Evidence map›Paper›PMID 35874292›Full record

ArticleUser modeling and user-adapted interaction2023

Personalised socially assistive robot for cardiac rehabilitation: Critical reflections on long-term interactions in the real world.

Bahar Irfan, Nathalia Céspedes, Jonathan Casas, Emmanuel Senft, Luisa F Gutiérrez, Mónica Rincon-Roncancio, Carlos A Cifuentes, Tony Belpaeme, Marcela Múnera

Open access · bronzeAbstract read
In one paragraph

Article in User modeling and user-adapted interaction, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
5.1field-weighted citation impact, top 4% of its field
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

3 citing papers in PubMed, 24 citations in OpenAlex.

  1. Review
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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

9 authors at 7 institutions in 5 countries.

Bahar IrfanCentre for Robotics and Neural Systems, University of Plymouth, Plymouth, UK.ORCID 0000-0002-7983-079X
Nathalia CéspedesDepartment of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá, Colombia.
Jonathan CasasDepartment of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá, Colombia.
Emmanuel SenftCentre for Robotics and Neural Systems, University of Plymouth, Plymouth, UK.
Luisa F GutiérrezInstituto de Cardiología, Fundación Cardioinfantil, Bogotá, Colombia.
Mónica Rincon-RoncancioInstituto de Cardiología, Fundación Cardioinfantil, Bogotá, Colombia.
Carlos A CifuentesPresent Address: School of Engineering, Science and Technology, Universidad del Rosario, Bogotá, Colombia.
Tony BelpaemeCentre for Robotics and Neural Systems, University of Plymouth, Plymouth, UK.
Marcela MúneraDepartment of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá, Colombia.
Escuela Colombiana de Ingenieria Julio Garavito · COInstitute of Cardiology · COGhent University · BEIstanbul Commerce University · TRQueen Mary University of London · GBUniversidad del Rosario · COUniversity of Wisconsin–Madison · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lack of motivation and low adherence rates are critical concerns of long-term rehabilitation programmes, such as cardiac rehabilitation. Socially assistive robots are known to be effective in improving motivation in therapy. However, over longer durations, generic and repetitive behaviours by the robot often result in a decrease in motivation and engagement, which can be overcome by personalising the interaction, such as recognising users, addressing them with their name, and providing feedback on their progress and adherence. We carried out a real-world clinical study, lasting 2.5 years with 43 patients to evaluate the effects of using a robot and personalisation in cardiac rehabilitation. Due to dropouts and other factors, 26 patients completed the programme. The results derived from these patients suggest that robots facilitate motivation and adherence, enable prompt detection of critical conditions by clinicians, and improve the cardiovascular functioning of the patients. Personalisation is further beneficial when providing high-intensity training, eliciting and maintaining engagement (as measured through gaze and social interactions) and motivation throughout the programme. However, relying on full autonomy for personalisation in a real-world environment resulted in sensor and user recognition failures, which caused negative user perceptions and lowered the perceived utility of the robot. Nonetheless, personalisation was positively perceived, suggesting that potential drawbacks need to be weighed against various benefits of the personalised interaction.

Indexed as

Cardiac rehabilitationHuman–robot interactionLong-term interactionPersonalisation in social robotsReal-world studySocially assistive robotics

Identifiers

PMID35874292
PMCPMC9294801
OpenAlexW4285799106

What OpenQuestion holds

Textmetadata
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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 OpenQuestion graph.