Feasibility of pediatric obesity and prediabetes treatment support through Tess, the AI behavioral coaching chatbot
Abstract
Behavioral intervention technologies (BITs) are unique ways to incorporate the benefits of technology and psychology to address differing health needs through various media, including Internet interventions, mobile apps, and video games. BITs present several possible benefits, including increased dissemination and accessibility, cost-effectiveness, increased engagement, and decreased stigma, especially among youth. A behavioral coaching chatbot, Tess, addresses different facets of behavioral health, such as depression and anxiety. Available 24/7, Tess delivers customized integrative support, psychoeducation, and interventions through brief conversations via existing communication channels (i.e., SMS text messaging and Facebook Messenger). This study assessed the feasibility of integrating Tess in behavioral counseling of adolescent patients (n = 23; Mage = 15.20 years; Rangeage = 9.78-18.54 years; 57% female) coping with weight management and prediabetes symptoms. Tess engaged patients via a preferred method of communication (SMS text messaging) in individualized conversations to promote treatment adherence, behavior change, and overall wellness. Adolescent patients reported experiencing positive progress toward their goals 81% of the time. The 4,123 messages exchanged and patients' reported usefulness ratings (96% of the time) illustrate that adolescents engaged with and viewed this chatbot as helpful. These results highlight the feasibility and benefit of support through artificial intelligence, specifically in a pediatric setting, which could be scaled to serve larger groups of patients. As a partner to clinicians, Tess can continue the therapeutic interaction outside office hours while maintaining patient satisfaction. Due to Tess's capacity for continuous learning, future iterations may have additional features to increase the user experience.
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AI chatbots can collect data sets from diverse sources including electronic health records, unstructured clinical notes, real-time physiological data points using additional sensors, and user interactions.
“First, AI chatbots can collect data sets from diverse sources: electronic health records, unstructured clinical notes, real-time physiological data points using additional sensors (eye-movement tracking, facial recognition, movement tracking, and heartbeat), and user interactions [5,6].”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [6]
AI chatbots can mimic real-life human support by offering services that can assist users in achieving their health behavior goals.
“Third, AI chatbots can mimic real-life human support by offering services that can assist users in achieving their health behavior goals [6].”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [6]
Of the 15 studies, 11 (73%) reported feasibility of AI chatbots in terms of safety, messages exchanged, retention rate, and duration of engagement.
“Out of the 15 studies, 11 (73%) reported the feasibility of AI chatbots in terms of (1) safety [22] (ie, no adverse events were reported), (2) messages exchanged with the chatbot [6,26,29,31,32], (3) retention rate [22,26], and (4) duration of engagement.”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [6]
Many studies (5/15, 33%) reported total number of messages exchanged with the chatbot; only 7% (1/15) reported exact proportion of user-initiated conversations (approximately 30%).
“Many studies reported the total number of messages exchanged with the chatbot (5/15, 33%) [6,26,29,31,32]; however, only 7% (1/15) of studies reported the exact proportion of user-initiated conversations (approximately 30%) [6], which depicted the participants’ level of interest in having health-related conversations with the chatbot.”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [6]
Of the 15 studies, 3 (20%) targeted medication or treatment adherence, but only 2 (67%) reported efficacy of AI chatbots in increasing treatment or medication adherence through timely and personalized reminders.
“Out of the 15 studies, 3 (20%) studies [6,26,30] targeted medication or treatment adherence, but only 2 (67%) of these studies [26,30] reported the efficacy of AI chatbots in increasing treatment or medication adherence through timely and personalized reminders.”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [6]
In Tess, CBT was combined with emotionally focused therapy and motivational interviewing to assist behavioral counseling of adolescent patients.
“In Tess [6], CBT was clubbed with the theory of emotionally focused therapy and motivational interviewing to assist the behavioral counseling of adolescent patients.”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [6]
Cognitive behavioral therapy was used in Tess, Lark Health Coach (HCAI), and Woebot to devise strategies that enhance self-efficacy and sustain behavior change.
“The cognitive behavioral therapy (CBT) was used in Tess [6], Lark Health Coach (HCAI) [28], and Woebot [31] to devise strategies that enhance self-efficacy and sustain behavior change.”
From Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. (2023) · cites this paper as [6]
In a feasibility study, adolescent patients actively engaged with the Tess behavioral coaching chatbot, reported experiencing positive progress toward their goals, and deemed the chatbot helpful.
“Patients actively engaged with the chatbot, reported experiencing positive progress toward their goals, and deemed the chatbot helpful.”
AI chatbots have great potential to increase the accessibility and efficacy of personalized lifestyle modification programs.
“These limitations can be overcome by utilizing AI chatbots, which have great potential to increase the accessibility and efficacy of personalized lifestyle modification programs [20, 21].”
From A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss (2021) · cites this paper as [21]
AI chatbots communicating via web or mobile applications can make personalized programs available 24/7.
“Enabling AI chatbots to communicate with individuals via web or mobile applications can make these personalized programs available 24/7 [21, 22].”
From A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss (2021) · cites this paper as [21]
Only 1 study documented data security.
“While only 1 study [21] documented data security, none of the studies provided information on participant safety or ethics (i.e., ethical principle or standards with which the chatbot is designed).”
From A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss (2021) · cites this paper as [21]
One study did not specify when and how often messages were delivered.
“One study did not specify when and how often the messages were delivered [21].”
From A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss (2021) · cites this paper as [21]
Open-text answers are highly appreciated.
“It was previously suggested that conversational agents can be influential and engaging for young patients, and that open-text answers are highly appreciated [104].”
Previous studies in other health domains have demonstrated promising results in using conversational agents to improve outcomes, such as promoting physical activity for childhood obesity.
“Previous studies in other health domains have demonstrated promising results in using conversational agents to improve outcomes, such as promoting physical activity for childhood obesity [104,114].”
Conversational agents can be influential and engaging for young patients.
“It was previously suggested that conversational agents can be influential and engaging for young patients, and that open-text answers are highly appreciated [104].”
Cites
Barlow, S. E., & and the Expert Committee (2007) · Pediatrics · doi:10.1542/peds.2007-2329c · Cited by 4,497 · see citing works
ELIZA—a computer program for the study of natural language communication between man and machine
Weizenbaum, J. (1966) · Communications of the ACM · doi:10.1145/365153.365168 · Cited by 4,452 · see citing works
2013 AHA/ACC/TOS Guideline for the Management of Overweight and Obesity in Adults
Jensen, M. D., Ryan, D. H., Apovian, C. M., Ard, J. D., Comuzzie, A. G., Donato, K. A., Hu, F. B., Hubbard, V. S., Jakicic, J. M., Kushner, R. F., et al. (2014) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.11.004 · Cited by 4,058 · see citing works
Prevalence of Obesity Among Adults and Youth: United States, 2015-2016.
Hales, C. M., Carroll, M. D., Fryar, C. D., & Ogden, C. L. (2017) · PubMed · Cited by 3,889 · see citing works
Gulliver, A., Griffiths, K. M., & Christensen, H. (2010) · BMC Psychiatry · doi:10.1186/1471-244x-10-113 · Cited by 3,292 · see citing works
ELIZA — a computer program for the study of natural language communication between man and machine
Weizenbaum, J. (1983) · Communications of the ACM · doi:10.1145/357980.357991 · Cited by 2,765 · see citing works
Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017) · JMIR Mental Health · doi:10.2196/mental.7785 · Cited by 2,730 · see citing works
Styne, D. M., Arslanian, S. A., Connor, E. L., Farooqi, I. S., Murad, M. H., Silverstein, J. H., & Yanovski, J. A. (2017) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2016-2573 · Cited by 1,322 · see citing works
Screening for Obesity in Children and Adolescents
US Preventive Services Task Force, Grossman, D. C., Bibbins-Domingo, K. B., Curry, S. J., Barry, M. J., Davidson, K. W., Doubeni, C. A., Epling, J. W., Kemper, A. R., Krist, A. H., et al. (2017) · JAMA · doi:10.1001/jama.2017.6803 · Cited by 822 · see citing works
Fulmer, R., Joerin, A., Gentile, B., Lakerink, L., & Rauws, M. (2018) · doi:10.2196/preprints.9782 · Cited by 715 · see citing works
Self-citationMohr, D. C., Burns, M. N., Schueller, S. M., Clarke, G., & Klinkman, M. (2013) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2013.03.008 · Cited by 693 · see citing works
Novel Models for Delivering Mental Health Services and Reducing the Burdens of Mental Illness
Kazdin, A. E., & Rabbitt, S. M. (2013) · Clinical Psychological Science · doi:10.1177/2167702612463566 · Cited by 465 · see citing works
Attrition in paediatric weight management: a review of the literature and new directions
Skelton, J. A., & Beech, B. M. (2010) · Obesity Reviews · doi:10.1111/j.1467-789x.2010.00803.x · Cited by 401 · see citing works
Dyrbye, L. N., & Shanafelt, T. D. (2011) · JAMA · doi:10.1001/jama.2011.652 · Cited by 371 · see citing works
Artificial Intelligence in Psychological Practice: Current and Future Applications and Implications
Luxton, D. D. (2014) · Professional Psychology Research and Practice · doi:10.1037/a0034559 · Cited by 297 · see citing works
The Surgeon General's Vision for a Healthy and Fit Nation 2010
(2010) · PsycEXTRA Dataset · doi:10.1037/e511782010-001 · Cited by 164 · see citing works
The Impact of Obesity on Medical Care Costs and Labor Market Outcomes in the US
Biener, A. I., Cawley, J., & Meyerhoefer, C. D. (2017) · Clinical Chemistry · doi:10.1373/clinchem.2017.272450 · Cited by 136 · see citing works
Psychological Adjustment of Obese Youth Presenting for Weight Management Treatment
Zeller, M. H., Saelens, B. E., Roehrig, H. R., Kirk, S., & Daniels, S. R. (2004) · Obesity Research · doi:10.1038/oby.2004.197 · Cited by 132 · see citing works
Parent Perspectives on Attrition From Tertiary Care Pediatric Weight Management Programs
Hampl, S. E., Demeule, M., Eneli, I., Frank, M., Hawkins, M. J., Kirk, S., Morris, P., Sallinen, B. J., Santos, M., Ward, W. L., et al. (2013) · Clinical Pediatrics · doi:10.1177/0009922813482515 · Cited by 77 · see citing works
Satisfaction and attrition in paediatric weight management
Skelton, J. A., Martin, S. B., & Irby, M. B. (2016) · Clinical Obesity · doi:10.1111/cob.12138 · Cited by 59 · see citing works
Parents and Pediatric Weight Management Attrition: Experiences and Recommendations
Gaffka, B. J. S., Frank, M., Hampl, S. E., Santos, M., & Rhodes, E. T. (2013) · Childhood Obesity · doi:10.1089/chi.2013.0069 · Cited by 44 · see citing works
Bjørnelv, S., Nordahl, H. M., & Holmen, T. L. (2010) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-010-0197-z · Cited by 44 · see citing works
Patient Attendance and Outcomes in a Structured Weight Management Program
Hampl, S. E., Borner, K. B., Dean, K. M., Papa, A. E., Cordts, K. M. P., Smith, T. R., Wade, K. R., & Davis, A. M. (2016) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2016.05.032 · Cited by 31 · see citing works
Mood Management Effects of a Brief Behavioral Activation Internet Intervention
Bunge, E. L., Beard, C. L., Stephens, T. N., Leykin, Y., & Muñoz, R. F. (2017) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-017-0026-2 · Cited by 19 · see citing works
Self-citationChallenges and Successes of a Multidisciplinary Pediatric Obesity Treatment Program
Walsh, S., Palmer, W., Welsh, J. A., & Vos, M. B. (2014) · Nutrition in Clinical Practice · doi:10.1177/0884533614551839 · Cited by 17 · see citing works
Digital Mental Health – Innovations in Consumer Driven Care
Ackerman, M. L., Virani, T., & Billings, B. (2017) · Nursing leadership · doi:10.12927/cjnl.2018.25384 · Cited by 17 · see citing works
Caregiving for Older Adults with Obesity in the United States
Ankuda, C. K., Harris, J. A., Ornstein, K. A., Levine, D. A., Langa, K. M., & Kelley, A. S. (2017) · Journal of the American Geriatrics Society · doi:10.1111/jgs.14918 · Cited by 7 · see citing works
The Challenge of Treating Obesity and Overweight
Solutions, R. O. O., Food and Nutrition Board, Division, H. A. M., & National Academies of Sciences, E. (2017) · National Academies Press eBooks · doi:10.17226/24855 · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A future for the world's children? A WHO–UNICEF–Lancet Commission
Clark, H., Coll-Seck, A. M., Banerjee, A., Peterson, S., Dalglish, S. L., Ameratunga, S., Balabanova, D., Bhan, M. K., Bhutta, Z. A., Borrazzo, J., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(19)32540-1 · Cited by 1,105 · see citing works
Fulmer, R., Joerin, A., Gentile, B., Lakerink, L., & Rauws, M. (2018) · doi:10.2196/preprints.9782 · Cited by 715 · see citing works
Self-citationArtificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review.
Aggarwal, A., Tam, C. C., Wu, D., Li, X., & Qiao, S. (2023) · Journal of medical Internet research · doi:10.2196/40789 · Cited by 683 · see citing works
“The results of the quality assessment are presented in Multimedia Appendix 2 [5,6,21-33].”
InfluentialMilne-Ives, M., Lam, C., De Cock, C., Van Velthoven, M. H., & Meinert, E. (2020) · JMIR mhealth and uhealth · doi:10.2196/17046 · Cited by 393 · see citing works
Zhang, J., Oh, Y. J., Lange, P., Yu, Z., & Fukuoka, Y. (2020) · Journal of Medical Internet Research · doi:10.2196/22845 · Cited by 363 · see citing works
Oh, Y. J., Zhang, J., Fang, M.-L., & Fukuoka, Y. (2021) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-021-01224-6 · Cited by 278 · see citing works
Klos, M. C., Escoredo, M., Joerin, A., Lemos, V. N., Rauws, M., & Bunge, E. L. (2021) · JMIR Formative Research · doi:10.2196/20678 · Cited by 200 · see citing works
Self-citationSystematic review and meta-analysis of the effectiveness of chatbots on lifestyle behaviours
Singh, B., Olds, T., Brinsley, J., Dumuid, D., Virgara, R., Matricciani, L., Watson, A., Szeto, K., Eglitis, E., Miatke, A., et al. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00856-1 · Cited by 215 · see citing works
Maher, C. A., Davis, C. R., Curtis, R. G., Short, C. E., & Murphy, K. J. (2020) · JMIR mHealth and uHealth · doi:10.2196/17558 · Cited by 165 · see citing works
Coaching With Artificial Intelligence: Concepts and Capabilities
Graßmann, C., & Schermuly, C. C. (2020) · Human Resource Development Review · doi:10.1177/1534484320982891 · Cited by 177 · see citing works
Chew, H. S. J. (2022) · JMIR Medical Informatics · doi:10.2196/32578 · Cited by 136 · see citing works
Artificial Intelligence Chatbot for Depression: Descriptive Study of Usage.
Dosovitsky, G., Pineda, B. S., Jacobson, N. C., Chang, C., Escoredo, M., & Bunge, E. L. (2020) · JMIR Formative Research · doi:10.2196/17065 · Cited by 118 · see citing works
Bin Sawad, A., Narayan, B., Alnefaie, A., Maqbool, A., Mckie, I., Smith, J., Yuksel, B., Puthal, D., Prasad, M., & Kocaballi, A. B. (2022) · Sensors · doi:10.3390/s22072625 · Cited by 113 · see citing works
Kowatsch, T., Schachner, T., Harperink, S., Barata, F., Dittler, U., Xiao, G., Stanger, C., v Wangenheim, F., Fleisch, E., Oswald, H., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/25060 · Cited by 103 · see citing works
Artificial intelligence for diabetes care: current and future prospects
Sheng, B., Pushpanathan, K., Guan, Z., Lim, Q. H., Lim, Z. W., Yew, S. M. E., Goh, J. H. L., Bee, Y. M., Sabanayagam, C., Sevdalis, N., et al. (2024) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(24)00154-2 · Cited by 106 · see citing works
Nonhuman humanitarianism: when 'AI for good' can be harmful
Madianou, M. (2021) · Information, Communication & Society · doi:10.1080/1369118x.2021.1909100 · Cited by 91 · see citing works
The Potential Role of Digital Health in Obesity Care
Hinchliffe, N., Capehorn, M., Bewick, M., & Feenie, J. (2022) · Advances in Therapy · doi:10.1007/s12325-022-02265-4 · Cited by 87 · see citing works
Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies
An, R., Shen, J., & Xiao, Y. (2022) · Journal of Medical Internet Research · doi:10.2196/40589 · Cited by 86 · see citing works
TensioBot: a chatbot assistant for self-managed in-house blood pressure checking
Echeazarra, L., Pereira, J., & Saracho, R. (2021) · Journal of Medical Systems · doi:10.1007/s10916-021-01730-x · Cited by 66 · see citing works
Anisha, S. A., Sen, A., & Bain, C. (2024) · Journal of Medical Internet Research · doi:10.2196/56114 · Cited by 78 · see citing works
Shah, J., DePietro, B., D’Adamo, L., Firebaugh, M., Laing, O., Fowler, L. A., Smolar, L., Sadeh‐Sharvit, S., Taylor, C. B., Wilfley, D. E., et al. (2022) · International Journal of Eating Disorders · doi:10.1002/eat.23798 · Cited by 74 · see citing works
Development of a chatbot for depression: adolescent perceptions and recommendations
Dosovitsky, G., & Bunge, E. (2023) · Child and Adolescent Mental Health · doi:10.1111/camh.12627 · Cited by 65 · see citing works
Mobile Applications for Health and Wellness: A Systematic Review
Alslaity, A., Suruliraj, B., Oyebode, O., Fowles, J. R., Steeves, D., & Orji, R. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3534525 · Cited by 66 · see citing works
Davis, C. R., Murphy, K. J., Curtis, R., & Maher, C. A. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17239137 · Cited by 61 · see citing works
Application of Chatbots to Help Patients Self-Manage Diabetes: Systematic Review and Meta-Analysis
Wu, Y., Zhang, J., Ge, P., Duan, T., Zhou, J., Wu, Y., Zhang, Y.-P., Liu, S., Liu, X., Wan, E., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/60380 · Cited by 66 · see citing works
Ding, H., Simmich, J., Vaezipour, A., Andrews, N. E., & Russell, T. G. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad222 · Cited by 57 · see citing works
Martinengo, L., Jabir, A. I., Goh, W. W. T., Lo, N. Y. W., Ho, M. R., Kowatsch, T., Atun, R. A., Michie, S., & Car, L. T. (2022) · Journal of Medical Internet Research · doi:10.2196/39243 · Cited by 48 · see citing works
Han, R., Todd, A., Wardak, S., Partridge, S. R., & Raeside, R. (2023) · JMIR Human Factors · doi:10.2196/43227 · Cited by 51 · see citing works
Passmore, J., Olafsson, B., & Tee, D. (2025) · Journal of Work-Applied Management · doi:10.1108/jwam-11-2024-0164 · Cited by 53 · see citing works
Diller, S. J., Stenzel, L.-C., & Passmore, J. (2024) · Human Resource Development International · doi:10.1080/13678868.2024.2375934 · Cited by 46 · see citing works
AI Chatbots in Clinical Laboratory Medicine: Foundations and Trends
Yang, H. S., Wang, F., Greenblatt, M. B., Huang, X., & Zhang, Y. (2023) · Clinical Chemistry · doi:10.1093/clinchem/hvad106 · Cited by 40 · see citing works
Machine Learning in Health Promotion and Behavioral Change: Scoping Review
Goh, Y. S., Yong, J. Q. Y. O., Chee, B. Q. H., Kuek, J. H. L., & Ho, C. S. H. (2022) · Journal of Medical Internet Research · doi:10.2196/35831 · Cited by 38 · see citing works
Clinical, Research, and Educational Applications of ChatGPT in Dentistry: A Narrative Review
Puleio, F., Giudice, G. L., Bellocchio, A. M., Boschetti, C. E., & Giudice, R. L. (2024) · Applied Sciences · doi:10.3390/app142310802 · Cited by 39 · see citing works
Ding, X., Shang, B., Xie, C., Xin, J., & Yu, F. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-04564-x · Cited by 40 · see citing works
Ethical Artificial Intelligence for Digital Health Organizations
Joerin, A., Rauws, M., Fulmer, R., & Black, V. (2020) · Cureus · doi:10.7759/cureus.7202 · Cited by 38 · see citing works
Self-citationConversational Agents for Body Weight Management: Systematic Review
Noh, E., Won, J., Jo, S., Hahm, D., & Lee, H. (2023) · Journal of Medical Internet Research · doi:10.2196/42238 · Cited by 34 · see citing works
Using AI–ML to Augment the Capabilities of Social Media for Telehealth and Remote Patient Monitoring
Leung, R. (2023) · Healthcare · doi:10.3390/healthcare11121704 · Cited by 37 · see citing works
Plotkina, L., & Ramalu, S. S. (2024) · Journal of Management Development · doi:10.1108/jmd-06-2024-0182 · Cited by 32 · see citing works
The application of artificial intelligence in health communication development: A scoping review
Weingott, S., & Parkinson, J. E. (2024) · Health Marketing Quarterly · doi:10.1080/07359683.2024.2422206 · Cited by 42 · see citing works
Chatbots in Healthcare: Challenges, Technologies and Applications
Sharma, D., Kaushal, S., Kumar, H., & Gainder, S. (2022) · International Conference on Artificial Intelligence and Speech Technology (AIST) · doi:10.1109/aist55798.2022.10065328 · Cited by 32 · see citing works
The Ethics of Psychological Artificial Intelligence: Clinical Considerations
Fulmer, R., Davis, T. N., Costello, C., & Joerin, A. (2021) · Counseling and Values · doi:10.1002/cvj.12153 · Cited by 31 · see citing works
Self-citationClinical Utility of Smartphone Applications in Ophthalmology
Nagino, K., Sung, J., Midorikawa‐Inomata, A., Eguchi, A., Fujimoto, K., Okumura, Y., Miura, M., Yee, A., Shokirova, H., Fujio, K., et al. (2023) · Ophthalmology Science · doi:10.1016/j.xops.2023.100342 · Cited by 32 · see citing works
Lyzwinski, L. N., Elgendi, M., & Menon, C. (2023) · JMIR mHealth and uHealth · doi:10.2196/39649 · Cited by 24 · see citing works
Agher, D., Sedki, K., Desprès, S., Albinet, J.-P., Jaulent, M., & Tsopra, R. (2022) · Journal of Medical Internet Research · doi:10.2196/25384 · Cited by 26 · see citing works
Fernández-Luque, L., Herbish, A. S. A., Shammari, R. A., Argente, J., Bin-Abbas, B. S., Deeb, A., Dixon, D., Zary, N., Koledova, E. B., & Savage, M. O. (2021) · Frontiers in Pediatrics · doi:10.3389/fped.2021.715705 · Cited by 26 · see citing works
Thompson, D., & Baranowski, T. (2019) · Translational Behavioral Medicine · doi:10.1093/tbm/ibz065 · Cited by 22 · see citing works
Applications of artificial intelligence in the management of childhood obesity
Alghalyini, B. (2023) · Journal of Family Medicine and Primary Care · doi:10.4103/jfmpc.jfmpc_469_23 · Cited by 25 · see citing works
“Good” and “Bad” Machine Agency in the Context of Human-AI Communication: The Case of ChatGPT
Brandtzaeg, P. B., You, Y., Wang, X., & Lao, Y. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-48057-7_1 · Cited by 25 · see citing works
Publication Trends in Psychotherapy: Bibliometric Analysis of the Past 5 Decades
Soares, E. E., Thrall, J., Stephens, T. N., Biglieri, R. R., Consoli, A. J., & Bunge, E. L. (2020) · American Journal of Psychotherapy · doi:10.1176/appi.psychotherapy.20190045 · Cited by 24 · see citing works
Self-citationCuesta, S. A., Blanes-Selva, V., Conejero, J. A., Frígola, A., Portolés, M. G., Merino-Torres, J. F., Rubio-Almanza, M., Syed-Abdul, S., Li, Y., Vilar-Mateo, R., et al. (2021) · JMIR Medical Informatics · doi:10.2196/17503 · Cited by 25 · see citing works
An eHealth Framework for Managing Pediatric Growth Disorders and Growth Hormone Therapy
Dimitri, P., Fernández-Luque, L., Banerjee, I., Bergadá, I., Calliari, L. E., Dahlgren, J., Muñoz, A. D. A., Lapatto, R., Reinehr, T., Senniappan, S., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/27446 · Cited by 21 · see citing works
Fridolin: participatory design and evaluation of a nutrition chatbot for older adults
Weber, P. P., Mahmood, F., Ahmadi, M., von Jan, V., Ludwig, T. G., & Wieching, R. (2023) · i-com · doi:10.1515/icom-2022-0042 · Cited by 21 · see citing works
Prediabetes in children and adolescents: An updated review
Ng, H. Y., & Chan, T. W. L. (2023) · World Journal of Clinical Pediatrics · doi:10.5409/wjcp.v12.i5.263 · Cited by 20 · see citing works
Artificial Intelligence in Health Promotion and Disease Reduction: Rapid Review
Yousefi, F., Naye, F., Ouellet, S., Yameogo, A. R., Sasseville, M., Bergeron, F., Ozkan, M., Cousineau, M., Amil, S., Rhéaume, C., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/70381 · Cited by 26 · see citing works
AI-Enhanced Virtual Reality Self-Talk for Psychological Counseling: Formative Qualitative Study
Zisquit, ., Shoa, A., Oliva, R., Perry, S., Spanlang, B., Klomek, A. B., Slater, M., & Friedman, D. A. (2025) · JMIR Formative Research · doi:10.2196/67782 · Cited by 18 · see citing works
Depression intervention using AI chatbots with social cues: a randomized trial of effectiveness
Xu, S., & Ma, T. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.119760 · Cited by 21 · see citing works
Diabetes and conversational agents: the AIDA project case study
Alloatti, F., Bosca, A., Di, L., & Pieraccini, F. (2021) · Discover Artificial Intelligence · doi:10.1007/s44163-021-00005-1 · Cited by 17 · see citing works
The Role of Text Messaging and Telehealth Messaging Apps
Ganapathy, S., de Korne, D. F., Chong, N. K., & Car, J. (2020) · Pediatric Clinics of North America · doi:10.1016/j.pcl.2020.04.002 · Cited by 17 · see citing works
Ollier, J., Nißen, M., & von Wangenheim, F. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2021.691595 · Cited by 17 · see citing works
Siddiqi, D. A., Miraj, F., Raza, H., Hussain, O. A., Munir, M., Dharma, V. K., Shah, M. T., Habib, A., & Chandir, S. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105288 · Cited by 15 · see citing works
Künstliche Intelligenz in der Radiologie
Haubold, J. (2019) · Der Radiologe · doi:10.1007/s00117-019-00621-0 · Cited by 16 · see citing works
Artificial Intelligence and Mental Health Issues: A Narrative Review
Salcedo, Z. B. V., Tari, I. D. A. E. P. D., Ratsameemonthon, C., & Setiyani, R. Y. (2023) · Journal of Public Health Sciences · doi:10.56741/jphs.v2i02.282 · Cited by 16 · see citing works
Zheng, Q.-J., Liu, F., Xu, S., Hu, J., Lu, H.-X., & Liu, T. (2023) · Journal of Safety Science and Resilience · doi:10.1016/j.jnlssr.2023.10.002 · Cited by 15 · see citing works
IoT-Based Wearable and Smart Health Device Solutions for Capnography: Analysis and Perspectives
Morais, D. F. T., Fernandes, G., Lima, G. D., & Rodrigues, J. J. P. C. (2023) · Electronics · doi:10.3390/electronics12051169 · Cited by 15 · see citing works
Singla, S. K. (2020) · Springer eBooks · doi:10.1007/978-3-030-37526-3_1 · Cited by 14 · see citing works
Unaddressed challenges in persuasive dieting chatbots
Balloccu, S., Reiter, E., Collu, M. G., Sanna, F., Sanguinetti, M., & Atzori, M. (2021) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3450614.3463602 · Cited by 15 · see citing works
Inteligencia artificial en pediatría: actualidad y retos
Galdo, B., García, C. P., Pardo, J., Solar, A., Llamas, D., Fernández-Blanco, E., & Pazos, A. (2024) · Anales de Pediatría · doi:10.1016/j.anpedi.2024.02.006 · Cited by 13 · see citing works
Mobile Apps for Health Behavior Change: Protocol for a Systematic Review
Milne‐Ives, M., Lam, C., van Velthoven, M. H., & Meinert, E. (2019) · JMIR Research Protocols · doi:10.2196/16931 · Cited by 13 · see citing works
The Development and Use of AI Chatbots for Health Behavior Change: Scoping Review
Fu, L., Burns, R. D., Xie, Y., Shen, J., Zhe, S., Estabrooks, P. A., & Bai, Y. (2025) · Journal of Medical Internet Research · doi:10.2196/79677 · Cited by 13 · see citing works
Pervanidou, P., Chatzidaki, E., Nicolaides, N. C., Voutetakis, A., Polychronaki, N., Chioti, V., Kitani, R.-A., Kyrkopoulou, E., Zarkogianni, K., Kalafatis, E., et al. (2023) · Nutrients · doi:10.3390/nu15071777 · Cited by 12 · see citing works
Comulada, W. S., Rezai, R., Sumstine, S., Flores, D. D., Kerin, T., Ocasio, M. A., Swendeman, D., Fernandez, M. I., & the Adolescent Trials Network (ATN) CARES Team (2023) · AIDS Care · doi:10.1080/09540121.2023.2216926 · Cited by 12 · see citing works
Applications of artificial intelligence and machine learning in prediabetes: a scoping review.
Lalani, B., Herur, R., Zade, D., Collins, G., Dishong, D. M., Mehta, S., Shim, J., Valdez, Y., & Mathioudakis, N. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251351995 · Cited by 11 · see citing works
Chatbots in Pharmacy: A Boon or a Bane for Patient Care and Pharmacy Practice?
Ramadhani, C. (2023) · Sciences of Pharmacy · doi:10.58920/sciphar02030001 · Cited by 14 · see citing works
Giovanni, C. B. S., Dawley, E. H., Pope, C. A., Steffen, M., & Roberts, J. R. (2021) · Southern Medical Journal · doi:10.14423/smj.0000000000001273 · Cited by 10 · see citing works
Liu, Y., Calle, P., Vadakekut, M., Rubin, D. A., Nagykaldi, Z. J., Doescher, M. P., Hightow‐Weidman, L., Pan, C., & Shao, R. (2025) · JMIR Formative Research · doi:10.2196/73319 · Cited by 10 · see citing works
Ganapathy, S., Chang, S. S. Y., Tan, J. M. C., Lim, C., & Ng, K.-C. (2021) · Singapore Medical Journal · doi:10.11622/smedj.2021119 · Cited by 9 · see citing works
Brandtzæg, P. B., Skjuve, M. B., & Følstad, A. (2025) · Oxford University Press eBooks · doi:10.1093/9780198945215.003.0099 · Cited by 9 · see citing works
Kim, Y., Lee, H., Park, J., Kim, Y., Kim, D. H., & Lee, Y. (2024) · JMIR Formative Research · doi:10.2196/59087 · Cited by 8 · see citing works
Lightening the Load: Generative AI to Mitigate the Burden of the New Era of Obesity Medical Therapy
Stevens, E. R., Elmaleh-Sachs, A., Lofton, H. F., & Mann, D. M. (2024) · JMIR Diabetes · doi:10.2196/58680 · Cited by 8 · see citing works
Taseen, S., Yousafzai, M., & Qureshi, M. F. H. (2025) · Digital Health · doi:10.1177/20552076251382086 · Cited by 11 · see citing works
Translational Medicine in the Era of Social Media: A Survey of Scientific and Clinical Communities
Sandalova, E., Ledford, J. G., Baskaran, M., & Dijkstra, S. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00152 · Cited by 8 · see citing works
Fredes, A. L., Cano, S., Cubillos, C., & Díaz, M. E. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17902-0_8 · Cited by 8 · see citing works
Kandaswamy, S., Knake, L. A., Dziorny, A. C., Hernandez, S., McCoy, A. B., Hess, L. M., Orenstein, E. W., White, M. S., Kirkendall, E. S., Molloy, M. J., et al. (2025) · Applied Clinical Informatics · doi:10.1055/a-2521-1508 · Cited by 7 · see citing works
Palacios, C. S., Hernández, J., Ajmal, A., Rodriguez, A. J., Hassan, A. Y. I., Metzendorf, M., & Ramella‐Roman, J. C. (2025) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd015968 · Cited by 8 · see citing works
HR Recruitment Through Chatbot- An Innovative Approach
(2021) · Journal of Contemporary Issues in Business and Government · doi:10.47750/cibg.2020.26.02.075 · Cited by 7 · see citing works
Consciousness: effect of coaching process and specifics through AI usage
Movsumova, E., Alexandrov, V., Rudenko, L., Aizen, V., Sidelnikova, S., & Voytko, M. (2020) · e-mentor · doi:10.15219/em86.1485 · Cited by 6 · see citing works
Lee, J., You, J. S., & Lee, D. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2307695 · Cited by 7 · see citing works
The Rise of the Mental Health Chatbot
Rauws, M. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_218 · Cited by 6 · see citing works
Self-citationDejal@Bot: Un chatbot aplicable en el tratamiento de la deshabituación tabáquica
de Tomás, J. F. Á., Olano-Espinosa, E., Minué-Lorenzo, C., Martinez-Suberviola, F. J., Pardo, B. M., Serrano, M. E. S., Güeto-Rubio, M. V., & Dej@lo, G. (2020) · Revista de Investigación y Educación en Ciencias de la Salud (RIECS) · doi:10.37536/riecs.2020.5.1.196 · Cited by 6 · see citing works
Artificial intelligence in paediatrics: Current events and challenges
Galdo, B., García, C. P., Pardo, J., Solar, A., Llamas, D., Fernández-Blanco, E., & Pazos, A. (2024) · Anales de Pediatría (English Edition) · doi:10.1016/j.anpede.2024.02.009 · Cited by 5 · see citing works
Usman, A., & Hartati, T. A. W. (2024) · JPBI (Jurnal Pendidikan Biologi Indonesia) · doi:10.22219/jpbi.v10i1.32576 · Cited by 5 · see citing works
Wu, Y. P., Herbert, L., Walker-Harding, L. R., & Tercyak, K. P. (2019) · Translational Behavioral Medicine · doi:10.1093/tbm/ibz056 · Cited by 5 · see citing works
Artificial Intelligence in Health Care: Focus on Diabetes Management
Unnikrishnan, A. (2019) · Indian Journal of Endocrinology and Metabolism · doi:10.4103/ijem.ijem_549_19 · Cited by 5 · see citing works
Continuous and Flash Glucose Monitoring in Adults at Risk of Type 2 Diabetes: A Scoping Review
Amoh, P., Broom, D. R., Kyrou, I., Nartey, S., Paul, A., Esliger, D., & Whelan, M. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251315497 · Cited by 4 · see citing works
Bosschaerts, K., Kashefi, A., Marez, L. D., Conradie, P. D., Hoecke, S. V., & Ongenae, F. (2025) · Digital Health · doi:10.1177/20552076251381747 · Cited by 5 · see citing works
A pilot study of the potential role of ChatGPT in stated-calorie diet planning
Aslan, S., & Sözlü, S. (2025) · International Journal of Obesity · doi:10.1038/s41366-025-01839-w · Cited by 4 · see citing works
The promises and challenges of clinical AI in community paediatric medicine
Singh, D. A., Nagaraj, S., Daniel, R. C., Flood, C. M., Kulik, D. M., Flook, R., Goldenberg, A., Brudno, M., & Stedman, I. (2023) · Paediatrics & Child Health · doi:10.1093/pch/pxac080 · Cited by 4 · see citing works
HelpBot: A Web-Based Chatbot to Handle Depression Among Adolescents
Jusoh, S., Alfawareh, H. M., Kadir, R. A., & Hosseinzadeh, H. (2024) · International Conference on Computing and Artificial Intelligence (ICCAI) · doi:10.1145/3669754.3669777 · Cited by 4 · see citing works
Chatbots as Tools for Psychoeducation and Self-Help in Mental Health
Sowbarnika, A., Patra, A., Kaur, L., Suresh, S., Srivastava, S., & Chawla, D. (2025) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-3112-5.ch003 · Cited by 4 · see citing works
Design of a Chatbot for Four- to Ten-Year-Old Children Based on Emotional Intelligence
Rajwal, S. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-2821-5_57 · Cited by 4 · see citing works
- Authors
- Stephens, T. N., Joerin, A., Rauws, M., & Werk, L. N.
- Affiliations
- Palo Alto University (US)
- xAI (United States) (US)
- University of Central Florida (US)
- Corresponding author
- Stephens, Taylor N
- Venue
- Translational Behavioral Medicine
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-05-16
- Volume
- 9
- Issue
- 3
- Pages
- 440-447
- ISSN
- 1613-9860 (ISSN-L)
- 1869-6716
- OpenAlex
- W2945732805
- Cited by
- 150
- References
- 28
- Citations by year
- 2026: 16 · 2025: 23 · 2024: 21 · 2023: 25 · 2022: 21 · 2021: 19 · 2020: 18 · 2019: 6 · 2018: 1
- FWCI
- 21.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Translational Behavioral Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- childhood obesity, prediabetes, adolescents, weight management, safety management systems, Facebook Messenger, medication adherence, behavior change, feasibility, psychoeducation
- MeSH terms
- Mentoring, Adolescent, Behavior Therapy, Communication, Feasibility Studies, Female, Humans, Male, Prediabetic State, Text Messaging, Mobile Applications, Pediatric Obesity
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