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Leon Straker and Rebecca Abbott

This study compared the cardiovascular responses and energy costs of new and traditional screen based entertainments, as played by twenty 9- to 12-year-old children. Playing traditional electronic games resulted in little change to heart rate or energy expenditure compared with watching a DVD. In contrast, playing an active-input game resulted in a 59% increase in heart rate (p < .001) and a 224% increase in energy expenditure (p < .001) for boys and girls. The average heart rate of 130 bpm and energy expenditure of 0.13 kcal · min−1 · kg−1 achieved during active-input game use equates with moderate intensity activities such as basketball and jogging. Active-input electronic games might provide children with opportunities to engage with technology and be physically active at the same time.

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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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Amity Campbell, Leon Straker, David Whiteside, Peter O’Sullivan, Bruce Elliott, and Machar Reid

Adolescent tennis players are at risk for low back pain (LBP). Recent research has demonstrated a potential mechanical etiology during serves; however, groundstrokes have also been suggested to load this region. Therefore, this study compared lumbar mechanics between players with and without a history of LBP during open and square stance tennis forehands and backhands. Nineteen elite, adolescent, male tennis players participated, 7 with a history of recurrent disabling LBP and 12 without. Differences in three-dimensional lumbar kinetics and kinematics were compared between pain/no pain groups and groundstrokes using linear mixed models (P < .01). There were no significant differences between pain/no pain groups. Relative to a right-handed player, groundstroke comparisons revealed that forehands had greater racquet velocity, greater lumbar right lateral flexion force, as well as upper lumbar extension/rightward rotation and lower lumbar right rotation/lateral flexion movements that were closer to or further beyond end of range than backhands. Backhands required upper lumbar leftward rotation that was beyond end range, while forehands did not. Given that players typically rotated near to their end of range during the backswing of both forehands and backhands, independent of pain, groundstrokes may contribute to the cumulative strain linked to LBP in tennis players.

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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