Self-monitoring is the cornerstone of many health and wellness persuasive
interventions. However, applications designed to promote health and wellness
that use this strategy have recorded varying degrees of success. In this study,
we investigated why the self-monitoring strategy might work in some contexts and
fail in others. We conducted a series of large-scale studies, with a total of
1768 participants, to explore the strengths and weaknesses of the
self-monitoring strategy. Our results uncover important strengths and weaknesses
that could facilitate or hinder the effectiveness of self-monitoring to promote
the health and wellness of its users. The strengths include its tendency to
reveal problem behaviours, provide real and
concrete information, foster reflection,
make people accept responsibility, create awareness
and raise users’ consciousness about their health and wellness.
Some of the weaknesses include its tendency to provoke health
disorder, be tedious and boring.
We contribute to the digital health community by offering design guidelines for
operationalising self-monitoring to overcome its weaknesses and amplify its
strengths.
Research has shown that persuasive technologies aimed at behavior change will be more effective if behavioral determinants are targeted. However, research on the determinants of bodyweight exercise performance in the context of behavior modeling in fitness apps is scarce. To bridge this gap, we conducted an empirical study among 659 participants resident in North America using social cognitive theory as a framework to uncover the determinants of the performance of bodyweight exercise behavior. To contextualize our study, we modeled, in a hypothetical context, two popular bodyweight exercise behaviors – push ups and squats – featured in most fitness apps on the market using a virtual coach (aka behavior model). Our social cognitive model shows that users’ perceived self-efficacy (βT = 0.23, p < 0.001) and perceived social support (βT = 0.23, p < 0.001) are the strongest determinants of bodyweight exercise behavior, followed by outcome expectation (βT = 0.11, p < 0.05). However, users’ perceived self-regulation (βT = –0.07, p = n.s.) turns out to be a non-determinant of bodyweight exercise behavior. Comparatively, our model shows that perceived self-efficacy has a stronger direct effect on exercise behavior for men (β = 0.31, p < 0.001) than for women (β = 0.10, p = n.s.). In contrast, perceived social support has a stronger direct effect on exercise behavior for women (β = 0.15, p < 0.05) than for men (β = −0.01, p = n.s.). Based on these findings and qualitative analysis of participants’ comments, we provide a set of guidelines for the design of persuasive technologies for promoting regular exercise behavior.
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