Predictors of Health-Related Quality of Life in Neurodivergent Children: A Systematic Review.

Health-related Quality of Life (HRQoL) is a multi-faceted construct influenced by a myriad of environmental, demographic, and individual characteristics. Our understanding of these influencers remains highly limited in neurodevelopmental conditions. Existing research in this area is sparse, highly siloed by diagnosis labels, and focused on symptoms. This review synthesized the evidence in this area using a multi-dimensional model of HRQoL and trans-diagnostically across neurodevelopmental conditions. The systematic review, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Checklist, was completed in June 2023 using Medline, PsycInfo, Embase, PubMed, and Cochrane Library. Our search revealed 78 studies that examined predictors of HRQoL in neurodevelopmental conditions. The majority of these studies focused on autism and ADHD with a paucity of literature in other conditions. Cross-diagnosis investigations were limited despite the fact that many of the examined predictors transcend diagnostic boundaries. Significant gaps were revealed in domains of biology/physiology, functioning, health perceptions, and environmental factors. Very preliminary evidence suggested potentially shared predictors of HRQoL across conditions including positive associations between HRQoL and adaptive functioning, male sex/gender, positive self-perception, physical activity, resources, and positive family context, and negative associations with diagnostic features and mental health symptoms. Studies of transdiagnostic predictors across neurodevelopmental conditions are critically needed to enable care models that address shared needs of neurodivergent individuals beyond diagnostic boundaries. Further understanding of HRQoL from the perspective of neurodivergent communities is a critical area of future work.

Indoor Location Data for Tracking Human Behaviours: A Scoping Review.

Real-time location systems (RTLS) record locations of individuals over time and are valuable sources of spatiotemporal data that can be used to understand patterns of human behaviour. Location data are used in a wide breadth of applications, from locating individuals to contact tracing or monitoring health markers. To support the use of RTLS in many applications, the varied ways location data can describe patterns of human behaviour should be examined. The objective of this review is to investigate behaviours described using indoor location data, and particularly the types of features extracted from RTLS data to describe behaviours. Four major applications were identified: health status monitoring, consumer behaviours, developmental behaviour, and workplace safety/efficiency. RTLS data features used to analyse behaviours were categorized into four groups: dwell time, activity level, trajectory, and proximity. Passive sensors that provide non-uniform data streams and features with lower complexity were common. Few studies analysed social behaviours between more than one individual at once. Less than half the health status monitoring studies examined clinical validity against gold-standard measures. Overall, spatiotemporal data from RTLS technologies are useful to identify behaviour patterns, provided there is sufficient richness in location data, the behaviour of interest is well-characterized, and a detailed feature analysis is undertaken.