Quarantine and spatial distancing measures associated with COVID-19 resulted in substantial changes to individuals' everyday lives. Prominent among these lifestyle changes was the way in which people ...interacted with media-including music listening. In this repeated assessment study, we assessed Australian university students' media use (i.e., listening to music, playing video/computer games, watching TV/movies/streaming videos, and using social media) throughout early stages of the COVID-19 pandemic in Australia, and determined whether media use was related to changes in life satisfaction. Participants (
= 127) were asked to complete six online questionnaires, capturing pre- and during-pandemic experiences. The results indicated that media use varied substantially throughout the study period, and at the within-person level, life satisfaction was positively associated with music listening and negatively associated with watching TV/videos/movies. The findings highlight the potential benefits of music listening during COVID-19 and other periods of social isolation.
Amidst strong efforts to promote the therapeutic benefits of physical activity for reducing depression and anxiety in clinical populations, little focus has been directed towards the mental health ...benefits of activity for non-clinical populations. The objective of this meta-meta-analysis was to systematically aggregate and quantify high-quality meta-analytic findings of the effects of physical activity on depression and anxiety for non-clinical populations. A systematic search identified eight meta-analytic outcomes of randomised trials that investigated the effects of physical activity on depression or anxiety. The subsequent meta-meta-analyses were based on a total of 92 studies with 4310 participants for the effect of physical activity on depression and 306 study effects with 10,755 participants for the effect of physical activity on anxiety. Physical activity reduced depression by a medium effect standardised mean difference (SMD) = −0.50; 95% CI: −0.93 to −0.06 and anxiety by a small effect (SMD = −0.38; 95% CI: −0.66 to −0.11). Neither effect showed significant heterogeneity across meta-analyses. These findings represent a comprehensive body of high-quality evidence that physical activity reduces depression and anxiety in non-clinical populations.
People with knowledge of the benefits of physical activity tend to be more active; however, such knowledge is typically operationalized as a basic understanding that physical activity is 'good' for ...health. Therefore, the aim of this study was to investigate whether there are differences in how detailed a person's knowledge is about the benefits of physical activity. Participants (N = 615) completed an online survey to measure their current physical activity behaviour, as well as their level of knowledge of the benefits and risks of physical (in)activity. The majority of participants (99.6%) strongly agreed that physical activity is good for health, however on average, participants only identified 13.8 out of 22 diseases associated with physical inactivity and over half of participants (55.6%) could not identify how much physical activity is recommended for health benefits. Furthermore, 45% of the participants overestimated, 9% underestimated and 27% did not know the increased risk of disease resulting from inactivity as indicated by the Australian Department of Health. Participants were significantly more active when they correctly identified more diseases associated with physical inactivity and when they overestimated the risks associated with inactivity. Therefore, health promotion initiatives should increase knowledge of the types of diseases associated with inactivity. Low knowledge of physical activity guidelines suggest they should be promoted more, as this knowledge provides guidance on frequency, types and duration of physical activity needed for health.
The number of commercial apps to improve health behaviours in children is growing rapidly. While this provides opportunities for promoting health, the content and quality of apps targeting children ...and adolescents is largely unexplored. This review systematically evaluated the content and quality of apps to improve diet, physical activity and sedentary behaviour in children and adolescents, and examined relationships of app quality ratings with number of app features and behaviour change techniques (BCTs) used.
Systematic literature searches were conducted in iTunes and Google Play stores between May-November 2016. Apps were included if they targeted children or adolescents, focused on improving diet, physical activity and/or sedentary behaviour, had a user rating of at least 4+ based on at least 20 ratings, and were available in English. App inclusion, downloading and user-testing for quality assessment and content analysis were conducted independently by two reviewers. Spearman correlations were used to examine relationships between app quality, and number of technical app features and BCTs included.
Twenty-five apps were included targeting diet (n = 12), physical activity (n = 18) and sedentary behaviour (n = 7). On a 5-point Mobile App Rating Scale (MARS), overall app quality was moderate (total MARS score: 3.6). Functionality was the highest scoring domain (mean: 4.1, SD: 0.6), followed by aesthetics (mean: 3.8, SD: 0.8), and lower scoring for engagement (mean: 3.6, SD: 0.7) and information quality (mean: 2.8, SD: 0.8). On average, 6 BCTs were identified per app (range: 1-14); the most frequently used BCTs were providing 'instructions' (n = 19), 'general encouragement' (n = 18), 'contingent rewards' (n = 17), and 'feedback on performance' (n = 13). App quality ratings correlated positively with numbers of technical app features (rho = 0.42, p < 0.05) and BCTs included (rho = 0.54, p < 0.01).
Popular commercial apps to improve diet, physical activity and sedentary behaviour in children and adolescents had moderate quality overall, scored higher in terms of functionality. Most apps incorporated some BCTs and higher quality apps included more app features and BCTs. Future app development should identify factors that promote users' app engagement, be tailored to specific population groups, and be informed by health behaviour theories.
Ecological momentary assessment (EMA) has the potential to yield new insights into the prediction and modeling of physical activity (PA) and sedentary behavior (SB). The objective of this study was ...to determine the feasibility and validity of an EMA protocol to assess older adults' PA and SB. Feasibility was determined by examining factors associated with EMA survey compliance and if PA or SB were impacted by EMA survey compliance. Validity was determined by comparing EMA-reported PA and SB to objectively measured PA and SB at the EMA prompt. Over 10 days, older adults (
= 104; Age
= 60-98 years) received 6 randomly prompted EMA questionnaires on a smartphone each day and wore an ActivPAL activity monitor to provide a device-based measure of PA and SB. Participants reported whether they were currently engaged in PA or SB. Older adults were compliant with the EMA and ActivPAL protocol on 92% of occasions. Differences in EMA compliance differed by weight status. Among overweight and obese older adults EMA compliance differed by sex (OR = 3.15, 95% CI: 1.43, 6.92) and day of week (OR = 1.79, 95% CI: 1.33, 2.41). Among normal weight older adults, EMA compliance differed by time of day (OR = 1.52, 95% CI: 1.02, 2.30). EMA compliance did not differ for device-based PA or SB in the 15 min before versus the 15 min after the EMA prompt, suggesting that these behaviors did not influence likelihood of responding and responding did not influence these behaviors (
s > 0.05). When PA was reported through EMA, participants engaged in less device-based PA in the 15 min after compared to the 15 min before the EMA prompt (
= 0.01), suggesting possible reactance or a disruption of PA. EMA-reported PA and SB were positively associated with higher device-based PA and SB in the ±15 min, respectively, supporting criterion validity (
s < 0.05). The assessment of older adults' PA and SB through EMA is feasible and valid, although there may be PA reactance to EMA prompting. Therefore, EMA represents a significant methodological tool that can aid in our understanding of the environmental, social, and psychological processes regulating older adults' PA and SB in the context of everyday life.
Poor neighborhood conditions are associated with lower levels of physical activity for older adults but socio-ecological models posit that physical activity depends on both environmental and ...individual factors. Older adults' ability to overcome environmental barriers to physical activity may partially rely on cognitive resources. However, evidence on the moderating role of these cognitive resources in the associations between environmental barriers and physical activity is still lacking. We analyzed cross-national and longitudinal data on 28,393 adults aged 50 to 96 years as part of the SHARE. Lack of access to services and neighborhood nuisances were used as indicators of poor neighborhood conditions. Delayed recall and verbal fluency were used as indicators of cognitive resources. Confounder-adjusted generalized estimation equations were conducted to test associations between neighborhood conditions and self-reported moderate physical activity, as well as the moderating role of cognitive resources. Results showed that poor neighborhood conditions reduced the odds of engagement in physical activity. Cognitive resources robustly reduced the adverse influence of poor neighborhood conditions on physical activity. Participants with lower cognitive resource scores showed lower odds of engaging in physical activity when neighborhood conditions were poorer, whereas these conditions were not related to this engagement for participants with higher cognitive resource scores. These findings suggest that cognitive resources can temper the detrimental effect of poor neighborhood conditions on physical activity. Public policies should target both individual and environmental factors to tackle the current pandemic of physical inactivity more comprehensively.
•Poor neighborhood conditions explain less frequent engagement in physical activity.•Poor neighborhood conditions explain a steeper decline in physical activity across aging.•Cognitive resources reduce the adverse influence of poor neighborhood conditions.•Both individual and contextual factors influence the pandemic of physical inactivity.
Intention has been an extremely important concept in physical activity theory and research but is complicated by a double-barreled definition of a decision to perform physical activity and the ...commitment to enact that decision. We put forth the hypothesis that these separate meanings have different measurement requirements, are situated in distinctly different intention-based models, and show discrete findings when explaining physical activity motives.
Studies of the physical activity intention-behavior gap, and factors that may moderate the gap (e.g., habit, perceived behavioral control), can inform physical activity promotion efforts. Yet, these ...studies typically apply linear modeling procedures, and so conclusions rely on linearity and homoscedasticity assumptions, which may not hold.
We modelled and plotted physical activity intention-behavior associations and the moderation effects of habit using simulated data based on (a) normal distributions with no shared variance, (b) correlated parameters with normal distribution, and (c) realistically correlated and non-normally distributed parameters.
In the uncorrelated and correlated normal distribution datasets, no violations were unmet, and the moderation effects applied across the entire data range. However, because in the realistic dataset, few people who engaged in physical activity behavior had low intention scores, the intention-behavior association was non-linear, resulting in inflated linear moderation estimations of habit. This finding was replicated when tested with intention-behavior moderation of perceived behavioral control.
Comparisons of the three scenarios illustrated how an identical correlation coefficient may mask different types of intention-behavior association and moderation effects. These findings highlight the risk of misinterpreting tests of the intention-behavior gap and its moderators for physical activity due to unfounded statistical assumptions. The previously well-documented moderating effects of habit, whereby the impact of intention on behavior weakens as habit strength increases, may be based on statistical byproducts of unmet model assumptions.
PDF
