ABSTRACT
This systematic review primarily aims to provide a summary of the game mechanics implemented in eHealth tools supporting young people's self-management of their chronic diseases. This review secondarily investigates the rationale for implementing game mechanics and the effects of these tools. A systematic search was conducted in Embase, Medline, PsycINFO, and Web of Science, from inception until August 30, 2022. Studies were eligible if focus was on the utilization of gamification in eHealth self-management interventions for young people (age = 10-25 years) with chronic diseases. Primary quantitative, qualitative, and mixed-method studies written in English were included. We identified 34 eHealth tools, of which 20 (59%) were gamified tools and 14 (41%) were serious games. We found that 55 unique game mechanics were implemented. The most commonly used were rewards (50%), score (44%), creative control (41%), and social interaction (32%). In comparison with gamified tools, the number and diversity of game mechanics applied were higher in serious games. For most tools (85%), a general rationale was provided for utilizing gamification, which often was to promote engaging experiences. A rationale for using specific game mechanics was less commonly provided (only for 45% of the game mechanics). The limited availability of experimental research precludes to test the effectiveness of using gamification in eHealth to support self-management in young people with chronic diseases. In this study, we highlight the importance of reporting the rationale for utilizing specific game mechanics in eHealth tools to ensure a proper alignment with evidence-based practice and the need of conducting experimental research. PROSPERO: CRD42021293037.
ABSTRACT
BACKGROUND: Aging is a multifactorial process that affects multiple tissues and is characterized by changes in homeostasis over time, leading to increased morbidity. Whole blood gene expression signatures have been associated with aging and have been used to gain information on its biological mechanisms, which are still not fully understood. However, blood is composed of many cell types whose proportions in blood vary with age. As a result, previously observed associations between gene expression levels and aging might be driven by cell type composition rather than intracellular aging mechanisms. To overcome this, previous aging studies already accounted for major cell types, but the possibility that the reported associations are false positives driven by less prevalent cell subtypes remains. RESULTS: Here, we compared the regression model from our previous work to an extended model that corrects for 33 additional white blood cell subtypes. Both models were applied to whole blood gene expression data from 3165 individuals belonging to the general population (age range of 18-81 years). We evaluated that the new model is a better fit for the data and it identified fewer genes associated with aging (625, compared to the 2808 of the initial model; P ≤ 2.5⨯10-6). Moreover, 511 genes (~ 18% of the 2808 genes identified by the initial model) were found using both models, indicating that the other previously reported genes could be proxies for less abundant cell types. In particular, functional enrichment of the genes identified by the new model highlighted pathways and GO terms specifically associated with platelet activity. CONCLUSIONS: We conclude that gene expression analyses in blood strongly benefit from correction for both common and rare blood cell types, and recommend using blood-cell count estimates as standard covariates when studying whole blood gene expression.
