Just-in-time adaptive interventions: A novel approach for enhancing skill utilization and acquisition in cognitive behavioral therapy for eating disorders

Proof-of-concept testing of a brief virtual ACT workshop for emotional eating

BACKGROUND

Emotional eating, or eating in response to negative emotions, is a commonly reported short-term emotion regulation strategy but has been shown to be ineffective in the long term. Most emotional eating interventions based on Acceptance and Commitment Therapy (ACT) have been delivered in the context of weight loss trials, highlighting a need for ACT-based emotional eating interventions in weight-neutral contexts.

AIMS

This proof-of-concept study aimed to test the acceptability and efficacy potential of a brief virtual ACT workshop for emotional eating in a small sample of adults identifying as emotional eaters.

METHODS

Twenty-six adult emotional eaters completed an ACT workshop delivered in two 1.5-h sessions over two weeks. The workshop targeted awareness and acceptance of emotions and eating urges, and valued actions around eating.

RESULTS

The acceptability of the workshop was demonstrated by high participant satisfaction. Significant improvements on all outcome measures were found and maintained up to 3 months follow-up.

CONCLUSIONS

These proof-of-concept findings suggest that a brief virtual ACT workshop may improve emotional eating and associated ACT processes. Results from this study can inform a future randomized controlled trial to test the efficacy of the workshop and the role of theoretical processes of change.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT04457804.

LEVEL OF EVIDENCE

Level IV, evidence obtained from multiple time series with the intervention.

Bulimia Nervosa and Binge-Eating Disorder Across the Lifespan

This article aims to review the current evidence-based psychotherapy and psychopharmacological treatments for adults and youths with bulimia nervosa (BN) and binge-eating disorder (BED). Treatments for adults and for children and adolescents are discussed separately, including developmental considerations in the management of these disorders among youths. Although several evidence-based psychotherapy and psychopharmacological treatment options have been established for adults with BN or BED, there is much less empirical support for the management of these eating disorders among children and adolescents. This review concludes by discussing promising modalities and innovations, highlighting the potential utility of integrating technology into treatment approaches. Despite decades of treatment development and testing, a sizable proportion of individuals with BN or BED do not respond to the current evidence-based treatments, highlighting the need for continued research in these domains. Future research should focus on testing psychotherapy treatments among diverse samples in large, randomized controlled trials, as well as on treatments that can be easily scaled and implemented in community settings.

Treatment of Binge-Eating Disorder Across the Lifespan: An Updated Review of the Literature and Considerations for Future Research

PURPOSE OF REVIEW

The present review describes the recent literature on treatment for binge-eating disorder (BED) in adults and youth, with a particular focus on research gaps, emerging treatments, and future research directions.

RECENT FINDINGS

Evidence supports the efficacy of several treatment modalities in adults, including self-help treatment, clinician-led psychotherapy, and pharmacotherapy; the largest effect sizes have been found for psychotherapies, most of which were cognitive-behavioral in orientation. Adapted psychotherapies for youth also show promise but lack a robust body of evidence. Predictors, moderators, and mediators of treatment outcome remain poorly understood; individuals with BED continue to experience significant barriers to treatment; and research is needed to address suboptimal treatment response. Recent work has highlighted the potential of adaptive interventions and investigation of novel mechanisms to address these gaps. Research on BED treatment continues to grow, though critical questions must be answered to improve treatment efficacy across the lifespan.

Predicting the onset of psychotic experiences in daily life with the use of ambulatory sensor data - A proof-of-concept study

INTRODUCTION

Predictive models of psychotic symptoms could improve ecological momentary interventions by dynamically providing help when it is needed. Wearable sensors measuring autonomic arousal constitute a feasible base for predictive models since they passively collect physiological data linked to the onset of psychotic experiences. To explore this potential, we investigated whether changes in autonomic arousal predict the onset of hallucination spectrum experiences (HSE) and paranoia in individuals with an increased likelihood of experiencing psychotic symptoms.

METHOD

For 24 h of ambulatory assessment, 62 participants wore electrodermal activity and heart rate sensors and were provided with an Android smartphone to answer questions about their HSE-, and paranoia-levels every 20 min. We calculated random forests to detect the onset of HSEs and paranoia. The generalizability of our models was tested using leave-one-assessment-out and leave-one-person-out cross-validation.

RESULTS

Leave-one-assessment-out models that relied on physiological data and participant ID yielded balanced accuracy scores of 80 % for HSE and 66 % for paranoia. Adding baseline information about lifetime experiences of psychotic symptoms increased balanced accuracy to 82 % (HSE) and 70 % (paranoia). Leave-one-person-out models yielded lower balanced accuracy scores (51 % to 58 %).

DISCUSSION

Using passively collectible variables to predict the onset of psychotic experiences is possible and prediction models improve with additional information about lifetime experiences of psychotic symptoms. Generalizing to new individuals showed poor performance, so including personal data from a recipient may be necessary for symptom prediction. Completely individualized prediction models built solely with the data of the person to be predicted might increase accuracy further.

Using machine learning with passive wearable sensors to pilot the detection of eating disorder behaviors in everyday life

BACKGROUND

Eating disorders (ED) are serious psychiatric disorders, taking a life every 52 minutes, with high relapse. There are currently no support or effective intervention therapeutics for individuals with an ED in their everyday life. The aim of this study is to build idiographic machine learning (ML) models to evaluate the performance of physiological recordings to detect individual ED behaviors in naturalistic settings.

METHODS

From an ongoing study (Final N = 120), we piloted the ability for ML to detect an individual's ED behavioral episodes (e.g. purging) from physiological data in six individuals diagnosed with an ED, all of whom endorsed purging. Participants wore an ambulatory monitor for 30 days and tapped a button to denote ED behavioral episodes. We built idiographic (N = 1) logistic regression classifiers (LRC) ML trained models to identify onset of episodes (~600 windows) v. baseline (~571 windows) physiology (Heart Rate, Electrodermal Activity, and Temperature).

RESULTS

Using physiological data, ML LRC accurately classified on average 91% of cases, with 92% specificity and 90% sensitivity.

CONCLUSIONS

This evidence suggests the ability to build idiographic ML models that detect ED behaviors from physiological indices within everyday life with a high level of accuracy. The novel use of ML with wearable sensors to detect physiological patterns of ED behavior pre-onset can lead to just-in-time clinical interventions to disrupt problematic behaviors and promote ED recovery.

Changes in evening-shifted loss of control eating severity following treatment for binge-eating disorder

BACKGROUND

Loss of control eating is more likely to occur in the evening and is uniquely associated with distress. No studies have examined the effect of treatment on within-day timing of loss of control eating severity. We examined whether time of day differentially predicted loss of control eating severity at baseline (i.e. pretreatment), end-of-treatment, and 6-month follow-up for individuals with binge-eating disorder (BED), hypothesizing that loss of control eating severity would increase throughout the day pretreatment and that this pattern would be less pronounced following treatment. We explored differential treatment effects of cognitive-behavioral guided self-help (CBTgsh) and Integrative Cognitive-Affective Therapy (ICAT).

METHODS

Individuals with BED (N = 112) were randomized to receive CBTgsh or ICAT and completed a 1-week ecological momentary assessment protocol at baseline, end-of-treatment, and 6-month follow-up to assess loss of control eating severity. We used multilevel models to assess within-day slope trajectories of loss of control eating severity across assessment periods and treatment type.

RESULTS

Within-day increases in loss of control eating severity were reduced at end-of-treatment and 6-month follow-up relative to baseline. Evening acceleration of loss of control eating severity was greater at 6-month follow-up relative to end-of-treatment. Within-day increases in loss of control severity did not differ between treatments at end-of-treatment; however, evening loss of control severity intensified for individuals who received CBTgsh relative to those who received ICAT at 6-month follow-up.

CONCLUSIONS

Findings suggest that treatment reduces evening-shifted loss of control eating severity, and that this effect may be more durable following ICAT relative to CBTgsh.

Noradrenergic control of neurobehavior in human binge-eating disorder and obesity (NOBEAD): A smartphone-supported behavioral emotion regulation intervention study protocol integrating molecular brain imaging

OBJECTIVE

The neurobehavioral underpinnings of binge-eating disorder (BED), co-occurring with obesity (OB), are largely unknown. This research project conceptualizes BED as a disorder with dysfunctional emotion regulation (ER) linked with changes in central noradrenaline (NA) transmission and NA-modulated neuronal networks.

METHODS

We expect abnormalities in NA activity in both BED and OB, but most pronounced in BED. We expect these abnormalities to be modifiable through state-of-the-art ER intervention, specifically in BED. To assess the role of NA transmission, we will quantify changes in NA transporter (NAT) availability using the highly NAT-specific [11 C]methylreboxetin (MRB) and positron emission tomography-magnetic resonance imaging (PET-MRI) that allows measuring molecular and neuronal changes before and after an ER intervention. Individual 12-session smartphone-supported acceptance-based behavioral therapy will be conducted to improve ER. Thirty individuals with OB and BED (OB + BED), 30 individuals with OB without BED (OB - BED), and 20 individuals with normal weight will undergo assessments of NAT availability and neuronal network activity under rest and stimulated conditions, clinical interviews, self-report questionnaires on eating behavior, ER, mental and physical health, and quality of life, and neuropsychological tests on executive function. Afterwards, in an experimental randomized-controlled design, individuals with OB + BED and OB - BED will be allocated to smartphone-supported ER intervention versus a waitlist and re-assessed after 10 weeks.

DISCUSSION

By obtaining biological and behavioral markers, the proposed study will disentangle the involvement of NAT and the central NA system in the modulation of emotion-supporting neuronal networks that influence eating behavior. Neurobehavioral mechanisms of change during an ER intervention will be determined.

TRIAL REGISTRATION

German Clinical Trials Register (DRKS): DRKS00029367.

PUBLIC SIGNIFICANCE

This study investigates the central noradrenaline system by using hybrid brain imaging in conjunction with emotion regulation as a putative core biological mechanism in individuals with obesity with or without binge-eating disorder that is targeted by emotion regulation intervention. The results will provide a molecular signature beyond functional imaging biomarkers as a predictive biomarker toward precision medicine for tailoring treatments for individuals with binge-eating disorders and obesity.

Weight status is associated with clinical characteristics among individuals with bulimia nervosa

Recent studies have found increasing rates of overweight and obesity in bulimia nervosa (BN). However, the relationships between body mass index (BMI) and BN symptoms and other clinically relevant constructs are unknown. Participants (N = 152 adults with BN) were assigned to three groups by BMI: group with no overweight or obesity (NOW-BN; BMI <25; N = 32), group with overweight (OW-BN; BMI ≥25 and <30; N = 66), and group with obesity (O-BN; BMI ≥30; N = 54). We compared the groups on demographics, diet and weight histories, body esteem, BN symptoms, and depression using chi square, analysis of variance, analysis of covariance, and Poisson regression models. The O-BN group was older (d = 0.57) and OW-BN and O-BN groups had greater proportions of race/ethnic minorities than NOW-BN group. The O-BN group was significantly younger at first diet (d = 0.41) and demonstrated significantly higher cognitive dietary restraint (d = 0.31). Compared to NOW-BN, O-BN participants had lower incidence of objective binge eating (incidence rate ratio [IRR] = 4.86) and driven exercise (IRR = 7.13), and greater incidence of vomiting (IRR = 9.30), laxative misuse (IRR = 4.01), and diuretic misuse (d = 2.08). O-BN participants also experienced higher shape (d = 0.41) and weight (d = 0.42) concerns than NOW-BN and OW-BN, although NOW-BN experienced higher shape (d = 0.44) and weight (d = 0.39) concerns than OW-BN. Groups did not differ on depression scores. These results were replicated when examining BMI as a continuous predictor across the full sample, with the exception of objective binge eating and driven exercise, which were not significantly associated with BMI. Individuals with BN and comorbid obesity have distinct clinical characteristics. Existing interventions may need to be adapted to meet clinical needs of these individuals.

A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health

Advances in computer science and data-analytic methods are driving a new era in mental health research and application. Artificial intelligence (AI) technologies hold the potential to enhance the assessment, diagnosis, and treatment of people experiencing mental health problems and to increase the reach and impact of mental health care. However, AI applications will not mitigate mental health disparities if they are built from historical data that reflect underlying social biases and inequities. AI models biased against sensitive classes could reinforce and even perpetuate existing inequities if these models create legacies that differentially impact who is diagnosed and treated, and how effectively. The current article reviews the health-equity implications of applying AI to mental health problems, outlines state-of-the-art methods for assessing and mitigating algorithmic bias, and presents a call to action to guide the development of fair-aware AI in psychological science.

An End-to-End Energy-Efficient Approach for Intake Detection With Low Inference Time Using Wrist-Worn Sensor

Automated detection of intake gestures with wearable sensors has been a critical area of research for advancing our understanding and ability to intervene in people's eating behavior. Numerous algorithms have been developed and evaluated in terms of accuracy. However, ensuring the system is not only accurate in making predictions but also efficient in doing so is critical for real-world deployment. Despite the growing research on accurate detection of intake gestures using wearables, many of these algorithms are often energy inefficient, impeding on-device deployment for continuous and real-time monitoring of diet. This article presents a template-based optimized multicenter classifier that enables accurate intake gesture detection while maintaining low-inference time and energy consumption using a wrist-worn accelerometer and gyroscope. We designed an Intake Gesture Counter smartphone application (CountING) and validated the practicality of our algorithm against seven state-of-the-art approaches on three public datasets (In-lab FIC, Clemson, and OREBA). Compared with other methods, we achieved optimal accuracy (81.60% F1 score) and very low inference time (15.97 msec per 2.20-sec data sample) on the Clemson dataset, and among the top performing algorithms, we achieve comparable accuracy (83.0% F1 score compared with 85.6% in the top performing algorithm) but superior inference time (13.8x faster, 33.14 msec per 2.20-sec data sample) on the In-lab FIC dataset and comparable accuracy (83.40% F1 score compared with 88.10% in the top-performing algorithm) but superior inference time (33.9x faster, 16.71 msec inference time per 2.20-sec data sample) on the OREBA dataset. On average, our approach achieved a 25-hour battery lifetime (44% to 52% improvement over state-of-the-art approaches) when tested on a commercial smartwatch for continuous real-time detection. Our approach demonstrates an effective and efficient method, enabling real-time intake gesture detection using wrist-worn devices in longitudinal studies.

Autonomic indices and loss-of-control eating in adolescents: an ecological momentary assessment study

BACKGROUND

Loss-of-control (LOC) eating commonly develops during adolescence, and it predicts full-syndrome eating disorders and excess weight gain. Although negative emotions and emotion dysregulation are hypothesized to precede and predict LOC eating, they are rarely examined outside the self-report domain. Autonomic indices, including heart rate (HR) and heart rate variability (HRV), may provide information about stress and capacity for emotion regulation in response to stress.

METHODS

We studied whether autonomic indices predict LOC eating in real-time in adolescents with LOC eating and body mass index (BMI) ⩾70th percentile. Twenty-four adolescents aged 12-18 (67% female; BMI percentile mean ± standard deviation = 92.6 ± 9.4) who reported at least twice-monthly LOC episodes wore biosensors to monitor HR, HRV, and physical activity for 1 week. They reported their degree of LOC after all eating episodes on a visual analog scale (0-100) using a smartphone.

RESULTS

Adjusting for physical activity and time of day, higher HR and lower HRV predicted higher self-reported LOC after eating. Parsing between- and within-subjects effects, there was a significant, positive, within-subjects association between pre-meal HR and post-meal LOC rating. However, there was no significant within-subjects effect for HRV, nor were there between-subjects effects for either electrophysiologic variable.

CONCLUSIONS

Findings suggest that autonomic indices may either be a marker of risk for subsequent LOC eating or contribute to LOC eating. Linking physiological markers with behavior in the natural environment can improve knowledge of illness mechanisms and provide new avenues for intervention.

Smartphone-supported behavioural weight loss treatment in adults with severe obesity: study protocol for an exploratory randomised controlled trial (SmartBWL)

INTRODUCTION

Behavioural weight loss (BWL) treatment is the standard evidence-based treatment for severe obesity (SO; body mass index ≥40.0 kg/m² or ≥35.0 kg/m² with obesity-related comorbidity), leading to moderate weight loss which often cannot be maintained in the long term. Because weight loss depends on patients' use of weight management skills, it is important to support them in daily life. In an ecological momentary intervention design, this clinical trial aims to adapt, refine and evaluate a personalised cognitive-behavioural smartphone application (app) in BWL treatment to foster patients' weight management skills use in everyday life. It is hypothesised that using the app is feasible and acceptable, improves weight loss and increases skills use and well-being.

METHODS AND ANALYSIS

In the pilot phase, the app will be adapted, piloted and optimised for BWL treatment following a participatory patient-oriented approach. In the subsequent single-centre, assessor-blind, exploratory randomised controlled trial, 90 adults with SO will be randomised to BWL treatment over 6 months with versus without adjunctive app. Primary outcome is the amount of weight loss (kg) at post-treatment (6 months), compared with pretreatment, derived from measured body weight. Secondary outcomes encompass feasibility, acceptance, weight management skills use, well-being and anthropometrics assessed at pretreatment, midtreatment (3 months), post-treatment (6 months) and 6-month follow-up (12 months). An intent-to-treat linear model with randomisation arm, pretreatment weight and stratification variables as covariates will serve to compare arms regarding weight at post-treatment. Secondary analyses will include linear mixed models, generalised linear models and regression and mediation analyses. For safety analysis (serious) adverse events will be analysed descriptively.

ETHICS AND DISSEMINATION

The study was approved by the Ethics Committee of the University of Leipzig (DE-21-00013674) and notified to the Federal Institute for Drugs and Medical Devices. Study results will be disseminated through peer-reviewed publications.

REGISTRATION

This study was registered at the German Clinical Trials Register (DRKS00026018), www.drks.de.

TRIAL REGISTRATION NUMBER

DRKS00026018.

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.

Optimizing digital health technologies to improve therapeutic skill use and acquisition alongside enhanced cognitive-behavior therapy for binge-spectrum eating disorders: Protocol for a randomized controlled trial

OBJECTIVE

Adjunctive mobile health (mHealth) technologies offer promise for improving treatment response to enhanced cognitive-behavior therapy (CBT-E) among individuals with binge-spectrum eating disorders, but research on the key "active" components of these technologies has been very limited. The present study will use a full factorial design to (1) evaluate the optimal combination of complexity of two commonly used mHealth components (i.e., self-monitoring and microinterventions) alongside CBT-E and (2) test whether the optimal complexity level of these interventions is moderated by baseline self-regulation. Secondary aims of the present study include evaluating target engagement associated with each level of these intervention components and quantifying the component interaction effects (i.e., partially additive, fully additive, or synergistic effects).

METHOD

Two hundred and sixty-four participants with binge-spectrum eating disorders will be randomized to six treatment conditions determined by the combination of self-monitoring condition (i.e., standard self-monitoring or skills monitoring) and microinterventions condition (i.e., no microinterventions, automated microinterventions, or just-in-time adaptive interventions) as an augmentation to 16 sessions of CBT-E. Treatment outcomes will be measured using the Eating Disorder Examination and compared by treatment condition using multilevel models.

RESULTS

Results will clarify the "active" components in mHealth interventions for binge eating.

DISCUSSION

The present study will provide critical insight into the efficacy of commonly used digital intervention components (i.e., skills monitoring and microinterventions) alongside CBT-E. Furthermore, results of this study may inform personalization of digital intervention intensity based on patient profiles of self-regulation.

PUBLIC SIGNIFICANCE

This study will examine the relative effectiveness of commonly used components of application-based interventions as an augmentation to cognitive-behavioral therapy for binge eating. Findings from this study will inform the development of an optimized digital intervention for individuals with binge eating.

Sensor Technology and Intelligent Systems in Anorexia Nervosa: Providing Smarter Healthcare Delivery Systems

Ubiquitous technology, big data, more efficient electronic health records, and predictive analytics are now at the core of smart healthcare systems supported by artificial intelligence. In the present narrative review, we focus on sensing technologies for the healthcare of Anorexia Nervosa (AN). We employed a framework inspired by the Interpersonal Neurobiology Theory (IPNB), which posits that human experience is characterized by a flow of energy and information both within us (within our whole body), and between us (in the connections we have with others and with nature). In line with this framework, we focused on sensors designed to evaluate bodily processes (body sensors such as implantable sensors, epidermal sensors, and wearable and portable sensors), human social interaction (sociometric sensors), and the physical environment (indoor and outdoor ambient sensors). There is a myriad of man-made sensors as well as nature-based sensors such as plants that can be used to design and deploy intelligent systems for human monitoring and healthcare. In conclusion, sensing technologies and intelligent systems can be employed for smarter healthcare of AN and help to relieve the burden of health professionals. However, there are technical, ethical, and environmental sustainability issues that must be considered prior to implementing these systems. A joint collaboration of professionals and other members of the society involved in the healthcare of individuals with AN can help in the development of these systems. The evolution of cyberphysical systems should also be considered in these collaborations.

Efficacy of a transdiagnostic cognitive-behavioral intervention for eating disorder psychopathology delivered through a smartphone app: a randomized controlled trial

BACKGROUND

Although effective treatments exist for diagnostic and subthreshold-level eating disorders (EDs), a significant proportion of affected individuals do not receive help. Interventions translated for delivery through smartphone apps may be one solution towards reducing this treatment gap. However, evidence for the efficacy of smartphones apps for EDs is lacking. We developed a smartphone app based on the principles and techniques of transdiagnostic cognitive-behavioral therapy for EDs and evaluated it through a pre-registered randomized controlled trial.

METHODS

Symptomatic individuals (those who reported the presence of binge eating) were randomly assigned to the app (n = 197) or waiting list (n = 195). Of the total sample, 42 and 31% exhibited diagnostic-level bulimia nervosa and binge-eating disorder symptoms, respectively. Assessments took place at baseline, 4 weeks, and 8 weeks post-randomization. Analyses were intention-to-treat. The primary outcome was global levels of ED psychopathology. Secondary outcomes were other ED symptoms, impairment, and distress.

RESULTS

Intervention participants reported greater reductions in global ED psychopathology than the control group at post-test (d = -0.80). Significant effects were also observed for secondary outcomes (d's = -0.30 to -0.74), except compensatory behavior frequency. Symptom levels remained stable at follow-up. Participants were largely satisfied with the app, although the overall post-test attrition rate was 35%.

CONCLUSION

Findings highlight the potential for this app to serve as a cost-effective and easily accessible intervention for those who cannot receive standard treatment. The capacity for apps to be flexibly integrated within current models of mental health care delivery may prove vital for addressing the unmet needs of people with EDs.

Potential benefits and limitations of machine learning in the field of eating disorders: current research and future directions

Advances in machine learning and digital data provide vast potential for mental health predictions. However, research using machine learning in the field of eating disorders is just beginning to emerge. This paper provides a narrative review of existing research and explores potential benefits, limitations, and ethical considerations of using machine learning to aid in the detection, prevention, and treatment of eating disorders. Current research primarily uses machine learning to predict eating disorder status from females' responses to validated surveys, social media posts, or neuroimaging data often with relatively high levels of accuracy. This early work provides evidence for the potential of machine learning to improve current eating disorder screening methods. However, the ability of these algorithms to generalise to other samples or be used on a mass scale is only beginning to be explored. One key benefit of machine learning over traditional statistical methods is the ability of machine learning to simultaneously examine large numbers (100s to 1000s) of multimodal predictors and their complex non-linear interactions, but few studies have explored this potential in the field of eating disorders. Machine learning is also being used to develop chatbots to provide psychoeducation and coping skills training around body image and eating disorders, with implications for early intervention. The use of machine learning to personalise treatment options, provide ecological momentary interventions, and aid the work of clinicians is also discussed. Machine learning provides vast potential for the accurate, rapid, and cost-effective detection, prevention, and treatment of eating disorders. More research is needed with large samples of diverse participants to ensure that machine learning models are accurate, unbiased, and generalisable to all people with eating disorders. There are important limitations and ethical considerations with utilising machine learning methods in practice. Thus, rather than a magical solution, machine learning should be seen as an important tool to aid the work of researchers, and eventually clinicians, in the early identification, prevention, and treatment of eating disorders.

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.

Micro-level de-coupling of negative affect and binge eating in relationship to macro-level outcomes in binge eating disorder treatment

BACKGROUND

While negative affect reliably predicts binge eating, it is unknown how this association may decrease or 'de-couple' during treatment for binge eating disorder (BED), whether such change is greater in treatments targeting emotion regulation, or how such change predicts outcome. This study utilized multi-wave ecological momentary assessment (EMA) to assess changes in the momentary association between negative affect and subsequent binge-eating symptoms during Integrative Cognitive Affective Therapy (ICAT-BED) and Cognitive Behavior Therapy Guided Self-Help (CBTgsh). It was predicted that there would be stronger de-coupling effects in ICAT-BED compared to CBTgsh given the focus on emotion regulation skills in ICAT-BED and that greater de-coupling would predict outcomes.

METHODS

Adults with BED were randomized to ICAT-BED or CBTgsh and completed 1-week EMA protocols and the Eating Disorder Examination (EDE) at pre-treatment, end-of-treatment, and 6-month follow-up (final N = 78). De-coupling was operationalized as a change in momentary associations between negative affect and binge-eating symptoms from pre-treatment to end-of-treatment.

RESULTS

There was a significant de-coupling effect at follow-up but not end-of-treatment, and de-coupling did not differ between ICAT-BED and CBTgsh. Less de-coupling was associated with higher end-of-treatment EDE global scores at end-of-treatment and higher binge frequency at follow-up.

CONCLUSIONS

Both ICAT-BED and CBTgsh were associated with de-coupling of momentary negative affect and binge-eating symptoms, which in turn relate to cognitive and behavioral treatment outcomes. Future research is warranted to identify differential mechanisms of change across ICAT-BED and CBTgsh. Results also highlight the importance of developing momentary interventions to more effectively de-couple negative affect and binge eating.

The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality

As the COVID-19 pandemic has largely increased the utilization of telehealth, mobile mental health technologies - such as smartphone apps, vir-tual reality, chatbots, and social media - have also gained attention. These digital health technologies offer the potential of accessible and scalable interventions that can augment traditional care. In this paper, we provide a comprehensive update on the overall field of digital psychiatry, covering three areas. First, we outline the relevance of recent technological advances to mental health research and care, by detailing how smartphones, social media, artificial intelligence and virtual reality present new opportunities for "digital phenotyping" and remote intervention. Second, we review the current evidence for the use of these new technological approaches across different mental health contexts, covering their emerging efficacy in self-management of psychological well-being and early intervention, along with more nascent research supporting their use in clinical management of long-term psychiatric conditions - including major depression; anxiety, bipolar and psychotic disorders; and eating and substance use disorders - as well as in child and adolescent mental health care. Third, we discuss the most pressing challenges and opportunities towards real-world implementation, using the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework to explain how the innovations themselves, the recipients of these innovations, and the context surrounding innovations all must be considered to facilitate their adoption and use in mental health care systems. We conclude that the new technological capabilities of smartphones, artificial intelligence, social media and virtual reality are already changing mental health care in unforeseen and exciting ways, each accompanied by an early but promising evidence base. We point out that further efforts towards strengthening implementation are needed, and detail the key issues at the patient, provider and policy levels which must now be addressed for digital health technologies to truly improve mental health research and treatment in the future.

Stress appraisal prospectively predicts binge eating through increases in negative affect

PURPOSE

Ecological momentary assessment (EMA) studies preliminarily support the transactional model of emotion regulation in eating disorders, such that heightened stress appraisal (i.e., the cognitive evaluation of an event's demands) results in increased negative affect (NA) and subsequent binge eating (BE). However, the temporal relationships between these variables and the magnitude of stress appraisal that is clinically significant require clarification. The current study aimed to extend previous research by (1) examining the temporal relationship between stress appraisal, changes in NA, and BE using three timepoints, (2) exploring what magnitude of momentary stress appraisal results in clinically significant increases in NA and BE, and (3) characterizing what stressors are associated with clinically significant stress appraisal.

METHODS

37 adult females completed an EMA protocol assessing momentary stressors, stress appraisal, NA, and BE over 2 week duration. Multilevel mediation models were used to test the study aims.

RESULTS

Momentary increases in stress appraisal significantly predicted binge eating through increases in NA. Stress appraisal ratings of 0.50 SD higher relative to one's average stress appraisal began to significantly predict the likelihood of BE through increases in NA, and the likelihood of BE occurrence increased with every 0.25 increments in momentary stress appraisal. Work/school stressors and interpersonal stressors were the most commonly endorsed stressors of clinically significant stress appraisal.

CONCLUSION

The current study supported the transactional model of emotion dysregulation in a binge eating sample and supports the use of momentary interventions at times of clinically significant stress appraisal to reduce BE risk.

LEVEL OF EVIDENCE

Level II, controlled trial without randomization.

NeckSense: A Multi-Sensor Necklace for Detecting Eating Activities in Free-Living Conditions

We present the design, implementation, and evaluation of a multi-sensor, low-power necklace, NeckSense, for automatically and unobtrusively capturing fine-grained information about an individual's eating activity and eating episodes, across an entire waking day in a naturalistic setting. NeckSense fuses and classifies the proximity of the necklace from the chin, the ambient light, the Lean Forward Angle, and the energy signals to determine chewing sequences, a building block of the eating activity. It then clusters the identified chewing sequences to determine eating episodes. We tested NeckSense on 11 participants with and 9 participants without obesity, across two studies, where we collected more than 470 hours of data in a naturalistic setting. Our results demonstrate that NeckSense enables reliable eating detection for individuals with diverse body mass index (BMI) profiles, across an entire waking day, even in free-living environments. Overall, our system achieves an F1-score of 81.6% in detecting eating episodes in an exploratory study. Moreover, our system can achieve an F1-score of 77.1% for episodes even in an all-day-long free-living setting. With more than 15.8 hours of battery life, NeckSense will allow researchers and dietitians to better understand natural chewing and eating behaviors. In the future, researchers and dietitians can use NeckSense to provide appropriate real-time interventions when an eating episode is detected or when problematic eating is identified.

Evaluating associations between fitspiration and thinspiration content on Instagram and disordered-eating behaviors using ecological momentary assessment: A registered report

INTRODUCTION

Greater use of appearance-focused social media, such as Instagram, is associated with increased body dissatisfaction and eating disorder (ED) symptoms; however, questions remain about the mechanism connecting social media use to disordered-eating behaviors (DEBs). The proposed study evaluates how and for whom exposure to fitspiration or thinspiration on Instagram is associated with DEBs.

METHODS

We will evaluate a hypothesized pathway from Instagram use to disordered-eating mediated by negative affect. We will test how individual differences in internalized weight stigma, trait self-esteem, and trait self-comparison moderate the pathway from social media use to negative affect. We will recruit 175 undergraduate women who report engaging in DEBs on average at least once per week over the past 3 months. Participants will complete a 7-day ecological momentary assessment protocol, during which they will report their Instagram use, affect, and engagement in DEBs.

RESULTS

Multi-level modeling will be used to assess moderated mediation. Results from this study will provide increased specificity about how Instagram usage is linked to eating pathology and who may be most vulnerable to experiencing distress.

DISCUSSION

Information about negative affect from Instagram and engagement in DEBs could contribute to the development of Just-In-Time Interventions for problematic social media use.

Usability Evaluation of a Cognitive-Behavioral App-Based Intervention for Binge Eating and Related Psychopathology: A Qualitative Study

Despite their promise as a scalable intervention modality for binge eating and related problems, reviews show that engagement of app-based interventions is variable. Issues with usability may account for this. App developers should undertake usability testing so that any problems can be identified and fixed prior to dissemination. We conducted a qualitative usability evaluation of a newly-developed app for binge eating in 14 individuals with a diagnostic- or subthreshold-level binge eating symptoms. Participants completed a semi-structured interview and self-report measures. Qualitative data were organized into six themes: usability, visual design, user engagement, content, therapeutic persuasiveness, and therapeutic alliance. Qualitative and quantitative results indicated that the app demonstrated good usability. Key advantages reported were its flexible content-delivery formats, level of interactivity, easy-to-understand information, and ability to track progress. Concerns with visual aesthetics and lack of professional feedback were raised. Findings will inform the optimal design of app-based interventions for eating disorder symptoms.

A Clinician-Controlled Just-in-time Adaptive Intervention System (CBT+) Designed to Promote Acquisition and Utilization of Cognitive Behavioral Therapy Skills in Bulimia Nervosa: Development and Preliminary Evaluation Study

Background

Cognitive behavioral therapy (CBT) for bulimia nervosa (BN) is most effective when patients demonstrate adequate skill utilization (ie, the frequency with which a patient practices or uses therapeutic skills) and skill acquisition (ie, the ability to successfully perform a skill learned in treatment). However, rates of utilization and acquisition of key treatment skills (eg, regular eating, urge management skills, and mood management skills) by the end of the treatment are frequently low; as a result, outcomes from CBT for BN are affected. Just-in-time adaptive interventions (JITAIs) may improve skill acquisition and utilization by delivering real-time interventions during algorithm-identified opportunities for skill practice.

Objective

In this manuscript, we describe a newly developed JITAI system called CBT+ that is designed to facilitate the acquisition and utilization of CBT for BN treatment skills when used as a treatment augmentation. We also present feasibility, acceptability, and preliminary outcomes data from a small proof-of-concept pilot trial (n=5 patients and n=3 clinicians) designed to identify opportunities for iterative development of CBT+ ahead of a larger ongoing randomized controlled trial.

Methods

A total of 5 individuals with BN received 16 sessions of outpatient CBT for BN while using the CBT+ app. Data were collected from patients and clinicians to evaluate the feasibility (eg, app use and user adherence), acceptability (eg, qualitative patient and clinician feedback, including usefulness ratings of CBT+ on a 6-point Likert scale ranging from 1=extremely useless to 6=extremely useful), and preliminary outcomes (eg, improvements in skill utilization and acquisition and BN symptoms) of the CBT+ system.

Results

Patients reported that CBT+ was a relatively low burden (eg, quick and easy-to-use self-monitoring interface), and adherence to in-app self-monitoring was high (mean entries per day 3.13, SD 1.03). JITAIs were perceived as useful by both patients (median rating 5/6) and clinicians (median rating 5/6) for encouraging the use of CBT skills. Large improvements in CBT skills and clinically significant reductions in BN symptoms were observed post treatment. Although preliminary findings indicated that the CBT+ system was acceptable to most patients and clinicians, the overall study dropout was relatively high (ie, 2/5, 40% patients), which could indicate some moderate concerns regarding feasibility.

Conclusions

CBT+, the first-ever JITAI system designed to facilitate the acquisition and utilization of CBT for BN treatment skills when used as a treatment augmentation, was shown to be feasible and acceptable. The results indicate that the CBT+ system should be subjected to more rigorous evaluations with larger samples and should be considered for wider implementation if found effective. Areas for iterative improvement of the CBT+ system ahead of a randomized controlled trial are also discussed.

Perspectives of e-health interventions for treating and preventing eating disorders: descriptive study of perceived advantages and barriers, help-seeking intentions, and preferred functionality

PURPOSE

Digital interventions that consider end-user needs, preferences, and concerns may address suboptimal rates of e-health uptake, usage, and engagement. We explored target-user perspectives of e-health treatment and prevention programs for eating disorders (EDs), with a focus on investigating (1) perceived advantages and barriers of e-health; (2) help-seeking intentions; and (3) preferences for different digital functionality, device types, and content-delivery formats.

METHODS

Survey data were analysed from 722 community-based participants. Participants were categorized into one of four groups based on symptom presentation and severity, ranging from low risk to probable bulimia nervosa or binge-eating disorder.

RESULTS

e-health advantages that received the highest endorsement (~ 84%) were "always there in times of need" and "travel not required". e-health barriers that received the highest endorsement (~ 50%) were concerns about data privacy and the accuracy of content presented. Nearly three-quarters reported an intention to use an e-health platform for preventing or treating EDs. Preference ratings were highest for programs to be available on all digital devices (relative to restricting the program to one type of device) and for content to be presented via graphics and video tutorials (rather than audio-based). e-health functionality that received highest preference ratings (~ 80%) were added clinician support, tailored feedback, strategies to change unhelpful ED thoughts, screening scales to assess symptoms, ED psychoeducation, and just-in-time intervention prompts. Preference and intention ratings were strikingly similar across all subgroups.

CONCLUSION

Findings may inform the development and design of e-health platforms that meet the needs of people at different stages of an ED.

LEVEL OF EVIDENCE

Level V, cross-sectional descriptive study.

Data-Driven Implications for Translating Evidence-Based Psychotherapies into Technology-Delivered Interventions

Mobile mental health interventions have the potential to reduce barriers and increase engagement in psychotherapy. However, most current tools fail to meet evidence-based principles. In this paper, we describe data-driven design implications for translating evidence-based interventions into mobile apps. To develop these design implications, we analyzed data from a month-long field study of an app designed to support dialectical behavioral therapy, a psychotherapy that aims to teach concrete coping skills to help people better manage their mental health. We investigated whether particular skills are more or less effective in reducing distress or emotional intensity. We also characterized how an individual's disorders, characteristics, and preferences may correlate with skill effectiveness, as well as how skill-level improvements correlate with study-wide changes in depressive symptoms. We then developed a model to predict skill effectiveness. Based on our findings, we present design implications that emphasize the importance of considering different environmental, emotional, and personal contexts. Finally, we discuss promising future opportunities for mobile apps to better support evidence-based psychotherapies, including using machine learning algorithms to develop personalized and context-aware skill recommendations.

Networks of stress, affect and eating behaviour: anticipated stress coping predicts goal-congruent eating in young adults

BACKGROUND

Many people aim to eat healthily. Yet, affluent food environments encourage consumption of energy dense and nutrient-poor foods, making it difficult to accomplish individual goals such as maintaining a healthy diet and weight. Moreover, goal-congruent eating might be influenced by affects, stress and intense food cravings and might also impinge on these in turn. Directionality and interrelations of these variables are currently unclear, which impedes targeted intervention. Psychological network models offer an exploratory approach that might be helpful to identify unique associations between numerous variables as well as their directionality when based on longitudinal time-series data.

METHODS

Across 14 days, 84 diet-interested participants (age range: 18-38 years, 85.7% female, mostly recruited via universities) reported their momentary states as well as retrospective eating episodes four times a day. We used multilevel vector autoregressive network models based on ecological momentary assessment data of momentary affects, perceived stress and stress coping, hunger, food craving as well as goal-congruent eating behaviour.

RESULTS

Neither of the momentary measures of stress (experience of stress or stress coping), momentary affects or craving uniquely predicted goal-congruent eating. Yet, temporal effects indicated that higher anticipated stress coping predicted subsequent goal-congruent eating. Thus, the more confident participants were in their coping with upcoming challenges, the more they ate in line with their goals.

CONCLUSION

Most eating behaviour interventions focus on hunger and craving alongside negative and positive affect, thereby overlooking additional important variables like stress coping. Furthermore, self-regulation of eating behaviours seems to be represented by how much someone perceives a particular eating episode as matching their individual eating goal. To conclude, stress coping might be a potential novel intervention target for eating related Just-In-Time Adaptive Interventions in the context of intensive longitudinal assessment.

Predicting Response to Group Cognitive Behavioral Therapy in Asthma by a Small Number of Abnormal Resting-State Functional Connections

Group cognitive behavioral therapy (GCBT) is a successful psychotherapy for asthma. However, response varies considerably among individuals, and identifying biomarkers of GCBT has been challenging. Thus, the aim of this study was to predict an individual's potential response by using machine learning algorithms and functional connectivity (FC) and to improve the personalized treatment of GCBT. We use the lasso method to make the feature selection in the functional connections between brain regions, and we utilize t-test method to test the significant difference of these selected features. The feature selections are performed between controls (size = 20) and pre-GCBT patients (size = 20), pre-GCBT patients (size = 10) and post-GCBT patients (size = 10), and post-GCBT patients (size = 10) and controls (size = 10). Depending on these features, support vector classification was used to classify controls and pre- and post-GCBT patients. Pearson correlation analysis was employed to analyze the associations between clinical symptoms and the selected discriminated FCs in post-GCBT patients. At last, linear support vector regression was applied to predict the therapeutic effect of GCBT. After feature selection and significant analysis, five discriminated FC regarding neuroimaging biomarkers of GCBT were discovered, which are also correlated with clinical symptoms. Using these discriminated functional connections, we could accurately classify the patients before and after GCBT (classification accuracy, 80%) and predict the therapeutic effect of GCBT in asthma (predicted accuracy, 67.8%). The findings in this study would provide a novel sight toward GCBT response prediction and further confirm neural underpinnings of asthma. Moreover, our findings had clinical implications for personalized treatment by identifying asthmatic patients who will be appropriate for GCBT.

CLINICAL TRIAL REGISTRATION

The brain mechanisms of group cognitive behavioral therapy to improve the symptoms of asthma (Registration number: Chi-CTR-15007442, http://www.chictr.org.cn/index.aspx).

Ecological momentary assessment in eating disorders research: recent findings and promising new directions

PURPOSE OF REVIEW

Ecological momentary assessment (EMA) is an important tool for clarifying common precipitants and consequences of eating disorder symptoms that might be meaningfully targeted in treatments for these pernicious disorders. This article reviews recent advances in EMA work conducted within clinical eating disorder samples.

RECENT FINDINGS

Published studies from the past 2.5 years can broadly be categorized as involving functional analysis of eating disorder behaviors, examining hypothesized predictors of eating disorder symptoms, or applying novel approaches to EMA data. Examples of the latter category include the use of latent profile analysis with EMA data, integration of neurocognitive (e.g., ambulatory inhibitory control task) or biological indicators (e.g., fMRI, plasma leptin), and examining changes in associations between momentary variables over time through multiwave EMA data collection.

SUMMARY

EMA studies in eating disorders have advanced significantly in recent years, with findings demonstrating strong support for the emotion regulation function of eating disorder behaviors and momentary predictors of distinct eating disorder symptoms. The use of novel statistical and data collection approaches represent exciting areas of growth, with likely implications for intervention approaches, including those that utilize ambulatory technology to deliver treatment.

An mHealth intervention for the treatment of patients with an eating disorder: A multicenter randomized controlled trial

OBJECTIVE

The current multicentre randomized controlled trial assessed the clinical efficacy of a combined mHealth intervention for eating disorders (EDs) based on cognitive behavioral therapy (CBT).

METHOD

A total of 106 ED patients from eight different public and private mental health services in Spain were randomly assigned to two parallel groups. Patients of the experimental group (N = 53) received standard face-to-face CBT plus a mobile intervention through an application called "TCApp," which provides self-monitoring and an online chat with the therapist. The control group (N = 53) received standard face-to-face CBT only. Patients completed self-report questionnaires on ED symptomatology, anxiety, depression, and quality of life, before and after treatment.

RESULTS

Significant reductions in primary and secondary outcomes were observed for participants of both groups, with no differences between groups. Results also suggested that the frequency with which patients attended their referral mental health institution after the intervention was lower for patients in the experimental group than for those in the control group.

DISCUSSION

The current study showed that CBT can help to reduce symptoms relating to ED, regardless of whether its delivery includes online components in addition to traditional face-to-face treatment. Besides, the additional component offered by the TCApp does not appear to be promising from a purely therapeutic perspective but perhaps as a cost-effective tool, reducing thus the costs and time burden associated with weekly visits to health professionals.

Emotional eating in healthy individuals and patients with an eating disorder: evidence from psychometric, experimental and naturalistic studies

Emotional eating has traditionally been defined as (over)eating in response to negative emotions. Such overeating can impact general health because of excess energy intake and mental health, due to the risks of developing binge eating. Yet, there is still significant controversy on the validity of the emotional eating concept and several theories compete in explaining its mechanisms. The present paper examines the emotional eating construct by reviewing and integrating recent evidence from psychometric, experimental and naturalistic research. Several psychometric questionnaires are available and some suggest that emotions differ fundamentally in how they affect eating (i.e. overeating, undereating). However, the general validity of such questionnaires in predicting actual food intake in experimental studies is questioned and other eating styles such as restrained eating seem to be better predictors of increased food intake under negative emotions. Also, naturalistic studies, involving the repeated assessment of momentary emotions and eating behaviour in daily life, are split between studies supporting and studies contradicting emotional eating in healthy individuals. Individuals with clinical forms of overeating (i.e. binge eating) consistently show positive relationships between negative emotions and eating in daily life. We will conclude with a summary of the controversies around the emotional eating construct and provide recommendations for future research and treatment development.

The role of affect in the maintenance of binge-eating disorder: Evidence from an ecological momentary assessment study

Affect regulation models of eating disorder behavior, which predict worsening of affect prior to binge-eating episodes and improvement in affect following such episodes, have received support in anorexia nervosa and bulimia nervosa. However, limited work has examined the trajectories of affect surrounding binge eating in binge-eating disorder (BED). In the current study, ecological momentary assessment data from 112 men and women with BED were used to examine the trajectories of positive affect (PA), negative affect (NA), guilt, fear, hostility, and sadness relative to binge-eating episodes. Prior to binge episodes, PA significantly decreased, whereas NA and guilt significantly increased. Following binge episodes, levels of NA and guilt significantly decreased and PA stabilized. Overall, results indicate improvements in affect following binge-eating episodes, suggesting that binge eating may function to alleviate unpleasant emotional experiences among individuals with BED, which is consistent with affect regulation models of eating pathology. Because improvements in negative affect were primarily driven by change in guilt, findings also highlight the relative importance of understanding the relationship between guilt and binge-eating behavior within this population. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Moving beyond self-report data collection in the natural environment: A review of the past and future directions for ambulatory assessment in eating disorders

OBJECTIVE

In recent years, ecological momentary assessment (EMA) has been used to repeatedly assess eating disorder (ED) symptoms in naturalistic settings, which has allowed for increased understanding of temporal processes that potentiate ED behaviors. However, there remain notable limitations of self-report EMA, and with the rapid proliferation of technology there are ever-increasing possibilities to improve ambulatory assessment methods to further the understanding and treatment of EDs. Therefore, the purpose of this review was to (a) systematically review the studies in EDs that have utilized ambulatory assessment methods other than self-report, and (b) provide directions for future research and clinical applications.

METHOD

A systematic literature search of electronic databases was conducted, and data regarding study characteristics and methodological quality were extracted.

RESULTS

The search identified 17 studies that used ambulatory assessment methods to gather objective data, and focused primarily on autonomic functioning, physical activity, and cognitive processes in ED and control groups.

DISCUSSION

Together the literature demonstrates the promise of using a range of ecologically valid ambulatory assessment approaches in EDs, though there remains limited research that has utilized methods other than self-report (e.g., wearable sensors), particularly in recent years. Going forward, there are several technology-enhanced momentary assessment methods that have potential to improve the understanding and treatment of EDs.

From Ecological Momentary Assessment (EMA) to Ecological Momentary Intervention (EMI): Past and Future Directions for Ambulatory Assessment and Interventions in Eating Disorders

PURPOSE OF REVIEW

Ambulatory assessment methods, including ecological momentary assessment (EMA), have often been used in eating disorders (EDs) to assess the type, frequency, and temporal sequencing of ED symptoms occurring in naturalistic environments. Relatedly, growing research in EDs has explored the utility of ecological momentary interventions (EMIs) to target ED symptoms. The aims of the present review were to (1) synthesize recent literature pertaining to ambulatory assessment/EMA and EMI in EDs, and (2) identify relevant limitations and future directions in these domains.

RECENT FINDINGS

With respect to ambulatory assessment and EMA, there has been substantial growth in the expansion of constructs assessed with EMA, the exploration of state- vs. trait-level processes, integration of objective and passive assessment approaches, and consideration of methodological issues. The EMI literature in EDs also continues to grow, though most of the recent research focuses on mobile health (mHealth) technologies with relatively minimal EMI components that adapt to momentary contextual information. Despite these encouraging advances, there remain several promising areas of ambulatory assessment research and clinical applications in EDs going forward. These include integration of passive data collection, use of EMA in treatment evaluation and design, evaluation of dynamic system processes, inclusion of diverse samples, and development and evaluation of adaptive, tailored EMIs such as just-in-time adaptive interventions. While much remains to be learned in each of these domains, the continual growth in mobile technology has potential to facilitate and refine our understanding of the nature of ED psychopathology and ultimately improve intervention approaches.

A multimodal, naturalistic investigation of relationships between behavioral impulsivity, affect, and binge eating

While binge eating is associated with both emotion regulation deficits and cognitive control impairments related to impulsivity, thus far research has not examined how dimensions of behavioral impulsivity may influence momentary relationships between affect and binge-eating episodes. The present study utilized multimodal methods to examine the extent to which individual differences in impulsive choice (i.e., delay and probabilistic discounting) and impulsive action (i.e., response inhibition) moderated momentary relationships between negative and positive affect (NA and PA) and binge eating measured in the natural environment. Participants were 30 adult women with binge-eating symptoms who completed measures of behavioral impulsivity (i.e., Monetary Choice Questionnaire, Cued Go/No-Go task, Game of Dice Task), followed by a 14-day ecological momentary assessment protocol during which they reported affect levels and binge-eating episodes. Results of generalized estimating equations indicated that greater delay discounting (i.e., preference for immediate, yet smaller rewards) strengthened momentary relationships between both PA and NA and binge eating. However, and unexpectedly, the relationship between momentary PA and binge eating was negative among individuals with greater Cued Go/No-go commission errors, suggesting that higher PA actually attenuated risk of binge episode occurring in these individuals. Together these findings highlight important distinctions between facets of behavioral impulsivity as well as their relationships with affect valence and intensity in predicting binge episodes. Specifically, temporal rather than probabilistic discounting may be most relevant to momentary processes that contribute to binge eating, and promotion of momentary positive affect may be helpful for individuals with poorer response inhibition.

Digital Interventions for Mental Disorders: Key Features, Efficacy, and Potential for Artificial Intelligence Applications

Mental disorders are highly prevalent and often remain untreated. Many limitations of conventional face-to-face psychological interventions could potentially be overcome through Internet-based and mobile-based interventions (IMIs). This chapter introduces core features of IMIs, describes areas of application, presents evidence on the efficacy of IMIs as well as potential effect mechanisms, and delineates how Artificial Intelligence combined with IMIs may improve current practices in the prevention and treatment of mental disorders in adults. Meta-analyses of randomized controlled trials clearly show that therapist-guided IMIs can be highly effective for a broad range of mental health problems. Whether the effects of unguided IMIs are also clinically relevant, particularly under routine care conditions, is less clear. First studies on IMIs for the prevention of mental disorders have shown promising results. Despite limitations and challenges, IMIs are increasingly implemented into routine care worldwide. IMIs are also well suited for applications of Artificial Intelligence and Machine Learning, which provides ample opportunities to improve the identification and treatment of mental disorders. Together with methodological innovations, these approaches may also deepen our understanding of how psychological interventions work, and why. Ethical and professional restraints as well as potential contraindications of IMIs, however, should also be considered. In sum, IMIs have a high potential for improving the prevention and treatment of mental health disorders across various indications, settings, and populations. Therefore, implementing IMIs into routine care as both adjunct and alternative to face-to-face treatment is highly desirable. Technological advancements may further enhance the variability and flexibility of IMIs, and thus even further increase their impact in people's lives in the future.