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Curumsing MK, Rivera Villicana J, Vouliotis A, Burns K, Babaei M, Petrovich T, Mouzakis K, Vasa R. Talk with Ted: an embodied conversational agent for caregivers. Gerontol Geriatr Educ 2024:1-18. [PMID: 38252487 DOI: 10.1080/02701960.2024.2302584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Communication is key to the success of any relationship. When it comes to caregivers, having a conversation with a person living with some form of cognitive impairment, such as dementia, can be a struggle. Most people living with dementia experience some form of communication impairment that reduces their ability to express their needs. In this case study, we present the design of an embodied conversation agent (ECA), Ted, designed to educate caregivers about the importance of good communication principles when engaging with people living with dementia. This training tool was trialed and compared to an online training tool, with 23 caregivers divided into two cohorts (12 in the ECA condition, and 11 in the online training tool condition), over a period of 8 weeks using a mixed evaluation approach. Our findings suggest that (a) caregivers developed an emotional connection with the ECA and retained the learning from their interactions with Ted even after 8 weeks had elapsed, (b) caregivers implemented the learnings in their practice, and (c) the changes in care practice were well received by people living with dementia.
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Affiliation(s)
| | | | - Andrew Vouliotis
- Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia
| | - Kelly Burns
- Business Innovation, Dementia Australia Kaleen, Australia
| | - Mahdi Babaei
- Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia
| | | | - Kon Mouzakis
- Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia
| | - Rajesh Vasa
- Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia
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2
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Shvetcov A, Whitton A, Kasturi S, Zheng WY, Beames J, Ibrahim O, Han J, Hoon L, Mouzakis K, Gupta S, Venkatesh S, Christensen H, Newby J. Machine learning identifies a COVID-19-specific phenotype in university students using a mental health app. Internet Interv 2023; 34:100666. [PMID: 37746637 PMCID: PMC10511781 DOI: 10.1016/j.invent.2023.100666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 06/19/2023] [Accepted: 09/07/2023] [Indexed: 09/26/2023] Open
Abstract
Background Advances in smartphone technology have allowed people to access mental healthcare via digital apps from wherever and whenever they choose. University students experience a high burden of mental health concerns. Although these apps improve mental health symptoms, user engagement has remained low. Studies have shown that users can be subgrouped based on unique characteristics that just-in-time adaptive interventions (JITAIs) can use to improve engagement. To date, however, no studies have examined the effect of the COVID-19 pandemic on these subgroups. Objective Here, we sought to examine user subgroup characteristics across three COVID-19-specific timepoints: during lockdown, immediately following lockdown, and three months after lockdown ended. Methods To do this, we used a two-step machine learning approach combining unsupervised and supervised machine learning. Results We demonstrate that there are three unique subgroups of university students who access mental health apps. Two of these, with either higher or lower mental well-being, were defined by characteristics that were stable across COVID-19 timepoints. The third, situational well-being, had characteristics that were timepoint-dependent, suggesting that they are highly influenced by traumatic stressors and stressful situations. This subgroup also showed feelings and behaviours consistent with burnout. Conclusions Overall, our findings clearly suggest that user subgroups are unique: they have different characteristics and therefore likely have different mental healthcare goals. Our findings also highlight the importance of including questions and additional interventions targeting traumatic stress(ors), reason(s) for use, and burnout in JITAI-style mental health apps to improve engagement.
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Affiliation(s)
| | | | | | - Wu-Yi Zheng
- Black Dog Institute, UNSW, Sydney, NSW, Australia
| | | | - Omar Ibrahim
- Black Dog Institute, UNSW, Sydney, NSW, Australia
| | - Jin Han
- Black Dog Institute, UNSW, Sydney, NSW, Australia
| | - Leonard Hoon
- Applied Artificial Intelligence Institute, Deakin University, Geelong, VIC, Australia
| | - Kon Mouzakis
- Applied Artificial Intelligence Institute, Deakin University, Geelong, VIC, Australia
| | - Sunil Gupta
- Applied Artificial Intelligence Institute, Deakin University, Geelong, VIC, Australia
| | - Svetha Venkatesh
- Applied Artificial Intelligence Institute, Deakin University, Geelong, VIC, Australia
| | | | - Jill Newby
- Black Dog Institute, UNSW, Sydney, NSW, Australia
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3
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Huckvale K, Hoon L, Stech E, Newby JM, Zheng WY, Han J, Vasa R, Gupta S, Barnett S, Senadeera M, Cameron S, Kurniawan S, Agarwal A, Kupper JF, Asbury J, Willie D, Grant A, Cutler H, Parkinson B, Ahumada-Canale A, Beames JR, Logothetis R, Bautista M, Rosenberg J, Shvetcov A, Quinn T, Mackinnon A, Rana S, Tran T, Rosenbaum S, Mouzakis K, Werner-Seidler A, Whitton A, Venkatesh S, Christensen H. Protocol for a bandit-based response adaptive trial to evaluate the effectiveness of brief self-guided digital interventions for reducing psychological distress in university students: the Vibe Up study. BMJ Open 2023; 13:e066249. [PMID: 37116996 PMCID: PMC10151864 DOI: 10.1136/bmjopen-2022-066249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/30/2023] Open
Abstract
INTRODUCTION Meta-analytical evidence confirms a range of interventions, including mindfulness, physical activity and sleep hygiene, can reduce psychological distress in university students. However, it is unclear which intervention is most effective. Artificial intelligence (AI)-driven adaptive trials may be an efficient method to determine what works best and for whom. The primary purpose of the study is to rank the effectiveness of mindfulness, physical activity, sleep hygiene and an active control on reducing distress, using a multiarm contextual bandit-based AI-adaptive trial method. Furthermore, the study will explore which interventions have the largest effect for students with different levels of baseline distress severity. METHODS AND ANALYSIS The Vibe Up study is a pragmatically oriented, decentralised AI-adaptive group sequential randomised controlled trial comparing the effectiveness of one of three brief, 2-week digital self-guided interventions (mindfulness, physical activity or sleep hygiene) or active control (ecological momentary assessment) in reducing self-reported psychological distress in Australian university students. The adaptive trial methodology involves up to 12 sequential mini-trials that allow for the optimisation of allocation ratios. The primary outcome is change in psychological distress (Depression, Anxiety and Stress Scale, 21-item version, DASS-21 total score) from preintervention to postintervention. Secondary outcomes include change in physical activity, sleep quality and mindfulness from preintervention to postintervention. Planned contrasts will compare the four groups (ie, the three intervention and control) using self-reported psychological distress at prespecified time points for interim analyses. The study aims to determine the best performing intervention, as well as ranking of other interventions. ETHICS AND DISSEMINATION Ethical approval was sought and obtained from the UNSW Sydney Human Research Ethics Committee (HREC A, HC200466). A trial protocol adhering to the requirements of the Guideline for Good Clinical Practice was prepared for and approved by the Sponsor, UNSW Sydney (Protocol number: HC200466_CTP). TRIAL REGISTRATION NUMBER ACTRN12621001223820.
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Affiliation(s)
- Kit Huckvale
- Centre for Digital Transformation of Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Leonard Hoon
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Eileen Stech
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | - Jill M Newby
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
- School of Psychology, UNSW Sydney, Sydney, New South Wales, Australia
| | - Wu Yi Zheng
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | - Jin Han
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | - Rajesh Vasa
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Sunil Gupta
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Scott Barnett
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Manisha Senadeera
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Stuart Cameron
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Stefanus Kurniawan
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Akash Agarwal
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Joost Funke Kupper
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Joshua Asbury
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - David Willie
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Alasdair Grant
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Henry Cutler
- Centre for the Health Economy, Macquarie University, Sydney, New South Wales, Australia
| | - Bonny Parkinson
- Centre for the Health Economy, Macquarie University, Sydney, New South Wales, Australia
| | | | - Joanne R Beames
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | - Rena Logothetis
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Marya Bautista
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | - Jodie Rosenberg
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | - Artur Shvetcov
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | - Thomas Quinn
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Andrew Mackinnon
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | - Santu Rana
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Truyen Tran
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Simon Rosenbaum
- School of Psychiatry, UNSW Sydney, Sydney, New South Wales, Australia
| | - Kon Mouzakis
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | | | - Alexis Whitton
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | - Svetha Venkatesh
- Applied Artificial Intelligence Institute, Deakin University, Melbourne, Victoria, Australia
| | - Helen Christensen
- Black Dog Institute, UNSW Sydney, Sydney, New South Wales, Australia
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Wong S, Simmons A, Rivera-Villicana J, Barnett S, Sivathamboo S, Perucca P, Ge Z, Kwan P, Kuhlmann L, Vasa R, Mouzakis K, O'Brien TJ. EEG datasets for seizure detection and prediction- A review. Epilepsia Open 2023. [PMID: 36740244 DOI: 10.1002/epi4.12704] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 01/28/2023] [Indexed: 02/07/2023] Open
Abstract
Electroencephalogram (EEG) datasets from epilepsy patients have been used to develop seizure detection and prediction algorithms using machine learning (ML) techniques with the aim of implementing the learned model in a device. However, the format and structure of publicly available datasets are different from each other, and there is a lack of guidelines on the use of these datasets. This impacts the generatability, generalizability, and reproducibility of the results and findings produced by the studies. In this narrative review, we compiled and compared the different characteristics of the publicly available EEG datasets that are commonly used to develop seizure detection and prediction algorithms. We investigated the advantages and limitations of the characteristics of the EEG datasets. Based on our study, we identified 17 characteristics that make the EEG datasets unique from each other. We also briefly looked into how certain characteristics of the publicly available datasets affect the performance and outcome of a study, as well as the influences it has on the choice of ML techniques and preprocessing steps required to develop seizure detection and prediction algorithms. In conclusion, this study provides a guideline on the choice of publicly available EEG datasets to both clinicians and scientists working to develop a reproducible, generalizable, and effective seizure detection and prediction algorithm.
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Affiliation(s)
- Sheng Wong
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | - Anj Simmons
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | | | - Scott Barnett
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | - Shobi Sivathamboo
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia.,Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia.,Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
| | - Piero Perucca
- Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia.,Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, Victoria, Australia.,Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australia.,Comprehensive Epilepsy Program, Austin Health, Heidelberg, Victoria, Australia
| | - Zongyuan Ge
- Monash eResearch Centre, Monash University, Clayton, Victoria, Australia
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia.,Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia
| | - Rajesh Vasa
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | - Kon Mouzakis
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | - Terence J O'Brien
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia.,Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia.,Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
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Barnett S, Huckvale K, Christensen H, Venkatesh S, Mouzakis K, Vasa R. Intelligent Sensing to Inform and Learn (InSTIL): A Scalable and Governance-Aware Platform for Universal, Smartphone-Based Digital Phenotyping for Research and Clinical Applications. J Med Internet Res 2019; 21:e16399. [PMID: 31692450 PMCID: PMC6868504 DOI: 10.2196/16399] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 10/22/2019] [Accepted: 10/22/2019] [Indexed: 02/06/2023] Open
Abstract
In this viewpoint we describe the architecture of, and design rationale for, a new software platform designed to support the conduct of digital phenotyping research studies. These studies seek to collect passive and active sensor signals from participants' smartphones for the purposes of modelling and predicting health outcomes, with a specific focus on mental health. We also highlight features of the current research landscape that recommend the coordinated development of such platforms, including the significant technical and resource costs of development, and we identify specific considerations relevant to the design of platforms for digital phenotyping. In addition, we describe trade-offs relating to data quality and completeness versus the experience for patients and public users who consent to their devices being used to collect data. We summarize distinctive features of the resulting platform, InSTIL (Intelligent Sensing to Inform and Learn), which includes universal (ie, cross-platform) support for both iOS and Android devices and privacy-preserving mechanisms which, by default, collect only anonymized participant data. We conclude with a discussion of recommendations for future work arising from learning during the development of the platform. The development of the InSTIL platform is a key step towards our research vision of a population-scale, international, digital phenotyping bank. With suitable adoption, the platform will aggregate signals from large numbers of participants and large numbers of research studies to support modelling and machine learning analyses focused on the prediction of mental illness onset and disease trajectories.
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Affiliation(s)
- Scott Barnett
- Applied Artificial Intelligence Institute (A2I2), Deakin University, Geelong, Australia
| | - Kit Huckvale
- Black Dog Institute, UNSW Sydney, Randwick, Australia
| | - Helen Christensen
- Black Dog Institute, UNSW Sydney, Randwick, Australia.,Mindgardens Neuroscience Network, Sydney, Australia
| | - Svetha Venkatesh
- Applied Artificial Intelligence Institute (A2I2), Deakin University, Geelong, Australia
| | - Kon Mouzakis
- Black Dog Institute, UNSW Sydney, Randwick, Australia
| | - Rajesh Vasa
- Applied Artificial Intelligence Institute (A2I2), Deakin University, Geelong, Australia
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6
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Grundy J, Mouzakis K, Vasa R, Cain A, Curumsing M, Abdelrazek M, Fernando N. Supporting Diverse Challenges of Ageing with Digital Enhanced Living Solutions. Stud Health Technol Inform 2018; 246:75-90. [PMID: 29507261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
By the 2050, it is estimated that the proportion of people over the age of 80 will have risen from 3.9% to 9.1% of population of Organisation for Economic Cooperation and Development countries. A large proportion of these people will need significant help to manage various chronic illnesses, including dementia, heart disease, diabetes, limited physical movement and many others. Current approaches typically focus on acute episodes of illness and are not well designed to provide adequately for daily living care support. In our rapidly ageing society, a critical need exists for effective, affordable, scalable and safe in-home and in-residential care solutions leveraging a range of current and emerging sensor, interaction and integration technologies. Key aims are to support the ageing to live longer in their own homes; make daily challenges associated with ageing less limiting through use of technology supports; better support carers - both professional and family - in providing monitoring, proactive intervention, and community connectedness; enable in-home and in-residential care organisations to scale their support services and better use their workforces; and ultimately provide better quality of life. Deakin University researchers have been investigating a range of emerging technologies and platforms to realise this vision, which we in broad terms coin Digital Enhanced Living, in the ageing space but also supporting those with anxiety and depression, sleep disorders, various chronic diseases, recovery from injury, and various predictive analytics. A Smart Home solution, carried out in conjunction with a local start-up, has produced and trialled a novel sensor, interaction, and AI-based technology. Virtual Reality (VR) solutions have been used to support carers in the set-up of dementia-friendly homes, in conjunction with Alzheimers Australia. Activity and nutrition solutions, including the use of conversational agents, have been used to build dialogue to engage and change behaviour. Predictive analytics, in conjunction with major hospitals, have been applied to large medical datasets to better support professionals making judgements around discharge outcomes. A set of lessons have been learned from the design, deployment and trialing of these diverse solutions and new development approaches have been crafted to address the challenges faced. In particular, we found that there is a need to consider user emotional expectations as first-class citizens and create methodologies that consider the user needs during the creation of the software solutions. We find that quality and emotional aspects have to be engineered into the solution, rather than added after a technical solution is deployed.
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Affiliation(s)
- John Grundy
- School of Information Technology, Deakin University, Victoria, Australia
| | - Kon Mouzakis
- School of Information Technology, Deakin University, Victoria, Australia
| | - Rajesh Vasa
- School of Information Technology, Deakin University, Victoria, Australia
| | - Andrew Cain
- School of Information Technology, Deakin University, Victoria, Australia
| | | | - Mohamed Abdelrazek
- School of Information Technology, Deakin University, Victoria, Australia
| | - Niroshine Fernando
- School of Information Technology, Deakin University, Victoria, Australia
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