With this concept mapping study we aimed to identify predictors of relapse in physical activity and dietary behavior from the perspectives of health practitioners and the key population. The majority of perceived predictors were mentioned by both health practitioners and the key population, and the great majority of perceived predictors belonged to the individual domain. However, the predictors that were rated as most important differed between the two groups of stakeholders. According to the health practitioners, the important predictors were: change in daily structure, stress, lack of effective coping skills, habitual behavior, and lack of self-efficacy regarding losing weight. The key population groups rated none of the recurrent perceived predictors as important (> 4.0), however recurrent perceived predictors with the highest rating scores (between 3.7 and 3.9) were: lifestyle imbalance or experiencing a life event, lack of perseverance, negative emotional state, abstinence violation effect, decrease in motivation and indulgence.
The majority of our results is coherent with findings from quantitative relapse studies. A recent systematic review of prospective studies on predictors of relapse in dietary behavior and physical activity confirmed self-efficacy and coping skills as predictors (11). The abstinence violation effect has also been confirmed as predictor in the past (20–25). Besides, the majority of the perceived predictors rated as most important are also covered in the Relapse Prevention Model (RPM) (4), including stress, lifestyle imbalance or experiencing a life event, indulgence, negative emotional state, coping skills, self-efficacy, and the abstinence violation effect.
However, although covered in the RPM, for stress, negative emotional state, indulgence, and lifestyle imbalance or experiencing a life event, who were rated as most important by our stakeholders, further scientific evidence on their relation with relapse in physical activity and dietary behavior is lacking (11). Previous research on factors influencing (maintenance of) physical activity and dietary behavior may provide some first indications. For example, recent reviews showed that stress impairs efforts to be physically active, and stress and negative emotions unfavorably influences dietary behavior (26–29); however, evidence for an association between negative emotions and physical activity remains inconclusive (30, 31). Furthermore, indulgence has been associated with unhealthy eating behavior by multiple studies, especially when the indulgence is justified (“I worked so hard today, I deserve it”) (32, 33). Despite the indicated association between lifestyle imbalance and relapse according to the RPM, studies assessing this potential association are, to our knowledge, currently lacking.
Although not part of the RPM, our stakeholders also indicated decrease in motivation, habitual behavior, change of daily rhythm and lack of perseverance as most important perceived predictors. As again scientific evidence on their association with relapse in physical activity and dietary behavior is lacking (11), general theories on motivation and behavior change and research on factors influencing (maintenance of) physical activity and dietary behavior may provide more insight. First, the self-determination theory indicates intrinsic motivation as an important factor for behavior change maintenance: behavior change is more likely to be maintained if the new behavior is perceived as personally relevant and reflects an individual's values (34). As most individuals start behavior change attempts when their motivation is high, a decrease in motivation over time could lead to a relapse into previous behavior (34). The association between intrinsic motivation and health behavior change has been shown present in the area of physical activity and dietary behavior (35–37). Second, habits, defined as a form of automaticity that is triggered by situational cues and enacted with little conscious awareness, play an important role in people’s failure to adopt and maintain healthy behavior (38, 39). Therefore, both breaking and creating habits are central to behavior change (39). Especially eating habits are striking: research shows eating habits can be directly activated by environmental cues, without the activation of preferences and goals (40, 41). The formation of new habits is triggered by stable features in the environment (42); therefore, it may explain why a change in an individual’s daily rhythm, often related to change of environment (e.g. going on holidays), can lead to a relapse into previous behavior. Last, for perseverance, described as the continuation of a goal-directed action in spite of obstacles (43), knowledge on the potential association with behavior change is currently lacking. More research is recommended to confirm the relation between these perceived predictors and relapse in physical activity and dietary behavior.
Although the majority of perceived predictors were mentioned by both stakeholder groups, both groups had somewhat different opinions. Different perceived predictors were rated as most important according to the health practitioners, when compared to the key population. Also, a few perceived predictors were mentioned by all practitioner groups, but not by the key population: change in daily structure, perceived financial barriers, and habitual behavior. Vice versa, perceived predictors that were mentioned by all key population groups, but not by the health practitioners were: tempting social environment, indulgence, and lack of knowledge. The differences may be due to health practitioners basing their knowledge on their experience with many clients, and therefore generate and rate statements on the average person (seeing ‘the bigger picture’). The key population may have generated and rated statements based on their own experiences, leaving more room for diversity. This emphasizes the importance of including multiple stakeholders to gather diverse views and a more complete picture. Furthermore, results show that both stakeholder groups predominantly rate individual factors as most important perceived predictors of relapse. However, previous research indicates that environmental factors, such as an tempting environment, also influence relapse (11). A possible explanation for this difference might be that individuals do not know or like to admit that they are being influenced by their social or physical environment. Also, the influence of the social or physical environment is often through individual factors (e.g. not being able to cope with the social pressure at a party), which might make individual factors more proximal and therefore easier to recall. It may also be that more individual factors are mentioned due to the current stigma that overweight and obese individuals are to blame for their weight (44).
Strengths and limitations
Our study provides new insights into the predictors of relapse in physical activity and dietary behavior from the perspective of key stakeholders, contributing to a more in-depth understanding of relapse by means of personal perspectives and daily practice. The focus on both health practitioners and key population perspectives is an important strength of this study, providing insight from multiple points of view. Furthermore, the concept mapping method allows multiple points of view in each group of stakeholders to be integrated whilst taking the relative importance of each statement into account, using valid statistical methods (45). However, there are also some limitations worth mentioning. During the sorting of the statements we noticed that both stakeholder groups found it challenging to start sorting the statements into piles. This was mainly since they were not allowed to create more than 10 piles, due to the settings of the software program. As the stakeholders identified a wide range of predictors, it might have been easier to place the statements into better fitting categories if they were allowed to create more piles. Furthermore, as each cluster represented multiple perceived predictors, which appeared during the interpretation of the maps, it seems that the data may have been too complex to base the results entirely on an mathematical model. Therefore, expert opinion and existing theoretical categories were needed to be able to analyze the results on a predictor level instead of cluster level. Last, a more diverse sample would have been preferred: males were underrepresented and the majority of the key population had a middle educational level.
Several recommendations for future research can be made. First, as we wanted to keep the generation of statements feasible and non-confusing for the participants, we formulated one focus statement in which the predictors of physical activity and dietary behavior were combined and no distinction between lapse and relapse was made. Future research could investigate potential differences between the predictors of relapse in physical activity and dietary behavior, and between lapse and relapse. Second, for several predictors only studies that examined their association with general behavior change instead of relapse have been conducted. Therefore, to examine whether the identified perceived predictors indeed predict relapse in physical activity and dietary behavior, a larger prospective study is recommended. We suggest an ecological momentary assessment (EMA) study to track experiences over time and get insight into the process of behavior change, among which lapsing and relapsing (46). EMA has been proven useful in measuring lapses and relapses in previous studies (47–52), and therefore provides an opportunity to confirm the perceived predictors as identified in this study.
Although more research on the predictors of relapse in physical activity and dietary behavior is recommended, careful implications for practice can be made. For example, the differences between health practitioners and key population regarding the perceived predictors in relapse may provide an opportunity to enhance lifestyle coaching, by ensuring it is patient-centered and tailored (53). Clients are more likely to be satisfied and follow advice on health behavior change when they feel they have been heard and understood, and are given information they recognize as relevant to them (54). The identified predictors could be relevant for future weight loss interventions that combine evidence-based techniques for altering relevant changeable predictors (e.g. effective coping skills) and coping with relevant non-changeable predictors (e.g. experiencing a life event), to prevent relapse in physical activity and dietary behavior. Planning coping responses on anticipated personal high risk situations helps an individual to cope with difficult situations, such as dealing with negative emotions or being tempted by their social or physical environment (55). Coping planning has been showed as an efficacious technique for promoting health behavior change, especially when individuals are supported in forming coping plans (56). Therefore, we recommend health practitioners to support their clients by helping to identify personal risk situations and formulate corresponding coping plans.