Valle Carmina G, Queen Tara L, Martin Barbara A, Ribisl Kurt M, Mayer Deborah K, Tate Deborah F
Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
J Med Internet Res. 2018 Mar 1;20(3):e63. doi: 10.2196/jmir.7613.
Health risk assessments with tailored feedback plus health education have been shown to be effective for promoting health behavior change. However, there is limited evidence to guide the development and delivery of online automated tailored feedback.
The goal of this study was to optimize tailored feedback messages for an online health risk assessment to promote enhanced user engagement, self-efficacy, and behavioral intentions for engaging in healthy behaviors. We examined the effects of three theory-based message factors used in developing tailored feedback messages on levels of engagement, self-efficacy, and behavioral intentions.
We conducted a randomized factorial experiment to test three different components of tailored feedback messages: tailored expectancy priming, autonomy support, and use of an exemplar. Individuals (N=1945) were recruited via Amazon Mechanical Turk and randomly assigned to one of eight different experimental conditions within one of four behavioral assessment and feedback modules (tobacco use, physical activity [PA], eating habits, and weight). Participants reported self-efficacy and behavioral intentions pre- and postcompletion of an online health behavior assessment with tailored feedback. Engagement and message perceptions were assessed at follow-up.
For the tobacco module, there was a significant main effect of the exemplar factor (P=.04); participants who received exemplar messages (mean 3.31, SE 0.060) rated their self-efficacy to quit tobacco higher than those who did not receive exemplar messages (mean 3.14, SE 0.057). There was a three-way interaction between the effect of message conditions on self-efficacy to quit tobacco (P=.02), such that messages with tailored priming and an exemplar had the greatest impact on self-efficacy to quit tobacco. Across PA, eating habits, and weight modules, there was a three-way interaction among conditions on self-efficacy (P=.048). The highest self-efficacy scores were reported among those who were in the standard priming condition and received both autonomy supportive and exemplar messages. In the PA module, autonomy supportive messages had a stronger effect on self-efficacy for PA in the standard priming condition. For PA, eating habits, and weight-related behaviors, the main effect of exemplar messages on behavioral intentions was in the hypothesized direction but did not reach statistical significance (P=.08). When comparing the main effects of different message conditions, there were no differences in engagement and message perceptions.
Findings suggest that tailored feedback messages that use exemplars helped improve self-efficacy related to tobacco cessation, PA, eating habits, and weight control. Combining standard priming and autonomy supportive message components shows potential for optimizing tailored feedback for tobacco cessation and PA behaviors.
有针对性的反馈加上健康教育的健康风险评估已被证明对促进健康行为改变有效。然而,指导在线自动定制反馈的开发和提供的证据有限。
本研究的目标是优化在线健康风险评估的定制反馈信息,以促进增强用户参与度、自我效能感以及参与健康行为的行为意向。我们研究了在开发定制反馈信息时使用的三个基于理论的信息因素对参与度、自我效能感和行为意向水平的影响。
我们进行了一项随机析因实验,以测试定制反馈信息的三个不同组成部分:定制期望启动、自主支持和示例的使用。通过亚马逊土耳其机器人招募了个体(N = 1945),并将其随机分配到四个行为评估和反馈模块(烟草使用、身体活动[PA]、饮食习惯和体重)之一内的八个不同实验条件之一。参与者在完成带有定制反馈的在线健康行为评估之前和之后报告自我效能感和行为意向。在随访时评估参与度和信息感知。
对于烟草模块,示例因素有显著的主效应(P = 0.04);收到示例信息的参与者(均值3.31,标准误0.060)对戒烟的自我效能感评分高于未收到示例信息的参与者(均值3.14,标准误0.057)。信息条件对戒烟自我效能感的影响之间存在三向交互作用(P = 0.02),因此带有定制启动和示例的信息对戒烟自我效能感的影响最大。在PA、饮食习惯和体重模块中,自我效能感的条件之间存在三向交互作用(P = 0.048)。在处于标准启动条件且同时收到自主支持和示例信息的参与者中,自我效能感得分最高。在PA模块中,自主支持信息在标准启动条件下对PA自我效能感的影响更强。对于PA、饮食习惯和与体重相关的行为,示例信息对行为意向的主效应朝着假设的方向,但未达到统计学显著性(P = 0.08)。比较不同信息条件的主效应时,参与度和信息感知没有差异。
研究结果表明,使用示例的定制反馈信息有助于提高与戒烟、PA、饮食习惯和体重控制相关的自我效能感。结合标准启动和自主支持信息成分显示出优化戒烟和PA行为定制反馈的潜力。