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Ayenigbara IO. The evolving nature of artificial intelligence: role in public health and health promotion. J Public Health (Oxf) 2024; 46:e322-e323. [PMID: 37973395 DOI: 10.1093/pubmed/fdad240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 10/30/2023] [Indexed: 11/19/2023] Open
Affiliation(s)
- Israel Oluwasegun Ayenigbara
- Department of Health Education, School and Community Health Education Unit, University of Ibadan, Ibadan, 200284, Nigeria
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Meyer A, Soleman A, Riese J, Streichert T. Comparison of ChatGPT, Gemini, and Le Chat with physician interpretations of medical laboratory questions from an online health forum. Clin Chem Lab Med 2024; 0:cclm-2024-0246. [PMID: 38804035 DOI: 10.1515/cclm-2024-0246] [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: 02/24/2024] [Accepted: 05/13/2024] [Indexed: 05/29/2024]
Abstract
OBJECTIVES Laboratory medical reports are often not intuitively comprehensible to non-medical professionals. Given their recent advancements, easier accessibility and remarkable performance on medical licensing exams, patients are therefore likely to turn to artificial intelligence-based chatbots to understand their laboratory results. However, empirical studies assessing the efficacy of these chatbots in responding to real-life patient queries regarding laboratory medicine are scarce. METHODS Thus, this investigation included 100 patient inquiries from an online health forum, specifically addressing Complete Blood Count interpretation. The aim was to evaluate the proficiency of three artificial intelligence-based chatbots (ChatGPT, Gemini and Le Chat) against the online responses from certified physicians. RESULTS The findings revealed that the chatbots' interpretations of laboratory results were inferior to those from online medical professionals. While the chatbots exhibited a higher degree of empathetic communication, they frequently produced erroneous or overly generalized responses to complex patient questions. The appropriateness of chatbot responses ranged from 51 to 64 %, with 22 to 33 % of responses overestimating patient conditions. A notable positive aspect was the chatbots' consistent inclusion of disclaimers regarding its non-medical nature and recommendations to seek professional medical advice. CONCLUSIONS The chatbots' interpretations of laboratory results from real patient queries highlight a dangerous dichotomy - a perceived trustworthiness potentially obscuring factual inaccuracies. Given the growing inclination towards self-diagnosis using AI platforms, further research and improvement of these chatbots is imperative to increase patients' awareness and avoid future burdens on the healthcare system.
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Affiliation(s)
- Annika Meyer
- Institute of Clinical Chemistry, Faculty of Medicine and University Hospital, 27182 University Hospital Cologne , Cologne, Germany
| | - Ari Soleman
- Faculty of Medicine and University Hospital, 27182 University Hospital Cologne , Cologne, Germany
| | - Janik Riese
- Institute of Pathology, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
| | - Thomas Streichert
- Institute of Clinical Chemistry, Faculty of Medicine and University Hospital, 27182 University Hospital Cologne , Cologne, Germany
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Chew HSJ, Chew NW, Loong SSE, Lim SL, Tam WSW, Chin YH, Chao AM, Dimitriadis GK, Gao Y, So JBY, Shabbir A, Ngiam KY. Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation. J Med Internet Res 2024; 26:e46036. [PMID: 38713909 PMCID: PMC11109864 DOI: 10.2196/46036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 12/12/2023] [Accepted: 03/12/2024] [Indexed: 05/09/2024] Open
Abstract
BACKGROUND A plethora of weight management apps are available, but many individuals, especially those living with overweight and obesity, still struggle to achieve adequate weight loss. An emerging area in weight management is the support for one's self-regulation over momentary eating impulses. OBJECTIVE This study aims to examine the feasibility and effectiveness of a novel artificial intelligence-assisted weight management app in improving eating behaviors in a Southeast Asian cohort. METHODS A single-group pretest-posttest study was conducted. Participants completed the 1-week run-in period of a 12-week app-based weight management program called the Eating Trigger-Response Inhibition Program (eTRIP). This self-monitoring system was built upon 3 main components, namely, (1) chatbot-based check-ins on eating lapse triggers, (2) food-based computer vision image recognition (system built based on local food items), and (3) automated time-based nudges and meal stopwatch. At every mealtime, participants were prompted to take a picture of their food items, which were identified by a computer vision image recognition technology, thereby triggering a set of chatbot-initiated questions on eating triggers such as who the users were eating with. Paired 2-sided t tests were used to compare the differences in the psychobehavioral constructs before and after the 7-day program, including overeating habits, snacking habits, consideration of future consequences, self-regulation of eating behaviors, anxiety, depression, and physical activity. Qualitative feedback were analyzed by content analysis according to 4 steps, namely, decontextualization, recontextualization, categorization, and compilation. RESULTS The mean age, self-reported BMI, and waist circumference of the participants were 31.25 (SD 9.98) years, 28.86 (SD 7.02) kg/m2, and 92.60 (SD 18.24) cm, respectively. There were significant improvements in all the 7 psychobehavioral constructs, except for anxiety. After adjusting for multiple comparisons, statistically significant improvements were found for overeating habits (mean -0.32, SD 1.16; P<.001), snacking habits (mean -0.22, SD 1.12; P<.002), self-regulation of eating behavior (mean 0.08, SD 0.49; P=.007), depression (mean -0.12, SD 0.74; P=.007), and physical activity (mean 1288.60, SD 3055.20 metabolic equivalent task-min/day; P<.001). Forty-one participants reported skipping at least 1 meal (ie, breakfast, lunch, or dinner), summing to 578 (67.1%) of the 862 meals skipped. Of the 230 participants, 80 (34.8%) provided textual feedback that indicated satisfactory user experience with eTRIP. Four themes emerged, namely, (1) becoming more mindful of self-monitoring, (2) personalized reminders with prompts and chatbot, (3) food logging with image recognition, and (4) engaging with a simple, easy, and appealing user interface. The attrition rate was 8.4% (21/251). CONCLUSIONS eTRIP is a feasible and effective weight management program to be tested in a larger population for its effectiveness and sustainability as a personalized weight management program for people with overweight and obesity. TRIAL REGISTRATION ClinicalTrials.gov NCT04833803; https://classic.clinicaltrials.gov/ct2/show/NCT04833803.
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Affiliation(s)
- Han Shi Jocelyn Chew
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Nicholas Ws Chew
- Department of Cardiology, National University Hospital, Singapore, Singapore
| | - Shaun Seh Ern Loong
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Su Lin Lim
- Department of Dietetics, National University Hospital, Singapore, Singapore
| | - Wai San Wilson Tam
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Yip Han Chin
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ariana M Chao
- School of Nursing, Johns Hopkins University, Baltimore, MD, United States
| | - Georgios K Dimitriadis
- Department of Endocrinology ASO/EASO COM, King's College Hospital NHS Foundation Trust, London, United Kingdom
| | - Yujia Gao
- Division of Hepatobiliary & Pancreatic Surgery, Department of Surgery, National University Hospital, Singapore, Singapore
| | - Jimmy Bok Yan So
- Division of General Surgery (Upper Gastrointestinal Surgery), Department of Surgery, National University Hospital, Singapore, Singapore
| | - Asim Shabbir
- Division of General Surgery (Upper Gastrointestinal Surgery), Department of Surgery, National University Hospital, Singapore, Singapore
| | - Kee Yuan Ngiam
- Division of Thyroid & Endocrine Surgery, Department of Surgery, National University Hospital, Singapore, Singapore
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Yaghy A, Yaghy M, Shields JA, Shields CL. Large Language Models in Ophthalmology: Potential and Pitfalls. Semin Ophthalmol 2024; 39:289-293. [PMID: 38179986 DOI: 10.1080/08820538.2023.2300808] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 12/06/2023] [Indexed: 01/06/2024]
Abstract
Large language models (LLMs) show great promise in assisting clinicians in general, and ophthalmology in particular, through knowledge synthesis, decision support, accelerating research, enhancing education, and improving patient interactions. Specifically, LLMs can rapidly summarize the latest literature to keep clinicians up-to-date. They can also analyze patient data to highlight crucial insights and recommend appropriate tests or referrals. LLMs can automate tedious research tasks like data cleaning and literature reviews. As AI tutors, LLMs can fill knowledge gaps and assess competency in trainees. As chatbots, they can provide empathetic, personalized responses to patient inquiries and improve satisfaction. The visual capabilities of LLMs like GPT-4 allow assisting the visually impaired by describing environments. However, there are significant ethical, technical, and legal challenges around the use of LLMs that should be addressed regarding privacy, fairness, robustness, attribution, and regulation. Ongoing oversight and refinement of models is critical to realize benefits while minimizing risks and upholding responsible AI principles. If carefully implemented, LLMs hold immense potential to push the boundaries of care, discovery, and quality of life for ophthalmology patients.
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Affiliation(s)
- Antonio Yaghy
- Ocular Oncology Service, Wills Eye Hospital, Thomas Jefferson University, Philadelphia, PA, USA
| | - Maria Yaghy
- Pediatric Emergency and Infectious Disease, Centre Hospitalier Universitaire Timone Enfants, Marseille, France
| | - Jerry A Shields
- Ocular Oncology Service, Wills Eye Hospital, Thomas Jefferson University, Philadelphia, PA, USA
| | - Carol L Shields
- Ocular Oncology Service, Wills Eye Hospital, Thomas Jefferson University, Philadelphia, PA, USA
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Yi X, He Y, Gao S, Li M. A review of the application of deep learning in obesity: From early prediction aid to advanced management assistance. Diabetes Metab Syndr 2024; 18:103000. [PMID: 38604060 DOI: 10.1016/j.dsx.2024.103000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 01/23/2024] [Accepted: 03/29/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND AND AIMS Obesity is a chronic disease which can cause severe metabolic disorders. Machine learning (ML) techniques, especially deep learning (DL), have proven to be useful in obesity research. However, there is a dearth of systematic reviews of DL applications in obesity. This article aims to summarize the current trend of DL usage in obesity research. METHODS An extensive literature review was carried out across multiple databases, including PubMed, Embase, Web of Science, Scopus, and Medline, to collate relevant studies published from January 2018 to September 2023. The focus was on research detailing the application of DL in the context of obesity. We have distilled critical insights pertaining to the utilized learning models, encompassing aspects of their development, principal results, and foundational methodologies. RESULTS Our analysis culminated in the synthesis of new knowledge regarding the application of DL in the context of obesity. Finally, 40 research articles were included. The final collection of these research can be divided into three categories: obesity prediction (n = 16); obesity management (n = 13); and body fat estimation (n = 11). CONCLUSIONS This is the first review to examine DL applications in obesity. It reveals DL's superiority in obesity prediction over traditional ML methods, showing promise for multi-omics research. DL also innovates in obesity management through diet, fitness, and environmental analyses. Additionally, DL improves body fat estimation, offering affordable and precise monitoring tools. The study is registered with PROSPERO (ID: CRD42023475159).
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Affiliation(s)
- Xinghao Yi
- Department of Endocrinology, NHC Key Laboratory of Endocrinology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Yangzhige He
- Department of Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing 100730, China
| | - Shan Gao
- Department of Endocrinology, Xuan Wu Hospital, Capital Medical University, Beijing 10053, China
| | - Ming Li
- Department of Endocrinology, NHC Key Laboratory of Endocrinology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China.
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Chew HSJ, Achananuparp P, Dalakoti M, Chew NWS, Chin YH, Gao Y, So BYJ, Shabbir A, Peng LE, Ngiam KY. Public acceptance of using artificial intelligence-assisted weight management apps in high-income southeast Asian adults with overweight and obesity: a cross-sectional study. Front Nutr 2024; 11:1287156. [PMID: 38385011 PMCID: PMC10879329 DOI: 10.3389/fnut.2024.1287156] [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: 09/01/2023] [Accepted: 01/19/2024] [Indexed: 02/23/2024] Open
Abstract
Introduction With in increase in interest to incorporate artificial intelligence (AI) into weight management programs, we aimed to examine user perceptions of AI-based mobile apps for weight management in adults with overweight and obesity. Methods 280 participants were recruited between May and November 2022. Participants completed a questionnaire on sociodemographic profiles, Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), and Self-Regulation of Eating Behavior Questionnaire. Structural equation modeling was performed using R. Model fit was tested using maximum-likelihood generalized unweighted least squares. Associations between influencing factors were analyzed using correlation and linear regression. Results 271 participant responses were analyzed, representing participants with a mean age of 31.56 ± 10.75 years, median (interquartile range) BMI, and waist circumference of 27.2 kg/m2 (24.2-28.4 kg/m2) and 86.4 (80.0-94.0) cm, respectively. In total, 188 (69.4%) participants intended to use AI-assisted weight loss apps. UTAUT2 explained 63.3% of the variance in our intention of the sample to use AI-assisted weight management apps with satisfactory model fit: CMIN/df = 1.932, GFI = 0.966, AGFI = 0.954, NFI = 0.909, CFI = 0.954, RMSEA = 0.059, SRMR = 0.050. Only performance expectancy, hedonic motivation, and the habit of using AI-assisted apps were significant predictors of intention. Comparison with existing literature revealed vast variabilities in the determinants of AI- and non-AI weight loss app acceptability in adults with and without overweight and obesity. UTAUT2 produced a good fit in explaining the acceptability of AI-assisted apps among a multi-ethnic, developed, southeast Asian sample with overweight and obesity. Conclusion UTAUT2 model is recommended to guide the development of AI-assisted weight management apps among people with overweight and obesity.
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Affiliation(s)
- Han Shi Jocelyn Chew
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Palakorn Achananuparp
- School of Computing and Information Systems, Singapore Management University, Singapore, Singapore
| | - Mayank Dalakoti
- Department of Cardiology, National University Heart Centre, Singapore, Singapore
| | - Nicholas W. S. Chew
- Department of Cardiology, National University Heart Centre, Singapore, Singapore
| | - Yip Han Chin
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Yujia Gao
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, National University Hospital, Singapore, Singapore
| | - Bok Yan Jimmy So
- Division of General Surgery (Upper Gastrointestinal Surgery), Department of Surgery, National University Hospital, Singapore, Singapore
| | - Asim Shabbir
- Division of General Surgery (Upper Gastrointestinal Surgery), Department of Surgery, National University Hospital, Singapore, Singapore
| | - Lim Ee Peng
- School of Computing and Information Systems, Singapore Management University, Singapore, Singapore
| | - Kee Yuan Ngiam
- Division of General Surgery (Upper Gastrointestinal Surgery), Department of Surgery, National University Hospital, Singapore, Singapore
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Gleason KT, Wu MMJ, Wec A, Powell DS, Zhang T, Gamper MJ, Green AR, Nothelle S, Amjad H, Wolff JL. Use of the patient portal among older adults with diagnosed dementia and their care partners. Alzheimers Dement 2023; 19:5663-5671. [PMID: 37354066 PMCID: PMC10808947 DOI: 10.1002/alz.13354] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 04/28/2023] [Accepted: 05/29/2023] [Indexed: 06/26/2023]
Abstract
INTRODUCTION Care partners are at the forefront of dementia care, yet little is known about patient portal use in the context of dementia diagnosis. METHODS We conducted an observational cohort study of date/time-stamped patient portal use for a 5-year period (October 3, 2017-October 2, 2022) at an academic health system. The cohort consisted of 3170 patients ages 65+ with diagnosed dementia with 2+ visits within 24 months. Message authorship was determined by manual review of 970 threads involving 3065 messages for 279 patients. RESULTS Most (71.20%) older adults with diagnosed dementia were registered portal users but far fewer (10.41%) had a registered care partner with shared access. Care partners authored most (612/970, 63.09%) message threads, overwhelmingly using patient identity credentials (271/279, 97.13%). DISCUSSION The patient portal is used by persons with dementia and their care partners. Organizational efforts that facilitate shared access may benefit the support of persons with dementia and their care partners. Highlights Patient portal registration and use has been increasing among persons with diagnosed dementia. Two thirds of secure messages from portal accounts of patients with diagnosed dementia were identified as being authored by care partners, primarily using patient login credentials. Care partners who accessed the patient portal using their own identity credentials through shared access demonstrate similar levels of activity to patients without dementia. Organizational initiatives should recognize and support the needs of persons with dementia and their care partners by encouraging awareness, registration, and use of proper identity credentials, including shared, or proxy, portal access.
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Affiliation(s)
- Kelly T. Gleason
- Johns Hopkins University School of Nursing, Baltimore, Maryland, USA
| | - Mingche M. J. Wu
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Aleksandra Wec
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Danielle S. Powell
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Talan Zhang
- Division of Geriatric Medicine and Gerontology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Mary Jo Gamper
- Johns Hopkins University School of Nursing, Baltimore, Maryland, USA
| | - Ariel R. Green
- Division of Geriatric Medicine and Gerontology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Stephanie Nothelle
- Division of Geriatric Medicine and Gerontology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Halima Amjad
- Division of Geriatric Medicine and Gerontology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jennifer L. Wolff
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
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Berry M, Taylor L, Huang Z, Chwyl C, Kerrigan S, Forman E. Automated Messaging Delivered Alongside Behavioral Treatment for Weight Loss: Qualitative Study. JMIR Form Res 2023; 7:e50872. [PMID: 37930786 PMCID: PMC10660236 DOI: 10.2196/50872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 10/12/2023] [Accepted: 10/14/2023] [Indexed: 11/07/2023] Open
Abstract
BACKGROUND Mobile health interventions for weight loss frequently use automated messaging. However, this intervention modality appears to have limited weight loss efficacy. Furthermore, data on users' subjective experiences while receiving automated messaging-based interventions for weight loss are scarce, especially for more advanced messaging systems providing users with individually tailored, data-informed feedback. OBJECTIVE The purpose of this study was to characterize the experiences of individuals with overweight or obesity who received automated messages for 6-12 months as part of a behavioral weight loss trial. METHODS Participants (n=40) provided Likert-scale ratings of messaging acceptability and completed a structured qualitative interview (n=39) focused on their experiences with the messaging system and generating suggestions for improvement. Interview data were analyzed using thematic analysis. RESULTS Participants found the messages most useful for summarizing goal progress and least useful for suggesting new behavioral strategies. Overall message acceptability was moderate (2.67 out of 5). From the interviews, 2 meta-themes emerged. Participants indicated that although the messages provided useful reminders of intervention goals and skills, they did not adequately capture their lived experiences while losing weight. CONCLUSIONS Many participants found the automated messages insufficiently tailored to their personal weight loss experiences. Future studies should explore alternative methods for message tailoring (eg, allowing for a higher degree of participant input and interactivity) that may boost treatment engagement and efficacy. TRIAL REGISTRATION ClinicalTrials.gov NCT05231824; https://clinicaltrials.gov/study/NCT05231824.
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Affiliation(s)
- Michael Berry
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, United States
| | - Lauren Taylor
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, United States
- Center for Weight, Eating and Lifestyle Science, Drexel University, Philadelphia, PA, United States
| | - Zhuoran Huang
- Center for Weight, Eating and Lifestyle Science, Drexel University, Philadelphia, PA, United States
| | - Christina Chwyl
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, United States
- Center for Weight, Eating and Lifestyle Science, Drexel University, Philadelphia, PA, United States
| | | | - Evan Forman
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, United States
- Center for Weight, Eating and Lifestyle Science, Drexel University, Philadelphia, PA, United States
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Gleason KT, Powell DS, Wec A, Zou X, Gamper MJ, Peereboom D, Wolff JL. Patient portal interventions: a scoping review of functionality, automation used, and therapeutic elements of patient portal interventions. JAMIA Open 2023; 6:ooad077. [PMID: 37663406 PMCID: PMC10469545 DOI: 10.1093/jamiaopen/ooad077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 08/03/2023] [Accepted: 08/14/2023] [Indexed: 09/05/2023] Open
Abstract
Objectives We sought to understand the objectives, targeted populations, therapeutic elements, and delivery characteristics of patient portal interventions. Materials and Methods Following Arksey and O-Malley's methodological framework, we conducted a scoping review of manuscripts published through June 2022 by hand and systematically searching PubMed, PSYCHInfo, Embase, and Web of Science. The search yielded 5403 manuscripts; 248 were selected for full-text review; 81 met the eligibility criteria for examining outcomes of a patient portal intervention. Results The 81 articles described: trials involving comparison groups (n = 37; 45.7%), quality improvement initiatives (n = 15; 18.5%), pilot studies (n = 7; 8.6%), and single-arm studies (n = 22; 27.2%). Studies were conducted in primary care (n = 33, 40.7%), specialty outpatient (n = 24, 29.6%), or inpatient settings (n = 4, 4.9%)-or they were deployed system wide (n = 9, 11.1%). Interventions targeted specific health conditions (n = 35, 43.2%), promoted preventive services (n = 19, 23.5%), or addressed communication (n = 19, 23.4%); few specifically sought to improve the patient experience (n = 3, 3.7%). About half of the studies (n = 40, 49.4%) relied on human involvement, and about half involved personalized (vs exclusively standardized) elements (n = 42, 51.8%). Interventions commonly collected patient-reported information (n = 36, 44.4%), provided education (n = 35, 43.2%), or deployed preventive service reminders (n = 14, 17.3%). Discussion This scoping review finds that most patient portal interventions have delivered education or facilitated collection of patient-reported information. Few interventions have involved pragmatic designs or been deployed system wide. Conclusion The patient portal is an important tool in real-world efforts to more effectively support patients, but interventions to date rely largely on evidence from consented participants rather than pragmatically implemented systems-level initiatives.
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Affiliation(s)
- Kelly T Gleason
- Johns Hopkins University School of Nursing, Baltimore, MD 21225, United States
| | - Danielle S Powell
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, United States
| | - Aleksandra Wec
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, United States
| | - Xingyuan Zou
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, United States
| | - Mary Jo Gamper
- Johns Hopkins University School of Nursing, Baltimore, MD 21225, United States
| | - Danielle Peereboom
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, United States
| | - Jennifer L Wolff
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, United States
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Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, Aldairem A, Alrashed M, Bin Saleh K, Badreldin HA, Al Yami MS, Al Harbi S, Albekairy AM. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC MEDICAL EDUCATION 2023; 23:689. [PMID: 37740191 PMCID: PMC10517477 DOI: 10.1186/s12909-023-04698-z] [Citation(s) in RCA: 76] [Impact Index Per Article: 76.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 09/19/2023] [Indexed: 09/24/2023]
Abstract
INTRODUCTION Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI's role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools. RESEARCH SIGNIFICANCE This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI's significance in healthcare and supports healthcare organizations in effectively adopting AI technologies. MATERIALS AND METHODS The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application. RESULTS Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust. CONCLUSION AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.
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Affiliation(s)
- Shuroug A Alowais
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia.
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia.
| | - Sahar S Alghamdi
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
- Department of Pharmaceutical Sciences, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Nada Alsuhebany
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Tariq Alqahtani
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
- Department of Pharmaceutical Sciences, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Abdulrahman I Alshaya
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Sumaya N Almohareb
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Atheer Aldairem
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Mohammed Alrashed
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Khalid Bin Saleh
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Hisham A Badreldin
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Majed S Al Yami
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Shmeylan Al Harbi
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Abdulkareem M Albekairy
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
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11
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Su JJ, Liu JYW, Cheung DSK, Wang S, Christensen M, Kor PPK, Tyrovolas S, Leung AYM. Long-term effects of e-Health secondary prevention on cardiovascular health: a systematic review and meta-analysis. Eur J Cardiovasc Nurs 2023; 22:562-574. [PMID: 36695341 DOI: 10.1093/eurjcn/zvac116] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 12/06/2022] [Accepted: 12/08/2023] [Indexed: 01/26/2023]
Abstract
AIMS Despite the well-documented short-to-medium-term effectiveness of e-Health (electronic health) secondary prevention interventions on patients with cardiovascular disease (CVD), there is limited empirical evidence regarding long-term effectiveness. This review aims to evaluate the long-term effects of e-Health secondary prevention interventions on the health outcomes of patients with CVD. METHODS AND RESULTS This systematic review and meta-analysis followed Cochrane Handbook for Systematic Reviews of Interventions. EMBASE, Medline, Web of Science, and Scopus were searched from 1990 to May 2022. Randomized controlled trials investigating the effects of e-Health secondary prevention on health outcomes of CVD patients that collected endpoint data at ≥ 12 months were included. RevMan 5.3 was used for risk of bias assessment and meta-analysis. Ten trials with 1559 participants were included. Data pooling suggested that e-Health programmes have significantly reduced LDL cholesterol [n = 6; SMD = -0.26, 95% confidence interval (CI): (-0.38, -0.14), I2 = 17%, P < 0.001]; systolic blood pressure [n = 5; SMD = -0.46, 95% CI: (-0.84, -0.08), I2 = 90%, P = 0.02]; and re-hospitalization, reoccurrence, and mortality [risk ratio = 0.36, 95% CI: (0.17, 0.77), I2 = 0%, P = 0.009]. Effects on behavioural modification, physiological outcomes of body weight and blood glucose, and quality of life were inconclusive. CONCLUSION e-Health secondary prevention is effective in improving long-term management of risk factors and reducing the reoccurrence of cardiac events in patients with CVD. Results are inconclusive for behaviour modification and quality of life. Exploring, implementing, and strengthening strategies in e-Health secondary prevention programmes that focus on maintaining behaviour changes and enhancing psychosocial elements should be undertaken. REGISTRATION PROSPERO CRD42022300551.
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Affiliation(s)
- Jing Jing Su
- School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Kowloon 999077, Hong Kong
- World Health Organization for Community Health Services, School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
- Centre for Gerontological Nursing, School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong
| | - Justina Yat Wa Liu
- School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Kowloon 999077, Hong Kong
- Centre for Gerontological Nursing, School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR 999077, China
| | - Daphne Sze Ki Cheung
- School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Kowloon 999077, Hong Kong
- Centre for Gerontological Nursing, School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR 999077, China
| | - Shanshan Wang
- School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Kowloon 999077, Hong Kong
- World Health Organization for Community Health Services, School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
- Centre for Gerontological Nursing, School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong
| | - Martin Christensen
- School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Kowloon 999077, Hong Kong
- Centre for Gerontological Nursing, School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong
| | - Patrick Pui Kin Kor
- School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Kowloon 999077, Hong Kong
- World Health Organization for Community Health Services, School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
- Centre for Gerontological Nursing, School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong
| | - Stefanos Tyrovolas
- School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Kowloon 999077, Hong Kong
- World Health Organization for Community Health Services, School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
- Centre for Gerontological Nursing, School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong
- Research, Innovation and Teaching Unit, Parc Sanitari Sant Joan de Déu, 08830 Sant Boi de Llobregat, Spain
- Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, 28029 Madrid, Spain
| | - Angela Yee Man Leung
- School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Kowloon 999077, Hong Kong
- World Health Organization for Community Health Services, School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
- Centre for Gerontological Nursing, School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR 999077, China
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12
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Jayasinghe S, Hills AP. Strategies to Improve Physical Activity and Nutrition Behaviours in Children and Adolescents: A Review. Nutrients 2023; 15:3370. [PMID: 37571307 PMCID: PMC10420868 DOI: 10.3390/nu15153370] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
Despite widespread acknowledgement of the multifarious health benefits of physical activity (PA), including prevention and control of obesity, an overwhelming majority of children and adolescents are not sufficiently active to realise such benefits. Concurrently, young people are significantly impacted by the rapid global rise of sedentarism, and suboptimal dietary patterns during key phases of development. Regrettably, the cumulative effects of unhealthy behaviours during the growing years predisposes young people to the early stages of several chronic conditions, including obesity. Clear and consistent approaches are urgently needed to improve eating and activity behaviours of children and adolescents. Based on existing evidence of "best bets" to prevent and control obesity and its comorbidities, we present a set of non-negotiable strategies as a 'road map' to achieving prevention and improving the health of children and adolescents.
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Affiliation(s)
| | - Andrew P. Hills
- College of Health and Medicine, University of Tasmania, Hobart, TAS 7005, Australia;
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13
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Singh B, Olds T, Brinsley J, Dumuid D, Virgara R, Matricciani L, Watson A, Szeto K, Eglitis E, Miatke A, Simpson CEM, Vandelanotte C, Maher C. Systematic review and meta-analysis of the effectiveness of chatbots on lifestyle behaviours. NPJ Digit Med 2023; 6:118. [PMID: 37353578 DOI: 10.1038/s41746-023-00856-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 06/01/2023] [Indexed: 06/25/2023] Open
Abstract
Chatbots (also known as conversational agents and virtual assistants) offer the potential to deliver healthcare in an efficient, appealing and personalised manner. The purpose of this systematic review and meta-analysis was to evaluate the efficacy of chatbot interventions designed to improve physical activity, diet and sleep. Electronic databases were searched for randomised and non-randomised controlled trials, and pre-post trials that evaluated chatbot interventions targeting physical activity, diet and/or sleep, published before 1 September 2022. Outcomes were total physical activity, steps, moderate-to-vigorous physical activity (MVPA), fruit and vegetable consumption, sleep quality and sleep duration. Standardised mean differences (SMD) were calculated to compare intervention effects. Subgroup analyses were conducted to assess chatbot type, intervention type, duration, output and use of artificial intelligence. Risk of bias was assessed using the Effective Public Health Practice Project Quality Assessment tool. Nineteen trials were included. Sample sizes ranged between 25-958, and mean participant age ranged between 9-71 years. Most interventions (n = 15, 79%) targeted physical activity, and most trials had a low-quality rating (n = 14, 74%). Meta-analysis results showed significant effects (all p < 0.05) of chatbots for increasing total physical activity (SMD = 0.28 [95% CI = 0.16, 0.40]), daily steps (SMD = 0.28 [95% CI = 0.17, 0.39]), MVPA (SMD = 0.53 [95% CI = 0.24, 0.83]), fruit and vegetable consumption (SMD = 0.59 [95% CI = 0.25, 0.93]), sleep duration (SMD = 0.44 [95% CI = 0.32, 0.55]) and sleep quality (SMD = 0.50 [95% CI = 0.09, 0.90]). Subgroup analyses showed that text-based, and artificial intelligence chatbots were more efficacious than speech/voice chatbots for fruit and vegetable consumption, and multicomponent interventions were more efficacious than chatbot-only interventions for sleep duration and sleep quality (all p < 0.05). Findings from this systematic review and meta-analysis indicate that chatbot interventions are efficacious for increasing physical activity, fruit and vegetable consumption, sleep duration and sleep quality. Chatbot interventions were efficacious across a range of populations and age groups, with both short- and longer-term interventions, and chatbot only and multicomponent interventions being efficacious.
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Affiliation(s)
- Ben Singh
- Alliance for Research in Exercise Nutrition and Activity (ARENA), University of South Australia, Adelaide, SA, Australia.
| | - Timothy Olds
- Alliance for Research in Exercise Nutrition and Activity (ARENA), University of South Australia, Adelaide, SA, Australia
| | - Jacinta Brinsley
- Alliance for Research in Exercise Nutrition and Activity (ARENA), University of South Australia, Adelaide, SA, Australia
| | - Dot Dumuid
- Alliance for Research in Exercise Nutrition and Activity (ARENA), University of South Australia, Adelaide, SA, Australia
| | - Rosa Virgara
- Alliance for Research in Exercise Nutrition and Activity (ARENA), University of South Australia, Adelaide, SA, Australia
| | - Lisa Matricciani
- Alliance for Research in Exercise Nutrition and Activity (ARENA), University of South Australia, Adelaide, SA, Australia
| | - Amanda Watson
- Alliance for Research in Exercise Nutrition and Activity (ARENA), University of South Australia, Adelaide, SA, Australia
| | - Kimberley Szeto
- Alliance for Research in Exercise Nutrition and Activity (ARENA), University of South Australia, Adelaide, SA, Australia
| | - Emily Eglitis
- Alliance for Research in Exercise Nutrition and Activity (ARENA), University of South Australia, Adelaide, SA, Australia
| | - Aaron Miatke
- Alliance for Research in Exercise Nutrition and Activity (ARENA), University of South Australia, Adelaide, SA, Australia
| | - Catherine E M Simpson
- Alliance for Research in Exercise Nutrition and Activity (ARENA), University of South Australia, Adelaide, SA, Australia
| | - Corneel Vandelanotte
- School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, QLD, Australia
| | - Carol Maher
- Alliance for Research in Exercise Nutrition and Activity (ARENA), University of South Australia, Adelaide, SA, Australia
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Bays HE, Fitch A, Cuda S, Gonsahn-Bollie S, Rickey E, Hablutzel J, Coy R, Censani M. Artificial intelligence and obesity management: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023. OBESITY PILLARS (ONLINE) 2023; 6:100065. [PMID: 37990659 PMCID: PMC10662105 DOI: 10.1016/j.obpill.2023.100065] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 04/18/2023] [Indexed: 11/23/2023]
Abstract
Background This Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) provides clinicians an overview of Artificial Intelligence, focused on the management of patients with obesity. Methods The perspectives of the authors were augmented by scientific support from published citations and integrated with information derived from search engines (i.e., Chrome by Google, Inc) and chatbots (i.e., Chat Generative Pretrained Transformer or Chat GPT). Results Artificial Intelligence (AI) is the technologic acquisition of knowledge and skill by a nonhuman device, that after being initially programmed, has varying degrees of operations autonomous from direct human control, and that performs adaptive output tasks based upon data input learnings. AI has applications regarding medical research, medical practice, and applications relevant to the management of patients with obesity. Chatbots may be useful to obesity medicine clinicians as a source of clinical/scientific information, helpful in writings and publications, as well as beneficial in drafting office or institutional Policies and Procedures and Standard Operating Procedures. AI may facilitate interactive programming related to analyses of body composition imaging, behavior coaching, personal nutritional intervention & physical activity recommendations, predictive modeling to identify patients at risk for obesity-related complications, and aid clinicians in precision medicine. AI can enhance educational programming, such as personalized learning, virtual reality, and intelligent tutoring systems. AI may help augment in-person office operations and telemedicine (e.g., scheduling and remote monitoring of patients). Finally, AI may help identify patterns in datasets related to a medical practice or institution that may be used to assess population health and value-based care delivery (i.e., analytics related to electronic health records). Conclusions AI is contributing to both an evolution and revolution in medical care, including the management of patients with obesity. Challenges of Artificial Intelligence include ethical and legal concerns (e.g., privacy and security), accuracy and reliability, and the potential perpetuation of pervasive systemic biases.
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Affiliation(s)
- Harold Edward Bays
- Louisville Metabolic and Atherosclerosis Research Center, University of Louisville School of Medicine, 3288 Illinois Avenue, Louisville, KY, 40213, USA
| | | | - Suzanne Cuda
- Alamo City Healthy Kids and Families, 1919 Oakwell Farms Parkway Ste 145, San Antonio, TX, 78218, USA
| | - Sylvia Gonsahn-Bollie
- Embrace You Weight & Wellness, 8705 Colesville Rd Suite 103, Silver Spring, MD, 10, USA
| | - Elario Rickey
- Obesity Medicine Association, 7173 S. Havana St. #600-130, Centennial, CO, 80112, USA
| | - Joan Hablutzel
- Obesity Medicine Association, 7173 S. Havana St. #600-130, Centennial, CO, 80112, USA
| | - Rachel Coy
- Obesity Medicine Association, 7173 S. Havana St. #600-130, Centennial, CO, 80112, USA
| | - Marisa Censani
- Division of Pediatric Endocrinology, Department of Pediatrics, New York Presbyterian Hospital, Weill Cornell Medicine, 525 East 68th Street, Box 103, New York, NY, 10021, USA
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15
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Noh E, Won J, Jo S, Hahm DH, Lee H. Conversational Agents for Body Weight Management: Systematic Review. J Med Internet Res 2023; 25:e42238. [PMID: 37234029 DOI: 10.2196/42238] [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: 08/28/2022] [Revised: 03/20/2023] [Accepted: 04/14/2023] [Indexed: 05/27/2023] Open
Abstract
BACKGROUND Obesity is a public health issue worldwide. Conversational agents (CAs), also frequently called chatbots, are computer programs that simulate dialogue between people. Owing to better accessibility, cost-effectiveness, personalization, and compassionate patient-centered treatments, CAs are expected to have the potential to provide sustainable lifestyle counseling for weight management. OBJECTIVE This systematic review aimed to critically summarize and evaluate clinical studies on the effectiveness and feasibility of CAs with unconstrained natural language input for weight management. METHODS PubMed, Embase, the Cochrane Library (CENTRAL), PsycINFO, and ACM Digital Library were searched up to December 2022. Studies were included if CAs were used for weight management and had a capability for unconstrained natural language input. No restrictions were imposed on study design, language, or publication type. The quality of the included studies was assessed using the Cochrane risk-of-bias assessment tool or the Critical Appraisal Skills Programme checklist. The extracted data from the included studies were tabulated and narratively summarized as substantial heterogeneity was expected. RESULTS In total, 8 studies met the eligibility criteria: 3 (38%) randomized controlled trials and 5 (62%) uncontrolled before-and-after studies. The CAs in the included studies were aimed at behavior changes through education, advice on food choices, or counseling via psychological approaches. Of the included studies, only 38% (3/8) reported a substantial weight loss outcome (1.3-2.4 kg decrease at 12-15 weeks of CA use). The overall quality of the included studies was judged as low. CONCLUSIONS The findings of this systematic review suggest that CAs with unconstrained natural language input can be used as a feasible interpersonal weight management intervention by promoting engagement in psychiatric intervention-based conversations simulating treatments by health care professionals, but currently there is a paucity of evidence. Well-designed rigorous randomized controlled trials with larger sample sizes, longer treatment duration, and follow-up focusing on CAs' acceptability, efficacy, and safety are warranted.
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Affiliation(s)
- Eunyoung Noh
- Department of Medical Science of Meridian, College of Korean Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Jiyoon Won
- Department of Meridian & Acupoint, College of Korean Medicine, Dong-eui University, Busan, Republic of Korea
| | - Sua Jo
- Department of Medical Science of Meridian, College of Korean Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Dae-Hyun Hahm
- Department of Physiology, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Hyangsook Lee
- Department of Medical Science of Meridian, College of Korean Medicine, Kyung Hee University, Seoul, Republic of Korea
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16
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Han R, Todd A, Wardak S, Partridge SR, Raeside R. Feasibility and Acceptability of Chatbots for Nutrition and Physical Activity Health Promotion Among Adolescents: Systematic Scoping Review With Adolescent Consultation. JMIR Hum Factors 2023; 10:e43227. [PMID: 37145858 PMCID: PMC10199392 DOI: 10.2196/43227] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 03/15/2023] [Accepted: 04/13/2023] [Indexed: 05/06/2023] Open
Abstract
BACKGROUND Reducing lifestyle risk behaviors among adolescents depends on access to age-appropriate health promotion information. Chatbots-computer programs designed to simulate conversations with human users-have the potential to deliver health information to adolescents to improve their lifestyle behaviors and support behavior change, but research on the feasibility and acceptability of chatbots in the adolescent population is unknown. OBJECTIVE This systematic scoping review aims to evaluate the feasibility and acceptability of chatbots in nutrition and physical activity interventions among adolescents. A secondary aim is to consult adolescents to identify features of chatbots that are acceptable and feasible. METHODS We searched 6 electronic databases from March to April 2022 (MEDLINE, Embase, Joanna Briggs Institute, the Cumulative Index to Nursing and Allied Health, the Association for Computing Machinery library, and the IT database Institute of Electrical and Electronics Engineers). Peer-reviewed studies were included that were conducted in the adolescent population (10-19 years old) without any chronic disease, except obesity or type 2 diabetes, and assessed chatbots used nutrition or physical activity interventions or both that encouraged individuals to meet dietary or physical activity guidelines and support positive behavior change. Studies were screened by 2 independent reviewers, with any queries resolved by a third reviewer. Data were extracted into tables and collated in a narrative summary. Gray literature searches were also undertaken. Results of the scoping review were presented to a diverse youth advisory group (N=16, 13-18 years old) to gain insights into this topic beyond what is published in the literature. RESULTS The search identified 5558 papers, with 5 (0.1%) studies describing 5 chatbots meeting the inclusion criteria. The 5 chatbots were supported by mobile apps using a combination of the following features: personalized feedback, conversational agents, gamification, and monitoring of behavior change. Of the 5 studies, 2 (40.0%) studies focused on nutrition, 2 (40.0%) studies focused on physical activity, and 1 (20.0%) focused on both nutrition and physical activity. Feasibility and acceptability varied across the 5 studies, with usage rates above 50% in 3 (60.0%) studies. In addition, 3 (60.0%) studies reported health-related outcomes, with only 1 (20.0%) study showing promising effects of the intervention. Adolescents presented novel concerns around the use of chatbots in nutrition and physical activity interventions, including ethical concerns and the use of false or misleading information. CONCLUSIONS Limited research is available on chatbots in adolescent nutrition and physical activity interventions, finding insufficient evidence on the acceptability and feasibility of chatbots in the adolescent population. Similarly, adolescent consultation identified issues in the design features that have not been mentioned in the published literature. Therefore, chatbot codesign with adolescents may help ensure that such technology is feasible and acceptable to an adolescent population.
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Affiliation(s)
- Rui Han
- Engagement and Co-Design Research Hub, School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Westmead, Australia
| | - Allyson Todd
- Engagement and Co-Design Research Hub, School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Westmead, Australia
| | - Sara Wardak
- Engagement and Co-Design Research Hub, School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Westmead, Australia
| | - Stephanie R Partridge
- Engagement and Co-Design Research Hub, School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Westmead, Australia
| | - Rebecca Raeside
- Engagement and Co-Design Research Hub, School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Westmead, Australia
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Černý M. Educational Psychology Aspects of Learning with Chatbots without Artificial Intelligence: Suggestions for Designers. Eur J Investig Health Psychol Educ 2023; 13:284-305. [PMID: 36826206 PMCID: PMC9955713 DOI: 10.3390/ejihpe13020022] [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: 11/30/2022] [Revised: 01/22/2023] [Accepted: 01/25/2023] [Indexed: 02/03/2023] Open
Abstract
Chatbots without artificial intelligence can play the role of practical and easy-to-implement learning objects in e-learning environments, allowing a reduction in social or psychological isolation. This research, with a sample of 79 students, explores the principles that need to be followed in designing this kind of chatbot in education in order to ensure an acceptable outcome for students. Research has shown that students interacting with a chatbot without artificial intelligence expect similar psychological and communicative responses to those of a live human, project the characteristics of the chatbot from the dialogue, and are taken aback when the chatbot does not understand or cannot help them sufficiently. The study is based on a design through research approach, in which students in information studies and library science interacted with a specific chatbot focused on information retrieval, and recorded their experiences and feelings in an online questionnaire. The study intends to find principles for the design of chatbots without artificial intelligence so that students feel comfortable interacting with them.
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Affiliation(s)
- Michal Černý
- Faculty of Art, Masaryk University in Brno, 602 00 Brno, Czech Republic
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18
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Chew HSJ, Lim SL, Kim G, Kayambu G, So BYJ, Shabbir A, Gao Y. Essential elements of weight loss apps for a multi-ethnic population with high BMI: a qualitative study with practical recommendations. Transl Behav Med 2023; 13:140-148. [PMID: 36689306 DOI: 10.1093/tbm/ibac090] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Smartphone weight loss apps are constantly being developed but the essential elements needed by a multi-ethnic population with overweight and obesity remains unclear. Purpose: To explore the perceptions of an Asian multi-ethnic population with overweight and obesity on the essential elements of weight loss apps. Twenty two participants were purposively sampled from a specialist weight management clinic in Singapore from 13 April to 30 April 2021. Recorded interviews were conducted using face-to-face and videoconferencing modalities. Data saturation was reached at the 18th participant. Data analysis was performed using inductive content analysis with constant comparison between and within transcripts. Findings: Three themes and eight subthemes on the essential app components emerged-(a) comprehensive and flexible calorie counters; (b) holistic, gradual and individualized behavior change recommendations tailored for people with overweight and obesity, and (c) just-in-time reminders of future consequences. There was a need to incorporate flexible options for food logging; break down general recommendations into small steps towards sustainable changes; tailor app contents for people with overweight and obesity; and evoke one's considerations of future consequences. Future weight loss apps should be designed to meet the needs of those with overweight and obesity, the very population that needs assistance with weight loss. Future apps could consider leveraging the capacity of artificial intelligence to provide personalized weight management in terms of sustaining self-regulation behaviors, optimizing goal-setting and providing personalized and timely recommendations for weight loss.
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Affiliation(s)
- Han Shi Jocelyn Chew
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Dr, Singapore 117597, Singapore
| | - Su Lin Lim
- Dietetics Department, National University Hospital, 5 Lower Kent Ridge Road Singapore 119074, Singapore
| | - Guowei Kim
- Department of Surgery, National University Hospital, 5 Lower Kent Ridge Road Singapore 119074, Singapore
| | - Geetha Kayambu
- Rehabilitation Department, National University Hospital, 5 Lower Kent Ridge Road Singapore 119074, Singapore
| | - Bok Yan Jimmy So
- Department of Surgery, National University Hospital, 5 Lower Kent Ridge Road Singapore 119074, Singapore
| | - Asim Shabbir
- Department of Surgery, National University Hospital, 5 Lower Kent Ridge Road Singapore 119074, Singapore
| | - Yujia Gao
- Department of Surgery, National University Hospital, 5 Lower Kent Ridge Road Singapore 119074, Singapore
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Chew HSJ, Koh WL, Ng JSHY, Tan KK. Sustainability of Weight Loss Through Smartphone Apps: Systematic Review and Meta-analysis on Anthropometric, Metabolic, and Dietary Outcomes. J Med Internet Res 2022; 24:e40141. [PMID: 36129739 PMCID: PMC9536524 DOI: 10.2196/40141] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/14/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Background Evidence on the long-term effects of weight management smartphone apps on various weight-related outcomes remains scarce. Objective In this review, we aimed to examine the effects of smartphone apps on anthropometric, metabolic, and dietary outcomes at various time points. Methods Articles published from database inception to March 10, 2022 were searched, from 7 databases (Embase, CINAHL, PubMed, PsycINFO, Cochrane Library, Scopus, and Web of Science) using forward and backward citation tracking. All randomized controlled trials that reported weight change as an outcome in adults with overweight and obesity were included. We performed separate meta-analyses using random effects models for weight, waist circumference, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, blood glucose level, blood pressure, and total energy intake per day. Methodological quality was assessed using the Cochrane Risk of Bias tool. Results Based on our meta-analyses, weight loss was sustained between 3 and 12 months, with a peak of 2.18 kg at 3 months that tapered down to 1.63 kg at 12 months. We did not find significant benefits of weight loss on the secondary outcomes examined, except for a slight improvement in systolic blood pressure at 3 months. Most of the included studies covered app-based interventions that comprised of components beyond food logging, such as real-time diet and exercise self-monitoring, personalized and remote progress tracking, timely feedback provision, smart devices that synchronized activity and weight data to smartphones, and libraries of diet and physical activity ideas. Conclusions Smartphone weight loss apps are effective in initiating and sustaining weight loss between 3 and 12 months, but their effects are minimal in their current states. Future studies could consider the various aspects of the socioecological model. Conversational and dialectic components that simulate health coaches could be useful to enhance user engagement and outcome effectiveness. Trial Registration International Prospective Register of Systematic Reviews (PROSPERO) CRD42022329197; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=329197
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Affiliation(s)
- Han Shi Jocelyn Chew
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Wee Ling Koh
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Janelle Shaina Hui Yi Ng
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ker Kan Tan
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Department of Surgery, National University Hospital, Singapore, Singapore
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Chew HSJ, Rajasegaran NN, Chin YH, Chew WSN, Kim KM. Effectiveness of combined health coaching and self-monitoring apps on weight-related outcomes in people with overweight and obesity: A systematic review and meta-analysis (Preprint). J Med Internet Res 2022; 25:e42432. [PMID: 37071452 PMCID: PMC10155083 DOI: 10.2196/42432] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 11/04/2022] [Accepted: 03/09/2023] [Indexed: 03/11/2023] Open
Abstract
BACKGROUND Self-monitoring smartphone apps and health coaching have both individually been shown to improve weight-related outcomes, but their combined effects remain unclear. OBJECTIVE This study aims to examine the effectiveness of combining self-monitoring apps with health coaching on anthropometric, cardiometabolic, and lifestyle outcomes in people with overweight and obesity. METHODS Relevant articles published from inception till June 9, 2022, were searched through 8 databases (Embase, CINAHL, PubMed, PsycINFO, Scopus, The Cochrane Library, and Web of Science). Effect sizes were pooled using random-effects models. Behavioral strategies used were coded using the behavior change techniques taxonomy V1. RESULTS A total of 14 articles were included, representing 2478 participants with a mean age of 39.1 years and a BMI of 31.8 kg/m2. Using combined intervention significantly improved weight loss by 2.15 kg (95% CI -3.17 kg to -1.12 kg; P<.001; I2=60.3%), waist circumference by 2.48 cm (95% CI -3.51 cm to -1.44 cm; P<.001; I2=29%), triglyceride by 0.22 mg/dL (95% CI -0.33 mg/dL to 0.11 mg/dL; P=.008; I2=0%), glycated hemoglobin by 0.12% (95% CI -0.21 to -0.02; P=.03; I2=0%), and total calorie consumption per day by 128.30 kcal (95% CI -182.67 kcal to -73.94 kcal; P=.003; I2=0%) kcal, but not BMI, blood pressure, body fat percentage, cholesterol, and physical activity. Combined interventional effectiveness was superior to receiving usual care and apps for waist circumference but only superior to usual care for weight loss. CONCLUSIONS Combined intervention could improve weight-related outcomes, but more research is needed to examine its added benefits to using an app. TRIAL REGISTRATION PROSPERO CRD42022345133; https://tinyurl.com/2zxfdpay.
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Affiliation(s)
- Han Shi Jocelyn Chew
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Nagadarshini Nicole Rajasegaran
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Yip Han Chin
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - W S Nicholas Chew
- Department of Cardiology, National University Hospital Singapore, Singapore, Singapore
| | - Kyung Mi Kim
- Office of Research, Patient Care Services, Stanford Health Care, Menlo Park, CA, United States
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Rahmanti AR, Yang HC, Bintoro BS, Nursetyo AA, Muhtar MS, Syed-Abdul S, Li YCJ. SlimMe, a Chatbot With Artificial Empathy for Personal Weight Management: System Design and Finding. Front Nutr 2022; 9:870775. [PMID: 35811989 PMCID: PMC9260382 DOI: 10.3389/fnut.2022.870775] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 05/12/2022] [Indexed: 11/23/2022] Open
Abstract
As the obesity rate continues to increase persistently, there is an urgent need to develop an effective weight loss management strategy. Nowadays, the development of artificial intelligence (AI) and cognitive technologies coupled with the rapid spread of messaging platforms and mobile technology with easier access to internet technology offers professional dietitians an opportunity to provide extensive monitoring support to their clients through a chatbot with artificial empathy. This study aimed to design a chatbot with artificial empathic motivational support for weight loss called “SlimMe” and investigate how people react to a diet bot. The SlimMe infrastructure was built using Dialogflow as the natural language processing (NLP) platform and LINE mobile messenger as the messaging platform. We proposed a text-based emotion analysis to simulate artificial empathy responses to recognize the user's emotion. A preliminary evaluation was performed to investigate the early-stage user experience after a 7-day simulation trial. The result revealed that having an artificially empathic diet bot for weight loss management is a fun and exciting experience. The use of emoticons, stickers, and GIF images makes the chatbot response more interactive. Moreover, the motivational support and persuasive messaging features enable the bot to express more empathic and engaging responses to the user. In total, there were 1,007 bot responses from 892 user input messages. Of these, 67.38% (601/1,007) of the chatbot-generated responses were accurate to a relevant user request, 21.19% (189/1,007) inaccurate responses to a relevant request, and 10.31% (92/1,007) accurate responses to an irrelevant request. Only 1.12% (10/1,007) of the chatbot does not answer. We present the design of an artificially empathic diet bot as a friendly assistant to help users estimate their calorie intake and calories burned in a more interactive and engaging way. To our knowledge, this is the first chatbot designed with artificial empathy features, and it looks very promising in promoting long-term weight management. More user interactions and further data training and validation enhancement will improve the bot's in-built knowledge base and emotional intelligence base.
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Affiliation(s)
- Annisa Ristya Rahmanti
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan
- Department of Health Policy and Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Hsuan-Chia Yang
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan
| | - Bagas Suryo Bintoro
- Department of Health Behavior, Environment, and Social Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Aldilas Achmad Nursetyo
- Center for Health Policy Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | | | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan
| | - Yu-Chuan Jack Li
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan
- Taipei Medical University Research Center of Cancer Translational Medicine, Taipei, Taiwan
- Department of Dermatology, Wan Fang Hospital, Taipei, Taiwan
- *Correspondence: Yu-Chuan Jack Li
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