Background Physical activity (PA) is important for children with a chronic disease. Serious games may be useful to promote PA levels among these children. Objective The primary purpose of this systematic review was to evaluate the effectiveness of serious games on PA levels in children with a chronic disease. Methods PubMed, EMBASE, PsycINFO, ERIC, Cochrane Library, and CINAHL were systematically searched for articles published from January 1990 to May 2018. Both randomized controlled trials and controlled clinical trials were included to examine the effects of serious games on PA levels in children with a chronic disease. Two investigators independently assessed the intervention, methods, and methodological quality in all articles using the Cochrane risk of bias tool. Both qualitative and quantitative analyses were performed. Results This systematic review included 9 randomized controlled trials (886 participants). In 2 of the studies, significant between-group differences in PA levels in favor of the intervention group were reported. The meta-analysis on PA levels showed a nonsignificant effect on moderate to vigorous PA (measured in minutes per day) between the intervention and control groups (standardized mean difference 0.30, 95% CI –0.15 to 0.75, P=.19). The analysis of body composition resulted in significantly greater reductions in BMI in the intervention group (standardized mean difference –0.24, 95% CI –0.45 to 0.04, P=.02). Conclusions This review does not support the hypothesis that serious games improve PA levels in children with a chronic disease. The meta-analysis on body composition showed positive intervention effects with significantly greater reductions in BMI in favor of the intervention group. A high percentage of nonuse was identified in the study of serious games, and little attention was paid to behavior change theories and specific theoretical approaches to enhance PA in serious games. Small sample sizes, large variability between intervention designs, and limited details about the interventions were the main limitations. Future research should determine which strategies enhance the effectiveness of serious games, possibly by incorporating behavior change techniques.
For children with asthma, physical activity (PA) can decrease the impact of their asthma. Thus far, effective PA promoting interventions for this group are lacking. To develop an intervention, the current study aimed to identify perspectives on physical activity of children with asthma, their parents, and healthcare providers. Children with asthma between 8 and 12 years old (n = 25), their parents (n = 17), and healthcare providers (n = 21) participated in a concept mapping study. Participants generated ideas that would help children with asthma to become more physically active. They sorted all ideas and rated their importance on influencing PA. Clusters were created with multidimensional scaling and cluster analysis. The researchers labelled the clusters as either environmental or personal factors using the Physical Activity for people with a Disability model. In total, 26 unique clusters were generated, of which 17 were labelled as environmental factors and 9 as personal factors. Important factors that promote physical activity in children with asthma according to all participating groups are asthma control, stimulating environments and relatives, and adapted facilities suiting the child’s needs. These factors, supported by the future users, enable developing an intervention that helps healthcare providers to promote PA in children with asthma.
Background Approximately 5%-10% of elementary school children show delayed development of fine motor skills. To address these problems, detection is required. Current assessment tools are time-consuming, require a trained supervisor, and are not motivating for children. Sensor-augmented toys and machine learning have been presented as possible solutions to address this problem. Objective This study examines whether sensor-augmented toys can be used to assess children’s fine motor skills. The objectives were to (1) predict the outcome of the fine motor skill part of the Movement Assessment Battery for Children Second Edition (fine MABC-2) and (2) study the influence of the classification model, game, type of data, and level of difficulty of the game on the prediction. Methods Children in elementary school (n=95, age 7.8 [SD 0.7] years) performed the fine MABC-2 and played 2 games with a sensor-augmented toy called “Futuro Cube.” The game “roadrunner” focused on speed while the game “maze” focused on precision. Each game had several levels of difficulty. While playing, both sensor and game data were collected. Four supervised machine learning classifiers were trained with these data to predict the fine MABC-2 outcome: k-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), and support vector machine (SVM). First, we compared the performances of the games and classifiers. Subsequently, we compared the levels of difficulty and types of data for the classifier and game that performed best on accuracy and F1 score. For all statistical tests, we used α=.05. Results The highest achieved mean accuracy (0.76) was achieved with the DT classifier that was trained on both sensor and game data obtained from playing the easiest and the hardest level of the roadrunner game. Significant differences in performance were found in the accuracy scores between data obtained from the roadrunner and maze games (DT, P =.03; KNN, P =.01; LR, P =.02; SVM, P =.04). No significant differences in performance were found in the accuracy scores between the best performing classifier and the other 3 classifiers for both the roadrunner game (DT vs KNN, P =.42; DT vs LR, P =.35; DT vs SVM, P =.08) and the maze game (DT vs KNN, P =.15; DT vs LR, P =.62; DT vs SVM, P =.26). The accuracy of only the best performing level of difficulty (combination of the easiest and hardest level) achieved with the DT classifier trained with sensor and game data obtained from the roadrunner game was significantly better than the combination of the easiest and middle level ( P =.046). Conclusions The results of our study show that s...
This paper describes experiments with a game device that was used for early detection of delays in motor skill development in primary school children. Children play a game by bi-manual manipulation of the device which continuously collects accelerometer data and game state data. Features of the data are used to discriminate between normal children and children with delays. This study focused on the feature selection. Three features were compared: mean squared jerk (time domain); power spectral entropy (fourier domain) and cosine similarity measure (quality of game play). The discriminatory power of the features was tested in an experiment where 28 children played games of different levels of difficulty. The results show that jerk and cosine similarity have reasonable discriminatory power to detect fine-grained motor skill development delays especially when taking the game level into account. Duration of a game level needs to be at least 30 seconds in order to achieve good classification results.
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