In this meta-analysis, research has been integrated to determine the strength of the relationship between print exposure and reading comprehension as well as reading attitude in primary and...Show moreIn this meta-analysis, research has been integrated to determine the strength of the relationship between print exposure and reading comprehension as well as reading attitude in primary and secondary school students. Furthermore, possible grade-level differences in this relationship were examined. The 26 included studies, which measured print exposure with an author- or title-recognition checklist and reading comprehension and/or reading attitude with various instruments, showed positive, large correlations between print exposure and reading comprehension. Positive, moderate correlations were found between print exposure and reading attitude. The heterogeneity was high in all analyses but could not be explained by the age-groups of the participants. The relationships appeared to be comparable between primary and secondary school students aged 7-16 years. It should be noted that conclusions about the direction of the relationships cannot be indicated. Further research about this direction would be recommended along with more research including different variables that could explain the heterogeneity. Besides the need for more extensive research about the relationship between print exposure and reading comprehension as well as reading attitude it is clear that stimulating children to read more is helpful.Show less
General abstract Background: Unhealthy lifestyle increases the change of early mortality of patients with or at risk of Cardiometabolic Diseases (CMDs). Positive effects of eHealth physical...Show moreGeneral abstract Background: Unhealthy lifestyle increases the change of early mortality of patients with or at risk of Cardiometabolic Diseases (CMDs). Positive effects of eHealth physical activity interventions have been recently found. In human supported eHealth interventions feedback and support is primarily provided by a healthcare professional, and in self-help eHealth interventions feedback is primarily automated and displayed via text or graphical form. Incorporating human contact by healthcare professionals showed its first improvements on physical activity. However, influential differences between human supported eHealth interventions and self-help eHealth interventions on physical activity remain unclear. Objective: In this meta-analysis we first evaluated the overall effectiveness of eHealth physical activity intervention in patients with or at risk of Cardiometabolic Diseases (CMD). Second, we analyzed the influence of human support and self-help on the effectiveness of different type(s) of eHealth physical activity intervention. Methods: Our meta-analysis was based on the PICO and PRISMA statements. We searched for English and Dutch studies between 1995 and 2020. We included randomized controlled trials focused on interactive web- mobile based physical activity interventions, and with the goal to modify healthy physical activity in patients of 18 years and older. Baseline and post measures of outcome measurement ‘BMI’ and ‘step count’ were collected. Risk of bias assessment was performed by Cochrane Risk of Bias Tool. We estimated the pooled effect sizes with a random-effects model meta-analysis. Results: A total of 36 studies (N=6,919) fulfilled the eligibility criteria and were included in our meta-analysis, including seven self-help interventions and 29 human supported interventions. We found a significant increase in BMI (Hedges’G 0.29, 95% CI. 0.45 - 0.12) and daily step count (Hedges’G 0.79, 95% CI. 1.37 - 0.22) in the eHealth physical activity intervention group. The subgroup analysis showed that both human-supported and self-help interventions significantly reduced BMI (Hedges’G 0.29; 95% CI 0.45 - 0.12). We found a significant increase in daily step count in the human-supported interventions (0.71; 95% CI 1.34 - 0.09; p = < 0.01), but no significant increase was found in the (group) with self-help interventions (0.11; 95% CI 2.34 - 0.32; p = 0.13). We found no significant difference in effectiveness between human-supported and self-help interventions on BMI (Q(1) = 0.35; 95% CI -0.47 - -0.18; p = 0.552) and daily step count (Q(1) = 1.06 CI -0.32 - 2.44). Discussion: eHealth physical interventions are effective on BMI and daily step count, but there is no significant variety in physical activity outcomes between human supported interventions and self-help interventions. Our results support that physical activity is one of the greatest modifiable behaviors for eHealth physical activity interventions. Future meta-analysis should continue with a multimorbid approach by adapting the shared underlying risk factors.Show less
