List of Titles

Classification of Twitter users with eating disorder engagement : learning from the biographies

- Abuhassan, Mohammad, Anwar, Tarique, Fuller-Tyszkiewicz, Matthew, Jarman, Hannah, Shatte, Adrian, Liu, Chengfei, Sukunesan, Suku

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

EDBase : generating a lexicon base for eating disorders via social media

- Anwar, Tarique, Fuller-Tyszkiewicz, Matthew, Jarman, Hannah, Abuhassan, Mohammad, Shatte, Adrian, WIRED Team, Sukunesan, Suku

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.

Ensemble regression modelling for genetic network inference

- Gamage, Hasini, Chetty, Madhu, Shatte, Adrian, Hallinan, Jennifer

Authors: Gamage, Hasini , Chetty, Madhu , Shatte, Adrian , Hallinan, Jennifer
Date: 2022
Type: Text , Conference paper
Relation: 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022, Ottawa Canada, 15-17 August 2022, 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022
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Description: An accurate reconstruction of Gene Regulatory Networks (GRNs) from time series gene expression data is crucial for discovering complex biological interactions. Among many different approaches for inferring GRNs, there are several methods which produce high false positive interactions, and are unstable, requiring fine tuning for many of their parameters. In this paper, we consider the GRN inference problem as a regression problem, and propose a simple ensemble regression-based feature selection model which is a combination of cross-validated Lasso and cross-validated Ridge algorithms for reconstructing GRNs. Due to the novelty of the proposed ensemble model, it is able to eliminate overfitting, multi co-linearity issues, and irrelevant genes within one computational approach. While observing the type of gene-gene regulatory interactions the regression model also identifies the direction of these interactions. A new coefficient of determination (R2)-based approach identifies the best model to fit the data among LassoCV and RidgeCV, and evaluates the model importance in term of gene-wise maximum in-degree which decides the maximum number of regulatory genes including self-regulations that can be selected from a given method. Then, an evaluated gene score-based majority voting technique aggregates the selected gene lists from each method. In our experiments, the performance of the proposed ensemble approach was evaluated using gene expression datasets from three small-scale real gene networks. Our proposed model outperformed other state-of-the-art methods, producing high true positives, reducing false positives, and obtaining high Structural Accuracy, while maintaining model stability and efficiency. © 2022 IEEE.

Filter feature selection based boolean modelling for genetic network inference

- Gamage, Hasini, Chetty, Madhu, Shatte, Adrian, Hallinan, Jennifer

Authors: Gamage, Hasini , Chetty, Madhu , Shatte, Adrian , Hallinan, Jennifer
Date: 2022
Type: Text , Journal article
Relation: BioSystems Vol. 221, no. (2022), p.
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Description: The reconstruction of Gene Regulatory Networks (GRNs) from time series gene expression data is highly relevant for the discovery of complex biological interactions and dynamics. Various computational strategies have been developed for this task, but most approaches have low computational efficiency and are not able to cope with high-dimensional, low sample-number, gene expression data. In this paper, we introduce a novel combined filter feature selection approach for efficient and accurate inference of GRNs. A Boolean framework for network modelling is used to demonstrate the efficacy of the proposed approach. Using discretized microarray expression data, the genes most relevant to each target gene are first filtered using ReliefF, an instance-based feature ranking method that is here applied for the first time to GRN inference. Then, further gene selection from the filtered-gene list is done using a mutual information-based min-redundancy max-relevance criterion by eliminating irrelevant genes. This combined method is executed on resampled datasets to finalize the optimal set of regulatory genes. Building upon our previous research, a Pearson correlation coefficient-based Boolean modelling approach is utilized for the efficient identification of the optimal regulatory rules associated with selected regulatory genes. The proposed approach was evaluated using gene expression datasets from small-scale and medium-scale real gene networks, and was observed to be more effective than Linear Discriminant Analysis, performed better than the individual feature selection methods, and obtained improved Structural Accuracy with a higher number of true positives than other state-of-the-art methods, while outperforming these methods with respect to Dynamic Accuracy and efficiency. © 2022 Elsevier B.V.

Dynamically regulated initialization for S-system modelling of genetic networks

- Gill, Jaskaran, Chetty, Madhu, Shatte, Adrian, Hallinan, Jennifer

Authors: Gill, Jaskaran , Chetty, Madhu , Shatte, Adrian , Hallinan, Jennifer
Date: 2021
Type: Text , Conference paper
Relation: 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2021, Virtual, Online, 13-15 October 2021, 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2021
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Description: Reverse engineering of gene regulatory networks through temporal gene expression data is an active area of research. Among the plethora of modelling techniques under investigation is the decoupled S-system model, which attempts to capture the non-linearity of biological systems in detail. For the model, number of parameters to be estimated are significantly high even when the network is of small or medium scale. Thus, the inference process poses a significant computational burden. In this paper, we propose: (1) a novel population initialization technique, Dynamically Regulated Prediction Initialization (DRPI), which utilises prior knowledge of biological gene expression data to create a feedback loop to produce dynamically regulated high-quality individuals for initial population; (2) an adaptive fitness function; and (3) a method for the maintenance of population diversity. The aim of this work is to reduce the computational complexity of the inference algorithm, to speed up the entire process of reverse engineering. The performance of the proposed algorithm was evaluated against a benchmark dataset and compared with other methods from earlier work. The experimental results show that we succeeded in achieving higher accuracy results in lesser fitness evaluations, considerably reducing the computational burden of the inference process. © 2021 IEEE.

Integrating steady-state and dynamic gene expression data for improving genetic network modelling

- Gill, Jaskaran, Chetty, Madhu, Shatte, Adrian, Hallinan, Jennifer

Authors: Gill, Jaskaran , Chetty, Madhu , Shatte, Adrian , Hallinan, Jennifer
Date: 2022
Type: Text , Conference paper
Relation: 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022, Ottawa Canada, 15-17 August 2022, 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022
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Description: Reverse engineering of Gene Regulatory Networks (GRNs) from experimentally obtained high-throughput data is an active and promising area of research. Among several modelling techniques, the S-System model, a set of tightly coupled differential equations, mimics the complexities and dynamics of biochemical systems, and thus provides realistic GRN representation. While it offers mathematical flexibility and biological relevance, the high number of learning parameters can lead to a computational burden. In our earlier work, we addressed this issue by judicious use of prior knowledge. However, another major cause of computational load is the need for numerical integration of the differential equations for the estimation of S-system model parameters. In this paper, we propose a method to obtain initial model parameter values from the steady state of the system, thereby computing simpler and less complex algebraic equations compared to the regular differential equations of S-systems. These network parameters are input as prior knowledge for the optimization of the dynamic S-System using differential equations. The proposed framework includes a novel fitness evaluation for steady-state S-System models, a novel evolutionary parameter learning framework, and a technique to incorporate the candidate solutions in dynamic S-System modelling. Our proposed methodology reached optimal model parameter values quickly, requiring only one-third of the fitness function evaluations, compared to our previously reported DRNI (Dynamically regulated network initialization) method for S-System modelling. © 2022 IEEE.

Combining kinetic orders for efficient S-System modelling of gene regulatory network

- Gill, Jaskaran, Chetty, Madhu, Shatte, Adrian, Hallinan, Jennifer

Authors: Gill, Jaskaran , Chetty, Madhu , Shatte, Adrian , Hallinan, Jennifer
Date: 2022
Type: Text , Journal article
Relation: BioSystems Vol. 220, no. (2022), p.
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Description: S-System models, non-linear differential equation models, are widely used for reconstructing gene regulatory networks from temporal gene expression data. An S-System model involves two states, generation and degeneration, and uses the kinetic parameters gij and hij, to represent the direction, nature, and intensity of the genetic interactions. The need for learning a large number of model parameters results in increased computational expense. Previously, we improved the performance of the algorithm using dynamic allocation of the maximum in-degree for each gene. While the method was effective for smaller networks, a large amount of computation was still needed for larger networks. This problem arose mainly due to the increased occurrence of invalid networks during optimization, primarily because the two kinetic parameters (gij and hij) of the S-System model converge independently during optimization. Being independent, these two parameters can converge to values that can indicate contradictory gene interactions, specifically inhibition or activation. In this study, to address this major challenge in S-System modelling, we developed a novel method that includes two features: a penalty term that penalizes those networks with invalid kinetic orders, and a parameter, wij, derived by combining the kinetic parameters gij and hij. The novel penalty term was used for candidate selection during the process of optimizing the DRNI (Dynamically Regulated Network Initialization) algorithm. Rather than remaining constant, it is dynamic, with its magnitude dependent on the number of invalid interactions in the given network. This approach encourages the generation of valid candidate solutions, and eliminates invalid networks in a systematic manner. The previous DRNI method, a two-stage approach which uses dynamic allocation of the maximum in-degree for each gene, was further improved by adding a third stage which applies the proposed wij to handle the invalid regulations that may still exist in that candidate solutions. The method was tested on different gene expression datasets, and was able to reduce the number of iterations and produce improved network accuracies. For a 20 gene network, the number of generations required for convergence was reduced by 300, and the F-score improved by 0.05 compared to our previously reported DRNI approach. For the well-known 10 gene networks of the DREAM challenge, our method produced an improvement in the average area under the ROC curve of the DREAM4 10 gene networks. © 2022

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

Cost effective annotation framework using zero-shot text classification

- Kasthuriarachchy, Buddhika, Chetty, Madhu, Shatte, Adrian, Walls, Darren

Authors: Kasthuriarachchy, Buddhika , Chetty, Madhu , Shatte, Adrian , Walls, Darren
Date: 2021
Type: Text , Conference paper
Relation: 2021 International Joint Conference on Neural Networks, IJCNN 2021 Vol. 2021-July
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Description: Manual and high-quality annotation of social media data has enabled companies and researchers to develop improved implementations using natural language processing. However, human text-annotation is expensive and time-consuming. Crowd-sourcing platforms such as Amazon's Mechanical Turk (MTurk) can be leveraged for the creation of large training corpora for text classification tasks using social media data. Nevertheless, the quality of annotations can vary significantly, based on the interpretations and motivations of annotators completing the tasks. Further, the labelling cost of data through MTurk will increase if target messages are small and having a significant amount of noise (e.g. promotional messages on Twitter). In this work, we propose a new annotation framework to create high-quality human-annotated datasets for text classification from social media data. We present a zero-shot text classification based pre-annotation technique reducing the adverse effects arising due to the highly skewed distribution of data across target classes. The proposed framework significantly reduces the cost and time while maintaining the quality of the annotations. Being generic, it can be applied to annotating text data from any discipline. Our experiment with a Twitter data annotation using the proposed annotation framework shows a cost reduction of 80% with no compromise to quality. © 2021 IEEE.

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From general language understanding to noisy text comprehension

- Kasthuriarachchy, Buddhika, Chetty, Madhu, Shatte, Adrian, Walls, Darren

Authors: Kasthuriarachchy, Buddhika , Chetty, Madhu , Shatte, Adrian , Walls, Darren
Date: 2021
Type: Text , Journal article
Relation: Applied Sciences (Switzerland) Vol. 11, no. 17 (2021), p.
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Description: Obtaining meaning-rich representations of social media inputs, such as Tweets (unstructured and noisy text), from general-purpose pre-trained language models has become challenging, as these inputs typically deviate from mainstream English usage. The proposed research establishes effective methods for improving the comprehension of noisy texts. For this, we propose a new generic methodology to derive a diverse set of sentence vectors combining and extracting various linguistic characteristics from latent representations of multi-layer, pre-trained language models. Further, we clearly establish how BERT, a state-of-the-art pre-trained language model, comprehends the linguistic attributes of Tweets to identify appropriate sentence representations. Five new probing tasks are developed for Tweets, which can serve as benchmark probing tasks to study noisy text comprehension. Experiments are carried out for classification accuracy by deriving the sentence vectors from GloVe-based pre-trained models and Sentence-BERT, and by using different hidden layers from the BERT model. We show that the initial and middle layers of BERT have better capability for capturing the key linguistic characteristics of noisy texts than its latter layers. With complex predictive models, we further show that the sentence vector length has lesser importance to capture linguistic information, and the proposed sentence vectors for noisy texts perform better than the existing state-of-the-art sentence vectors. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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

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 (

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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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.

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

- Linardon, Jake, Tylka, Tracy, Burnette, C., Shatte, Adrian, Fuller-Tyszkiewicz, Matthew

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

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

- Linardon, Jake, Shatte, Adrian, Rosato, John, Fuller-Tyszkiewicz, Matthew

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.

The role of pre-existing knowledge and knowledge acquisition in internet-based cognitive-behavioural therapy for eating disorders

- Linardon, Jake, Broadbent, Jaclyn, Shatte, Adrian, Fuller-Tyszkiewicz, Matthew

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

Effects of an acceptance-facilitating intervention on acceptance and usage of digital interventions for binge eating

- Linardon, Jake, Anderson, Cleo, Chapneviss, Tara, Hants, Emma, Shatte, Adrian, Fuller-Tyszkiewicz, Matthew

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.

Effects of participant's choice of different digital interventions on outcomes for binge-spectrum eating disorders : a pilot doubly randomized preference trial

- Linardon, Jake, Shatte, Adrian, Messer, Mariel, McClure, Zoe, Fuller-Tyszkiewicz, Matthew

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

The significance and impact of winning an academic award : a study of early career academics

- Ren, Jing, Shi, Yajie, Shatte, Adrian, Kong, Xiangjie, Xia, Feng

Authors: Ren, Jing , Shi, Yajie , Shatte, Adrian , Kong, Xiangjie , Xia, Feng
Date: 2022
Type: Text , Conference paper
Relation: 22nd ACM/IEEE Joint Conference on Digital Libraries, JCDL 2022, Virtual, online, 20-24 June 2022, Proceedings of the ACM/IEEE Joint Conference on Digital Libraries
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Description: Academic award plays an important role in an academic's careerparticularly for early career academics. Previous studies have primarilyfocused on the impact of awards conferred to academics whoe made outstanding contributions to a specific research field, such as the Nobel Prize. In contrast, this paper aims to investigatethe effect of awards conferred to academics at an earlier careerstage, who have the potential to make a great impact in the future. We devise a metric named Award Change Factor (ACF), to evaluatethe change of a recipient's academic behavior after winningan academic award. Next, we propose a model to compare awardrecipients with academics who have similar performance beforewinning an academic award. In summary, we analyze the impact ofan award on the recipients' academic impact and their teams fromdifferent perspectives. Experimental results show that most recipientsdo have improvements in both productivity and citations afterwinning an academic award, while there is no significant impacton publication quality. In addition, receipt of an academic awardnot only expands recipients' collaboration network, but also has apositive effect on their team size. © 2022 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

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