The influence of success experience on self-efficacy when providing feedback through technology
Introduction
More and more people live a sedentary lifestyle, resulting in a decrease in health and posing a risk for various diseases (e.g. Bankoski et al., 2011, Warren et al., 2010). On the other hand, a physically active lifestyle has significant positive effects on prevention of chronic diseases, such as cardiovascular disease, diabetes and cancer (Warburton, Nicol, & Bredin, 2006). Also, a sufficient level of physical activity has positive effects on mental health condition through reduced perceived stress and lower levels of burnout, depression and anxiety (Jonsdottir, Rödjer, Hadzibajramovic, Börjesson, & Ahlborg, 2010). Numerous interventions have already been developed to improve the level of physical activity in the general population (e.g. Dishman and Buckworth, 1996, Marcus et al., 1998). They are usually delivered through public media, flyers, e-mails, or consist of face to face (group) consultations, and show moderate effect sizes (Dishman & Buckworth, 1996).
A recent development regarding physical activity interventions is using mobile, technology-supported applications to achieve the desired effect. Examples include UbiFit Garden (Consolvo et al., 2008), BeWell+ (Lin et al., 2012) and Move2Play (Bielik et al., 2012). A study by Op den Akker, Jones, and Hermens (2014) concluded that many interventions apply tailoring, i.e. personalization of information or feedback, which increases the effect of the intervention (Hawkins, Kreuter, Resnicow, Fishbein, & Dijkstra, 2008). The most common technique is to provide previously obtained information about the individual and some also include a tailored goal and tailored inter-human interaction. Although the effectiveness of tailoring based on constructs from behavioral science – or adaptation (Hawkins et al., 2008) – has been proven, Op den Akker et al. (2014) show that none of the interventions used adaptation as a tailoring strategy. Such lack of adaptation in technology-supported physical activity interventions was also noticed by Achterkamp et al. (submitted for publication), who developed specific feedback strategies for these types of intervention. Four of the six feedback strategies include a focus on increasing self-efficacy, making it an important aspect when designing mobile activity coaches (Achterkamp et al., submitted for publication).
The concept op tailoring information or feedback enhances relevance for the individual and increases the impact of the message; guidelines for designing effective physical activity interventions strongly recommend tailoring feedback (Greaves et al., 2011). Traditional, non-technology-supported interventions that apply adaptation, e.g. by providing tailored information based on subjects’ attitudes, stage of change, social support or processes of change, show significantly larger effect sizes than interventions that do not tailor on these constructs (Noar, Benac, & Harris, 2007). Additionally, self-efficacy seems of major importance (Hawkins et al., 2008); a construct that is common in models and theories that explain behavior and behavioral change. High self-efficacy not only increases intention to perform the target behavior, it also leads to actual performance of the target behavior (Gist & Mitchell, 1992). Additionally, Achterkamp et al. (submitted for publication) showed that the level of self-efficacy is related to (1) level of activity at baseline: the higher the subjects’ level of self-efficacy, the higher their level of physical activity; and (2) the percentage of change as a result of a twelve week intervention: for subjects who are inactive at the start of the intervention, a higher level of self-efficacy is associated with a higher level of increase in physical activity. Bandura (1994) describes four sources of self-efficacy:
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Mastery experience: the subject successfully performs the target behavior.
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Vicarious experience: the subject observes a similar other perform the target behavior.
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Verbal (or social) persuasion: expressing faith in the subject’s capabilities.
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Physiological/affective states: correcting misinterpretations of bodily states.
A systematic review with meta-analysis (Ashford, Edmunds, & French, 2010) shows that the most successful strategy to increase self-efficacy for physical activity is using enactive mastery experience, including feedback about previous performance/successes, followed by vicarious experience and feedback about similar others’ performance.
So, traditional non-technology-supported interventions emphasize the importance of increasing self-efficacy to maximize the chance of positive results, but this knowledge is rarely applied in technology-supported interventions and it is not yet clear how this should be done. Therefore, the aim of the current study is to investigate whether experiencing success also leads to an increase in self-efficacy when using technology-supported feedback strategies. To our knowledge, no such experiment has been performed until now. Specifically, we aim to answer the following questions: what is the effect of a feedback strategy that focuses on success experience on (1) level of self-efficacy regarding a specific task, (2) level of self-efficacy regarding physical activity, and (3) task performance?
Section snippets
Participants
The call for participation was distributed through e-mail, social media and the involved researchers personally. Subjects were included if they were Dutch-speaking and did not have walking disabilities. These criteria were necessary considering instructions were in Dutch and, as much as, possible rule out the influence of walking ability.
Fifteen subjects were included and participated in the study; nine women and six men. Age ranged from 22 to 36 years and averaged 27 years (SD = 4). All
Results
The average level of task-specific self-efficacy on the trials with feedback was 58.69 (SD = 23.00), 31.49 (SD = 18.75), and 59.11 (SD = 21.59) for the correct, negative and positive feedback conditions respectively. The Repeated Measures ANOVA shows a main effect for task-specific self-efficacy (F(2, 28) = 37.37, p < .001). Fig. 1 clearly shows that in the negative feedback condition the task-specific self-efficacy decreases, initially steeply, whereas it increases in the positive and correct feedback
Discussion
The main aim of the current study was to investigate whether experiencing success leads to an increase in self-efficacy when using technology-supported feedback strategies. Specifically, we focused on the effect of a feedback strategy that focuses on success experience on (1) level of self-efficacy regarding a specific task, (2) level of self-efficacy regarding physical activity, and (3) task performance. The task was to walk from A to B (eight meters), in exactly 14, 16, or 18 s (target time),
Conclusion
Self-efficacy can be influenced when using technology-supported feedback strategies. This study is a first step towards adaptation of technology-supported interventions, it shows self-efficacy can indeed be increased by experiencing success; the next step is to incorporate this knowledge into tailored feedback strategies of mobile activity coaches and test its effect on both level of self-efficacy and performance. Overall, the role of self-efficacy in these types of intervention deserves more
Conflict of interest
The authors declare that there are no conflicts of interest.
Acknowledgement
This publication was supported by the Dutch national program COMMIT (project P7 SWELL).
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