Social and Behavioral Sciences Commons^™

Health Behaviour Profiles In Young Australian Adults In Relation To Physical And Mental Health: The Raine Study, Cecilie Thøgersen-Ntoumani, Daniel F. Gucciardi, Joanne A. Mcveigh, Therese A. O'Sullivan, Manon Dontje, Emmanuel Stamatakis, Peter R. Eastwood, Leon Straker

Research outputs 2022 to 2026

Issues Addressed: We aimed to identify latent health behaviour profiles of young adults and examine their associations with physical and mental health outcomes. We also characterised the profiles by socio-demographic characteristics. Methods: Data were collected between 2012 and 2014. Participants (N = 476) were young adults (M age [SD] = 22.1 [.57] years) from Generation 2 of the Raine Study longitudinal cohort. Health behaviours were measured via ActiGraph GT3X waist monitors (physical activity, sedentary behaviour) and questionnaires (diet quality, alcohol, smoking and sleep). Physical and mental health were measured using clinical health assessments, blood biomarkers, and questionnaires. Latent Profile Analysis …

Cannabis Use, Sedentary Behavior, And Physical Activity In A Nationally Representative Sample Of Us Adults, Lydia Q. Ong, John Belletierre, Citlali Alvorado, Paul Chavez, Vincent Berardi

Psychology Faculty Articles and Research

Background

Prior research examining the relationship between cannabis use, sedentary behavior, and physical activity has generated conflicting findings, potentially due to biases in the self-reported measures used to assess physical activity. This study aimed to more precisely explore the relationship between cannabis use and sedentary behavior/physical activity using objective measures.

Methods

Data were obtained from the 2005–2006 National Health and Nutrition Examination Survey. A total of 2,092 participants (ages 20–59; 48.8% female) had accelerometer-measured sedentary behavior, light physical activity, and moderate-to-vigorous physical activity. Participants were classified as light, moderate, frequent, or non-current cannabis users depending on how often they used …

Prediction Of Activity Type In Preschool Children Using Machine Learning Techniques, M. Hagenbuchner, Dylan P. Cliff, Stewart Trost, Van Tuc Nguyen, Gregory E. Peoples

Faculty of Science, Medicine and Health - Papers: part A

Objectives Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children. Design Participants completed 12 standardised activity trials (TV, reading, tablet game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits. Methods Eleven children aged 3-6 years (mean age = 4.8 ± 0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised …

Physical Activity Levels Of Older Community-Dwelling Adults Are Influenced By Summer Weather Variables, Caitlin A. Brandon, Dawn P. Gill, Mark Speechley, Jason Gilliland, Gareth R. Jones

Geography & Environment Publications

Adequate daily physical activity (PA) is important for maintaining functional capacity and independence in older adults. However, most older adults in Canada do not engage in enough PA to sustain fitness and functional independence. Environmental influences, such as warmer daytime temperatures, may influence PA participation; however, few studies have examined the effect of summertime temperatures on PA levels in older adults. This investigation measured the influence of summertime weather variables on PA in 48 community-dwelling older adults who were randomly recruited from a local seniors’ community centre. Each participant wore an accelerometer for a single 7-consecutive-day period (between 30 May …