While physical and mental health benefits of regular physical activity are well known, increasing evidence suggests that limiting sedentary behaviour is also important for health. Evidence shows ...associations of physical activity and sedentary behaviour with health-related quality of life (HRQoL), however, these findings are based predominantly on duration measures of physical activity and sedentary behaviour (e.g., minutes/week), with less attention on frequency measures (e.g., number of bouts). We examined the association of HRQoL with physical activity and sedentary behaviour, using both continuous duration (average daily minutes) and frequency (average daily bouts≥10 min) measures. Baseline data from the WALK 2.0 trial were analysed. WALK 2.0 is a randomised controlled trial investigating the effects of Web 2.0 applications on engagement, retention, and subsequent physical activity change. Daily physical activity and sedentary behaviour (duration = average minutes, frequency = average number of bouts ≥10 minutes) were measured (ActiGraph GT3X) across one week, and HRQoL was assessed with the 'general health' subscale of the RAND 36-Item Health Survey. Structural equation modelling was used to evaluate associations. Participants (N = 504) were 50.8±13.1 (mean±SD) years old with a BMI of 29.3±6.0. The 465 participants with valid accelerometer data engaged in an average of 24.0±18.3 minutes and 0.64±0.74 bouts of moderate-vigorous physical activity per day, 535.2±83.8 minutes and 17.0±3.4 bouts of sedentary behaviour per day, and reported moderate-high general HRQoL (64.5±20.0). After adjusting for covariates, the duration measures of physical activity (path correlation = 0.294, p<0.05) and sedentary behaviour were related to general HRQoL (path coefficient = -0.217, p<0.05). The frequency measure of physical activity was also significant (path coefficient = -0.226, p<0.05) but the frequency of sedentary behaviour was not significantly associated with general HRQoL. Higher duration levels of physical activity in fewer bouts, and lower duration of sedentary behaviour are associated with better general HRQoL. Further prospective studies are required to investigate these associations in different population groups over time.
This study aimed to investigate the validity of the Active Australia Survey across different subgroups and its responsiveness to change, as few previous studies have examined this.
The Active ...Australia Survey was validated against the ActiGraph as an objective measure of physical activity. Participants (n = 465) wore the ActiGraph for 7 days and subsequently completed the Active Australia Survey. Moderate activity, vigorous activity and total moderate and vigorous physical activity were compared using Spearman rank-order correlations. Changes in physical activity between baseline and 3-month assessments were correlated to examine responsiveness to change. The data were stratified to assess outcomes according to different subgroups (e.g., gender, age, weight, activity levels).
With regards to the validity, a significant correlation of ρ = 0.19 was found for moderate physical activity, ρ = 0.33 for vigorous physical activity and ρ = 0.23 for moderate and vigorous physical activity combined. For vigorous physical activity correlations were higher than 0.3 for most subgroups, whereas they were only higher than 0.3 in those with a healthy weight for the other activity outcomes. With regards to responsiveness to change, a correlation of ρ = 0.32 was found for moderate physical activity, ρ = 0.19 for vigorous physical activity and ρ = 0.35 for moderate and vigorous physical activity combined. For moderate and vigorous activity combined correlations were higher than 0.4 for several subgroups, but never for vigorous physical activity.
Little evidence for the validity of Active Australia Survey was found, although the responsiveness to change was acceptable for several subgroups. Findings from studies using the Active Australia Survey should be interpreted with caution.
World Health Organisation Universal Trial Number: U111-1119-1755. Australian New Zealand Clinical Trials Registry, ACTRN12611000157976 . Registration date: 8 March 2011.
Global population aging has raised academic interest in successful aging to a public policy priority. Currently there is no consensus regarding the definition of successful aging. However, a ...synthesis of research shows successful aging can be defined as a late-life process of change characterized by high physical, psychological, cognitive, and social functioning. Masters athletes systematically train for, and compete in, organized forms of team and individual sport specifically designed for older adults. Masters athletes are often proposed as exemplars of successful aging. However, their aging status has never been examined using a comprehensive multidimensional successful aging definition. Here, we examine the successful aging literature, propose a successful aging definition based on this literature, present evidence which suggests masters athletes could be considered exemplars of successful aging according to the proposed definition, and list future experimental research directions.
Many adults are insufficiently physically active, have prolonged sedentary behaviour and report poor sleep. These behaviours can be improved by interventions that include education, goal setting, ...self-monitoring, and feedback strategies. Few interventions have explicitly targeted these behaviours simultaneously or examined the relative efficacy of different self-monitoring methods.
This study aims to compare the efficacy of two self-monitoring methods in an app-based multi-behaviour intervention to improve objectively measured physical activity, sedentary, and sleep behaviours, in a 9 week 2-arm randomised trial. Participants will be adults (n = 64) who report being physically inactive, sitting >8 h/day and frequent insufficient sleep (≥14 days out of last 30). The "Balanced" intervention is delivered via a smartphone 'app', and includes education materials (guidelines, strategies to promote change in behaviour), goal setting, self-monitoring and feedback support. Participants will be randomly allocated to either a device-entered or user-entered self-monitoring method. The device-entered group will be provided with a activity tracker to self-monitor behaviours. The user-entered group will recall and manually record behaviours. Assessments will be conducted at 0, 3, 6, and 9 weeks. Physical activity, sedentary behaviour and sleep-wake behaviours will be measured using the wrist worn Geneactiv accelerometer. Linear mixed models will be used to examine differences between groups and over time using an alpha of 0.01.
This study will evaluate an app-based multi-behavioural intervention to improve physical activity, sedentary behaviour and sleep; and the relative efficacy of two different approaches to self-monitoring these behaviours. Outcomes will provide information to inform future interventions and self-monitoring targeting these behaviours.
ACTRN12615000182594 (Australian New Zealand Clinical Trials Registry. Registry URL: www.anzctr.org.au ; registered prospectively on 25 February 2015).
Web-based interventions that provide personalized physical activity advice have demonstrated good effectiveness but rely on self-reported measures of physical activity, which are prone to ...overreporting, potentially reducing the accuracy and effectiveness of the advice provided.
This study aimed to examine whether the effectiveness of a Web-based computer-tailored intervention could be improved by integrating Fitbit activity trackers.
Participants received the 3-month TaylorActive intervention, which included 8 modules of theory-based, personally tailored physical activity advice and action planning. Participants were randomized to receive the same intervention either with or without Fitbit tracker integration. All intervention materials were delivered on the Web, and there was no face-to-face contact at any time point. Changes in physical activity (Active Australia Survey), sitting time (Workforce Sitting Questionnaire), and body mass index (BMI) were assessed 1 and 3 months post baseline. Advice acceptability, website usability, and module completion were also assessed.
A total of 243 Australian adults participated. Linear mixed model analyses showed a significant increase in total weekly physical activity (adjusted mean increase=163.2; 95% CI 52.0-274.5; P=.004) and moderate-to-vigorous physical activity (adjusted mean increase=78.6; 95% CI 24.4-131.9; P=.004) in the Fitbit group compared with the non-Fitbit group at the 3-month follow-up. The sitting time and BMI decreased more in the Fitbit group, but no significant group × time interaction effects were found. The physical activity advice acceptability and the website usability were consistently rated higher by participants in the Fitbit group. Non-Fitbit group participants completed 2.9 (SD 2.5) modules, and Fitbit group participants completed 4.4 (SD 3.1) modules.
Integrating physical activity trackers into a Web-based computer-tailored intervention significantly increased intervention effectiveness.
Australian New Zealand Clinical Trials Registry ACTRN12616001555448; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=371793 (Archived by WebCite at http://www.webcitation.org/73ioTxQX2).
Physical inactivity, sitting behaviour, and mental health problems are detrimental to health-related quality of life but typically are considered as independent determinants. This study tested how ...these factors clustered together as profiles of subgroups of people and whether the clusters differed as a function of physical and mental health-related quality of life.
In 2012, Australian adults (N =1,014) self-reported their physical and mental health-related quality of life, physical activity, sitting time, depression, anxiety, and stress using a web-based survey. Cluster analysis was used to identify subgroups of health behaviour and mental health profiles, and ANOVA was used to test for between-cluster differences in health-related quality of life.
Three subgroups were identified: people with higher psychological stress (n =13%), people with higher amounts of sitting time (n =45%), and people with lower amounts of sitting time (n =42%). There were no differences in mental health-related quality of life between subgroups; however people represented by the subgroup of higher amounts of sitting time had significantly lower physical health-related quality of life than the other two subgroups, F(2, 1011) =10.04, p < .01.
Interventions should consider that (1) physical activity, sitting time, and psychological distress are aspects of multifaceted behavioural-psychological profiles, and (2) reductions of sitting time may have major impacts for physical health-related quality of life.
Action planning is a common approach used in physical activity interventions. The aim of this study was to assess the association of frequency, consistency and content of action planning with ...physical activity behaviour, intention strength and habit strength.
Within a 3-month web-based, computer-tailored physical activity intervention, participants (
= 115; 68.7% female,
age =43.9; range = 22-73 years) could create 6 rounds of action plans for 4 activities each (24 total).
Consistency of action planning during the intervention was associated with change in physical activity at 9-months, and intention and habit strength at 3-months and 9-months. Frequency of action planning was negatively associated with intention at 3-months and 9-months. The effect of action planning consistency on physical activity behaviour was no longer significant when accounting for change in intention and habit strength.
Consistency of how, where, when and with whom people plan their physical activity may translate into stronger physical activity habits. Interventions should avoid encouraging making many distinct action plans, but rather encourage stable contexts through consistent action planning.
Studying at university is stressful, which can lead to unhealthy lifestyle behaviors. This study aimed to explore perceived reasons and barriers preventing Australian nursing students from engaging ...in a healthy lifestyle and strategies to overcome barriers. Fifty‐four bachelor of nursing students participated in seven focus groups between July and November 2018. Participants defined healthy lifestyle behaviors as eating well; regular physical activity; regular water consumption; limiting alcohol, caffeine, and nicotine; good sleep quality; stress management and relaxation; and regular social interaction and support. They identified individual (lack of motivation, existing bad habits, lack of knowledge), environmental (time, finances, limited access to healthy food and physical activity resources), and psychosocial (competing priorities, increased learning cognitive load, lack of social interaction and support, compassion fatigue, and shift work) barriers preventing healthy lifestyle. Participants proposed several individual and system‐related strategies to overcome barriers. Despite portraying a comprehensive understanding of healthy lifestyle behaviors, students reported finding difficulty in attaining healthy lifestyles. Strategies proposed by students may inform targeted interventions aiming to increase overall health of students, reduce attrition rates, and promote workforce retention post‐graduation.
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