Mapping Canadian Men’s Recent and Intended Health Behavior Changes Through the Don’t Change Much Electronic Health Program

Abstract

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.

Promoting Men’s Health With the “Don’t Change Much” e-Program

“…The current study findings confirm and extend our previous work ( Oliffe et al, 2020a ), and by extension affirm the strong potential of men’s e-health promotion programs ( Robinson & Robertson, 2010 ). Though not claiming attribution, the statistically significant positive associations between men’s DCM exposure levels and their recent and intended health behavior changes warrant discussion of three key issues as a means to scoping potential DCM adjustments, and offering recommendations to advance men’s e-health promotion work.…”

“…Third, while direct comparisons with previous DCM findings ( Oliffe et al, 2020a ) were not the aim of the current study, it is important to note that there were subtle changes to behavior change rankings and the strength of the associations with DCM exposure. For most end-users’ recent and intended health behavior changes, the odds ratios were lower (though significant) in the current study compared to the previous study results.…”

“…Data were collected from two Canadian male cohorts: (1) benchmark (reference group who had not accessed DCM), and (2) DCM users. Benchmark data collection procedures, and cross-sectional ( Oliffe et al, 2020c ; Flannigan et al, 2019 ; McCreary et al, 2020 ) and comparative findings ( Oliffe et al, 2020a ) have been published elsewhere. In brief, the 15-min benchmark questionnaire was administered April 20–28, 2017, via an online panel provider, and 2000 men, stratified by age and location based on Canadian census data, completed the survey.…”

“…Older age has been associated with reduced e-health literacy in men, which can impose significant barriers for some males ( Wills et al, 2020 ). In exploring the demographics of men who access e-health programs, and the relative benefits and barriers associated with specific end-user subgroups, important content and platform decisions and adjustments can be made ( Oliffe, et al, 2020a ; Soprovich et al, 2020 ).…”

“…Many health promotion programs have focused on reducing male risk factors such as unhealthy diets, substance use, smoking, and sedentary lifestyles ( Lahoud & Franco, 2016 ). Investigations of the challenges for engaging men with self-health have highlighted the need for gender–sensitized health promotion programs ( Oliffe et al, 2020a ; Oliffe et al, 2020b ; Soprovich et al, 2020 ). While the delivery of men’s health promotion programs is diverse, the e-health sector has grown significantly during the 2000s ( Fogarty et al, 2017 ).…”