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
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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
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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