Relationship of device measured physical activity type and posture with cardiometabolic health markers: pooled dose-response associations from the Prospective Physical Activity, Sitting and Sleep Consortium.

AIMS/HYPOTHESIS: The aim of this study was to examine the dose-response associations of device-measured physical activity types and postures (sitting and standing time) with cardiometabolic health. METHODS: We conducted an individual participant harmonised meta-analysis of 12,095 adults (mean ± SD age 54.5±9.6 years; female participants 54.8%) from six cohorts with thigh-worn accelerometry data from the Prospective Physical Activity, Sitting and Sleep (ProPASS) Consortium. Associations of daily walking, stair climbing, running, standing and sitting time with a composite cardiometabolic health score (based on standardised z scores) and individual cardiometabolic markers (BMI, waist circumference, triglycerides, HDL-cholesterol, HbA1c and total cholesterol) were examined cross-sectionally using generalised linear modelling and cubic splines. RESULTS: We observed more favourable composite cardiometabolic health (i.e. z score <0) with approximately 64 min/day walking (z score [95% CI] -0.14 [-0.25, -0.02]) and 5 min/day stair climbing (-0.14 [-0.24, -0.03]). We observed an equivalent magnitude of association at 2.6 h/day standing. Any amount of running was associated with better composite cardiometabolic health. We did not observe an upper limit to the magnitude of the dose-response associations for any activity type or standing. There was an inverse dose-response association between sitting time and composite cardiometabolic health that became markedly less favourable when daily durations exceeded 12.1 h/day. Associations for sitting time were no longer significant after excluding participants with prevalent CVD or medication use. The dose-response pattern was generally consistent between activity and posture types and individual cardiometabolic health markers. CONCLUSIONS/INTERPRETATION: In this first activity type-specific analysis of device-based physical activity, ~64 min/day of walking and ~5.0 min/day of stair climbing were associated with a favourable cardiometabolic risk profile. The deleterious associations of sitting time were fully attenuated after exclusion of participants with prevalent CVD and medication use. Our findings on cardiometabolic health and durations of different activities of daily living and posture may guide future interventions involving lifestyle modification.

Physical activity across midlife and health-related quality of life in Australian women: A target trial emulation using a longitudinal cohort.

BACKGROUND: There is little long-term causal evidence on the effect of physical activity on health-related quality of life. This study aimed to examine the associations between longitudinal patterns of physical activity over 15 years and health-related quality of life in both the physical and mental health domains, in a cohort of middle-aged Australian women. METHODS AND FINDINGS: We used data collected at 3-year intervals (1998 to 2019) from 11,336 participants in the Australian Longitudinal Study on Women's Health (ALSWH) (1946 to 1951 birth cohort). Primary outcomes were the physical (PCS) and mental health component summary (MCS) scores (range from 0 to 100; higher scores indicate higher perceived physical/mental health) from the SF-36 in 2019 (when women aged 68 to 73 years). Using target trial emulation to imitate a randomized controlled trial (RCT), we tested 2 interventions: (1) meeting the World Health Organization (WHO) physical activity guidelines consistently throughout the 15-year "exposure period" (2001 to 2016; when women aged 50-55 to 65-70 years; physical activity assessed every 3 years); and (2) not meeting the guidelines at the beginning of the exposure period but starting to first meet the guidelines at age 55, 60, or 65; against the control of not meeting the guidelines throughout the exposure period. Analysis controlled for confounding using marginal structural models which were adjusted for sociodemographic and health variables and conditions. Consistent adherence to guidelines during the exposure period (PCS: 46.93 [99.5% confidence interval [CI]: 46.32, 47.54]) and first starting to meet the guidelines at age 55 (PCS: 46.96 [99.5% CI: 45.53, 48.40]) were associated with three-point higher PCS (mean score difference: 3.0 [99.5% CI: 1.8, 4.1] and 3.0 [99.5% CI:1.2, 4.8]) than consistent non-adherence (PCS: 43.90 [99.5% CI: 42.79, 45.01]). We found a similar pattern for most SF-36 subscales but no significant effects of the interventions on MCS. The main limitations of the study were that it may not account for all underlying health conditions and/or other unmeasured or insufficiently measured confounders, the use of self-reported physical activity and that findings may not be generalizable to all mid-age women. CONCLUSIONS: Results from the emulated RCT suggest women should be active throughout mid-age, ideally increasing activity levels to meet the guidelines by age 55, to gain the most benefits for physical health in later life.

Prepregnancy maternal BMI and trajectories of BMI-for-age in children up to four years of age: findings from the 2015 Pelotas (Brazil) birth cohort.

BACKGROUND: The aims of the study were to: (a) describe BMI-for-age trajectories in children up to four years of age; (b) evaluate the association between prepregnancy maternal BMI and the BMI-for-age trajectories. METHODS: Data from 3218 (75.3% of the original cohort) children from the Pelotas 2015 Birth Cohort were analyzed. Prepregnancy BMI (kg/m2) was measured on the perinatal interview. Z-scores of BMI-for-age were calculated for children at three months, 1, 2 and 4 years. Trajectories were identified using a semi-parametric group-based modeling approach. Multinomial logistic regression was used to test the association between prepregnancy BMI (weight excess: BMI ≥ 25 kg/m2) and BMI-for-age trajectories. RESULTS: Four trajectories of the BMI-for-age, in z-score, were identified and represent children in the "increasing", "adequate", "stabilized" and "risk for weight excess" group. A total of 196 children (7.1%) belonged to the group that was at risk of weight excess. Adjusted analyses showed that children whose mothers presented prepregnancy weight excess had 2.36 (95%CI 1.71; 3.24) times more risk of belonging to group "risk for weight excess" when compared to those children whose mothers presented underweight/normal weight before pregnancy. CONCLUSION: The risk of weight excess in children up to 4 years of age were greater in mothers who presented prepregnancy weight excess.

Trajectories of physical activity from mid to older age in women: 21 years of data from the Australian Longitudinal Study on Women's Health.

BACKGROUND: Women's physical activity varies across the adult lifespan. However, changes in physical activity among mid-aged women are not well understood. We analysed 21 years of data from women born in 1946-51 to identify: (1) trajectories of physical activity in the transition from mid- to old-age and (2) determinants of different physical activity trajectories. METHODS: Data were from the 1946-51 cohort of the Australian Longitudinal Study on Women's Health (N = 10,371). Surveys were mailed at three-year intervals from 1998 (age 47-52) to 2019 (age 68-73) to collect data on physical activity, sociodemographic factors (country of birth, area of residence, educational attainment, marital status, income management, paid work hours, living with children age < 18, providing care), health indicators (menopause status, BMI, physical and mental health, chronic conditions), and health behaviours (smoking, alcohol status). Group-based trajectory modelling was used to identify trajectories of physical activity. Multinomial logistic regression models were used to examine the determinants of physical activity trajectories. RESULTS: Five trajectories were identified: Low-stable (13.3% of participants), Moderate-stable (50.4%), Moderate-increasing (22.2%), High-declining (7.7%), and High-stable (6.6%). Sociodemographic characteristics (area of residence, education, income management, living with children, and providing care) were determinants of physical activity trajectories, but the strongest factors were BMI, physical and mental health. Women who were overweight/obese and had poor physical and mental health were less likely to be in the High-stable group than in any other group. Changes in these variables (increasing BMI, and declining physical and mental health) and in marital status (getting married) were positively associated with being in trajectories other than the High-stable group. CONCLUSIONS: Although most women maintained physical activity at or above current guidelines, very low physical activity levels in the Low-stable group, and declining levels in the High-declining group are concerning. The data suggest that physical activity promotion strategies could be targeted to these groups, which are characterised by socioeconomic disadvantage, high (and increasing) BMI, and poor (and worsening) physical and mental health. Removing barriers to physical activity in these women, and increasing opportunities for activity, may reduce chronic disease risk in older age.

Ecological momentary assessment of physical activity and sedentary behaviour in shift workers and non-shift workers: Validation study.

This study examined the criterion validity of an ecological momentary assessment (EMA)-reported physical activity and sedentary time compared with accelerometry in shift workers and non-shift workers. Australian workers (n = 102) received prompts through a mobile EMA app and wore the Actigraph accelerometer on the right hip for 7-10 days. Participants received five EMA prompts per day at 3-hour intervals on their mobile phones. EMA prompts sent to shift workers (SW-T) were tailored according to their work schedule. Non-shift workers (NSW-S) received prompts at standardised times. To assess criterion validity, the association of EMA-reported activities and the Actigraph accelerometer activity counts and number of steps were used. Participants were 36 ± 11 years and 58% were female. On occasions where participants reported physical activity, acceleration counts per minute (CPM) and steps were significantly higher (ß = 1184 CPM, CI 95%: 1034, 1334; ß = 20.9 steps, CI 95%: 18.2, 23.6) than each of the other EMA activities. Acceleration counts and steps were lower when sitting was reported than when no sitting was reported by EMA. Our study showed that EMA-reported physical activity and sedentary time was significantly associated with accelerometer-derived data. Therefore, EMA can be considered to assess shift workers' movement-related behaviours with accelerometers to provide rich contextual data.

Adherence to 24-h movement behaviour guidelines in families with multiple children.

BACKGROUND: In 2019, the World Health Organization (WHO) launched the first global movement guidelines for children that combined sleep, physical activity and screen time. Our previous research showed that adherence to age-specific guidelines for screen time was challenging for families with children in different age groups. We aimed to determine whether families with children in different age-based movement guideline categories have poorer adherence to the broader 24-h movement guidelines than those with all children in the same age category. METHODS: Data were from the 1973-1978 cohort of the Australian Longitudinal Study on Women's Health (seventh survey, 2015) and the women's three youngest children (aged ≤12) (Mothers and their Children's Health sub-study, 2016/2017). The sample was 1787 women (families) with 4064 children (mean age 7.2 [SD 2.9]). Whether children in the family were in the same or different age-based category was determined by matching children's ages in a family against age-based guideline categories for the 24-h movement behaviours. The association between children in the family being in the same or different age-based guideline category on adherence to 24-h movement guidelines, both collectively and individually, was analysed by adjusted logistic regression (binary and multinomial). RESULTS: Families with children in the same age guideline categories had double the odds of having all children meet 24-h movement guidelines (adjusted odds ratio [OR] 1.95 [95% confidence interval, CI: 1.32, 2.86]). Families with children in the same age categories on the screen guideline had higher odds of all children meeting (2.25 [1.73, 2.93]) and lower odds of some meeting/some failing the screen guideline (0.18 [0.14, 0.25]), than families with all children in different age categories. Families with children in the same age categories on the physical activity guideline had lower odds of all children meeting (0.57 [0.43, 0.75]) or some meeting/some failing the physical activity guideline (0.08 [0.06, 0.12]). No associations were found for sleep guidelines. CONCLUSIONS: Families with multiple children may need practical advice and strategies on how to adhere to guidelines when children span age categories. This could form part of public health strategies that raise awareness of the guidelines and may improve guideline adherence.

Cross-sectional and prospective associations between screen time and childhood neurodevelopment in two Brazilian cohorts born 11 years apart.

BACKGROUND: The aim of this study was to investigate the associations between screen time from ages 2 to 4 years and child neurodevelopment at age 4. METHODS: The participants were from the 2004 (N = 3787) and 2015 (N = 3604) Pelotas (Brazil) birth cohort studies. Childhood neurodevelopment was assessed at age 4 using the Battelle Development Inventory. The time children spent on screen devices was reported by their guardians at ages 2 and 4 years. Linear regression models were used to investigate the association of: (i) time spent on television at ages 2 and 4 years; (ii) time spent on other screens at age 4; and (iii) total screen time at age 4 (television + other screens) with childhood neurodevelopment at age 4. RESULTS: Average daily screen time among children born in 2004 and those born in 2005 aged 4 years were 3.4 (SD: 2.4) and 4.4 h (SD: 2.9), respectively. Overall, few associations of very small magnitude between screen time and child neurodevelopment were observed. Television time at 2 years of age was statistically associated with lower neurodevelopment at 4 years of age in the 2015 cohort (ß = -0.30, 95%CI = -0.55; -0.05). Conversely, television time (ß = 0.17, 95%CI = 0.07, 0.26) and total screen time (ß = 0.22, 95%CI = 0.13, 0.31) at age 4 were associated with higher neurodevelopment at age 4 in the 2004 cohort. CONCLUSIONS: The findings of this study suggest that the amount of time spent on screen devices might not be associated with neurodevelopment of children under 5 years of age. The small magnitude and inconsistencies in the direction of associations did not find evidence to support the current guidelines for screen time at this age. Therefore, more studies, especially those with longitudinal data, are important to comprehend the true effect of screen time on neurodevelopment and other health outcomes.

Association of 'Tortoise' and 'Hare' movement behavior patterns with cardiometabolic health, body composition, and functional fitness in older adults.

We investigated the association of movement behavior patterns with cardiometabolic health, body composition, and functional fitness in older adults. A total of 242 older adults participated of this cross-sectional study. Sedentary time, light physical activity (LPA) and moderate-vigorous physical activity (MVPA), steps/day, and step cadence were assessed by accelerometry. The movement behavior patterns were derived by principal component analysis. Cardiometabolic health was defined by a metabolic syndrome score (cMetS). Body composition was determined by appendicular lean mass/body mass index (ALM/BMI). Functional fitness was assessed by a composite z-score from the Senior Fitness Test battery. Two patterns were identified: 'Tortoise' (low sedentary time, high LPA and steps/day) and 'Hare' (high MVPA, steps/day, and step cadence). 'Tortoise' and 'Hare' were associated with better cMetS. 'Hare' was positively associated with ALM/BMI and Functional Fitness. While 'Tortoise' and 'Hare' were associated with better cMetS, only 'Hare' was associated with better ALM/BMI and functional fitness.

The impact of selected methodological factors on data collection outcomes in observational studies of device-measured physical behaviour in adults: A systematic review.

BACKGROUND: Accelerometer measures of physical behaviours (physical activity, sedentary behaviour and sleep) in observational studies offer detailed insight into associations with health and disease. Maximising recruitment and accelerometer wear, and minimising data loss remain key challenges. How varying methods used to collect accelerometer data influence data collection outcomes is poorly understood. We examined the influence of accelerometer placement and other methodological factors on participant recruitment, adherence and data loss in observational studies of adult physical behaviours. METHODS: The review was in accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). Observational studies of adults including accelerometer measurement of physical behaviours were identified using database (MEDLINE (Ovid), Embase, PsychINFO, Health Management Information Consortium, Web of Science, SPORTDiscus and Cumulative Index to Nursing & Allied Health Literature) and supplementary searches to May 2022. Information regarding study design, accelerometer data collection methods and outcomes were extracted for each accelerometer measurement (study wave). Random effects meta-analyses and narrative syntheses were used to examine associations of methodological factors with participant recruitment, adherence and data loss. RESULTS: 123 accelerometer data collection waves were identified from 95 studies (92.5% from high-income countries). In-person distribution of accelerometers was associated with a greater proportion of invited participants consenting to wear an accelerometer (+ 30% [95% CI 18%, 42%] compared to postal distribution), and adhering to minimum wear criteria (+ 15% [4%, 25%]). The proportion of participants meeting minimum wear criteria was higher when accelerometers were worn at the wrist (+ 14% [ 5%, 23%]) compared to waist. Daily wear-time tended to be higher in studies using wrist-worn accelerometers compared to other wear locations. Reporting of information regarding data collection was inconsistent. CONCLUSION: Methodological decisions including accelerometer wear-location and method of distribution may influence important data collection outcomes including recruitment and accelerometer wear-time. Consistent and comprehensive reporting of accelerometer data collection methods and outcomes is needed to support development of future studies and international consortia. Review supported by the British Heart Foundation (SP/F/20/150002) and registered (Prospero CRD42020213465).

High-Intensity Interval Training is Safe, Feasible and Efficacious in Nonalcoholic Steatohepatitis: A Randomized Controlled Trial.

BACKGROUND: High-Intensity Interval Training (HIIT) involves bursts of high-intensity exercise interspersed with lower-intensity exercise recovery. HIIT may benefit cardiometabolic health in people with nonalcoholic steatohepatitis (NASH). AIMS: We aimed to examine the safety, feasibility, and efficacy of 12-weeks of supervised HIIT compared with a sham-exercise control (CON) for improving aerobic fitness and peripheral insulin sensitivity in biopsy-proven NASH. METHODS: Participants based in the community [(n = 14, 56 ± 10 years, BMI 39.2 ± 6.7 kg/m2, 64% male), NAFLD Activity Score 5 (range 3-7)] were randomized to 12-weeks of supervised HIIT (n = 8, 4 × 4 min at 85-95% maximal heart rate, interspersed with 3 min active recovery; 3 days/week) or CON (n = 6, stretching; 3 days/week). Safety (adverse events) and feasibility determined as ≥ 70% program completion and ≥ 70% global adherence (including session attendance, interval intensity adherence, and duration adherence) were assessed. Changes in cardiorespiratory fitness (V̇O2peak), exercise capacity (time-on-test) and peripheral insulin sensitivity (euglycemic hyperinsulinemic clamp) were assessed. Data were analysed using ANCOVA with baseline value as the covariate. RESULTS: There were no HIIT-related adverse events and HIIT was globally feasible [program completion 75%, global adherence 100% (including adherence to session 95.4 ± 7.3%, interval intensity 95.3 ± 6.0% and duration 96.8 ± 2.4%)]. A large between-group effect was observed for exercise capacity [mean difference 134.2 s (95% CI 19.8, 248.6 s), ƞ2 0.44, p = 0.03], improving in HIIT (106.2 ± 97.5 s) but not CON (- 33.4 ± 43.3 s), and for peripheral insulin sensitivity [mean difference 3.4 mg/KgLegFFM/min (95% CI 0.9,6.8 mg/KgLegFFM/min), ƞ2 0.32, p = 0.046], improving in HIIT (1.0 ± 0.8 mg/KgLegFFM/min) but not CON (- 3.1 ± 1.2 mg/KgLegFFM/min). CONCLUSIONS: HIIT is safe, feasible and efficacious for improving exercise capacity and peripheral insulin sensitivity in people with NASH. CLINICAL TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trial Registry (anzctr.org.au) identifier ACTRN12616000305426 (09/03/2016).

Absolute intensity thresholds for tri-axial wrist and waist accelerometer-measured movement behaviors in adults.

AIM: This study was aimed to: (1) compare raw triaxial acceleration data from GENEActiv (GA) and ActiGraph GT3X+ (AG) placed on the non-dominant wrist; (2) compare AG placed on the non-dominant and dominant wrist, and waist; (3) derive brand- and placement-specific absolute intensity thresholds for inactive and sedentary time, and physical activity intensity in adults. METHODS: Eighty-six adults (44 men; 34.6 ± 10.8 years) performed nine activities while simultaneously wearing GA and AG on wrist and waist. Acceleration (in gravitational equivalent units; mg) was compared with oxygen uptake (measured with indirect calorimetry). RESULTS: Increases in acceleration mirrored increases in intensity of activities, regardless of device brand and placement. Differences in acceleration between GA and AG worn at the non-dominant wrist were small but tended to be high at lower intensity activities. Thresholds for differentiating inactivity (<1.5 MET) from activity (≥1.5 MET) ranged from 25 mg (AG non-dominant wrist; sensitivity 93%, specificity 95%) to 40 mg (AG waist; sensitivity 78%, specificity 100%). For moderate intensity (≥3 METs), thresholds ranged from 65 mg (AG waist; sensitivity 96%, specificity 94%) to 92 mg (GA non-dominant; sensitivity 93%, specificity 98%); vigorous intensity (≥6 METs) thresholds ranged from 190 mg (AG waist; sensitivity 82%, specificity 92%) to 283 mg (GA non-dominant; sensitivity 93%, specificity 98%). CONCLUSION: Raw triaxial acceleration outputs from two widely used accelerometer brands may have limited comparability in low intensity activities. Thresholds derived in this study can be utilized in adults to reasonably classify movement behaviors into categories of intensity.

Day-to-day variability in accelerometer-measured physical activity in mid-aged Australian adults.

PURPOSE: The aim was to use accelerometer data to describe day-to-day variability in physical activity in a single week, according to sociodemographic variables, in mid-aged Australian adults. METHODS: Data were from participants in the How Areas in Brisbane Influence HealTh and AcTivity (HABITAT) study who took part in a 2014 sub-study (N = 612; Mean age 60.6 [SD 6.9; range 48-73]). Participants wore a triaxial accelerometer (ActiGraph wGT3X-BT) on their non-dominant wrist for seven days, and data were expressed as acceleration in gravitational equivalent units (1 mg = 0.001 g). These were, used to estimate daily acceleration (during waking hours) and daily time spent in moderate-vigorous physical activity (MVPA, defined as ≥ 100mg). Coefficient of variation (calculated as [standard deviation/mean of acceleration and MVPA across the seven measurement days] * 100%) was used to describe day-to-day variability. RESULTS: Average values for both acceleration (24.1-24.8 mg/day) and MVPA (75.9-79.7 mins/day) were consistent across days of the week, suggesting little day-to-day variability (at the group level). However, over seven days, average individual day-to-day variability in acceleration was 18.8% (SD 9.3%; range 3.4-87.7%) and in MVPA was 35.4% (SD 15.6%; range 7.3-124.6%), indicating considerable day-to-day variability in some participants. While blue collar workers had the highest average acceleration (28.6 mg/day) and MVPA (102.5 mins/day), their day-to-day variability was low (18.3% for acceleration and 31.9% for MVPA). In contrast, variability in acceleration was highest in men, those in professional occupations and those with high income; and variability in MVPA was higher in men than in women. CONCLUSION: Results show group-level estimates of average acceleration and MVPA in a single week conceal considerable day-to-day variation in how mid-age Australians accumulate their acceleration and MVPA on a daily basis. Overall, there was no clear relationship between overall volume of activity and variability. Future studies with larger sample sizes and longitudinal data are needed to build on the findings from this study and increase the generalisability of these findings to other population groups.

Use of electronic devices in leisure time modifies the prevalence and factors associated with sedentary behavior derived exclusively from excessive television viewing among Brazilian adults.

The growing interest in the study of sedentary behavior is justified by its increasing presence in people's daily lives, particularly in leisure time. The aim of this study was to compare the prevalence and factors associated with sedentary behavior derived exclusively from TV time and from its combination with the time spent using other electronic devices among Brazilian adults (n = 52,443). This cross-sectional study used data from the Vigitel survey (2019), which included subjects ≥ 18 years old who resided in the capitals of the 26 Brazilian states and Federal District. High TV time (≥ 4 h/day), and its combination with computer, tablet, or cell phone use (≥ 4 h/day), as well as sociodemographic, behavioral, and health characteristics were self-reported. Adjusted logistic regression was used to estimate odds ratios and 95% confidence intervals (95%CI). The prevalence of high sedentary behavior almost tripled when TV viewing was added to the time spent using other electronic devices (from 12.2%; 95%CI: 11.6; 12.8, to 34.7%; 95%CI: 33.8; 35.6), notably among the youngest (32.0 percentage points). Individuals living without a partner, who smoked, consumed alcohol and processed foods excessively, were physically inactive, and had hypertension were more likely to have both outcomes than their counterparts. Older and less educated individuals were more likely to spend excessive time watching TV and less likely to have high use of other electronic devices in addition to TV viewing than their peers. Including computer, tablet, or cell phone led to an increase in the prevalence of high sedentary behavior. The magnitude and direction of the associations of age and education with high sedentary behavior varied according to the method how high sedentary behavior was defined. Projects, programs, and policies must consider the different indicators of sedentary behavior in monitoring and promoting a healthier lifestyle.

Bi-directional associations between physical activity and growth indicators of pre-school aged children.

Physical activity for young children provides a wealth of benefits for health and development. However, little is known about the inter-relationship of physical activity and growth indicators. The aim of this study was to test the bi-directional associations of physical activity and growth indicators in children under five years of age. This prospective study included 1,575 children with data on physical activity and growth indicators at ages 12, 24 and 48 months. Accelerometers were used to measure physical activity. Z-scores for length/height-for-age, weight-for-length/height, weight-for-age and body mass index (BMI)-for-age were calculated. Bi-directional associations between physical activity and growth indicators were evaluated using cross-lagged panels based on Generalized Estimating Equations and cross-lagged structural equation models. Physical activity was consistently associated with lower weight-related growth indicators: BMI-for-age: ß=-0.12; Weight-for-age: ß=-0.11; Weight-for-length/height: ß=-0.12. Higher BMI-for-age indicated lower physical activity (ß=-0.06). When the exposure was lagged, the association of physical activity on weight-related growth indicators remained, but weight-related growth indicators showed a negative association on physical activity. A bi-directional association between physical activity and weight-related growth indicators was observed. The magnitude of associations were stronger when physical activity was modelled as exposure. These results reinforce the importance of physical activity since early years.

A stepped down physical activity support program for military service veterans: The Active Choices pilot study.

ISSUE ADDRESSED: Referral to supervised physical activity (PA) programs is an effective treatment for military service veterans (MSVs) suffering from a range of chronic diseases. However, many MSVs fail to maintain PA regimes once discharged from supervision. This pilot study assessed Active Choices, a stepped-down program to support MSVs in the transition from allied health treatment to self-managed PA. METHODS: Participants were 34 Australian MSVs (mean [SD] age = 61 [15.8] years) who were completing supervised referral to an exercise physiologist or physiotherapist. MSVs stepped-down to Active Choices and received a 12-week, evidence-based PA support program (2020-2021). Analyses compared within-group changes in accelerometer-assessed PA at three time points (Weeks 0, 12, and 24; linear mixed model). Program retention, PA choices, and allied healthcare service costs were also evaluated. RESULTS: Relative to baseline (64 [26] min/day), mean (SD) moderate-to-vigorous PA increased (74 [28] min/day; p < .05) and was maintained (62 [28] min/day) at weeks 12 and 24, respectively. Retention in the program was high (86% [29/34 participants] completion rate at 12 weeks), with water-based group activities the most popular PAs of choice (14/24 activities). Average allied healthcare service costs during the study were lower than typical costs for MSVs (60.51 vs. 97.06 AUD/week). CONCLUSION: The findings highlight the potential of Active Choices to support MSVs in the transition from supervised to self-managed MVPA. SO WHAT?: The program could promote the health of veterans and reduce costs for ongoing referral if impact is replicated at scale.

Influence of maternal excess weight before pregnancy on breast-feeding and weaning: pooled analyses of four population-based cohorts born between 1982 and 2015.

To investigate the associations of maternal excess weight before pregnancy with (1) weaning at 3 months of age, (2) duration of exclusive breast-feeding at 6 months of age, (3) duration of any breast-feeding at 12 months of age and (4) to compare the magnitude of these associations over four decades. Data were from participants in the Pelotas (Brazil) Birth Cohorts born in 1982 (n 5334), 1993 (n 1442), 2004 (n 4092) and 2015 (n 4102). Maternal pre-pregnancy weight was collected after the delivery and breast-feeding status was assessed when children were 3 and 12 months old. Only in the most recent cohort (2015), women with excess weight (BMI ≥ 25 kg/m2) before pregnancy had higher risk of discontinuing exclusive breast-feeding within the first 6 months postpartum than women with normal weight (hazard ratio = 1·22 (95 % CI 1·15, 1·30)). Duration of any type of breast-feeding until 12 months of age was not affected by pre-pregnancy weight. Excess weight before pregnancy is associated with exclusive breast-feeding only in the most recent birth cohort coinciding with increases in excess weight and breast-feeding over time.

All are equal, but some are more equal than others: social determinants of leisure time physical activity through the lens of intersectionality.

BACKGROUND: To date, no research has investigated social determinants of leisure time physical activity through the lens of intersectionality in a low- and middle-income country. Therefore, the aim of this study was to explore the intersectionality in leisure time physical activity in a nationwide sample of Brazilian adults. METHODS: Data from the Brazilian National Health Survey conducted in 2013 were analysed (N = 58,429). Prevalence of sufficient leisure time physical activity (150+ minutes per week in moderate-to-vigorous physical activity) was estimated according to gender, racial identity, education and income, and according to multiple combinations of these sociodemographic characteristics (i.e., multiple jeopardy index). RESULTS: The prevalence of sufficient leisure time physical activity was 22.9% (95%CI: 22.3 to 23.6). Overall, the prevalence of sufficient leisure time physical activity was highest among men, individuals with white skin colour, and among those in the highest group of education and income. Among men, white, with a university degree and in the highest quartile of income (3% of the population), the prevalence of sufficient leisure time physical activity was 48%. Among non-white women with low education and low income (8.1% of the population), the prevalence of sufficient leisure time physical activity was 9.8%. CONCLUSION: Informed by the theory of intersectionality, findings of this study have shown that intersections of gender, racial identity and socioeconomic position of the Brazilian society strongly influence leisure time physical activity at the individual level. Targeted interventions to increase leisure time physical activity should address the complexities of social status intersections.

Accelerometer-measured physical activity in mid-age Australian adults.

BACKGROUND: Raw data from accelerometers can provide valuable insights into specific attributes of physical activity, such as time spent in intensity-specific activity. The aim of this study was to describe physical activity assessed with raw data from triaxial wrist-worn accelerometers in mid-age Australian adults. METHODS: Data were from 700 mid-age adults living in Brisbane, Australia (mean age: 60.4; SD:7.1 years). Data from a non-dominant wrist worn triaxial accelerometer (Actigraph wGT3X-BT), expressed as acceleration in gravitational equivalent units (1 mg = 0.001 g), were used to estimate time spent in moderate-vigorous intensity physical activity (MVPA; >100 mg) using different bout criteria (non-bouted, 1-, 5-, and 10-min bouts), and the proportion of participants who spent an average of at least one minute per day in vigorous physical activity. RESULTS: Mean acceleration was 23.2 mg (SD: 7.5) and did not vary by gender (men: 22.4; women: 23.7; p-value: 0.073) or education (p-value: 0.375). On average, mean acceleration was 10% (2.5 mg) lower per decade of age from age 55y. The median durations in non-bouted, 1-min, 5-min and 10-min MVPA bouts were, respectively, 68 (25th -75th : 45-99), 26 (25th -75th : 12-46), 10 (25th -75th : 3-24) and 8 (25th -75th : 0-19) min/day. Around one third of the sample did at least one minute per day in vigorous intensity activities. CONCLUSION: This population-based cohort provided a detailed description of physical activity based on raw data from accelerometers in mid-age adults in Australia. Such data can be used to investigate how different patterns and intensities of physical activity vary across the day/week and influence health outcomes.

Comparison of bone mineral density according to domains of sedentary behavior in children and adolescents.

BACKGROUND: Somatic maturation and the age at onset of puberty are closely related to bone mineral density (BMD), and are potential confounders of the associations between physical activity, sedentary behavior (SB) and BMD in adolescents. Thus the aim was compare BMD at different anatomical sites according to different domains of SB. METHODS: The sample consisted of 88 young people (54 boys and 34 girls; 9.5 ± 1.5 years). The self-reported SB was measured by the time spent on TV, computer, video game and smartphone. BMD at each location and throughout the body was assessed by DEXA. Physical activity was assessed by a questionnaire. The comparison of the different types of BMD sites according to the SB levels for each screen device and the total SB were analyzed by Covariance Analysis (ANCOVA). RESULTS: Whole-body BMD was higher in young people with low total SB (Total BMD = 0.957 ± 0.042) than in those with moderate (Total BMD = 0.921 ± 0.053) and high SB (Total BMD = 0.929 ± 0.051) (p-value = 0.011). Children and adolescents with low total SB had higher BMD legs (0.965 ± 0.056) than young people with high total SB (BMD legs = 0.877 ± 0.209), but this relationship was attenuated when the analyzes were adjusted for physical activity (p-value = 0.068). CONCLUSION: Adolescents with high sedentary behavior tend to have lower whole body bone mineral density than those with low sedentary behavior.

Rural-urban differences in physical activity and TV-viewing in Brazil.

INTRODUCTION: This study aims to compare differences regarding physical activity (PA) indicators and TV viewing according to location of residence and sociodemographic variables in Brazil. METHODS: This cross-sectional study with individuals aged 18 years or older using data from the Brazilian National Health Survey was carried out in 2013. PA (total and domains) and TV viewing (≥3 hours) were measured by self-report. Stratifiers were location of residence, sex, age, wealth quintiles and regions of the country. RESULTS: This study included 60 202 individuals (49 245 from urban areas). In general, total PA was not different according to location of residence (urban 59.4%; rural 57.4%). Higher differences were found for leisure-time PA (urban 24.1%; rural 13.9%), work-based PA (urban 12.9%; rural 21.1%) and TV viewing of more than 3 hours (urban 30.1%; rural 21.4%). The differences between urban and rural areas were enhanced according to sex, age and wealth quintiles. CONCLUSION: The differences between urban and rural areas vary according to PA domains and population groups. To explore specific domains of PA in each location of residence may improve the understanding of the analyzed outcomes in different communities and consequently guide future interventions.