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Research Letter (31) Internet Access and Digital Technology Use in an Elderly Population (179) Demographics of Users, Social & Digital Divide (677)

Published on in Vol 24, No 10 (2022): October

This is a member publication of Lancaster University (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41536, first published .
The Impact of Social Isolation, Loneliness, and Technology Use During the COVID-19 Pandemic on Health-Related Quality of Life: Observational Cross-sectional Study

The Impact of Social Isolation, Loneliness, and Technology Use During the COVID-19 Pandemic on Health-Related Quality of Life: Observational Cross-sectional Study

The Impact of Social Isolation, Loneliness, and Technology Use During the COVID-19 Pandemic on Health-Related Quality of Life: Observational Cross-sectional Study

Authors of this article:

Eric Balki1 Author Orcid Image ;   Niall Hayes2 Author Orcid Image ;   Carol Holland1 Author Orcid Image
Article Authors Cited by (7) Tweetations (9) Metrics

    Research Letter

    1Division of Health Research, Lancaster University, Lancaster, United Kingdom

    2Directorate, Nottingham Trent University, Nottingham, United Kingdom

    Corresponding Author:

    Eric Balki, BSc, MSc, MA, PhD

    Division of Health Research

    Lancaster University

    Furness Building

    Hazelrigg Ln

    Lancaster, LA1 4YG

    United Kingdom

    Phone: 44 7801972693

    Email: e.balkhi@lancaster.ac.uk




    Health-related quality of life (HRQoL), defined as a person’s self-perceived health status in relation to their social, cultural, and environmental context, is linked to better health and the ability to deal with adverse life events [1]. Social factors such as loneliness are known to influence HRQoL negatively [2]. The COVID-19 pandemic has disproportionately impacted older adults, with social distancing measures worsening isolation levels [3], which we hypothesize has resulted in lower levels of HRQoL (hypothesis 1).

    Further, technology use is linked to improved self-rated health and psychological well-being, alleviating loneliness among older adults, and encouraging behaviors that may lead to better levels of HRQoL [4]. Digital communication tools became critical during the pandemic to remain socially connected and helped prevent social health risks [5], potentially benefiting those with lower HRQoL [6]. We hypothesized that technology use could predict higher HRQoL (hypothesis 2). Moreover, disease containment measures resulted in increased isolation and loneliness among older adults [3], which could impact HRQoL (hypothesis 3). Increased knowledge about how HRQoL was impacted by pandemic loneliness, isolation, and technology use may better inform health care workers, policy makers, and the public.


    This was an observational cross-sectional study from March 16, 2020, to June 21, 2021, when social distancing mandates were in force. Participants were recruited in England.

    Ethics Approval

    The study received ethical approval from the University Research Ethics Committee (Ref FHMREC19121).

    Participants

    Eligible participants were living in their own homes, proficient in English, and aged ≥65 years. The sample (G*Power confirmed effect size of 87) consisted of 89 people aged 65 to 92 (mean 73.2, SD 7.46) years.

    Variables and Measures

    Participants completed a background questionnaire capturing age, gender, and ethnicity. We used the following standardized measures: UCLA Loneliness Scale [7], Technology Experience Questionnaire [8], Lubben’s Social Isolation Scale, and Short-Form 36 [9], a measure of HRQoL comprising eight health scales (physical/mental).

    Procedure

    Surveys were conducted via telephone, with further analysis done using SPSS Ver 28 (IBM Corp).

    Statistical Methods

    Higher scores on the UCLA Loneliness Scale and technology use measures indicated greater loneliness and technology use; lower scores on Lubben’s scale indicated greater isolation. Pearson correlation determined whether lower social isolation (hypothesis 1) and greater technology use (hypothesis 2) were associated with higher HRQoL. Multiple linear regression models were built to evaluate whether loneliness predicted HRQoL after controlling for social isolation and technology use (hypothesis 3).


    Low social isolation (hypothesis 1) and higher technology use (hypothesis 2) were significantly associated with higher HRQoL (Table 1).

    Multiple linear regression was calculated (Table 2) for hypothesis 3. Model 1, incorporating loneliness, explained 24.9% of the variance in HRQoL. Model 2, incorporating social isolation, explained an additional nonsignificant 0.1% of the variance (F1,89=0.112; P=.74). Model 3, adding technology use, explained an additional 5.5% of the variance (F1,88=6.93; P=.01).

    Semipartial correlations squared showed unique amount of variance; only technology use predicted a significant unique amount of the variance in HRQoL (sr2=0.0547; P=.01), followed by loneliness (sr2=0.0179; P=.14) and social isolation (sr2=0.0004; P=.82).

    Table 1. Correlational analysis between variables (N=89).

    		UCLA Loneliness scoreHRQoLaTechnology useSocial isolation
    UCLA Loneliness score

    		Pearson correlation—b—0.499–0.631–0.853

    		P value—<.001<.001<.001
    HRQoL

    		Pearson correlation−0.499—0.4970.442

    		P value<.001—<.001<.001
    Technology use

    		Pearson correlation−0.6310.497—0.577

    		P value<.001<.001—<.001
    Social isolation

    		Pearson correlation−0.8530.4420.557—

    		P value<.001<.001<.001—

    aHRQoL: health-related quality of life.

    bNot applicable.

    Table 2. Model output and coefficients of multiple linear regression models for health-related quality of life (N=89).
    Independent variablesModel 1Model 2Model 3

    		b (SE)BP valueb (SE)BP valueb (SE)BP value
    Loneliness−4.07 (0.745)−0.499<.001−3.66 (1.436)−0.449.01−2.246 (1.490)−0.275.14
    Social isolationN/AaN/AN/A0.559 (1.671)0.059.740.369 (1.62)0.039.82
    Technology useN/AN/AN/AN/AN/AN/A1.071 (0.407)0.302.01
    Intercept757.851 (37.75)N/A<.001723.318 (109.926)N/A<.001536.117 (128.009)N/A<.001
    R2 (ΔR2)0.249N/A<.0010.250 (0.001)N/A.740.305 (0.055)N/A.01
    F test (df)29.871 (1,90)N/A<.00114.844 (2,89)N/A<.00112.865 (3,88)N/A<.001

    aN/A: not applicable.


    Few studies to date have examined the impact of social isolation, loneliness, and technology use together on HRQoL in older adults in England during the pandemic. We found that loneliness negatively impacts HRQoL, and technology use positively impacts it. Although social isolation has been linked to HRQoL, it had a low impact when loneliness was accounted for. Technology use was related to higher HRQoL, aligning our findings with the results of previous studies [9]. However, the magnitude of the positive effect was notable when considering prepandemic studies [10]. Loneliness impacted HRQoL even when social isolation and technology use were accounted for, in agreement with previous literature [10]. The cross-sectional design prevented us from determining causality and was the main limitation of this study. Our study has relevant implications for health professionals such as health psychologists seeking to improve the HRQoL of older adults, especially through adverse life events like the pandemic or other circumstances that would put older adults in a similar situation where their mobility has been restricted. Our study informs that loneliness should be addressed, in conjunction with increasing technology use, in interventions. The absence of longitudinal studies examining the same cohort before and after the pandemic makes this interpretation speculative. Further research is needed to determine causes, and future studies need to examine pandemic-linked long-term impacts on the mental health and well-being of older adults.

    Conflicts of Interest

    None declared.

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    ‎
    HRQoL: health-related quality of life.

    Edited by G Eysenbach, T Leung; submitted 29.07.22; peer-reviewed by B Wang, H Lum, SGS Shah; comments to author 23.08.22; revised version received 15.09.22; accepted 08.10.22; published 19.10.22

    Copyright

    ©Eric Balki, Niall Hayes, Carol Holland. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 19.10.2022.

    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, 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.