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
- Full Text: false
- Reviewed:
- 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
Machine learning in mental health: a scoping review of methods and applications
- Shatte, Adrian, Hutchinson, Delyse, Teague, Samantha
- Authors: Shatte, Adrian , Hutchinson, Delyse , Teague, Samantha
- Date: 2019
- Type: Text , Journal article
- Relation: Psychological Medicine Vol. 49, no. 9 (2019), p. 1426-1448
- Full Text: false
- Reviewed:
- Description: This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice. We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review. Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis (ii) prognosis, treatment and support (iii) public health, and (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering. Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.
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.
- 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.
- Full Text:
- Reviewed:
- 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.
Peer support of fathers on reddit : quantifying the stressors, behaviors, and drivers
- Teague, Samantha, Shatte, Adrian
- Authors: Teague, Samantha , Shatte, Adrian
- Date: 2021
- Type: Text , Journal article
- Relation: Psychology of Men and Masculinity Vol. 22, no. 4 (2021), p. 757-766
- Full Text:
- Reviewed:
- Description: This article aimed to delineate the behavioral patterns of fathers in seeking and providing peer support on the popular social media site Reddit using a sample of 2,393 users. First, fathers’ support-seeking posts were characterized, finding that fathers self-disclosed a range of individual, familial, and societal stressors, including topics sensitive to traditional male gender roles. Second, peers’ comments were differentiated by support type, with differences observed in the behaviors, emotions, and language that peers use when providing advice, confirmation, and encouragement. Third, the relationship between types of fatherhood stressors and their associated peer comments was mapped. While fathers seeking support for individual stressors received fewer comments, the support provided utilized more actionoriented language. Finally, a statistical model was developed to examine the factors that drive peer support on the fatherhood forums, which are observed to influence the quality of peers’ comments and peers’ commenting behaviors. Combined, the findings provide a comprehensive understanding of the peer support environment for fathers on social media like Reddit, strengthening the research literature that is limited to qualitative evidence to date. The results have important implications for formal support services targeting fathers, both online and offline © 2021 American Psychological Association
- Authors: Teague, Samantha , Shatte, Adrian
- Date: 2021
- Type: Text , Journal article
- Relation: Psychology of Men and Masculinity Vol. 22, no. 4 (2021), p. 757-766
- Full Text:
- Reviewed:
- Description: This article aimed to delineate the behavioral patterns of fathers in seeking and providing peer support on the popular social media site Reddit using a sample of 2,393 users. First, fathers’ support-seeking posts were characterized, finding that fathers self-disclosed a range of individual, familial, and societal stressors, including topics sensitive to traditional male gender roles. Second, peers’ comments were differentiated by support type, with differences observed in the behaviors, emotions, and language that peers use when providing advice, confirmation, and encouragement. Third, the relationship between types of fatherhood stressors and their associated peer comments was mapped. While fathers seeking support for individual stressors received fewer comments, the support provided utilized more actionoriented language. Finally, a statistical model was developed to examine the factors that drive peer support on the fatherhood forums, which are observed to influence the quality of peers’ comments and peers’ commenting behaviors. Combined, the findings provide a comprehensive understanding of the peer support environment for fathers on social media like Reddit, strengthening the research literature that is limited to qualitative evidence to date. The results have important implications for formal support services targeting fathers, both online and offline © 2021 American Psychological Association
Schema : an open-source, distributed mobile platform for deploying mHealth research tools and interventions
- Shatte, Adrian, Teague, Samantha
- Authors: Shatte, Adrian , Teague, Samantha
- Date: 2020
- Type: Text , Journal article
- Relation: BMC Medical Research Methodology Vol. 20, no. 1 (2020), p.
- Full Text:
- Reviewed:
- Description: Background: Mobile applications for health, also known as 'mHealth apps', have experienced increasing popularity over the past ten years. However, most publicly available mHealth apps are not clinically validated, and many do not utilise evidence-based strategies. Health researchers wishing to develop and evaluate mHealth apps may be impeded by cost and technical skillset barriers. As traditionally lab-based methods are translated onto mobile platforms, robust and accessible tools are needed to enable the development of quality, evidence-based programs by clinical experts. Results: This paper introduces schema, an open-source, distributed, app-based platform for researchers to deploy behavior monitoring and health interventions onto mobile devices. The architecture and design features of the platform are discussed, including flexible scheduling, randomisation, a wide variety of survey and media elements, and distributed storage of data. The platform supports a range of research designs, including cross-sectional surveys, ecological momentary assessment, randomised controlled trials, and micro-randomised just-in-time adaptive interventions. Use cases for both researchers and participants are considered to demonstrate the flexibility and usefulness of the platform for mHealth research. Conclusions: The paper concludes by considering the strengths and limitations of the platform, and a call for support from the research community in areas of technical development and evaluation. To get started with schema, please visit the GitHub repository: Https://github.com/schema-app/schema. © 2020 The Author(s).
- Authors: Shatte, Adrian , Teague, Samantha
- Date: 2020
- Type: Text , Journal article
- Relation: BMC Medical Research Methodology Vol. 20, no. 1 (2020), p.
- Full Text:
- Reviewed:
- Description: Background: Mobile applications for health, also known as 'mHealth apps', have experienced increasing popularity over the past ten years. However, most publicly available mHealth apps are not clinically validated, and many do not utilise evidence-based strategies. Health researchers wishing to develop and evaluate mHealth apps may be impeded by cost and technical skillset barriers. As traditionally lab-based methods are translated onto mobile platforms, robust and accessible tools are needed to enable the development of quality, evidence-based programs by clinical experts. Results: This paper introduces schema, an open-source, distributed, app-based platform for researchers to deploy behavior monitoring and health interventions onto mobile devices. The architecture and design features of the platform are discussed, including flexible scheduling, randomisation, a wide variety of survey and media elements, and distributed storage of data. The platform supports a range of research designs, including cross-sectional surveys, ecological momentary assessment, randomised controlled trials, and micro-randomised just-in-time adaptive interventions. Use cases for both researchers and participants are considered to demonstrate the flexibility and usefulness of the platform for mHealth research. Conclusions: The paper concludes by considering the strengths and limitations of the platform, and a call for support from the research community in areas of technical development and evaluation. To get started with schema, please visit the GitHub repository: Https://github.com/schema-app/schema. © 2020 The Author(s).
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
- Full Text:
- Reviewed:
- 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.
- 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
- Full Text:
- Reviewed:
- 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.
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