Evidence map›Paper›PMID 37174828›Full record

ArticleHealthcare (Basel, Switzerland)2023

Robots for Elderly Care: Review, Multi-Criteria Optimization Model and Qualitative Case Study.

Bartosz Sawik, Sławomir Tobis, Ewa Baum, Aleksandra Suwalska, Sylwia Kropińska, Katarzyna Stachnik, Elena Pérez-Bernabeu, Marta Cildoz, Alba Agustin, Katarzyna Wieczorowska-Tobis

Open access · goldAbstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 5 pooled it
26.7field-weighted citation impact, top 1% 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

29 citing papers in PubMed, 5 syntheses or guidelines pooled it, 80 citations in OpenAlex.

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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

10 authors at 3 institutions in 3 countries.

Bartosz SawikDepartment of Business Informatics and Engineering Management, AGH University of Science and Technology, 30-059 Krakow, Poland.ORCID 0000-0001-5748-3961
Sławomir TobisOccupational Therapy Unit, Chair of Geriatric Medicine and Gerontology, Poznan University of Medical Sciences, ul. Swiecickiego 6, 60-781 Poznan, Poland.ORCID 0000-0002-0764-3258
Ewa BaumDepartment of Social Sciences and the Humanities, Poznan University of Medical Sciences, 60-806 Poznan, Poland.
Aleksandra SuwalskaDepartment of Mental Health, Chair of Psychiatry, Poznan University of Medical Sciences, ul. Szpitalna 27/33, 60-572 Poznan, Poland.ORCID 0000-0002-5759-5151
Sylwia KropińskaGeriatrics Unit, Chair of Palliative Medicine, Poznan University of Medical Sciences, os. Rusa 55, 61-245 Poznan, Poland.
Katarzyna StachnikGeriatrics Unit, Chair of Palliative Medicine, Poznan University of Medical Sciences, os. Rusa 55, 61-245 Poznan, Poland.
Elena Pérez-BernabeuDepartment of Applied Statistics and Operations Research, Universitat Politècnica de València, Plaza Ferrandiz y Carbonell, sn, 03801 Alcoy, Spain.ORCID 0000-0002-9221-7623
Marta CildozInstitute of Smart Cities, Department of Statistics, Computer Science and Mathematics, Public University of Navarre, 31006 Pamplona, Spain.
Alba AgustinInstitute of Smart Cities, Department of Statistics, Computer Science and Mathematics, Public University of Navarre, 31006 Pamplona, Spain.ORCID 0000-0001-9173-1906
Katarzyna Wieczorowska-TobisGeriatrics Unit, Chair of Palliative Medicine, Poznan University of Medical Sciences, os. Rusa 55, 61-245 Poznan, Poland.
Poznan University of Medical Sciences · PLUniversidad Publica de Navarra · ESUniversitat Politècnica de València · ES

Funding

The European Union Horizon 2020 Program 643691C
6 · The paper itself

Abstract

This paper focuses on three areas: the first is a review of current knowledge about social and service robots for elderly care. The second is an optimization conceptual model aimed at maximizing the efficiency of assigning robots to serve the elderly. The proposed multi-criteria optimization model is the first one proposed in the area of optimization for robot assignment for the elderly with robot utilization level and caregiver stress level. The third is the findings of studies on the needs, requirements, and adoption of technology in elderly care. We consider the use of robots as a part of the ENRICHME project for long-term interaction and monitoring of older persons with mild cognitive impairment, to optimize their independence. Additionally, we performed focus group discussions (FGD) to collect opinions about robot-related requirements of the elderly and their caregivers. Four FDGs of six persons were organized: two comprising older adults, and two of the other formal and informal caregivers, based on a detailed script. The statements of older participants and their caregivers were consistent in several areas. The analysis revealed user characteristics, robot-related issues, functionality, and barriers to overcome before the deployment of the robot. An introduction of the robot must be thoroughly planned, include comprehensive pre-training, and take the ethical and practical issues into account. The involvement of future users in the customization of the robot is essential.

Indexed as

artificial intelligencecaregiverelderly carehealthcarehuman-robot interactionmathematical integer programmingmulti-criteria optimizationrobotic assistantservice robotsocial robot

Identifiers

PMID37174828
PMCPMC10178192
OpenAlexW4367627475

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.