Optimizing prediction of binge eating episodes : A comparison approach to test alternative conceptualizations of the affect regulation model
- Authors: Fuller-Tyszkiewicz, Matthew , Richardson, Ben , Skouteris, Helen , Austin, David , Castle, David , Busija, Lucy , Klein, Britt , Holmes, Milllicent , Broadbent, Jaclyn
- Date: 2014
- Type: Text , Journal article
- Relation: Journal of Eating Disorders Vol. 2, no. 1 (2014), p. 1-8
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- Description: Background: Although a wealth of studies have tested the link between negative mood states and likelihood of a subsequent binge eating episode, the assumption that this relationship follows a typical linear dose-response pattern (i.e., that risk of a binge episode increases in proportion to level of negative mood) has not been challenged. The present study demonstrates the applicability of an alternative, non-linear conceptualization of this relationship, in which the strength of association between negative mood and probability of a binge episode increases above a threshold value for the mood variable relative to the slope below this threshold value (threshold dose response model).Methods: A sample of 93 women aged 18 to 40 completed an online survey at random intervals seven times per day for a period of one week. Participants self-reported their current mood state and whether they had recently engaged in an eating episode symptomatic of a binge.Results: As hypothesized, the threshold approach was a better predictor than the linear dose-response modeling of likelihood of a binge episode. The superiority of the threshold approach was found even at low levels of negative mood (3 out of 10, with higher scores reflecting more negative mood). Additionally, severity of negative mood beyond this threshold value appears to be useful for predicting time to onset of a binge episode.Conclusions: Present findings suggest that simple dose-response formulations for the association between negative mood and onset of binge episodes miss vital aspects of this relationship. Most notably, the impact of mood on binge eating appears to depend on whether a threshold value of negative mood has been breached, and elevation in mood beyond this point may be useful for clinicians and researchers to identify time to onset. © 2014 Fuller-Tyszkiewicz et al.; licensee BioMed Central Ltd.
Can intelligent agents improve data quality in online questionnaires? A pilot study
- Authors: Söderström, Arne , Shatte, Adrian , Fuller-Tyszkiewicz, Matthew
- Date: 2021
- Type: Text , Journal article
- Relation: Behavior Research Methods Vol. 53, no. 5 (2021), p. 2238-2251
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- Description: We explored the utility of chatbots for improving data quality arising from collection via sonline surveys. Three-hundred Australian adults sampled via Prolific Academic were randomized across chatbot-supported or unassisted online questionnaire conditions. The questionnaire comprised validated measures, along with challenge items formulated to be confusing yet aligned with the validated targets. The chatbot condition provided optional assistance with item clarity via a virtual support agent. Chatbot use and user satisfaction were measured through session logs and user feedback. Data quality was operationalized as between-group differences in relationships among validated and challenge measures. Findings broadly supported chatbot utility for online surveys, showing that most participants with chatbot access utilized it, found it helpful, and demonstrated modestly improved data quality (vs. controls). Absence of confusion for one challenge item is believed to have contributed to an underestimated effect. Findings show that assistive chatbots can enhance data quality, will be utilized by many participants if available, and are perceived as beneficial by most users. Scope constraints for this pilot study are believed to have led to underestimated effects. Future testing with longer-form questionnaires incorporating expanded item difficulty may further understanding of chatbot utility for survey completion and data quality. © 2021, The Psychonomic Society, Inc.
E-mental health interventions for the treatment and prevention of eating disorders : an updated systematic review and meta-analysis
- Authors: Linardon, Jake , Shatte, Adrian , Messer, Mariel , Firth, Joseph , Fuller-Tyszkiewicz, Matthew
- Date: 2020
- Type: Text , Journal article
- Relation: Journal of Consulting and Clinical Psychology Vol. 88, no. 11 (2020), p. 994-1007
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- Description: 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)What is the public health significance of this article?: E-mental health interventions show promise in addressing eating disorder symptoms and risk factors. However, issues with study quality, drop-out, and engagement are noted, and researchers are encouraged to involve the end-user in all stages of the intervention development and implementation to optimize outcomes. (PsycInfo Database Record (c) 2020 APA, all rights reserved) © 2020 American Psychological Association
Social media markers to identify fathers at risk of postpartum depression : a machine learning approach
- Authors: Shatte, Adrian , Hutchinson, Delyse , Fuller-Tyszkiewicz, Matthew , Teague, Samantha
- Date: 2020
- Type: Text , Journal article
- Relation: Cyberpsychology, Behavior, and Social Networking Vol. 23, no. 9 (2020), p. 611-618
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- Description: Postpartum depression (PPD) is a significant mental health issue in mothers and fathers alike; yet at-risk fathers often come to the attention of health care professionals late due to low awareness of symptoms and reluctance to seek help. This study aimed to examine whether passive social media markers are effective for identifying fathers at risk of PPD. We collected 67,796 Reddit posts from 365 fathers, spanning a 6-month period around the birth of their child. A list of "at-risk"words was developed in collaboration with a perinatal mental health expert. PPD was assessed by evaluating the change in fathers' use of words indicating depressive symptomatology after childbirth. Predictive models were developed as a series of support vector machine classifiers using behavior, emotion, linguistic style, and discussion topics as features. The performance of these classifiers indicates that fathers at risk of PPD can be predicted from their prepartum data alone. Overall, the best performing model used discussion topic features only with a recall score of 0.82. These findings could assist in the development of support and intervention tools for fathers during the prepartum period, with specific applicability to personalized and preventative support tools for at-risk fathers. © Copyright 2020, Mary Ann Liebert, Inc., publishers 2020.
A survey study of attitudes toward, and preferences for, e-therapy interventions for eating disorder psychopathology
- Authors: Linardon, Jake , Shatte, Adrian , Tepper, Hannah , Fuller-Tyszkiewicz, Matthew
- Date: 2020
- Type: Text , Journal article
- Relation: International Journal of Eating Disorders Vol. 53, no. 6 (2020), p. 907-916
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- Description: 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 (
Usability evaluation of a cognitive-behavioral app-based intervention for binge eating and related psychopathology : a qualitative study
- Authors: Linardon, Jake , King, Teagan , Shatte, Adrian , Fuller-Tyszkiewicz, Matthew
- Date: 2022
- Type: Text , Journal article
- Relation: Behavior Modification Vol. 46, no. 5 (2022), p. 1002-1020
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- Description: 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. © The Author(s) 2021.
Methods and applications of social media monitoring of mental health during disasters : scoping review
- Authors: Teague, Samantha , Shatte, Adrian , Weller, Emmelyn , Fuller-Tyszkiewicz, Matthew , Hutchinson, Delyse
- Date: 2022
- Type: Text , Journal article , Review
- Relation: JMIR Mental Health Vol. 9, no. 2 (2022), p.
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- Description: Background: With the increasing frequency and magnitude of disasters internationally, there is growing research and clinical interest in the application of social media sites for disaster mental health surveillance. However, important questions remain regarding the extent to which unstructured social media data can be harnessed for clinically meaningful decision-making. Objective: This comprehensive scoping review synthesizes interdisciplinary literature with a particular focus on research methods and applications. Methods: A total of 6 health and computer science databases were searched for studies published before April 20, 2021, resulting in the identification of 47 studies. Included studies were published in peer-reviewed outlets and examined mental health during disasters or crises by using social media data. Results: Applications across 31 mental health issues were identified, which were grouped into the following three broader themes: estimating mental health burden, planning or evaluating interventions and policies, and knowledge discovery. Mental health assessments were completed by primarily using lexical dictionaries and human annotations. The analyses included a range of supervised and unsupervised machine learning, statistical modeling, and qualitative techniques. The overall reporting quality was poor, with key details such as the total number of users and data features often not being reported. Further, biases in sample selection and related limitations in generalizability were often overlooked. Conclusions: The application of social media monitoring has considerable potential for measuring mental health impacts on populations during disasters. Studies have primarily conceptualized mental health in broad terms, such as distress or negative affect, but greater focus is required on validating mental health assessments. There was little evidence for the clinical integration of social media-based disaster mental health monitoring, such as combining surveillance with social media-based interventions or developing and testing real-world disaster management tools. To address issues with study quality, a structured set of reporting guidelines is recommended to improve the methodological quality, replicability, and clinical relevance of future research on the social media monitoring of mental health during disasters. © 2022 Samantha J Teague, Adrian B R Shatte, Emmelyn Weller, Matthew Fuller-Tyszkiewicz, Delyse M Hutchinson.
An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms
- Authors: Linardon, Jake , Fuller-Tyszkiewicz, Matthew , Shatte, Adrian , Greenwood, Christopher
- Date: 2022
- Type: Text , Journal article
- Relation: International Journal of Eating Disorders Vol. 55, no. 6 (2022), p. 845-850
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- Description: 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.
Understanding the role of positive body image during digital interventions for eating disorders : secondary analyses of a randomized controlled trial
- Authors: Linardon, Jake , Tylka, Tracy , Burnette, C. , Shatte, Adrian , Fuller-Tyszkiewicz, Matthew
- Date: 2022
- Type: Text , Journal article
- Relation: Body Image Vol. 43, no. (2022), p. 1-7
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- Description: 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. © 2022 Elsevier Ltd
The role of pre-existing knowledge and knowledge acquisition in internet-based cognitive-behavioural therapy for eating disorders
- Authors: Linardon, Jake , Broadbent, Jaclyn , Shatte, Adrian , Fuller-Tyszkiewicz, Matthew
- Date: 2022
- Type: Text , Journal article
- Relation: Computers in Human Behavior Vol. 134, no. (2022), p.
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- Description: Knowledge is a relevant concept in internet-based cognitive-behaviour therapy (I-CBT), yet little research has sought to understand the role of knowledge in I-CBT for eating disorders. This study addressed this gap. Data were analysed from 293 participants enrolled in a RCT of I-CBT for eating disorder symptoms. A test assessing knowledge of CBT principles and eating disorders was administered before and after I-CBT. Participants had high knowledge to begin with, correctly answering 72% of items. A significant increase in knowledge scores and knowledge confidence was observed after ICBT. While no relationship between the degree of knowledge gain and the degree of symptom improvement emerged, an increase in confidence in one's knowledge was associated with greater symptom improvement. Higher baseline knowledge levels predicted lower likelihood of drop-out and a higher likelihood of adherence, but were unrelated to symptom-level improvement. Findings suggest that while new knowledge can be acquired through I-CBT, the degree of knowledge gain alone is not sufficient to explain improvement in symptoms. Pre-existing knowledge levels may be an important prognostic indicator of patient progress and compliance to I-CBT. Ensuring that patients can correctly apply the key I-CBT skills may be more important than knowledge gain. © 2022 Elsevier Ltd
Critical measurement issues in the assessment of social media influence on body image
- Authors: Jarman, Hannah , McLean, Sian , Griffiths, Scott , Teague, Samantha , Rodgers, Rachel , Paxton, Susan , Austen, Emma , Harris, Emily , Steward, Trevor , Shatte, Adrian , Khanh-Dao Le, Long , Anwar, Tarique , Mihalopoulos, Cathrine , Parker, Alexandra , Yager, Zali , Fuller-Tyszkiewicz, Matthew
- Date: 2022
- Type: Text , Journal article , Review
- Relation: Body Image Vol. 40, no. (2022), p. 225-236
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- Description: 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. © 2022 Elsevier Ltd
Efficacy of a transdiagnostic cognitive-behavioral intervention for eating disorder psychopathology delivered through a smartphone app: a randomized controlled trial
- Authors: Linardon, Jake , Shatte, Adrian , Rosato, John , Fuller-Tyszkiewicz, Matthew
- Date: 2022
- Type: Text , Journal article
- Relation: Psychological Medicine Vol. 52, no. 9 (2022), p. 1679-1690
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- Description: Abstract 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.
Targeting dietary restraint to reduce binge eating : a randomised controlled trial of a blended internet- and smartphone app-based intervention
- Authors: Linardon, Jake , Messer, Mariel , Shatte, Adrian , Skvarc, David , Rosato, John , Rathgen, April , Fuller-Tyszkiewicz, Matthew
- Date: 2023
- Type: Text , Journal article
- Relation: Psychological Medicine Vol. 53, no. 4 (2023), p. 1277-1287
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- Description: 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. © The Author(s), 2021. Published by Cambridge University Press.
Effects of an acceptance-facilitating intervention on acceptance and usage of digital interventions for binge eating
- Authors: Linardon, Jake , Anderson, Cleo , Chapneviss, Tara , Hants, Emma , Shatte, Adrian , Fuller-Tyszkiewicz, Matthew
- Date: 2022
- Type: Text , Journal article
- Relation: Psychiatric services (Washington, D.C.) Vol. 73, no. 10 (2022), p. 1173-1176
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- Description: 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.
EDBase : generating a lexicon base for eating disorders via social media
- Authors: Anwar, Tarique , Fuller-Tyszkiewicz, Matthew , Jarman, Hannah , Abuhassan, Mohammad , Shatte, Adrian , WIRED Team , Sukunesan, Suku
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Journal of Biomedical and Health Informatics Vol. 26, no. 12 (2022), p. 6116-6125
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- Description: Eating disorders (EDs) are characterised by abnormal eating habits and obsessive thought about food, weight, shape, and body image. EDs are experienced by a significant portion of our population. Social media is identified as a possible source of influence for EDs, and there is growing evidence of a large amount of ED-related discussions on the Web via social media platforms, such as Twitter. With this growing trend, automatic content analysis for EDs is becoming increasingly important. To date, there does not exist any comprehensive benchmark ED lexicon to identify ED-related conversations that would, in turn, facilitate these content analysis tasks. In this paper, we propose a novel method for generating a lexicon base for ED language, called EDBase. The method starts with collecting over 3.7 million ED-focused tweets. In order to semantically represent potential ED terminology in a vector space, an ED word embedding model (EDModel) is trained. Then we develop a novel multi-seeded hierarchical density-based algorithm with contrasting corpora for ED lexicon expansion. The EDModel is queried by the proposed lexicon expansion algorithm to expand the seed terms to a comprehensive lexicon base. Our EDBase consists of a (further expandable) list of 3794 high-quality ED terms, quantified by an ED score, and linked to their parent terms. The proposed method significantly outperforms all existing alternative baseline methods and models by over 25% in terms of precision and 1500 in terms of true positives. This research is expected to be impactful in the health data science and healthcare community. © 2021 IEEE.
Classification of Twitter users with eating disorder engagement : learning from the biographies
- Authors: Abuhassan, Mohammad , Anwar, Tarique , Fuller-Tyszkiewicz, Matthew , Jarman, Hannah , Shatte, Adrian , Liu, Chengfei , Sukunesan, Suku
- Date: 2023
- Type: Text , Journal article
- Relation: Computers in Human Behavior Vol. 140, no. (2023), p.
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- Description: Individuals with an Eating Disorder (ED) are typically reluctant to seek help via traditional means (e.g., psychologists). However, recent evidence suggests that many individuals seek assistance via social media for weight and diet related concerns. Sophisticated approaches are needed to better distinguish those who may be in need of help for an ED from those who are simply commenting on ED in online social environments. In order to facilitate effective communication between individuals with or at-risk of an ED and healthcare professionals, this research exploits a deep learning model to differentiate the users with ED engagement (e.g., ED sufferers, healthcare professionals or communicators) over social media. For this purpose, a collection of Twitter data is compiled using Twitter application programming interface (API) on the Australian Research Data Commons (ARDC) Nectar research cloud. After collecting 1,400,000 Twitter biographies in total, a subset of 4000 biographies are annotated manually. This annotation enables the differentiation of users engaged with ED-focused language on social media into five categories: ED-user, healthcare professional, communicator, healthcare professional-communicator, and other. Based on these annotated categories, a predictive deep learning model based on bidirectional encoder representations from transformers (BERT) and long short-term memory (LSTM) is developed. The model achieves an F1 score of 98.19% and an accuracy of 98.37%. It demonstrates the viability of detecting the individuals with possible ED risk and distinguishes them from other categories using their biography data. We further conducted a network analysis for investigating the communication network between these categories. Our analysis shows that ED-users are more secretive and self-protective, whereas the healthcare professionals and communicators frequently interact with each other and a wide range of other people. To the best of our knowledge, our research is the first of its kind for identifying the different user categories engaged with ED-focused communications on social media. © 2022
Effects of participant's choice of different digital interventions on outcomes for binge-spectrum eating disorders : a pilot doubly randomized preference trial
- Authors: Linardon, Jake , Shatte, Adrian , Messer, Mariel , McClure, Zoe , Fuller-Tyszkiewicz, Matthew
- Date: 2023
- Type: Text , Journal article
- Relation: Behavior Therapy Vol. 54, no. 2 (2023), p. 303-314
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- Description: 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. © 2023