Evidence map›Paper›PMID 31426766›Full record

ArticleBMC geriatrics2019

Virtual reality among the elderly: a usefulness and acceptance study from Taiwan.

Shabbir Syed-Abdul, Shwetambara Malwade, Aldilas Achmad Nursetyo, Mishika Sood, Madhu Bhatia, Diana Barsasella, Megan F Liu, Chia-Chi Chang, Kathiravan Srinivasan, Raja M and 1 more

Abstract read
In one paragraph

Article in BMC geriatrics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 3 pooled it
–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

48 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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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

11 authors.

Shabbir Syed-AbdulInternational Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.
Shwetambara MalwadeInternational Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.
Aldilas Achmad NursetyoInternational Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.
Mishika SoodChitkara University, Punjab, India.
Madhu BhatiaI.K. Gujral Punjab Technical University, Kapurthala, Punjab, India.
Diana BarsasellaInternational Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.
Megan F LiuSchool of Gerontology Health Management, College of Nursing, Taipei Medical University, 250 Wuxing Street, 11031, Taipei, Taiwan. fangliu@tmu.edu.tw.ORCID 0000-0001-5942-8659
Chia-Chi ChangSchool of Gerontology Health Management, College of Nursing, Taipei Medical University, 250 Wuxing Street, 11031, Taipei, Taiwan.
Kathiravan SrinivasanSchool of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore, India.
Raja MSchool of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore, India.
Yu-Chuan Jack LiInternational Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundVirtual reality (VR) has several applications in the medical domain and also generates a secure environment to carry out activities. Evaluation of the effectiveness of VR among older populations revealed positive effects of VR as a tool to reduce risks of falls and also improve the social and emotional well-being of older adults. The decline in physical and mental health, the loss of functional capabilities, and a weakening of social ties represent obstacles towards active aging among older adults and indicate a need for support. Existing research focused on the effects of VR among older populations, and its uses and benefits. Our study investigated the acceptance and use of VR by the elderly.

methodsThis pilot study was conducted on 30 older adults who voluntarily participated during March to May 2018. Nine VR applications that promote physical activities, motivate users, and provide entertainment were chosen for this study. Participants were asked to use any one of the applications of their choice for 15 min twice a week for 6 weeks. At the end of 6 weeks, participants were asked to fill out a questionnaire based on the Technology Acceptance Model and a literature review, to evaluate their acceptance of VR technology. Cronbach's alpha reliability analysis was used to test the internal consistency of the questionnaire items. Pearson's product moment correlation was used to examine the validity of the questionnaire. A linear regression and mediation analysis were utilized to identify relationships among the variables of the questionnaire.

resultsIn total, six male and 24 female participants aged 60~95 years volunteered to participate in the study. Perceived usefulness, perceived ease of use, social norms, and perceived enjoyment were seen to have had significant effects on the intention to use VR. Participants agreed to a large extent regarding the perceived usefulness, perceived enjoyment, and their experience of using VR. Thus, VR was seen to have high acceptance among this elderly population.

conclusionsOlder people have positive perceptions towards accepting and using VR to support active aging. They perceived VR to be useful, easy to use, and an enjoyable experience, implying positive attitudes toward adopting this new technology.

Indexed as

IntentionVirtual RealityAccidental FallsAgedAged, 80 and overAgingEmotionsExerciseFemaleHumansMaleMiddle AgedPatient Acceptance of Health CarePilot ProjectsReproducibility of ResultsSurveys and QuestionnairesActive agingOlder peopleTechnology acceptance modelVirtual reality

Identifiers

PMID31426766
PMCPMC6699111

What OpenQuestion holds

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Registered trials

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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.