Validity of the Global Physical Activity Questionnaire (GPAQ) in assessing levels and change in moderate-vigorous physical activity and sedentary behaviour

Background

Feasible, cost-effective instruments are required for the surveillance of moderate-to-vigorous physical activity (MVPA) and sedentary behaviour (SB) and to assess the effects of interventions. However, the evidence base for the validity and reliability of the World Health Organisation-endorsed Global Physical Activity Questionnaire (GPAQ) is limited. We aimed to assess the validity of the GPAQ, compared to accelerometer data in measuring and assessing change in MVPA and SB.

Participants (n = 101) were selected randomly from an on-going research study, stratified by level of physical activity (low, moderate or highly active, based on the GPAQ) and sex. Participants wore an accelerometer (Actigraph GT3X) for seven days and completed a GPAQ on Day 7. This protocol was repeated for a random sub-sample at a second time point, 3–6 months later. Analysis involved Wilcoxon-signed rank tests for differences in measures, Bland-Altman analysis for the agreement between measures for median MVPA and SB mins/day, and Spearman’s rho coefficient for criterion validity and extent of change.

95 participants completed baseline measurements (44 females, 51 males; mean age 44 years, (SD 14); measurements of change were calculated for 41 (21 females, 20 males; mean age 46 years, (SD 14). There was moderate agreement between GPAQ and accelerometer for MVPA mins/day (r = 0.48) and poor agreement for SB (r = 0.19). The absolute mean difference (self-report minus accelerometer) for MVPA was −0.8 mins/day and 348.7 mins/day for SB; and negative bias was found to exist, with those people who were more physically active over-reporting their level of MVPA: those who were more sedentary were less likely to under-report their level of SB. Results for agreement in change over time showed moderate correlation (r = 0.52, p = 0.12) for MVPA and poor correlation for SB (r = −0.024, p = 0.916).

Levels of agreement with objective measurements indicate the GPAQ is a valid measure of MVPA and change in MVPA but is a less valid measure of current levels and change in SB. Thus, GPAQ appears to be an appropriate measure for assessing the effectiveness of interventions to promote MVPA.

Questionnaire Survey of Physical activITy in Genral Practictioners (PHIT GP Study)

Objectives: To assess the levels of physical activity and other health related behaviours of General Practitioners (GPs) and compare their reported levels of physical activity with those of the general population. Study Design: Cross sectional postal questionnaire survey. Methods: A questionnaire, which did not allow identification of individual respondents, was posted to all 1074 (GPs) in Northern Ireland. It included the validated International Physical Activity Questionnaire (IPAQ) and questions relating to smoking and alcohol consumption. A national survey of a representative sample of the general population of similar age (29-67 years; n = 3010) provided comparative data. Results: 735 GPs responded (68.4%). IPAQ data indicated that fewer GPs (43.4%) were “physically inactive” compared to the general population (56.2%) (p

The effectiveness of physical activity interventions in socio-economically disadvantaged communities: A systematic review.

OBJECTIVE: Interventions to increase levels of physical activity (PA) in socio-economically disadvantaged communities are needed but little is known about their effectiveness. This review examines the effectiveness of interventions designed to increase PA in these communities and the theoretical frameworks and components used. METHODS: Five databases were searched for papers published in English between January 2000 and December 2010 that reported outcomes of PA interventions in socio-economically disadvantaged communities. Studies targeting individuals with pre-existing disease and not reporting a measure of free-living PA were excluded. Two reviewers independently extracted data and evaluated quality of evidence against pre-defined criteria. RESULTS: Of 478 publications identified, 27 were included. We found that group-based interventions were effective for adults but not for children; evidence for the effectiveness of interventions targeting individuals was insufficient; limited evidence suggested that community-wide interventions produced small changes in PA. Interventions underpinned by any theoretical framework, compared to none, were more likely to be effective. Several effective interventions included education, PA and social support components. CONCLUSION: Compared to other approaches, multi-component adult group-based interventions with theoretical frameworks are most effective in increasing PA in socio-economically disadvantaged communities. More robust evaluations of interventions targeting individuals in these 'hard-to-reach' communities are required. Copyright © 2012. Published by Elsevier Inc.

Social support and the likelihood of maintaining and improving levels of physical activity:the Whitehall II Study

Background: Evidence on the association between social support and leisure time physical activity (LTPA) is scarce and mostly based on cross-sectional data with different types of social support collapsed into a single index. The aim of this study was to investigate whether social support from the closest person was associated with LTPA.

Exploring the use of physical activity loyalty cards for behaviour change in public health: randomised controlled trial

Abstract
Background Physical inactivity is a major public health concern, and more innovative approaches are urgently needed to address it. The UK Government supports the use of incentives and so-called nudges to encourage healthy behaviour changes, and has encouraged business sector involvement in public health through the Public Health Responsibility Deal. To test the effectiveness of provision of incentives to encourage adults to increase their physical activity, we
recruited 406 adults from a workplace setting (office-based) to take part in an assessor-blind randomised controlled trial.
Methods
We developed the physical activity loyalty card scheme, which integrates a novel physical activity tracking system with web-based monitoring (palcard). Participants were recruited from two buildings at Northern Ireland’s main
government offices and were randomly allocated (grouped by building [n=2] to reduce contamination) to either incentive group (n=199) or no incentive group (n=207). We included participants aged 16–65 years, based at the worksite 4 days or more per week and for 6 h or more per day, and able to complete 15 min of moderate-paced walking (self-report). Exclusion criteria included having received specific advice by a general practitioner not to exercise. A statistician not involved in administration of the trial prepared a computer-generated random allocation sequence. Random assignments were placed in individually numbered, sealed envelopes by the statistician to ensure concealment of allocation. Only the assessor was masked to assignment. Sensors were placed along footpaths and the gym in the workplace. Participants scanned their loyalty card at the sensor when undertaking physical activity (eg, walking), which logged activity. Participants in the incentive group monitored their physical activity, collected points, and received rewards (retail vouchers) for minutes of physical activity completed over the 12-week intervention. Rewards were vouchers sponsored by local retailers. Participants in the no incentive group used their loyalty card to self-monitor their physical activity but were not able to earn points or receive rewards. The primary outcome was change in minutes of moderate to vigorous physical activity with the Global Physical Activity Questionnaire, measured at baseline, week 12, and 6 months. Activity was objectively measured with the tracking system over the 12-week intervention. Mann Whitney U tests were done to assess change between groups.
Findings
The mean age of participants was 43·32 years (SD 9·37), and 272 (67%) were women. We obtained follow-up data from 353 (87%) participants at week 12 and 341 (84%) at 6 months. At week 12, participants in the incentive group increased moderate to vigorous physical activity by a median of 60 min per week (IQR –10 to 120) compared with 30 min per week (–60 to 90) in the no incentive group (p=0·05). At 6 months, participants in the incentive group had
increased their moderate to vigorous physical activity by 30 min per week (–60 to 100) from baseline compared with 0 min per week (–115 to 1110) in the no incentive group (p=0·099). We noted no significant differences between groups
for use of loyalty card (p=0·18). Participants in the incentive group recorded a mean of 60·22 min (95% CI 50·90–69·55) of physical activity per week with their loyalty card on week 1 and 23·56 min (17·06–30·06) at week 12, which was similar to that for those in the no incentive group (59·74 min, 51·24–68·23, at week 1; 20·25 min, 14·45–26·06, at week 12; p=0·94 for differences between groups at week 1; p=0·45 for differences between groups at week 12).
Interpretation:
Financial incentives showed a short-term behaviour change in physical activity. This innovative study contributes to the necessary evidence base, and has important implications for physical activity promotion and business engagement in health. The optimum incentive-based approach needs to be established. Results should be interpreted with some caution as the analyses of secondary outcomes were not adjusted for multiple comparisons.

Association of physical activity with future mental health in older, mid-life and younger women

Background: Mental ill-health, particularly depression and anxiety, is a leading and increasing cause of disability worldwide, especially for women.

Methods: We examined the prospective association between physical activity and symptoms of mental ill-health in younger, mid-life and older working women. Participants were 26 913 women from the ongoing cohort Finnish Public Sector Study with complete data at two phases, excluding those who screened positive for mental ill-health at baseline. Mental health was assessed using the 12-item General Health Questionnaire. Self-reported physical activity was expressed in metabolic equivalent task (MET) hours per week. Logistic regression models were used to analyse associations between physical activity levels and subsequent mental health.

Results: There was an inverse dose–response relationship between physical activity and future symptoms of mental ill-health. This association is consistent with a protective effect of physical activity and remained after adjustments for socio-demographic, work-related and lifestyle factors, health and body mass index. Furthermore, those mid-life and older women who reported increased physical activity by more than 2 MET hours per week demonstrated a reduced risk of later mental ill-health in comparison with those who did not increase physical activity. This protective effect of increased physical activity did not hold for younger women.

Conclusions: This study adds to the evidence for the protective effect of physical activity for later mental health in women. It also suggests that increasing physical activity levels may be beneficial in terms of mental health among mid-life and older women. The alleviation of menopausal symptoms may partly explain age effects but further research is required.

Individual Characteristics Associated with Mismatches between Self-Reported and Accelerometer-Measured Physical Activity

Background: Accurate assessment tools are required for the surveillance of physical activity (PA) levels and the assessment of the effect of interventions. In addition, increasing awareness of PA is often used as the first step in pragmatic behavioural interventions, as discrepancies between the amount of activity an individual perceives they do and the amount actually undertaken may act as a barrier to change. Previous research has demonstrated differences in the amount of activity individuals report doing, compared to their level of physical activity when measured with an accelerometer. Understanding the characteristics of those whose PA level is ranked differently when measured with either self-report or accelerometry is important as it may inform the choice of instrument for future research. The aim of this project was to determine which individual characteristics are associated with differences between self-reported and accelerometer measured physical activity.

Methods: Participant data from the 2009 wave of the Commuting and Health in Cambridge study were used. Quartiles of self-reported and accelerometer measured PA were derived by ranking each measure from lowest to highest. These quartiles were compared to determine whether individuals’ physical activity was ranked higher by either method. Multinomial logistic regression models were used to investigate the individual characteristics associated with different categories of mismatch.

Results: Data from 486 participants (70% female) were included in the analysis. In adjusted analyses, the physical activity of overweight or obese individuals was significantly more likely to be ranked higher by self-report than by accelerometer than that of normal-weight individuals (OR = 2.07, 95%CI = 1.28–3.34), particularly among women (OR = 3.97, 95%CI = 2.11–7.47).

Conclusions: There was a greater likelihood of mismatch between self-reported and accelerometer measured physical activity levels in overweight or obese adults. Future studies in overweight or obese adults should consider employing both methods of measurement.

Learning from Outdoor Webcams: Surveillance of Physical Activity Across Environments

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.

The validation of Fibit ZipTM physical activity monitor as a measure of free-living physical activity

BACKGROUND: The new generation of activity monitors allow users to upload their data to the internet and review progress. The aim of this study is to validate the Fitbit Zip as a measure of free-living physical activity.

FINDINGS: Participants wore a Fitbit Zip, ActiGraph GT3X accelerometer and a Yamax CW700 pedometer for seven days. Participants were asked their opinion on the utility of the Fitbit Zip. Validity was assessed by comparing the output using Spearman's rank correlation coefficients, Wilcoxon signed rank tests and Bland-Altman plots. 59.5% (25/47) of the cohort were female. There was a high correlation in steps/day between the Fitbit Zip and the two reference devices (r = 0.91, p < 0.001). No statistically significant difference between the Fitbit and Yamax steps/day was observed (Median (IQR) 7477 (3597) vs 6774 (3851); p = 0.11). The Fitbit measured significantly more steps/day than the Actigraph (7477 (3597) vs 6774 (3851); p < 0.001). Bland-Altman plots revealed no systematic differences between the devices.

CONCLUSIONS: Given the high level of correlation and no apparent systematic biases in the Bland Altman plots, the use of Fitbit Zip as a measure of physical activity. However the Fitbit Zip recorded a significantly higher number of steps per day than the Actigraph.