This study examined the test-retest reliability and criterion validity of light (LPA), moderate (MPA), vigorous (VPA), and moderate-to-vigorous (MVPA) physical activity survey items in a subset of participants from a large prospective cohort. Participants included 423 women and 290 men aged 31–72 years in the Cancer Prevention Study-3 (CPS-3). Information on physical activity (PA) was collected using two different surveys: one survey which captures all activity performed during a typical 24-hour period in broad categories (24-hour survey), and a more detailed survey focused primarily on leisure-time PA (LTPA survey). One-year reliability was assessed by computing Spearman correlation coefficients between responses from pre- and post-study periods for both surveys. Validity was assessed by comparing survey-estimated PA with accelerometry, seven-day diaries, and a latent variable representing ‘true’ PA estimated through the method of triads. Reliability was considered acceptable for most items on the LTPA survey (range ρ = 0.45–0.92) and the 24-hour survey (range ρ = 0.37–0.61). LPA validity coefficients were higher for the 24-hour survey, while MPA, VPA, and MVPA coefficients were higher for the LTPA survey. Study results suggest that both CPS-3 PA surveys are suitable for ranking or classifying participants in our population according to overall PA category or intensity-specific activity level.
Erika Rees-Punia, Charles E. Matthews, Ellen M. Evans, Sarah K. Keadle, Rebecca L. Anderson, Jennifer L. Gay, Michael D. Schmidt, Susan M. Gapstur and Alpa V. Patel
Emma L. J. Eyre, Jason Tallis, Susie Wilson, Lee Wilde, Liam Akhurst, Rildo Wanderleys and Michael J. Duncan
Background: The ability to objectively assess physical activity and inactivity in free living individuals is important in understanding activity patterns and the dose response relationship with health. Currently, a large number of research tools exist, but little evidence has examined the validity/utility of the Research Tracker 6 (RT6) monitor. Questions remain in regard to the best placements, positions, and cut-points in young adults to determine activity intensity across a range of activities. This study sought to address this gap in young adults. The study aims were 1) to examine criterion validity of RT6 in comparison to breath-by-breath gas analysis; 2) convergent validity of RT6 in comparison to ActiGraph and GENEActiv; 3) development of RT6 tri-axial vector magnitude cut-points to classify physical activity at different intensities (i.e., for sedentary, moderate, and vigorous); 4) to compare the generated cut-points of the RT6 in comparison to other tools. Methods: Following ethics approval and informed consent, 31 young adults (age = 22±3 years: BMI = 23±3 kg/m2) undertook five modes of physical activity/sedentary behaviors while wearing three different accelerometers at hip and wrist locations (ActiGraph GT9X Link, GENEActiv, RT6). Expired gas was sampled during the five activities (MetaMax 3B). Correlational analysis assessed the relationship between accelerometer devices and METs/VO2. Receiver Operating Characteristic Curves analysis were used to calculate area under the curve and define cut-points for physical activity intensities. Results: The RT6 demonstrated criterion and convergent validity (r = 0.662–0.966, P < .05). RT6 generally performed good to excellent across activity intensities and monitor position (sedentary [AUC = 0.862–0.911], moderate [AUC = 0.849–0.830], vigorous [AUC = 0.872–0.877]) for non-dominant and dominant position, respectively. Cut-points were derived across activity intensities for non-dominant- and dominant-worn RT6 devices. Comparison of the RT6 derived cut-points identified appropriate agreement with comparative tools but yields the strongest agreement with the ActiGraph monitor at the hip location during sedentary, light, and moderate activity. Conclusion: The RT6 performed similar to the ActiGraph and GENEActiv and is capable of classifying the intensity of physical activity in young adults. As such this may offer a more useable tool for understanding current physical activity levels and in intervention studies to monitor and track changes without the excessive need for downloading and making complex analysis, especially given the option to view energy expenditure data while wearing it. The RT6 should be placed on the dominant hip when determining activities that are sedentary, moderate, or vigorous intensity.
Katherine L. Downing, Jo Salmon, Anna Timperio, Trina Hinkley, Dylan P. Cliff, Anthony D. Okely and Kylie D. Hesketh
Background: Although there is increasing evidence regarding children’s screen time, little is known about children’s sitting. This study aimed to determine the correlates of screen time and sitting in 6- to 8-year-old children. Methods: In 2011–2012, parents in the Healthy Active Preschool and Primary Years (HAPPY) study (n = 498) reported their child’s week/weekend day recreational screen time and potential correlates. ActivPALs™ measured children’s nonschool sitting. In model 1, linear regression analyses were performed, stratified by sex and week/weekend day and controlling for age, clustered recruitment, and activPAL™ wear time (for sitting analyses). Correlates significantly associated with screen time or sitting (P < .05) were included in model 2. Results: Children (age 7.6 y) spent 99.6 and 119.3 minutes per day on week and weekend days engaging in screen time and sat for 119.3 and 374.6 minutes per day on week and weekend days, respectively. There were no common correlates for the 2 behaviors. Correlates largely differed by sex and week/weekend day. Modifiable correlates of screen time included television in the child’s bedroom and parental logistic support for, encouragement of, and coparticipation in screen time. Modifiable correlates of sitting included encouragement of and coparticipation in physical activity and provision of toys/equipment for physical activity. Conclusions: Interventions may benefit from including a range of strategies to ensure that all identified correlates are targeted.
Susanna Kola-Palmer, Samantha Buckley, Gabrielle Kingston, Jonathan Stephen, Alison Rodriguez, Nicole Sherretts and Kiara Lewis
Player welfare is an important development in supporting elite athletes during their professional careers. Little is known about how player engagement with player welfare provision impact on mental health. Over two consecutive years, professional rugby football league (RFL) players were invited to complete an anonymous online survey assessing psychological stress, athletic identity, and attitudes to player welfare provision. Findings indicate that nearly half of respondents experienced symptoms of anxiety and depression. Multivariate analyses suggest that higher psychological stress and athletic identity and less knowledge and less positive attitudes to RFL mental health support is associated with worse mental health, whereas older age is associated with better mental health. The study has identified some key variables to focus on in developing player care and support management, and also suggest directions for future research guiding player welfare support, especially regarding increasing positive attitudes to mental health supports.
Meera Sreedhara, Karin Valentine Goins, Christine Frisard, Milagros C. Rosal and Stephenie C. Lemon
Background: Local health departments (LHDs) are increasingly involved in Community Health Improvement Plans (CHIPs), a collaborative planning process that represents an opportunity for prioritizing physical activity. We determined the proportion of LHDs reporting active transportation strategies in CHIPs and associations between LHD characteristics and such strategies. Methods: A national probability survey of US LHDs (<500,000 residents; 30.2% response rate) was conducted in 2017 (n = 162). LHDs reported the inclusion of 8 active transportation strategies in a CHIP. We calculated the proportion of LHDs reporting each strategy. Multivariate logistic regression models determined the associations between LHD characteristics and inclusion of strategies in a CHIP. Inverse probability weights were applied for each stratum. Results: 45.6% of US LHDs reported participating in a CHIP with ≥1 active transportation strategy. Proportions for specific strategies ranged from 22.3% (Safe Routes to School) to 4.1% (Transit-Oriented Development). Achieving national accreditation (odds ratio [OR] = 3.67; 95% confidence interval [CI], 1.11–12.05), pursuing accreditation (OR = 3.40; 95% CI, 1.25–9.22), using credible resources (OR = 5.25; 95% CI, 1.77–15.56), and collaborating on a Community Health Assessment (OR = 4.48; 95% CI, 1.23–16.29) were associated with including a strategy in a CHIP after adjusting for covariates. Conclusions: CHIPs are untapped tools, but national accreditation, using credible resources, and Community Health Assessment collaboration may support strategic planning efforts to improve physical activity.
Chelsea Steel, Carolina Bejarano and Jordan A. Carlson
Purpose: To investigate potential time drift between devices when using Global Positioning Systems (GPS) and accelerometers in field-based research. Methods: Six Qstarz BT-Q1000XT GPS trackers, activPAL3 accelerometers, and ActiGraph GT3X+ and GT3X accelerometers were tested over 1–3 waves, each lasting 9–14 days. Once per day an event marker was created on each pair of devices concurrently. The difference in seconds between the time stamps for each event marker were calculated between each pair of GPS and activPAL devices and GPS and ActiGraph devices. Mixed-effects linear regression tested time drift across days and waves and between two rooms/locations (in an inner room vs. on a windowsill in an outer room). Results: The GPS trackers remained within one second of the computer clock across days and waves and between rooms. The activPAL devices drifted an average of 8.38 seconds behind the GPS devices over 14 days (p < .001). The ActiGraph GT3X+ devices drifted an average of 11.67 seconds ahead of the GPS devices over 14 days (p < .001). The ActiGraph GT3X devices drifted an average of 28.83 seconds behind the GPS devices over 9 days (p < .001). Time drift did not differ across waves but did differ between rooms and across devices. Conclusions: Time drift between the GPS and accelerometer models tested was minimal and is unlikely to be problematic when addressing many common research questions. However, studies that require high levels of precision when matching short (e.g., 1-second) time intervals may benefit from consideration of time drift and potential adjustments.
Tracy Nau, Karen Lee, Ben J. Smith, William Bellew, Lindsey Reece, Peter Gelius, Harry Rutter and Adrian Bauman
Background: The value of a systems thinking approach to tackling population physical inactivity is increasingly recognized. This study used conceptual systems thinking to develop a cognitive map for physical activity (PA) influences and intervention points, which informed a standardized approach to the coding and notation of PA-related policies in Australia. Methods: Policies were identified through desktop searches and input from 33 nominated government representatives attending 2 national PA policy workshops. Documents were audited using predefined criteria spanning policy development, strategic approaches to PA, implementation processes, and evaluation. Data were analyzed using descriptive statistics. Results: The audit included 110 policies, mainly led by the health or planning/infrastructure sectors (n = 54, 49%). Most policies purporting to promote PA did so as a cobenefit of another objective that was not focused on PA (n = 63, 57%). An intention to monitor progress was indicated in most (n = 94, 85%); however, fewer than half (n = 52, 47%) contained evaluable goals/actions relevant to PA. Descriptions of resourcing/funding arrangements were generally absent or lacked specific commitment (n = 67, 61%). Conclusions: This study describes current PA-relevant policy in Australia and identifies opportunities for improving coordination, implementation, and evaluation to strengthen a whole-of-system and cross-agency approach to increasing population PA.
Meredith C. Peddie, Matthew Reeves, Millie K. Keown, Tracy L. Perry and C. Murray Skeaff
Background: Regular activity breaks positively impact markers of cardiometabolic health when performed in a laboratory. However, identifying compliance to a free-living regular activity breaks intervention is challenging, particularly if intensity is prescribed. Methods: This study had two parts. In Part A, 20 participants performed activity breaks similar to those shown to impart health benefits while wearing an ActiGraph and activPAL accelerometer, and a heart rate monitor. In Part B, the threshold found to identify these activities was used to identify the activity breaks performed by 78 sedentary, university employees wearing an ActiGraph accelerometer for seven days. Results: A cut-point of 1,000 vector magnitude counts per minute accurately identified activity breaks performed in the laboratory. Applying this cut-point to data collected in free living, sedentary participants identified, on average, seven activity breaks were being performed during work-hours. Conclusions: Using a cut-point of 1,000 vector magnitude counts per minute will identify activity breaks of a similar intensity to those found to elicit acute cardiometabolic benefit. Sedentary university employees may benefit from interventions to increase the number of activity breaks performed across their entire day.
Christina M. Patch, Caterina G. Roman, Terry L. Conway, Ralph B. Taylor, Kavita A. Gavand, Brian E. Saelens, Marc A. Adams, Kelli L. Cain, Jessa K. Engelberg, Lauren Mayes, Scott C. Roesch and James F. Sallis
Background: A common hypothesis is that crime is a major barrier to physical activity, but research does not consistently support this assumption. This article advances research on crime-related safety and physical activity by developing a multilevel conceptual framework and reliable measures applicable across age groups. Methods: Criminologists and physical activity researchers collaborated to develop a conceptual framework. Survey development involved qualitative data collection and resulted in 155 items and 26 scales. Intraclass correlation coefficients (ICCs) were computed to assess test–retest reliability in a subsample of participants (N = 176). Analyses were conducted separately by age groups. Results: Test–retest reliability for most scales (63 of 104 ICCs across 4 age groups) was “excellent” or “good” (ICC ≥ .60) and only 18 ICCs were “poor” (ICC < .40). Reliability varied by age group. Adolescents (aged 12–17 y) had ICCs above the .40 threshold for 21 of 26 scales (81%). Young adults (aged 18–39 y) and middle-aged adults (aged 40–65 y) had ICCs above .40 for 24 (92%) and 23 (88%) scales, respectively. Older adults (aged 66 y and older) had ICCs above .40 for 18 of 26 scales (69%). Conclusions: The conceptual framework and reliable measures can be used to clarify the inconclusive relationships between crime-related safety and physical activity.
Zachary Zenko and Panteleimon Ekkekakis
Studies of automatic associations of sedentary behavior, physical activity, and exercise are proliferating, but the lack of information on the psychometric properties of relevant measures is a potential impediment to progress. The purpose of this review was to critically summarize measurement practices in studies examining automatic associations related to sedentary behavior, physical activity, and exercise. Of 37 studies, 27 (73%) did not include a justification for the measure chosen to assess automatic associations. Additional problems have been noted, including the nonreporting of psychometric information (validity, internal consistency, test–retest reliability) and the lack of standardization of procedures (e.g., number, type of stimuli). The authors emphasize the need to select measures based on conceptual arguments and psychometric evidence and to standardize measurement procedures. To facilitate progress, the review concludes with a proposal for conceptually appropriate validation criteria to be used in future studies.