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BACKGROUND: Physical activity (PA), sleep and sedentary time are now recognised as mutually exclusive and exhaustive parts of the 24-h day-if PA decreases, time spent sleeping, being sedentary or both must increase so that all components equate to 24 h. Recent advances in time-use epidemiology suggest that we should not consider time-use domains (PA, sleep and sedentary time) in isolation from each other, but in terms of a composition-the mix of time-use domains across the 24-h day. While interrelated daily activities are known to be important in the management of diabetes mellitus, few studies have investigated the interrelated daily activities in people with an active diabetes-related foot ulcer (DFU) and their impact on important outcomes such as wound severity, blood glucose control and health-related quality of life (HRQoL). This feasibility study aims to determine the acceptability and practicality of measuring 24-h use of time data in people with a DFU and its associations on important outcome measures for this population. METHODS: Participants wore a wrist-worn accelerometer for two weeks and completed demographic and HRQoL questionnaires. Outcomes were participant engagement, reported levels of study burden and value and compositional data analysis as a methodological approach for evaluating 24-h use of time data. RESULTS: Twenty-six participants reported low levels of study burden and rated the study value highly. The protocol appears feasible in terms of recruitment (81%) and retention rate (86%). On average, participants were relatively sedentary spending 747, 172 and 18 min in sedentary time, light physical activity and moderate-to-vigorous activity, respectively. Sleep appeared adequate with participants obtaining an average of 485 min, but quality of sleep was notably poor with average sleep efficiency of 75%. Compositional data analysis was able to quantify the integrated associations of 24-h use of time with HRQoL. CONCLUSION: The protocol provides an acceptable method to collect 24-h use of time data in people with a DFU. Efforts to consider and analyse PA as part of a 24-h activity composition may provide holistic and realistic understandings of PA in this clinical population.
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Pie Diabético , Ejercicio Físico , Estudios de Factibilidad , Calidad de Vida , Conducta Sedentaria , Sueño , Humanos , Pie Diabético/fisiopatología , Femenino , Masculino , Persona de Mediana Edad , Ejercicio Físico/fisiología , Sueño/fisiología , Anciano , Factores de Tiempo , Acelerometría/métodos , Encuestas y Cuestionarios , Actividades Cotidianas , AdultoRESUMEN
Physical activity (PA) during childhood and adolescence is important for the accrual of maximal peak bone mass. The precise dose that benefits bone remains unclear as methods commonly used to analyze PA data are unsuitable for measuring bone-relevant PA. Using improved accelerometry methods, this study identified the amount and intensity of PA most strongly associated with bone outcomes in 11-12-year-olds. Participants (n = 770; 382 boys) underwent tibial peripheral quantitative computed tomography to assess trabecular and cortical density, endosteal and periosteal circumference and polar stress-strain index. Seven-day wrist-worn raw acceleration data averaged over 1-s epochs was used to estimate time accumulated above incremental PA intensities (50 milli-gravitational unit (mg) increments from 200 to 3000 mg). Associations between time spent above each 50 mg increment and bone outcomes were assessed using multiple linear regression, adjusted for age, sex, height, weight, maturity, socioeconomic position, muscle cross-sectional area and PA below the intensity of interest. There was a gradual increase in mean R2 change across all bone-related outcomes as the intensity increased in 50 mg increments from >200 to >700 mg. All outcomes became significant at >700 mg (R2 change = 0.6%-1.3% and p = 0.001-0.02). Any further increases in intensity led to a reduction in mean R2 change and associations became non-significant for all outcomes >1500 mg. Using more appropriate accelerometry methods (1-s epochs; no a priori application of traditional cut-points) enabled us to identify that â¼10 min/day of PA >700 mg (equivalent to running â¼10 km/h) was positively associated with pQCT-derived measures of bone density, geometry and strength in 11-12-year-olds.
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Acelerometría , Densidad Ósea , Ejercicio Físico , Humanos , Niño , Masculino , Estudios Transversales , Femenino , Ejercicio Físico/fisiología , Australia , Tibia/fisiología , Tibia/diagnóstico por imagen , Tomografía Computarizada por Rayos X , Muñeca/fisiología , Muñeca/diagnóstico por imagenRESUMEN
BACKGROUND: The school day provides a supportive and stimulating environment that may protect children and adolescents (5-18 years) from behaviours that are adverse for health and wellbeing. OBJECTIVE: To review the literature regarding changes in children's academic achievement or overall wellbeing during the extended school summer break and evaluate if the outcomes are different for children experiencing disadvantage. METHODS: The peer-reviewed literature was searched across six electronic databases for studies tracking changes in any academic, health or wellbeing outcome in children over the summer holidays. Studies were screened in duplicate for inclusion. Data were extracted using a standardized data extraction form. Outcomes were coded as decline (suggestive or significant), increase (suggestive or significant) or mixed/neutral and then compared to the school year or according to disadvantaged. RESULTS: Seventy-six studies (n = 14,230,846 participants) were included. Strong evidence was found of a decline in academic outcomes and increases in adiposity, sedentary behaviour and screen time. There was moderate evidence of declines in cardiovascular fitness and physical activity. These patterns were magnified for disadvantaged children. Limited data were available on muscular fitness, sleep, diet quality and social, emotional or mental wellbeing. A total of 80% of studies were from the United States. Most data were from children 12 years of age and younger. CONCLUSIONS: Over the summer break, children's academic and health outcomes decline. Children experiencing disadvantage display magnified losses that warrant further investigation. The summer holidays present an opportunity to improve children's health and wellbeing.
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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.
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Acelerometría , Conducta Sedentaria , Adulto , Humanos , Estudios de Cohortes , Estudios Prospectivos , Australia , Ejercicio Físico , SueñoRESUMEN
BACKGROUND: Increasing physical activity (PA) is an effective strategy to slow reductions in cortical volume and maintain cognitive function in older adulthood. However, PA does not exist in isolation, but coexists with sleep and sedentary behaviour to make up the 24-hour day. We investigated how the balance of all three behaviours (24-hour time-use composition) is associated with grey matter volume in healthy older adults, and whether grey matter volume influences the relationship between 24-hour time-use composition and cognitive function. METHODS: This cross-sectional study included 378 older adults (65.6 ± 3.0 years old, 123 male) from the ACTIVate study across two Australian sites (Adelaide and Newcastle). Time-use composition was captured using 7-day accelerometry, and T1-weighted magnetic resonance imaging was used to measure grey matter volume both globally and across regions of interest (ROI: frontal lobe, temporal lobe, hippocampi, and lateral ventricles). Pairwise correlations were used to explore univariate associations between time-use variables, grey matter volumes and cognitive outcomes. Compositional data analysis linear regression models were used to quantify associations between ROI volumes and time-use composition, and explore potential associations between the interaction between ROI volumes and time-use composition with cognitive outcomes. RESULTS: After adjusting for covariates (age, sex, education), there were no significant associations between time-use composition and any volumetric outcomes. There were significant interactions between time-use composition and frontal lobe volume for long-term memory (p = 0.018) and executive function (p = 0.018), and between time-use composition and total grey matter volume for executive function (p = 0.028). Spending more time in moderate-vigorous PA was associated with better long-term memory scores, but only for those with smaller frontal lobe volume (below the sample mean). Conversely, spending more time in sleep and less time in sedentary behaviour was associated with better executive function in those with smaller total grey matter volume. CONCLUSIONS: Although 24-hour time use was not associated with total or regional grey matter independently, total grey matter and frontal lobe grey matter volume moderated the relationship between time-use composition and several cognitive outcomes. Future studies should investigate these relationships longitudinally to assess whether changes in time-use composition correspond to changes in grey matter volume and cognition.
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Sustancia Gris , Imagen por Resonancia Magnética , Humanos , Masculino , Anciano , Persona de Mediana Edad , Sustancia Gris/diagnóstico por imagen , Sustancia Gris/patología , Estudios Transversales , Imagen por Resonancia Magnética/métodos , Australia , Cognición/fisiologíaRESUMEN
BACKGROUND: This study aimed to investigate associations of meeting 24-h movement behavior (24-HMB: physical activity [PA], screen time [ST] in the school-aged youth, and sleep) guidelines with indicators of academic engagement, psychological functioning, and cognitive function in a national representative sample of U.S. youth. METHODS: In this cross-sectional study, 1794 participants aged 6 to 17 years old were included for multivariable logistic regression to determine the above-mentioned associations, while adjusting for sociodemographic and health covariates. RESULTS: The proportion of participants who met 24-HMB guideline(s) varied greatly (PA+ ST+ sleep = 34 [weighted 1.17 %], PA + ST = 23 [weighted 1.72 %], PA + sleep = 52 [weighted 2.15 %], PA = 34 [weighted 2.88 %], ST = 142 [weighted 7.5 %], ST+ sleep = 209 [weighted 11.86 %], sleep = 725 [weighted 35.5 %], none = 575 [weighted 37.22 %]). Participants who met ST guideline alone and integrated (ST + Sleep and ST + sleep + PA) guidelines demonstrated the consistently beneficial associations with learning interest/curiosity, caring for school performance, completing required homework, resilience, cognitive difficulties, self-regulation (ps < 0.05). CONCLUSION: Meeting 24-HMB guidelines in an isolated or integrative manner was associated with improved academic engagement, psychological functioning, and reduced cognitive difficulties. These findings highlight the importance of the promotion of 24-HMB guidelines in youth with internalizing problems. Future longitudinal studies are needed to investigate whether changes or modifications of meeting specific 24-HMB guidelines (especially ST) is beneficial for youth with internalizing problems.
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Adhesión a Directriz , Conducta Sedentaria , Humanos , Adolescente , Niño , Estudios Transversales , Cognición , Instituciones Académicas , Sueño/fisiologíaRESUMEN
PURPOSE: There are well-known socioeconomic status (SES) gradients in children and adolescents' health which may be associated with time use. Our aim was to evaluate the association between Australian children's 24-hour time use and SES using four separate surveys from 2005 to 2021. METHODS: Time use was assessed in 4526 8-19-year-olds from the 2005 Health of Young Victorians, 2007 National Children's Nutrition and Physical Activity, 2015 Child Health CheckPoint, and 2019-21 Life on Holidays study. Each survey used the same reliable, valid, 24-hour recall instrument. SES was quantified using tertiles of household income, education, and postcode-level measures. Compositional data analysis was used to compare 24-hour time use between SES categories, adjusting for age, sex, and puberty. RESULTS: Time-use compositions differed significantly by SES in each survey. Relative to the lowest SES, children from the highest SES accumulated on average 31 min/day more School-related time, 6 min/day more Passive Transport and 6 min/day more Self-care. Conversely, they accumulated 30 min/day less Screen Time (which included computer time), 11 min/day less sleep, and spent 7 min/day less in Domestic/Social activities. There were only small differences in Quiet Time and Physical Activity. DISCUSSION: SES-related differences in time use were robust across ages 8-19, a 16-year timespan, diverse Australian geographical regions, and using different SES metrics. The exchange of about 30 min/day between School-related activities and Screen Time amounts to >180 hours extra exposure to School-related activities annually in the highest SES category relative to the lowest, equivalent to >6 weeks of school time per year.
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Estatus Socioeconómico Bajo , Clase Social , Humanos , Adolescente , Niño , Australia , Instituciones Académicas , Salud del AdolescenteRESUMEN
BACKGROUND: Obesity is a growing, global public health issue. This study aimed to describe the weight management strategies used by a sample of Australian adults; examine the socio-demographic characteristics of using each strategy; and examine whether use of each strategy was associated with 12-month weight change. METHODS: This observational study involved a community-based sample of 375 healthy adults (mean age: 40.1 ± 5.8 years, 56.8% female). Participants wore a Fitbit activity monitor, weighed themselves daily, and completed eight online surveys on socio-demographic characteristics. Participants also recalled their use of weight management strategies over the past month, at 8 timepoints during the 12-month study period. RESULTS: Most participants (81%) reported using at least one weight management strategy, with exercise/physical activity being the most common strategy at each timepoint (40-54%). Those who accepted their current bodyweight were less likely to use at least one weight management strategy (Odds ratio = 0.38, 95% CI = 0.22-0.64, p < 0.01) and those who reported being physically active for weight maintenance had a greater reduction in bodyweight, than those who did not (between group difference: -1.2 kg, p < 0.01). The use of supplements and fasting were associated with poorer mental health and quality of life outcomes (p < 0.01). CONCLUSIONS: The use of weight management strategies appears to be common. Being physically active was associated with greater weight loss. Individuals who accepted their current body weight were less likely to use weight management strategies. Fasting and the use of supplements were associated with poorer mental health. Promoting physical activity as a weight management strategy appears important, particularly considering its multiple health benefits.
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Obesidad , Calidad de Vida , Adulto , Humanos , Femenino , Persona de Mediana Edad , Masculino , Índice de Masa Corporal , Australia , Obesidad/epidemiología , Obesidad/terapia , Obesidad/complicaciones , AyunoRESUMEN
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.
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Obesidad , Aumento de Peso , Humanos , Adulto , Femenino , Masculino , Estaciones del Año , Estudios de Cohortes , Australia/epidemiología , Obesidad/epidemiología , Obesidad/prevención & control , Peso CorporalRESUMEN
[This corrects the article DOI: 10.3389/fnhum.2022.1051793.].
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Chatbots (also known as conversational agents and virtual assistants) offer the potential to deliver healthcare in an efficient, appealing and personalised manner. The purpose of this systematic review and meta-analysis was to evaluate the efficacy of chatbot interventions designed to improve physical activity, diet and sleep. Electronic databases were searched for randomised and non-randomised controlled trials, and pre-post trials that evaluated chatbot interventions targeting physical activity, diet and/or sleep, published before 1 September 2022. Outcomes were total physical activity, steps, moderate-to-vigorous physical activity (MVPA), fruit and vegetable consumption, sleep quality and sleep duration. Standardised mean differences (SMD) were calculated to compare intervention effects. Subgroup analyses were conducted to assess chatbot type, intervention type, duration, output and use of artificial intelligence. Risk of bias was assessed using the Effective Public Health Practice Project Quality Assessment tool. Nineteen trials were included. Sample sizes ranged between 25-958, and mean participant age ranged between 9-71 years. Most interventions (n = 15, 79%) targeted physical activity, and most trials had a low-quality rating (n = 14, 74%). Meta-analysis results showed significant effects (all p < 0.05) of chatbots for increasing total physical activity (SMD = 0.28 [95% CI = 0.16, 0.40]), daily steps (SMD = 0.28 [95% CI = 0.17, 0.39]), MVPA (SMD = 0.53 [95% CI = 0.24, 0.83]), fruit and vegetable consumption (SMD = 0.59 [95% CI = 0.25, 0.93]), sleep duration (SMD = 0.44 [95% CI = 0.32, 0.55]) and sleep quality (SMD = 0.50 [95% CI = 0.09, 0.90]). Subgroup analyses showed that text-based, and artificial intelligence chatbots were more efficacious than speech/voice chatbots for fruit and vegetable consumption, and multicomponent interventions were more efficacious than chatbot-only interventions for sleep duration and sleep quality (all p < 0.05). Findings from this systematic review and meta-analysis indicate that chatbot interventions are efficacious for increasing physical activity, fruit and vegetable consumption, sleep duration and sleep quality. Chatbot interventions were efficacious across a range of populations and age groups, with both short- and longer-term interventions, and chatbot only and multicomponent interventions being efficacious.
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BACKGROUND: While there is evidence that physical activity, sedentary behaviour (SB) and sleep may all be associated with modified levels of inflammatory markers in adolescents and children, associations with one movement behaviour have not always been adjusted for other movement behaviours, and few studies have considered all movement behaviours in the 24-hour day as an exposure. PURPOSE: The aim of the study was to explore how longitudinal reallocations of time between moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), SB and sleep are associated with changes in inflammatory markers in children and adolescents. METHODS: A total of 296 children/adolescents participated in a prospective cohort study with a 3-year follow-up. MVPA, LPA and SB were assessed by accelerometers. Sleep duration was assessed using the Health Behavior in School-aged Children questionnaire. Longitudinal compositional regression models were used to explore how reallocations of time between movement behaviours are associated with changes in inflammatory markers. RESULTS: Reallocations of time from SB to sleep were associated with increases in C3 levels (difference for 60 min/d reallocation [d60] = 5.29 mg/dl; 95% confidence interval [CI] = 0.28, 10.29) and TNF-α (d60 = 1.81 mg/dl; 95% CI = 0.79, 15.41) levels. Reallocations from LPA to sleep were also associated with increases in C3 levels (d60 = 8.10 mg/dl; 95% CI = 0.79, 15.41). Reallocations from LPA to any of the remaining time-use components were associated with increases in C4 levels (d60 ranging from 2.54 to 3.63 mg/dl; p < 0.05), while any reallocation of time away from MVPA was associated with unfavourable changes in leptin (d60 ranging from 3088.44 to 3448.07 pg/ml; p < 0.05). CONCLUSIONS: Reallocations of time between 24-h movement behaviours are prospectively associated with some inflammatory markers. Reallocating time away from LPA appears to be most consistently unfavourably associated with inflammatory markers. Given that higher levels of inflammation during childhood and adolescence are associated with an increased risk of chronic diseases in adulthood, children and adolescents should be encouraged to maintain or increase the level of LPA to preserve a healthy immune system.
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Ejercicio Físico , Sueño , Humanos , Niño , Adolescente , Estudios Prospectivos , Conducta Sedentaria , Acelerometría , InflamaciónRESUMEN
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).
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Cambio Climático , Conducta Sedentaria , Humanos , Femenino , Adulto , Masculino , Estudios Longitudinales , Estudios Prospectivos , Australia , Ejercicio Físico , Tiempo (Meteorología) , SueñoRESUMEN
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).
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Hábitos , Conducta Sedentaria , Adulto , Humanos , Estudios de Cohortes , Australia , RecreaciónRESUMEN
OBJECTIVE: To synthesise the evidence on the effects of physical activity on symptoms of depression, anxiety and psychological distress in adult populations. DESIGN: Umbrella review. DATA SOURCES: Twelve electronic databases were searched for eligible studies published from inception to 1 January 2022. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Systematic reviews with meta-analyses of randomised controlled trials designed to increase physical activity in an adult population and that assessed depression, anxiety or psychological distress were eligible. Study selection was undertaken in duplicate by two independent reviewers. RESULTS: Ninety-seven reviews (1039 trials and 128 119 participants) were included. Populations included healthy adults, people with mental health disorders and people with various chronic diseases. Most reviews (n=77) had a critically low A MeaSurement Tool to Assess systematic Reviews score. Physical activity had medium effects on depression (median effect size=-0.43, IQR=-0.66 to -0.27), anxiety (median effect size=-0.42, IQR=-0.66 to -0.26) and psychological distress (effect size=-0.60, 95% CI -0.78 to -0.42), compared with usual care across all populations. The largest benefits were seen in people with depression, HIV and kidney disease, in pregnant and postpartum women, and in healthy individuals. Higher intensity physical activity was associated with greater improvements in symptoms. Effectiveness of physical activity interventions diminished with longer duration interventions. CONCLUSION AND RELEVANCE: Physical activity is highly beneficial for improving symptoms of depression, anxiety and distress across a wide range of adult populations, including the general population, people with diagnosed mental health disorders and people with chronic disease. Physical activity should be a mainstay approach in the management of depression, anxiety and psychological distress. PROSPERO REGISTRATION NUMBER: CRD42021292710.
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Depresión , Trastornos Mentales , Adulto , Femenino , Humanos , Embarazo , Ansiedad/terapia , Enfermedad Crónica , Depresión/terapia , Estado de Salud , Calidad de Vida , Revisiones Sistemáticas como AsuntoRESUMEN
Introduction: Physical activity, sedentary behaviour and sleep are associated with cognitive function in older adults. However, these behaviours are not independent, but instead make up exclusive and exhaustive components of the 24-h day. Few studies have investigated associations between 24-h time-use composition and cognitive function in older adults. Of these, none have considered how the quality of sleep, or the context of physical activity and sedentary behaviour may impact these relationships. This study aims to understand how 24-h time-use composition is associated with cognitive function across a range of domains in healthy older adults, and whether the level of recreational physical activity, amount of television (TV) watching, or the quality of sleep impact these potential associations. Methods: 384 healthy older adults (age 65.5 ± 3.0 years, 68% female, 63% non-smokers, mean education = 16.5 ± 3.2 years) participated in this study across two Australian sites (Adelaide, n = 207; Newcastle, n = 177). Twenty-four-hour time-use composition was captured using triaxial accelerometry, measured continuously across 7 days. Total time spent watching TV per day was used to capture the context of sedentary behaviours, whilst total time spent in recreational physical activity was used to capture the context of physical activity (i.e., recreational accumulation of physical activity vs. other contexts). Sleep quality was measured using a single item extracted from the Pittsburgh Sleep Quality Index. Cognitive function was measured using a global cognition index (Addenbrooke's Cognitive Examination III) and four cognitive domain composite scores (derived from five tests of the Cambridge Neuropsychological Test Automated Battery: Paired Associates Learning; One Touch Stockings of Cambridge; Multitasking; Reaction Time; Verbal Recognition Memory). Pairwise correlations were used to describe independent relationships between time use variables and cognitive outcomes. Then, compositional data analysis regression methods were used to quantify associations between cognition and 24-h time-use composition. Results: After adjusting for covariates and false discovery rate there were no significant associations between time-use composition and global cognition, long-term memory, short-term memory, executive function, or processing speed outcomes, and no significant interactions between TV watching time, recreational physical activity engagement or sleep quality and time-use composition for any cognitive outcomes. Discussion: The findings highlight the importance of considering all activities across the 24-h day against cognitive function in older adults. Future studies should consider investigating these relationships longitudinally to uncover temporal effects.
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People's perceptions of the mental effort required for everyday activities may drive variation in the relationships between lifestyles and cognitive ability. We asked n = 259 healthy older adults aged 60 to 70 years (90 males, 169 females) to provide a rating of the Perceived Mental Effort (PME) for each activity instance they recalled over a 48-h period as part of a time-use recall. PME was rated on a 9-point scale from "very, very low" (score of 1) to "very, very high" (score of 9). Across the entire sample, participants rated a total of 196 different activities and 17,433 activity instances. The mean PME for individual activities was 3.50 ± 1.58. PMEs varied significantly by activity domain, with highest ratings being for Work (5.48 ± 1.72) and the lowest for Self-Care (2.89 ± 0.98). In multivariate analyses, PME ratings were higher in males than females (+0.30), PMEs were higher later in the day, increased with task duration, and decreased with age (all p < 0.0001). Time-weighted average individual PMEs across the two days of recall ranged from 1.86 to 6.50, and were 0.3 units higher for males, but unrelated to age. Repeated intra-individual PME ratings for the same activity were very reliable (ICC = 0.995, mean absolute difference = 0.03 ± 0.17). PMEs show promise as a reliable measure of mental effort.
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Cognición , Recuerdo Mental , Anciano , Femenino , Humanos , Masculino , Actividades Cotidianas , Envejecimiento , Persona de Mediana EdadRESUMEN
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.
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Aplicaciones Móviles , Calidad de Vida , Australia , Niño , Ejercicio Físico , Femenino , Humanos , Masculino , Conducta SedentariaRESUMEN
Wearable activity trackers offer an appealing, low-cost tool to address physical inactivity. This systematic review of systematic reviews and meta-analyses (umbrella review) aimed to examine the effectiveness of activity trackers for improving physical activity and related physiological and psychosocial outcomes in clinical and non-clinical populations. Seven databases (Embase, MEDLINE, Ovid Emcare, Scopus, SPORTDiscus, the Cochrane Library, and Web of Science) were searched from database inception to April 8, 2021. Systematic reviews of primary studies using activity trackers as interventions and reporting physical activity, physiological, or psychosocial outcomes were eligible for inclusion. In total, 39 systematic reviews and meta-analyses were identified, reporting results from 163â992 participants spanning all age groups, from both healthy and clinical populations. Taken together, the meta-analyses suggested activity trackers improved physical activity (standardised mean difference [SMD] 0·3-0·6), body composition (SMD 0·7-2·0), and fitness (SMD 0·3), equating to approximately 1800 extra steps per day, 40 min per day more walking, and reductions of approximately 1 kg in bodyweight. Effects for other physiological (blood pressure, cholesterol, and glycosylated haemoglobin) and psychosocial (quality of life and pain) outcomes were typically small and often non-significant. Activity trackers appear to be effective at increasing physical activity in a variety of age groups and clinical and non-clinical populations. The benefit is clinically important and is sustained over time. Based on the studies evaluated, there is sufficient evidence to recommend the use of activity trackers.
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Monitores de Ejercicio , Calidad de Vida , Ejercicio Físico , Humanos , Revisiones Sistemáticas como AsuntoRESUMEN
Background: Gamification is purported to enhance engagement with health behavior apps, ultimately improving their effectiveness. This study aimed to examine (1) whether the inclusion of gamification features in a physical activity smartphone app was associated with improved app usage and goal adherence, describe (2) use of the gamification features, and (3) by whom, and determine (4) whether engagement was associated with increased physical activity. Methods: Data from community-dwelling adult participants (mean age 42.1 years, standard deviation [SD 11.9], 74% female) in the gamified (n = 134) and nongamified (n = 155) conditions from a three-group randomized controlled trial were analyzed. Physical activity was assessed at baseline and 9 months using a survey and accelerometers. App usage (number of days steps were logged), goal adherence (number of days step count was ≥10,000), and behavioral engagement with gamification features were obtained from server logs. Multilevel modeling was used to examine the study aims. Results: Participants who received the gamified app showed more days of usage than those who received the nongamified app (M = 113 days [SD 88] vs. M = 81 days [SD 54], P = 0.006), whereas goal adherence did not differ between groups. The leaderboard and "status" gamification features were the most frequently used gamification features (M = 83 [SD 114] and M = 50 [SD 67] views, respectively). Older age (P = 0.008) and lower body mass index (P = 0.004) were associated with more status views. Participants who reported higher stress symptoms sent more gifts (P = 0.04). The use of gamification features was associated with increased physical activity (P = 0.04). Conclusion: The gamified app was used substantially longer than the nongamified app. Use of gamification features was positively associated with change in physical activity. Leaderboards promoting social comparison may be a promising form of gamification. Research on different forms of gamification is warranted.