Evidence map›Paper›PMID 41623724›Full record

ArticleDigital health

Feasibility and usability of a ChatGPT-based app to support physical activity: A pilot study.

Dillys Larbi, Paolo Zanaboni, Eirik Årsand, Pietro Randine, Marianne Vibeke Trondsen, Kerstin Denecke, Rolf Wynn, Elia Gabarron

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

8 authors.

Dillys LarbiNorwegian Centre for E-health Research, Tromsø, Norway.ORCID https://orcid.org/0000-0002-1556-017X
Paolo ZanaboniNorwegian Centre for E-health Research, Tromsø, Norway.ORCID https://orcid.org/0000-0002-5469-092X
Eirik ÅrsandNorwegian Centre for E-health Research, Tromsø, Norway.ORCID https://orcid.org/0000-0002-9520-1408
Pietro RandineDepartment of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway.ORCID https://orcid.org/0000-0001-7188-0138
Marianne Vibeke TrondsenNorwegian Centre for E-health Research, Tromsø, Norway.ORCID https://orcid.org/0000-0002-2502-5031
Kerstin DeneckeInstitute for Patient-Centered Digital Health, Bern University of Applied Sciences, Bern, Switzerland.ORCID https://orcid.org/0000-0001-6691-396X
Rolf WynnDepartment of Clinical Medicine, UiT The Arctic University of Norway, Tromsø, Norway.ORCID https://orcid.org/0000-0002-2254-3343
Elia GabarronDepartment of Education, ICT and Learning, Østfold University College, Halden, Norway.ORCID https://orcid.org/0000-0002-7188-550X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: FysBot is a ChatGPT-based mobile app developed to promote physical activity among adults living with obesity. This pilot study aimed to evaluate the feasibility and usability of FysBot. Methods: A 6-week single-arm pilot study was conducted in which patients from an obesity rehabilitation clinic in Norway used FysBot. This pilot study employed an explanatory sequential mixed-methods design combining questionnaires and post-intervention interviews. Participants completed questionnaires at baseline and weeks 2, 4, and 6, assessing leisure-time physical activity (Godin Leisure-Time Exercise Questionnaire (GODIN)), motivation (Behavioral Regulation in Exercise Questionnaire-2 and relative autonomy index (RAI)), Self-Efficacy for Exercise (SEE), and System Usability Scale (SUS). Semi-structured interviews were conducted to explore user experiences further. Quantitative data were analyzed descriptively, with multiple imputations for missing data, while qualitative data were analyzed thematically. Results: Fifty-three participants were eligible, 36 completed baseline, and 17 completed the final follow-up. App engagement declined steadily, with most participants ceasing use after week 2. The mean SUS score was 51.3, indicating below-average usability. The median of self-reported leisure-time physical activity (GODIN: 34-40) and overall motivation (RAI: 8.3-9.8) showed small, non-significant increases, while identified regulation increased significantly (2.8-3.3; Conclusions: This study offers insight into the potential of a ChatGPT-based physical activity app for adults living with obesity and highlights key areas for refinement. Future iterations should incorporate user-requested features through iterative co-design, with enhanced personalization and guidance to improve relevance and engagement.

Indexed as

Artificial intelligencebehavior changechatbotChatGPTdigital healthphysical activityusability

Identifiers

PMID41623724
PMCPMC12855731

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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 Socratic graph.