A comparison of children's diet and movement behaviour patterns derived from three unsupervised multivariate methods

Clusters of 24-hour movement behavior and diet and their relationship with health indicators among youth: a systematic review

Movement-related behaviors (physical activity [PA], sedentary behavior [SB], and sleep) and diet interact with each other and play important roles in health indicators in youth. This systematic review aimed to investigate how PA, SB, sleep, and diet cluster in youth by biological sex; and to examine which cluster are associated with health indicators. This study was registered in PROSPERO (number: CRD42018094826). Five electronic databases were assessed. Eligibility criteria allowed studies that included youth (aged 19 years and younger), and only the four behaviors {PA, SB, sleep, and diet (ultra-processed foods [UPF]; fruits and vegetables [FV])} analyzed by applying data-based cluster procedures. From 12,719 articles screened; 23 were included. Of these, four investigated children, and ten identified clusters by biological sex. Sixty-six mixed cluster were identified including, 34 in mixed-sex samples, 10 in boys and 11 in girls. The most frequent clusters in mixed-sex samples were "High SB UPF Low Sleep", "Low PA High SB Satisfactory Sleep", and "High PA". The main difference in profiles according to sex was that girls' clusters were characterized by high sleep duration, whereas boys' clusters by high PA. There were a few associations found between cluster types and health indicators, highlighting that youth assigned to cluster types with low PA exhibited higher adiposity. In conclusion, the youth presented a range of clusters of behaviors, typically exhibiting at least one unhealthy behavior. Similar patterns were observed in both sexes with the biggest difference in time of sleep for girls and PA for boys. These findings underscore the importance of intervention strategies targeting multiple behaviors simultaneously to enhance health risk profiles and indicators in children and adolescents.

Cross-sectional and prospective associations between behavioural patterns and adiposity in school-aged children

OBJECTIVE

Behavioural patterns are important in understanding the synergistic effect of multiple health behaviours on childhood adiposity. Most previous evidence assessing associations between patterns and adiposity were cross-sectional and investigated two or three behaviour domains within patterns. This study aimed to identify behavioural patterns comprising four behaviour domains and investigate associations with adiposity risk in children.

DESIGN

Parent-report and accelerometry data were used to capture daily dietary, physical activity, sedentary behaviour and sleep data. Variables were standardised and included in the latent profile analysis to derive behavioural patterns. Trained researchers measured children's height, weight and waist circumference using standardised protocols. Associations of patterns and adiposity measures were tested using multiple linear regression.

SETTING

Melbourne, Australia.

PARTICIPANTS

A total of 337 children followed up at 6-8 years (T2) and 9-11 years (T3).

RESULTS

Three patterns derived at 6-8 years were broadly identified to be healthy, unhealthy and mixed patterns. Patterns at 9-11 years were dissimilar except for the unhealthy pattern. Individual behaviours characterising the patterns varied over time. No significant cross-sectional or prospective associations were observed with adiposity at both time points; however, children displaying the unhealthy pattern had higher adiposity measures than other patterns.

CONCLUSION

Three non-identical patterns were identified at 6-8 and 9-11 years. The individual behaviours that characterised patterns (dominant behaviours) at both ages are possible drivers of the patterns obtained and could explain the lack of associations with adiposity. Identifying individual behaviour pattern drivers and strategic intervention are key to maintain and prevent the decline of healthy patterns.

Screen media and non-screen media habits among preschool children in Singapore, South Korea, Japan, and Finland: Insights from an unsupervised clustering approach

The main purpose of the research was to describe the daily screen media habits and non-screen media habits like indoor and outdoor play, and sleep of preschool children aged 2 to 6 years from Singapore, South Korea, Japan, and Finland using a content-validated online questionnaire (SMALLQ®) and unsupervised cluster analysis. Unsupervised cluster analysis on 5809 parent-reported weekday and weekend screen and non-screen media habits of preschool children from the four countries resulted in seven emergent clusters. Cluster 2 (n = 1288) or the Early-screen media, screen media-lite and moderate-to-vigorous physical activity-lite family made up 22.2% and Cluster 1 (n = 261) or the High-all-round activity and screen media-late family made up 4.5%, respectively represented the largest and smallest clusters among the seven clusters that were emergent from the pooled dataset. Finland was best represented by Cluster 2 and Japan was best represented by Cluster 3 (High-screen media-for-entertainment and low-engagement family). Parents from Finland and Japan displayed greater homogeneity in terms of the screen media and non-screen media habits of preschool children than the parents from South Korea and Singapore. South Korea was best represented by Clusters 6 (Screen media-physical activity-engagement hands-off family) and 7 (Screen media-lite, screen media-late and high-physical activity family). Singapore was best represented by Clusters 4, 5, 6 and 7, and these clusters ranged from Low all-round activity-high nap time family to Screen media-lite, screen media-late and high-physical activity family. Future research should explore in-depth reasons for the across-country and within-country cluster characteristics of screen media and non-screen media habits among preschool children to allow for more targeted interventions.

Differing associations with childhood outcomes using behavioural patterns derived from three data reduction techniques

BACKGROUND

Behavioural patterns help to understand the influence of multiple health behaviours on childhood outcomes. Behavioural patterns derived using different data reduction techniques can be non-identical and may differentially associate with childhood outcomes. This study aimed to compare associations of behavioural patterns derived from three methods with three childhood outcomes.

METHODS

Data were from the Healthy Active Preschool and Primary Years study when children were 6-8 years old (n = 432). Cluster analysis (CA), latent profile analysis (LPA) and principal component analysis (PCA) were used to derive behavioural patterns from children's diet, physical activity, sedentary behaviour and sleep data. Behavioural data were obtained through parent report and accelerometry. Children's height, weight and waist circumference were measured by trained study staff. Health-related quality of life data were obtained using the Pediatric Quality of Life Inventory and academic performance scores were from a national test. Associations between derived patterns from each method and each of the outcomes were tested using linear regression (adjusted for child age and sex and parent education).

RESULTS

Three patterns were each derived using CA and LPA, and four patterns were derived using PCA. Each method identified a healthy, an unhealthy and a mixed (comprising healthy and unhealthy behaviours together) pattern. Differences in associations were observed between pattern groups from CA and LPA and pattern scores from PCA with the three outcomes.

CONCLUSIONS

Discrepancies in associations across pattern derivation methods suggests that the choice of method can influence subsequent associations with outcomes. This has implications for comparison across studies that have employed different methods.

Conceptualizing and Measuring Appetite Self-Regulation Phenotypes and Trajectories in Childhood: A Review of Person-Centered Strategies

This review uses person-centered research and data analysis strategies to discuss the conceptualization and measurement of appetite self-regulation (ASR) phenotypes and trajectories in childhood (from infancy to about ages 6 or 7 years). Research that is person-centered provides strategies that increase the possibilities for investigating ASR phenotypes. We first examine the utility of examining underlying phenotypes using latent profile/class analysis drawing on cross-sectional data. The use of trajectory analysis to investigate developmental change is then discussed, with attention to phenotypes using trajectories of individual behaviors as well as phenotypes based on multi-trajectory modeling. Data analysis strategies and measurement approaches from recent examples of these person-centered approaches to the conceptualization and investigation of appetite self-regulation and its development in childhood are examined. Where relevant, examples from older children as well as developmental, clinical and educational psychology are drawn on to discuss when and how person-centered approaches can be used. We argue that there is scope to incorporate recent advances in biological and psychoneurological knowledge about appetite self-regulation as well as fundamental processes in the development of general self-regulation to enhance the examination of phenotypes and their trajectories across childhood (and beyond). The discussion and conclusion suggest directions for future research and highlight the potential of person-centered approaches to progress knowledge about the development of appetite self-regulation in childhood.