Journal article
Unique digital images as incentives in clinical trials: a digital shift toward meaningful participation
- Abstract:
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Incentivization in clinical trial participation can be challenging, with many studies failing to meet recruitment or retention goals despite traditional compensation strategies. Digital health evolves, and with it, new approaches can emerge to engage participants meaningfully. We propose unique digital images as a novel, symbolic incentive for clinical trials. Digital images combine qualities such as personalization, ownership, and digital visibility, which may drive engagement more effectively than monetary rewards alone. In our illustrative study, participants complete artificial intelligence–personalized digital therapeutics training using CURATE.DTx, generating individualized learning trajectories. These are transformed into digital artworks and minted as nonfungible tokens given as a reward upon trial completion. This concept integrates gamification, personalization, and blockchain technology to support both intrinsic and extrinsic motivation. We explore the implications for decentralized health care, long-term behavior change, and participant recognition in the context of preventive medicine and longevity science. Our aim is to encourage research into the use of digital incentives to transform the trial participant experience and promote sustained engagement in health interventions.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.7MB, Terms of use)
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- Publisher copy:
- 10.2196/88022
Authors
- Publisher:
- JMIR Publications
- Journal:
- Journal of Medical Internet Research More from this journal
- Volume:
- 28
- Article number:
- e88022
- Publication date:
- 2026-06-08
- Acceptance date:
- 2026-04-20
- DOI:
- EISSN:
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1438-8871
- Language:
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English
- Keywords:
- Pubs id:
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2430166
- Local pid:
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pubs:2430166
- Source identifiers:
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W4416334283
- Deposit date:
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2026-06-16
- ARK identifier:
Terms of use
- Copyright holder:
- Tadeo et al.
- Copyright date:
- 2026
- Rights statement:
- © Xavier Tadeo, Gyula Seres, Peter Wang, Yoann Sapanel, Alexandria Remus, Jasmine L Eyal, Jason Kai-Wei Lee, Simon Chesterman, Julian Savulescu, John Eu-Li Wong, Christopher L Asplund, R Brian Stone, Benjamin C K Tee, Reza Shokri, Marlena Raczkowska, Li Ming Chong, Siong Peng Kwek, Agata Blasiak, Dean Ho. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 8.Jun.2026. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
- Licence:
- CC Attribution (CC BY)
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