Relationship between coaches’ health promotion activities, sports experience and health among adults

Hoye, Aurélie Van; Johnson, Stacey; Geidne, Susanna; Vuillemin, Anne

doi:10.1177/0017896920919777

Cited by 10 publications

(5 citation statements)

References 39 publications

(75 reference statements)

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“…Enjoyment in PA will be measured by using the Intrinsic Motivation Inventory (IMI) Questionnaire. This scale, not validated in French but used in different studies 23 among the sport population, determines the enjoyment in the practice of PA over a given period. Using four statements (eg, ‘I liked the activities I did’), responses are rated on a scale of 1–5, from 1, ‘strongly disagree’, to 5, ‘strongly agree’.…”

Section: Methodsmentioning

confidence: 99%

“…In 2017 in France, 10.7 million patients (17% of the insured population) 1 were diagnosed with at least 1 of the 30 chronic diseases defined as 'diseases with an expensive and long-term treatment, which can be paid by the health insurance'. 23 Moreover, the prevalence of patients with chronic disease has increased by 4% between 2006 and 2011 and 5% between 2011 and 2017. 4 Beyond affecting individual health, chronic diseases also have high financial cost, estimated at €86 billion in 2015 in France.…”

Section: Introductionmentioning

confidence: 99%

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PERSISTE: a mixed methods protocol to identify barriers and levers to a sustainable physical activity practice among patients with chronic disease after physical activity resumption programs

Riquier

Vuillemin

Hoye

2022

BMJ Open Sport Exerc Med

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Physical activity (PA) has evidence-based benefits for physical, social and mental health, but investigation of how PA interventions for patients with chronic disease affects their PA practice up to 1 year after programmes are rare. Moreover, few studies document how the context and intervention mechanisms of PA programmes affect sustainable PA practice and its determinants. The present protocol describes a mixed methods study comparing the effectiveness and conditions for the effectiveness of two PA resumption programmes (a hospital-led and a community-based programme). Using a comparative longitudinal study, 60 patients (3-month duration) will be followed for 1 year though four data collections: before (T0) and at the end (T1) of the intervention, 6 months (T2) and 1 year (T3) after the start of the programme. The primary outcome will be PA practice in min/week and categorised as light, moderate or vigorous (using International Physical Activity Questionnaire (IPAQ)), and secondary outcomes will include sedentary time in min/week (IPAQ), quality of life evaluated though the physical and mental composite scores (‘Medical Outcome Study Short Form 12’), and enjoyment using four statements rated from 1 to 5, a high score being synonymous of enjoyment (Intrinsic Motivation Inventory). Qualitative data on further determinants of PA practice and intervention mechanisms will be collected. The expected results will offer the opportunity to understand how the intervention context contributes to a more effective, sustainable PA practice. Trial registration number: NCT04954209.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PERSISTE: a mixed methods protocol to identify barriers and levers to a sustainable physical activity practice among patients with chronic disease after physical activity resumption programs

Riquier

Vuillemin

Hoye

2022

BMJ Open Sport Exerc Med

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“…Several epidemiological studies have demonstrated that physical activity levels are closely associated with an individual's health status. Lack of physical activity often accompanies illnesses like cardiovascular disease, diabetes, and obesity [2]. It is also true that low levels of physical activity are strongly correlated to the incidence of diseases like cardiovascular disease [3].…”

Section: Introductionmentioning

confidence: 99%

Research on recognition of physical activity types based on a single triaxial acceleration sensor

Li,

Yang

2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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Single triaxial acceleration sensor was used to acquire acceleration information generated by physical activities, and classify physical activities with widely varying degrees of intensity into three types: resting (including sitting, lying down and standing), walking (including running, walking up and down stairs), and running and jumping (including running and jumping in place), so as to provide an objective basis for assessing the level of physical activity. Methods: Forty-four participants wearing accelerometers performed eight physical activities, namely meditation, lying down, standing, running, going upstairs, going downstairs, sprinting, and jumping in place. The accelerometer data were collected, cleaned, and preprocessed using a band-pass filter. The signal amplitude region, mean, standard deviation, correlation coefficient of triaxial, frequency domain entropy and other characteristic quantities are then extracted from the filtered signal. Lastly, a radial basis kernel function support vector machine recognition model is used for physical activity recognition and validated using a leave-one-subject-cross-validation method. The results show that The highest recognition rate of 98.50% was achieved using the frequency domain composite feature subset, whereas the time domain feature subset achieved a slightly lower recognition rate of 98.3%. The time taken was calculated to be 223ms and 146ms. The window size was found to have the highest recognition time of 5.12s. In conclusion: A single triaxial acceleration sensor can collect substantial data about human activities for identification purposes. Creating a physical activity type recognition model that uses the radial basis function kernel and support vector machine (SVM) can allow for real-time and accurate identification of physical activity types in natural environments.

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“…With the development of science and technology and economic takeoff, people are constantly improving their standard of living, but also face the increasing pressure of life; thus, the country is more concerned about the health of the people [1][2][3][4]. Exercise has been shown to reduce the risk of death by more than 25%, even when it is less than 5 MET-h per week, according to studies.…”

Section: Introductionmentioning

confidence: 99%

Research on a parametric model-based algorithm for sports intensity assessment

Fang

2024

Applied Mathematics and Nonlinear Sciences

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Nowadays, people’s demand for healthy exercise is growing rapidly, appropriate exercise can effectively improve physical function, and good exercise effect cannot be separated from the assessment of exercise intensity. In this paper, through the construction of a knowledge graph and parameterized inference model, the elastic network algorithm is introduced to assess the exercise intensity, and the three indexes of myocardial force, heart rate and blood supply are chosen as references through the test experiments to compare the exercise intensity level and the prediction results of the parameter model, to validate the validity and accuracy of parameter model assessment method, and then to compare the assessment effect of BP neural network and the parameter model of the present paper on the exercise prescription. Finally, it was concluded that the critical values of exercise intensity classifications obtained from the experimental tests of the three indexes of myocardial force, heart rate and cardiac blood supply were 4.002, 25.742 and −0.301, respectively, and the assessment results obtained from the parametric model were 3.722, 23.793 and −0.276, which were close to each other. A comparison of different levels of exercise intensity testing and assessment results can be obtained. The accuracy of the three test groups is 96%, 93% and 89%, indicating that the accuracy of the parametric model assessment is higher. The best values of the BP neural network and the parametric model in the paper in terms of the accuracy of the assessment of exercise prescription are 95% and 96%, respectively, in terms of the combined parametric model convergence better error loss.

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Relationship between coaches’ health promotion activities, sports experience and health among adults

Cited by 10 publications

References 39 publications

PERSISTE: a mixed methods protocol to identify barriers and levers to a sustainable physical activity practice among patients with chronic disease after physical activity resumption programs

PERSISTE: a mixed methods protocol to identify barriers and levers to a sustainable physical activity practice among patients with chronic disease after physical activity resumption programs

Research on recognition of physical activity types based on a single triaxial acceleration sensor

Research on a parametric model-based algorithm for sports intensity assessment

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