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Barbaric A, Christofferson K, Benseler SM, Lalloo C, Mariakakis A, Pham Q, Swart JF, Yeung RSM, Cafazzo JA. Health recommender systems to facilitate collaborative decision-making in chronic disease management: A scoping review. Digit Health 2025; 11:20552076241309386. [PMID: 39777064 PMCID: PMC11705346 DOI: 10.1177/20552076241309386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2024] [Accepted: 12/04/2024] [Indexed: 01/11/2025] Open
Abstract
Objective Health recommender systems (HRSs) are increasingly used to complement existing clinical decision-making processes, but their use for chronic diseases remains underexplored. Recognizing the importance of collaborative decision making (CDM) and patient engagement in chronic disease treatment, this review explored how HRSs support patients in managing their illness. Methods A scoping review was conducted using the framework proposed by Arksey and O'Malley, advanced by Levac et al., in line with the PRISMA-ScR checklist. Quantitative (descriptive numerical summary) and qualitative (inductive content analysis) methods wered used to synthesize the data. Results Forty-five articles were included in the final review, most commonly covering diabetes (9/45, 20%), mental health (9/45, 20.0%), and tobacco dependence (7/45, 15.6%). Behavior change theories (10/45, 22.2%) and authoritative sources (10/45, 22.2%) were the most commonly referenced sources for design and development work. From the thematic analysis, we conclude: (a) the main goal of HRSs is to induce behavior change, but limited research investigates their effectiveness in achieving this aim; (b) studies acknowledge that theories, models, frameworks, and/or guidelines help design HRSs to elicit specific behavior change, but they do not implement them; (c) connections between CDM and HRS purpose should be more explicit; and (d) HRSs can often offer other self-management services, such as progress tracking and chatbots. Conclusions We recommend a greater emphasis on evaluation outcomes beyond algorithmic performance to determine HRS effectiveness and the creation of an evidence-driven, methodological approach to creating HRSs to optimize their use in enhancing patient care. Lay summary Our work aims to provide a summary of the current landscape of health recommender system (HRS) use for chronic disease management. HRSs are digital tools designed to help people manage their health by providing personalized recommendations based on their health history, behaviors, and preferences, enabling them to make more informed health decisions. Given the increased use of these tools for personalized care, and especially with advancements in generative artificial intelligence, understanding the current methods and evaluation processes used is integral to optimizing their effectiveness. Our findings show that HRSs are most used for diabetes, mental health, and tobacco dependence, but only a small percentage of publications directly reference and/or use relevant frameworks to help guide their design and evaluation processes. Furthermore, the goal for most of these HRSs is to induce behavior change, but there is limited research investigating how effective they are in accomplishing this. Given these findings, we recommend that evaluations shift their focus from algorithms to more holistic approaches and to be more intentional about the processes used when designing the tool to support an evidence-driven approach and ultimately create more effective and useful HRSs for chronic disease management.
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Affiliation(s)
- Antonia Barbaric
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
- Centre for Digital Therapeutics, University Health Network, Toronto, ON, Canada
| | - Kenneth Christofferson
- Centre for Digital Therapeutics, University Health Network, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Susanne M Benseler
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Children's Health Ireland, Dublin, Ireland
| | - Chitra Lalloo
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
- Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada
| | - Alex Mariakakis
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Quynh Pham
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
- Centre for Digital Therapeutics, University Health Network, Toronto, ON, Canada
- Telfer School of Management, University of Ottawa, Ottawa, ON, Canada
| | - Joost F Swart
- Department of Pediatric Rheumatology and Immunology, Wilhelmina, Children’s Hospital, University Medical Center Utrecht, Utrecht, The Netherlands
- Faculty of Medicine, Utrecht University, Utrecht, The Netherlands
| | - Rae S M Yeung
- Department of Immunology and Medical Science, University of Toronto, Toronto, ON, Canada
- Division of Rheumatology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Joseph A Cafazzo
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
- Centre for Digital Therapeutics, University Health Network, Toronto, ON, Canada
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Weingott S, Parkinson J. The application of artificial intelligence in health communication development: A scoping review. Health Mark Q 2024:1-43. [PMID: 39556410 DOI: 10.1080/07359683.2024.2422206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2024]
Abstract
This scoping review explores the integration of Artificial Intelligence (AI) with communication, behavioral, and social theories to enhance health behavior interventions. A systematic search of articles published through February 2024, following PRISMA guidelines, identified 28 relevant studies from 13,723 screened. These studies, conducted across various countries, addressed health issues such as smoking cessation, musculoskeletal injuries, diabetes, chronic diseases and mental health using AI-driven tools like chatbots and apps. Despite AI's potential, a gap exists in aligning technical advancements with theoretical frameworks. The proposed AI Impact Communications Model (AI-ICM) aims to bridge this gap, offering a road map for future research and practice.
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Affiliation(s)
- Sam Weingott
- Peter Faber Business School, Australian Catholic University, Brisbane, QLD, Australia
| | - Joy Parkinson
- Faculty of Law and Business, Australian Catholic University, Brisbane, QLD, Australia
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Ananthakrishnan A, Milne-Ives M, Cong C, Shankar R, Lakey B, Alexander J, Tapuria A, Marchal A, Joy E, Meinert E. The evaluation of health recommender systems: A scoping review. Int J Med Inform 2024; 195:105697. [PMID: 39608231 DOI: 10.1016/j.ijmedinf.2024.105697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 10/30/2024] [Accepted: 11/08/2024] [Indexed: 11/30/2024]
Abstract
BACKGROUND People often look online for information about health concerns, but the vast amount of available and unregulated content can cause misinformation and potential harm. Health recommender systems (HRSs) can address this issue by recommending personalised health information. Previous research has evaluated individual systems, but there is a lack of reviews synthesising their evaluation findings. Such a synthesis is needed to ensure that future recommender designs have a positive impact on target health or behavioural outcomes. OBJECTIVE This review aimed to provide a summary of the evidence obtained from previous studies evaluating HRSs and highlight methodological considerations and gaps in the current research. METHODS The review was developed using the PRISMA-ScR and PICOS frameworks. PubMed, ACM Digital Library, IEEE Xplore, Web of Science, ScienceDirect, and Scopus were searched for studies that evaluated at least one HRS and involved human participants. A descriptive analysis was conducted on included studies and key themes and gaps in the literature were assessed. RESULTS 36 papers evaluating 34 HRSs were included. The systems targeted 13 different health conditions and provided different types of recommendations. Evaluation designs varied, with sample sizes ranging from 1 to 8057, and study durations from a single session to three years. A variety of outcome measures were used, including accuracy, engagement, clinical or behavioural outcomes, and participant perspectives. CONCLUSIONS The number of studies about HRSs is increasing, but there is a distinct lack of robust scientific research. The heterogeneity of outcome measures made it difficult to draw conclusions about their efficacy, but the data suggest that HRSs can help with the self-management of a wide range of conditions. There is a need to strengthen the available early-stage evidence with further research, evaluating multiple outcome measures including clinical outcomes, usability, and acceptability over a longer period to show real-world impact.
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Affiliation(s)
- Ananya Ananthakrishnan
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
| | - Madison Milne-Ives
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom; Centre for Health Technology, School of Nursing and Midwifery, University of Plymouth, Plymouth PL4 6DN, United Kingdom
| | - Cen Cong
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
| | - Rohit Shankar
- Peninsula Medical School, Faculty of Health, University of Plymouth, Plymouth PL4 8AA, United Kingdom; Cornwall Partnership NHS Foundation Trust, Carew House, Beacon Technology Park, Dunmere Rd, Bodmin PL31 2QN, United Kingdom
| | - Ben Lakey
- Syndi Health, Unit 1, Cambridge House, Camboro Business Park, Oakington Road, Girton, Cambridge CB3 0QH, United Kingdom
| | - Jorge Alexander
- Syndi Health, Unit 1, Cambridge House, Camboro Business Park, Oakington Road, Girton, Cambridge CB3 0QH, United Kingdom
| | - Archana Tapuria
- Syndi Health, Unit 1, Cambridge House, Camboro Business Park, Oakington Road, Girton, Cambridge CB3 0QH, United Kingdom
| | - Ariane Marchal
- Syndi Health, Unit 1, Cambridge House, Camboro Business Park, Oakington Road, Girton, Cambridge CB3 0QH, United Kingdom
| | - Elizabeth Joy
- Cornwall Partnership NHS Foundation Trust, Carew House, Beacon Technology Park, Dunmere Rd, Bodmin PL31 2QN, United Kingdom
| | - Edward Meinert
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom; Department of Primary Care and Public Health, School of Public Health, Imperial College London, London W6 8RP, United Kingdom.
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Andree R, Mujcic A, den Hollander W, van Laar M, Boon B, Engels R, Blankers M. Digital Smoking Cessation Intervention for Cancer Survivors: Analysis of Predictors and Moderators of Engagement and Outcome Alongside a Randomized Controlled Trial. JMIR Cancer 2024; 10:e46303. [PMID: 38901028 PMCID: PMC11229662 DOI: 10.2196/46303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 01/26/2024] [Accepted: 02/25/2024] [Indexed: 06/22/2024] Open
Abstract
BACKGROUND Recent studies have shown positive, though small, clinical effects of digital smoking cessation (SC) interventions for cancer survivors. However, research on associations among participant characteristics, intervention engagement, and outcomes is limited. OBJECTIVE This study aimed to explore the predictors and moderators of engagement and outcome of MyCourse-Quit Smoking (in Dutch: "MijnKoers-Stoppen met Roken"), a digital minimally guided intervention for cancer survivors. METHODS A secondary analysis of data from the randomized controlled trial was performed. The number of cigarettes smoked in the past 7 days at 6-month follow-up was the primary outcome measure. We analyzed interactions among participant characteristics (11 variables), intervention engagement (3 variables), and outcome using robust linear (mixed) modeling. RESULTS In total, 165 participants were included in this study. Female participants accessed the intervention less often than male participants (B=-11.12; P=.004). A higher Alcohol Use Disorders Identification Test score at baseline was associated with a significantly higher number of logins (B=1.10; P<.001) and diary registrations (B=1.29; P<.001). A higher Fagerström Test for Nicotine Dependence score at baseline in the intervention group was associated with a significantly larger reduction in tobacco use after 6 months (B=-9.86; P=.002). No other associations and no moderating effects were found. CONCLUSIONS Overall, a limited number of associations was found between participant characteristics, engagement, and outcome, except for gender, problematic alcohol use, and nicotine dependence. Future studies are needed to shed light on how this knowledge can be used to improve the effects of digital SC programs for cancer survivors. TRIAL REGISTRATION Netherlands Trial register NTR6011/NL5434; https://onderzoekmetmensen.nl/nl/trial/22832.
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Affiliation(s)
- Rosa Andree
- Trimbos Institute, Netherlands Institute of Mental Health and Addiction, Utrecht, Netherlands
| | - Ajla Mujcic
- PsyQ, Parnassia Groep, The Hague, Netherlands
| | - Wouter den Hollander
- Trimbos Institute, Netherlands Institute of Mental Health and Addiction, Utrecht, Netherlands
| | - Margriet van Laar
- Trimbos Institute, Netherlands Institute of Mental Health and Addiction, Utrecht, Netherlands
| | - Brigitte Boon
- Siza, Center for Long-term Care for People with Disabilities, Arnhem, Netherlands
- Academy Het Dorp, Research & Advisory on Technology in Long-term Care, Arnhem, Netherlands
- Tranzo, Tilburg School of Social and Behavioral Sciences, Tilburg University, Tilburg, Netherlands
| | - Rutger Engels
- Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, Netherlands
| | - Matthijs Blankers
- Trimbos Institute, Netherlands Institute of Mental Health and Addiction, Utrecht, Netherlands
- Department of Research, Arkin Mental Health Care, Amsterdam, Netherlands
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Bickel WK, Tomlinson DC, Craft WH, Ma M, Dwyer CL, Yeh YH, Tegge AN, Freitas-Lemos R, Athamneh LN. Predictors of smoking cessation outcomes identified by machine learning: A systematic review. ADDICTION NEUROSCIENCE 2023; 6:100068. [PMID: 37214256 PMCID: PMC10194042 DOI: 10.1016/j.addicn.2023.100068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
This systematic review aims to characterize the utility of machine learning to identify the predictors of smoking cessation outcomes and identify the machine learning methods applied in this area. In the current study, multiple searches occurred through December 9, 2022 in MEDLINE, Science Citation Index, Social Science Citation Index, EMBASE, CINAHL Plus, APA PsycINFO, PubMed, Cochrane Central Register of Controlled Trials, and the IEEE Xplore were performed. Inclusion criteria included various machine learning techniques, studies reporting cigarette smoking cessation outcomes (smoking status and the number of cigarettes), and various experimental designs (e.g., cross-sectional and longitudinal). Predictors of smoking cessation outcomes were assessed, including behavioral markers, biomarkers, and other predictors. Our systematic review identified 12 papers fitting our inclusion criteria. In this review, we identified gaps in knowledge and innovation opportunities for machine learning research in the field of smoking cessation.
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Affiliation(s)
- Warren K. Bickel
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA
| | - Devin C. Tomlinson
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA
- Graduate Program in Translational Biology, Medicine, and Health, Virginia Tech, Blacksburg, VA, USA
| | - William H. Craft
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA
- Graduate Program in Translational Biology, Medicine, and Health, Virginia Tech, Blacksburg, VA, USA
| | - Manxiu Ma
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA
| | - Candice L. Dwyer
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA
- Department of Psychology, Virginia Tech, Blacksburg, VA, USA
| | - Yu-Hua Yeh
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA
| | - Allison N. Tegge
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA
- Department of Statistics, Virginia Tech, Blacksburg, VA, USA
| | | | - Liqa N. Athamneh
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA
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Applying Collective Intelligence in Health Recommender Systems for Smoking Cessation: A Comparison Trial. ELECTRONICS 2022. [DOI: 10.3390/electronics11081219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Background: Health recommender systems (HRSs) are intelligent systems that can be used to tailor digital health interventions. We compared two HRSs to assess their impact providing smoking cessation support messages. Methods: Smokers who downloaded a mobile app to support smoking abstinence were randomly assigned to two interventions. They received personalized, ratable motivational messages on the app. The first intervention had a knowledge-based HRS (n = 181): it selected random messages from a subset matching the users’ demographics and smoking habits. The second intervention had a hybrid HRS using collective intelligence (n = 190): it selected messages applying the knowledge-based filter first, and then chose the ones with higher ratings provided by other similar users in the system. Both interventions were compared on: (a) message appreciation, (b) engagement with the system, and (c) one’s own self-reported smoking cessation status, as indicated by the last seven-day point prevalence report in different time intervals during a period of six months. Results: Both interventions had similar message appreciation, number of rated messages, and abstinence results. The knowledge-based HRS achieved a significantly higher number of active days, number of abstinence reports, and better abstinence results. The hybrid algorithm led to more quitting attempts in participants who completed their user profiles.
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