The aim of this study was to investigate expert practitioners’ approaches to conducting a first sport psychology session with individual clients as there is sparse empirical literature on this topic. Nine expert Certified Mental Performance Consultants completed a semistructured interview where they discussed experiences conducting a first meeting with an athlete. Primary objectives included establishing the relationship, setting guidelines and expectations, understanding the client’s background, identifying presenting concerns, and formulating the treatment plan and building skills. Building rapport was an aspect used to establish the relationship while discussing confidentiality was utilized to set guidelines. Important strategies employed to increase the perceived benefits to services included conveying the consulting approach and philosophy. Lessons learned centered around doing too much and not appreciating individual differences of clients. Findings show expert consultants aim to achieve similar broad objectives in the first session and provide a basis for best practices in this area.
Graig M. Chow, Lindsay M. Garinger, Jaison Freeman, Savanna K. Ward, and Matthew D. Bird
Kimberly A. Clevenger, Kelly A. Mackintosh, Melitta A. McNarry, Karin A. Pfeiffer, Alexander H.K. Montoye, and Jan Christian Brønd
ActiGraph counts are commonly used for characterizing physical activity intensity and energy expenditure and are among the most well-studied accelerometer metrics. Researchers have recently replicated the counts processing method using a mechanical setup, now allowing users to generate counts from raw acceleration data. Purpose: The purpose of this study was to compare ActiGraph-generated counts to open-source counts and assess the impact on free-living physical activity levels derived from cut points, machine learning, and two-regression models. Methods: Children (n = 488, 13.0 ± 1.1 years of age) wore an ActiGraph wGT3X-BT on their right hip for 7 days during waking hours. ActiGraph counts and counts generated from raw acceleration data were compared at the epoch-level and as overall means. Seven methods were used to classify overall and epoch-level activity intensity. Outcomes were compared using weighted kappa, correlations, mean absolute deviation, and two one-sided equivalence testing. Results: All outcomes were statistically equivalent between ActiGraph and open-source counts; weighted kappa was ≥.971 and epoch-level correlations were ≥.992, indicating very high agreement. Bland–Altman plots indicated differences increased with activity intensity, but overall differences between ActiGraph and open-source counts were minimal (e.g., epoch-level mean absolute difference of 23.9 vector magnitude counts per minute). Regardless of classification model, average differences translated to 1.4–2.6 min/day for moderate- to vigorous-intensity physical activity. Conclusion: Open-source counts may be used to enhance comparability of future studies, streamline data analysis, and enable researchers to use existing developed models with alternative accelerometer brands. Future studies should verify the performance of open-source counts for other outcomes, like sleep.
Mary C. Hidde, Kate Lyden, Josiane L. Broussard, Kim L. Henry, Julia L. Sharp, Elizabeth A. Thomas, Corey A. Rynders, and Heather J. Leach
Introduction: Patterns of physical activity (PA) and time in bed (TIB) across the 24-hr cycle have important implications for many health outcomes; therefore, wearable accelerometers are often implemented in behavioral research to measure free-living PA and TIB. Two accelerometers, the activPAL and Actiwatch, are common accelerometers for measuring PA (activPAL) and TIB (Actiwatch), respectively. Both accelerometers have the capacity to measure TIB, but the degree to which these accelerometers agree is not clear. Therefore, this study compared estimates of TIB between activPAL and the Actiwatch accelerometers. Methods: Participants (mean ± SDage = 39.8 ± 7.6 years) with overweight or obesity (N = 83) wore an activPAL and Actiwatch continuously for 7 days, 24 hr per day. TIB was assessed using manufacturer-specific algorithms. Repeated-measures mixed-effect models and Bland–Altman plots were used to compare the activPAL and Actiwatch TIB estimates. Results: Statistical differences between TIB assessed by activPAL versus Actiwatch (p < .001) were observed. There was not a significant interaction between accelerometer and day of wear (p = .87). The difference in TIB between accelerometers ranged from −72.9 ± 15.7 min (Day 7) to −98.6 ± 14.5 min (Day 3), with the Actiwatch consistently estimating longer TIB compared with the activPAL. Conclusion: Data generated by the activPAL and Actiwatch accelerometers resulted in divergent estimates of TIB. Future studies should continue to explore the validity of activity monitoring accelerometers for estimating TIB.
Avril Johnstone, Paul McCrorie, Rita Cordovil, Ingunn Fjørtoft, Susanna Iivonen, Boris Jidovtseff, Frederico Lopes, John J. Reilly, Hilary Thomson, Valerie Wells, and Anne Martin
Background: The purpose was to synthesize evidence on the association between nature-based Early Childhood Education (ECE) and children’s physical activity (PA) and motor competence (MC). Methods: A literature search of 9 databases was concluded in August 2020. Studies were eligible if (1) children were aged 2–7 years old and attending ECE, (2) ECE settings integrated nature, and (3) assessed physical outcomes. Two reviewers independently screened full-text articles and assessed study quality. Synthesis was conducted using effect direction (quantitative), thematic analysis (qualitative), and combined using a results-based convergent synthesis. Results: 1370 full-text articles were screened and 39 (31 quantitative and 8 qualitative) studies were eligible; 20 quantitative studies assessed PA and 6 assessed MC. Findings indicated inconsistent associations between nature-based ECE and increased moderate to vigorous PA, and improved speed/agility and object control skills. There were positive associations between nature-based ECE and reduced sedentary time and improved balance. From the qualitative analysis, nature-based ECE affords higher intensity PA and risky play, which could improve some MC domains. The quality of 28/31 studies was weak. Conclusions: More controlled experimental designs that describe the dose and quality of nature are needed to better inform the effectiveness of nature-based ECE on PA and MC.
Rylee A. Dionigi, Maria Horne, Anne-Marie Hill, and Mary Ann Kluge
Melissa A. Jones, Sara J. Diesel, Bethany Barone Gibbs, and Kara M. Whitaker
Introduction: Current best practice for objective measurement of sedentary behavior and moderate-to-vigorous intensity physical activity (MVPA) requires two separate devices. This study assessed concurrent agreement between the ActiGraph GT3X and the activPAL3 micro for measuring MVPA to determine if activPAL can accurately measure MVPA in addition to its known capacity to measure sedentary behavior. Methods: Forty participants from two studies, including pregnant women (n = 20) and desk workers (n = 20), provided objective measurement of MVPA from waist-worn ActiGraph GT3X and thigh-worn activPAL micro3. MVPA from the GT3X was compared with MVPA from the activPAL using metabolic equivalents of task (MET)- and step-based data across three epochs. Intraclass correlation coefficient and Bland–Altman analyses, overall and by study sample, compared MVPA minutes per day across methods. Results: Mean estimates of activPAL MVPA ranged from 22.7 to 35.2 (MET based) and 19.7 to 25.8 (step based) minutes per day, compared with 31.4 min/day (GT3X). MET-based MVPA had high agreement with GT3X, intraclass correlation coefficient ranging from .831 to .875. Bland–Altman analyses revealed minimal bias between 15- and 30-s MET-based MVPA and GT3X MVPA (−3.77 to 8.63 min/day, p > .10) but with wide limits of agreement (greater than ±27 min). Step-based MVPA had moderate to high agreement (intraclass correlation coefficient: .681–.810), but consistently underestimated GT3X MVPA (bias: 5.62–11.74 min/day, p < .02). For all methods, activPAL appears to better estimate GT3X at lower quantities of MVPA. Results were similar when repeated separately by pregnant women and desk workers. Conclusion: activPAL can measure MVPA in addition to sedentary behavior, providing an option for concurrent, single device monitoring. MET-based MVPA using 30-s activPAL epochs provided the best estimate of GT3X MVPA in pregnant women and desk workers.
Kyle R. Leister, Jessica Garay, and Tiago V. Barreira
Purpose: To determine accuracy of activPAL Technologies’ CREA algorithm to assess bedtime, wake time, and sleep time. Methods: As part of a larger study, 104 participants recorded nightly sleep logs (LOGs) and wore the activPAL accelerometer at the thigh and ActiGraph accelerometer at the hip for 24 hr/day, for seven consecutive days. For sleep LOGs, participants recorded nightly bed and daily wake times. Previously validated ActiGraph, proprietary activPAL, and the Winkler sleep algorithm were used to compute sleep variables. Eighty-seven participants provided 2+ days of valid data. Pearson correlations, paired samples t tests, and equivalency tests were used to examine relationships and differences between methods (activPAL vs. ActiGraph, activPAL vs. LOG, and activPAL vs. Winkler algorithm). Results: For screened data, moderately high to high correlations but significant mean differences were found between activPAL versus ActiGraph for bedtime (t 86 = −6.80, p ≤ .01, r = .84), wake time (t 86 = 4.80, p ≤ .01, r = .93), and sleep time (t 86 = 7.99, p ≤ .01, r = .88). activPAL versus LOG comparisons also yielded significant mean differences and moderately high to high correlations for bedtime (t 86 = −4.68, p ≤ .01, r = .82), wake time (t 86 = 8.14, p ≤ .01, r = .93), and sleep time (t 86 = 8.60, p ≤ .01, r = .72). Equivalency testing revealed that equivalency could not be claimed between activPAL versus LOG or activPAL versus ActiGraph comparisons, though the activPAL and Winkler algorithm were equivalent. Conclusion: The activPAL algorithm overestimated sleep time by detecting earlier bedtimes and later wake times. Because of the significant differences between algorithms, bedtime, wake time, and sleep time are not interchangeable between methods.