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.

Combining passive eating monitoring and ecological momentary assessment to characterize dietary lapses from a lifestyle modification intervention

Dietary lapses (i.e., specific instances of nonadherence to recommended dietary goals) contribute to suboptimal weight loss outcomes during lifestyle modification programs. Passive eating monitoring could enhance lapse measurement via objective assessment of eating characteristics that could be markers for lapse (e.g., more bites consumed). The purpose of this study was to evaluate if passively-inferred eating characteristics (i.e., bites, eating duration, and eating rate), measured via wrist-worn device, could distinguish dietary lapses from non-lapse eating. Adults (n = 25) with overweight/obesity received a 24-week lifestyle modification intervention. Participants completed ecological momentary assessment (EMA; repeated smartphone surveys) biweekly to self-report on dietary lapses and non-lapse eating episodes. Participants wore a wrist device that captured continuous wrist motion. Previously-validated algorithms inferred eating episodes from wrist data, and calculated bite count, duration, and rate (seconds per bite). Mixed effects logistic regressions revealed no simple effects of bite count, duration, or eating rate on the likelihood of dietary lapse. Moderation analyses revealed that eating episodes in the evening were more likely to be lapses if they involved fewer bites (B = -0.16, p < .05), were shorter (B = -0.54, p < .05), or had a slower rate (B = 1.27, p < .001). Statistically significant interactions between eating characteristics (Bs = -0.30 to -0.08, ps < .001) revealed two distinct patterns. Eating episodes that were 1. smaller, slower, and shorter than average, or 2. larger, quicker, and longer than average were associated with increased probability of lapse. This study is the first to use objective eating monitoring to characterize dietary lapses throughout a lifestyle modification intervention. Results demonstrate the potential of sensors to identify non-adherence using only patterns of passively-sensed eating characteristics, thereby minimizing the need for self-report in future studies. CLINICAL TRIALS REGISTRY NUMBER: NCT03739151.

Momentary predictors of dietary lapse from a mobile health weight loss intervention

Identifying factors that influence risk of dietary lapses (i.e., instances of dietary non-adherence) is important because lapses contribute to suboptimal weight loss outcomes. Existing research examining lapse risk factors has had methodological limitations, including retrospective recall biases, subjective operationalizations of lapse, and has investigated lapses among participants in gold-standard behavioral weight loss programs (which are not accessible to most Americans). The current study will address these limitations by being the first to prospectively assess several risk factors of lapse (objectively operationalized) in the context of a commercial mobile health (mHealth) intervention, a highly popular and accessible method of weight loss. N = 159 adults with overweight or obesity enrolled in an mHealth commercial weight loss program completed ecological momentary assessments (EMAs) of 15 risk factors and lapses (defined as exceeding a point target for a meal/snack) over a 2-week period. N = 9 participants were excluded due to low EMA compliance, resulting in a sample of N = 150. Dietary lapses were predicted by momentary increases in urges to deviate from one's eating plan (b = .55, p < .001), cravings (b = .55, p < .001), alcohol consumption (b = .51, p < .001), and tiredness (b = .19, p < .001), and decreases in confidence related to meeting dietary goals (b = -.21, p < .001) and planning food intake (b = -.15, p < .001). This study was among the first to identify prospective predictors of lapse in the context of a commercial mHealth weight loss program. Findings can inform mHealth weight loss programs, including just-in-time interventions that measure these risk factors, calculate when risk of lapse is high, and deliver momentary interventions to prevent lapses.

Identifying behavioral types of dietary lapse from a mobile weight loss program: Preliminary investigation from a secondary data analysis

Success in behavioral weight loss (BWL) programs depends on adherence to the recommended diet to reduce caloric intake. Dietary lapses (i.e., deviations from the BWL diet) occur frequently and can adversely affect weight loss outcomes. Research indicates that lapse behavior is heterogenous; there are many eating behaviors that could constitute a dietary lapse, but they are rarely studied as distinct contributors to weight outcomes. This secondary analysis aims to evaluate six behavioral lapse types during a 10-week mobile BWL program (eating a large portion, eating when not intended, eating an off-plan food, planned lapse, being unaware of caloric content, and endorsing multiple types of lapse). Associations between weekly behavioral lapse type frequency and weekly weight loss were investigated, and predictive contextual characteristics (psychological, behavioral, and environmental triggers for lapse) and individual difference (e.g., age, gender) factors were examined across lapse types. Participants (N = 121) with overweight/obesity (M_BMI = 34.51; 84.3% female; 69.4% White) used a mobile BWL program for 10 weeks, self-weighed weekly using Bluetooth scales, completed daily ecological momentary assessment of lapse behavior and contextual characteristics, and completed a baseline demographics questionnaire. Linear mixed models revealed significant negative associations between unplanned lapses and percent weight loss. Unplanned lapses from eating a large portion, eating when not intended, and having multiple "types" were significantly negatively associated with weekly percent weight loss. A lasso regression showed that behavioral lapse types share many similar stable factors, with other factors being unique to specific lapse types. Results add to the prior literature on lapses and weight loss in BWL and provide preliminary evidence that behavioral lapse types could aid in understanding adherence behavior and developing precision medicine tools to improve dietary adherence.

Combining ecological momentary assessment, wrist-based eating detection, and dietary assessment to characterize dietary lapse: A multi-method study protocol

Objectives

Behavioral obesity treatment (BOT) produces clinically significant weight loss and health benefits for many individuals with overweight/obesity. Yet, many individuals in BOT do not achieve clinically significant weight loss and/or experience weight regain. Lapses (i.e., eating that deviates from the BOT prescribed diet) could explain poor outcomes, but the behavior is understudied because it can be difficult to assess. We propose to study lapses using a multi-method approach, which allows us to identify objectively-measured characteristics of lapse behavior (e.g., eating rate, duration), examine the association between lapse and weight change, and estimate nutrition composition of lapse.

Method

We are recruiting participants (n = 40) with overweight/obesity to enroll in a 24-week BOT. Participants complete biweekly 7-day ecological momentary assessment (EMA) to self-report on eating behavior, including dietary lapses. Participants continuously wear the wrist-worn ActiGraph Link to characterize eating behavior. Participants complete 24-hour dietary recalls via structured interview at 6-week intervals to measure the composition of all food and beverages consumed.

Results

While data collection for this trial is still ongoing, we present data from three pilot participants who completed EMA and wore the ActiGraph to illustrate the feasibility, benefits, and challenges of this work.

Conclusion

This protocol will be the first multi-method study of dietary lapses in BOT. Upon completion, this will be one of the largest published studies of passive eating detection and EMA-reported lapse. The integration of EMA and passive sensing to characterize eating provides contextually rich data that will ultimately inform a nuanced understanding of lapse behavior and enable novel interventions.Trial registration: Registered clinical trial NCT03739151; URL: https://clinicaltrials.gov/ct2/show/NCT03739151.