scholarly journals Moderating Infl uences of Baseline Activity Levels in School Physical Activity Programming for Children: The Ready for Recess Project

2015 ◽  
pp. 155-172 ◽  
Keyword(s):  
Physical Activity ◽  
Activity Levels ◽  
Baseline Activity ◽  
Physical Activity Programming

scholarly journals The Intricacies of Children’s Physical Activity

2015 ◽  
Vol 47 (1) ◽  
pp. 269-275 ◽  
Author(s):  
Timothy A Brusseau
Keyword(s):  
Physical Activity ◽  
Physical Education ◽  
Activity Patterns ◽  
Activity Levels ◽  
Physical Activity Patterns ◽  
Change Behavior ◽  
Before And After ◽  
School Recess ◽  
Physical Activity Programming ◽  
Children’S Physical Activity

AbstractUnderstanding the physical activity patterns of youth is an essential step in preparing programming and interventions needed to change behavior. To date, little is known about the intricacies of youth physical activity across various physical activity segments (i.e. in school, out of school, recess, classroom physical activity, physical education, weekends, etc.). Therefore, the purpose of the study was to examine the physical activity patterns of elementary school children across various segments and during two seasons. A total of 287 fourth and fifth graders from the Southwest US wore the Yamax Digiwalker SW-200 pedometer for 7 consecutive days during the Fall and Spring seasons. Children were prompted to record their step counts when arriving and leaving school, before and after physical education and recess, as well as on the weekends. Means and standard deviations were calculated and ANOVAs and t tests were utilized to examine difference by sex, season, and segment. Youth were more active outside of school and on weekdays (p<0.05). Boys were generally more active than girls and all youth were more active during the milder Spring season. There is a clear need for Comprehensive School Physical Activity Programming and weekend physical activity opportunities. Furthermore, greater emphasis is needed on PE and across other activity segments for girls to increase their physical activity levels.

scholarly journals Getting Moving Together; a field experiment on workplace physical activity

2021 ◽  
Author(s):  
Michael Sanders ◽  
Karen Tindall ◽  
Alex Gyani ◽  
Susannah Hume ◽  
Min-Taec Kim ◽  
...  
Keyword(s):  
Physical Activity ◽  
Social Comparison ◽  
Comparison Group ◽  
Controlled Trial ◽  
Wearable Devices ◽  
Control Group ◽  
Activity Levels ◽  
Additional Information ◽  
Baseline Activity ◽  
The Social

Importance: Wearable devices are widely used in an effort to increase physical activity and consequently to improve health. The evidence for this is patchy, and it does not appear that wearables alone are sufficient to achieve this end.Objective: To determine whether social comparisons in a workplace setting can increase the effectiveness of wearables at promoting physical activity.Design: A four week randomized controlled trial conducted in November 2015 with employees of a large firm. Participants were randomised to one of two treatment conditions (control vs social comparison) at team level, and teams are formed into ‘leagues’ based on their activity levels before the study. Impact is measured through wearable devices issued to all participants throughout the study duration.Setting: Offices of a large Australian employer.Participants: 646 employees of an Australian employer, issued with wearable activity trackers prior to the beginning of the study. Intervention(s) (for clinical trials) or Exposure(s) (for observational studies). Participants used a wearable device to track steps. Participants had been wearing these for at least four weeks at the outset of the trial, establishing a baseline level of activity. Teams (n=646, k=49), were randomly assigned to either control (k=24), or a social comparison (k=25) treatment. All participants took part in a step-count competition between their team and others at their employer, in which their team’s ranking within a mini-league of five teams, as well as their own activity was communicated each week. The control group had access to the usual features of the wearable, while the social comparison group received additional information about the performance of the other teams in their league, including how far behind and ahead their nearest rival teams were.Main Outcome(s) and Measure(s): Number of steps taken per day on average, measured by the wearable devices issued to all participants. Results: A total of 646 participants were included in the study. Compared to the control, participants in the social comparison group took significantly more steps per day during the trial period (an additional 620 steps, 8.2%, p&lt;0.001). These effects are largest in both relative and absolute terms for people whose prior steps were in the bottom quartile of steps (an additional 948 steps, 40%, p&lt;0.001), while the effect on people with highest levels of activity was a precisely estimated null (an additional 6 steps, 0.01%, p=0.98).Conclusions and Relevance: Social comparison increased the effectiveness of wearables at improving physical activity, particularly for those with the lowest baseline activity.

Planning Mediates Between Self-Efficacy and Physical Activity Among Motivated Young Adults

2016 ◽  
Vol 13 (1) ◽  
pp. 87-93 ◽  
Author(s):  
Guangyu Zhou ◽  
Dongmei Wang ◽  
Nina Knoll ◽  
Ralf Schwarzer
Keyword(s):  
Physical Activity ◽  
Young Adults ◽  
Stages Of Change ◽  
Self Efficacy ◽  
Activity Levels ◽  
Observation Study ◽  
Physically Active ◽  
Moderator Effect ◽  
Baseline Activity ◽  
Physical Activity Levels

Background:Often, motivation to be physically active is a necessary precondition of action but still does not suffice to initiate the target behavior. Instead, motivation needs to be translated into action by a self-regulatory process. Self-efficacy and planning are considered to be useful constructs that help to facilitate such translations.Objective:The aim is to examine the roles of motivation, planning, and self-efficacy as well as the mechanisms that operate in the change of physical activity levels.Methods:In a longitudinal observation study with 249 young adults, self-efficacy, planning, motivation, and physical activity were assessed at 2 points in time, 3 months apart.Results:Planning served as a mediator between self-efficacy and physical activity, controlling for baseline activity. In addition to this indirect effect, a moderator effect was found between self-efficacy and stages of change on planning. The mediation operated only in motivated, but not in unmotivated students.Conclusions:A mediation from self-efficacy via planning to physical activity seems to be likely only when people are motivated to become more active.

scholarly journals Association between behavioral phenotypes and sustained use of smartphones and wearable devices to remotely monitor physical activity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sarah J. Fendrich ◽  
Mohan Balachandran ◽  
Mitesh S. Patel
Keyword(s):  
Physical Activity ◽  
Risk Taking ◽  
Latent Class ◽  
Wearable Devices ◽  
Individual Characteristics ◽  
Wearable Device ◽  
Activity Levels ◽  
Behavioral Phenotypes ◽  
Baseline Activity ◽  
Sustained Use

AbstractSmartphones and wearable devices can be used to remotely monitor health behaviors, but little is known about how individual characteristics influence sustained use of these devices. Leveraging data on baseline activity levels and demographic, behavioral, and psychosocial traits, we used latent class analysis to identify behavioral phenotypes among participants randomized to track physical activity using a smartphone or wearable device for 6 months following hospital discharge. Four phenotypes were identified: (1) more agreeable and conscientious; (2) more active, social, and motivated; (3) more risk-taking and less supported; and (4) less active, social, and risk-taking. We found that duration and consistency of device use differed by phenotype for wearables, but not smartphones. Additionally, “at-risk” phenotypes 3 and 4 were more likely to discontinue use of a wearable device than a smartphone, while activity monitoring in phenotypes 1 and 2 did not differ by device type. These findings could help to better target remote-monitoring interventions for hospitalized patients.

scholarly journals Physical Activity Behaviour Before, During and After COVID-19 Restrictions: A Longitudinal Smartphone Tracking Study of 5395 UK Adults. (Preprint)

2020 ◽  
Author(s):  
Hannah McCarthy ◽  
HWW Potts ◽  
A Fisher
Keyword(s):  
Physical Activity ◽  
Age Groups ◽  
Physical Activity Behaviour ◽  
Activity Levels ◽  
Activity Data ◽  
Significant Drop ◽  
Viral Spread ◽  
First Case ◽  
Baseline Activity ◽  
The Impact

BACKGROUND The COVID-19 Pandemic led to the implementation of worldwide restrictive measures to reduce social contact and viral spread. These measures have been reported to have a negative effect on physical activity (PA). Studies of PA during the pandemic have primarily used self -reported data. Only one academic study using tracked data this did not report on demographics. OBJECTIVE The study aimed to explore patterns of tracked activity before, during and immediately after Lockdown in the UK and examine differences in sociodemographic characteristics and prior levels of PA. METHODS Tracked longitudinal weekly minutes of physical activity were captured using the BetterPoints smartphone app between January and June 2020. Data was plotted by week, demographics and activity levels at baseline. Non-parametric tests of difference were used to assess mean and median weekly minutes of activity at significant points, before, during and as lockdown was eased. Changes over time by demographics (age, gender, Index of Multiple Deprivation, baseline activity levels) were examined using generalised estimating equations (GEEs). RESULTS There were 5395 users with mean age of 41 (SD 12), 61% were female. At baseline, 26% of users were inactive, 23% fairly active and 51% active. There was a relatively even spread across deprivation deciles (31% in the least deprived deciles and 23% in the most.). We found significant changes in PA from the week before the first case of COVID-19 was announced (baseline), to the week that social distancing restrictions were relaxed (Friedman test: χ2(2) = 2331, p < 0.001.) By the first full week of lockdown, the median change in PA 57 minutes less than baseline. This represents a 37% reduction in weekly minutes of PA. Overall, 63% of people decreased their level of activity between baseline and the first week of COVID-19 restrictions. Younger people showed more PA before lockdown but the least PA after lockdown. In contrast, the over-65s appeared to remain more active throughout and increased their activity levels as soon as lockdown was eased. Levels of physical activity levels among those classed as active at baseline showed a dramatic drop compared with those considered to be fairly active or inactive. Socioeconomic group and gender did not appear to be associated with changes in PA. CONCLUSIONS Our tracked physical activity data suggests a significant drop in PA during the UK’s COVID-19 lockdown Significant differences by age group and prior PA levels suggests that Government response to COVID-19 needs to be sensitive to these individual differences and react accordingly. Specifically, considering the impact on younger age groups and those that were fairly active not meeting recommended PA levels prior to lockdown.

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