Published on in Vol 9, No 10 (2022): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31251, first published .
Factors Influencing Increased Use of Technology to Communicate With Others During the COVID-19 Pandemic: Cross-sectional Web-Based Survey Study

Factors Influencing Increased Use of Technology to Communicate With Others During the COVID-19 Pandemic: Cross-sectional Web-Based Survey Study

Factors Influencing Increased Use of Technology to Communicate With Others During the COVID-19 Pandemic: Cross-sectional Web-Based Survey Study

Original Paper

1Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom

2Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom

3The McPin Foundation, London, United Kingdom

Corresponding Author:

Sara Simblett, BSc, MSc, PhD, DClinPsy

Department of Psychology

Institute of Psychiatry, Psychology and Neuroscience

King's College London

16 De Crespigny Park

Henry Wellcome Building

London, SE5 8AF

United Kingdom

Phone: 44 2078480762

Email: sara.simblett@kcl.ac.uk


Background: Communication via technology is regarded as an effective way of maintaining social connection and helping individuals to cope with the psychological impact of social distancing measures during a pandemic. However, there is little information about which factors have influenced increased use of technology to communicate with others during lockdowns and whether this has changed over time.

Objective: The aim of this study is to explore which psychosocial factors (eg, mental health and employment) and pandemic-related factors (eg, shielding and time) influenced an increase in communication via technology during the first lockdown in the United Kingdom.

Methods: A cross-sectional, web-based survey was conducted between April and July 2020, examining thoughts, feelings, and behaviors associated with the pandemic, including communicating more using technology (eg, via messaging, phone, or video). We collected sociodemographic information, employment status, mental health service user status, and depression symptoms. We used hierarchical logistic regression to test which factors were associated with communicating more using technology during the lockdown.

Results: Participants (N=1464) were on average 41.07 (SD 14.61) years old, and mostly women (n=1141; 77.9%), White (n=1265; 86.4%), and employed (n=1030; 70.4%). Participants reported a mild level of depression (mean 9.43, SD 7.02), and were communicating more using technology (n=1164; 79.5%). The hierarchical regression indicated that people who were employed and experiencing lower levels of depression were more likely to report increased communication using technology during a lockdown period of the COVID-19 pandemic, and over time, men communicated more using technology. Increased use of technology to communicate was related to greater communication and the inability to see others due to the social distancing measures enacted during the lockdown. It was not related to a general increase in technology use during the lockdown.

Conclusions: Although most participants reported increased use of technology to communicate during a lockdown period of the COVID-19 pandemic, this was more apparent in the employed and those experiencing low levels of depression. Moving forward, we should continue to monitor groups who may have been excluded from the benefits of support and communication using technology.

JMIR Ment Health 2022;9(10):e31251

doi:10.2196/31251

Keywords



The COVID-19 pandemic and concomitant restrictions have had an enormous effect on our day-to-day lives [1,2], and technology has been fundamental in enabling us to contact others, access support, and maintain employment throughout periods of lockdown [3]. We wanted to explore whether there was an increase in technological communication during the first COVID-19–related lockdown in the United Kingdom (a period of time when stringent social distancing measures were implemented and people were told to severely limit time spent outside of their own home) and to investigate whether some psychosocial factors (eg, mental health, employment status, and other demographics) and pandemic-related factors (eg, shielding and time) may have contributed to this change.


Design

This cross-sectional study used the results of a web-based survey administered during the United Kingdom’s first national lockdown period of the COVID-19 pandemic with a snowball sampling technique.

Procedure

The survey was published online between April 24 and June 27, 2020. Participants were presented with an information sheet and asked to provided consent before completing the survey questions.

Ethics Approval

Ethical approval was received from the King’s College London Research Ethics Committee (HR-19/20-18180).

Participants

Participants were UK residents aged ≥16 years and were recruited using social media and other web-based platforms (eg, community mental health forums and newsletters) as well as through mental health service user advisory groups.

Measures

A clinical measure was selected to establish symptoms of depression, but pandemic-specific questions were developed through themes extracted from a series of qualitative interviews with mental health service users and caregivers about coping during the COVID-19 pandemic [4]. Measures of time and demographics were also collected.

Clinical Measure

Depression was measured using the patient health questionnaire (PHQ-9) [5], a self-report measure with 9 items corresponding to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition diagnostic criteria for major depressive disorder. Scores of 5, 10, 15, and 20 represent the cutoff point for mild, moderate, moderately severe, and severe depression, respectively. We divided participants into severe (>19) and mild to moderate (5-19) groups.

Pandemic-Specific Questions
  1. Communicating using technology (whether they had been communicating more using technology [eg, messaging, phone, or video] during a lockdown period of the COVID-19 pandemic)
  2. Social distancing (whether they were “shielding” and/or “cannot see people I want to see”)
  3. Communication (whether they were “talking to people more” and/or “speaking about my problems with someone”)
  4. Technology use (had they been “watching TV and films excessively to fill the time” and/or “checking social media and news” and/or “using health and wellness apps”).
Time

The survey completion date was subtracted from the date the UK lockdown period of the COVID-19 pandemic started (March 23, 2020).

Demographic Characteristics

Participants were queried about their age, gender, ethnicity, employment status, and current mental health service use.

Data Analysis

A hierarchical logistic regression was performed, using a forced entry method, and included time since the start of the lockdown, age, gender, ethnicity, mental health service user status, level of depression, and employment status (step 1), social distancing impacts (step 2), communication impacts (step 3), and technology use impacts (step 4) of the COVID-19 pandemic lockdown on the dependent variable—increased communication using technology. Interaction terms with “time” were included for all variables to investigate the change at different stages of the COVID-19 pandemic lockdown.


Participants (N=1464) were on average 41.07 (SD 14.61) years old, and mostly women (n=1141; 77.9%), White (n=1265; 86.4%), and employed (n=1030; 70.4%). Participants reported a mild level of depression (mean 9.43, SD 7.02). Moreover, most participants were communicating more using technology (n=1164; 79.5%) and some (15.1%; n=221) were shielding (Table 1).

Demographic and clinical variables contributed significantly to increased communication using technology and explained 13% of the variance (step 1: X213=86.25, P<.001; Nagelkerke R2=.13; see Multimedia Appendix 1). People who were employed and had lower levels of depression were more likely to report increased communication via technology. As time in the lockdown period of the COVID-19 pandemic increased, men were more likely to be communicating more using technology. Social distancing (step 2: X24=25.66, P<.001; Nagelkerke R2=.17) and communication (step 3, X24=44.78, P<.001; Nagelkerke R2=.23) significantly contributed to the model, explaining an additional 3.7% and 6.3% of the variance, respectively. Those who reported social distancing (“I cannot see the people I want to see”) and/or communication (“I am talking to people more” and “I am speaking openly about my problems with someone”) impacts of lockdown were more likely to be communicating more using technology. Finally, technology impacts of lockdown did not significantly contribute to the model (step 4, X26=8.53, P=.20; Nagelkerke R2=.24). These results were not affected by removing nonsignificant interaction terms.

Table 1. Participant characteristics split by mental health service user status.a
CharacteristicsService users (n=285)Nonservice users (n=1179)Total (N=1464)P value
Age (years), mean (SD)36.9 (13.0)42.1 (14.8)41.1 (14.6)<.001
Gender, n (%)


.02

Women234 (82.1)907 (76.9)1141 (77.9)

Men44 (15.4)259 (22.0)303 (20.7)
Ethnicity, n (%).30

White251 (88.1)1014 (86)1265 (86.4)

Ethnic minorities (excluding White minorities)30 (10.5)151 (12.8)181 (12.4)
Employed, “yes,” n (%)156 (54.7)874 (74.1)1030 (70.4)<.001
Depression severity, n (%)<.001

Minimal30 (10.5)349 (29.6)379 (25.9)

Mild41 (14.4)318 (27)359 (24.5)

Moderate59 (20.7)177 (15)236 (16.1)

Moderately severe44 (15.4)103 (8.7)147 (10)

Severe70 (24.6)86 (7.3)156 (10.7)
Shielding, “yes,” n (%)66 (23.2)155 (13.1)221 (15.1)<.001
Communicating more using technology, “yes,” n (%)206 (72.3)958 (81.3)1164 (79.5)<.001

aPercentages do not add up to 100% where data were missing.


Principal Findings

We found that the employed and those experiencing lower levels of depression were more likely to report that they were using technology to communicate more during the first lockdown period of the COVID-19 pandemic and that changes in technology use were motivated by social distancing and communication rather than changes in general technology usage during the first lockdown. Overall, these results indicate that use of technology to communicate with others (eg, to maintain social connection and access support networks) rose independently of any general change in technology use during this period (eg, watching television or checking social media).

During the COVID-19 pandemic, social distancing and self-isolation guidelines meant that people had to shift toward using technology to continue to communicate with others and seek social support. Our results suggest that people experiencing depression were less likely to have adapted to the changes in communication and this was largely anticipated, when one considers that depression can reduce motivation to engage in social interaction [6] and limit social problem-solving abilities [7]. Research has shown that people that have felt more connected to members of their community during the pandemic have experienced lower levels of depression, anxiety, and loneliness [8]. Communication via technology could be an effective coping strategy that individuals employ to maintain social connection, access support, and manage the psychological effects of lockdown [4,9-11]. It is concerning that people experiencing high levels of depression were less likely to report an increased use of technology to communicate with others because this may have left them more vulnerable to the deleterious mental health effects of the lockdown.

Those who were employed during the first COVID-19 lockdown period were more likely to report increased communication using technology. Technology has been an efficacious and convenient tool for communicating with colleagues, working, and accessing vital support networks during the lockdown [12,13]. Social distancing regulations and government advice stipulated that people should work from home wherever possible, and an increase in the prevalence of working from home offers a reasonable explanation as to why those in employment were using technology for communication during the lockdown [13]. Conversely, individuals who were unemployed are likely to have been using technology to communicate with others prior to the COVID-19 lockdown period and to have continued to do so at a similar rate, because they did not face the same pressure to increase their use of technology. Additionally, individuals who are unemployed or from low-income households are more vulnerable to digital exclusion [14] and may have lacked access to the technological resources (eg, data packages, internet access) necessary to increase communication via technology.

The interaction between gender and time is harder to interpret but demonstrates that men were more likely to report a change in their use of technology as the lockdown progressed. This supports evidence that suggests that women found social isolation and distancing more difficult and were more likely to report using internet-based technologies to cope with the stress of the pandemic [10,15]. However, our results suggest that men may experience a delayed response to the impacts of social distancing, turning to technological communication much later in periods of lockdown.

Strengths and Limitations

Cross-sectional designs cannot determine causality and data from longitudinal studies are needed to disentangle the precise relationship between the different factors examined. The variables we explored may be interrelated; for example, people who reported higher levels of depression may have been less likely to be employed and therefore less likely to report using technology earlier on during the COVID-19 pandemic. Furthermore, withdrawal associated with depression may not be specific to the effects of the COVID-19 pandemic. Although our sample had enough variation to identify specific factors that affect communication via technology, participants needed access to technology and the internet to take part in the web-based survey, which will have introduced sampling bias and likely concealed the most digitally excluded members of society.

Conclusion

In a climate of unprecedented uncertainty, people have shown incredible resilience and resourcefulness, but have become ever more reliant on technology. Although many people reported that they were communicating more using technology, this change has been most apparent in people who were employed and less prominent in those experiencing higher levels of depression. It is also evident that increased technology use was partly driven by the social distancing and communication consequences of the lockdown rather than a general increase in technology use. As the COVID-19 pandemic continues, we expect that there will be a greater integration of technology into our lives, and therefore we must continue to examine changing technology use and monitor groups who may be excluded from the benefits of support and communication using technology.

Acknowledgments

This paper represents independent research part-funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley National Health Service (NHS) Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. TW would specifically like to acknowledge support from the NIHR for their Senior Investigator Awards. Further thanks go to all the charitable organizations, including the McPin Foundation, who helped with recruitment.

Conflicts of Interest

None declared.

Multimedia Appendix 1

Hierarchical logistic regression analysis demonstrating the predictors of communicating more using technology during lockdown.

DOC File , 147 KB

  1. Dawson DL, Golijani-Moghaddam N. COVID-19: Psychological flexibility, coping, mental health, and wellbeing in the UK during the pandemic. J Contextual Behav Sci 2020 Jul;17:126-134 [FREE Full text] [CrossRef] [Medline]
  2. Holmes EA, O'Connor RC, Perry VH, Tracey I, Wessely S, Arseneault L, et al. Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science. Lancet Psychiatry 2020 Jun;7(6):547-560 [FREE Full text] [CrossRef] [Medline]
  3. Shah SGS, Nogueras D, van Woerden HC, Kiparoglou V. The COVID-19 pandemic: a pandemic of lockdown loneliness and the role of digital technology. J Med Internet Res 2020 Nov 05;22(11):e22287 [FREE Full text] [CrossRef] [Medline]
  4. Bruno E, Simblett S, Lang A, Biondi A, Odoi C, Schulze-Bonhage A, RADAR-CNS Consortium. Wearable technology in epilepsy: the views of patients, caregivers, and healthcare professionals. Epilepsy Behav 2018 Aug;85:141-149. [CrossRef] [Medline]
  5. Spitzer RL, Kroenke K, Williams JB. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. Primary Care Evaluation of Mental Disorders. Patient Health Questionnaire. JAMA 1999 Nov 10;282(18):1737-1744. [CrossRef] [Medline]
  6. Pelizza L, Ferrari A. Anhedonia in schizophrenia and major depression: state or trait? Ann Gen Psychiatry 2009 Oct 08;8:22 [FREE Full text] [CrossRef] [Medline]
  7. Donaldson C, Lam D. Rumination, mood and social problem-solving in major depression. Psychol Med 2004 Oct;34(7):1309-1318. [CrossRef] [Medline]
  8. White RG, Van Der Boor C. Impact of the COVID-19 pandemic and initial period of lockdown on the mental health and well-being of adults in the UK. BJPsych Open 2020 Aug 17;6(5):e90 [FREE Full text] [CrossRef] [Medline]
  9. Bavel JJV, Baicker K, Boggio PS, Capraro V, Cichocka A, Cikara M, et al. Using social and behavioural science to support COVID-19 pandemic response. Nat Hum Behav 2020 May;4(5):460-471. [CrossRef] [Medline]
  10. Prowse R, Sherratt F, Abizaid A, Gabrys RL, Hellemans KGC, Patterson ZR, et al. Coping with the COVID-19 pandemic: examining gender differences in stress and mental health among university students. Front Psychiatry 2021;12:650759 [FREE Full text] [CrossRef] [Medline]
  11. Brooks SK, Webster RK, Smith LE, Woodland L, Wessely S, Greenberg N, et al. The psychological impact of quarantine and how to reduce it: rapid review of the evidence. Lancet 2020 Mar 14;395(10227):912-920 [FREE Full text] [CrossRef] [Medline]
  12. Kamal MM. The triple-edged sword of COVID-19: understanding the use of digital technologies and the impact of productive, disruptive, and destructive nature of the pandemic. Information Systems Management 2020 Sep 12;37(4):310-317. [CrossRef]
  13. Robbins T, Hudson S, Ray P, Sankar S, Patel K, Randeva H, et al. COVID-19: A new digital dawn? Digit Health 2020;6:2055207620920083 [FREE Full text] [CrossRef] [Medline]
  14. Beaunoyer E, Dupéré S, Guitton MJ. COVID-19 and digital inequalities: reciprocal impacts and mitigation strategies. Comput Human Behav 2020 Oct;111:106424 [FREE Full text] [CrossRef] [Medline]
  15. Pahayahay A, Khalili-Mahani N. What media helps, what media hurts: a mixed methods survey study of coping with COVID-19 using the media repertoire framework and the appraisal theory of stress. J Med Internet Res 2020 Aug 06;22(8):e20186 [FREE Full text] [CrossRef] [Medline]

Edited by J Torous; submitted 15.06.21; peer-reviewed by S Gordon, CY Lin; comments to author 12.01.22; revised version received 14.04.22; accepted 15.04.22; published 25.10.22

Copyright

©Erin Dawe-Lane, Magano Mutepua, Daniel Morris, Clarissa M Odoi, Emma Wilson, Joanne Evans, Vanessa Pinfold, Til Wykes, Sagar Jilka, Sara Simblett. Originally published in JMIR Mental Health (https://mental.jmir.org), 25.10.2022.

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 JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on https://mental.jmir.org/, as well as this copyright and license information must be included.