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Alessandra de Carvalho Bastone, Bruno de Souza Moreira, Renata Alvarenga Vieira, Renata Noce Kirkwood, João Marcos Domingues Dias and Rosângela Corrêa Dias

The purpose of this study was to assess the validity of the Human Activity Profile (HAP) by comparing scores with accelerometer data and by objectively testing its cutoff points. This study included 120 older women (age 60–90 years). Average daily time spent in sedentary, moderate, and hard activity; counts; number of steps; and energy expenditure were measured using an accelerometer. Spearman rank order correlations were used to evaluate the correlation between the HAP scores and accelerometer variables. Significant relationships were detected (rho = .47−.75, p < .001), indicating that the HAP estimates physical activity at a group level well; however, scatterplots showed individual errors. Receiver operating characteristic curves were constructed to determine HAP cutoff points on the basis of physical activity level recommendations, and the cutoff points found were similar to the original HAP cutoff points. The HAP is a useful indicator of physical activity levels in older women.

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Scott J. Strath, Ann M. Swartz and Susan E. Cashin

This study examined objectively determined walking profiles of older adults across a wide range of sociocultural backgrounds. All individuals (N = 415; 131 men age 70.5 ± 9.2 yr and 284 women age 71.5 ± 9.0 yr) underwent physiological measurements, completed pen-and-paper surveys, and wore a pedometer for 7 consecutive days. The total sample accumulated a mean of 3,987 ± 2,680 steps/day. Age (r = –.485, p < .001) and body-mass index (BMI; r = –.353, p < .001) were negatively associated with steps per day. Multivariate analysis revealed that race/ethnic category (F = 3.15, df = 3), gender (F = 2.46, df = 1), BMI (F = 6.23, df = 2), income (F = 9.86, df = 1), education (F = 43.3, df = 1), and retirement status (F = 52.3, df = 1) were significantly associated with steps per day. Collectively these categories accounted for 56% of the variance in walking activity in this independently living, community-dwelling older adult sample. Sedentary characteristics highlighted within, and step-per-day values specific to, older adults have implications for planning targeted physical activity interventions related to walking activity in this population.

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Nick Garrett, Philip J. Schluter and Grant Schofield

Background:

A minority of adults in developed countries engage in sufficient physical activity (PA) to achieve health benefits. This study aims to identify modifiable perceived resources and barriers to PA among New Zealand adults.

Methods:

Secondary analysis of a 2003 nationally representative cross-sectional mail survey, stratified by region, age, and ethnicity, and analyzed utilizing ordinal logistic regression.

Results:

Overall, n = 8038 adults responded to the survey, of whom 49% met updated guidelines for sufficient PA. Perceived accessibility of local resources was associated with PA; however, for some resources there was more awareness among individuals whose predominant activity was not commonly associated with that resource (eg, health clubs and walkers). Perceived local environmental barriers demonstrated negative (steep hills, crime, dogs) and positive (unmaintained footpaths) associations. The absence of perceived environmental barriers was strongly associated with increased activity, suggesting the number of barriers may be a critical factor.

Conclusion:

Complex relationships between perceptions of local environments and activity patterns among adults were found. Although complex, these results demonstrate positive associations between awareness of resources and perceived lack of barriers with being sufficiently physically active for health. Therefore, investments in provision and/or promotion of local resources have the potential to enable active healthy communities.

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P. Margaret Grant, Malcolm H. Granat, Morag K. Thow and William M. Maclaren

This study measured objectively the postural physical activity of 4 groups of older adults (≥65 yr). The participants (N = 70) comprised 3 patient groups—2 from rehabilitation wards (city n = 20, 81.8 ± 6.7 yr; rural n = 10, 79.4 ± 4.7 yr) and the third from a city day hospital (n = 20, 74.7 ± 7.9 yr)—and a healthy group to provide context (n = 20, 73.7 ± 5.5 yr). The participants wore an activity monitor (activPAL) for a week. A restricted maximum-likelihood-estimation analysis of hourly upright time (standing and walking) revealed significant differences between day, hour, and location and the interaction between location and hour (p < .001). Differences in the manner in which groups accumulated upright and sedentary time (sitting and lying) were found, with the ward-based groups sedentary for prolonged periods and upright for short episodes. This information may be used by clinicians to design appropriate rehabilitation interventions and monitor patient progress.

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Alex V. Rowlands, Tatiana Plekhanova, Tom Yates, Evgeny M. Mirkes, Melanie Davies, Kamlesh Khunti and Charlotte L. Edwardson

 al., 2018 ). As they are not population specific, reflect directly measured acceleration, and together describe the volume and intensity of the entire activity profile in two metrics ( Rowlands, Edwardson, et al., 2018 ), they are good candidates for comparing and/or pooling physical activity data. Methods

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Paul R. Ford, Jeffrey Low, Allistair P. McRobert and A. Mark Williams

We examined the developmental activities that contribute to the development of superior anticipation skill among elite cricket batters. The batters viewed 36 video clips involving deliveries from bowlers that were occluded at ball release and were required to predict delivery type. Accuracy scores were used to create two subgroups: high-performing and low-performing anticipators. Questionnaires were used to record the participation history profiles of the groups. In the early stages of development, hours accumulated in cricket and other sports, as well as milestones achieved, did not differentiate groups. Significant between-group differences in activity profiles were found between 13 and 15 years of age, with high-performing anticipators accumulating more hours in structured cricket activity, and specifically in batting, compared with their low-performing counterparts.

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Don W. Morgan

A growing body of literature has confirmed the health benefits of regular physical activity in school-aged youth. However, less systematic attention has been directed toward establishing activity profiles and evaluating the impact of community-based interventions designed to increase physical activity and reduce sedentary behavior in preschool children. In this paper, current findings are reviewed to determine whether preschoolers are achieving sufficient levels of structured and unstructured physical activity and to identify potential correlates of activity and sedentary behavior in the young child. In addition, promotion of physical activity among preschool-aged children in selected community settings is discussed and future research initiatives are highlighted. Given current trends in the overweight and obesity status of children aged two to five years, efforts aimed at increasing physical activity levels and documenting gains in health-related fitness and movement skillfulness in this pediatric population should be accelerated.

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Sheri J. Brock, Danielle Wadsworth, Nikki Hollett and Mary E. Rudisill

The School of Kinesiology at Auburn University is using Movband Technology to support online learning in their physical activity program. Active Auburn is a 2-hr credit course that encourages students (n = 2,000/year) to become physically active through online instruction and tracking physical activity using Movband technology. Movband technology allows for uploading and monitoring group physical activity data. The implementation of this technology has allowed the School of Kinesiology to: (a) promote physical activity on our campus, (b) serve a large number of students, (c) reduce demand on classroom/physical activity space, and (d) promote our research and outreach scholarship as well, by collecting physical activity profiles for students enrolled in the course. Students report they enjoy the course and that they appreciate the “freedom to exercise” when it best fits into their schedule. This course generates considerable revenue to support course instruction and much more for the School of Kinesiology.

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Kathleen B. Watson, Ginny M. Frederick, Carmen D. Harris, Susan A. Carlson and Janet E. Fulton

Background:

There is little information on national estimates for participation in types of aerobic activities among U.S. adults. Current estimates are important to develop appropriate and effective interventions to promote physical activity and interpret bias for some activities measured with devices.

Methods:

The percentage of adults participating in specific aerobic activities was estimated overall and by demographic subgroups. The 2011 Behavioral Risk Factor Surveillance System respondents (N = 446,216) reported up to 2 aerobic activities they spent the most time doing during the past month.

Results:

Overall, walking (47%) was the most common activity reported and was reported more by women (54%) than men (41%). Participation in most activities declined with increasing age (P < .006). There were a number of differences in participation between race/ethnic subgroups. Participation increased with more education (P for trend < 0.006) for all activities. Participation in most activities was different (P < .002) across BMI subgroups.

Conclusions:

Walking is the most common activity, overall and among most subgroups. Other activity profiles differ by demographic subgroup. Physical activity promotion strategies that focus on identifying and addressing personal and environmental barriers and understanding demographic subgroup differences could lead to more tailored interventions and public health programs.

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Matthew Pearce, David H. Saunders, Peter Allison and Anthony P. Turner

daily MVPA, producing a unique physical activity profile. The use of accelerometry does not record swimming and underestimates the contributions of movement during activities such as cycling, upper-body exercise, and load bearing, and this must be considered when viewing these results. The GPS receiver