Who Follows eHealth Interventions as Recommended? A Study of Participants’ Personal Characteristics From the Experimental Arm of a Randomized Controlled Trial.

Auteur(s) :
De Vries H., Schulz DN., Kremers SP., Crutzen R., Reinwand DA.
Date :
Mai, 2015
Source(s) :
Journal of medical Internet research. #17:5 pe115
Adresse :
CAPHRI School for Public Health and Primary Care, Department of Health Promotion, Maastricht University, Maastricht, Netherlands. [email protected].

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BACKGROUND
Computer-tailored eHealth interventions to improve health behavior have been demonstrated to be effective and cost-effective if they are used as recommended. However, different subgroups may use the Internet differently, which might also affect intervention use and effectiveness. To date, there is little research available depicting whether adherence to intervention recommendations differs according to personal characteristics.

OBJECTIVE
The aim was to assess which personal characteristics are associated with using an eHealth intervention as recommended.

METHODS
A randomized controlled trial was conducted among a sample of the adult Dutch population (N=1638) testing an intervention aimed at improving 5 healthy lifestyle behaviors: increasing fruit and vegetable consumption, increasing physical activity, reducing alcohol intake, and promoting smoking cessation. Participants were asked to participate in those specific online modules for which they did not meet the national guideline(s) for the respective behavior(s). Participants who started with fewer than the recommended number of modules of the intervention were defined as users who did not follow the intervention recommendation.

RESULTS
The fewer modules recommended to participants, the better participants adhered to the intervention modules. Following the intervention recommendation increased when participants were older (χ(2) 1=39.8, P<.001), female (χ(2) 1=15.8, P<.001), unemployed (χ(2) 1=7.9, P=.003), ill (χ(2) 1=4.5, P=.02), or in a relationship (χ(2) 1=7.8, P=.003). No significant relevant differences were found between groups with different levels of education, incomes, or quality of life.

CONCLUSION
Our findings indicate that eHealth interventions were used differently by subgroups. The more frequent as-recommended intervention use by unemployed, older, and ill participants may be an indication that these eHealth interventions are attractive to people with a greater need for health care information. Further research is necessary to make intervention use more attractive for people with unhealthy lifestyle patterns.

Source : Pubmed
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