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Open access

Pedro C. Hallal

Open access

Sebastien Pollet, James Denison-Day, Katherine Bradbury, Rosie Essery, Elisabeth Grey, Max Western, Fiona Mowbray, Kirsten A. Smith, Joanna Slodkowska-Barabasz, Nanette Mutrie, Paul Little, and Lucy Yardley

Purpose: This study explored participant views of a web-based physical activity intervention for older adults and examined how they resonate with the key principles that guided intervention development. Methods: Qualitative interviews were carried out with 52 older adults. A deductive qualitative analysis approach was taken, based around the intervention’s key principles. Results: Participants expressed mostly positive views of the intervention features, broadly confirming the appropriateness of the key principles, which were to: (a) encourage intrinsic motivation for physical activity, (b) minimize the risk of users receiving activity suggestions that are inappropriate or unsafe, (c) offer users choice regarding the activities they engage with and build confidence to undertake more activity, and (d) minimize the cognitive load and need to engage with the intervention website. The findings also identified ways in which content could be improved to further increase acceptability. Conclusion: This study illustrates how using the person-based approach has enabled the identification and implementation of features that older adults appreciate.

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Priya Patel, Seungmin Lee, Nicholas D. Myers, and Mei-Hua Lee

Missing data incidents are common in experimental studies of motor learning and development. Inadequate handling of missing data may lead to serious problems, such as addition of bias, reduction in power, and so on. Thus, this study aimed to conduct a systematic review of the past (2007) and present (2017) practices used for reporting and analyzing missing data in motor learning and development. For this purpose, the authors reviewed 309 articles from five journals focusing on motor learning and development studies and published in 2007 and 2017. The authors carefully reviewed each article using a six-stage review process to assess the reporting and analyzing practices. Reporting of missing data along with reasons for their presence was consistently high across time, which slightly increased in 2017. Researchers predominantly used older methods (mainly deletion) for analysis, which only showed a small increase in the use of newer methods in 2017. While reporting practices were exemplary, missing data analysis calls for serious attention. Improvements in missing data handling may have the merit to address some of the major issues, such as underpowered studies, in motor learning and development.

Open access

Paddy C. Dempsey, Christine M. Friedenreich, Michael F. Leitzmann, Matthew P. Buman, Estelle Lambert, Juana Willumsen, and Fiona Bull

Background: In 2020, the World Health Organization (WHO) released global guidelines on physical activity (PA) and sedentary behavior, for the first time providing population-based recommendations for people living with selected chronic conditions. This article briefly presents the guidelines, related processes and evidence, and, importantly, considers how they may be used to support research, practice, and policy. Methods: A brief overview of the scope, agreed methods, selected chronic conditions (adults living with cancer, hypertension, type 2 diabetes, and human immunodeficiency virus), and appraisal of systematic review evidence on PA/sedentary behavior is provided. Methods were consistent with World Health Organization protocols for developing guidelines. Results: Moderate to high certainty evidence (varying by chronic condition and outcome examined) supported that PA can reduce the risk of disease progression or premature mortality and improve physical function and quality of life in adults living with chronic conditions. Direct evidence on sedentary behavior was lacking; however, evidence extrapolated from adult populations was considered applicable, safe, and likely beneficial (low certainty due to indirectness). Conclusions: Clinical and public health professionals and policy makers should promote the World Health Organization 2020 global guidelines and develop and implement services and programs to increase PA and limit sedentary behavior in adults living with chronic conditions.

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Michelle Ogrodnik, Jillian Halladay, Barbara Fenesi, Jennifer Heisz, and Katholiki Georgiades

Background: Participation in physical activity (PA) is a modifiable factor that contributes to academic success, yet the optimal dose (ie, frequency) and mechanisms underlying the effect require further exploration. Methods: Using data from 19,886 elementary and 11,238 secondary school students across Ontario, Canada, this study examined associations between PA participation frequency, academic achievement, and inattention and hyperactivity. Results: Among elementary students, there was a positive association between PA frequency and academic achievement. Participating in 1 to 2 days per week of PA related to higher academic achievement compared with no days, whereas 7 days per week had the largest associations. For secondary students, a minimum of 3 to 4 days per week was associated with higher academic achievement with no significant benefit of additional days. Indirect effects of inattention and hyperactivity were found for both groups, suggesting that the benefits of PA on academic achievement may be partly explained by reductions in inattention and hyperactivity, especially for secondary school students. Conclusion: Students may experience academic benefits from PA even if they are not meeting the guidelines of exercising daily. These benefits may occur, in part, through reductions in inattention and hyperactivity. Further work is needed to determine the temporality and mechanism of these associations.

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Jessica Gorzelitz, Chloe Farber, Ronald Gangnon, and Lisa Cadmus-Bertram

Background: The evidence base regarding validity of wearable fitness trackers for assessment and/or modification of physical activity behavior is evolving. Accurate assessment of moderate- to vigorous-intensity physical activity (MVPA) is important for measuring adherence to physical activity guidelines in the United States and abroad. Therefore, this systematic review synthesizes the state of the validation literature regarding wearable trackers and MVPA. Methods: A systematic search of the PubMed, Scopus, SPORTDiscus, and Cochrane Library databases was conducted through October 2019 (PROSPERO registration number: CRD42018103808). Studies were eligible if they reported on the validity of MVPA and used devices from Fitbit, Apple, or Garmin released in 2012 or later or available on the market at the time of review. A meta-analysis was conducted on the correlation measures comparing wearables with the ActiGraph. Results: Twenty-two studies met the inclusion criteria; all used a Fitbit device; one included a Garmin model and no Apple-device studies were found. Moderate to high correlations (.7–.9) were found between MVPA from the wearable tracker versus criterion measure (ActiGraph n = 14). Considerable heterogeneity was seen with respect to the specific definition of MVPA for the criterion device, the statistical techniques used to assess validity, and the correlations between wearable trackers and ActiGraph across studies. Conclusions: There is a need for standardization of validation methods and reporting outcomes in individual studies to allow for comparability across the evidence base. Despite the different methods utilized within studies, nearly all concluded that wearable trackers are valid for measuring MVPA.

Open access

Fahim A. Salim, Fasih Haider, Dees Postma, Robby van Delden, Dennis Reidsma, Saturnino Luz, and Bert-Jan van Beijnum

Automatic tagging of video recordings of sports matches and training sessions can be helpful to coaches and players and provide access to structured data at a scale that would be unfeasible if one were to rely on manual tagging. Recognition of different actions forms an essential part of sports video tagging. In this paper, the authors employ machine learning techniques to automatically recognize specific types of volleyball actions (i.e., underhand serve, overhead pass, serve, forearm pass, one hand pass, smash, and block which are manually annotated) during matches and training sessions (uncontrolled, in the wild data) based on motion data captured by inertial measurement unit sensors strapped on the wrists of eight female volleyball players. Analysis of the results suggests that all sensors in the inertial measurement unit (i.e., magnetometer, accelerometer, barometer, and gyroscope) contribute unique information in the classification of volleyball actions types. The authors demonstrate that while the accelerometer feature set provides better results than other sensors, overall (i.e., gyroscope, magnetometer, and barometer) feature fusion of the accelerometer, magnetometer, and gyroscope provides the bests results (unweighted average recall = 67.87%, unweighted average precision = 68.68%, and κ = .727), well above the chance level of 14.28%. Interestingly, it is also demonstrated that the dominant hand (unweighted average recall = 61.45%, unweighted average precision = 65.41%, and κ = .652) provides better results than the nondominant (unweighted average recall = 45.56%, unweighted average precision = 55.45, and κ = .553) hand. Apart from machine learning models, this paper also discusses a modular architecture for a system to automatically supplement video recording by detecting events of interests in volleyball matches and training sessions and to provide tailored and interactive multimodal feedback by utilizing an HTML5/JavaScript application. A proof of concept prototype developed based on this architecture is also described.