Background Although evaluation studies confirm the strong potential of men’s electronic health (eHealth) programs, there have been calls to more fully understand acceptability, engagement, and behavior change to guide future work. Relatedly, mapping of behavior changes using health promotion theories including the transtheoretical model (or stages of change) has been recommended to build a translatable empirical base to advance design and evaluation considerations for men’s eHealth programs. Objective This study aimed 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) eHealth program. The hypothesis being tested was that increased exposure to DCM 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 limited exposure (257/863, 29.8%), low exposure (431/863, 49.9%), and high exposure (175/863, 20.3%) subgroups. 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 10 categorical dependent variables (recent and intended health behavior changes). Binary logistic regression models were computed for each of the 10 recent and intended health behavior changes. Linear regression was used to model the association between the number of recent and intended changes and the level of exposure to DCM. Results Compared with the benchmark reference group, DCM high-exposure respondents had significantly increased odds for 9 of the 10 health behavior changes, with the largest effect size observed for Changed diet or Improved eating habits (odds ratio [OR] 5.628, 95% CI 3.932-8.055). High-exposure respondents also had significantly increased odds for 9 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 (F12,2850=25.52; P.001; adjusted R2=.093) and intended health behavior changes (F12,2850=36.30; P.001; adjusted R2=.129) among high-exposure respondents. Conclusions DCM respondents contrasted the predominately precontemplative benchmark sample mapping across the contemplative, preparation, and action stages of the transtheoretical health behavior change model. Almost 10% of variation in the recent and 13% of variation in the intended health behavior changes can be explained by DCM exposure and demographic factors, indicating the acceptability of this men’s eHealth resource.

Men’s e-health promotion programs can offer end-user anonymity and autonomy that provide avenues for supporting positive health behavior change. The twofold purpose of the current study was to use a benchmark cohort as a reference group to: (1) describe associations between men’s usage levels of the e-health program Don’t Change Much (DCM) and their recent and intended health behavior changes, and (2) report an exploratory analysis of the moderating effects of demographic variables on the associations between DCM users and their recent and intended health behavior changes. Based on self-report, DCM users were classified into limited ( n = 613, 34.7%), low ( n = 826, 46.8%), and high ( n = 327, 18.5%) exposure groups. Compared with the benchmark cohort, DCM high-exposure respondents had significantly increased odds for eight of the nine recent behavior changes, with the largest effect size observed for “Made an effort to sit less and walk more” (odds ratio [OR] 2.996, 95% CI [2.347, 3.826]). Eight of the nine intended health behavior changes in the DCM high-exposure group had significantly increased odds compared to the benchmark cohort, with “Reduce stress level” (OR 3.428, 95% CI [2.643, 4.447]) having the largest effect size. Significantly greater total numbers of recent ( F(12, 2850) = 29.32; p = .001; R2 = .086) and intended health behavior changes ( F(12, 2850) = 34.59; p = .001; R2 = 0.100) were observed among high exposure respondents while adjusting for demographics. Younger age, being employed, and household income <$120,000 had an enhancing moderator effect on DCM users’ number of intended behavior changes.

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BACKGROUND Although evaluation studies confirm the strong potential of men’s electronic health (eHealth) programs, there have been calls to more fully understand acceptability, engagement, and behavior change to guide future work. Relatedly, mapping of behavior changes using health promotion theories including the transtheoretical model (or stages of change) has been recommended to build a translatable empirical base to advance design and evaluation considerations for men’s eHealth programs. OBJECTIVE This study aimed to use a benchmark sample as a reference group to map the recent and intended health behavior changes in Canadian men who use the <i>Don’t Change Much</i> (DCM) eHealth program. The hypothesis being tested was that increased exposure to DCM 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 limited exposure (257/863, 29.8%), low exposure (431/863, 49.9%), and high exposure (175/863, 20.3%) subgroups. 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 10 categorical dependent variables (recent and intended health behavior changes). Binary logistic regression models were computed for each of the 10 recent and intended health behavior changes. Linear regression was used to model the association between the number of recent and intended changes and the level of exposure to DCM. RESULTS Compared with the benchmark reference group, DCM high-exposure respondents had significantly increased odds for 9 of the 10 health behavior changes, with the largest effect size observed for Changed diet or Improved eating habits (odds ratio [OR] 5.628, 95% CI 3.932-8.055). High-exposure respondents also had significantly increased odds for 9 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_12,2850=25.52; <i>P</i>.001; adjusted <i>R</i>²=.093) and intended health behavior changes (F_12,2850=36.30; <i>P</i>.001; adjusted <i>R</i>²=.129) among high-exposure respondents. CONCLUSIONS DCM respondents contrasted the predominately precontemplative benchmark sample mapping across the contemplative, preparation, and action stages of the transtheoretical health behavior change model. Almost 10% of variation in the recent and 13% of variation in the intended health behavior changes can be explained by DCM exposure and demographic factors, indicating the acceptability of this men’s eHealth resource.