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Erin K. Howie, Joanne A. McVeigh, and Leon M. Straker

Background:

There are several practical issues when considering the use of hip-worn or wrist-worn accelerometers. This study compared compliance and outcomes between hip- and wrist-worn accelerometers worn simultaneously by children during an active video games intervention.

Methods:

As part of a larger randomized crossover trial, participants (n = 73, age 10 to 12 years) wore 2 Actical accelerometers simultaneously during waking hours for 7 days, on the hip and wrist. Measurements were repeated at 4 timepoints: 1) at baseline, 2) during traditional video games condition, 3) during active video games condition, 4) during no video games condition. Compliance and intervention effects were compared between hip and wrist.

Results:

There were no statistically significant differences at any timepoint in percentage compliance between hip (77% to 87%) and wrist (79% to 89%). Wrist-measured counts (difference of 64.3 counts per minute, 95% CI 4.4–124.3) and moderate-to-vigorous physical activity (MVPA) (12 min/day, 95% CI 0.3–23.7) were higher during the no video games condition compared with the traditional video games condition. There were no differences in hip-measured counts per minute or MVPA between conditions or sedentary time for hip or wrist.

Conclusions:

There were no differences in compliance between hip- and wrist-worn accelerometers during an intervention trial, however, intervention findings differed between hip and wrist.

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Erin Kaye Howie, Timothy Olds, Joanne A. McVeigh, Rebecca A. Abbott, and Leon Straker

Background:

The detailed patterns of physical activity and sedentary behaviors of overweight and obese adolescents are unknown, but may be important for health outcomes and targeted intervention design.

Methods:

Participants completed Curtin University’s Activity, Food and Attitudes Program (CAFAP), an 8-week intervention with 12 months of maintenance intervention. Physical activity and sedentary time were assessed at 6 time periods with accelerometers and were analyzed by 1) time and type of day, 2) intensity bout patterns using exposure variation analysis, and 3) individual case analysis.

Results:

Participants (n = 56) spent a lower percentage of time at baseline in light activity during school days compared with weekend days (24.4% vs 29.0%, P = .004). The majority of time was in long uninterrupted sedentary bouts of greater than 30 minutes (26.7% of total time, 36.8% of sedentary time at baseline). Moderate activity was accumulated in short bouts of less than 5 minutes (3.1% of total time, 76.0% moderate time). Changes varied by individuals.

Conclusions:

Exposure variation analysis revealed specific changes in activity patterns in overweight and obese adolescents who participated in a lifestyle intervention. A better understanding of these patterns can help to design interventions that meaningfully affect specific behaviors, with unique health consequences.

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Leon Straker, Amity Campbell, Svend Erik Mathiassen, Rebecca Anne Abbott, Sharon Parry, and Paul Davey

Background:

Capturing the complex time pattern of physical activity (PA) and sedentary behavior (SB) using accelerometry remains a challenge. Research from occupational health suggests exposure variation analysis (EVA) could provide a meaningful tool. This paper (1) explains the application of EVA to accelerometer data, (2) demonstrates how EVA thresholds and derivatives could be chosen and used to examine adherence to PA and SB guidelines, and (3) explores the validity of EVA outputs.

Methods:

EVA outputs are compared with accelerometer data from 4 individuals (Study 1a and1b) and 3 occupational groups (Study 2): seated workstation office workers (n = 8), standing workstation office workers (n = 8), and teachers (n = 8).

Results:

Line graphs and related EVA graphs highlight the use of EVA derivatives for examining compliance with guidelines. EVA derivatives of occupational groups confirm no difference in bouts of activity but clear differences as expected in extended bouts of SB and brief bursts of activity, thus providing evidence of construct validity.

Conclusions:

EVA offers a unique and comprehensive generic method that is able, for the first time, to capture the time pattern (both frequency and intensity) of PA and SB, which can be tailored for both occupational and public health research.

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Ashley A. Fenner, Erin K. Howie, Leon M. Straker, and Martin S. Hagger

The current study explored whether a multidisciplinary family-based intervention underpinned by self-determination theory could enhance perceptions of parent need support, autonomous motivation, and quality of life in overweight and obese adolescents. Using a staggered-entry waitlist-period control design, adolescents (n = 56) were assessed at baseline and preintervention (within-participant control), immediately following intervention, and at 3, 6, and 12 month follow-ups. Parents were trained in need-supportive behaviors within the broader context of an 8-week multidisciplinary intervention attended jointly with adolescents. Following intervention, significant improvements were demonstrated in adolescent perceptions of parent need support, autonomous motivation, and quality of life, and changes were maintained at the 1-year follow-up. Mediation analyses revealed changes in perceptions of parent need support predicted changes in quality of life indirectly via changes in autonomous motivation. Findings suggest overweight and obese adolescents are likely to benefit from multidisciplinary family-based interventions that aim to train parents in need-supportive behaviors.

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Nicholas D. Gilson, Caitlin Hall, Andreas Holtermann, Allard J. van der Beek, Maaike A. Huysmans, Svend Erik Mathiassen, and Leon Straker

Background: This systematic review assessed evidence on the accelerometer-measured sedentary and physical activity (PA) behavior of nonoffice workers in “blue-collar” industries. Methods: The databases CINAHL, Embase, MEDLINE, PubMed, and Scopus were searched up to April 6, 2018. Eligibility criteria were accelerometer-measured sedentary, sitting, and/or PA behaviors in “blue-collar” workers (≥10 participants; agricultural, construction, cleaning, manufacturing, mining, postal, or transport industries). Data on participants’ characteristics, study protocols, and measured behaviors during work and/or nonwork time were extracted. Methodologic quality was assessed using a 12-item checklist. Results: Twenty studies (representing 11 data sets), all from developed world economies, met inclusion criteria. The mean quality score for selected studies was 9.5 (SD 0.8) out of a maximum of 12. Data were analyzed using a range of analytical techniques (eg, accelerometer counts or pattern recognition algorithms). “Blue-collar” workers were more sedentary and less active during nonwork compared with work time (eg, sitting 5.7 vs 3.2 h/d; moderate to vigorous PA 0.5 vs 0.7 h/d). Drivers were the most sedentary (work time 5.1 h/d; nonwork time 8.2 h/d). Conclusions: High levels of sedentary time and insufficient PA to offset risk are health issues for “blue-collar” workers. To better inform interventions, research groups need to adopt common measurement and reporting methodologies.

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Leon Straker, Erin Kaye Howie, Dylan Paul Cliff, Melanie T. Davern, Lina Engelen, Sjaan R. Gomersall, Jenny Ziviani, Natasha K. Schranz, Tim Olds, and Grant Ryan Tomkinson

Background:

Australia has joined a growing number of nations that have evaluated the physical activity and sedentary behavior status of their children. Australia received a “D minus” in the first Active Healthy Kids Australia Physical Activity Report Card.

Methods:

An expert subgroup of the Australian Report Card Research Working Group iteratively reviewed available evidence to answer 3 questions: (a) What are the main sedentary behaviors of children? (b) What are the potential mechanisms for sedentary behavior to impact child health and development? and (c) What are the effects of different types of sedentary behaviors on child health and development?

Results:

Neither sedentary time nor screen time is a homogeneous activity likely to result in homogenous effects. There are several mechanisms by which various sedentary behaviors may positively or negatively affect cardiometabolic, neuromusculoskeletal, and psychosocial health, though the strength of evidence varies. National surveillance systems and mechanistic, longitudinal, and experimental studies are needed for Australia and other nations to improve their grade.

Conclusions:

Despite limitations, available evidence is sufficiently convincing that the total exposure and pattern of exposure to sedentary behaviors are critical to the healthy growth, development, and wellbeing of children. Nations therefore need strategies to address these common behaviors.

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Joanne A. McVeigh, Jennifer Ellis, Caitlin Ross, Kim Tang, Phoebe Wan, Rhiannon E. Halse, Satvinder Singh Dhaliwal, Deborah A. Kerr, and Leon Straker

Activity trackers provide real-time sedentary behavior (SB) and physical activity (PA) data enabling feedback to support behavior change. The validity of activity trackers in an obese population in a free-living environment is largely unknown. This study determined the convergent validity of the Fitbit Charge 2 in measuring SB and PA in overweight adults. The participants (n = 59; M ± SD: age = 48 ± 11 years; body mass index = 34 ± 4 kg/m2) concurrently wore a Charge 2 and ActiGraph GT3X+ accelerometer for 8 days. The same waking wear periods were analyzed, and standard cut points for GT3X+ and proprietary algorithms for the Charge 2, together with a daily step count, were used. Associations between outputs, mean difference (MD) and limits of agreement (LOA), and relative differences were assessed. There was substantial association between devices (intraclass correlation coefficients from .504, 95% confidence interval [.287, .672] for SB, to .925, 95% confidence interval [.877, .955] for step count). In comparison to the GT3X+, the Charge 2 overestimated SB (MD = 37, LOA = −129 to 204 min/day), moderate to vigorous PA (MD = 15, LOA = −49 to 79 min/day), and steps (MD = 1,813, LOA = −1,066 to 4,691 steps/day), and underestimated light PA (MD = −32, LOA = −123 to 58 min/day). The Charge 2 may be a useful tool for self-monitoring of SB and PA in an overweight population, as mostly good agreement was demonstrated with the GT3X+. However, there were mean and relative differences, and the implications of these need to be considered for overweight adult populations who are already at risk of being highly sedentary and insufficiently active.

Open access

Natasha Schranz, Vanessa Glennon, John Evans, Sjaan Gomersall, Louise Hardy, Kylie D. Hesketh, David Lubans, Nicola D. Ridgers, Leon Straker, Michalis Stylianou, Grant R. Tomkinson, Stewart Vella, Jenny Ziviani, and Tim Olds

Open access

Natasha K. Schranz, Timothy Olds, Roslyn Boyd, John Evans, Sjaan R. Gomersall, Louise Hardy, Kylie Hesketh, David R. Lubans, Nicola D. Ridgers, Leon Straker, Stewart Vella, Jenny Ziviani, and Grant R. Tomkinson

Background:

Two years on from the inaugural Active Healthy Kids Australia (AHKA) Physical Activity Report Card, there has been little to no change with the majority of Australian children still insufficiently active.

Methods:

The 2016 AHKA Report Card was developed using the best available national- and state-based physical activity data, which were evaluated by the AHKA Research Working Group using predetermined weighting criteria and benchmarks to assign letter grades to the 12 Report Card indicators.

Results:

In comparison with 2014, Overall Physical Activity Levels was again assigned a D- with Organized Sport and Physical Activity Participation increasing to a B (was B-) and Active Transport declining to a C- (was C). The settings and sources of influence again performed well (A- to a C+), however Government Strategies and Investments saw a decline (C+ to a D). The traits associated with physical activity were also graded poorly (C- to a D).

Conclusions:

Australian youth are insufficiently active and engage in high levels of screen-based sedentary behaviors. While a range of support structures exist, Australia lacks an overarching National Physical Activity Plan that would unify the country and encourage the cultural shift needed to face the inactivity crisis head on.

Full access

Natasha Schranz, Tim Olds, Dylan Cliff, Melanie Davern, Lina Engelen, Billie Giles-Corti, Sjaan Gomersall, Louise Hardy, Kylie Hesketh, Andrew Hills, David Lubans, Doune Macdonald, Rona Macniven, Philip Morgan, Tony Okely, Anne-Maree Parish, Ron Plotnikoff, Trevor Shilton, Leon Straker, Anna Timperio, Stewart Trost, Stewart Vella, Jenny Ziviani, and Grant Tomkinson

Background:

Like many other countries, Australia is facing an inactivity epidemic. The purpose of the Australian 2014 Physical Activity Report Card initiative was to assess the behaviors, settings, and sources of influences and strategies and investments associated with the physical activity levels of Australian children and youth.

Methods:

A Research Working Group (RWG) drawn from experts around Australia collaborated to determine key indicators, assess available datasets, and the metrics which should be used to inform grades for each indicator and factors to consider when weighting the data. The RWG then met to evaluate the synthesized data to assign a grade to each indicator.

Results:

Overall Physical Activity Levels were assigned a grade of D-. Other physical activity behaviors were also graded as less than average (D to D-), while Organized Sport and Physical Activity Participation was assigned a grade of B-. The nation performed better for settings and sources of influence and Government Strategies and Investments (A- to a C). Four incompletes were assigned due to a lack of representative quality data.

Conclusions:

Evidence suggests that physical activity levels of Australian children remain very low, despite moderately supportive social, environmental and regulatory environments. There are clear gaps in the research which need to be filled and consistent data collection methods need to be put into place.