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
Li Yi, Shirlene D. Wang, Daniel Chu, Aditya Ponnada, Stephen S. Intille, and Genevieve F. Dunton
Background: Recent studies have shown potentially detrimental effects of the COVID-19 pandemic on physical activity (PA) in emerging adults (ages 18–29 y). However, studies that examined the effects of COVID-19 on PA location choices and maintenance for this age group remain limited. The current study investigated changes in PA location choices across 13 months during the pandemic and their associations with PA maintenance in this population. Methods: Emerging adults (N = 197) living in the United States completed weekly survey on personal smartphones (May 2020–June 2021) regarding PA location choices and maintenance. Mixed-effects models examined the main effects of PA location choice and its interaction with weeks into the pandemic on participants’ PA maintenance. Results: On a given week, participants performing PA on roads/sidewalks or at parks/open spaces were 1½ and 2 times as likely to maintain PA levels, respectively. Moreover, after September 2021, weeks when individuals performed PA on roads/sidewalks had a protective effect on PA maintenance. Conclusions: Performing PA on roads/sidewalks and at parks/open spaces was associated with PA maintenance during the COVID-19 pandemic. PA promotion and intervention efforts for emerging adults during large-scale disruptions to daily life should focus on providing programmed activities in open spaces to maintain their PA levels.
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.
Yiyan Li, Jiajia Liu, Minghui Quan, Jie Zhuang, Zhen-Bo Cao, Zheng Zhu, Yongming Li, Stephen D. Herrmann, and Barbara E. Ainsworth
Background: The 2011 Compendium of Physical Activities provides metabolic equivalent (MET) values for household and eldercare activities (physical activities [PAs]). METs are from published studies, estimated if values are not published, or combined with other PAs with different METs in a single entry. Some PAs are missing from the Compendium. This study measures the energy costs for 15 household and eldercare PAs with estimated METs, PAs in combined entries, and new PAs. Methods: Participants were 30 adults (14 males and 16 females), ages 22–58 years (33.7 [11.2] y). PAs were measured in a laboratory for 8 minutes with a 4-minute rest between PAs. A portable indirect calorimeter measured oxygen uptake (in milliliters per kilogram per minute). Standard METs were computed as activity VO2/3.5 mL·kg−1·min−1. Results: Cooking, meal tasks, laundry, light cleaning, and watering plants ranged from 1.8 to 2.3 METs. Sweeping, walking, and carrying groceries and boxes on the ground and stairs ranged from 3.0 to 5.5 METs. Eldercare ranged from 1.8 to 3.0 METs. Measured METs differed from estimated values by ±0.3 to 2.2 METs. Most measured METs were lower than estimated METs. Conclusion: Updating estimated METs with measured values and separating PAs from combined entries increases the accuracy of household and eldercare PAs presented in the Compendium.
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.