The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
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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In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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 the citing paper
“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]
In the citing paper
“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 the citing paper
“Patients actively engaged with the chatbot, reported experiencing positive progress toward their goals, and deemed the chatbot helpful.”
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“It was previously suggested that conversational agents can be influential and engaging for young patients, and that open-text answers are highly appreciated [104].”
In the citing paper
“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].”
In the citing paper
“It was previously suggested that conversational agents can be influential and engaging for young patients, and that open-text answers are highly appreciated [104].”
Citations
Cites
Barlow, S. E., & and the Expert Committee (2007) · Pediatrics · doi:10.1542/peds.2007-2329c · Cited by 4,497
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
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
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
Gulliver, A., Griffiths, K. M., & Christensen, H. (2010) · BMC Psychiatry · doi:10.1186/1471-244x-10-113 · Cited by 3,292
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 2,807
Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017) · JMIR Mental Health · doi:10.2196/mental.7785 · Cited by 2,730
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 1,375
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 841
Fulmer, R., Joerin, A., Gentile, B., Lakerink, L., & Rauws, M. (2018) · doi:10.2196/preprints.9782 · Cited by 715
Self-citation
Research works printed but not linked: 3
- Children’s Hospital Association, 2014, 2013 survey findings of children’s hospitals obesity services, 1
- Johnson, 2008, Professional roles
- Poushter, 2018, Social Media Use Continues to Rise in Developing Countries but Plateaus Across Developed Ones
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
Fulmer, R., Joerin, A., Gentile, B., Lakerink, L., & Rauws, M. (2018) · doi:10.2196/preprints.9782 · Cited by 715
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
“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
Zhang, J., Oh, Y. J., Lange, P., Yu, Z., & Fukuoka, Y. (2020) · Journal of Medical Internet Research · doi:10.2196/22845 · Cited by 363
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
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
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 224
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
Coaching With Artificial Intelligence: Concepts and Capabilities
Graßmann, C., & Schermuly, C. C. (2020) · Human Resource Development Review · doi:10.1177/1534484320982891 · Cited by 177
Chew, H. S. J. (2022) · JMIR Medical Informatics · doi:10.2196/32578 · Cited by 160
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
“Furthermore, in a feasibility study by Stephens et al [20] with a small sample (n=23) of adolescents coping with weight management and prediabetes symptoms, the authors found conversations with Tess useful in supporting them toward their goals and high usefulness ratings.”
BackgroundBin 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
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
“It was suggested [103] that CAs can be influential and engaging for young patients and that open text answers are much appreciated.”
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
Nonhuman humanitarianism: when 'AI for good' can be harmful
Madianou, M. (2021) · Information, Communication & Society · doi:10.1080/1369118x.2021.1909100 · Cited by 91
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 100
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 90
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
Anisha, S. A., Sen, A., & Bain, C. (2024) · Journal of Medical Internet Research · doi:10.2196/56114 · Cited by 86
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
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
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
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 63
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 73
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 63
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 54
Han, R., Todd, A., Wardak, S., Partridge, S. R., & Raeside, R. (2023) · JMIR Human Factors · doi:10.2196/43227 · Cited by 51
Passmore, J., Olafsson, B., & Tee, D. (2025) · Journal of Work-Applied Management · doi:10.1108/jwam-11-2024-0164 · Cited by 60
Diller, S. J., Stenzel, L.-C., & Passmore, J. (2024) · Human Resource Development International · doi:10.1080/13678868.2024.2375934 · Cited by 49
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 44
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 41
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 40
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 44
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
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 37
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 39
Plotkina, L., & Ramalu, S. S. (2024) · Journal of Management Development · doi:10.1108/jmd-06-2024-0182 · Cited by 32
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
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
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
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
Lyzwinski, L. N., Elgendi, M., & Menon, C. (2023) · JMIR mHealth and uHealth · doi:10.2196/39649 · Cited by 24
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
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
Thompson, D., & Baranowski, T. (2019) · Translational Behavioral Medicine · doi:10.1093/tbm/ibz065 · Cited by 22
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
“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
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 27
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
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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
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
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Ng, H. Y., & Chan, T. W. L. (2023) · World Journal of Clinical Pediatrics · doi:10.5409/wjcp.v12.i5.263 · Cited by 22
Artificial Intelligence in Health Promotion and Disease Reduction: Rapid Review
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Xu, S., & Ma, T. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.119760 · Cited by 23
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Alloatti, F., Bosca, A., Di, L., & Pieraccini, F. (2021) · Discover Artificial Intelligence · doi:10.1007/s44163-021-00005-1 · Cited by 17
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
Ollier, J., Nißen, M., & von Wangenheim, F. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2021.691595 · Cited by 17
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
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Haubold, J. (2019) · Der Radiologe · doi:10.1007/s00117-019-00621-0 · Cited by 15
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
Zheng, Q., Liu, F., Xu, S., Hu, J., Lu, H., & Liu, T. (2023) · Journal of Safety Science and Resilience · doi:10.1016/j.jnlssr.2023.10.002 · Cited by 16
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Morais, D. F. T., Fernandes, G., Lima, G. D., & Rodrigues, J. J. P. C. (2023) · Electronics · doi:10.3390/electronics12051169 · Cited by 16
Singla, S. K. (2020) · Springer eBooks · doi:10.1007/978-3-030-37526-3_1 · Cited by 14
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Inteligencia artificial en pediatría: actualidad y retos
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The Development and Use of AI Chatbots for Health Behavior Change: Scoping Review
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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 15
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
Applications of artificial intelligence and machine learning in prediabetes: a scoping review.
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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
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Brandtzæg, P. B., Skjuve, M. B., & Følstad, A. (2025) · Oxford University Press eBooks · doi:10.1093/9780198945215.003.0099 · Cited by 11
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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
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Metadata
- 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
- 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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