90 resultados para Physical activity measurement
Resumo:
Purpose:Physical activity is recommended for optimal prevention of cardiovascular disease(CVD) and participation in sport is associated with improved well-being. However, people with long-standing illness/disability are less likely to participate in sport than others. Evidence of factors associated with their participation is limited and the best approach to encourage participation is unknown. This study aimed to identify sport participation levels and their correlates, among adults with long standing illness/disability in Northern Ireland, where CVD prevalence is high. Method:Using routinely collected data in annual surveys of population samples from 2007 to 2011, descriptive statistics were derived. Chi-squared tests were used to compare characteristics of those with a long-term illness/disability and those without long-term health problems. Uni-variate binary regression analysis for the whole sample and those with a long-standing illness/disability, using sport participation as the dependent variable, was performed and variables with a p-value of 0.1 or less were taken into a multi-variate analysis. Results:The sample included 13,683 adults; 3550(26%) reported having long-term illness/disability. Fewer of those with, than without, long-term illness/disability reported sport participation in the previous year (868/3550(24.5%) v 5615/10133(55.6%)). Multi-variate analysis showed that, for those with long-standing illness/disability, being single and less socio-economically deprived correlated positively with sport participation. For both those with long-standing illness/disability and the full sample, sport participation correlated positively with being male, aged <56 years, access to a household car/van, sports club membership, health ‘fairly good’ or ‘good’ in the previous year, doing paid/unpaid work, and living in an urban location. For the full sample but not those with long-standing illness/disability, sport participation correlated positively with being a non-smoker, higher educational status and personal internet access. Of note, personal internet access was less for those with, than without, long-term illness/disability (41% v 70%). Conclusions:Efforts to promote physical activity in sport for those with long-standing illness/disability should target older people, married females, those who live rurally, and those who are socio-economically deprived and report their health as ‘not good’. Implementation of initiatives should not rely on the internet, to which these people may not have ready access, to help support their sport participation and physical activity in optimal CVD prevention.
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PURPOSE: Treatment of prostate cancer with androgen deprivation therapy (ADT) is associated with an increased fat mass, decreased lean mass, increased fatigue and a reduction in quality of life (QoL). The aim of this study was to evaluate the efficacy of a 6-month dietary and physical activity intervention for prostate cancer patients receiving ADT, to help minimise these side effects.
METHODS: Patients (n = 94) were recruited to this study if they were planned to receive ADT for prostate cancer for at least 6 months. Men randomised to the intervention arm received a dietary and exercise intervention, commensurate with UK healthy eating and physical activity recommendations. The primary outcome of interest was body composition; secondary outcomes included fatigue, QoL, functional capacity, stress and dietary change.
RESULTS: The intervention group had a significant (p < 0.001) reduction in weight, body mass index and percentage fat mass compared to the control group at 6 months; the between-group differences were -3.3 kg (95 % confidence interval (95 % CI) -4.5, -2.1), -1.1 kg/m(2) (95 % CI -1.5, -0.7) and -2.1 % (95 % CI -2.8, -1.4), respectively, after adjustment for baseline values. The intervention resulted in improvements in functional capacity (p < 0.001) and dietary intakes but did not significantly impact fatigue, QoL or stress scores at endpoint.
CONCLUSIONS: A 6-month diet and physical activity intervention can minimise the adverse body composition changes associated with ADT.
IMPLICATIONS FOR CANCER SURVIVORS: This study shows that a pragmatic lifestyle intervention is feasible and can have a positive impact on health behaviours and other key outcomes in men with prostate cancer receiving ADT.
Resumo:
It is unknown how interventions aimed at increasing physical activity (PA), other than traditional pulmonary rehabilitation, are structured and whether they are effective in increasing PA in chronic obstructive pulmonary disease (COPD). The primary aim of this review was to outline the typical components of PA interventions in patients with COPD. This review followed the PRISMA guidelines. A structured literature search of relevant electronic databases from inception to April 2014 was undertaken to outline typical components and examine outcome variables of PA interventions in patients with COPD. Over 12000 articles were screened and 20 relevant studies involving 31 PA interventions were included. Data extracted included patient demographics, components of the PA intervention, PA outcome measures and effects of the intervention. Quality was assessed using the PEDro and CASP scales. There were 13 randomised controlled trials and three randomised trials (PEDro score 5-7/10) and four cohort studies (CASP score 5/10). Interventions varied in duration, number of participant/researcher contacts and mode of delivery. The most common behaviour change techniques included information on when and where (n = 26/31) and how (n = 22/31) to perform PA behaviour and self-monitoring (n = 18/31). Significant between-group differences post-intervention in favour of the PA intervention, compared to a control group or to other PA interventions, in one or more PA assessments were found in 7/16 studies. All seven studies used walking as the main type of PA/exercise. In conclusion, although the components of PA interventions were variable, there is some evidence that PA interventions have the potential to increase PA in patients with COPD
Resumo:
BACKGROUND: The transtheoretical model has been successful in promoting health behavior change in general and clinical populations. However, there is little knowledge about the application of the transtheoretical model to explain physical activity behavior in individuals with non-cystic fibrosis bronchiectasis. The aim was to examine patterns of (1) physical activity and (2) mediators of behavior change (self-efficacy, decisional balance, and processes of change) across stages of change in individuals with non-cystic fibrosis bronchiectasis.
METHODS: Fifty-five subjects with non-cystic fibrosis bronchiectasis (mean age ± SD = 63 ± 10 y) had physical activity assessed over 7 d using an accelerometer. Each component of the transtheoretical model was assessed using validated questionnaires. Subjects were divided into groups depending on stage of change: Group 1 (pre-contemplation and contemplation; n = 10), Group 2 (preparation; n = 20), and Group 3 (action and maintenance; n = 25). Statistical analyses included one-way analysis of variance and Tukey-Kramer post hoc tests.
RESULTS: Physical activity variables were significantly (P < .05) higher in Group 3 (action and maintenance) compared with Group 2 (preparation) and Group 1 (pre-contemplation and contemplation). For self-efficacy, there were no significant differences between groups for mean scores (P = .14). Decisional balance cons (barriers to being physically active) were significantly lower in Group 3 versus Group 2 (P = .032). For processes of change, substituting alternatives (substituting inactive options for active options) was significantly higher in Group 3 versus Group 1 (P = .01), and enlisting social support (seeking out social support to increase and maintain physical activity) was significantly lower in Group 3 versus Group 2 (P = .038).
CONCLUSIONS: The pattern of physical activity across stages of change is consistent with the theoretical predictions of the transtheoretical model. Constructs of the transtheoretical model that appear to be important at different stages of change include decisional balance cons, substituting alternatives, and enlisting social support. This study provides support to explore transtheoretical model-based physical activity interventions in individuals with non-cystic fibrosis bronchiectasis.
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Background
Trials depend on good recruitment and retention, but efforts to improve these have had varying success. This may be due to inadequate understanding of what participants would value in return for taking part. An opportunity arose in one trial to investigate the incentives that might help recruit and retain participants to another.
Aim
To determine what adults value as an incentive for involvement in a trial.
Methods
In the PAL Scheme, employees used a ‘loyalty card’ to monitor their physical activity over 12 weeks. The incentive group (n=199) collected points and received rewards for physical activity (1 minute = 1 point, max: 30 pts/day). A comparator group (n=207) self-monitored their physical activity only. Points could be redeemed as retail vouchers. 17 different incentives were available, from 75 pts (£2.50, a sandwich) to 1800 pts (£60, 1 month gym membership).
Results
148 of the 199 intervention participants used their card at least once, earning a mean of 374 pts. 121 earned sufficient to collect a reward and 76 redeemed points for vouchers but only 48 exchanged the vouchers for rewards. The most popular reward was not that of highest monetary value: two cinema tickets (300 pts, £10).
Conclusions
The value that participants place on a reward might be more important than its monetary value. Some might appreciate receiving the voucher, without spending it. In choosing incentives to boost trial participation, it may help to allow people to choose from a variety of rewards, rather than reimbursing in money.
Resumo:
BACKGROUND: Pregnant women are recommended to 1) perform daily moderate-intensity physical activity and 2) limit the amount of sedentary time. Many women do not meet these recommendations. Reduced physical activity and increased sedentary behavior may result from women actively intending to rest during pregnancy. The Theory of Planned Behavior (TPB) has been used to assess attitudes (e.g., positive/negative beliefs), subjective norms (e.g., perception of others' views), perceived behavioral control (PBC) (e.g., self-efficacy), and intention toward exercising while pregnant but has not been applied to aspects pertaining to resting during pregnancy.
METHODS: Pregnant women (n = 345) completed a cross-sectional questionnaire that included two TPB Questionnaires where the target behaviors were 1) being physically active and 2) resting. Bootstrapped paired t tests, ANOVA, and linear hierarchal regression analyses were performed to identify predictors of intentions and whether intentions toward the two behaviors varied at different stages of pregnancy.
RESULTS: As women progressed in their pregnancy, their attitude, PBC, and intention toward being physically active all significantly declined. A positive attitude, subjective norms, and intention toward resting all significantly increased with the advancing trimester. Self-reported health conditions predicted lower intention for physical activity but not for resting.
DISCUSSION: The significantly inverse relationship between physical activity and resting across time suggests that women feel they should focus on one behavior at the expense of the other. Finding that women generally do not perceive these behaviors as mutually compatible has implications in strategizing as to how to encourage women to be active during pregnancy.
Resumo:
BACKGROUND: Physical inactivity has been associated with obesity and related chronic diseases. Understanding built environment (BE) influences on specific domains of physical activity (PA) around homes and workplaces is important for public health interventions to increase population PA.
PURPOSE: To examine the association of home and workplace BE features with PA occurring across specific life domains (work, leisure, and travel).
METHODS: Between 2012 and 2013, telephone interviews were conducted with participants in four Missouri metropolitan areas. Questions included sociodemographic characteristics, home and workplace supports for PA, and dietary behaviors. Data analysis was conducted in 2013; logistic regression was used to examine associations between BE features and domain-specific PA.
RESULTS: In home neighborhoods, seven of 12 BE features (availability of fruits and vegetables, presence of shops and stores, bike facilities, recreation facilities, crime rate, seeing others active, and interesting things) were associated with leisure PA. The global average score of home neighborhood BE features was associated with greater odds of travel PA (AOR=1.99, 95% CI=1.46, 2.72); leisure PA (AOR=1.84, 95% CI=1.44, 2.34); and total PA (AOR=1.41, 95% CI=1.04, 1.92). Associations between workplace neighborhoods' BE features and workplace PA were small but in the expected direction.
CONCLUSIONS: This study offers empirical evidence on BE supports for domain-specific PA. Findings suggest that diverse, attractive, and walkable neighborhoods around workplaces support walking, bicycling, and use of public transit. Public health practitioners, researchers, and worksite leaders could benefit by utilizing worksite domains and measures from this study for future BE assessments.
Resumo:
Introduction: Abundant evidence shows that regular physical activity (PA) is an effective strategy for preventing obesity in people of diverse socioeconomic status (SES) and racial groups. The proportion of PA performed in parks and how this differs by proximate neighborhood SES has not been thoroughly investigated. The present project analyzes online public web data feeds to assess differences in outdoor PA by neighborhood SES in St. Louis, MO, USA.
Methods: First, running and walking routes submitted by users of the website MapMyRun.com were downloaded. The website enables participants to plan, map, record, and share their exercise routes and outdoor activities like runs, walks, and hikes in an online database. Next, the routes were visually illustrated using geographic information systems. Thereafter, using park data and 2010 Missouri census poverty data, the odds of running and walking routes traversing a low-SES neighborhood, and traversing a park in a low-SES neighborhood were examined in comparison to the odds of routes traversing higher-SES neighborhoods and higher-SES parks.
Results: Results show that a majority of running and walking routes occur in or at least traverse through a park. However, this finding does not hold when comparing low-SES neighborhoods to higher-SES neighborhoods in St. Louis. The odds of running in a park in a low-SES neighborhood were 54% lower than running in a park in a higher-SES neighborhood (OR = 0.46, CI = 0.17-1.23). The odds of walking in a park in a low-SES neighborhood were 17% lower than walking in a park in a higher-SES neighborhood (OR = 0.83, CI = 0.26-2.61).
Conclusion: The novel methods of this study include the use of inexpensive, unobtrusive, and publicly available web data feeds to examine PA in parks and differences by neighborhood SES. Emerging technologies like MapMyRun.com present significant advantages to enhance tracking of user-defined PA across large geographic and temporal settings.
Resumo:
Introduction: Fewer than 50% of adults and 40% of youth meet US CDC guidelines for physical activity (PA) with the built environment (BE) a culprit for limited PA. A challenge in evaluating policy and BE change is the forethought to capture a priori PA behaviors and the ability to eliminate bias in post-change environments. The present objective was to analyze existing public data feeds to quantify effectiveness of BE interventions. The Archive of Many Outdoor Scenes (AMOS) has collected 135 million images of outdoor environments from 12,000 webcams since 2006. Many of these environments have experienced BE change. Methods: One example of BE change is the addition of protected bike lanes and a bike share program in Washington, DC.Weselected an AMOS webcam that captured this change. AMOS captures a photograph from eachwebcamevery half hour.AMOScaptured the 120 webcam photographs between 0700 and 1900 during the first work week of June 2009 and the 120 photographs from the same week in 2010. We used the Amazon Mechanical Turk (MTurk) website to crowd-source the image annotation. MTurk workers were paid US$0.01 to mark each pedestrian, cyclist and vehicle in a photograph. Each image was coded 5 unique times (n=1200). The data, counts of transportation mode, was downloaded to SPSS for analysis. Results: The number of cyclists per scene increased four-fold between 2009 and 2010 (F=36.72, p=0.002). There was no significant increase in pedestrians between the two years, however there was a significant increase in number of vehicles per scene (F=16.81, p
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Abstract
Publicly available, outdoor webcams continuously view the world and share images. These cameras include traffic cams, campus cams, ski-resort cams, etc. The Archive of Many Outdoor Scenes (AMOS) is a project aiming to geolocate, annotate, archive, and visualize these cameras and images to serve as a resource for a wide variety of scientific applications. The AMOS dataset has archived over 750 million images of outdoor environments from 27,000 webcams since 2006. Our goal is to utilize the AMOS image dataset and crowdsourcing to develop reliable and valid tools to improve physical activity assessment via online, outdoor webcam capture of global physical activity patterns and urban built environment characteristics.
This project’s grand scale-up of capturing physical activity patterns and built environments is a methodological step forward in advancing a real-time, non-labor intensive assessment using webcams, crowdsourcing, and eventually machine learning. The combined use of webcams capturing outdoor scenes every 30 min and crowdsources providing the labor of annotating the scenes allows for accelerated public health surveillance related to physical activity across numerous built environments. The ultimate goal of this public health and computer vision collaboration is to develop machine learning algorithms that will automatically identify and calculate physical activity patterns.