Physical Activity, Mental Activity, and Risk of Incident Stroke: A Prospective Cohort Study.

BACKGROUND: Cumulative evidence suggests a correlation between physical or mental activity and the risk of stroke. However, the combined impact of these activities on stroke onset remains unexplored. This study identified physical and mental activity patterns using principal component analysis and investigated their associations with risk of incident stroke in the general population. METHODS: Our study was sourced from the UK Biobank cohort between 2006 and 2010. Information on physical and mental-related activities were obtained through a touch-screen questionnaire. The incident stroke was diagnosed by physicians and subsequently verified through linkage to Hospital Episode Statistics. Principal component analysis was used to identify potential physical and mental activity patterns. Cox proportional hazard regression models were performed to calculate hazard ratios (HRs) and 95% CIs of incident stroke, adjusting for potential confounders. RESULTS: The initial UK Biobank cohort originally consisted of 502 411 individuals, of whom a total of 386 902 participants (aged 38-79 years) without any history of stroke at baseline were included in our study. During a median follow-up of 7.7 years, 6983 (1.8%) cases of stroke were documented. The mean age of the included participants was 55.9 years, and the proportion of women was 55.1%. We found that multiple individual items related to physical and mental activity showed significant associations with risk of stroke. We identified 4 patterns of physical activity and 3 patterns of mental activity using principal component analysis. The adherence to activity patterns of vigorous exercise, housework, and walking predominant patterns were associated with a lower risk of stroke by 17% (HR, 0.83 [95% CI, 0.78-0.89]; 20% (HR, 0.80 [95% CI, 0.75-0.85]; and 20% (HR, 0.80 [95% CI, 0.75-0.86), respectively. Additionally, the transportation predominant pattern (HR, 1.36 [95% CI, 1.28-1.45) and watching TV pattern (HR, 1.43 [95% CI, 1.33-1.53) were found to be significantly associated with a higher risk of stroke. These associations remained consistent across all subtypes of stroke. CONCLUSIONS: Activity patterns mainly related to frequent vigorous exercise, housework, and walking were associated with lower risks of stroke and all its subtypes. Our findings provide new insights for promoting suitable patterns of physical and mental activity for primary prevention of stroke.

Total and temporal patterning of physical activity in adolescents and associations with mental wellbeing.

BACKGROUND: There is limited understanding of the extent to which differences in physical activity across the day and week may be associated with mental wellbeing. Such an understanding is needed for better targeting of interventions. In this study, we describe total and temporal patterning of physical activity across the week in adolescents (age 13-14y) and assess their prospective associations with mental wellbeing. METHODS: 1,983 13-14-year-old adolescent participants based in Cambridgeshire and Essex, recruited between 2016 and 2017 into the Get Others Active Trial provided data at baseline and 4 months. Physical activity was measured at baseline using wrist-worn accelerometers across different time segments (whole week, weekday schooltime, weekday out of school, and weekend), and operationalized as average movement-related acceleration for each time segment. Mental Wellbeing at baseline and 4 months was measured using the Warwick Edinburgh MentalWellbeing Scale. Associations between physical activity across different time segments (whole week, weekday schooltime, weekday out of school, and weekend) and mental wellbeing at 4 months were investigated using sex-stratified multi-level regression models, adjusted for covariates, and both adjusted and unadjusted for baseline mental wellbeing. RESULTS: Our analyses found positive associations between physical activity and mental wellbeing at 4 months, unadjusted for baseline wellbeing. Among girls, positive associations were shown when considering physical activity across the whole week 0.07 (95% CI, 0.03-0.12), and across all separate time periods studied: weekday schooltime 0.07 (95% CI, 0.02-0.11), weekday out-of-school time 0.07 (95% CI, 0.03-0.12), and weekend 0.07 (95% CI, 0.02-0.11). For boys, similar associations were observed for activity across the week 0.07 (95% CI, 0.03-0.11), during weekday schooltime 0.08 (95% CI, 0.04-0.12), and weekday out-of-school time 0.07 (95% CI, 0.03-0.11), but not the weekend 0.01 (95% CI, -0.03-0.05). For both girls and boys, associations were attenuated below significance after adjusting for baseline wellbeing. CONCLUSIONS: This longitudinal analysis showed positive associations between physical activity and later mental wellbeing in both male and female adolescents across most time segments. Higher physical activity throughout the week may be associated with better mental wellbeing in the adolescent population. Further research is required to understand determinants of change in wellbeing over time. TRIAL REGISTRATION: Registration Number: ISRCTN31583496. Registered: 18/02/2014.

Associations of between- and within-day patterns of physical activity accumulation with arterial stiffness and indices of microvascular health-Evidence from The Maastricht study.

While physical activity (PA) is understood to promote vascular health, little is known about whether the daily and weekly patterns of PA accumulation associate with vascular health. Accelerometer-derived (activPAL3) 6- or 7-day stepping was analyzed for 6430 participants in The Maastricht Study (50.4% women; 22.4% Type 2 diabetes mellitus (T2DM)). Multivariable regression models examined associations between stepping metrics (average step count, and time spent slower and faster paced stepping) with arterial stiffness (measured as carotid-femoral pulse wave velocity (cfPWV)), and several indices of microvascular health (heat-induced skin hyperemia, retinal vessel reactivity and diameter), adjusting for confounders and moderators. PA pattern metrics were added to the regression models to identify associations with vascular health beyond that of stepping metrics. Analyses were stratified by T2DM status if an interaction effect was present. Average step count and time spent faster paced stepping was associated with better vascular health, and the association was stronger in those with compared to those without T2DM. In fully adjusted models a higher step count inter-daily stability was associated with a higher (worse) cfPWV in those without T2DM (std ß = 0.04, p = 0.007) and retinal venular diameter in the whole cohort (std ß = 0.07, p = 0.002). A higher within-day variability in faster paced stepping was associated with a lower (worse) heat-induced skin hyperemia in those with T2DM (std ß = -0.31, p = 0.008). Above and beyond PA volume, the daily and weekly patterns in which PA was accumulated were additionally associated with improved macro- and microvascular health, which may have implications for the prevention of vascular disease.

Morning physical activity may be more beneficial for blood lipids than afternoon physical activity in older adults: a cross-sectional study.

BACKGROUND: The effect of differences in daily physical activity patterns on blood lipids has not been determined. This study examines the effects of the differences in free-living daily physical activity patterns (amount and intensity) on blood lipid levels in older adults. METHODS: This cross-sectional study included 51 older participants (71.8 ± 0.6 years, men = 8, women = 43). A triaxial accelerometer was used to assess physical activity patterns. The time from awakening to bedtime for each participant was used for group classification based on the amount (number of steps) and intensity (moderate-to-vigorous physical activity, MVPA) of physical activity. The morning step group (M Step) was defined as those who took more steps in the morning, and the afternoon step group (A Step) was defined as those who took more steps in the afternoon. The same method was used for MVPA (morning MVPA: M MVPA; afternoon MVPA: A MVPA). Blood samples were collected at the start of the study to determine blood lipid levels. RESULTS: Number of steps taken showed a trend toward lower low-density lipoprotein cholesterol (LDL-C) levels in the M Step group compared with the A Step group. The LDL/high-density lipoprotein (HDL) ratio was significantly lower in the M Step group than the A Step group (p < 0.05). The M MVPA group also had higher HDL-C levels and significantly lower LDL/HDL ratios than the A MVPA group (p < 0.05). CONCLUSIONS: These results suggest that compared with afternoon physical activity, daily morning physical activity (amount and intensity) is more effective in improving blood lipid levels.

Advanced analytical methods to assess physical activity behavior using accelerometer time series: A scoping review.

Physical activity (PA) is a complex human behavior, which implies that multiple dimensions need to be taken into account in order to reveal a complete picture of the PA behavior profile of an individual. This scoping review aimed to map advanced analytical methods and their summary variables, hereinafter referred to as wearable-specific indicators of PA behavior (WIPAB), used to assess PA behavior. The strengths and limitations of those indicators as well as potential associations with certain health-related factors were also investigated. Three databases (MEDLINE, Embase, and Web of Science) were screened for articles published in English between January 2010 and April 2020. Articles, which assessed the PA behavior, gathered objective measures of PA using tri-axial accelerometers, and investigated WIPAB, were selected. All studies reporting WIPAB in the context of PA monitoring were synthesized and presented in four summary tables: study characteristics, details of the WIPAB, strengths, and limitations, and measures of association between those indicators and health-related factors. In total, 7247 records were identified, of which 24 articles were included after assessing titles, abstracts, and full texts. Thirteen WIPAB were identified, which can be classified into three different categories specifically focusing on (1) the activity intensity distribution, (2) activity accumulation, and (3) the temporal correlation and regularity of the acceleration signal. Only five of the thirteen WIPAB identified in this review have been used in the literature so far to investigate the relationship between PA behavior and health, while they may provide useful additional information to the conventional PA variables.

Adolescent Obesity and its Association with Socio-Demographic Profile, Lifestyle Factors, Dietary and Physical Activity Patterns; Findings from Southwestern Nigeria.

BACKGROUND: The prevalence of adolescent obesity is rising in all parts of the world, but only very few studies have considered factors influencing obesity among Nigerian adolescents. Therefore, this study aimed to determine the prevalence of obesity and its association with sociodemographic profile, lifestyle factors, dietary patterns, and physical activity patterns among in-school adolescents in Southwest Nigeria. METHODS: A total sample size of 400 in-school adolescents were selected through a multi-stage sampling technique from secondary schools in Ile-Ife. The dietary patterns were assessed using a 92-item FFQ, while the activity patterns were assessed using the physical activity questionnaire for older children and adolescents. Data were analyzed using IBM SPSS. RESULTS: There were 211 (52.8%) males and 189 (47.3%) females, with mean ages of 14.8 ± 2.1 and 14.4 ± 1.9 years, respectively. The prevalence of overweight/obesity was 12.8%. The associated factors were age, sex, class, the number of children in the family, birth orders and the dietary pattern dominated by starchy foods, roots and tubers (p<0.05). CONCLUSION: The prevalence of overweight/obesity was relatively high among the respondents. It was associated with age, the number of children in the family, and adolescents' birth order. WAJM 2022; 39(2): 119-126.

CONTEXTE: La prévalence de l'obésité chez les adolescents augmente dans toutes les régions du monde, mais très peu d'études ont facteurs considérés comme influençant l'obésité chez les adolescents Nigérians. Par conséquent, cette étude visait à déterminer le prévalence de l'obésité et son association avec le profil sociodémographique, les facteurs de style de vie, les habitudes alimentaires et les habitudes d'activité physique chez les adolescents scolarisés dans Sud-ouest du Nigeria. MÉTHODES: Un échantillon total de 400 adolescents scolarisés ont été sélectionnés par une technique d'échantillonnage en plusieurs étapes à partir de écoles secondaires d'Ile-Ife. Les habitudes alimentaires ont été évaluées à l'aide d'un questionnaire FFQ de 92 items, tandis que les habitudes d'activité ont été évaluées à l'aide du questionnaire sur l'activité physique pour les enfants plus âgés et les adolescents. Les données ont été analysées à l'aide d'IBM SPSS. RÉSULTATS: Il y avait 211 hommes (52.8 %) et 189 hommes (47.3 %) femelles, avec des âges moyens de 14.8 ± 2.1 et 14.4 ±1.9 ans, respectivement. La prévalence du surpoids / obésité était de 12.8%. Les facteurs associés étaient l'âge, le sexe, la classe, le nombre de enfants dans la famille, ordres de naissance et régime alimentaire dominé par les féculents, les racines et les tubercules (p <0.05). CONCLUSION: La prévalence du surpoids / de l'obésité était relativement élevé parmi les répondants. Il était associé à l'âge, le nombre d'enfants dans la famille et l'ordre de naissance des adolescents.

Children and Adolescents Treated for Valvular Aortic Stenosis Have Different Physical Activity Patterns Compared to Healthy Controls: A Methodological Study in a National Cohort.

Previous research in children and adolescents with congenital heart defects presents contradictory findings concerning their physical activity (PA) level, due to methodological limitations in the PA assessment. The aim of the present cross-sectional study was to compare PA in children and adolescents treated for valvular aortic stenosis with healthy controls using an improved accelerometer method. Seven-day accelerometer data were collected from the hip in a national Swedish sample of 46 patients 6-18 years old treated for valvular aortic stenosis and 44 healthy controls matched for age, gender, geography, and measurement period. Sports participation was self-reported. Accelerometer data were processed with the new improved Frequency Extended Method and with the traditional ActiGraph method for comparison. A high-resolution PA intensity spectrum was investigated as well as traditional crude PA intensity categories. Children treated for aortic stenosis had a pattern of less PA in the highest intensity spectra and had more sedentary time, while the adolescent patients tended to be less physically active in higher intensities overall and with less sedentary time, compared to the controls. These patterns were evident using the Frequency Extended Method with the detailed PA intensity spectrum, but not to the same degree using the ActiGraph method and traditional crude PA intensity categories. Patients reported less sports participation than their controls in both age-groups. Specific differences in PA patterns were revealed using the Frequency Extended Method with the high-resolution PA intensity spectrum in Swedish children and adolescents treated for valvular aortic stenosis.

Lifestyle-associated health risk indicators across a wide range of occupational groups: a cross-sectional analysis in 72,855 workers.

BACKGROUND: Identify and compare health risk indicators for common chronic diseases between different occupational groups. METHODS: A total of 72,855 participants (41% women) participating in an occupational health service screening in 2014-2019 were included. Occupation was defined by the Swedish Standard Classification of Occupation, and divided into nine major and additionally eight sub-major groups. These were analysed separately, as white- and blue-collar occupations and as low- and high-skilled occupations. Seven health risk indicators were self-reported: exercise, physical work situation, sitting at work and leisure, smoking, diet, and perceived health, whereas cardiorespiratory fitness, BMI and blood pressure were measured. These were further dichotomized (yes/no) and as clustering of risk indicators (≥3 vs. <3). RESULTS: The greatest variation in OR across sub-major and major occupational groups were seen for daily smoking (OR = 0.68 to OR = 5.12), physically demanding work (OR = 0.55 to OR = 45.74) and high sitting at work (OR = 0.04 to OR = 1.86). For clustering of health risk indicators, blue-collar workers had significantly higher clustering of health risks (OR: 1.80; 95% CI 1.71-1.90) compared to white-collar workers (reference). Compared to high-skilled white-collar workers, low-skilled white-collar workers had similar OR (2.00; 1.88-2.13) as high-skilled blue-collar workers (1.98; 1.86-2.12), with low-skilled blue-collar workers having the highest clustered risk (2.32; 2.17-2.48). CONCLUSION: There were large differences in health risk indicators across occupational groups, mainly between high-skilled white-collar occupations and the other occupations, with important variations also between major and sub-major occupational groups. Future health interventions should target the occupational groups identified with the highest risk for effective disease prevention.

Multidimensional Measures of Physical Activity and Their Association with Gross Motor Capacity in Children and Adolescents with Cerebral Palsy.

The current lack of adapted performance metrics leads clinicians to focus on what children with cerebral palsy (CP) do in a clinical setting, despite the ongoing debate on whether capacity (what they do at best) adequately reflects performance (what they do in daily life). Our aim was to measure these children's habitual physical activity (PA) and gross motor capacity and investigate their relationship. Using five synchronized inertial measurement units (IMU) and algorithms adapted to this population, we computed 22 PA states integrating the type (e.g., sitting, walking, etc.), duration, and intensity of PA. Their temporal sequence was visualized with a PA barcode from which information about pattern complexity and the time spent in each of the six simplified PA states (PAS; considering PA type and duration, but not intensity) was extracted and compared to capacity. Results of 25 children with CP showed no strong association between motor capacity and performance, but a certain level of motor capacity seems to be a prerequisite for the achievement of higher PAS. Our multidimensional performance measurement provides a new method of PA assessment in this population, with an easy-to-understand visual output (barcode) and objective data for clinical and scientific use.

Associations between physical activity and health-related fitness - volume versus pattern.

Approximately 3.2 million people die of non-communicable diseases (NCD) each year due to insufficient physical activity. Physical activity guidelines are possibly perceived as too demanding and might thus pose a barrier. We addressed the question if a more stable physical activity pattern is associated with higher levels of health-related fitness than one with high and low intensities, regardless of the physical activity level (PAL). Physical activity was objectively measured in 296 men and women (53.7 ± 8.94 years) with the SenseWear Pro Armband®. Using this data, the PAL and a Gini index were calculated to report the physical activity pattern. Health-related fitness was expressed as a fitness index. PAL was weakly correlated to health-related fitness (r = 0.38, P < .0001). The Gini index was also weakly correlated to the fitness index (r = 0.23, P < .0001). Results of the ANCOVA showed that participants in the first quartile of PAL always scored significantly lower for health-related fitness than participants in quartile four, after adjustment for the Gini index. These results suggest that as long as the volume of physical activity is high, health-related fitness will be high as well, independent of the physical activity pattern or variability in intensities throughout the day.

Temporal Patterns of Diet and Physical Activity and of Diet Alone Have More Numerous Relationships With Health and Disease Status Indicators Compared to Temporal Patterns of Physical Activity Alone.

BACKGROUND: Daily temporal patterns of energy intake (temporal dietary patterns [TDPs]) and physical activity (temporal physical activity patterns [TPAPs]) have been independently and jointly (temporal dietary and physical activity patterns [TDPAPs]) associated with health and disease status indicators. OBJECTIVE: The aim of this study was to compare the number and strength of association between clusters of daily TDPs, TPAPs, and TDPAPs and multiple health and disease status indicators. DESIGN: This cross-sectional study used 1 reliable weekday dietary recall and 1 random weekday of accelerometer data to partition to create clusters of participants representing the 3 temporal patterns. Four clusters were created via kernel-k means clustering algorithm of the same constrained dynamic time warping distance computed over the time series for each temporal pattern. PARTICIPANTS/SETTING: From the National Health and Nutrition Examination Survey (2003-2006), 1,836 US adults aged 20 through 65 years who were not pregnant and had valid diet, physical activity, sociodemographic, anthropometric, questionnaire, and health and disease status indicator data were included. MAIN OUTCOME MEASURES: Health status indicators used as outcome measures were body mass index, waist circumference, fasting plasma glucose, hemoglobin A1c, triglycerides, high-density lipoprotein cholesterol, total cholesterol, and systolic and diastolic blood pressure; disease status indicators included obesity, type 2 diabetes mellitus, and metabolic syndrome. STATISTICAL ANALYSES PERFORMED: Multivariate regression models determined associations between the clusters representing each pattern and health and disease status indicators, controlling for confounders and adjusting for multiple comparisons. The number of significant differences among clusters and adjusted R2 and Akaike information criterion compared the strength of associations between clusters of patterns and continuous and categorical health and disease status indicators. RESULTS: TDPAPs showed 21 significant associations with health and disease status indicators, including body mass index, waist circumference, obesity, and type 2 diabetes; TDPs showed 19 significant associations; and TPAPs showed 8 significant associations. CONCLUSIONS: TDPAPs and TDPs had stronger and more numerous associations with health and disease status indicators compared with TPAPs. Patterns representing the integration of daily dietary habits hold promise for early detection of obesity.

Cluster Analysis to Find Temporal Physical Activity Patterns Among US Adults.

Physical activity (PA) is known to be a risk factor for obesity and chronic diseases such as diabetes and metabolic syndrome. Few attempts have been made to pattern the time of physical activity while incorporating intensity and duration in order to determine the relationship of this multi-faceted behavior with health. In this paper, we explore a distance-based approach for clustering daily physical activity time series to estimate temporal physical activity patterns among U.S. adults (ages 20-65) from the National Health and Nutrition Examination Survey 2003-2006 (NHANES). A number of distance measures and distance-based clustering methods were investigated and compared using various metrics. These metrics include the Silhouette and the Dunn Index (internal criteria), and the associations of the clusters with health status indicators (external criteria). Our experiments indicate that using a distance-based cluster analysis approach to estimate temporal physical activity patterns through the day, has the potential to describe the complexity of behavior rather than characterizing physical activity patterns solely by sums or labels of maximum activity levels.

Patterns of physical activity and SARS-CoV-2 severe pneumonia: A case-control study.

BACKGROUND: Regular physical activity is associated with a low risk of severe community-acquired infections. However, the hypothesis that a physical inactivity pattern is associated with a higher risk for severe COVID-19 has not been completely proven, especially with severe pneumonia. OBJECTIVE: The goal of this study was to confirm the link between physical activity patterns and severe SARS-CoV-2 pneumonia. DESIGN: Case-control study. METHODS: This study involved 307 patients who developed SARS-CoV-2 severe pneumonia and were hospitalized in an intensive care unit. Age- and sex-matched controls (307) were selected from the same population: patients with mild to moderate forms of COVID-19 who were not hospitalized. Physical activity patterns were assessed using the short version of the International Physical Activity Questionnaire. RESULTS: The mean physical activity levels were lower in the SARS-CoV-2 severe pneumonia group as compared to the control group: 1576±2939 vs 2438±2999, metabolic equivalent of task (MET-min/week), p<0.001. A high or moderate physical activity level was more common in the control group, and a low physical activity level was more observed in the case group (p<0.001). Obesity was also associated with severe SARS-CoV-2 pneumonia (p<0.001). Multivariable analysis showed that a low physical activity level was associated with a higher risk for severe SARS-CoV-2 pneumonia, independent of nutritional status (CI 3.7; 2.24-5.99), p<0.001). CONCLUSION: A higher and moderate level of physical activity is linked to a lower risk of SARS-CoV-2 severe pneumonia.

Physical Activity Pattern of Adults With Metabolic Syndrome Risk Factors: Time-Series Cluster Analysis.

Background: Physical activity plays a crucial role in maintaining a healthy lifestyle, and wrist-worn wearables, such as smartwatches and smart bands, have become popular tools for measuring activity levels in daily life. However, studies on physical activity using wearable devices have limitations; for example, these studies often rely on a single device model or use improper clustering methods to analyze the wearable data that are extracted from wearable devices. Objective: This study aimed to identify methods suitable for analyzing wearable data and determining daily physical activity patterns. This study also explored the association between these physical activity patterns and health risk factors. Methods: People aged >30 years who had metabolic syndrome risk factors and were using their own wrist-worn devices were included in this study. We collected personal health data through a web-based survey and measured physical activity levels using wrist-worn wearables over the course of 1 week. The Time-Series Anytime Density Peak (TADPole) clustering method, which is a novel time-series method proposed recently, was used to identify the physical activity patterns of study participants. Additionally, we defined physical activity pattern groups based on the similarity of physical activity patterns between weekdays and weekends. We used the χ2 or Fisher exact test for categorical variables and the 2-tailed t test for numerical variables to find significant differences between physical activity pattern groups. Logistic regression models were used to analyze the relationship between activity patterns and health risk factors. Results: A total of 47 participants were included in the analysis, generating a total of 329 person-days of data. We identified 2 different types of physical activity patterns (early bird pattern and night owl pattern) for weekdays and weekends. The physical activity levels of early birds were less than that of night owls on both weekdays and weekends. Additionally, participants were categorized into stable and shifting groups based on the similarity of physical activity patterns between weekdays and weekends. The physical activity pattern groups showed significant differences depending on age (P=.004) and daily energy expenditure (P<.001 for weekdays; P=.003 for weekends). Logistic regression analysis revealed a significant association between older age (≥40 y) and shifting physical activity patterns (odds ratio 8.68, 95% CI 1.95-48.85; P=.007). Conclusions: This study overcomes the limitations of previous studies by using various models of wrist-worn wearables and a novel time-series clustering method. Our findings suggested that age significantly influenced physical activity patterns. It also suggests a potential role of the TADPole clustering method in the analysis of large and multidimensional data, such as wearable data.

Associations Between Wearable-Specific Indicators of Physical Activity Behaviour and Insulin Sensitivity and Glycated Haemoglobin in the General Population: Results from the ORISCAV-LUX 2 Study.

BACKGROUND: Parameters derived from an acceleration signal, such as the time accumulated in sedentary behaviour or moderate to vigorous physical activity (MVPA), may not be sufficient to describe physical activity (PA) which is a complex behaviour. Incorporating more advanced wearable-specific indicators of PA behaviour (WIPAB) may be useful when characterising PA profiles and investigating associations with health. We investigated the associations of novel objective measures of PA behaviour with glycated haemoglobin (HbA1c) and insulin sensitivity (Quicki index). METHODS: This observational study included 1026 adults (55% women) aged 18-79y who were recruited from the general population in Luxembourg. Participants provided ≥ 4 valid days of triaxial accelerometry data which was used to derive WIPAB variables related to the activity intensity, accumulation pattern and the temporal correlation and regularity of the acceleration time series. RESULTS: Adjusted general linear models showed that more time spent in MVPA and a higher average acceleration were both associated with a higher insulin sensitivity. More time accumulated in sedentary behaviour was associated with lower insulin sensitivity. With regard to WIPAB variables, parameters that were indicative of higher PA intensity, including a shallower intensity gradient and higher average accelerations registered during the most active 8 h and 15 min of the day, were associated with higher insulin sensitivity. Results for the power law exponent alpha, and the proportion of daily time accumulated in sedentary bouts > 60 min, indicated that activity which was characterised by long sedentary bouts was associated with lower insulin sensitivity. A greater proportion of time spent in MVPA bouts > 10 min was associated with higher insulin sensitivity. A higher scaling exponent alpha at small time scales (< 90 min), which shows greater correlation in the acceleration time series over short durations, was associated with higher insulin sensitivity. When measured over the entirety of the time series, metrics that reflected a more complex, irregular and unpredictable activity profile, such as the sample entropy, were associated with lower HbA1c levels and higher insulin sensitivity. CONCLUSION: Our investigation of novel WIPAB variables shows that parameters related to activity intensity, accumulation pattern, temporal correlation and regularity are associated with insulin sensitivity in an adult general population.

Physical Activity Patterns among Individuals with Prediabetes or Type 2 Diabetes across Two Years-A Longitudinal Latent Class Analysis.

BACKGROUND: This study aimed to identify distinct profiles of physical activity (PA) patterns among individuals with prediabetes or type 2 diabetes participating in a two-year PA trial and to investigate predictors of the profiles. METHODS: Data (n = 168, collected 2013-2020) from the cohort of a randomized trial aimed at increasing PA in individuals with prediabetes and type 2 diabetes were used. PA and sedentary behaviours were assessed by waist-worn ActiGraph GT1M accelerometers at baseline and at 6, 12, 18 and 24 months. Fifteen PA and sedentary variables were entered into a latent class mixed model for multivariate longitudinal outcomes. Multinominal regression analysis modelled profile membership based on baseline activity level, age, gender, BMI, disease status and group randomisation. RESULTS: Two profiles of PA patterns were identified: "Increased activity" (n = 37, 22%) included participants increasing time in PA and decreasing sedentary time. "No change in activity" (n = 131, 78%) included participants with no or minor changes. "Increased activity" were younger (p = 0.003) and more active at baseline (p = 0.011), compared to "No change in activity". No other predictor was associated with profile membership. CONCLUSIONS: A majority of participants maintained PA and sedentary patterns over two years despite being part of a PA intervention. Individuals improving PA patterns were younger and more active at baseline.

Exercise in Aging: Be Balanced.

The beneficial effects of exercise are recognized for preventing physical and cognitive decline during the aging process. However, there is still a gap concerning recommended intensity, volume, frequency and mode of exercise especially for older people. The aim of this study was to investigate an appropriate type of physical activity (PA) model for healthy aging. A commentary of the influence of PA and exercise on healthy aging through an online search of the databases Web of Science, PubMed and Google Scholar. Two living groups can be considered as potential references: modern hunter-gatherer small-scale population and master athletes. Greater physical activity is proposed for healthy aging than that recommended by WHO. Additionally, mindfulness meditation techniques during exercise are recommended especially for persons practicing long-duration exercises. Complex and compound exercise and workouts should include challenging exercises adjusted and balanced to provide clients, especially older people, with noticeable changes and progress.

A Systematic Review of Children's Physical Activity Patterns: Concept, Operational Definitions, Instruments, Statistical Analyses, and Health Implications.

Despite the widespread use of the expression "physical activity pattern" (PAP), there apparently is no general consensus regarding its definition. This systematic review aimed to examine available research focussing on (1) definitions of PAP, (2) instruments/techniques used to describe PAP, (3) statistical approaches used to analyse PAP, and (4) implications of PAP on children's health. A systematic review of the available literature was done to identify studies published up to October 2019, and 76 studies were eligible. None of the studies presented a formal definition of PAP; a wide range of instruments were used to investigate children's PAP, and most of the revised studies did not explicitly present a formal statistical model to define PAP. Twenty-four papers purported to examine associations between PAP and health indicators. The review highlights no consensus on a clear PAP definition whatever the instrument used to capture it, and we did not find any agreement regarding how best to analyse PAP. We suggest that PAP should be used when targeting the investigation of similarities/dissimilarities, as well as stabilities and/or changes in children's PA at an intra-personal level. In sum, PAP should be used to best describe individual streams of behaviours, and not exclusively PA levels/intensities.

Does exercise-induced bronchoconstriction affect physical activity patterns in asthmatic children?

Exercise-induced bronchoconstriction (EIB) is a sign of uncontrolled childhood asthma and classically occurs after exercise. Recent research shows that EIB frequently starts during exercise, called breakthrough-EIB (BT-EIB). It is unknown whether this more severe type of EIB forces children to adapt their physical activity (PA) pattern in daily life. Therefore, this pilot study aims to investigate daily life PA (amount, intensity, duration, and distribution) in children with BT-EIB, 'classic' EIB, and without EIB. A Fitbit Zip activity tracker was used for one week to objectively measure daily life PA at one-minute intervals. Thirty asthmatic children participated. Children with BT-EIB were less physically active compared to children without EIB (respectively 7994 and 11,444 steps/day, p = .02). Children with BT-EIB showed less moderate-to-vigorous PA compared to the children without (respectively 117 and 170 minutes/day, p = .02). Children with EIB (both BT and classic) had significant shorter bouts of activity and a less stretched distribution of bout lengths compared to the non-EIB group (all p < .05). These results emphasize a marked association between EIB severity and PA patterns in daily life, stressing the need for a thorough clinical evaluation of exercise-induced symptoms in childhood asthma.

Advanced analytical methods to assess physical activity behaviour using accelerometer raw time series data: a protocol for a scoping review.

BACKGROUND: Physical activity (PA) is a complex multidimensional human behaviour. Currently, there is no standardised approach for measuring PA using wearable accelerometers in health research. The total volume of PA is an important variable because it includes the frequency, intensity and duration of activity bouts, but it reduces them down to a single summary variable. Therefore, analytical approaches using accelerometer raw time series data taking into account the way PA are accumulated over time may provide more clinically relevant features of physical behaviour. Advances on these fields are highly needed in the context of the rapid development of digital health studies using connected trackers and smartwatches. The objective of this review will be to map advanced analytical approaches and their multidimensional summary variables used to provide a comprehensive picture of PA behaviour. METHODS: This scoping review will be guided by the Arksey and O'Malley methodological framework. A search for relevant publications will be undertaken in MEDLINE (PubMed), Embase and Web of Science databases. The selection of articles will be limited to studies published in English from January 2010 onwards. Studies including analytical methods that go beyond total PA volume, average daily acceleration and the conventional cut-point approaches, involving tri-axial accelerometer data will be included. Two reviewers will independently screen all citations, full-text articles and extract data. The data will be collated, stored and charted to provide a descriptive summary of the analytical methods and outputs, their strengths and limitations and their association with different health outcomes. DISCUSSION: This protocol describes a systematic method to identify, map and synthesise advanced analytical approaches and their multidimensional summary variables used to investigate PA behaviour and identify potentially clinically relevant features. The results of this review will be useful to guide future research related to analysing PA patterns, investigate their association with health conditions and suggest appropriate recommendations for changes in PA behaviour. The results may be of interest to sports scientists, clinical researchers, epidemiologists and smartphone application developers in the field of PA assessment. SCOPING REVIEW REGISTRATION: This protocol has been registered with the Open Science Framework (OSF): https://osf.io/yxgmb .