Applying models of self-regulated learning to understand engagement with digital health interventions: a narrative review

Digital health interventions (DHIs) are often burdened by poor user engagement and high drop-out rates, diminishing their potential public health impact. Identifying user-related factors predictive of engagement has therefore drawn significant research attention in recent years. Absent from this literature—yet implied by DHI design—is the notion that individuals who use DHIs have well-regulated learning capabilities that facilitate engagement with unguided intervention content. In this narrative review, we make the case that learning capacity can differ markedly across individuals, and that the requirements of self-guided learning for many DHIs do not guarantee that those who sign up for these interventions have good learning capabilities at the time of uptake. Drawing upon a rich body of theoretical work on self-regulated learning (SRL) in education research, we propose a user-as-learner perspective to delineate parameters and drivers of variable engagement with DHIs. Five prominent theoretical models of SRL were wholistically evaluated according to their relevance for digital health. Three key themes were drawn and applied to extend our current understanding of engagement with DHIs: (a) common drivers of engagement in SRL, (b) the temporal nature of engagement and its drivers, and (c) individuals may differ in learning capability. Integrating new perspectives from SRL models offered useful theoretical insights that could be leveraged to enhance engagement with intervention content throughout the DHI user journey. In an attempt to consolidate these differing—albeit complementary—perspectives, we develop an integrated model of engagement and provide an outline of future directions for research to extend the current understanding of engagement issues in self-guided DHIs

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. © 2022 The Authors. International Journal of Eating Disorders published by Wiley Periodicals LLC

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

Autism spectrum disorder and anorexia nervosa : investigating the behavioural and neurocognitive overlap

Autism spectrum disorder (autism) and anorexia nervosa (AN) share many clinical features. Two key neurocognitive correlates of the autistic dyad, specifically, mentalising (social impairment) and set-shifting (restricted and repetitive behaviours/interests [RRBI]) were investigated in a sample of 327 adult participants with autism (n = 100; 50 females, 50 male), AN (n = 82; 54 females, 28 male), autism and AN (n = 45; 36 females, 9 male), and 100 (50 female, 50 male) control participants from the general population. A battery of self-report (Autism Spectrum Quotient, Eating Disorder Examination Questionnaire, Reflective Function Questionnaire, and Repetitive Behaviour Questionnaire 2 – Adult version) and performance-based (Wisconsin Card Sort Task [WCST] and Penn Emotion Recognition Test [ER-40]) measures were administered online. Clinical participants reported greater mentalising difficulty, more repetitive behaviour, and displayed worse mentalising ability compared to controls, with no difference between the clinical groups. Eating disorder psychopathology predicted error (total and perseverative) rates on the WCST, while lower levels of autistic traits were positively associated with ER-40 accuracy. We provide evidence that clinical features of autism and AN might have specific neurocognitive relevance. Improved understanding of the mechanisms underlying the overlapping features of autism and AN can have critical implications for early detection and improved and tailored intervention. © 2024 The Author

Using chatbot technology to improve Brazilian adolescents' body image and mental health at scale: Randomized controlled trial

Accessible, cost-effective, and scalable mental health interventions are limited, particularly in low- and middle-income countries, where disparities between mental health needs and services are greatest. Microinterventions (ie, brief, stand-alone, or digital approaches) aim to provide immediate reprieve and enhancements in mental health states and offer a novel and scalable framework for embedding evidence-based mental health promotion techniques into digital environments. Body image is a global public health issue that increases young peoples' risk of developing more severe mental and physical health issues. Embedding body image microinterventions into digital environments is one avenue for providing young people with immediate and short-term reprieve and protection from the negative exposure effects associated with social media. This 2-armed, fully remote, and preregistered randomized controlled trial assessed the impact of a body image chatbot containing microinterventions on Brazilian adolescents' state and trait body image and associated well-being outcomes. Geographically diverse Brazilian adolescents aged 13-18 years (901/1715, 52.54% girls) were randomized into the chatbot or an assessment-only control condition and completed web-based self-assessments at baseline, immediately after the intervention time frame, and at 1-week and 1-month follow-ups. The primary outcomes were mean change in state (at chatbot entry and at the completion of a microintervention technique) and trait body image (before and after the intervention), with the secondary outcomes being mean change in affect (state and trait) and body image self-efficacy between the assessment time points. Most participants who entered the chatbot (258/327, 78.9%) completed ≥1 microintervention technique, with participants completing an average of 5 techniques over the 72-hour intervention period. Chatbot users experienced small significant improvements in primary (state: P<.001, Cohen d=0.30, 95% CI 0.25-0.34; and trait body image: P=.02, Cohen d range=0.10, 95% CI 0.01-0.18, to 0.26, 95% CI 0.13-0.32) and secondary outcomes across various time points (state: P<.001, Cohen d=0.28, 95% CI 0.22-0.33; trait positive affect: P=.02, Cohen d range=0.15, 95% CI 0.03-0.27, to 0.23, 95% CI 0.08-0.37; negative affect: P=.03, Cohen d range=-0.16, 95% CI -0.30 to -0.02, to -0.18, 95% CI -0.33 to -0.03; and self-efficacy: P=.02, Cohen d range=0.14, 95% CI 0.03-0.25, to 0.19, 95% CI 0.08-0.32) relative to the control condition. Intervention benefits were moderated by baseline levels of concerns but not by gender. This is the first large-scale randomized controlled trial assessing a body image chatbot among Brazilian adolescents. Intervention attrition was high (531/858, 61.9%) and reflected the broader digital intervention literature; barriers to engagement were discussed. Meanwhile, the findings support the emerging literature that indicates microinterventions and chatbot technology are acceptable and effective web-based service provisions. This study also offers a blueprint for accessible, cost-effective, and scalable digital approaches that address disparities between health care needs and provisions in low- and middle-income countries. Clinicaltrials.gov NCT04825184; http://clinicaltrials.gov/ct2/show/NCT04825184. RR2-10.1186/s12889-021-12129-1. [Abstract copyright: ©Emily L Matheson, Harriet G Smith, Ana C S Amaral, Juliana F F Meireles, Mireille C Almeida, Jake Linardon, Matthew Fuller-Tyszkiewicz, Phillippa C Diedrichs. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 19.06.2023.

Study Protocol for the COVID-19 Pandemic Adjustment Survey (CPAS): A Longitudinal Study of Australian Parents of a Child 0–18 Years

Background: The COVID-19 pandemic presents significant risks to the mental health and wellbeing of Australian families. Employment and economic uncertainty, chronic stress, anxiety, and social isolation are likely to have negative impacts on parent mental health, couple and family relationships, as well as child health and development. Objective: This study aims to: (1) provide timely information on the mental health impacts of the emerging COVID-19 crisis in a close to representative sample of Australian parents and children (0–18 years), (2) identify adults and families most at risk of poor mental health outcomes, and (3) identify factors to target through clinical and public health intervention to reduce risk. Specifically, this study will investigate the extent to which the COVID-19 pandemic is associated with increased risk for parents’ mental health, lower well-being, loneliness, and alcohol use; parent-parent and parent-child relationships (both verbal and physical); and child and adolescent mental health problems. Methods: The study aims to recruit a close to representative sample of at least 2,000 adults aged 18 years and over living in Australia who are parents of a child 0–4 years (early childhood, N = 400), 5–12 years (primary school N = 800), and 13–18 years (secondary school, N = 800). The design will be a longitudinal cohort study using an online recruitment methodology. Participants will be invited to complete an online baseline self-report survey (20 min) followed by a series of shorter online surveys (10 min) scheduled every 2 weeks for the duration of the COVID-19 pandemic (i.e., estimated to be 14 surveys over 6 months). Results: The study will employ post stratification weights to address differences between the final sample and the national population in geographic communities across Australia. Associations will be analyzed using multilevel modeling with time-variant and time-invariant predictors of change in trajectory over the testing period. Conclusions: This study will provide timely information on the mental health impacts of the COVID-19 crisis on parents and children in Australia; identify communities, parents, families, and children most at risk of poor outcomes; and identify potential factors to address in clinical and public health interventions to reduce risk

Psychiatric and medical comorbidities of eating disorders : findings from a rapid review of the literature

Background: Eating disorders (EDs) are potentially severe, complex, and life-threatening illnesses. The mortality rate of EDs is signifcantly elevated compared to other psychiatric conditions, primarily due to medical complications and suicide. The current rapid review aimed to summarise the literature and identify gaps in knowledge relating to any psychiatric and medical comorbidities of eating disorders. Methods: This paper forms part of a rapid review) series scoping the evidence base for the feld of EDs, conducted to inform the Australian National Eating Disorders Research and Translation Strategy 2021–2031, funded and released by the Australian Government. ScienceDirect, PubMed and Ovid/Medline were searched for English-language studies focused on the psychiatric and medical comorbidities of EDs, published between 2009 and 2021. High-level evidence such as meta-analyses, large population studies and Randomised Control Trials were prioritised. Results: A total of 202 studies were included in this review, with 58% pertaining to psychiatric comorbidities and 42% to medical comorbidities. For EDs in general, the most prevalent psychiatric comorbidities were anxiety (up to 62%), mood (up to 54%) and substance use and post-traumatic stress disorders (similar comorbidity rates up to 27%). The review also noted associations between specifc EDs and non-suicidal self-injury, personality disorders, and neurodevelopmental disorders. EDs were complicated by medical comorbidities across the neuroendocrine, skeletal, nutritional, gastrointestinal, dental, and reproductive systems. Medical comorbidities can precede, occur alongside or emerge as a complication of the ED. Conclusions: This review provides a thorough overview of the comorbid psychiatric and medical conditions cooccurring with EDs. High psychiatric and medical comorbidity rates were observed in people with EDs, with comorbidities contributing to increased ED symptom severity, maintenance of some ED behaviours, and poorer functioning as well as treatment outcomes. Early identifcation and management of psychiatric and medical comorbidities in people with an ED may improve response to treatment and overall outcomes

Informing the development of Australia's national eating disorders research and translation strategy : a rapid review methodology

Background Eating disorders (EDs) are highly complex mental illnesses associated with significant medical complications. There are currently knowledge gaps in research relating to the epidemiology, aetiology, treatment, burden, and outcomes of eating disorders. To clearly identify and begin addressing the major deficits in the scientific, medical, and clinical understanding of these mental illnesses, the Australian Government Department of Health in 2019 funded the InsideOut Institute (IOI) to develop the Australian Eating Disorder Research and Translation Strategy, the primary aim of which was to identify priorities and targets for building research capacity and outputs. A series of rapid reviews (RR) were conducted to map the current state of knowledge, identify evidence gaps, and inform development of the national research strategy. Published peer-reviewed literature on DSM-5 listed EDs, across eight knowledge domains was reviewed: (1) population, prevalence, disease burden, Quality of Life in Western developed countries; (2) risk factors; (3) co-occurring conditions and medical complications; (4) screening and diagnosis; (5) prevention and early intervention; (6) psychotherapies and relapse prevention; (7) models of care; (8) pharmacotherapies, alternative and adjunctive therapies; and (9) outcomes (including mortality). While RRs are systematic in nature, they are distinct from systematic reviews in their aim to gather evidence in a timely manner to support decision-making on urgent or high-priority health concerns at the national level. Results Three medical science databases were searched as the primary source of literature for the RRs: Science Direct, PubMed and OVID (Medline). The search was completed on 31st May 2021 (spanning January 2009-May 2021). At writing, a total of 1,320 articles met eligibility criteria and were included in the final review. Conclusions For each RR, the evidence has been organised to review the knowledge area and identify gaps for further research and investment. The series of RRs (published separately within the current series) are designed to support the development of research and translation practice in the field of EDs. They highlight areas for investment and investigation, and provide researchers, service planners and providers, and research funders rapid access to quality current evidence, which has been synthesised and organised to assist decision-making

Secondary effects of digital interventions for depression

Meta-analyses show that digital interventions can effectively alleviate symptoms of depression. However, it is unclear whether these interventions have broader effects beyond mere symptom reduction. Most RCTs in this field assess a range of secondary outcomes, such as quality of life, anxiety, social support, distress, disability, and occupational functioning, yet these findings have yet to be aggregated using meta-analysis techniques. This meta-analysis aims to assess the effects of digital interventions for depression on these secondary outcome measures