The Annual Rhythms in Sleep, Sedentary Behavior, and Physical Activity of Australian Adults: A Prospective Cohort Study.

BACKGROUND: Sleep, sedentary behavior, and physical activity have fundamental impacts on health and well-being. Little is known about how these behaviors vary across the year. PURPOSE: To investigate how movement-related behaviors change across days of the week and seasons, and describe movement patterns across a full year and around specific temporal events. METHODS: This cohort study included 368 adults (mean age = 40.2 years [SD = 5.9]) who wore Fitbit activity trackers for 12 months to collect minute-by-minute data on sleep, sedentary behavior, light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). Data were analyzed descriptively, as well as through multilevel mixed-effects linear regression to explore associations with specific temporal cycles (day-of-the-week, season) and events. RESULTS: Movement patterns varied significantly by day-of-the-week and season, as well as during annual events like Christmas-New Year and daylight saving time (DST) transitions. For example, sleep was longer on weekends (+32 min/day), during autumn and winter relative to summer (+4 and +11 min/day), and over Christmas-New Year (+24 min/day). Sedentary behavior was longer on weekdays, during winter, after Christmas-New Year, and after DST ended (+45, +7, +12, and +8 min/day, respectively). LPA was shorter in autumn, winter, and during and after Christmas-New Year (-6, -15, -17, and -31 min/day, respectively). Finally, there was less MVPA on weekdays and during winter (-5 min/day and -2 min/day, respectively). CONCLUSIONS: Across the year, there were notable variations in movement behaviors. Identifying high-risk periods for unfavorable behavior changes may inform time-targeted interventions and health messaging.

Sleep, sedentary behavior, and physical activity have fundamental impacts on health and well-being, yet little is known about how these behaviors vary across the year. This study investigated how these behaviors change across days of the week, seasons, and a year, and around specific temporal events. The study included 368 middle-aged adults who wore Fitbit activity trackers for 12 months to collect minute-by-minute movement data. Statistical analyses showed movement patterns varied significantly by day-of-the-week and season, as well as during annual events like Christmas-New Year and daylight saving time transitions. For example, sleep was longer on weekends, during autumn and winter relative to summer, and over Christmas-New Year. Sedentary behavior was longer on weekdays, during winter, after Christmas-New Year, and after daylight savings time ended. Light physical activity was shorter in autumn, winter, and during and after Christmas-New Year. Finally, there was less moderate-to-vigorous physical activity on weekdays and during winter. Across the year, there were notable variations in movement patterns. Identifying high-risk periods for unfavorable behavior changes may inform time-targeted interventions and health messaging.

The association between reallocations of time and health using compositional data analysis: a systematic scoping review with an interactive data exploration interface.

BACKGROUND: How time is allocated influences health. However, any increase in time allocated to one behaviour must be offset by a decrease in others. Recently, studies have used compositional data analysis (CoDA) to estimate the associations with health when reallocating time between different behaviours. The aim of this scoping review was to provide an overview of studies that have used CoDA to model how reallocating time between different time-use components is associated with health. METHODS: A systematic search of four electronic databases (MEDLINE, Embase, Scopus, SPORTDiscus) was conducted in October 2022. Studies were eligible if they used CoDA to examine the associations of time reallocations and health. Reallocations were considered between movement behaviours (sedentary behaviour (SB), light physical activity (LPA), moderate-to-vigorous physical activity (MVPA)) or various activities of daily living (screen time, work, household chores etc.). The review considered all populations, including clinical populations, as well as all health-related outcomes. RESULTS: One hundred and three studies were included. Adiposity was the most commonly studied health outcome (n = 41). Most studies (n = 75) reported reallocations amongst daily sleep, SB, LPA and MVPA. While other studies reported reallocations amongst sub-compositions of these (work MVPA vs. leisure MVPA), activity types determined by recall (screen time, household chores, passive transport etc.) or bouted behaviours (short vs. long bouts of SB). In general, when considering cross-sectional results, reallocating time to MVPA from any behaviour(s) was favourably associated with health and reallocating time away from MVPA to any behaviour(s) was unfavourably associated with health. Some beneficial associations were seen when reallocating time from SB to both LPA and sleep; however, the strength of the association was much lower than for any reallocations involving MVPA. However, there were many null findings. Notably, most of the longitudinal studies found no associations between reallocations of time and health. Some evidence also suggested the context of behaviours was important, with reallocations of leisure time toward MVPA having a stronger favourable association for health than reallocating work time towards MVPA. CONCLUSIONS: Evidence suggests that reallocating time towards MVPA from any behaviour(s) has the strongest favourable association with health, and reallocating time away from MVPA toward any behaviour(s) has the strongest unfavourable association with health. Future studies should use longitudinal and experimental study designs, and for a wider range of outcomes.

Changes in fitness and fatness in Australian schoolchildren during the summer holidays: fitness lost, fatness regained? A cohort study.

BACKGROUND: Emerging evidence suggests that children's fatness increases and fitness declines at a greater rate during the summer holiday period, compared with the school year. The aim of this study was to compare rates of change in fitness and fatness over the in-term and summer holiday periods among Australian schoolchildren. A secondary aim was to explore whether rates of change differed according to the child's sex, socio-economic status (SES), pubertal status and weight status. METHODS: Children (n = 381) initially in Grade 4 (age 9) were recruited for this 2-year longitudinal study. Fatness (% body fat, BMI z-score, waist-to-height ratio) and fitness (20-m shuttle run and standing broad jump) were measured at the start and end of two consecutive years. Rates of change were calculated for the two in-school periods (Grades 4 and 5) and for the summer holiday period. Rates of change in fatness and fitness between in-school and holiday periods were compared, and differences in rates of change according to sex, socio-economic status, and weight status were explored. RESULTS: During the holidays, percentage body fat increased at a greater rate (annualised rate of change [RoC]: +3.9 vs. Grade 4 and + 4.7 vs. Grade 5), and aerobic fitness declined at a greater rate (RoC - 4.7 vs. Grade 4 and - 4.4 vs. Grade 5), than during the in-school periods. There were no differences in rates of change for BMI z-score, waist-to-height ratio or standing broad jump. Body fatness increased faster in the holidays (relative to the in-school period) in children who are overweight and from low-SES families. Aerobic fitness declined more rapidly in the holidays in children who are overweight. CONCLUSION: This study highlights that during the summer holiday period, children experience greater increases in fatness and declines in fitness, with children who live with low-SES families and are overweight being more affected. The findings suggest the need for targeted interventions during this period to address these negative health trends. TRIAL REGISTRATION: Australia New Zealand Clinical Trials Registry, identifier ACTRN12618002008202. Retrospectively registered on 14 December 2018.

Almonds vs. carbohydrate snacks in an energy-restricted diet: Weight and cardiometabolic outcomes from a randomized trial.

OBJECTIVE: This study evaluated weight and cardiometabolic outcomes after a 3-month energy-restricted diet (-30%) containing almonds (almond-enriched diet [AED]) or containing carbohydrate-rich snacks (nut-free control diet [NFD]) (Phase 1), followed by 6 months of weight maintenance (Phase 2). METHODS: Participants (25-65 years old) with overweight or obesity (BMI 27.5-34.9 kg/m2 ) were randomly allocated to AED (n = 68) or NFD (n = 72). RESULTS: Both groups lost weight during Phase 1 (p < 0.001) (mean [SE], -7.0 [0.5] kg AED vs. -7.0 [0.5] kg NFD, p = 0.858) and Phase 2 (p = 0.009) (-1.1 [0.5] kg AED vs. -1.3 [0.6] NFD, p = 0.756), with improvements in percentage lean mass after Phase 2 (4.8% [0.3%], p < 0.001). Reductions occurred in fasting glucose (-0.2 [0.07] mmol/L, p = 0.003), insulin (-8.1 [4.0] pmol/L, p = 0.036), blood pressure (-4.9 [0.8] mm Hg systolic, -5.0 [0.5] mm Hg diastolic, p < 0.001), total cholesterol (-0.3 [0.1] mmol/L), low-density lipoprotein (LDL) (-0.2 [0.1] mmol/L), very low-density lipoprotein (-0.1 [0.03] mmol/L), and triglycerides (-0.3 [0.06] mmol/L) (all p < 0.001), and high-density lipoprotein increased (0.1 [0.02] mmol/L, p = 0.011) by the end of Phase 2 in both groups. There were group by time interactions for lipoprotein particle concentrations: very small triglyceride-rich (-31.0 [7.7] nmol/L AED vs. -4.8 [7.9] nmol/L NFD, p = 0.007), small LDL (-109.3 [40.5] nmol/L AED vs. -20.7 [41.6] nmol/L NFD, p = 0.017), and medium LDL (-24.4 [43.4] nmol/L AED vs. -130.5 [44.4] nmol/L NFD, p = 0.045). CONCLUSIONS: An energy-restricted AED resulted in weight loss and weight loss maintenance comparable to an energy-restricted NFD, and both diets supported cardiometabolic health. The AED resulted in greater improvements in some lipoprotein subfractions, which may enhance reductions in cardiovascular risk.

Weekly, Seasonal, and Festive Period Weight Gain Among Australian Adults.

Importance: Obesity is a major global health concern. A better understanding of temporal patterns of weight gain will enable the design and implementation of interventions with potential to alter obesity trajectories. Objective: To describe changes in daily weight across 12 months among Australian adults. Design, Setting, and Participants: This cohort study conducted between December 1, 2019, and December 31, 2021 in Adelaide, South Australia, involved 375 community-dwelling adults aged 18 to 65 years. Participants wore a fitness tracker and were encouraged to weigh themselves, preferably daily but at least weekly, using a body weight scale. Data were remotely gathered using custom-developed software. Exposure: Time assessed weekly, seasonally, and at Christmas/New Year and Easter. Main Outcomes and Measures: Data were visually inspected to assess the overall yearly pattern in weight change. Data were detrended (to remove systematic bias from intraindividual gradual increases or decreases in weight) by calculating a line of best fit for each individual's annual weight change relative to baseline and subtracting this from each participant's weight data. Multilevel mixed-effects linear regression analysis was used to compare weight across days of the week and seasons and at Christmas/New Year and Easter. Results: Of 375 participants recruited, 368 (mean [SD] age, 40.2 [5.9] years; 209 [56.8%] female; mean [SD] baseline weight, 84.0 [20.5] kg) provided at least 7 days of weight data for inclusion in analyses. Across the 12-month period, participants gained a median of 0.26% body weight (218 g) (range, -29.4% to 24.0%). Weight fluctuated by approximately 0.3% (252 g) each week, with Mondays and Tuesdays being the heaviest days of the week. Relative to Monday, participants' weight gradually decreased from Tuesday, although not significantly so (mean [SE] weight change, 0.01% [0.03%]; P = .83), to Friday (mean [SE] weight change, -0.18% [0.03%]; P < .001) and increased across the weekend to Monday (mean [SE] weight change for Saturday, -0.16% [0.03%]; P < .001; mean [SE] weight change for Sunday, -0.10% [0.03%]; P < .001). Participants' weight increased sharply at Christmas/New Year (mean [SE] increase, 0.65% [0.03%]; z score, 25.30; P < .001) and Easter (mean [SE] weight change, 0.29% [0.02%], z score, 11.51; P < .001). Overall, participants were heaviest in summer (significantly heavier than in all other seasons), were lightest in autumn (mean [SE] weight change relative to summer, -0.47% [0.07%]; P < .001), regained some weight in winter (mean [SE] weight change relative to summer, -0.23% [0.07%]; P = .001), and became lighter in spring (mean [SE] weight change relative to summer, -0.27% [0.07%]; P < .001). Conclusions and Relevance: In this cohort study of Australian adults with weekly and yearly patterns in weight gain observed across 12 months, high-risk times for weight gain were Christmas/New Year, weekends, and winter, suggesting that temporally targeted weight gain prevention interventions may be warranted.

How do 24-h movement behaviours change during and after vacation? A cohort study.

BACKGROUND: For adults, vacations represent a break from daily responsibilities of work - offering the opportunity to re-distribute time between sleep, sedentary behaviour, light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) across the 24-h day. To date, there has been minimal research into how activity behaviour patterns change on vacation, and whether any changes linger after the vacation. This study examined how daily movement behaviours change from before, to during and after vacations, and whether these varied based on the type of vacation and vacation duration. METHODS: Data collected during the Annual Rhythms In Adults' lifestyle and health (ARIA) study were used. 308 adults (mean age 40.4 years, SD 5.6) wore Fitbit Charge 3 fitness trackers 24 h a day for 13 months. Minute-by-minute movement behaviour data were aggregated into daily totals. Multi-level mixed-effects linear regressions were used to compare movement behaviours during and post-vacation (4 weeks) to pre-vacation levels (14 days), and to examine the associations with vacation type and duration. RESULTS: Participants took an average of 2.6 (SD = 1.7) vacations of 12 (SD = 14) days' (N = 9778 days) duration. The most common vacation type was outdoor recreation (35%) followed by family/social events (31%), rest (17%) and non-leisure (17%). Daily sleep, LPA and MVPA all increased (+ 21 min [95% CI = 19,24] p < 0.001, + 3 min [95% CI = 0.4,5] p < 0.02, and + 5 min [95% CI = 3,6] p < 0.001 respectively) and sedentary behaviour decreased (-29 min [95% CI = -32,-25] p < 0.001) during vacation. Post-vacation, sleep remained elevated for two weeks; MVPA returned to pre-vacation levels; and LPA and sedentary behaviour over-corrected, with LPA significantly lower for 4 weeks, and sedentary behaviour significantly higher for one week. The largest changes were seen for "rest" and "outdoor" vacations. The magnitude of changes was smallest for short vacations (< 3 days). CONCLUSIONS: Vacations are associated with favourable changes in daily movement behaviours. These data provide preliminary evidence of the health benefits of vacations. TRIAL REGISTRATION: The study was prospectively registered on the Australian New Zealand Clinical Trial Registry (Trial ID: ACTRN12619001430123).

Children's activity and diet behaviours in the summer holidays versus school year.

BACKGROUND: Evidence shows children gain more weight during the summer holidays versus the school year. OBJECTIVES: To examine within-child differences in activity and diet behaviours during the summer holidays versus the school year. METHODS: Children (mean age 9.4 years; 37% male) wore accelerometers (GENEActiv; n = 133), reported activities (Multimedia Activity Recall for Children and Adolescents; n = 133) and parents reported child diet (n = 133) at five timepoints over 2 years capturing school and summer holiday values. Mixed-effects models were used to compare school and summer holiday behaviours. RESULTS: Children spent less time in moderate- to vigorous-physical activity (-12 min/day; p = 0.001) and sleep (-12 min/day; p < 0.001) and more time sedentary (+27 min/day; p < 0.001) during summer holidays versus the school year. Screentime (+70 min/day; p < 0.001), domestic/social activities (+43 min/day; p = <0.001), self-care (+24 min/day; p < 0.001), passive transport (+22 min/day; p = 0.001) and quiet time (+16 min/day; p = 0.012) were higher during the summer holidays, compensating for less time in school-related activities (-164 min/day; p < 0.001). Diet quality was lower (-4 points; p < 0.001) and children consumed fewer serves of fruit (-0.4 serves; p < 0.001) during the summer holidays versus the school year. CONCLUSIONS: Children are displaying poorer activity and diet behaviours during the summer holidays, which may contribute to accelerated weight gain over the holiday period.

Weather associations with physical activity, sedentary behaviour and sleep patterns of Australian adults: a longitudinal study with implications for climate change.

BACKGROUND: Weather is a potentially important influence on how time is allocated to sleep, sedentary behaviour and physical activity across the 24-h day. Extremes of weather (very hot, cold, windy or wet) can create undesirable, unsafe outdoor environments for exercise or active transport, impact the comfort of sleeping environments, and increase time indoors. This 13-month prospective cohort study explored associations between weather and 24-h movement behaviour patterns. METHODS: Three hundred sixty-eight adults (mean age 40.2 years, SD 5.9, 56.8% female) from Adelaide, Australia, wore Fitbit Charge 3 activity trackers 24 h a day for 13 months with minute-by-minute data on sleep, sedentary behaviour, light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) collected remotely. Daily weather data included temperature, rainfall, wind, cloud and sunshine. Multi-level mixed-effects linear regression analyses (one model per outcome) were used. RESULTS: Ninety thousand eight hundred one days of data were analysed. Sleep was negatively associated with minimum temperature (-12 min/day change across minimum temperature range of 31.2 °C, p = 0.001). Sedentary behaviour was positively associated with minimum temperature (+ 12 min/day, range = 31.2 oC, p = 0.006) and wind speed (+ 10 min/day, range = 36.7 km/h, p< 0.001), and negatively associated with sunshine (-17 min/day, range = 13.9 h, p < 0.001). LPA was positively associated with minimum temperature (+ 11 min/day, range = 31.2 °C, p = 0.002), cloud cover (+ 4 min/day, range = 8 eighths, p = 0.008) and sunshine (+ 17 min/day, range = 13.9 h, p < 0.001), and negatively associated with wind speed (-8 min/day, range = 36.7 km/h, p < 0.001). MVPA was positively associated with sunshine (+ 3 min/day, range = 13.9 h, p < 0.001) and negatively associated with minimum temperature (-13 min/day, range = 31.2 oC, p < 0.001), rainfall (-3 min/day, range = 33.2 mm, p = 0.006) and wind speed (-4 min/day, range = 36.7 km/h, p < 0.001). For maximum temperature, a significant (p < 0.05) curvilinear association was observed with sleep (half-U) and physical activity (inverted-U), where the decrease in sleep duration appeared to slow around 23 °C, LPA peaked at 31 oC and MVPA at 27 °C. CONCLUSIONS: Generally, adults tended to be less active and more sedentary during extremes of weather and sleep less as temperatures rise. These findings have the potential to inform the timing and content of positive movement behaviour messaging and interventions. TRIAL REGISTRATION: The study was prospectively registered on the Australian New Zealand Clinical Trial Registry (Trial ID: ACTRN12619001430123).

The relationship between sleep and physical activity in an in-patient rehabilitation stroke setting: a cross-sectional study.

BACKGROUND: Appropriate sleep and physical activity are known to be important for positive neuroplastic changes in the brain and therefore may affect stroke recovery. OBJECTIVE: To investigate the relationship between sleep and participation in different intensity levels of physical activity; to investigate the convergent validity of a commercially available device (Fitbit Flex) in measuring sleep and physical activity in people with stroke. METHODS: A cross-sectional observational study in people with stroke undergoing rehabilitation. Participants wore two accelerometers on their unaffected wrist for seven consecutive days and recorded a sleep log, the Pittsburgh Sleep Quality Index and Fatigue Assessment Scale. Any relationships between sleep and activity were assessed with linear regression. Pearson and intra-class correlation coefficients were used to assess the validity of the two accelerometers (Fitbit Flex against the validated GENEActiv). RESULTS: Twenty-three patients with stroke were recruited. Twenty complete data sets were analyzed. Participants had approximately 9 hours of sleep and 13 hours of sedentary behavior per day, with 99 minutes spent on physical activity (16 min spent on moderate to vigorous activity). Time spent on sleep was significantly related to sedentary, light and moderate physical activity time (r = -.67, .22, .20). The ICC of the Fitbit Flex in measuring light physical activity was .884 p < .001 but was not correlated for other measures. CONCLUSIONS: More sleep was related to less sedentary behavior and more time spent on physical activity. Fitbit Flex was only valid for measuring light physical activity.

Parenting Styles and Their Associations With Children's Body Composition, Activity Patterns, Fitness, Diet, Health, and Academic Achievement.

Background: Evidence regarding the impact of parenting style on health and other outcomes is inconsistent and limited by measurement quality and type. This study will examine associations between parenting style and children's objectively assessed activity patterns, body composition, fitness, diet, health, and academic achievement. Methods: Two hundred fifty-five children (mean age: 9.4 years) from Adelaide, Australia, were included. Parenting style (items from Child Rearing Questionnaire and National Longitudinal Survey of Children and Youth to assess Authoritative, Authoritarian, Permissive, Disengaged parenting), diet, and health were proxy-reported by parents. Body composition, fitness, and 24 hour activity patterns were objectively measured, and children reported screen-time. Academic achievement was measured using standardized tests in reading and mathematics. Mixed models were used to regress parenting style against activity patterns, body composition, fitness, diet, health, and academic achievement, adjusted for age, sex, socioeconomic position, and pubertal stage. Results: Children with Disengaged parents had poorer activity patterns: less moderate to vigorous physical activity (standard mean difference [SMD] relative to grand mean = -0.23), light physical activity (SMD = -0.13) and sleep (SMD = -0.18), more sitting (SMD = 0.45), later bedtime (SMD = 0.18), lower overall energy expenditure (SMD = -0.23), and poorer overall self-reported health (SMD = -0.30). Children with Permissive parents had generally better activity patterns (SMD = 0.25-0.32). Children with Authoritative parents were more likely to meet dietary guidelines for fruit intake (SMD = 0.12). There were no associations for Authoritarian parenting style or for academic achievement, body composition, or fitness. Conclusions: Disengaged parenting was detrimental, while Permissive parenting was beneficial for activity patterns. As parenting styles may be malleable, future interventions may target Permissive parenting to improve children's activity patterns. Trial registration: Australia New Zealand Clinical Trials Registry, identifier ACTRN12618002008202. Retrospectively registered on 14 December 2018.

Your best day: An interactive app to translate how time reallocations within a 24-hour day are associated with health measures.

Reallocations of time between daily activities such as sleep, sedentary behavior and physical activity are differentially associated with markers of physical, mental and social health. An individual's most desirable allocation of time may differ depending on which outcomes they value most, with these outcomes potentially competing with each other for reallocations. We aimed to develop an interactive app that translates how self-selected time reallocations are associated with multiple health measures. We used data from the Australian Child Health CheckPoint study (n = 1685, 48% female, 11-12 y), with time spent in daily activities derived from a validated 24-h recall instrument, %body fat from bioelectric impedance, psychosocial health from the Pediatric Quality of Life Inventory and academic performance (writing) from national standardized tests. We created a user-interface to the compositional isotemporal substitution model with interactive sliders that can be manipulated to self-select time reallocations between activities. The time-use composition was significantly associated with body fat percentage (F = 2.66, P < .001), psychosocial health (F = 4.02, P < .001), and academic performance (F = 2.76, P < .001). Dragging the sliders on the app shows how self-selected time reallocations are associated with the health measures. For example, reallocating 60 minutes from screen time to physical activity was associated with -0.8 [95% CI -1.0 to -0.5] %body fat, +1.9 [1.4 to 2.5] psychosocial score and +4.5 [1.8 to 7.2] academic performance. Our app allows the health associations of time reallocations to be compared against each other. Interactive interfaces provide flexibility in selecting which time reallocations to investigate, and may transform how research findings are disseminated.

The EPIPHA-KNEE trial: Explaining Pain to target unhelpful pain beliefs to Increase PHysical Activity in KNEE osteoarthritis - a protocol for a multicentre, randomised controlled trial with clinical- and cost-effectiveness analysis.

BACKGROUND: Despite well-established benefits of physical activity for knee osteoarthritis (OA), nine of ten people with knee OA are inactive. People with knee OA who are inactive often believe that physical activity is dangerous, fearing that it will further damage their joint(s). Such unhelpful beliefs can negatively influence physical activity levels. We aim to evaluate the clinical- and cost-effectiveness of integrating physiotherapist-delivered pain science education (PSE), an evidence-based conceptual change intervention targeting unhelpful pain beliefs by increasing pain knowledge, with an individualised walking, strengthening, and general education program. METHODS: Two-arm, parallel-design, multicentre randomised controlled trial involving 198 people aged ≥50 years with painful knee OA who do not meet physical activity guideline recommendations or walk regularly for exercise. Both groups receive an individualised physiotherapist-led walking, strengthening, and OA/activity education program via 4x weekly in-person treatment sessions, followed by 4 weeks of at-home activities (weekly check-in via telehealth), with follow-up sessions at 3 months (telehealth) and 5 and 9 months (in-person). The EPIPHA-KNEE group also receives contemporary PSE about OA/pain and activity, embedded into all aspects of the intervention. Outcomes are assessed at baseline, 12 weeks, 6 and 12 months. Primary outcomes are physical activity level (step count; wrist-based accelerometry) and self-reported knee symptoms (WOMAC Total score) at 12 months. Secondary outcomes are quality of life, pain intensity, global rating of change, self-efficacy, pain catastrophising, depression, anxiety, stress, fear of movement, knee awareness, OA/activity conceptualisation, and self-regulated learning ability. Additional measures include adherence, adverse events, blinding success, COVID-19 impact on activity, intention to exercise, treatment expectancy/perceived credibility, implicit movement/environmental bias, implicit motor imagery, two-point discrimination, and pain sensitivity to activity. Cost-utility analysis of the EPIPHA-KNEE intervention will be undertaken, in addition to evaluation of cost-effectiveness in the context of primary trial outcomes. DISCUSSION: We will determine whether the integration of PSE into an individualised OA education, walking, and strengthening program is more effective than receiving the individualised program alone. Findings will inform the development and implementation of future delivery of PSE as part of best practice for people with knee OA. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry: ACTRN12620001041943 (13/10/2020).

Are all MVPA minutes equal? Associations between MVPA characteristics, independent of duration, and childhood adiposity.

BACKGROUND: The inverse relationship between moderate-to-vigorous physical activity (MVPA) duration and childhood adiposity is well established. Less is known about how characteristics of MVPA accumulation may be associated with adiposity, independent of MVPA duration. This study aimed to investigate how the MVPA characteristics of children, other than duration (bout length, time of day, day-to-day consistency, intensity), were associated with adiposity. METHODS: Cross-sectional study of the Australian arm of the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE) (participants: n = 424, age range 9-11, 44% male). Adiposity was determined by percent body fat via bioelectrical impedance. MVPA duration and characteristics (bout length, time of day, consistency, intensity) were derived from 7-day, 24-h accelerometry. Generalised estimating equations were used to examine the individual and multivariate associations between MVPA characteristics and adiposity. RESULTS: Univariate analyses showed that higher MVPA duration (ß range = - 0.26,-0.15), longer bouts of MVPA (ß range = 0.15,0.22) and higher MVPA intensity (ß range = - 0.20,-0.13) were all inversely associated with adiposity (all p < 0.05). When models were adjusted for MVPA duration, only MVPA intensity (ß range = - 0.16,-0.04) showed consistent significant associations with adiposity. CONCLUSIONS: Characteristics of MVPA other than duration and intensity appear to be unrelated to adiposity.

Annual, seasonal, cultural and vacation patterns in sleep, sedentary behaviour and physical activity: a systematic review and meta-analysis.

BACKGROUND: Time spent in daily activities (sleep, sedentary behaviour and physical activity) has important consequences for health and wellbeing. The amount of time spent varies from day to day, yet little is known about the temporal nature of daily activity patterns in adults. The aim of this review is to identify the annual rhythms of daily activity behaviours in healthy adults and explore what temporal factors appear to influence these rhythms. METHODS: Six online databases were searched for cohort studies exploring within-year temporal patterns (e.g. season effects, vacation, cultural festivals) in sleep, sedentary behaviour or physical activity in healthy 18 to 65-year-old adults. Screening, data extraction, and risk of bias scoring were performed in duplicate. Extracted data was presented as mean daily minutes of each activity type, with transformations performed as needed. Where possible, meta-analyses were performed using random effect models to calculate standardised mean differences (SMD). RESULTS: Of the 7009 articles identified, 17 studies were included. Studies were published between 2003 and 2019, representing 14 countries and 1951 participants, addressing variation in daily activities across season (n = 11), Ramadan (n = 4), vacation (n = 1) and daylight savings time transitions (n = 1). Meta-analyses suggested evidence of seasonal variation in activity patterns, with sleep highest in autumn (+ 12 min); sedentary behaviour highest in winter (+ 19 min); light physical activity highest in summer (+ 19 min); and moderate-to-vigorous physical activity highest in summer (+ 2 min) when compared to the yearly mean. These trends were significant for light physical activity in winter (SMD = - 0.03, 95% CI - 0.58 to - 0.01, P = 0.04). Sleep appeared 64 min less during, compared to outside Ramadan (non-significant). Narrative analyses for the impact of vacation and daylight savings suggested that light physical activity is higher during vacation and that sleep increases after the spring daylight savings transition, and decreases after the autumn transition. CONCLUSIONS: Research into temporal patterns in activity behaviours is scarce. Existing evidence suggests that seasonal changes and periodic changes to usual routine, such as observing religious events, may influence activity behaviours across the year. Further research measuring 24-h time use and exploring a wider variety of temporal factors is needed.

Changes in 24-Hour Physical Activity Patterns and Walking Gait Biomechanics After Primary Total Hip Arthroplasty: A 2-Year Follow-up Study.

BACKGROUND: Despite marked improvements in self-reported pain, perceived functional ability, and gait function following primary total hip arthroplasty (THA), it remains unclear whether these improvements translate into improved physical activity and sleep behaviors. The aim of this study was to determine the change in 24-hour activity profile (waking activities and sleep) and laboratory-based gait function from preoperatively to 2 years following the THA. METHODS: Fifty-one patients undergoing primary THA at a single public hospital were recruited. All THAs were performed using a posterior surgical approach with the same prosthesis type. A wrist-worn accelerometer was used to capture 24-hour activity profiles preoperatively and at 1 and 2 years postoperatively. Three-dimensional gait analysis was performed to determine changes in temporospatial and kinematic parameters of the hip and pelvis. RESULTS: Patients showed improvements in all temporospatial and kinematic parameters with time. Preoperatively, patients were sedentary or asleep for a mean time (and standard deviation) of 19.5 ± 2.2 hours per day. This remained unchanged up to 2 years postoperatively (19.6 ± 1.3 hours per day). Sleep efficiency remained suboptimal (<85%) at all time points and was worse at 2 years (77% ± 10%) compared with preoperatively (84% ± 5%). More than one-quarter of the sample were sedentary for >11 hours per day at 1 year (32%) and 2 years (41%), which was greater than the preoperative percentage (21%). Patients accumulated their activity performing light activities; however, patients performed less light activity at 2 years compared with preoperative levels. No significant differences (p = 0.935) were observed for moderate or vigorous activity across time. CONCLUSIONS: Together with improvements in self-reported pain and perceived physical function, patients had significantly improved gait function postoperatively. However, despite the opportunity for patients to be more physically active postoperatively, patients were more sedentary, slept worse, and performed less physical activity at 2 years compared with preoperative levels. LEVEL OF EVIDENCE: Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.

Development and evaluation of a method to define a tibial coordinate system through the fitting of geometric primitives.

Coordinate system definition is a critical element of biomechanical modeling of the knee, and cases of skeletal trauma present major technical challenges. This paper presents a method to define a tibial coordinate system by fitting geometric primitives to surface anatomy requiring minimal user input. The method presented here utilizes a conical fit to both the tibial shaft and femoral condyles to generate independent axes forming the basis of a tibial coordinate system. Definition of the tibial axis showed high accuracy when shape fitting to ≥50 mm of shaft with <3° of angular variation from the axis obtained using the full tibia. Repeatability and reproducibility of the axis was compared using intraclass correlation coefficients which showed excellent intra- and inter-observer agreement across cases. Additionally, shape fitting to the distal femoral condyles showed high accuracy compared to the reference axis established automatically through identifying the medial and lateral epicondyles (<4°). Utilizing geometric primitives to estimate functional axes for the tibia and femur removes reliance on anatomical landmarks that can be displaced by fracture or inaccurately identified by observers. Furthermore, fitting of such primitives provides a more complete understanding of the true bony anatomy, which cannot be done through simple landmark identification.

Comparisons of Daily Energy Intake vs. Expenditure Using the GeneActiv Accelerometer in Elite Australian Football Athletes.

ABSTRACT: Salagaras, BS, Mackenzie-Shalders, KL, Nelson, MJ, Fraysse, F, Wycherley, TP, Slater, GJ, McLellan, C, Kumar, K, and Coffey, VG. Comparisons of daily energy intake vs. expenditure using the GeneActiv accelerometer in elite Australian Football athletes. J Strength Cond Res 35(5): 1273-1278, 2021-To assess validity of the GeneActiv accelerometer for use within an athlete population and compare energy expenditure (EE) with energy and macronutrient intake of elite Australian Football athletes during a competition week. The GeneActiv was first assessed for utility during high-intensity exercise with indirect calorimetry. Thereafter, 14 professional Australian Football athletes (age, 24 ± 4 [SD] y; height, 1.87 ± 0.08 m; body mass, 86 ± 10 kg) wore the accelerometer and had dietary intake assessed via dietitian-led 24-hour recalls throughout a continuous 7 days of competition period (including match day). There was a significant relationship between metabolic equivalents and GeneActiv g·min-1 (SEE 1.77 METs; r2 = 0.64; p < 0.0001). Across the in-season week a significant difference only occurred on days 3 and 4 (day 3: energy intake [EI] EI 137 ± 31 kJ·kg-1·d-1; 11,763 ± 2,646 kJ·d-1 and EE: 186 ± 14 kJ·kg-1·d-1; 16,018 ± 1973 kJ·d-1; p < 0.05; d = -1.4; day 4: EI: 179 ± 44 kJ·kg-1·d-1, 15,413 ± 3,960 kJ·d-1 and EE: 225 ± 42 kJ·kg-1·d-1; 19,313 ± 3,072 kJ·d-1; d = -0.7). Carbohydrate intake (CI) was substantially below current sports nutrition recommendations on 6 of 7 days with deficits ranging from -1 to -7.2 g·kg-1·d-1 (p < 0.05), whereas daily protein and fat intake was adequate. In conclusion, the GeneActiv provides effective estimation of EE during weekly preparation for a professional team sport competition. Australian Footballers attempt to periodize dietary EI to varying daily training loads but fail to match expenditure on higher-training load days. Specific dietary strategies to increase CI may be beneficial to achieve appropriate energy balance and macronutrient distribution, particularly on days where athletes undertake multiple training sessions.

Compliance With Mobile Ecological Momentary Assessment of Self-Reported Health-Related Behaviors and Psychological Constructs in Adults: Systematic Review and Meta-analysis.

BACKGROUND: Mobile ecological momentary assessment (mEMA) permits real-time capture of self-reported participant behaviors and perceptual experiences. Reporting of mEMA protocols and compliance has been identified as problematic within systematic reviews of children, youth, and specific clinical populations of adults. OBJECTIVE: This study aimed to describe the use of mEMA for self-reported behaviors and psychological constructs, mEMA protocol and compliance reporting, and associations between key components of mEMA protocols and compliance in studies of nonclinical and clinical samples of adults. METHODS: In total, 9 electronic databases were searched (2006-2016) for observational studies reporting compliance to mEMA for health-related data from adults (>18 years) in nonclinical and clinical settings. Screening and data extraction were undertaken by independent reviewers, with discrepancies resolved by consensus. Narrative synthesis described participants, mEMA target, protocol, and compliance. Random effects meta-analysis explored factors associated with cohort compliance (monitoring duration, daily prompt frequency or schedule, device type, training, incentives, and burden score). Random effects analysis of variance (P≤.05) assessed differences between nonclinical and clinical data sets. RESULTS: Of the 168 eligible studies, 97/105 (57.7%) reported compliance in unique data sets (nonclinical=64/105 [61%], clinical=41/105 [39%]). The most common self-reported mEMA target was affect (primary target: 31/105, 29.5% data sets; secondary target: 50/105, 47.6% data sets). The median duration of the mEMA protocol was 7 days (nonclinical=7, clinical=12). Most protocols used a single time-based (random or interval) prompt type (69/105, 65.7%); median prompt frequency was 5 per day. The median number of items per prompt was similar for nonclinical (8) and clinical data sets (10). More than half of the data sets reported mEMA training (84/105, 80%) and provision of participant incentives (66/105, 62.9%). Less than half of the data sets reported number of prompts delivered (22/105, 21%), answered (43/105, 41%), criterion for valid mEMA data (37/105, 35.2%), or response latency (38/105, 36.2%). Meta-analysis (nonclinical=41, clinical=27) estimated an overall compliance of 81.9% (95% CI 79.1-84.4), with no significant difference between nonclinical and clinical data sets or estimates before or after data exclusions. Compliance was associated with prompts per day and items per prompt for nonclinical data sets. Although widespread heterogeneity existed across analysis (I2>90%), no compelling relationship was identified between key features of mEMA protocols representing burden and mEMA compliance. CONCLUSIONS: In this 10-year sample of studies using the mEMA of self-reported health-related behaviors and psychological constructs in adult nonclinical and clinical populations, mEMA was applied across contexts and health conditions and to collect a range of health-related data. There was inconsistent reporting of compliance and key features within protocols, which limited the ability to confidently identify components of mEMA schedules likely to have a specific impact on compliance.

Changes in diet, activity, weight, and wellbeing of parents during COVID-19 lockdown.

The COVID-19 pandemic has dramatically impacted lifestyle behaviour as public health initiatives aim to "flatten the curve". This study examined changes in activity patterns (physical activity, sedentary time, sleep), recreational physical activities, diet, weight and wellbeing from before to during COVID-19 restrictions in Adelaide, Australia. This study used data from a prospective cohort of Australian adults (parents of primary school-aged children; n = 61, 66% female, aged 41±6 years). Participants wore a Fitbit Charge 3 activity monitor and weighed themselves daily using Wi-Fi scales. Activity and weight data were extracted for 14 days before (February 2020) and 14 days during (April 2020) COVID-19 restrictions. Participants reported their recreational physical activity, diet and wellbeing during these periods. Linear mixed effects models were used to examine change over time. Participants slept 27 minutes longer (95% CI 9-51), got up 38 minutes later (95% CI 25-50), and did 50 fewer minutes (95% CI -69--29) of light physical activity during COVID-19 restrictions. Additionally, participants engaged in more cycling but less swimming, team sports and boating or sailing. Participants consumed a lower percentage of energy from protein (-0.8, 95% CI -1.5--0.1) and a greater percentage of energy from alcohol (0.9, 95% CI 0.2-1.7). There were no changes in weight or wellbeing. Overall, the effects of COVID-19 restrictions on lifestyle were small; however, their impact on health and wellbeing may accumulate over time. Further research examining the effects of ongoing social distancing restrictions are needed as the pandemic continues.