Effectiveness of a Web-based Intervention Aimed at Healthy Dietary and Physical Activity Behavior: A Randomized Controlled Trial About Users and Usage

Abstract

BackgroundRecent studies have shown the potential of Web-based interventions for changing dietary and physical activity (PA) behavior. However, the pathways of these changes are not clear. In addition, nonusage poses a threat to these interventions. Little is known of characteristics of participants that predict usage.ObjectiveIn this study we investigated the users and effect of the Healthy Weight Assistant (HWA), a Web-based intervention aimed at healthy dietary and PA behavior. We investigated the value of a proposed framework (including social and economic factors, condition-related factors, patient-related factors, reasons for use, and satisfaction) to predict which participants are users and which participants are nonusers. Additionally, we investigated the effectiveness of the HWA on the primary outcomes, self-reported dietary and physical activity behavior.MethodsOur design was a two-armed randomized controlled trial that compared the HWA with a waiting list control condition. A total of 150 participants were allocated to the waiting list group, and 147 participants were allocated to the intervention group. Online questionnaires were filled out before the intervention period started and after the intervention period of 12 weeks. After the intervention period, respondents in the waiting list group could use the intervention. Objective usage data was obtained from the application itself.ResultsIn the intervention group, 64% (81/147) of respondents used the HWA at least once and were categorized as “users.” Of these, 49% (40/81) used the application only once. Increased age and not having a chronic condition increased the odds of having used the HWA (age: beta = 0.04, P = .02; chronic condition: beta = 2.24, P = .003). Within the intervention group, users scored better on dietary behavior and on knowledge about healthy behavior than nonusers (self-reported diet: χ2 2 = 8.4, P = .02; knowledge: F1,125 = 4.194, P = .04). Furthermore, users underestimated their behavior more often than nonusers, and nonusers overestimated their behavior more often than users (insight into dietary behavior: χ2 2 = 8.2, P = .02). Intention-to-treat analyses showed no meaningful significant effects of the intervention. Exploratory analyses of differences between pretest and posttest scores of users, nonusers, and the control group showed that on dietary behavior only the nonusers significantly improved (effect size r = −.23, P = .03), while on physical activity behavior only the users significantly improved (effect size r = −.17, P = .03).ConclusionsRespondents did not use the application as intended. From the proposed framework, a social and economic factor (age) and a condition-related factor (chronic condition) predicted usage. Moreover, users were healthier and more knowledgeable about healthy behavior than nonusers. We found no apparent effects of the intervention, although exploratory analyses showed that choosing to use or not to use the intervention led to different outcomes. Combined with the differences between groups at...

The Efficacy of a Walking Intervention Using Social Media to Increase Physical Activity: A Randomized Trial

“…As has been seen in prior physical activity interventions delivered through the internet, [13][14][15][16] engagement declined as the intervention progressed. Although all participants engaged in the Facebook Social Support Group, there was also a wide range of engagement with a minimum of 6 total posts to a maximum of 32 total posts.…”

“…The use of Facebook may also aid in ameliorating a frequently reported hindrance of physical activity interventions delivered through the internet which is getting and keeping participants engaged and visiting the intervention website. [12][13][14][15][16] The average Facebook user logs into Facebook 3 times per day, 17 a login rate that is 7 times higher than that seen among previous internet-based physical activity interventions. 16 The use of social media in health-promoting interventions is not new and has resulted in improvements in several aspects of health (eg, sexual health, food safety, and weight loss).…”

The Theory, Development, and Implementation of an e-Intervention to Prevent Excessive Gestational Weight Gain: e-Moms Roc

“…Engagement with online or electronic pregnancy interventions has been examined only in a few studies and has been relatively low. Kelders et al 10 found that 64% of participants used their online…”

The Empirical Evidence for the Telemedicine Intervention in Diabetes Management

“…These were conducted in The Netherlands (2011) 98 and United States (2014). 99 They are summarized in Table 5.…”

“…In 2011, an RCT (n = 297) 98 was conducted in The Netherlands that investigated the predicted usage of a Web-based intervention aimed at promoting healthy diet and physical activity. In total, 297 respondents were recruited from various public sources, including advertisements in local newspapers, postings in supermarkets, and a health-related Web site.…”