Baseline and momentary predictors of ecological momentary assessment adherence in a sample of adults with binge-eating disorder

Predicting Completion of Ecological Momentary Assessments Among Substance-Using Women Experiencing Intimate Partner Violence

Noncompletion of ecological momentary assessment (EMA) surveys is a common issue and may yield bias in results if not properly handled. Using data observed at scheduled times as well as data retrieved later to fill missing responses, this study aims to investigate predictors of EMA completion, including demographic characteristics, time-related factors, and momentary experiences/behaviors. Data were from a 30-day EMA study including 145 women currently experiencing intimate partner violence (IPV) and using substances. The average rate of EMA completion was initially 51.4% at the scheduled times and increased to 72.6% after incorporating data from later-retrieved surveys. Participants who were younger, had more children, or had lower mean levels of negative affect dysregulation showed lower completion rates. At the momentary survey level, more days into the study and afternoon/evening reports (vs. morning reports) were associated with lower completion; lower levels of negative affect dysregulation, less smoking or alcohol use, and experiencing IPV were linked to lower momentary completion. Implications of the results for handling missing data in EMA are discussed and have important ramifications for future research, practice, and theory.

Evaluating dietary restriction as a maintaining factor in binge-eating disorder

OBJECTIVE

Prominent theories of binge-eating (BE) maintenance highlight dietary restriction as a key precipitant of BE episodes. Consequently, treatment approaches for eating disorders (including binge-eating disorder; BED) seek to reduce dietary restriction in order to improve BE symptoms. The present study tested the hypothesis that dietary restriction promotes BE among 112 individuals with BED.

METHODS

Participants completed a 7-day ecological momentary assessment (EMA) protocol before and after completing 17 weeks of either Integrative Cognitive-Affective Therapy or guided self-help cognitive behavioral therapy. Analyses examined whether dietary restriction on 1 day of the baseline EMA protocol predicted risk for BE later that same day, and on the following day. Changes in dietary restriction over the course of treatment were also evaluated as a predictor of change in BE from pre-treatment to post-treatment. Baseline dietary restraint was examined as a moderator of the above associations.

RESULTS

Dietary restriction did not predict BE later the same day, and changes in restriction were not related to changes in BE across treatment, regardless of baseline dietary restraint levels. Restriction on 1 day did predict increased BE risk on the following day for individuals with higher levels of dietary restraint, but not those with lower levels.

DISCUSSION

These findings challenge the assumption that dietary restriction maintains BE among all individuals with BED. Rather, results suggest that dietary restriction may be largely unrelated to BE maintenance in this population, and that reducing dietary restriction generally does not have the intended effect on BE frequency.

Development of Momentary Appetite Capture (MAC): A versatile tool for monitoring appetite over long periods

Understanding how an intervention impacts appetite in real-life settings and over several days remains a challenging and under-explored research question. To this end, we developed Momentary Appetite Capture (MAC), a form of ecological momentary assessment that combines automated text messaging with an online platform. Participants report their appetite using visual analogue scales (hunger, desire to eat, and fullness) and a virtual portion-size selection task. In two separate studies, we assessed the feasibility and test-retest reliability of MAC. Participants were prompted every 2 hours over a 14-hour window, and they repeated this assessment over two consecutive weekdays. For each participant, we calculated a daily time-averaged area under the curve (AUC) for each appetite measure. In Study One (N = 25) time-averaged AUC was significantly positively correlated across test days for hunger (r = 0.563, p = .003), desire to eat (r = 0.515, p = .008) and prospective portion size (r = 0.914, p < .001), but not for fullness (r = 0.342, p = .094). Participants completed 95% of MACs (380 of 400), and we used participant feedback to improve the MAC tool and study protocol for Study Two. In Study Two (N = 31), 94% of MACs were completed (468 of 496). Across days, time-averaged AUC was significantly positively correlated for hunger (r = 0.595, p = < .001), fullness (r = 0.501, p = .004), desire to eat (r = 0.585, p < .001), and prospective portion size (r = 0.757, p < .001). Together, these studies suggest that MAC could be an acceptable and reliable tool to track appetite throughout the day. In the future, MAC could be used to explore the impact of weight-loss interventions on natural fluctuations in appetite.

Respondent characteristics associated with adherence in a general population ecological momentary assessment study

OBJECTIVES

Ecological momentary assessment (EMA) has seen an explosion in popularity in recent years; however, an improved understanding of how to minimise (selective) non-adherence is needed.

METHODS

We examined a range of respondent characteristics predictors of adherence (defined as the number of EMA surveys completed) in the D2M EMA study. Participants were a sample of n = 255 individuals drawn from the longitudinal z-proso cohort who completed up to 4 EMA surveys per day for a period of 2 weeks.

RESULTS

In unadjusted analyses, lower moral shame, lower self-control, lower levels of self-injury, and higher levels of aggression, tobacco use, psychopathy, and delinquency were associated with lower adherence. In fully adjusted analyses with predictors selected using lasso, only alcohol use was related to adherence: beer and alcopops to higher adherence and spirits to lower adherence.

CONCLUSIONS

These findings provide potential insights into some of the psychological mechanisms that may underlie adherence in EMA. They also point to respondent characteristics for which additional or tailored efforts may be needed to promote adherence.

Prediction meets time series with gaps: User clusters with specific usage behavior patterns

With mHealth apps, data can be recorded in real life, which makes them useful, for example, as an accompanying tool in treatments. However, such datasets, especially those based on apps with usage on a voluntary basis, are often affected by fluctuating engagement and by high user dropout rates. This makes it difficult to exploit the data using machine learning techniques and raises the question of whether users have stopped using the app. In this extended paper, we present a method to identify phases with varying dropout rates in a dataset and predict for each. We also present an approach to predict what period of inactivity can be expected for a user in the current state. We use change point detection to identify the phases, show how to deal with uneven misaligned time series and predict the user's phase using time series classification. In addition, we examine how the evolution of adherence develops in individual clusters of individuals. We evaluated our method on the data of an mHealth app for tinnitus, and show that our approach is appropriate for the study of adherence in datasets with uneven, unaligned time series of different lengths and with missing values.

Using Wearable Passive Sensing to Predict Binge Eating in Response to Negative Affect Among Individuals With Transdiagnostic Binge Eating: Protocol for an Observational Study

BACKGROUND

Binge eating (BE), characterized by eating a large amount of food accompanied by a sense of loss of control over eating, is a public health crisis. Negative affect is a well-established antecedent for BE. The affect regulation model of BE posits that elevated negative affect increases momentary risk for BE, as engaging in BE alleviates negative affect and reinforces the behavior. The eating disorder field's capacity to identify moments of elevated negative affect, and thus BE risk, has exclusively relied on ecological momentary assessment (EMA). EMA involves the completion of surveys in real time on one's smartphone to report behavioral, cognitive, and emotional symptoms throughout the day. Although EMA provides ecologically valid information, EMA surveys are often delivered only 5-6 times per day, involve self-report of affect intensity only, and are unable to assess affect-related physiological arousal. Wearable, psychophysiological sensors that measure markers of affect arousal including heart rate, heart rate variability, and electrodermal activity may augment EMA surveys to improve accurate real-time prediction of BE. These sensors can objectively and continuously measure biomarkers of nervous system arousal that coincide with affect, thus allowing them to measure affective trajectories on a continuous timescale, detect changes in negative affect before the individual is consciously aware of them, and reduce user burden to improve data completeness. However, it is unknown whether sensor features can distinguish between positive and negative affect states, given that physiological arousal may occur during both negative and positive affect states.

OBJECTIVE

The aims of this study are (1) to test the hypothesis that sensor features will distinguish positive and negative affect states in individuals with BE with >60% accuracy and (2) test the hypothesis that a machine learning algorithm using sensor data and EMA-reported negative affect to predict the occurrence of BE will predict BE with greater accuracy than an algorithm using EMA-reported negative affect alone.

METHODS

This study will recruit 30 individuals with BE who will wear Fitbit Sense 2 wristbands to passively measure heart rate and electrodermal activity and report affect and BE on EMA surveys for 4 weeks. Machine learning algorithms will be developed using sensor data to distinguish instances of high positive and high negative affect (aim 1) and to predict engagement in BE (aim 2).

RESULTS

This project will be funded from November 2022 to October 2024. Recruitment efforts will be conducted from January 2023 through March 2024. Data collection is anticipated to be completed in May 2024.

CONCLUSIONS

This study is anticipated to provide new insight into the relationship between negative affect and BE by integrating wearable sensor data to measure affective arousal. The findings from this study may set the stage for future development of more effective digital ecological momentary interventions for BE.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/47098.

Introducing a Real-Time Method for Identifying the Predictors of Noncompliance with Event-Based Reporting of Tobacco Use in Ecological Momentary Assessment

BACKGROUND

Little is known about the factors that bias event-based (i.e., self-initiated) reporting of health behaviors in ecological momentary assessment (EMA) due to the difficulty inherent to tracking failures to self-initiate reports.

PURPOSE

To introduce a real-time method for identifying the predictors of noncompliance with event-based reporting.

METHODS

N = 410 adults who used both cigarettes and e-cigarettes completed a 1-week EMA protocol that combined random reporting of current contexts with event-based reporting of tobacco use. Each random assessment first asked if participants were currently using tobacco and, if so, the assessment converted into a "randomly captured" event report-indicating failure to self-initiate that report. Multilevel modeling tested predictors of failing to complete random reports and failing to self-initiate event reports.

RESULTS

On the person level, male sex, higher average cigarette rate, and higher average cigarette urge each predicted missing random reports. The person-level predictors of failing to self-initiate event reports were older age, higher average cigarette and e-cigarette rates, higher average cigarette urge, and being alone more on average; the moment-level predictors were lower cigarette urge, lower positive affect, alcohol use, and cannabis use. Strikingly, the randomly captured events comprised more of the total EMA reports (28%) than did the self-initiated event reports (24%). These report types were similar across most variables, with some exceptions, such as momentary cannabis use predicting the random capture of tobacco events.

CONCLUSIONS

This study demonstrated a method of identifying predictors of noncompliance with event-based reporting of tobacco use and enhancing the real-time capture of events.

Prompt-level predictors of compliance in an ecological momentary assessment study of young adults' mental health

BACKGROUND

Ecological momentary assessment (EMA) has become a popular method of gathering information about participants as they go about their daily lives. However, participant non-compliance, especially non-random compliance, in EMA is a concern. Better knowledge of the moment-to-moment factors that predict prompt non-response can inform the design of strategies to mitigate it.

METHOD

We used data from a general population young adult (n = 260) EMA study, 'decades-to-minutes' (D2M) and fitted dynamic structural equation models (DSEMs) to explore a range of candidate momentary predictors of missing the next prompt.

RESULTS

We found that higher levels of stress, overall negative affect, and the specific negative affective state of 'upset' at a given prompt predicted a greater likelihood of missing the next prompt. However, no other specific affective states, alcohol use, experiencing social provocations nor aggressive behaviour predicted missing the next prompt.

LIMITATIONS

The primary limitation of the present study was a lack of information on predictors concurrent with missed prompts.

CONCLUSIONS

Findings point to the potential value of gathering information on momentary negative affect (especially feeling upset) and stress to help inform strategies that intervene to prevent application disengagement at optimal moments and to feed into strategies to mitigate bias due to non-random non-response in EMA studies.

Real-time assessment of positive and negative affective fluctuations and mood lability in a transdiagnostic sample of youth

BACKGROUND

Emotional lability, defined as rapid and/or intense affect fluctuations, is associated with pediatric psychopathology. Although numerous studies have examined labile mood in clinical groups, few studies have used real-time assessments in a well-characterized transdiagnostic sample, and no prior study has included participants with disruptive mood dysregulation disorder (DMDD). The present study leverages ecological momentary assessment (EMA) to assess emotional lability in a transdiagnostic pediatric sample.

METHODS

One hundred thirty participants ages 8-18 with primary diagnoses of DMDD, attention-deficit/hyperactivity disorder (ADHD), an anxiety disorder (ANX), or healthy volunteers completed a previously validated 1-week EMA protocol. Clinicians determined diagnoses based on semi-structured interviews and assessed levels of functional impairment. Participants reported momentary affective states and mood change. Composite scores of fluctuations in positive and negative affect were generated. Affect fluctuations were compared between diagnostic groups and tested for their association with functional impairment.

RESULTS

Diagnostic groups differed in levels of negative and positive emotional lability. DMDD patients demonstrated the highest level of labile mood compared with other groups. Emotional lability was associated with global impairment in the whole sample.

CONCLUSIONS

Both positive and negative emotional lability is salient in pediatric psychopathology and is associated with functional impairment, particularly in DMDD youth.

Mobile survey engagement by older adults is high during multiple phases of the COVID-19 pandemic and is predicted by baseline and structural factors

Digital surveys, such as mobile phone ecological momentary assessment (EMA), bear the potential to assess and target individual wellbeing in a personalized, real-time approach and allow for interaction in situations when in-person contact is not possible, such as during the coronavirus pandemic. While the use of digital technology might especially benefit research in older adults who find themselves in circumstances of reduced mobility, little is known about their barriers to adherence. We investigated baseline and structural factors that predict study withdrawal and adherence from daily smartphone EMA self-report surveys in the StayWELL Study. The StayWELL study is a longitudinal, observational study on the relationship between social restrictions during the coronavirus pandemic and mental well-being in 95 community-dwelling older aged adults (67–87 years) who were participants in a randomized clinical trial using EMA. Withdrawal was associated with less research staff changes and less likely in participants that reached the study mid-point. No baseline characteristics predicted withdrawal. Main reasons for withdrawal were communication issues, i.e. staff not being able to contact participants. We found an adherence rate of 82% and no fatigue effects. Adherence was predicted by education status, study participation duration, reaching the study midpoint and time between study start and enrollment. COVID infections or supporting people in the household was not related to adherence. To conclude, it is feasible to conduct an EMA study in older people without impacting engagement during a pandemic. Furthermore, personal characteristics and smartphone operating system (Android vs. iOS) used did not relate to engagement, allowing for a broad distribution of digital health technologies. Our study adds information on single predictive variables relevant for adherence and withdrawal from EMA smartphone surveys in older people that can inform the design of future digital EMA research to maximize engagement and reliability of study results.