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

The efficacy of internet-based cognitive behavioral therapy for adult binge spectrum eating disorders: A meta-analysis

OBJECTIVE

Internet-Based Cognitive Behavioral Therapy (iCBT) is an innovative modality of cognitive-behavioral intervention that presents a promising therapeutic strategy for individuals diagnosed with binge spectrum eating disorders. This study employed a meta-analysis methodology to assess the clinical effectiveness and acceptability of iCBT.

METHODS

We conducted searches in databases such as PubMed, Embase, Web of Science, Cochrane Library, and PsycINFO, collecting literature that met the inclusion criteria until August 5, 2023.

RESULTS

A comprehensive analysis was conducted, encompassing a total of 11 randomized controlled studies that satisfied the predetermined criteria for inclusion. The summary results demonstrated that iCBT could significantly improve the pathological features related to eating in patients with binge spectrum eating disorders and also significantly reduce the frequency of binge episodes. Additionally, iCBT could ameliorate the depressive and anxious emotions of patients with binge spectrum eating disorders and boost their self-esteem. Furthermore, a notable disparity in dropout rates was seen in comparison to the control group.

LIMITATION

Heterogeneity across studies，limitations of self-assessment scales and potential publication bias.

CONCLUSION

iCBT can effectively assist patients with binge spectrum eating disorders in improving clinical symptoms. However, it is important to use caution when interpreting the findings of this study, as there are limitations pertaining to the quantity and quality of the included studies.

Framework Development for Reducing Attrition in Digital Dietary Interventions: Systematic Review and Thematic Synthesis

BACKGROUND

Dietary behaviors significantly influence health outcomes across populations. Unhealthy diets are linked to serious diseases and substantial economic burdens, contributing to approximately 11 million deaths and significant disability-adjusted life years annually. Digital dietary interventions offer accessible solutions to improve dietary behaviors. However, attrition, defined as participant dropout before intervention completion, is a major challenge, with rates as high as 75%-99%. High attrition compromises intervention validity and reliability and exacerbates health disparities, highlighting the need to understand and address its causes.

OBJECTIVE

This study systematically reviews the literature on attrition in digital dietary interventions to identify the underlying causes, propose potential solutions, and integrate these findings with behavior theory concepts to develop a comprehensive theoretical framework. This framework aims to elucidate the behavioral mechanisms behind attrition and guide the design and implementation of more effective digital dietary interventions, ultimately reducing attrition rates and mitigating health inequalities.

METHODS

We conducted a systematic review, meta-analysis, and thematic synthesis. A comprehensive search across 7 electronic databases (PubMed, MEDLINE, Embase, CENTRAL, Web of Science, CINAHL Plus, and Academic Search Complete) was performed for studies published between 2013 and 2023. Eligibility criteria included original research exploring attrition in digital dietary interventions. Data extraction focused on study characteristics, sample demographics, attrition rates, reasons for attrition, and potential solutions. We followed ENTREQ (Enhancing the Transparency in Reporting the Synthesis of Qualitative Research) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and used RStudio (Posit) for meta-analysis and NVivo for thematic synthesis.

RESULTS

Out of the 442 identified studies, 21 met the inclusion criteria. The meta-analysis showed mean attrition rates of 35% for control groups, 38% for intervention groups, and 40% for observational studies, with high heterogeneity (I²=94%-99%) indicating diverse influencing factors. Thematic synthesis identified 15 interconnected themes that align with behavior theory concepts. Based on these themes, the force-resource model was developed to explore the underlying causes of attrition and guide the design and implementation of future interventions from a behavior theory perspective.

CONCLUSIONS

High attrition rates are a significant issue in digital dietary interventions. The developed framework conceptualizes attrition through the interaction between the driving force system and the supporting resource system, providing a nuanced understanding of participant attrition, summarized as insufficient motivation and inadequate or poorly matched resources. It underscores the critical necessity for digital dietary interventions to balance motivational components with available resources dynamically. Key recommendations include user-friendly design, behavior-factor activation, literacy training, force-resource matching, social support, personalized adaptation, and dynamic follow-up. Expanding these strategies to a population level can enhance digital health equity. Further empirical validation of the framework is necessary, alongside the development of behavior theory-guided guidelines for digital dietary interventions.

TRIAL REGISTRATION

PROSPERO CRD42024512902; https://tinyurl.com/3rjt2df9.

Behavior Change Techniques Within Digital Interventions for the Treatment of Eating Disorders: Systematic Review and Meta-Analysis

BACKGROUND

Previous systematic reviews of digital eating disorder interventions have demonstrated effectiveness at improving symptoms of eating disorders; however, our understanding of how these interventions work and what contributes to their effectiveness is limited. Understanding the behavior change techniques (BCTs) that are most commonly included within effective interventions may provide valuable information for researchers and developers. Establishing whether these techniques have been informed by theory will identify whether they target those mechanisms of action that have been identified as core to changing eating disorder behaviors. It will also evaluate the importance of a theoretical approach to digital intervention design.

OBJECTIVE

This study aims to define the BCTs within digital self-management interventions or minimally guided self-help interventions for adults with eating disorders that have been evaluated within randomized controlled trials. It also assessed which of the digital interventions were grounded in theory and the range of modes of delivery included.

METHODS

A literature search identified randomized controlled trials of digital intervention for the treatment of adults with eating disorders with minimal therapist support. Each digital intervention was coded for BCTs using the established BCT Taxonomy v1; for the application of theory using an adapted version of the theory coding scheme (TCS); and for modes of delivery using the Mode of Delivery Ontology. A meta-analysis evaluated the evidence that any individual BCT moderated effect size or that other potential factors such as the application of theory or number of modes of delivery had an effect on eating disorder outcomes.

RESULTS

Digital interventions included an average of 14 (SD 2.6; range 9-18) BCTs. Self-monitoring of behavior was included in all effective interventions, with Problem-solving, Information about antecedents, Feedback on behavior, Self-monitoring of outcomes of behavior, and Action planning identified in >75% (13/17) of effective interventions. Social support and Information about health consequences were more evident in effective interventions at follow-up compared with postintervention measurement. The mean number of modes of delivery was 4 (SD 1.6; range 2-7) out of 12 possible modes, with most interventions (15/17, 88%) being web based. Digital interventions that had a higher score on the TCS had a greater effect size than those with a lower TCS score (subgroup differences: χ21=9.7; P=.002; I²=89.7%) within the meta-analysis. No other subgroup analyses had statistically significant results.

CONCLUSIONS

There was a high level of consistency in terms of the most common BCTs within effective interventions; however, there was no evidence that any specific BCT contributed to intervention efficacy. The interventions that were more strongly informed by theory demonstrated greater improvements in eating disorder outcomes compared to waitlist or treatment-as-usual controls. These results can be used to inform the development of future digital eating disorder interventions.

TRIAL REGISTRATION

PROSPERO CRD42023410060; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=410060.

A randomized controlled trial of an online single session intervention for body image in individuals with recurrent binge eating

OBJECTIVE

Theoretical models highlight body image disturbances as central to the maintenance of eating disorders, with empirical evidence finding negative body image to be a robust predictor of treatment nonresponse, relapse and symptom persistence. Accessible, scalable, and inexpensive interventions that can effectively target negative body image across the eating disorders are needed. We developed an online single session intervention (SSI) for negative body image and evaluated its acceptability and efficacy in a randomized controlled trial in individuals with recurrent binge eating.

METHOD

Participants with recurrent binge eating were randomly assigned to the SSI (n = 99) or a waitlist (n = 101). Assessments were conducted at baseline, 4 week follow-up, and 8 week follow-up.

RESULTS

81% of participants accessed the SSI, demonstrating reasonable uptake. However, issues with attrition were apparent; a 32% study dropout rate was observed at posttest, while a 58% dropout rate was observed at follow-up. 87% of participants who completed satisfaction measures would recommend the SSI. SSI participants experienced greater improvements in negative body image at both 4 (d = -0.65) and 8 week (d = -0.74) follow-up. Significant between-group effects were also observed on most other secondary symptom measures, though no differences were found for motivations and help-seeking beliefs and intentions.

CONCLUSION

Body image-focused self-guided SSIs for recurrent binge are largely accepted by those who are retained in the trial, but are associated with significant attrition. Although SSIs are not intended to replace standard treatment, they may help with short-term symptom management and could play an important role in eating disorder care.

PUBLIC SIGNIFICANCE

Single session interventions (SSI) are a potentially accessible, scalable, and cost-effective way to deliver evidence-based treatment content to people with eating disorders. This study shows that an online (SSI) designed to target body image among people with recurrent binge eating is accepted and produced short-term symptom relief.

Resilience eDBT: Development and Usability Evaluation of a Dialectical Behavior Therapy Skills Training App for Eating Disorders

OBJECTIVE

Dialectical behavior therapy (DBT) is an evidence-based treatment for disorders characterized by recurrent binge eating. Yet, access to specialized treatment like DBT remains limited. To increase the accessibility of DBT, we developed a DBT skills training app (Resilience: eDBT) for the management of eating disorder (ED) symptoms. This paper delineates the developmental process of Resilience and tests its usability.

METHODS

Descriptive information on the development and features of Resilience is provided, including its framework, content structure and delivery formats, functionality, data storage procedure, and privacy protocols. Usability was assessed via a mixed methods approach in 10 symptomatic individuals. Qualitative data were organized based on an existing framework, which included six themes: usability, visual design, user engagement, content, therapeutic persuasiveness, and therapeutic alliance.

RESULTS

Resilience demonstrated good usability via a Systems Usability Scale score of 85.5, which exceeded the recommended cutoff of 68. Positive aspects of the app, according to interview data, were the ease of use and the visual design, while the addition of peer support was suggested as an opportunity for improvement.

DISCUSSION

A novel DBT-based app may serve as an acceptable, low-intensity option or adjunct to traditional treatment for targeting ED symptoms that emerge in daily life. However, notable limitations include the small sample size and the single time point at which the usability assessment was conducted.

Cognitive training via mobile app for addressing eating disorders' cognitions in adolescents: a randomized control trial protocol

During adolescence, individuals are particularly vulnerable to developing eating disorders (EDs). To address the dysfunctional beliefs linked to these disorders, a new mobile app has been developed. This app, called GG eating disorders-Adolescents (GGED-AD), was created based on CBT to help adolescents work on their self-dialogue related to the core beliefs of eating disorders. The objective is to present the protocol for a randomized controlled trial to explore the efficacy of GGED-AD.Methods The study will be carried out in adolescents aged 13 to 16 from an educational center in the Valencian Community. The participants will be randomized into two groups: the experimental group will use the GGED-AD app during 14 consecutive days for approximately 5 min each day; and the control group will use a neutral app (GGNEUTRAL) during the same time. Both groups will complete instruments that assess dysfunctional beliefs related to eating disorders, eating symptoms, symptoms of depression and anxiety, body satisfaction and self-esteem before and after the intervention. A follow-up will be conducted one month later.Results A decrease in the degree of ascription to dysfunctional beliefs associated with eating disorders and eating symptomatology is expected, as well as an increase in body satisfaction and the self-esteem of the participants of the experimental group.Discussion The app in this study could help tackle and prevent ED-related symptoms in adolescents.Trial registration NCT06039514.

Predictors, mediators, and moderators of response to digital interventions for eating disorders: A systematic review

OBJECTIVE

Digital interventions show promise as an effective prevention or self-management option for eating disorders (EDs). However, it remains unclear how, for whom, and through what mechanisms they work in this population, as a synthesis of outcome predictors, moderators, and mediators is lacking. This systematic review synthesized empirical research investigating predictors, mediators, and moderators of response to digital interventions for EDs.

METHOD

Six databases were searched (PROSPERO CRD42022295565) for studies that assessed predictors, moderators, or mediators of response (i.e., uptake, drop-out, engagement, and symptom level change) to a digital prevention or treatment program for EDs. Variables were grouped into several overarching categories (demographic, symptom severity, psychological, etc.) and were synthesized qualitatively across samples without a formally diagnosed ED (typically prevention-focused) and samples with a formally diagnosed ED (typically treatment-focused).

RESULTS

Eighty-six studies were included. For studies recruiting samples without a formal diagnosis (n = 70 studies), most predictors explored were statistically unrelated to outcome, although participant age, baseline symptom severity, confidence to change, motivation, and program engagement showed preliminary evidence of prognostic potential. No robust moderators or mediators were identified. Few studies recruiting samples with a formal diagnosis emerged (n = 16), of which no reliable predictors, moderators, or mediators were identified.

DISCUSSION

It remains unclear how, for whom, and under what circumstances digital programs targeting EDs work. We offer several recommendations for future research with the aim of advancing understanding of client characteristics and intervention elements that signal success from this intervention modality.

PUBLIC SIGNIFICANCE

Digital interventions have shown potential as an effective, scalable, and accessible intervention option for EDs. However, responsiveness varies, so advancing understanding of predictors, mediators, and moderators of outcome to digital interventions for EDs is needed. Such knowledge is important for enabling safe and efficient treatment matching, and for informing future development of effective digital interventions.

Exploration of the individual and combined effects of predictors of engagement, dropout, and change from digital interventions for recurrent binge eating

OBJECTIVE

Our ability to predict responsiveness to digital interventions for eating disorders has thus far been poor, potentially for three reasons: (1) there has been a narrow set of predictors explored; (2) prediction has mostly focused on symptom change, ignoring other aspects of the user journey (uptake, early engagement); and (3) there is an excessive focus on the unique effects of predictors rather than the combined contributions of a predictor set. We evaluated the univariate and multivariate effects of outcome predictors in the context of a randomized trial (n = 398) of digitally delivered interventions for recurrent binge eating.

METHOD

Thirty baseline variables were selected as predictors, ranging from specific symptoms, to key protective factors, to technological acceptance, and to online treatment attitudes. Outcomes included uptake, early engagement, and remission. Univariate (d) and multivariate (D) standardized mean differences were calculated to estimate the individual and combined effects of predictors, respectively.

RESULTS

At the univariate level, few predictors produced an effect size larger than what is considered small (d > .20) across outcomes. However, our multivariate approach enhanced prediction (Ds = .65 to 1.12), producing accuracy rates greater than chance (63%-71% accuracy). Less than half of the chosen variables proved to be useful in contributing to predictions in multivariate models.

CONCLUSION

Findings suggest that accuracy in outcome prediction from digitally delivered interventions may be better driven by the aggregation of many small effects rather than one or several largely influential predictors. Replication with different data streams (sensor, neuroimaging) would be useful.

PUBLIC SIGNIFICANCE

Our ability to predict who will and will not benefit from digital interventions for eating disorders has been poor. We highlight the viability of a multivariate approach to outcome prediction, whereby prediction may be better driven by the aggregation of many small effects rather than one or a few influential predictors.

Efficacy of a therapist-guided smartphone-based intervention to support recovery from bulimia nervosa: Study protocol of a randomized controlled multi-centre trial

OBJECTIVE

Although inpatient treatment is highly effective for patients with bulimia nervosa (BN), some patients show a resurgence of symptoms and relapse after discharge. Therefore, the aim of this study is to evaluate the efficacy of a guided smartphone-based aftercare intervention following inpatient treatment to support recovery.

METHOD

172 female patients with BN (DSM-5: 307.51) will be randomized to receive a 16-week smartphone-based aftercare intervention (German version of 'Recovery Record') with therapist feedback as an add-on element to treatment as usual (TAU) or TAU alone. Assessments will take place at baseline (discharge, T0), during the intervention (after 4 weeks, T1), post-intervention (after 16 weeks, T2) and at 6-month follow-up (T3). Primary outcome will be remission at T2. Moderator and mediator analyses will investigate for whom the aftercare intervention suits best and how it works.

CONCLUSIONS

This is the first randomized controlled trial to examine a guided smartphone-based aftercare intervention following inpatient treatment of patients with BN. We expect that this innovative aftercare intervention is highly accepted by the patients and that it has the potential to support recovery after inpatient treatment and thereby could contribute to improving aftercare for patients with BN.

Target-user preferences, motivations, and acceptance for a dialectical behaviour therapy smartphone application for eating disorders

PURPOSE

Evidence shows that dialectical behavioural therapy (DBT) is efficacious for eating disorders (ED), yet few people have access to specialized treatments like DBT. Translating key DBT skills for delivery via a smartphone application may broaden the dissemination of evidence-based interventions. However, prior to developing a DBT-based app, it is crucial to gather information on target-user needs and preferences. Assessing overall acceptance and identifying predictors of acceptance, informed by the UTAUT framework, is also important. This process ensures not only a demand for such an app, but also that users receive content and features tailored to their needs.

METHOD

This study aimed to understand target-user preferences of DBT-based apps for EDs by assessing willingness to engage, overall acceptance levels, and preferred functionality/content delivery modes (n = 326 symptomatic participants).

RESULTS

Eighty-eight percent indicated they would be willing to use a DBT-based ED app if it were available. Acceptance levels of a DBT app were high (64%), which was uniquely predicted by performance expectancy (perceptions of how beneficial an intervention is) and facilitating conditions (expectations of technological infrastructure and support in interventions) in path analysis. Content perceived as important to contain were emotion regulation techniques, tailored intervention strategies, and psychoeducation.

CONCLUSION

Findings generate important information about target-user preferences of a DBT-based app for EDs, highlighting necessary design principles for apps of this kind. Level of evidence Level V, cross-sectional descriptive study.

Evidence-Based Assessments for Transdiagnostic Eating Disorder Symptoms: Guidelines for Current Use and Future Directions

Eating disorders are severe and often chronic mental illnesses that are associated with high impairment and mortality rates. Recent estimates suggest that eating disorder prevalence rates are on the rise, indicating an increased need for accurate assessment and detection. The current review provides an overview of transdiagnostic eating disorder assessments, including interview, self-report, health and primary care screeners, and technology-based and objective assessments. We focused on assessments that are transdiagnostic in nature and exhibit high impact in the field. We provide recommendations for how these assessments should be used in research and clinical settings. We also discuss considerations that are crucial for assessment, including the use of a categorical versus dimensional diagnostic framework, assessment of eating disorders in related fields (i.e., anxiety and depression), and measurement-based care for eating disorders. Finally, we provide suggestions for future research, including the need for more research on short transdiagnostic screeners for use in health care settings, standardized assessments for ecological momentary assessment, development of state-based assessment of eating disorder symptoms, and consideration of assessment across multiple timescales.

Digital Mental Health Interventions: Differences in Diet Culture Intervention Framing

Diet culture is a societal norm that ranks thin bodies as superior to other body types and has been associated with negative outcomes, such as eating disorders. Wellness has evolved into a term that is often used to promote diet culture messages. One possible way to combat diet culture is through single-session, digital mental health interventions (DMHIs), which allow for increased access to brief public health treatments. The framing of DMHIs is critical to ensure that the target population is reached. Participants (N = 397) were enrolled in a single-session DMHI, which was framed as either a Diet Culture Intervention (n = 201) or a Wellness Resource (n = 196). Baseline group differences in eating disorder pathology, body image, weight stigma concerns, fat acceptance, and demographic characteristics were analyzed. Across groups, participants reported moderately high eating disorder pathology, low-to-moderate levels of body dissatisfaction, moderate levels of fat acceptance, and either very low or very high weight stigma concerns. Participants in the Diet Culture Intervention group reported higher levels of fat acceptance than those in the Wellness Resource group (p < 0.001). No other framing group differences were identified, though post hoc analyses revealed differences based on recruitment source (i.e., social media versus undergraduate research portal). This study found that framing a DMHI as targeting diet culture or as a Wellness Resource can result in the successful recruitment of individuals at risk of disordered eating. Framing a DMHI as a Wellness Resource may increase recruitment of individuals with low levels of fat acceptance, which may be particularly important for dismantling diet culture, disordered eating, and weight stigma concerns. Future research should assess DMHI framing in other populations, such as men and adolescents.

Psychotherapies for eating disorders: findings from a rapid review

BACKGROUND

Psychotherapy is considered central to the effective treatment of eating disorders-focusing on behavioural, psychological, and social factors that contribute to the illness. Research indicates psychotherapeutic interventions out-perform placebo, waitlist, and/or other treatments; but, outcomes vary with room for major improvement. Thus, this review aims to (1) establish and consolidate knowledge on efficacious eating disorder psychotherapies; (2) highlight select emerging psychotherapeutic interventions; and (3) identify knowledge gaps to better inform future treatment research and development.

METHODS

The current review forms part of a series of Rapid Reviews published in a special issue in the Journal of Eating Disorders to inform the development of the Australian-government-funded National Eating Disorder Research and Translation Strategy 2021-2031. Three databases were searched for studies published between 2009 and 2023, published in English, and comprising high-level evidence studies (meta-analyses, systematic reviews, moderately sized randomised controlled studies, moderately sized controlled-cohort studies, and population studies). Data pertaining to psychotherapies for eating disorders were synthesised and outlined in the current paper.

RESULTS

281 studies met inclusion criteria. Behavioural therapies were most commonly studied, with cognitive-behavioural and family-based therapies being the most researched; and thus, having the largest evidence-base for treating anorexia nervosa, bulimia nervosa, and binge eating disorder. Other therapies, such as interpersonal and dialectical behaviour therapies also demonstrated positive treatment outcomes. Emerging evidence supports specific use of Acceptance and Commitment; Integrative Cognitive Affective; Exposure; Mindfulness; and Emotionally-Focused therapies; however further research is needed to determine their efficacy. Similarly, growing support for self-help, group, and computer/internet-based therapeutic modalities was noted. Psychotherapies for avoidant/restrictive food intake disorder; other, and unspecified feeding and eating disorders were lacking evidence.

CONCLUSIONS

Currently, clinical practice is largely supported by research indicating that behavioural and cognitive-behavioural psychotherapies are most effective for the treatment of eating disorders. However, the efficacy of psychotherapeutic interventions varies across studies, highlighting the need for investment and expansion of research into enhanced variants and novel psychotherapies to improve illness outcomes. There is also a pressing need for investigation into the whole range of eating disorder presentations and populations, to determine the most effective interventions.

A pilot multiple-baseline study of a mobile cognitive behavioral therapy for the treatment of eating disorders in university students

OBJECTIVE

Eating disorders (EDs) are serious psychiatric disorders associated with substantial morbidity and mortality that are prevalent among university students. Because many students do not receive treatment due to lack of access on university campuses, mobile-health (mHealth) adaptations of evidence-based treatments represent an opportunity to increase treatment accessibility and engagement. The purpose of this study was to test the initial efficacy of Building Healthy Eating and Self-Esteem Together for University Students (BEST-U), which is a 10-week mHealth self-guided cognitive-behavioral therapy (CBT-gsh) app that is paired with a brief 25-30-min weekly telehealth coaching, for reducing ED psychopathology in university students.

METHOD

A non-concurrent multiple-baseline design (N = 8) was used to test the efficacy of BEST-U for reducing total ED psychopathology (primary outcome), ED-related behaviors and cognitions (secondary outcomes), and ED-related clinical impairment (secondary outcome). Data were examined using visual analysis and Tau-BC effect-size calculations.

RESULTS

BEST-U significantly reduced total ED psychopathology and binge eating, excessive exercise, and restriction (effect sizes ranged from -0.39 to -0.92). Although body dissatisfaction decreased, it was not significant. There were insufficient numbers of participants engaging in purging to evaluate purging outcomes. Clinical impairment significantly reduced from pre-to-post-treatment.

DISCUSSION

The current study provided initial evidence that BEST-U is a potentially efficacious treatment for reducing ED symptoms and ED-related clinical impairment. Although larger-scale randomized controlled trials are needed, BEST-U may represent an innovative, scalable tool that could reach greater numbers of underserved university students than traditional intervention-delivery models.

PUBLIC SIGNIFICANCE

Using a single-case experimental design, we found evidence for the initial efficacy of a mobile guided-self-help cognitive-behavioral therapy program for university students with non-low weight binge-spectrum eating disorders. Participants reported significant reductions in ED symptoms and impairment after completion of the 10-week program. Guided self-help programs show promise for filling an important need for treatment among university students with an ED.

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.

Predicting smoking cessation, reduction and relapse six months after using the Stop-Tabac app for smartphones: a machine learning analysis

BACKGROUND

An analysis of predictors of smoking behaviour among users of smoking cessation apps can provide useful information beyond what is already known about predictors in other contexts. Therefore, the aim of the present study was to identify the best predictors of smoking cessation, smoking reduction and relapse six months after starting to use the smartphone app Stop-Tabac.

METHOD

Secondary analysis of 5293 daily smokers from Switzerland and France who participated in a randomised trial testing the effectiveness of this app in 2020, with follow-up at one and six months. Machine learning algorithms were used to analyse the data. The analyses for smoking cessation included only the 1407 participants who responded after six months; the analysis for smoking reduction included only the 673 smokers at 6-month follow-up; and the analysis for relapse at 6 months included only the 502 individuals who had quit smoking after one month.

RESULTS

Smoking cessation after 6 months was predicted by the following factors (in this order): tobacco dependence, motivation to quit smoking, frequency of app use and its perceived usefulness, and nicotine medication use. Among those who were still smoking at follow-up, reduction in cigarettes/day was predicted by tobacco dependence, nicotine medication use, frequency of app use and its perceived usefulness, and e-cigarette use. Among those who had quit smoking after one month, relapse after six months was predicted by intention to quit, frequency of app use, perceived usefulness of the app, level of dependence and nicotine medication use.

CONCLUSION

Using machine learning algorithms, we identified independent predictors of smoking cessation, smoking reduction and relapse. Studies on the predictors of smoking behavior among users of smoking cessation apps may provide useful insights for the future development of these apps and future experimental studies.

CLINICAL TRIAL REGISTRATION

ISRCTN Registry: ISRCTN11318024, 17 May 2018. http://www.isrctn.com/ISRCTN11318024 .

Reach and uptake of digital mental health interventions based on cognitive-behavioral therapy for college students: A systematic review

Background

Mental health problems are increasing in prevalence among college students, yet few students receive treatment due to barriers such as insufficient resources in college counseling centers. Digital mental health interventions (DMHIs) have potential to overcome barriers and offer accessible, evidence-based care to college students. However, to evaluate the true public health impact of evidence-based DMHIs, it is important to assess the reach and uptake rates of DMHIs on college campuses.

Objectives

We conducted a systematic review to examine the reach (i.e., % of invited students who express interest) and uptake (i.e., % of enrolled participants who initiate an intervention) of DMHIs based on cognitive-behavioral therapy (CBT) for college students.

Methods

Eight databases were searched. Inclusion criteria included: (1) college population; (2) experimental design; (3) CBT-based intervention; (4) intervention targeting specific mental health conditions; and (5) digital intervention. Reach and uptake rates were calculated from data reported. A systematic narrative review framework was used to synthesize results.

Results

Of 10,315 articles screened, 90 were included. Seventeen studies (19%) reported sufficient data to calculate reach; 35 studies (39%) reported uptake rates. Of studies that reported reach or uptake, most evaluated unguided (n = 20) or guided (n = 16) self-help programs. Measurement methods varied widely. Overall reach was low, whereas uptake was high among enrolled participants.

Discussion

Despite evidence that improving reach and uptake can increase the public health impact of DMHIs, most studies did not report on either outcome. Suggested practices to improve these outcomes, and their reporting, are discussed.

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 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.

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.

Factors Influencing Adherence to mHealth Apps for Prevention or Management of Noncommunicable Diseases: Systematic Review

BACKGROUND

Mobile health (mHealth) apps show vast potential in supporting patients and health care systems with the increasing prevalence and economic costs of noncommunicable diseases (NCDs) worldwide. However, despite the availability of evidence-based mHealth apps, a substantial proportion of users do not adhere to them as intended and may consequently not receive treatment. Therefore, understanding the factors that act as barriers to or facilitators of adherence is a fundamental concern in preventing intervention dropouts and increasing the effectiveness of digital health interventions.

OBJECTIVE

This review aimed to help stakeholders develop more effective digital health interventions by identifying factors influencing the continued use of mHealth apps targeting NCDs. We further derived quantified adherence scores for various health domains to validate the qualitative findings and explore adherence benchmarks.

METHODS

A comprehensive systematic literature search (January 2007 to December 2020) was conducted on MEDLINE, Embase, Web of Science, Scopus, and ACM Digital Library. Data on intended use, actual use, and factors influencing adherence were extracted. Intervention-related and patient-related factors with a positive or negative influence on adherence are presented separately for the health domains of NCD self-management, mental health, substance use, nutrition, physical activity, weight loss, multicomponent lifestyle interventions, mindfulness, and other NCDs. Quantified adherence measures, calculated as the ratio between the estimated intended use and actual use, were derived for each study and compared with the qualitative findings.

RESULTS

The literature search yielded 2862 potentially relevant articles, of which 99 (3.46%) were included as part of the inclusion criteria. A total of 4 intervention-related factors indicated positive effects on adherence across all health domains: personalization or tailoring of the content of mHealth apps to the individual needs of the user, reminders in the form of individualized push notifications, user-friendly and technically stable app design, and personal support complementary to the digital intervention. Social and gamification features were also identified as drivers of app adherence across several health domains. A wide variety of patient-related factors such as user characteristics or recruitment channels further affects adherence. The derived adherence scores of the included mHealth apps averaged 56.0% (SD 24.4%).

CONCLUSIONS

This study contributes to the scarce scientific evidence on factors that positively or negatively influence adherence to mHealth apps and is the first to quantitatively compare adherence relative to the intended use of various health domains. As underlying studies mostly have a pilot character with short study durations, research on factors influencing adherence to mHealth apps is still limited. To facilitate future research on mHealth app adherence, researchers should clearly outline and justify the app's intended use; report objective data on actual use relative to the intended use; and, ideally, provide long-term use and retention data.

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.

Smartphone apps for eating disorders: An overview of the marketplace and research trends

OBJECTIVE

Apps purporting to assist with the management of eating disorders are proliferating; although less is known about (a) the safety or efficacy of the apps on the marketplaces and (b) if the research evidence supports dissemination of these apps. In this research forum, we seek to synthesize the current data and provide practical considerations around the use and research of these apps.

METHODS

A search of the iOS and Google Play stores was conducted in June 2021 to identify publicly available apps targeting eating disorders. A PubMed search was also conducted in June 2021 to identify relevant publications around smartphone apps for eating disorders.

RESULTS

Sixty-five apps that support the treatment of eating disorders were identified and coded across 105 data points on the publicly available mindApps.org website. The literature search revealed 13 articles. Seven percent of marketplaces apps offered any research support and the 13 published studies focused on only four apps.

DISCUSSION

There is a need for more translational research on smartphone apps for eating disorders. Current marketplace offerings present risks that may not be balanced by their limited evidence. Research efforts should focus on offering evidenced-based apps for the marketplace. Clinicians should weigh known and emerging risks and benefits of these apps within the context of research gaps when making decisions around use.

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.

Food Addiction and Emotional Eating Behaviors Co-Occurring with Problematic Smartphone Use in Adolescents?

Addiction in adolescence is increasing and has a significant impact on physical and mental health. Notably, addictions can be comorbid and affect each other. Despite the recent growing interest in food addiction (FA) and problematic smartphone use (PSU), few studies have investigated their association in adolescents. We investigated the relationship between FA and PSU in adolescents and the effects of eating behaviors. A total of 209 adolescents (44.5% male; mean age = 12.86 ± 0.7 years) participated in the current school-based community study. We found a positive correlation between the dimensional Yale Food Addiction Scale for Children 2.0 (dYFAS-C2.0) and the Smartphone Overdependence Scale after adjusting for age, sex, body mass index, and socioeconomic status. The high-risk PSU group accounted for 17.2% of participants. Furthermore, this group showed 2.3 times higher dYFAS-C2.0 scores than the general group. Emotional overeating and satiety responsiveness were correlated with PSU. A comprehensive evaluation of addiction symptoms is needed for proper intervention, especially in adolescents with symptoms of abnormal eating behaviors.

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.

The costs and benefits of intensive day treatment programs and outpatient treatments for eating disorders: An idea worth researching

Outpatient care (e.g., individual, group, or self-help therapies) and day treatment programs (DTPs) are common and effective treatments for adults with eating disorders. Compared to outpatient care, DTPs have additional expenses and could have unintended iatrogenic effects (e.g., may create an overly protective environment that undermines self-efficacy). However, these potential downsides may be offset if DTPs are shown to have advantages over outpatient care. To explore this question, our team conducted a scoping review that aimed to synthesize the existing body of adult eating disorder literature (a) comparing outcomes for DTPs to outpatient care, and (b) examining the use of DTPs as a higher level of care in a stepped care model. Only four studies met the predefined search criteria. The limited results suggest that the treatments have similar effects and that outpatient care is more cost-effective. Furthermore, no studies explored the use of DTPs as a higher level of care in a stepped care model (despite international guidelines recommending this approach). Given the clear dearth of literature on this clinically relevant topic, we have provided specific avenues for further research.

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.

Binge Eating Disorder: A Psychiatrist's Commentary on Clinical Considerations

PURPOSE

Of the 3 major eating disorders, anorexia nervosa, bulimia nervosa, and binge eating disorder (BED), BED is the most common and exists in the practices of most primary care and psychiatric clinicians. However, BED often goes unrecognized and thus untreated.

METHODS

Reviewed in this commentary are the basic elements in the diagnosis of BED, demographic and clinical characteristics, screening options, the importance of comorbidities, pathophysiology, and available treatments.

FINDINGS

Psychological treatments, including cognitive-behavioral therapy, interpersonal therapy, and behavioral weight loss, have been recommended as first-line options and are supported by several different meta-analytic reviews. Lisdexamfetamine is currently the only medication approved by the US Food and Drug Administration for the treatment of BED. Effect sizes for lisdexamfetamine versus placebo for response, remission, and avoidance of relapse in BED are robust, but its use may be limited by tolerability. This is also the case for topiramate, an anticonvulsant that has been used "off-label" to treat BED.

IMPLICATIONS

Additional medication choices approved by the US Food and Drug Administration for the treatment of BED are needed. Moving forward, opportunities to leverage modern technology to broaden access to treatment are highly desirable.