JMIR Publications

ABSTRACT

Background:

While evaluation studies confirm the strong potential of men’s e-Health programs there have been calls to more fully understand acceptability, engagement and behavior change to guide future work. Relatedly, mapping behavior changes using health promotion theories including the transtheoretical model (or stages of change) have been recommended to build a translatable empirical base to advance design and evaluation considerations for men’s e-Health programs.

Objective:

The current study purpose was to use a benchmark sample as a reference group to map the recent and intended health behavior changes in Canadian men who use the ‘Don’t Change Much’ (DCM) e-Health program. The hypothesis being tested was that increased DCM exposure levels would be positively associated with men’s recent and intended health behavior changes.

Methods:

DCM users (n = 863) were sampled for demographic data and self-reported recent and intended health behavior changes. Respondents also reported their usage (frequency and duration) for each of the 3 DCM components (web, newsletter and social media), and were allocated to immediate (n = 257; 29.8%), medium (n = 431; 49.9%) and high (n = 175; 20.3%) exposure sub-groups. A benchmark sample (n = 2000), comprising respondents who had not accessed DCM provided a reference group. Bivariate analysis of recent and intended health behavior changes and DCM exposure levels were used to compute the strength of association between the independent variables (exposure levels) and the 11 categorical dependent variables (recent and intended health behavior changes). Binary logistic regression models were computed for each of the 11 recent and intended health behavior changes. Linear regression was used to model the association between the number of recent and intended changes and level of exposure to DCM.

Results:

Compared to the benchmark reference group, DCM high exposure respondents had significantly increased odds for 8 of the 11 health behavior changes, with the largest effect size observed for Changed diet or improved eating habits (OR = 5.628, 95% CI = 3.932, 8.055). High exposure respondents also had significantly increased odds for 8 intended health changes, with the largest effect sizes observed for Reduce stress level (OR = 4.282, 95% CI = 3.086, 5.941). Moderate effect size (Goodness of Fit) was observed for increased total number of recent (F=25.34, P <.001, Adjusted R2 = .093) and intended health behavior changes (DV) (F=36.16, P <.001, Adjusted R2 = .128) amongst high exposure respondents (IV).

Conclusions:

DCM respondents contrasted the predominately pre-contemplative benchmark sample mapping across the contemplative, preparation and action stages of the transtheoretical health behavior change model. Almost 10% of variation in recent, and 13% of variation in intended health behavior changes can be explained by DCM exposure and demographic factors, indicating the acceptability and usefulness of this men’s e-Health resource.