Effectiveness of school-based eHealth interventions to prevent multiple lifestyle risk behaviours among adolescents: a systematic review and meta-analysis

Abstract

Background Lifestyle risk behaviours typically emerge during adolescence, track into adulthood, and commonly co-occur. Interventions targeting multiple risk behaviours in adolescents have the potential to efficiently improve health outcomes, yet further evidence is required to determine their effect. We reviewed the effectiveness of eHealth school-based interventions targeting multiple lifestyle risk behaviours. Methods In this systematic review and meta-analysis, we searched Ovid MEDLINE, Embase, PsycINFO, and the Cochrane Library databases between Jan 1, 2000, and March 14, 2019, with no language restrictions, for publications on school-based eHealth multiple health behaviour interventions in humans. We also screened the grey literature for unpublished data. Eligible studies were randomised controlled trials of eHealth (internet, computers, tablets, mobile technology, or tele-health) interventions targeting two or more of six behaviours of interest: alcohol use, smoking, diet, physical activity, sedentary behaviour, and sleep. Primary outcomes of interest were the prevention or reduction of unhealthy behaviours, or improvement in healthy behaviours of the six behaviours. Outcomes were summarised in a narrative synthesis and combined using random-effects meta-analysis. This systematic review is registered with PROSPERO, identifier CRD42017072163. Findings Of 10 571 identified records, 22 publications assessing 16 interventions were included, comprising 18 873 students, of whom on average 56•2% were female, with a mean age of 13•41 years (SD 1•52). eHealth schoolbased multiple health behaviour change interventions significantly increased fruit and vegetable intake (standard mean difference 0•11, 95% CI 0•03 to 0•19; p=0•007) and both accelerometer-measured (0•33, 0•05 to 0•61; p=0•02) and self-reported (0•14, 0•05 to 0•23; p=0•003) physical activity, and reduced screen time (-0•09,-0•17 to-0•01; p=0•03) immediately after the intervention; however, these effects were not sustained at follow-up when data were available. No effect was seen for alcohol or smoking, fat or sugar-sweetened beverage or snack consumption. No studies examined sleep or used mobile health interventions. The risk of bias in masking of final outcome assessors and selective outcome reporting was high or unclear across studies and overall we deemd the quality of evidence to be low to very low. Interpretation eHealth school-based interventions addressing multiple lifestyle risk behaviours can be effective in improving physical activity, screen time, and fruit and vegetable intake. However, effects were small and only evident immediately after the intervention. Further high quality, adolescent-informed research is needed to develop eHealth interventions that can modify multiple behaviours and sustain long-term effects.

Clustering of Six Key Risk Behaviors for Chronic Disease among Adolescent Females

“…Further, the clustering of behaviors suggests that interventions that adopt a multiple health behavior change (MHBC) approach [ 47 ], whereby health behaviors are targeted together, rather than in isolation, may be beneficial among this population. However, given that clustering of health behaviors differs by sex [ 22 ], that adolescent females and males may respond differently to MHBC interventions [ 24 ], and that the clusters in the current study varied in perceived importance of life domains, considerations should be given to sex, risk profile, and value alignment when developing interventions. One approach involves utilizing web- and mobile-based technologies, which can be particularly useful for tailoring interventions [ 24 ].…”

“…However, given that clustering of health behaviors differs by sex [ 22 ], that adolescent females and males may respond differently to MHBC interventions [ 24 ], and that the clusters in the current study varied in perceived importance of life domains, considerations should be given to sex, risk profile, and value alignment when developing interventions. One approach involves utilizing web- and mobile-based technologies, which can be particularly useful for tailoring interventions [ 24 ]. Based on the present risk profiles and correlates, another idea would be to provide information within study skill sessions for females to highlight the important link between physical wellbeing, particularly in regard to healthy sleep and limited screen time, and academic achievement.…”

“…Evidence suggests this clustering differs by sex, with adolescent females comprising unhealthy clusters characterized by low physical activity [ 22 ], and females more likely than males to engage in multiple risk behaviors [ 23 ]. Moreover, a recent review of school-based eHealth interventions targeting multiple health behaviors found adolescent females do not benefit as much as males in terms of physical activity [ 24 ], suggesting tailored interventions for females may be required. There is therefore a need to better understand the risk behavior profiles of adolescent females, along with their correlates, to inform future prevention work.…”

Fitbit-Based Interventions for Healthy Lifestyle Outcomes: Systematic Review and Meta-Analysis

“…In our case, we included the following two types of conditions: the main intervention components that were present in the included studies and the same study characteristics as in the subgroup analysis (ie, grounded in a theory, length of the intervention, and chronic disease in the subjects). We focused on a range of outcomes (ie, steps, MVPA, sedentary behaviors, and weight) that are important components of a heathy lifestyle [ 64 - 66 ]. FsQCA is an analytical method that allows us to assess which configurations of conditions or factors (ie, intervention components and study characteristics) lead to successful outcomes.…”

Social networks as tools for the prevention and promotion of health among youth

“…Adolescents face challenges with respect to physical, psychological, and interpersonal development (Crosnoe & Johnson, (2011); Steinberg & Morris, (2001), such as a reconfiguration of their relationships with family and peers as well as pressure to assume vocational decisions Zimmer-Gembeck & Skinner, (2008). This is also a period that has been associated with the onset of risk behaviors (Defoe, Dubas, & Romer, (2010) that are maintained into adulthood and very often co-occur (Champion et al, (2019). Recent studies also highlight the links between social media use and risky behaviors during adolescence Vannucci, Simpson, Ggnon, & McCauley, (2020), but longitudinal data allow us to understand that adolescents who report frequently using social media face higher risks of mental health problems Riehm et al, (2019).…”