Momentary changes in heart rate variability can detect risk for emotional eating episodes

Exploring emotions beyond the laboratory: A review of emotional and physiological ecological momentary assessment methods in children and youth

Recent advancements in methodologies such as ecological momentary assessment (EMA) and ambulatory physiology devices have enhanced our ability to measure emotions experienced in daily life. Despite the feasibility of EMA for assessing children's and youth's emotional self-reports, the feasibility of combining it with physiological measurements in a real-life context has yet to be established. Our scoping review evaluates the feasibility and usability of implementing emotional and physiological EMA in children and youth. Due to the complexities of physiological EMA data, this review also synthesized existing methodological and statistical practices of existing studies. Following the PRISMA-ScR guidelines, we searched and screened PsycINFO, PubMed, and Web of Science electronic databases for studies that assessed children's and youth's subjective emotions and cardiac or electrodermal physiological responses outside the laboratory. Our initial search resulted in 4174 studies, 13 of which were included in our review. Findings showed significant variability in the feasibility of physiological EMA, with physiology device wear-time averaging 58.77% of study periods and data loss due to quality issues ranging from 0.2% to 77% across signals. Compliance for emotional EMA was approximately 60% of study periods when combined with physiological EMA. The review points to a lack of standardized procedures in physiological EMA and suggests a need for guidelines in designing, processing, and analyzing such data collected in real-life contexts. We offer recommendations to enhance participant engagement and develop standard practices for employing physiological EMA with children and youth for emotion, developmental, and psychophysiology researchers.

Measures of nutrition status and health for weight-inclusive patient care: A narrative review

In healthcare, weight is often equated to and used as a marker for health. In examining nutrition and health status, there are many more effective markers independent of weight. In this article, we review practical and emerging clinical applications of technologies and tools used to collect non-weight-related data in nutrition assessment, monitoring, and evaluation in the outpatient setting. The aim is to provide clinicians with new ideas about various types of data to evaluate and track in nutrition care.

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.

Boredom proneness, interoception, and emotional eating

Emotional eating is associated with weight gain and difficulty losing weight during weight loss interventions. Theoretical and empirical work suggest boredom may be an important predictor of problematic eating behaviors. Yet, little work has examined the role of boredom in emotional eating. Further, individual differences in the ability to recognize internal cues (i.e., interoception) may alter the impact of boredom on emotional eating. This study hypothesized that boredom proneness would predict unique variance in emotional eating after accounting for negative and positive affect, and that the association between boredom proneness and emotional eating would be stronger among those with poorer interoceptive ability compared to those with better interoceptive ability. Hypotheses were tested in two large samples using multiple linear regression. Participants aged 18-65 were recruited from MTurk (n = 365; 59.2% female) and an undergraduate research pool (n = 461; 52.9% female). Participants completed self-report measures: Boredom Proneness Scale; Dutch Eating Behavior Questionnaire- Emotional Eating; Multidimensional Assessment of Interoceptive Awareness-2; Intuitive Eating Scale-2- Reliance on Hunger and Satiety Cues; and Positive and Negative Affect Schedule. Boredom proneness was a significant predictor of emotional eating in both samples, even accounting for the broad dimensions of negative and positive affect (ps < .001). Interoception did not moderate the association between boredom proneness and emotional eating in either sample (ps > .05), but was an independent predictor of emotional eating (ps < .001). Boredom proneness and interoceptive ability may warrant attention as targets in the prevention and treatment of emotional eating. Future work should continue exploring different emotion categories and different facets of interoception in emotional eating, as well as examine novel mechanisms that could inform intervention efforts.

Detecting Mental Health Behaviors Using Mobile Interactions: Exploratory Study Focusing on Binge Eating

BACKGROUND

Binge eating is a subjective loss of control while eating, which leads to the consumption of large amounts of food. It can cause significant emotional distress and is often accompanied by purging behaviors (eg, meal skipping, overexercising, or vomiting).

OBJECTIVE

The aim of this study was to explore the potential of mobile sensing to detect indicators of binge-eating episodes, with a view toward informing the design of future context-aware mobile interventions.

METHODS

This study was conducted in 2 stages. The first involved the development of the DeMMI (Detecting Mental health behaviors using Mobile Interactions) app. As part of this, we conducted a consultation session to explore whether the types of sensor data we were proposing to capture were useful and appropriate, as well as to gather feedback on some specific app features relating to self-reporting. The second stage involved conducting a 6-week period of data collection with 10 participants experiencing binge eating (logging both their mood and episodes of binge eating) and 10 comparison participants (logging only mood). An optional interview was conducted after the study, which discussed their experience using the app, and 8 participants (n=3, 38% binge eating and n=5, 63% comparisons) consented.

RESULTS

The findings showed unique differences in the types of sensor data that were triangulated with the individuals' episodes (with nearby Bluetooth devices, screen and app use features, mobility features, and mood scores showing relevance). Participants had a largely positive opinion about the app, its unobtrusive role, and its ease of use. Interacting with the app increased participants' awareness of and reflection on their mood and phone usage patterns. Moreover, they expressed no privacy concerns as these were alleviated by the study information sheet.

CONCLUSIONS

This study contributes a series of recommendations for future studies wishing to scale our approach and for the design of bespoke mobile interventions to support this population.

Application of photoplethysmography signals for healthcare systems: An in-depth review

BACKGROUND AND OBJECTIVES

Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, heart rate variability, blood pressure, etc. Hence, PPG signals can produce a wide variety of biological information that can be useful for the detection and diagnosis of various health problems. In this review, we are interested in the possible health disorders that can be detected using PPG signals.

METHODS

We applied PRISMA guidelines to systematically search various journal databases and identified 43 PPG studies that fit the criteria of this review.

RESULTS

Twenty-five health issues were identified from these studies that were classified into six categories: cardiac, blood pressure, sleep health, mental health, diabetes, and miscellaneous. Various routes were employed in these PPG studies to perform the diagnosis: machine learning, deep learning, and statistical routes. The studies were reviewed and summarized.

CONCLUSIONS

We identified limitations such as poor standardization of sampling frequencies and lack of publicly available PPG databases. We urge that future work should consider creating more publicly available databases so that a wide spectrum of health problems can be covered. We also want to promote the use of PPG signals as a potential precision medicine tool in both ambulatory and hospital settings.

Using e-diaries to investigate ADHD - State-of-the-art and the promising feature of just-in-time-adaptive interventions

Attention-deficit/hyperactive disorder (ADHD) is characterized by symptoms which are dynamic in nature: states of hyperactivity, inattention and impulsivity as core symptoms, and emotion dysregulation as associated feature. Although tremendous work has been done to investigate between-subject differences (how patients with ADHD differ from healthy controls or patients with other disorders), little is known about the relationship between symptoms with triggers and contexts, that may allow us to better understand their causes and consequences. Understanding the temporal associations between symptoms and environmental triggers in an ecologically valid manner may be the basis to developing just-in-time adaptive interventions. Fortunately, recent years have seen advances in methodology, hardware and innovative statistical approaches to study dynamic processes in daily life. In this narrative review, we provide a description of the methodology (ambulatory assessment), summarize the existing literature in ADHD, and discuss future prospects for these methods, namely mobile sensing to assess contextual information, real-time analyses and just-in-time adaptive interventions.

The potential of artificial intelligence in enhancing adult weight loss: a scoping review

OBJECTIVE

To present an overview of how artificial intelligence (AI) could be used to regulate eating and dietary behaviours, exercise behaviours and weight loss.

DESIGN

A scoping review of global literature published from inception to 15 December 2020 was conducted according to Arksey and O'Malley's five-step framework. Eight databases (CINAHL, Cochrane-Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus and Web of Science) were searched. Included studies were independently screened for eligibility by two reviewers with good interrater reliability (k = 0·96).

RESULTS

Sixty-six out of 5573 potential studies were included, representing more than 2031 participants. Three tenets of self-regulation were identified - self-monitoring (n 66, 100 %), optimisation of goal setting (n 10, 15·2 %) and self-control (n 10, 15·2 %). Articles were also categorised into three AI applications, namely machine perception (n 50), predictive analytics only (n 6) and real-time analytics with personalised micro-interventions (n 10). Machine perception focused on recognising food items, eating behaviours, physical activities and estimating energy balance. Predictive analytics focused on predicting weight loss, intervention adherence, dietary lapses and emotional eating. Studies on the last theme focused on evaluating AI-assisted weight management interventions that instantaneously collected behavioural data, optimised prediction models for behavioural lapse events and enhance behavioural self-control through adaptive and personalised nudges/prompts. Only six studies reported average weight losses (2·4-4·7 %) of which two were statistically significant.

CONCLUSION

The use of AI for weight loss is still undeveloped. Based on the current study findings, we proposed a framework on the applicability of AI for weight loss but cautioned its contingency upon engagement and contextualisation.

Enhancing Integrative Cognitive-Affective Therapy with ecological momentary interventions: A pilot trial

OBJECTIVE

Although current treatments are effective for some patients with eating disorders, a large number of patients remain partially or fully symptomatic post-treatment. This may be related to poor utilization of treatment skills outside of the therapy office. Smartphone applications that can detect and intervene during moments of need could facilitate such skill use between sessions.

METHOD

Individuals (N = 16) participated in a small pilot open trial where they received 21 sessions of in-person Integrative Cognitive-Affective Therapy (ICAT) therapy an app (iCAT+) that delivers ecological momentary interventions (EMI) in response to user-entered data. Data were collected on the feasibility and acceptability of this treatment approach and on preliminary indicators of treatment outcomes.

RESULTS

Participants found iCAT+ as a treatment augmentation acceptable and indicated it had clinical utility as an adjunct to in-person therapy, although analyses indicated poor compliance with data entry needed to trigger EMI delivery. This suggests that long-term use of EMI requiring ongoing data entry is infeasible.

CONCLUSIONS

We describe lessons learned from our initial pilot trial and future directions for the development of impactful EMI systems that can be used to augment in-person therapies.