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Jill Dawson, Melvyn Hillsdon, Irene Boller and Charlie Foster

The authors investigated whether low levels of walking among older adults in the UK were associated with demographic and health characteristics, as well as perceived environmental attributes. Survey data were obtained from self-administered standard questionnaires given to 680 people age 50+ (mean age 64.4 yr) attending nationally led walking schemes. Items concerned with demographic characteristics and perceived barriers to neighborhood walking were analyzed using multiple logistic regression. Citing more than 1 environmental barrier to walking, versus not, was associated with significantly reduced levels of (leisure) walking (MET/hr) in the preceding week (Z = –2.35, p = .019), but physical activity levels overall did not differ significantly (Z = –0.71, p = .48). Citing a health-related barrier to walking significantly adversely affected overall physical activity levels (Z = –2.72, p = .006). The authors concluded that, among older people who favor walking, health problems might more seriously affect overall physical activity levels than perceived environmental barriers.

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Joshua Twaites, Richard Everson, Joss Langford and Melvyn Hillsdon

Purpose: Physical activity classifiers are typically trained on data obtained from sensors at a set orientation. Changes in this orientation (such as being on a different wrist) result in performance degradation. This work investigates a method to obtain sensor location and orientation invariance for classification of wrist-mounted accelerometry via a technique known as domain adaption. Methods: Data was gathered from 16 participants who wore accelerometers on both wrists. Physical activity classification models were created using data from each wrist and then used to predict activities when using data from the opposing wrist. Using subspace alignment domain adaption, this procedure was then repeated to align the training and testing data before the classification stage. Results: Prediction of activity when using data where the wearer’s wrist was incorrectly specified resulted in a significant (p = .01) decrease in performance of 12%. When using domain adaption this drop in performance became negligible (M difference < 1%, p = .73). Conclusion: Domain adaption is a valuable method for achieving accurate physical activity classification independent of sensor orientation in wrist-worn accelerometry.

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Joshua Twaites, Richard Everson, Joss Langford and Melvyn Hillsdon

Introduction: Data from wrist-worn accelerometers often has an inherent natural segmentation that reflects transitioning from one activity to another. The aim of this study was to develop an activity transition detection method to realize this natural segmentation. Methods: Data was gathered from 16 participants who wore triaxial wrist accelerometers in a lab-based protocol and 47 participants in a free-living protocol. Change point detection was used to create a method for detecting activity transitions. The agreement between observed and predicted transitions was assessed by the Matthews Correlation Coefficient (MCC), Root Mean Squared Error (RMSE), and two additional metrics created for this task; the Ratio of Minimum Mean Distance (RMMD) and the Ratio of Sensitivity (RoS). The effects of varying combinations of acceleration axes were also investigated to determine the most effective set of axes. A novel post-processing technique was developed to mitigate a major limitation identified in current transition detection methods. Results: The developed transition detection method achieved a MCC of 0.763, a RMSE of 3.17, a RoS of 2.40, and a RMMD of 3.21, outperforming existing techniques. The post-processing technique developed improved the performance of all methods when identifying transitions. It was found that using solely the y-axis (vertical acceleration) allowed for optimal performance. Conclusion: Change point detection is a valid method for identifying transitions in activity using wrist-worn accelerometer data. The new post processing technique developed improves the performance of transition detection methods.

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Lisa Price, Katrina Wyatt, Jenny Lloyd, Charles Abraham, Siobhan Creanor, Sarah Dean and Melvyn Hillsdon

Purpose: The purpose of this study was to assess children’s compliance with wrist-worn accelerometry during a randomized controlled trial and to examine whether compliance differed by allocated condition or gender. Methods: A total of 886 children within the Healthy Lifestyles Programme trial were randomly allocated to wear a GENEActiv accelerometer at baseline and 18-month follow-up. Compliance with minimum wear-time criteria (≥10 h for 3 weekdays and 1 weekend day) was obtained for both time points. Chi-square tests were used to determine associations between compliance, group allocation, and gender. Results: At baseline, 851 children had usable data, 830 (97.5%) met the minimum wear-time criteria, and 631 (74.1%) had data for 7 days at 24 hours per day. At follow-up, 789 children had usable data, 745 (94.4%) met the minimum wear-time criteria, and 528 (67%) had complete data. Compliance did not differ by gender (baseline: χ2 = 1.66, P = .2; follow-up: χ2 = 0.76, P = .4) or by group at follow-up (χ2 = 2.35, P = .13). Conclusion: The use of wrist-worn accelerometers and robust trial procedures resulted in high compliance at 2 time points regardless of group allocation, demonstrating the feasibility of using precise physical activity monitors to measure intervention effectiveness.

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Lisa Price, Katrina Wyatt, Jenny Lloyd, Charles Abraham, Siobhan Creanor, Sarah Dean and Melvyn Hillsdon

Background: Physical activity guidelines state that children should achieve at least 60 minutes of moderate to vigorous physical activity (MVPA) on each day of the week. Accurate assessment of adherence to these guidelines should, ideally, include measurement over 7 days. When less than 7 days of data are available, researchers often report the average minutes of MVPA per day as a proxy for 7-day measurement. The aim of this study was to compare prevalence estimates generated by average MVPA per day versus MVPA assessed over 7 days. Methods: Data were collected as part of the Healthy Lifestyles Programme. One class from each school was randomized to wear a GENEActiv accelerometer for 8 days. The percentages of children achieving an average of ≥60 minutes of MVPA per day and those achieving ≥60 minutes of MVPA on each of 7 days were calculated. Results: A total of 807 children provided 7 days of data. When the average MVPA per day was calculated, 30.6% (n = 247) of children accumulated ≥60 minutes of MVPA per day. Only 3.2% (n = 26) accumulated ≥60 minutes of MVPA on every day of the week. Conclusion: Previous studies utilizing average MVPA per day are likely to have overestimated the percentage of children meeting recommendations.

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Charlie Foster, Melvyn Hillsdon, Andy Jones, Chris Grundy, Paul Wilkinson, Martin White, Bart Sheehan, Nick Wareham and Margaret Thorogood

Background:

Physical activity has been positively associated with a range of objectively measured environmental variables. We explored the relationship of walking and other categories of physical activity with objectively measured activity specific environmental variables in a UK population.

Methods:

We used a geographical information system (GIS) and gender specific multivariate models to relate 13,927 participants’ reported levels of physical activity with a range of measures of the environment.

Results:

Access to green space and area levels of crime were not associated with walking for recreation. Distance to facilities had either no or only a small effect on the uptake of different activities. Odds ratios of cycling for leisure dropped as local traffic density increased for both genders. Compared with the lowest quartile for traffic density the likelihood of reporting any cycling for leisure was OR 0.42, (95% CI 0.32 to 0.52, P < .001) for women and OR 0.41, (95% CI 0.33 to 0.50, P < .001) for men in the highest quartile.

Conclusions:

We were unable to reproduce results observed in previous studies. Future research should use large representative population samples from multiple areas to maximize environmental variability and if feasible use both objective and subjective measures of physical activity and the environment.