Evidence map›Paper›PMID 37531187›Full record

ArticleJournal of medical Internet research2023

Use and Acceptance of Digital Communication Technology by Older Adults for Social Connectedness During the COVID-19 Pandemic: Mixed Methods Study.

Eric Balki, Carol Holland, Niall Hayes

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
34.8field-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

20 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 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

3 authors at 2 institutions in 1 country.

Eric BalkiCentre for Ageing and Research, Division of Health, Lancaster University, Lancaster, United Kingdom.ORCID 0000-0001-5404-7187
Carol HollandCentre for Ageing and Research, Division of Health, Lancaster University, Lancaster, United Kingdom.ORCID 0000-0001-7109-6554
Niall HayesNottingham Trent University, The Directorate, Notthingham, United Kingdom.ORCID 0000-0001-8718-4671
Lancaster University · GBNottingham Trent University · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOlder adults are at higher risk for health issues, including mental health problems. This was especially apparent during the COVID-19 pandemic, where older adults were simultaneously more vulnerable to the disease and the mental health concerns created by social distancing. Subsequently, the use of digital communication technology (DCT) became a critical option for maintaining social connectedness in older adults. Prior to the pandemic, the low uptake and use of technology by older adults was an established problem, known as the digital divide. However, not much is known about how this may have changed as a result of the pandemic.

objectiveThis study aims to explore how older adults maintained social connectedness through DCT during the pandemic and to understand factors influencing the use and acceptance of DCT.

methodsA mixed methods explorative field study was set up, involving surveys and interviews of 25 community-dwelling older adults (65-88 years old) living in the United Kingdom. The surveys included the internet acceptance questionnaire (based on the Technology Acceptance Model [TAM]); COVID-19 dysfunctional anxiety was captured using the COVID-19 Anxiety Scale (CAS). Background information (demographics, use of technology) was gathered before conducting semistructured interviews. We hypothesized that CAS would affect constructs of TAM and that predictive constructs of TAM would have remained valid during the pandemic. We also posited that there would be unidentified themes outside TAM that impacted the acceptance and use of DCT. We used the quantitative data to guide the semistructured interviews, which were then analyzed through thematic analysis to identify additional themes.

resultsCorrelational analysis showed that CAS influences all constructs of TAM. We also saw that the predictive constructs of TAM, especially the perceived ease of use (PEU) and perceived usefulness (PU), remained valid during the pandemic. Common acceptance-influencing themes were encountered in both quantitative and qualitative analyses, with 3 matching the known constructs of TAM (PU, PEU, and behavioral intention). We identified 2 additional themes affecting acceptance, namely influence of the pandemic (situational context) and privacy and security concerns. DCT use (especially email and videoconferencing use) increased during the pandemic, but the results related to social networking sites were mixed.

conclusionsThe COVID-19 pandemic impacted technology acceptance and use by older adults, encouraging their use of certain DCT apps (email and videoconferencing apps, such as WhatsApp). These apps helped insulate them from adverse effects (social isolation and loneliness). Other social networking apps, however, exerted a negative influence, increasing anxiety and a general feeling of negativity. Future studies should maximize older adult agency related to design, privacy, security, and user requirements for development. We also recommend that when studying DCT acceptance for older adults, our additional identified themes should be considered alongside the existing TAM constructs.

Indexed as

COVID-19PandemicsAgedAged, 80 and overCommunicationHumansIntentionTechnologyaging in placeCOVID-19 pandemic, Facebook, Metainformation and communication technologiesqualitative researchtechnology acceptancetechnology adoptionWhatsAppZoom, generative artificial intelligence, AI

Identifiers

PMID37531187
PMCPMC10433026
OpenAlexW4381094418

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.