Goal pursuit increases more after dietary success than after dietary failure: examining conflicting theories of self-regulation using ecological momentary assessment

BACKGROUND

Maintaining a healthy body weight and reaching long-term dietary goals requires ongoing self-monitoring and behavioral adjustments. How individuals respond to successes and failures is described in models of self-regulation: while cybernetic models propose that failures lead to increased self-regulatory efforts and successes permit a reduction of such efforts, motivational models (e.g., social-cognitive theory) make opposite predictions. Here, we tested these conflicting models in an ecological momentary assessment (EMA) context and explored whether effort adjustments are related to inter-individual differences in perceived self-regulatory success in dieting (i.e., weight management).

METHODS

Using linear mixed effects models, we tested in 174 diet-interested individuals whether current day dietary success or failure (e.g., on Monday) was followed by self-regulatory effort adjustment for the next day (e.g., on Tuesday) across 14 days. Success vs. failure was operationalized with two EMA items: first, whether food intake was higher vs. lower than usual and second, whether food intake was perceived as more vs. less goal-congruent than usual. Trait-level perceived self-regulatory success in dieting was measured on a questionnaire.

RESULTS

Intended self-regulatory effort increased more strongly after days with dietary success (i.e., eating less than usual / rating intake as goal-congruent) than after days with dietary failure (i.e., eating more than usual / rating intake as goal-incongruent), especially in those individuals with lower scores on perceived self-regulatory success in dieting.

CONCLUSIONS

Findings support mechanisms proposed by social-cognitive theory, especially in unsuccessful dieters. Thus, future dietary interventions could focus on preventing the decrease in self-regulatory effort after instances of dietary failures and thereby mitigate the potential risk that a single dietary failure initiates a downward spiral into unhealthy eating.

Toward Individualized Prediction of Binge-Eating Episodes Based on Ecological Momentary Assessment Data: Item Development and Pilot Study in Patients With Bulimia Nervosa and Binge-Eating Disorder

BACKGROUND

Prevention of binge eating through just-in-time mobile interventions requires the prediction of respective high-risk times, for example, through preceding affective states or associated contexts. However, these factors and states are highly idiographic; thus, prediction models based on averages across individuals often fail.

OBJECTIVE

We developed an idiographic, within-individual binge-eating prediction approach based on ecological momentary assessment (EMA) data.

METHODS

We first derived a novel EMA-item set that covers a broad set of potential idiographic binge-eating antecedents from literature and an eating disorder focus group (n=11). The final EMA-item set (6 prompts per day for 14 days) was assessed in female patients with bulimia nervosa or binge-eating disorder. We used a correlation-based machine learning approach (Best Items Scale that is Cross-validated, Unit-weighted, Informative, and Transparent) to select parsimonious, idiographic item subsets and predict binge-eating occurrence from EMA data (32 items assessing antecedent contextual and affective states and 12 time-derived predictors).

RESULTS

On average 67.3 (SD 13.4; range 43-84) EMA observations were analyzed within participants (n=13). The derived item subsets predicted binge-eating episodes with high accuracy on average (mean area under the curve 0.80, SD 0.15; mean 95% CI 0.63-0.95; mean specificity 0.87, SD 0.08; mean sensitivity 0.79, SD 0.19; mean maximum reliability of r_D 0.40, SD 0.13; and mean r_CV 0.13, SD 0.31). Across patients, highly heterogeneous predictor sets of varying sizes (mean 7.31, SD 1.49; range 5-9 predictors) were chosen for the respective best prediction models.

CONCLUSIONS

Predicting binge-eating episodes from psychological and contextual states seems feasible and accurate, but the predictor sets are highly idiographic. This has practical implications for mobile health and just-in-time adaptive interventions. Furthermore, current theories around binge eating need to account for this high between-person variability and broaden the scope of potential antecedent factors. Ultimately, a radical shift from purely nomothetic models to idiographic prediction models and theories is required.

Clustering individuals' temporal patterns of affective states, hunger, and food craving by latent class vector-autoregression

Background

Eating plays an important role in mental and physical health and is influenced by affective (e.g., emotions, stress) and appetitive (i.e., food craving, hunger) states, among others. Yet, substantial temporal variability and marked individual differences in these relationships have been reported. Exploratory data analytical approaches that account for variability between and within individuals might benefit respective theory development and subsequent confirmatory studies.

Methods

Across 2 weeks, 115 individuals (83% female) reported on momentary affective states, hunger, and food craving six times a day. Based on these ecological momentary assessment (EMA) data we investigated whether latent class vector-autoregression (LCVAR) can identify different clusters of participants based on similarities in their temporal associations between these states.

Results

LCVAR allocated participants into three distinct clusters. Within clusters, we found both positive and negative associations between affective states and hunger/food craving, which further varied temporally across lags. Associations between hunger/food craving and subsequent affective states were more pronounced than vice versa. Clusters differed on eating-related traits such as stress-eating and food craving as well as on EMA completion rates.

Discussion

LCVAR provides novel opportunities to analyse time-series data in affective science and eating behaviour research and uncovers that traditional models of affect-eating relationships might be overly simplistic. Temporal associations differ between subgroups of individuals with specific links to eating-related traits. Moreover, even within subgroups, differences in associations across time and specific affective states can be observed. To account for this high degree of variability, future research and theories should consider individual differences in direction and time lag of associations between affective states and eating behaviour, daytime and specific affective states. In addition to that, methodological implications for EMA research are discussed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12966-022-01293-1.