Pub Date : 2023-12-01DOI: 10.1186/s44167-023-00035-3
Charlotte C Gupta, Mitch J. Duncan, Sally A Ferguson, A. Rebar, C. Vandelanotte, M. Sprajcer, S. Khalesi, Lauren A. Booker, Caroline M Rampling, Gabrielle Rigney, G. Vincent
{"title":"Exploring the prioritisation of sleep, diet, and physical activity as pillars of health: correlates and associations with health behaviours in Australian adults","authors":"Charlotte C Gupta, Mitch J. Duncan, Sally A Ferguson, A. Rebar, C. Vandelanotte, M. Sprajcer, S. Khalesi, Lauren A. Booker, Caroline M Rampling, Gabrielle Rigney, G. Vincent","doi":"10.1186/s44167-023-00035-3","DOIUrl":"https://doi.org/10.1186/s44167-023-00035-3","url":null,"abstract":"","PeriodicalId":73581,"journal":{"name":"Journal of activity, sedentary and sleep behaviors","volume":"45 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138627115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1186/s44167-023-00039-z
C. Lamboglia, Geralyn R Ruissen, Nicholas Kuzik, Valerie Carson, John C. Spence
{"title":"Continuous-time modeling of the multivariate relationships between physical activity levels and stationary time in preschool-aged children: an investigation of the ActivityStat hypothesis","authors":"C. Lamboglia, Geralyn R Ruissen, Nicholas Kuzik, Valerie Carson, John C. Spence","doi":"10.1186/s44167-023-00039-z","DOIUrl":"https://doi.org/10.1186/s44167-023-00039-z","url":null,"abstract":"","PeriodicalId":73581,"journal":{"name":"Journal of activity, sedentary and sleep behaviors","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138615458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1186/s44167-023-00036-2
Stephanie E Chappel, Brad Aisbett, Julie Considine, N. Ridgers
{"title":"Measuring nurses’ on-shift physical activity and sedentary time by accelerometry or heart rate monitoring: a descriptive case study illustrating the importance of context","authors":"Stephanie E Chappel, Brad Aisbett, Julie Considine, N. Ridgers","doi":"10.1186/s44167-023-00036-2","DOIUrl":"https://doi.org/10.1186/s44167-023-00036-2","url":null,"abstract":"","PeriodicalId":73581,"journal":{"name":"Journal of activity, sedentary and sleep behaviors","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138627060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1186/s44167-023-00037-1
Ine Nieste, J. Spaas, W. Franssen, P. V. Asch, H. Savelberg, B. O. Eijnde
{"title":"The effect of a structured running exercise intervention on non-exercise physical activity and sedentary behaviour in persons with mild Multiple Sclerosis and healthy controls","authors":"Ine Nieste, J. Spaas, W. Franssen, P. V. Asch, H. Savelberg, B. O. Eijnde","doi":"10.1186/s44167-023-00037-1","DOIUrl":"https://doi.org/10.1186/s44167-023-00037-1","url":null,"abstract":"","PeriodicalId":73581,"journal":{"name":"Journal of activity, sedentary and sleep behaviors","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138615442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-03DOI: 10.1186/s44167-023-00032-6
Julie E. Campbell, Michelle R. Stone, Raktim Mitra, Maggie Locke, Cynthia MacDonald, Ashley Preston, Rebecca A. Feicht, Laurene Rehman, Sara F. L. Kirk, Guy Faulkner, Mark S. Tremblay, Sarah A. Moore
Abstract Background Like many places globally, the health and well-being of children and youth living in Canada were significantly affected by the COVID-19 pandemic. Restricted access to the outdoors, schools, and public green spaces impacted children’s physical activity (PA), sedentary behaviour, and sleep. Restrictions changed throughout the pandemic, and children’s and youth’s movement behaviours may have been differentially affected based on time and place. This paper aimed to examine the impact of the COVID-19 pandemic on the movement behaviours of children and youth living in Nova Scotia (NS), Canada, over time and by geographic region using mixed methods. Methods This study employed an explanatory sequential mixed-methods design. Secondary data from three repeated cross-sectional surveys of parent-reported demographic, movement, and geographic data of 291 children and youth aged 5–17 years were analyzed. Spatial cluster analyses were applied to identify geographic concentrations of children and youth who were more or less likely to meet the moderate-vigorous PA (MVPA) guideline during the pandemic. Semi-structured interviews were conducted with 14 Nova Scotian parents to understand their perspectives on their child’s movement behaviours during the pandemic. Interviews were analyzed deductively based on quantitative results using reflexive thematic analysis. Results Our findings showed only 5.5% of children and youth were meeting all guidelines throughout the pandemic. Of the movement behaviours, screen time (ST) differed across the pandemic and by age and gender. Clusters of children and youth meeting the MVPA recommendation on fewer days were found in regions within NS’s three largest population centres (Truro, Sydney, and the Halifax Regional Municipality (HRM)), and clusters of those meeting the MVPA recommendation on more days were also identified in the HRM. From semi-structured interviews, themes indicated: (1) escaping screens during early parts of the COVID-19 pandemic and when weather was colder was hard; (2) having access to spaces to be active near the home helped facilitate children’s movement; and (3) higher socioeconomic status enabled more opportunities for movement. Conclusion Overall, fewer public health restrictions led to more favourable movement behaviours and spatial and sociodemographic factors may have been at play. Decision-makers should consider these factors when identifying strategies to keep children active during future health crises.
{"title":"Children and youth’s movement behaviours differed across phases and by geographic region throughout the COVID-19 pandemic in Nova Scotia, Canada: an explanatory sequential mixed-methods study","authors":"Julie E. Campbell, Michelle R. Stone, Raktim Mitra, Maggie Locke, Cynthia MacDonald, Ashley Preston, Rebecca A. Feicht, Laurene Rehman, Sara F. L. Kirk, Guy Faulkner, Mark S. Tremblay, Sarah A. Moore","doi":"10.1186/s44167-023-00032-6","DOIUrl":"https://doi.org/10.1186/s44167-023-00032-6","url":null,"abstract":"Abstract Background Like many places globally, the health and well-being of children and youth living in Canada were significantly affected by the COVID-19 pandemic. Restricted access to the outdoors, schools, and public green spaces impacted children’s physical activity (PA), sedentary behaviour, and sleep. Restrictions changed throughout the pandemic, and children’s and youth’s movement behaviours may have been differentially affected based on time and place. This paper aimed to examine the impact of the COVID-19 pandemic on the movement behaviours of children and youth living in Nova Scotia (NS), Canada, over time and by geographic region using mixed methods. Methods This study employed an explanatory sequential mixed-methods design. Secondary data from three repeated cross-sectional surveys of parent-reported demographic, movement, and geographic data of 291 children and youth aged 5–17 years were analyzed. Spatial cluster analyses were applied to identify geographic concentrations of children and youth who were more or less likely to meet the moderate-vigorous PA (MVPA) guideline during the pandemic. Semi-structured interviews were conducted with 14 Nova Scotian parents to understand their perspectives on their child’s movement behaviours during the pandemic. Interviews were analyzed deductively based on quantitative results using reflexive thematic analysis. Results Our findings showed only 5.5% of children and youth were meeting all guidelines throughout the pandemic. Of the movement behaviours, screen time (ST) differed across the pandemic and by age and gender. Clusters of children and youth meeting the MVPA recommendation on fewer days were found in regions within NS’s three largest population centres (Truro, Sydney, and the Halifax Regional Municipality (HRM)), and clusters of those meeting the MVPA recommendation on more days were also identified in the HRM. From semi-structured interviews, themes indicated: (1) escaping screens during early parts of the COVID-19 pandemic and when weather was colder was hard; (2) having access to spaces to be active near the home helped facilitate children’s movement; and (3) higher socioeconomic status enabled more opportunities for movement. Conclusion Overall, fewer public health restrictions led to more favourable movement behaviours and spatial and sociodemographic factors may have been at play. Decision-makers should consider these factors when identifying strategies to keep children active during future health crises.","PeriodicalId":73581,"journal":{"name":"Journal of activity, sedentary and sleep behaviors","volume":"127 3‐4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135818813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-02DOI: 10.1186/s44167-023-00033-5
Philip von Rosen
Abstract Recently, there has been a paradigm shift from considering physical activity and sedentary behaviour as “independent” risk factors of health to acknowledging their co-dependency and compositional nature. The focus is now on how these behaviours relate to each other rather than viewing them in isolation. Compositional data analysis (CoDA) is a methodology that has been developed specifically for compositional data and the number of publications using CoDA in physical activity and sedentary behaviour research has increased rapidly in the past years. Yet, only a small proportion of the published studies in physical activity and sedentary behaviour research have investigated the time-use composition as dependent variables. This could be related to challenges regarding the interpretation of the results and the lack of guidelines for deciding which statistical approach to use. Therefore, in this paper, four different approaches for analysing the time-use composition as dependent variables are presented and discussed. This paper advocates that the aim of research should guide how the dependent variable is defined and which data analysis approach is selected, and it encourages researchers to consider analysing time-use components as dependent variables in physical activity and sedentary behaviour research.
{"title":"Analysing time-use composition as dependent variables in physical activity and sedentary behaviour research: different compositional data analysis approaches","authors":"Philip von Rosen","doi":"10.1186/s44167-023-00033-5","DOIUrl":"https://doi.org/10.1186/s44167-023-00033-5","url":null,"abstract":"Abstract Recently, there has been a paradigm shift from considering physical activity and sedentary behaviour as “independent” risk factors of health to acknowledging their co-dependency and compositional nature. The focus is now on how these behaviours relate to each other rather than viewing them in isolation. Compositional data analysis (CoDA) is a methodology that has been developed specifically for compositional data and the number of publications using CoDA in physical activity and sedentary behaviour research has increased rapidly in the past years. Yet, only a small proportion of the published studies in physical activity and sedentary behaviour research have investigated the time-use composition as dependent variables. This could be related to challenges regarding the interpretation of the results and the lack of guidelines for deciding which statistical approach to use. Therefore, in this paper, four different approaches for analysing the time-use composition as dependent variables are presented and discussed. This paper advocates that the aim of research should guide how the dependent variable is defined and which data analysis approach is selected, and it encourages researchers to consider analysing time-use components as dependent variables in physical activity and sedentary behaviour research.","PeriodicalId":73581,"journal":{"name":"Journal of activity, sedentary and sleep behaviors","volume":"34 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135876425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1186/s44167-023-00030-8
Andrew Dalziell, Xanne Janssen
Abstract Background This study will evaluate the barriers and facilitators that families experience in adhering to the 24-hour movement behaviours guidelines as outlined by World Health Organisation (WHO). Methods The study is a mixed-methods study and will recruit between 20 to 30 low-income families with children aged 3- to 4-years living in Scotland. For the quantitative part, children will be asked to wear an Actigraph (GT3X +) accelerometer to measure physical activity, sedentary behaviour, and sleep. Parents/guardians will be asked to keep an activity diary outlining when their child has had to remove the device (i.e., showering, bathing, swimming) and record the child’s screen time each day. Once the data has been analysed, a unique activity profile chart will be sent out to each family illustrating their child’s 24-hour movement behaviours (i.e., time spent active, time spent sedentary and on screens, time spent sleeping). The activity profile will provide a day-by-day output as well as a weekly average for each of the 24-hour movement behaviours. Qualitative data will be collected using the Asynchronous Remote Communities method (ARC). The ARC involves participants completing activities using an online closed Facebook group. Parents/guardians of 3- to 4-year-old children will be asked to engage in group discussion tasks using the private and closed-group online platform (a minimum of 6 and a maximum of 8 families per discussion group). The quantitative data collated from the questionnaire and activity monitor will be presented through descriptive analysis and after the 6-week asynchronous process is complete, qualitative data will be collated and analysed using Braun and Clarke’s reflexive approach to thematic analysis. Discussion The data collected will provide an understanding of what barriers and facilitators parent’s/guardians’ experience in relation to adhering to the 24-hour movement behaviour guidelines. This could potentially lead to the design and implementation of support and interventions to help families struggling to adhere to the guidelines.
{"title":"Barriers and facilitators of physical activity, sedentary and sleep behaviours in 3 to 4-year-old children from low-income families: a study protocol","authors":"Andrew Dalziell, Xanne Janssen","doi":"10.1186/s44167-023-00030-8","DOIUrl":"https://doi.org/10.1186/s44167-023-00030-8","url":null,"abstract":"Abstract Background This study will evaluate the barriers and facilitators that families experience in adhering to the 24-hour movement behaviours guidelines as outlined by World Health Organisation (WHO). Methods The study is a mixed-methods study and will recruit between 20 to 30 low-income families with children aged 3- to 4-years living in Scotland. For the quantitative part, children will be asked to wear an Actigraph (GT3X +) accelerometer to measure physical activity, sedentary behaviour, and sleep. Parents/guardians will be asked to keep an activity diary outlining when their child has had to remove the device (i.e., showering, bathing, swimming) and record the child’s screen time each day. Once the data has been analysed, a unique activity profile chart will be sent out to each family illustrating their child’s 24-hour movement behaviours (i.e., time spent active, time spent sedentary and on screens, time spent sleeping). The activity profile will provide a day-by-day output as well as a weekly average for each of the 24-hour movement behaviours. Qualitative data will be collected using the Asynchronous Remote Communities method (ARC). The ARC involves participants completing activities using an online closed Facebook group. Parents/guardians of 3- to 4-year-old children will be asked to engage in group discussion tasks using the private and closed-group online platform (a minimum of 6 and a maximum of 8 families per discussion group). The quantitative data collated from the questionnaire and activity monitor will be presented through descriptive analysis and after the 6-week asynchronous process is complete, qualitative data will be collated and analysed using Braun and Clarke’s reflexive approach to thematic analysis. Discussion The data collected will provide an understanding of what barriers and facilitators parent’s/guardians’ experience in relation to adhering to the 24-hour movement behaviour guidelines. This could potentially lead to the design and implementation of support and interventions to help families struggling to adhere to the guidelines.","PeriodicalId":73581,"journal":{"name":"Journal of activity, sedentary and sleep behaviors","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135216473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-11DOI: 10.1186/s44167-023-00031-7
Katika Akksilp, Falk Müller-Riemenschneider, Yot Teerawattananon, Cynthia Chen
Abstract Introduction Physical inactivity and sedentary behaviour independently increase morbidity and negatively affect quality of life. This study evaluates the associations between physical activity and sedentary behaviour with health-related quality of life, including the five dimensions of quality of life (mobility, self-care, usual activities, pain or discomfort, and anxiety or depression). Methods This cross-sectional study analysed baseline data from Thailand's Physical Activity at Work (PAW) trial. Physical activity data were collected using the ActiGraph™ accelerometer, worn on the right hip for a minimum of three ten-hour workdays. Accelerometer data were then used to categorise participants into: (i) not-sedentary and physically active (the Reference Group), (ii) not-sedentary but inactive, (iii) sedentary but active, and (iv) sedentary and inactive. We employed the EuroQol-5 dimensions questionnaire with five scoring levels (EQ-5D-5L) to measure health-related quality of life. The Thai EQ-5D-5D valuation was utilised to convert the EQ-5D profile into utility index scores (EQ-5D values). Tobit regression models were used to analyse EQ-5D value differences. Moreover, the odds of having problems in each EQ-5D dimension were compared between categories. Results 277 valid participant data were included. Older age (P = 0.007), higher education (P < 0.001), and higher prevalence of cardiovascular disease (P = 0.032) were observed in participants who were sedentary and physically inactive compared to other groups. We found − 0.0503 (95% CI: − 0.0946–− 0.00597) lower EQ-5D value and 1.39 (95% CI: 1.07–1.79) higher odds of reporting pain or discomfort problems in the sedentary and physically inactive group compared to the Reference Group. We also found 2.12 (95%CI: 1.14–5.40) higher odds of reporting usual activity problems in the not-sedentary but physically inactive group than in the Reference Group. Discussion We found further evidence of the potential benefit of higher physical activity levels and lower sedentary time for higher quality of life among healthy office workers in Thailand. Further research with larger cohorts and longitudinal data is needed to establish a stronger foundation for interventions and economic evaluations targeting physical activity promotion to improve quality of life.
{"title":"The association of physical activity and sedentary behaviour on health-related quality of life: a cross-sectional study from the physical activity at work (PAW) trial","authors":"Katika Akksilp, Falk Müller-Riemenschneider, Yot Teerawattananon, Cynthia Chen","doi":"10.1186/s44167-023-00031-7","DOIUrl":"https://doi.org/10.1186/s44167-023-00031-7","url":null,"abstract":"Abstract Introduction Physical inactivity and sedentary behaviour independently increase morbidity and negatively affect quality of life. This study evaluates the associations between physical activity and sedentary behaviour with health-related quality of life, including the five dimensions of quality of life (mobility, self-care, usual activities, pain or discomfort, and anxiety or depression). Methods This cross-sectional study analysed baseline data from Thailand's Physical Activity at Work (PAW) trial. Physical activity data were collected using the ActiGraph™ accelerometer, worn on the right hip for a minimum of three ten-hour workdays. Accelerometer data were then used to categorise participants into: (i) not-sedentary and physically active (the Reference Group), (ii) not-sedentary but inactive, (iii) sedentary but active, and (iv) sedentary and inactive. We employed the EuroQol-5 dimensions questionnaire with five scoring levels (EQ-5D-5L) to measure health-related quality of life. The Thai EQ-5D-5D valuation was utilised to convert the EQ-5D profile into utility index scores (EQ-5D values). Tobit regression models were used to analyse EQ-5D value differences. Moreover, the odds of having problems in each EQ-5D dimension were compared between categories. Results 277 valid participant data were included. Older age (P = 0.007), higher education (P < 0.001), and higher prevalence of cardiovascular disease (P = 0.032) were observed in participants who were sedentary and physically inactive compared to other groups. We found − 0.0503 (95% CI: − 0.0946–− 0.00597) lower EQ-5D value and 1.39 (95% CI: 1.07–1.79) higher odds of reporting pain or discomfort problems in the sedentary and physically inactive group compared to the Reference Group. We also found 2.12 (95%CI: 1.14–5.40) higher odds of reporting usual activity problems in the not-sedentary but physically inactive group than in the Reference Group. Discussion We found further evidence of the potential benefit of higher physical activity levels and lower sedentary time for higher quality of life among healthy office workers in Thailand. Further research with larger cohorts and longitudinal data is needed to establish a stronger foundation for interventions and economic evaluations targeting physical activity promotion to improve quality of life.","PeriodicalId":73581,"journal":{"name":"Journal of activity, sedentary and sleep behaviors","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136057963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-03DOI: 10.1186/s44167-023-00029-1
Tetsuhiro Kidokoro, Naruki Kitano, Natsuko Imai, Justin J. Lang, Grant R. Tomkinson, Costan G. Magnussen
Abstract Background Optimizing childhood domain-specific physical activity (PA) and sedentary behavior (SB) for blood lipid profile is not well understood. We aimed to (1) determine the associations between accelerometer-measured PA and SB for each domain (school time and out-of-school time) with blood lipid profile and (2) estimate predicted changes in blood lipid profile with hypothetical time-reallocation between domain-specific SB and PA among Japanese children using compositional data analysis (CoDA). Methods This cross-sectional study included 284 children (147 boys and 137 girls) aged 9–12 years (mean age [years]: 10.1 ± 1.2 for boys, 10.0 ± 1.1 for girls; mean body mass index: 18.2 ± 3.2 for boys, 17.5 ± 2.5 for girls). Time spent in domain-specific SB, light-intensity PA (LPA), moderate-intensity PA (MPA), and vigorous-intensity PA (VPA) was assessed using accelerometry. The non-fasting lipid profile considered measures of triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and non-HDL-C levels. CoDA and isotemporal substitution model were performed to examine the associations of domain-specific PA and SB with blood lipids. Results Time spent in out-of-school VPA relative to the other behaviors was negatively associated with non-HDL-C (boys: β ilr1 = − 0.10, 95% confidence interval [CI] = − 0.19 to − 0.01), TG (boys: β ilr1 = − 0.45, 95% CI = − 0.68 to − 0.22), and positively associated with HDL-C (girls: β ilr1 = 0.09, 95% CI = 0.02 to 0.16) after adjusting for age, body mass index, and time spent in SB, LPA, and MPA. During the out-of-school period, a replacement of 1 min of any other behavior with VPA was associated with decreases in LDL-C in boys (predicted changes [95% CI] − 0.03 mmol/L [− 0.05 to − 0.00] for LPA), non-HDL-C in boys (predicted changes [95% CI] − 0.03 mmol/L [− 0.06 to − 0.01] for SB and LPA) and TG in boys (predicted changes [95% CI] − 0.04 mmol/L [− 0.06 to − 0.02] for SB and LPA, − 0.05 mmol/L [− 0.07 to − 0.02] for MPA) and in girls (predicted changes [95% CI] − 0.02 mmol/L [− 0.04 to − 0.00] for LPA), and increases in HDL-C in girls (predicted changes [95% CI] 0.02 mmol/L [0.00 to 0.04] for SB and LPA, 0.03 mmol/L [0.00 to 0.05] for MPA). Conclusions Increasing out-of-school VPA might be an effective approach to improve blood lipid profiles among Japanese children.
{"title":"Optimal domain-specific physical activity and sedentary behaviors for blood lipids among Japanese children: a compositional data analysis","authors":"Tetsuhiro Kidokoro, Naruki Kitano, Natsuko Imai, Justin J. Lang, Grant R. Tomkinson, Costan G. Magnussen","doi":"10.1186/s44167-023-00029-1","DOIUrl":"https://doi.org/10.1186/s44167-023-00029-1","url":null,"abstract":"Abstract Background Optimizing childhood domain-specific physical activity (PA) and sedentary behavior (SB) for blood lipid profile is not well understood. We aimed to (1) determine the associations between accelerometer-measured PA and SB for each domain (school time and out-of-school time) with blood lipid profile and (2) estimate predicted changes in blood lipid profile with hypothetical time-reallocation between domain-specific SB and PA among Japanese children using compositional data analysis (CoDA). Methods This cross-sectional study included 284 children (147 boys and 137 girls) aged 9–12 years (mean age [years]: 10.1 ± 1.2 for boys, 10.0 ± 1.1 for girls; mean body mass index: 18.2 ± 3.2 for boys, 17.5 ± 2.5 for girls). Time spent in domain-specific SB, light-intensity PA (LPA), moderate-intensity PA (MPA), and vigorous-intensity PA (VPA) was assessed using accelerometry. The non-fasting lipid profile considered measures of triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and non-HDL-C levels. CoDA and isotemporal substitution model were performed to examine the associations of domain-specific PA and SB with blood lipids. Results Time spent in out-of-school VPA relative to the other behaviors was negatively associated with non-HDL-C (boys: β ilr1 = − 0.10, 95% confidence interval [CI] = − 0.19 to − 0.01), TG (boys: β ilr1 = − 0.45, 95% CI = − 0.68 to − 0.22), and positively associated with HDL-C (girls: β ilr1 = 0.09, 95% CI = 0.02 to 0.16) after adjusting for age, body mass index, and time spent in SB, LPA, and MPA. During the out-of-school period, a replacement of 1 min of any other behavior with VPA was associated with decreases in LDL-C in boys (predicted changes [95% CI] − 0.03 mmol/L [− 0.05 to − 0.00] for LPA), non-HDL-C in boys (predicted changes [95% CI] − 0.03 mmol/L [− 0.06 to − 0.01] for SB and LPA) and TG in boys (predicted changes [95% CI] − 0.04 mmol/L [− 0.06 to − 0.02] for SB and LPA, − 0.05 mmol/L [− 0.07 to − 0.02] for MPA) and in girls (predicted changes [95% CI] − 0.02 mmol/L [− 0.04 to − 0.00] for LPA), and increases in HDL-C in girls (predicted changes [95% CI] 0.02 mmol/L [0.00 to 0.04] for SB and LPA, 0.03 mmol/L [0.00 to 0.05] for MPA). Conclusions Increasing out-of-school VPA might be an effective approach to improve blood lipid profiles among Japanese children.","PeriodicalId":73581,"journal":{"name":"Journal of activity, sedentary and sleep behaviors","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135689772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1186/s44167-023-00028-2
Philip von Rosen
Abstract Background More and more researchers have started to analyse device-measured physical activity data using compositional data analysis (CoDA), which has led to that the effect of relative time in different behaviours can be explored. However, there are challenges related to the interpretation of the results based on CoDA. This is partly related to that CoDA provides estimates based on the relative time that is difficult to interpret relative to the 2020 guidelines of physical activity and sedentary behaviour. Since many data cohorts do not have data on sleep, the proportion of time in physical activity may vary depending on accelerometer wear time. Therefore, there is a need to explore cut-points for relative time to distinguish between individuals that do and do not reach 150–300 min of moderate-to-vigorous intensity physical activity (MVPA) per week. The aim was to establish a ratio of MVPA to awaken time that corresponds to meeting the 2020 guidelines of physical activity and sedentary behaviour in adults. Method To estimate the cut-off points of relative time in MVPA, the publicly available data from NHANES 2003–2004 was used and cut-off points were explored in different subsets of the total population. Values for sensitivity, specificity and cut-off values were explored; i) in total sample, ii) by tertiles of wear time, iii) in individuals with ± 5 min from 150 to 300 min of MVPA, iiii) in individuals with ± 5 min from 150 to 300 min of MVPA in the middle tertile of wear time. Results Overall, the analyses show high values for sensitivity (88–100%) and specificity (66–99%) for different cut-off values associated with 150–300 min of MVPA. Spending 2.4–4.7% of the time awake in MVPA was found to correspond to the 2020 guidelines of physical activity and sedentary behaviour. Conclusion Based on publicly available data from NHANES 2003–2004, spending 2.4–4.7% of time awake in MVPA corresponds to meeting the 2020 guidelines of physical activity and sedentary behaviour.
背景越来越多的研究者开始使用成分数据分析(CoDA)来分析设备测量的身体活动数据，从而可以探索相对时间在不同行为中的影响。然而，基于CoDA的结果解释存在一些挑战。这在一定程度上与CoDA提供的基于相对时间的估计有关，相对于2020年的身体活动和久坐行为指南，这很难解释。由于许多数据队列没有睡眠数据，因此运动时间的比例可能会因加速度计佩戴时间的不同而变化。因此，有必要探索相对时间的临界值，以区分每周中等至高强度体力活动(MVPA)达到150-300分钟和未达到150-300分钟的个体。目的是建立一个MVPA与觉醒时间的比例，该比例符合2020年成人身体活动和久坐行为指南。方法利用2003-2004年NHANES公开数据，在不同人群中探索相对时间的分界点，估计MVPA相对时间的分界点。探讨敏感性、特异性和截止值的值;i)总样本，ii)磨损时间的分位数，iii)在150至300分钟的MVPA中±5分钟的个体，iii)在150至300分钟的MVPA中±5分钟的个体，磨损时间的中间分位数。结果总的来说，分析显示，对于与150-300 min MVPA相关的不同截止值，灵敏度(88-100%)和特异性(66-99%)很高。研究发现，在MVPA中，2.4-4.7%的清醒时间符合2020年的身体活动和久坐行为指南。根据NHANES 2003-2004的公开数据，在MVPA中花费2.4-4.7%的清醒时间符合2020年的身体活动和久坐行为指南。
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