospholipase A2 (Lp-PLA2) is a protein produced by inflammatory cells in circulation and is associated with cardiovascular disease (CVD) risk. Physical activity (PA) is known to reduce inflammation and risk for CVD. However, Lp-PLA2 has yet to be examined in relation to PA and sedentary time. The purpose of this study was to determine if PA and sedentary time impacts Lp-PLA2 mass. A total of 25 subjects with an average BMI of 30.6 ± 5.7 were included in the data analysis. Methods and resultsData collected included anthropometric data, Lp-PLA2 mass, peak oxygen uptake (VO2peak), resting heart rate and blood pressure, obstructive sleep apnea (OSA) risk, and assessment of PA using an accelerometer. Sedentary minutes per day was positively associated with Lp-PLA2 (r = 0.41, P < 0.05). Light intensity PA was negatively associated (r = −0.51. P = 0.01) with Lp-PLA2. When subjects were divided into 2-quantiles by Lp-PLA2, the group with the higher Lp-PLA2 mass accumulated more sedentary time per day (P < 0.001) and less light intensity PA per day (P = 0.001). OSA risk and Lp-PLA2 showed no relationship. Sedentary behavior was higher, and light intensity PA was lower in subjects with hiLp-PLA2 mass. No difference was seen in moderate-to-vigorous intensity PA or steps per day. ConclusionsThis suggests that, total PA habits, including time spent sedentary and lower intensity PA, impacts the levels of Lp-PLA2, an important inflammatory marker and marker of CVD risk.

Dog-Assisted Physical Activity Intervention in Children with Autism Spectrum Disorder: A Feasibility and Efficacy Exploratory Study

ABSTRACT Integrating a therapy dog into physical activity sessions may help children with autism spectrum disorder (ASD) to increase physical activity and gain-related health benefits. This exploratory intervention assessed the feasibility of integrating a therapy dog into exercise sessions and its efficacy to improve physical activity outcomes in children with ASD. After two familiarization sessions, we randomly assigned 18 children with ASD (mean age = 10.1, SD = 2.5) into two groups (n = 9). We used a crossover design and randomized groups to attend a weekly physical activity session with or without a therapy dog for four weeks. Each group had two sessions with the presence of 1–2 therapy dogs and two sessions without a therapy dog. We assessed feasibility by measuring participant attendance to the crossover sessions and retention in the intervention. We measured efficacy by recording light physical activity, moderate to vigorous physical activity (MVPA), number of bone-impacts, and sedentary time using activity monitors (accelerometers) in each session. We compared physical activity outcomes between the crossover sessions with and without a therapy dog using repeated measures MANOVA. Attendance at the sessions was 92% and the retention rate was 90%. Participants had 13% more minutes of light physical activity (mean difference = 3.5 min; 95% CI: 1.2, 5.8 min) and 22% less sedentary minutes (–2.4; –4.3, –0.1) in the sessions with a therapy dog. MVPA and the number of bone-impacts did not differ between the sessions (p > 0.05). Our results suggest that integrating therapy dogs into physical activity sessions is feasible and it increases light physical activity and decreases sedentary time in children with ASD.

Objectively Measured Patterns of Daily Physical Activity and Phenotypic Frailty.

Self-reported low physical activity is a defining feature of phenotypic frailty but does not adequately capture physical activity performed throughout the day. This study examined associations between accelerometer-derived patterns of routine daily physical activity and frailty. Wrist accelerometer and frailty data from 638 participants (mean age 77 [SD = 5.5] years; 44% women) were used to derive 5 physical activity metrics: active minutes/day, sedentary minutes/day, total activity counts/day, activity fragmentation (reciprocal of the average active bout length), and sedentary fragmentation (reciprocal of the average sedentary bout length). Robust, prefrail, and frail statuses were identified using the physical frailty phenotype defined as having 0, 1-2, or ≥3 of the following criterion: weight loss, exhaustion, slowness, self-reported low activity, and weakness. Frailty was collapsed into not frail (robust and prefrail) and frail, and each frailty criteria was dichotomized. Multiple logistic regression was used to model each accelerometer metric. Separate frailty criteria and interactions with age and sex were also examined. With higher amounts and intensity of daily activity (more active minutes, fewer sedentary minutes, higher activity counts) and lower activity fragmentation, the odds of frailty were lower compared to robust/prefrail states (p < .02 for all). For interactions, only an age by sedentary fragmentation interaction on the odds of frailty was observed (p = .01). For each separate criteria, accelerometer metrics were associated with odds of slowness, low activity, and weakness. Less favorable patterns of objectively measured daily physical activity are associated with frailty and the components of slowness, low self-reported activity, and weakness.

Relationship quality and objectively measured physical activity before and after implementation of COVID-19 stay-home orders.

In a sample of 28 individuals cohabiting with a partner in NYC, Boston, or Chicago, this study tested whether implementation of stay-home orders to combat the spread of COVID-19 disrupted physical activity and whether high-quality romantic relationships buffered adverse effects. Participants provided FitBit data between February and October, 2020. Stay-home orders were associated with a reduction in daily step counts, B = -1595.72, p = 0.018, increased sedentary minutes, B = 33.75, p = 0.002, and reduced daily minutes of light and moderate physical activity, B = -25.01, p = 0.011; B = -0.72, p = 0.021. No moderation effects emerged.

How Pokemon Go Playing Style Impacts The Volume And Pattern Of Physical Activity

PURPOSE: Two self-selected playing styles of Pokémon Go (PG) have been observed: intermittent and continuous game play. However, it is unknown how playing style impacts volume and pattern of physical activity (PA) that may be accrued during game play. METHODS: Twenty experienced PG players followed a 1.77 km loop during a 30-minute walking condition in which they were instructed to play PG as they normally would. Objective measurement tools (pedometer, accelerometer, heart rate monitor, GPS watch) were used to measure steps, Caloric expenditure, intensity, heart rate, and distance travelled. Direct observation was used to determine number and duration of any stops, which allowed for determination playing style. Specifically, continuous PG was defined as either no stops or 1 stop of ≤15 seconds, and intermittent PG was defined as ≥1 stop of >15 seconds or ≥ 2 stops of any duration during the 30-minute condition. RESULTS: Most (60%) PG players used the intermittent playing style, and the remainder (40%) used a continuous style. PA levels between participants utilizing continuous and intermittent PG play styles were similar, however, total steps (3394.3 ± 255.8 vs. 2779.1 ± 317.5), aerobic steps (3314.3 ± 318.9 vs. 2387.5 ± 771.8), Caloric expenditure (146.0 (130.0-201.3) vs. 110.0 (89.3-132.3 Kcals)), distance (2.4 (2.3-2.6) vs. 2.0 (1.8-2.1 km)), and moderate minutes (29.7 (27.5-29.8) vs. 25.8 (23.1-27.1)) were significantly greater, while sedentary minutes (0.2 (0.0-1.0) vs. 2.7 (1.7-4.3)) and number of stops (0 (0-0.8) vs. 4.5 (2.3-7.0)) were less, for continuous compared to intermittent PG playing styles. CONCLUSION: Continuous and intermittent game play both appear to be common approaches to playing PG. While continuous PG elicited in more PA than intermittent PG, both resulted in substantial PA accumulation, suggesting PG using either play style may be a strategy for increasing PA participation.

Feasibility, preliminary efficacy, and acceptability of a twitter-based social support group vs Fitbit only to decrease sedentary behavior in women

BackgroundHealth behavior change interventions delivered by social media allow for real-time, dynamic interaction, peer social support, and experimenter-provided content. AimsWe tested the feasibility, acceptability, and preliminary efficacy of a novel Twitter-based walking break intervention with daily behavior change strategies and prompts for social support, combined with a Fitbit, vs. Fitbit alone. MethodsIn a 2-group pilot, 45 sedentary women from a heart clinic were randomized to Twitter + Fitbit activity tracker (Tweet4Wellness, n = 23) or Fitbit-only (control, n = 22). All received a Fitbit and 13 weeks of tailored weekly step goals. Tweet4Wellness consisted of a private Twitter support group, with daily automated behavior change “tweets” informed by behavior change theory, and encouragement to communicate within the group. Feasibility outcomes included recruitment and enrollment numbers, implementation challenges, and number and type of help requests from participants throughout the study period. Preliminary efficacy outcomes provided by Fitbit data were sedentary minutes, number of hours with >250 steps, maximum sitting bout, weighted sedentary median bout length, total steps, intensity minutes (>3.0 METS), and ratio of time spent sitting-to-moving. Acceptability outcomes included level of Twitter participation within Tweet4Wellness, and Likert scale plus open-ended survey questions on enjoyment and perceived effectiveness of intervention components. Survey data on acceptability of the features of the intervention were collected at 13 weeks (end-of-treatment [EOT]) and 22 weeks (follow-up). ResultsThe study was feasible, with addressable implementation challenges. Tweet4Wellness participants changed significantly from baseline to EOT relative to control participants on number of active hours p = .018, total steps p = .028, and ratio of sitting-to-moving, p = .014. Only sitting-to-moving was significant at follow-up (p = .047). Among Tweet4Wellness participants, each tweet sent during treatment was associated with a 0.11 increase in active hours per day (p = .04) and a 292-step increase per day (p < .001). Tweet4Wellness participants averaged 54.8 (SD = 35.4) tweets, totaling 1304 tweets, and reported liking the accountability and peer support provided by the intervention. ConclusionA Twitter-delivered intervention for promoting physical activity among inactive women from a heart clinic was feasible, acceptable, and demonstrated preliminary efficacy in increasing daily active hours, daily total steps, and the ratio of sitting-to-moving from pre to post for the intervention compared with the control. Lessons learned from this pilot suggest that the next study should expand the recruitment pool, refine the intervention to increase group engagement, and select active hours, total steps, and ratio of sitting-to-movement as primary sedentary behavior measures.