Victor Elijah Adeyemo

Victor Elijah Adeyemo, Anna Palczewska, Ben Jones et al.

The application of pattern mining algorithms to extract movement patterns from sports big data can improve training specificity by facilitating a more granular evaluation of movement. As there are various pattern mining algorithms, this study aimed to validate which algorithm discovers the best set of movement patterns for player movement profiling in professional rugby league and the similarity in extracted movement patterns between the algorithms. Three pattern mining algorithms (l-length Closed Contiguous [LCCspm], Longest Common Subsequence [LCS] and AprioriClose) were used to profile elite rugby football league hookers (n = 22 players) and wingers (n = 28 players) match-games movements across 319 matches. Machine learning classification algorithms were used to identify which algorithm gives the best set of movement patterns to separate playing positions with Jaccard similarity score identifying the extent of similarity between algorithms' movement patterns. LCCspm and LCS movement patterns shared a 0.19 Jaccard similarity score. AprioriClose movement patterns shared no significant similarity with LCCspm and LCS patterns. The closed contiguous movement patterns profiled by LCCspm best-separated players into playing positions. Multi-layered Perceptron algorithm achieved the highest accuracy of 91.02% and precision, recall and F1 scores of 0.91 respectively. Therefore, we recommend the extraction of closed contiguous (consecutive) over non-consecutive movement patterns for separating groups of players.

Suncica Hadzidedic, Jingyun Wang, Victor Elijah Adeyemo et al.

Obesity is a global health challenge. According to the World Health Organization (WHO), between 1990 and 2022, adult obesity more than doubled. Weight management interventions (WMIs) support individuals in achieving and maintaining a healthy weight through dietary guidance, physical activity promotion and behavioural counselling. However, traditional WMIs often have limited accessibility. Digital WMIs or DWMIs are delivered via websites or smartphone applications and provide scalable and cost-effective alternatives. However, user needs for digital services and their prevalence in the existing commercial solutions remain underexplored. Hence, our study systematically identified 26 commercial DWMIs to identify their features, services, and data collection practices. Additionally, we performed a user needs analysis by recruiting 207 individuals involved in a real-life WMI. Our findings indicated that DWMIs integrated self-monitoring, goal setting, and behaviour change strategies, yet lack social support, virtual reality applications and adaptive personalisation. WMI clients prefer smartphone Apps and fitness trackers for tracking weight management progress and have varying levels of comfort in using digital resources. The presented results serve as recommendations for future directions in the design and implementation of services for DWMIs.