Evidence map›Paper›PMID 42847389›Full record

ArticleAnnals of behavioral medicine : a publication of the Society of Behavioral Medicine2026

Feasibility study of an artificial intelligence chatbot-powered cognitive-behavioral lifestyle modification program.

Evan M Forman, Charlotte J Hagerman, Asher E Hong, Zhuoran Huang, Hannah C McCausland, Jasmine H Sun, Lauren C Taylor, Meghan L Butryn, Preetha Chatterjee, Ramtin Ehsani and 1 more

Abstract read
In one paragraph

Article in Annals of behavioral medicine : a publication of the Society of Behavioral Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Evan M FormanSidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, United States.ORCID 0000-0001-7233-1384
Charlotte J HagermanOregon Research Institute, Springfield, OR 97477, United States.ORCID 0000-0002-9386-8444
Asher E HongDepartment of Psychology, University of Illinois Chicago, Chicago, IL 60607, United States.ORCID 0009-0007-1752-9938
Zhuoran HuangKhoury College of Computer Science, Northeastern University, Boston, MA 02115, United States.ORCID 0000-0002-0270-6096
Hannah C McCauslandDepartment of Psychological and Brain Sciences Drexel University, Philadelphia, PA 19104, United States.ORCID 0000-0001-6001-8243
Jasmine H SunDepartment of Psychological and Brain Sciences Drexel University, Philadelphia, PA 19104, United States.ORCID 0009-0007-9659-741X
Lauren C TaylorDepartment of Psychological and Brain Sciences Drexel University, Philadelphia, PA 19104, United States.ORCID 0000-0002-3633-5553
Meghan L ButrynDepartment of Psychological and Brain Sciences Drexel University, Philadelphia, PA 19104, United States.ORCID 0000-0001-5573-5707
Preetha ChatterjeeDepartment of Computer Science, Drexel University, Philadelphia, PA 19104, United States.ORCID 0000-0003-3057-7807
Ramtin EhsaniDepartment of Computer Science, Drexel University, Philadelphia, PA 19104, United States.ORCID 0000-0003-1517-7135
Nawal SyedCollege of Medicine, Drexel University, Philadelphia, PA 19104, United States.ORCID 0009-0001-6753-8089

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundApproximately 2 billion adults seek lifestyle modification to improve their health and well-being. However, because of cost and access barriers, only a tiny fraction receive gold-standard lifestyle modification programs led by trained coaches. Digital programs without human coaches are more disseminable but have limited efficacy. Recent advances in large language model (LLM) AI chatbots have the potential to revolutionize digital programs by emulating human coaches, offering a safe, low-cost, and scalable lifestyle modification intervention. Yet, no studies have developed and tested such a program. PURPOSE: This study aims to evaluate the feasibility, safety, and user engagement of a new AI chatbot-powered lifestyle modification program named Lyra, which utilizes recent advances in LLM technology to potentially emulate the benefits of human coaching.

methodsWe developed Lyra, which integrates structured psychoeducational programming, digital tracking of health behaviors, and text messaging, all powered by an LLM engine. We tested the 8-week program, which included interactive training in behavioral strategies, customized meal plans, tailored feedback on goal progress, and on-demand coaching, with 17 participants in a sample of participants with overweight/obesity seeking lifestyle modification.

resultsThe program was successfully implemented at a cost of $1.78/participant/week. Coders rated 2288 messages from and 475 conversations with Lyra. All were deemed safe, and the mean expertise was 8.5/10. Participants (mean BMI = 31.5) rated Lyra as helpful (69%), empathic (88%), and easy to use (94%); 88% valued having 24-h availability, and 75% developed a connection to Lyra. Participants initiated an average of 6.2 conversations, ranging from coping with cravings to unprompted "confessions" of having consumed unhealthy food.

conclusionThis new form of intervention proved feasible, acceptable, safe, clinically adept, and capable of fostering a connection. However, rigorous testing of Lyra's effectiveness is necessary.

Indexed as

Artificial IntelligenceCognitive Behavioral TherapyHealth BehaviorLife StyleAdultDigital HealthFeasibility StudiesFemaleHumansLarge Language ModelsMaleMiddle AgedText Messagingartificial intelligencechatbotdigital health interventionhealth behavior changelarge language modellifestyle modification

Identifiers

PMID42847389
PMCPMC13355595

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.