List of Titles

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

- Linardon, Jake, Shatte, Adrian, Tepper, Hannah, Fuller-Tyszkiewicz, Matthew

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 (

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

- Linardon, Jake, Fuller-Tyszkiewicz, Matthew, Shatte, Adrian, Greenwood, Christopher

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.

Can intelligent agents improve data quality in online questionnaires? A pilot study

- Söderström, Arne, Shatte, Adrian, Fuller-Tyszkiewicz, Matthew

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.

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

- 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

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

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

- Linardon, Jake, Shatte, Adrian, Messer, Mariel, Firth, Joseph, Fuller-Tyszkiewicz, Matthew

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

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Methods and applications of social media monitoring of mental health during disasters : scoping review

- Teague, Samantha, Shatte, Adrian, Weller, Emmelyn, Fuller-Tyszkiewicz, Matthew, Hutchinson, Delyse

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.

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Optimizing prediction of binge eating episodes : A comparison approach to test alternative conceptualizations of the affect regulation model

- Fuller-Tyszkiewicz, Matthew, Richardson, Ben, Skouteris, Helen, Austin, David, Castle, David, Busija, Lucy, Klein, Britt, Holmes, Milllicent, Broadbent, Jaclyn

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.

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Social media markers to identify fathers at risk of postpartum depression : a machine learning approach

- Shatte, Adrian, Hutchinson, Delyse, Fuller-Tyszkiewicz, Matthew, Teague, Samantha

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.

Usability evaluation of a cognitive-behavioral app-based intervention for binge eating and related psychopathology : a qualitative study

- Linardon, Jake, King, Teagan, Shatte, Adrian, Fuller-Tyszkiewicz, Matthew

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.

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