A broad v. focused digital intervention for recurrent binge eating: a randomized controlled non-inferiority trial

BACKGROUND

Empirically validated digital interventions for recurrent binge eating typically target numerous hypothesized change mechanisms via the delivery of different modules, skills, and techniques. Emerging evidence suggests that interventions designed to target and isolate one key change mechanism may also produce meaningful change in core symptoms. Although both 'broad' and 'focused' digital programs have demonstrated efficacy, no study has performed a direct, head-to-head comparison of the two approaches. We addressed this through a randomized non-inferiority trial.

METHOD

Participants with recurrent binge eating were randomly assigned to a broad (n = 199) or focused digital intervention (n = 199), or a waitlist (n = 202). The broad program targeted dietary restraint, mood intolerance, and body image disturbances, while the focused program exclusively targeted dietary restraint. Primary outcomes were eating disorder psychopathology and binge eating frequency.

RESULTS

In intention-to-treat analyses, both intervention groups reported greater improvements in primary and secondary outcomes than the waitlist, which were sustained at an 8-week follow-up. The focused intervention was not inferior to the broad intervention on all but one outcome, but was associated with higher rates of attrition and non-compliance.

CONCLUSION

Focused digital interventions that are designed to target one key change mechanism may produce comparable symptom improvements to broader digital interventions, but appear to be associated with lower engagement.

Targeting dietary restraint to reduce binge eating: a randomised controlled trial of a blended internet- and smartphone app-based intervention

BACKGROUND

Existing internet-based prevention and treatment programmes for binge eating are composed of multiple distinct modules that are designed to target a broad range of risk or maintaining factors. Such multi-modular programmes (1) may be unnecessarily long for those who do not require a full course of intervention and (2) make it difficult to distinguish those techniques that are effective from those that are redundant. Since dietary restraint is a well-replicated risk and maintaining factor for binge eating, we developed an internet- and app-based intervention composed solely of cognitive-behavioural techniques designed to modify dietary restraint as a mechanism to target binge eating. We tested the efficacy of this combined selective and indicated prevention programme in 403 participants, most of whom were highly symptomatic (90% reported binge eating once per week).

METHOD

Participants were randomly assigned to the internet intervention (n = 201) or an informational control group (n = 202). The primary outcome was objective binge-eating frequency. Secondary outcomes were indices of dietary restraint, shape, weight, and eating concerns, subjective binge eating, disinhibition, and psychological distress. Analyses were intention-to-treat.

RESULTS

Intervention participants reported greater reductions in objective binge-eating episodes compared to the control group at post-test (small effect size). Significant effects were also observed on each of the secondary outcomes (small to large effect sizes). Improvements were sustained at 8 week follow-up.

CONCLUSIONS

Highly focused digital interventions that target one central risk/maintaining factor may be sufficient to induce meaningful change in core eating disorder symptoms.

Effects of Participant's Choice of Different Digital Interventions on Outcomes for Binge-Spectrum Eating Disorders: A Pilot Doubly Randomized Preference Trial

It is unclear whether offering individuals a choice between different digital intervention programs affects treatment outcomes. To generate initial insights, we conducted a pilot doubly randomized preference trial to test whether offering individuals with binge-spectrum eating disorder a choice between two digital interventions is causally linked with superior outcomes than random assignment to these interventions. Participants with recurrent binge eating were randomized to either a choice (n = 77) or no-choice (n = 78) group. Those in the choice group could choose one of the two digital programs, while those in the no-choice group were assigned a program at random. The two digital interventions (a broad and a focused program) took 4 weeks to complete, were based on cognitive-behavioral principles and have demonstrated comparable efficacy, but differ in scope, content, and targeted change mechanisms. Most participants (79%) allocated to the choice condition chose the broad program. While both groups experienced improvements in primary (Eating Disorder Examination Questionnaire global scores and number of binge eating episodes over the past month) and secondary outcomes (dietary restraint, body image concerns, etc.), no significant between-group differences were observed. The two groups did not differ on dropout rates, nor on most indices of intervention engagement. Findings provide preliminary insights towards the role of client preferences in digital mental health interventions for eating disorders. Client preferences may not determine outcomes when digital interventions are based on similar underlying principles, although larger trials are needed to confirm this.

Understanding the role of positive body image during digital interventions for eating disorders: Secondary analyses of a randomized controlled trial

Despite growing interest in the possible link between positive body image and eating disorder (ED) symptoms, little is known about what role this adaptive construct plays in ED treatment. This study investigated whether: (1) interventions principally designed to target ED psychopathology also lead to improvements in positive body image indices (i.e., body appreciation, functionality appreciation, and body image flexibility); (2) changes in ED symptoms correlate with changes in positive body image, both concurrently and prospectively; and (3) baseline positive body image levels moderate the degree of symptom improvement. Secondary analyses from a randomized controlled trial on digital interventions for EDs (n=600) were conducted. Intervention participants reported greater increases in the three positive body image constructs than the control group (ds=0.15-0.41). Greater pre-post reductions in ED psychopathology and binge eating were associated with greater pre-post improvements in positive body image indices. However, earlier reductions in ED psychopathology and binge eating did not predict later improvements in positive body image at follow-up. None of the positive body image constructs at baseline moderated degree of symptom change. Standard ED interventions can cultivate a more positive body image, although this is not explained by earlier symptom reduction. Understanding the mechanisms through which ED interventions enhance positive body image is needed.

Informing mHealth and Web-Based Eating Disorder Interventions: Combining Lived Experience Perspectives With Design Thinking Approaches

BACKGROUND

App-based interventions designed to prevent and treat eating disorders have considerable potential to overcome known barriers to treatment seeking. Existing apps have shown efficacy in terms of symptom reduction; however, uptake and retention issues are common. To ensure that apps meet the needs and preferences of those for whom they were designed, it is critical to understand the lived experience of potential users and involve them in the process of design, development, and delivery. However, few app-based interventions are pretested on and co-designed with end users before randomized controlled trials.

OBJECTIVE

To address the issue, this study used a highly novel design thinking approach to provide the context and a lived experience perspective of the end user, thus allowing for a deeper level of understanding.

METHODS

In total, 7 young women (mean age 25.83, SD 5.34, range 21-33 years) who self-identified as having a history of body image issues or eating disorders were recruited. Participants were interviewed about their lived experience of body image and eating disorders and reported their needs and preferences for app-based eating disorder interventions. Traditional (thematic analysis) and novel (empathy mapping; visually depicting and empathizing with the user's personal experience) analyses were performed, providing a lived experience perspective of eating disorders and identifying the needs and preferences of this population in relation to app-based interventions for eating disorders. Key challenges and opportunities for app-based eating disorder interventions were also identified.

RESULTS

Findings highlighted the importance of understanding and identifying problematic eating disorder symptoms for the user, helpful practices for recovery that identify personal values and goals, the role of social support in facilitating hope, and aspects of usability to promote continued engagement and recovery.

CONCLUSIONS

Practical guidance and recommendations are described for those developing app-based eating disorder interventions. These findings have the potential to inform practices to enhance participant uptake and retention in the context of app-based interventions for this population.

Effects of an Acceptance-Facilitating Intervention on Acceptance and Usage of Digital Interventions for Binge Eating

OBJECTIVE

The authors aimed to test the impact of an acceptance-facilitating intervention (AFI) on acceptance ratings and usage patterns of digital interventions for binge eating.

METHOD

Participants with recurrent binge eating (N=398) were randomly assigned to an AFI or control condition. The AFI was an educational video providing information about digital interventions, including their capabilities, benefits, evidence base, and misconceptions. The primary outcome was acceptance of digital interventions. Secondary outcomes included drivers of acceptance and usage patterns.

RESULTS

The AFI group reported higher scores than the control group on acceptance, effort expectancy, facilitating conditions, motivations, and positive attitudes toward digital interventions. No group differences were observed on uptake or adherence rates at follow-up.

CONCLUSION

AFIs can positively influence participants' acceptance of digital interventions for binge eating and can address common barriers associated with their use. Further research is needed to understand how AFIs can best facilitate help seeking and treatment engagement in this population.

Filter feature selection based Boolean Modelling for Genetic Network Inference

The reconstruction of Gene Regulatory Networks (GRNs) from time series gene expression data is highly relevant for the discovery of complex biological interactions and dynamics. Various computational strategies have been developed for this task, but most approaches have low computational efficiency and are not able to cope with high-dimensional, low sample-number, gene expression data. In this paper, we introduce a novel combined filter feature selection approach for efficient and accurate inference of GRNs. A Boolean framework for network modelling is used to demonstrate the efficacy of the proposed approach. Using discretized microarray expression data, the genes most relevant to each target gene are first filtered using ReliefF, an instance-based feature ranking method that is here applied for the first time to GRN inference. Then, further gene selection from the filtered-gene list is done using a mutual information-based min-redundancy max-relevance criterion by eliminating irrelevant genes. This combined method is executed on resampled datasets to finalize the optimal set of regulatory genes. Building upon our previous research, a Pearson correlation coefficient-based Boolean modelling approach is utilized for the efficient identification of the optimal regulatory rules associated with selected regulatory genes. The proposed approach was evaluated using gene expression datasets from small-scale and medium-scale real gene networks, and was observed to be more effective than Linear Discriminant Analysis, performed better than the individual feature selection methods, and obtained improved Structural Accuracy with a higher number of true positives than other state-of-the-art methods, while outperforming these methods with respect to Dynamic Accuracy and efficiency.

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.

An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms

OBJECTIVE

Digital interventions show promise to address eating disorder (ED) symptoms. However, response rates are variable, and the ability to predict responsiveness to digital interventions has been poor. We tested whether machine learning (ML) techniques can enhance outcome predictions from digital interventions for ED symptoms.

METHOD

Data were aggregated from three RCTs (n = 826) of self-guided digital interventions for EDs. Predictive models were developed for four key outcomes: uptake, adherence, drop-out, and symptom-level change. Seven ML techniques for classification were tested and compared against the generalized linear model (GLM).

RESULTS

The seven ML methods used to predict outcomes from 36 baseline variables were poor for the three engagement outcomes (AUCs = 0.48-0.52), but adequate for symptom-level change (R²  = .15-.40). ML did not offer an added benefit to the GLM. Incorporating intervention usage pattern data improved ML prediction accuracy for drop-out (AUC = 0.75-0.93) and adherence (AUC = 0.92-0.99). Age, motivation, symptom severity, and anxiety emerged as influential outcome predictors.

CONCLUSION

A limited set of routinely measured baseline variables was not sufficient to detect a performance benefit of ML over traditional approaches. The benefits of ML may emerge when numerous usage pattern variables are modeled, although this validation in larger datasets before stronger conclusions can be made.

Does the Method of Content Delivery Matter? Randomized Controlled Comparison of an Internet-Based Intervention for Eating Disorder Symptoms With and Without Interactive Functionality

Despite their potential as a scalable, cost-effective intervention format, self-guided Internet-based interventions for eating disorder (ED) symptoms continue to be associated with suboptimal rates of adherence and retention. Improving this may depend on the design of an Internet intervention and its method of content delivery, with interactive programs expected to be more engaging than static, text-based programs. However, causal evidence for the added benefits of interactive functionality is lacking. We conducted a randomized controlled comparison of an Internet-based intervention for ED symptoms with and without interactive functionality. Participants were randomized to a 4-week interactive (n = 148) or static (n = 145) version of an Internet-based, cognitive-behavioral program. The interactive version included diverse multimedia content delivery channels (video tutorials, graphics, written text), a smartphone app allowing users to complete the required homework exercises digitally (quizzes, symptom tracking, self-assessments), and progress monitoring features. The static version delivered identical intervention content but only via written text, and contained none of those interactive features. Dropout rates were high overall (58%), but were significantly-yet slightly-lower for the interactive (51%) compared to the static intervention (65%). There were no significant differences in adherence rates and symptom-level improvements between the two conditions. Adding basic interactive functionality to a digital intervention may help with study retention. However, present findings challenge prior speculations that interactive features are crucial for enhancing user engagement and symptom improvement.

Critical measurement issues in the assessment of social media influence on body image

Progress towards understanding how social media impacts body image hinges on the use of appropriate measurement tools and methodologies. This review provides an overview of common (qualitative, self-report survey, lab-based experiments) and emerging (momentary assessment, computational) methodological approaches to the exploration of the impact of social media on body image. The potential of these methodologies is detailed, with examples illustrating current use as well as opportunities for expansion. A key theme from our review is that each methodology has provided insights for the body image research field, yet is insufficient in isolation to fully capture the nuance and complexity of social media experiences. Thus, in consideration of gaps in methodology, we emphasise the need for big picture thinking that leverages and combines the strengths of each of these methodologies to yield a more comprehensive, nuanced, and robust picture of the positive and negative impacts of social media.

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.

E-mental health interventions for the treatment and prevention of eating disorders: An updated systematic review and meta-analysis

OBJECTIVES

E-mental health (digital) interventions can help overcome existing barriers that stand in the way of people receiving help for an eating disorder (ED). Although e-mental health interventions for treating and preventing EDs have been met with enthusiasm, earlier reviews brought attention to poor quality of evidence, and offered solutions to enhance their evidence base. To assess developments in the field, we conducted an updated meta-analysis on the efficacy of e-mental health interventions for treating and preventing EDs, paying attention to whether trial quality and outcomes have improved in recent trials. We also assessed whether user-centered design principles have been implemented in existing digital interventions.

METHOD

Four databases were searched for RCTs of digital interventions for treating and preventing EDs. Thirty-six RCTs (28 prevention- and 8 treatment-focused) were included.

RESULTS

Some evidence that study quality improved in recent prevention-focused trials was found. Few trials involved the end-user in the design or development stage of the intervention. Issues with intervention engagement were noted, and 1 in 4 participants dropped out from prevention- and treatment-focused trials. Digital interventions were more effective than control conditions in reducing established risk factors and symptoms in prevention- (g's = 0.19 to 0.43) and treatment-focused trials (g's = 0.29 to 0.69), respectively. Effect sizes have not increased in recent trials. Few trials compared a digital intervention with a face-to-face intervention. Whether digital interventions can prevent ED onset is unclear.

CONCLUSION

Digital interventions are a promising approach to ED treatment and prevention, but improvements are still needed. Three key recommendations are provided. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

State-Based Markers of Disordered Eating Symptom Severity

Recent work using naturalistic, repeated, ambulatory assessment approaches have uncovered a range of within-person mood- and body image-related dynamics (such as fluctuation of mood and body dissatisfaction) that can prospectively predict eating disorder behaviors (e.g., a binge episode following an increase in negative mood). The prognostic significance of these state-based dynamics for predicting trait-level eating disorder severity, however, remains largely unexplored. The present study uses within-person relationships among state levels of negative mood, body image, and dieting as predictors of baseline, trait-level eating pathology, captured prior to a period of state-based data capture. Two-hundred and sixty women from the general population completed baseline measures of trait eating pathology and demographics, followed by a 7 to 10-day ecological momentary assessment phase comprising items measuring state body dissatisfaction, negative mood, upward appearance comparisons, and dietary restraint administered 6 times daily. Regression-based analyses showed that, in combination, state-based dynamics accounted for 34-43% variance explained in trait eating pathology, contingent on eating disorder symptom severity. Present findings highlight the viability of within-person, state-based dynamics as predictors of baseline trait-level disordered eating severity. Longitudinal testing is needed to determine whether these dynamics account for changes in disordered eating over time.

A survey study of attitudes toward, and preferences for, e-therapy interventions for eating disorder psychopathology

OBJECTIVE

E-therapy shows promise as a solution to the barriers that stand in the way of people receiving eating disorder (ED) treatment. Despite the potential for e-therapy to reduce the well-known treatment gap, little is known about public views and perspectives on this mode of intervention delivery. This study explored attitudes toward, and preferences for, e-therapy among individuals spanning the spectrum of eating pathology.

METHOD

Survey data assessing e-therapy attitudes and preferences were analyzed from 713 participants recruited from the public. Participants were categorized into one of five subgroups based on the type of self-reported ED symptoms and severity/risk level, ranging from high risk to a probable threshold or subthreshold ED.

RESULTS

Attitudes toward e-therapies appeared to be relatively positive; participants largely supported health care insurance coverage of costs for e-therapies, and were optimistic about the wide-ranging benefits of e-therapy. Although three-quarters of participants expressed a preference for face-to-face therapy, a significant percentage of participants (∼50%) reported an intention to use an e-therapy program for current or future eating problems, with intention ratings highest (70%) among those with probable bulimia nervosa (BN). Variables associated with an e-therapy preference were not currently receiving psychotherapy, more positive e-therapy attitudes, and greater stigma associated with professional help-seeking. Variables associated with e-therapy intentions were more positive e-therapy attitudes and a probable BN classification.

CONCLUSIONS

Present findings have important implications for increasing online intervention acceptance, engagement, and help-seeking among those at different stages of illness.

Retention strategies in longitudinal cohort studies: a systematic review and meta-analysis

BACKGROUND

Participant retention strategies that minimise attrition in longitudinal cohort studies have evolved considerably in recent years. This study aimed to assess, via systematic review and meta-analysis, the effectiveness of both traditional strategies and contemporary innovations for retention adopted by longitudinal cohort studies in the past decade.

METHODS

Health research databases were searched for retention strategies used within longitudinal cohort studies published in the 10-years prior, with 143 eligible longitudinal cohort studies identified (141 articles; sample size range: 30 to 61,895). Details on retention strategies and rates, research designs, and participant demographics were extracted. Meta-analyses of retained proportions were performed to examine the association between cohort retention rate and individual and thematically grouped retention strategies.

RESULTS

Results identified 95 retention strategies, broadly classed as either: barrier-reduction, community-building, follow-up/reminder, or tracing strategies. Forty-four of these strategies had not been identified in previous reviews. Meta-regressions indicated that studies using barrier-reduction strategies retained 10% more of their sample (95%CI [0.13 to 1.08]; p = .01); however, studies using follow-up/reminder strategies lost an additional 10% of their sample (95%CI [- 1.19 to - 0.21]; p = .02). The overall number of strategies employed was not associated with retention.

CONCLUSIONS

Employing a larger number of retention strategies may not be associated with improved retention in longitudinal cohort studies, contrary to earlier narrative reviews. Results suggest that strategies that aim to reduce participant burden (e.g., flexibility in data collection methods) might be most effective in maximising cohort retention.

A Mobile App-Based Intervention for Depression: End-User and Expert Usability Testing Study

BACKGROUND

Despite the growing number of mental health apps available for smartphones, the perceived usability of these apps from the perspectives of end users or health care experts has rarely been reported. This information is vital, particularly for self-guided mHealth interventions, as perceptions of navigability and quality of content are likely to impact participant engagement and treatment compliance.

OBJECTIVE

The aim of this study was to conduct a usability evaluation of a personalized, self-guided, app-based intervention for depression.

METHODS

Participants were administered the System Usability Scale and open-ended questions as part of a semistructured interview. There were 15 participants equally divided into 3 groups: (1) individuals with clinical depression who were the target audience for the app, (2) mental health professionals, and (3) researchers who specialize in the area of eHealth interventions and/or depression research.

RESULTS

The end-user group rated the app highly, both in quantitative and qualitative assessments. The 2 expert groups highlighted the self-monitoring features and range of established psychological treatment options (such as behavioral activation and cognitive restructuring) but had concerns that the amount and layout of content may be difficult for end users to navigate in a self-directed fashion. The end-user data did not confirm these concerns.

CONCLUSIONS

Encouraging participant engagement via self-monitoring and feedback, as well as personalized messaging, may be a viable way to maintain participation in self-guided interventions. Further evaluation is necessary to determine whether levels of engagement with these features enhance treatment effects.