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Towards best practice in developing motor skills

ObjectiveRepeated practice, or spacing, can improve various types of skill acquisition. Similarly, virtual reality (VR) simulators have demonstrated their effectiveness in fostering surgical skill acquisition and provide a promising, realistic environment for spaced training. To explore how spacing impacts VR simulator-based acquisition of surgical psychomotor skills, we performed a systematic literature review.MethodsWe systematically searched the databases PubMed, PsycINFO, Psychology and Behavioral Sciences Collection, ERIC and CINAHL for studies investigating the influence of spacing on the effectiveness of VR simulator training focused on psychomotor skill acquisition in healthcare professionals. We assessed the quality of all included studies using the Medical Education Research Study Quality Instrument (MERSQI) and the risk of bias using the Cochrane Collaboration’s risk of bias assessment tool. We extracted and aggregated qualitative data regarding spacing interval, psychomotor task performance and several other performance metrics.ResultsThe searches yielded 1662 unique publications. After screening the titles and abstracts, 53 publications were retained for full text screening and 7 met the inclusion criteria. Spaced training resulted in better performance scores and faster skill acquisition when compared to control groups with a single day (massed) training session. Spacing across consecutive days seemed more effective than shorter or longer spacing intervals. However, the included studies were too heterogeneous in terms of spacing interval, obtained performance metrics and psychomotor skills analysed to allow for a meta-analysis to substantiate our outcomes.ConclusionSpacing in VR simulator-based surgical training improved skill acquisition when compared to massed training. The overall number and quality of available studies were only moderate, limiting the validity and generalizability of our findings.

Perspectives on theory and application of implicit and explicit motor learning in neurological rehabilitation

Movement is an essential part of our lives. Throughout our lifetime, we acquire many different motor skills that are necessary to take care of ourselves (e.g., eating, dressing), to work (e.g., typing, using tools, care for others) and to pursue our hobbies (e.g., running, dancing, painting). However, as a consequence of aging, trauma or chronic disease, motor skills may deteriorate or become “lost”. Learning, relearning, and improving motor skills may then be essential to maintain or regain independence. There are many different ways in which the process of learning a motor skill can be shaped in practice. The conceptual basis for this thesis was the broad distinction between implicit and explicit forms of motor learning. Physiotherapists and occupational therapists are specialized to provide therapy that is tailored to facilitate the process of motor learning of patients with a wide range of pathologies. In addition to motor impairments, patients suffering from neurological disorders often also experience problems with cognition and communication. These problems may hinder the process of learning at a didactic level, and make motor learning especially challenging for those with neurological disorders. This thesis focused on the theory and application of motor learning during rehabilitation of patients with neurological disorders. The overall aim of this thesis was to provide therapists in neurological rehabilitation with knowledge and tools to support the justified and tailored use of motor learning in daily clinical practice. The thesis is divided into two parts. The aim of the first part (Chapters 2‐5) was to develop a theoretical basis to apply motor learning in clinical practice, using the implicit‐explicit distinction as a conceptual basis. Results of this first part were used to develop a framework for the application of motor learning within neurological rehabilitation (Chapter 6). Afterwards, in the second part, strategies identified in first part were tested for feasibility and potential effects in people with stroke (Chapters 7 and 8). Chapters 5-8 are non-final versions of an article published in final form in: Chapter 5: Kleynen M, Moser A, Haarsma FA, Beurskens AJ, Braun SM. Physiotherapists use a great variety of motor learning options in neurological rehabilitation, from which they choose through an iterative process: a retrospective think-aloud study. Disabil Rehabil. 2017 Aug;39(17):1729-1737. doi: 10.1080/09638288.2016.1207111. Chapter 6: Kleynen M, Beurskens A, Olijve H, Kamphuis J, Braun S. Application of motor learning in neurorehabilitation: a framework for health-care professionals. Physiother Theory Pract. 2018 Jun 19:1-20. doi: 10.1080/09593985.2018.1483987 Chapter 7: Kleynen M, Wilson MR, Jie LJ, te Lintel Hekkert F, Goodwin VA, Braun SM. Exploring the utility of analogies in motor learning after stroke: a feasibility study. Int J Rehabil Res. 2014 Sep;37(3):277-80. doi: 10.1097/MRR.0000000000000058. Chapter 8: Kleynen M, Jie LJ, Theunissen K, Rasquin SM, Masters RS, Meijer K, Beurskens AJ, Braun SM. The immediate influence of implicit motor learning strategies on spatiotemporal gait parameters in stroke patients: a randomized within-subjects design. Clin Rehabil. 2019 Apr;33(4):619-630. doi: 10.1177/0269215518816359.

Gymmermansoog

Development of fundamental motor skills between 2006 and 2016 in Dutch primary school children

BackgroundFundamental Motor Skills (FMS) are important building blocks for children’s sport-participation and lifelong physical activity. In the last decade, several international studies have reported delays in the development of FMS. To get better insight into the Dutch situation and to provide future directions, this study examined the development of FMS in Dutch primary school children.MethodThe main goal of this study is to compare FMS of 11–12-year-old Dutch children in 2016 with scores of similarly-aged-children in 2006. In addition, gender, age, BMI were taken into account, to see whether changes in motor performance are related to these child characteristics. FMS-test scores on seven motor competence tests (balance, swing, jump, roll, shoot, throwing and catching, and tennis) from 1939 children in 2016 were set side by side with those of 1648 children in 2006. Temporal changes in motor competence scores were analyzed using regression-analysis.ResultsThis cross-sectional study shows better results for the children in 2006 compared to similarly-aged-children in 2016. Lower scores were found on six out of seven tested FMS, with the largest declines on the object control skills tennis and throwing and catching. Only vaulting jump skills remained on the same level. Overall, children with a higher BMI scored lower on all tests, except for throwing and catching via the wall. On the balancing, jumping and tennis test, the gap with children with a lower BMI widened over the last decade. Girls showed a lower competence level on rolling, shooting and throwing and catching compared to boys. During the last decade, their performance on the tennis test decreased more than for boys.ConclusionsResults of this study are alarming as diminishing motor skills are related to lower sport participation and poorer health outcomes. For the future generation, new interventions are needed to help children reach a sufficient proficiency level in FMS, to prevent or overcome the negative effects of lowered motor skills. Targeting FMS components during physical education and outside of school hours may potentially be a valuable strategy in reverting the lowering FMS levels amongst children.

Hand motor skills affect the intake of finger foods in toddlers (12–18 months)

Starting with finger foods is recommended from 7 months in typically developing children. However, information on which finger foods are appropriate and accepted for which age is largely lacking. The purpose of this exploratory study was to determine whether chewing skills, hand motor skills, and other personal and food characteristics influence the intake of finger foods in early life. Thirty children aged 12 to 18 months participated in this study. All children were offered four finger foods in a fixed order on four consecutive days at their home. Two finger foods varied mainly in texture (fresh banana vs. freeze-dried banana) and two other finger foods mainly in shape (stick vs. heart shaped cracker). The intake was measured after ten minutes of exposure to the product. Chewing skills were measured with the Mastication Observation and Evaluation instrument and fine motor skills with selected items of the Bayley-III-NL scales. The results suggest that texture but not shape was found to affect intake, as fresh banana was eaten more than freeze-dried banana and the consumed quantity of the two crackers was not significantly different. Hand motor skills affected the intake of fresh banana only and chewing skills did not affect intake of any of the finger foods. Age and experience with chewable foods were associated with an increased intake of some of the finger foods. In conclusion, the intake of the four finger foods in this study was found to be mainly affected by texture, hand motor skills, age and experience.

Assessing Children's Fine Motor Skills With Sensor-Augmented Toys: Machine Learning Approach

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 sensor-augmented toys can efficiently predict the fine MABC-2 scores for children in elementary school. Selecting the game type (focusing on speed or precision) and data type (sensor or game data) is more important for determining the performance than selecting the machine learning classifier or level of difficulty.