Family Caregiver Comfort with Telehealth Technologies: Differences by Race and Ethnicity in a Cross-Sectional Survey

Abstract

Background:

Telehealth has seen widespread use since the onset of the COVID-19 pandemic, and 82% patients required assistance in accessing their telehealth appointments. This assistance commonly comes from a family caregiver who may or may not be comfortable using the technologies associated with telehealth. The objective of our study was to analyze a demographically representative survey of U.S. family caregivers to understand the level of comfort using telehealth technologies among family caregivers.

Methods:

A secondary analysis of survey data collected during the COVID-19 pandemic in 2020. Level of caregiver comfort using computers, smartphones, and tablets was determined through three Likert-style questions. Proportional odds logistic regression was used to understand the associations between demographic variables and level of caregiver comfort using each technology, when adjusting for covariates.

Results:

A total of 340 caregivers were included in the analysis. Compared with non-Hispanic white caregivers, Asian caregivers had higher odds (odds ratio [OR] 3.14; 95% confidence interval [CI] 1.36, 8.02; p = 0.01) of expressing comfort using computers; black caregivers (OR 0.46; 95% CI 0.21, 0.98; p = 0.04) and Hispanic caregivers (OR 0.36; 95% CI 0.17, 0.79; p = 0.01) expressed lower odds of comfort using smartphones; and Asian caregivers had higher odds (OR 4.64; 95% CI 2.05, 11.69; p = 0.001) of expressing comfort using tablets.

Conclusion and Implications:

There are identified disparities in the level of technological comfort using computers, smartphones, and tablets by different racial and ethnic groups. Health systems should consider early stakeholder involvement in the design of telehealth technologies, culturally responsive training materials on telehealth technology use to reduce disparities in comfort using telehealth technologies.

Introduction

Telehealth has become widespread since the onset of the COVID-19 pandemic.^1,2 The technologies required for telehealth visits are most often web-enabled devices, primarily smartphones, tablets, and computers.³ The efficacy of telehealth as a means to deliver high-quality care hinges on user engagement.^4,5 A prerequisite to high engagement with technology is perceived comfort using technology.⁶ However, lack of familiarity with the technology required to conduct telehealth visits is identified as a barrier for patient access to telehealth-delivered medical care.⁷ Family caregivers are often called upon to assist patients in using the technologies required for telehealth. In a cross-sectional survey of older adults, 82% of patients reported requiring assistance from a family member to access or complete their telehealth visit, and patients reported that the reasons for their challenges were often due to a lack of comfort using the technology required for telehealth.^8,9

There are disparities in the level of technological comfort between patients of different racial and ethnic backgrounds.⁵ A cross-sectional study of 344 patients at an academic medical center found that compared with white patients, black patients were less likely to self-report technological comfort.⁵ Other qualitative work has found that Hispanic patients required technological comfort training to ease the transition to telemedicine during COVID-19,¹⁰ and a study of 197,076 telehealth visits found that patient race and ethnicity are associated with lower rates of video-based telehealth visit utilization.¹¹

When patients and caregivers cannot effectively use telehealth to engage with clinic visits, patients experience increasing emergency department visits, longer hospitalizations, and a higher likelihood for medication misuse.⁷ Caregiver ability to use technology is identified as a critical component of the Technology-Enabled Caregiving in the Home (TECH) model (Fig. 1), related to caregiver burden, caregiver decision confidence, and feelings of empowerment.^6,12 It is currently unknown whether there are disparities in technological comfort among family caregivers.

Fig. 1.

TECH model. TECH, Technology-Enabled Caregiving in the Home.

Understanding whether disparities exist in technological comfort is important to identify potential areas and groups for future interventions that support equity and reduce disparities in telehealth utilization. To address this gap, we conducted a cross-sectional survey of family caregivers to determine how the COVID-19 pandemic has affected family caregiver communication with health care providers, including their experiences with telehealth.¹³ The aim of this secondary analysis was to examine racial and ethnic differences in caregiver level of comfort using telehealth technologies. We hypothesized that there would be differences in the level of comfort using technologies among family caregivers by race and ethnicity, with similar patterns to patient populations. We believed caregivers identifying as black or Hispanic displaying less comfort with telehealth technologies than caregivers identifying as non-Hispanic white.

Methods

STUDY DESIGN

A secondary analysis was conducted from a cross-sectional online survey of adult family caregivers in the United States (U.S.) from June to August 2020. Details of the survey and its administration can be found elsewhere, although in brief, the goal of the survey was to understand that the shift to telemedicine during COVID-19 had affected family caregiver communications with health care systems.¹³ After viewing and acknowledging an information sheet and clicking a button indicating their informed consent page, English-speaking U.S. adults (18+) who self-identified as an unpaid individual most responsible for the health care of a family member or friend were eligible for the study, who may or may not live in the same home.

This study followed the Checklist for Reporting the Results of Internet E-Surveys (Supplementary Appendix SA1) and no personally identifiable information was collected.¹⁴ This study was deemed exempt from Institutional Review Board (IRB) review by Dartmouth College's Committee for the Protection of Human Subjects.

SURVEY DEVELOPMENT AND DESIGN

This survey was developed by OpenRecordings, a group of researchers, physicians, patients, and caregivers at Dartmouth College, and the National Alliance for Caregiving (NAC), a nonprofit coalition of organizations that support family caregivers. The survey was pilot-tested and refined based on feedback from a six-member NAC caregiver panel. Respondents were asked Likert-style questions about their level of comfort using smartphones, tablets, and computers. Responses included “Don't Use,” “Not Very Comfortable,” “Comfortable,” and “Very Comfortable.”

SURVEY PROCEDURES

The survey was distributed through the NAC's email newsletter, Twitter, and Facebook on June 1, 2020. On July 27, 2020, we analyzed the demographics of this initial sample and supplemented with an additional sample through Qualtrics Panels, an online survey platform that draws broad participant demographics. Quotas were applied to create a demographic profile by race and ethnicity in our sample similar to the national caregiver demographics described in the 2020 Caregiving in the U.S. Report, a 5-year report that seeks to understand the demographics and experiences of caregivers nationally.¹⁵ To ensure completeness, all questions were forced response, although respondents were reminded they could opt-out of the survey at any time by closing their webpage. Participants viewed 31–36 questions, depending on their answer selections. There was no “back” button on the survey and all questions were delivered in English only.

We excluded “speeders,” respondents who completed the survey under half the piloted median time to completion.¹⁶ Surveys were analyzed if they were more than 97% complete, which indicated they reached the final page of the survey. Finally, we used the “Ballot Box Stuffing” feature in Qualtrics to ensure that an individual only took the survey once, although we allowed respondents to resume responses up to 1 week after starting the survey. More information about the survey can be found in the primary survey report.¹³

PARTICIPANTS

The NAC newsletter was sent to 5,986 people and the survey section was viewed by 449. The social media post received 676 unique views. Of the 1,125 potential participants who saw the survey link from the NAC sample, 268 visited the survey introduction page, and 113 fully completed the survey. Through Qualtrics, we received an additional 227 respondents to fill quotas based on race and ethnicity, but the overall number of potential participants from Qualtrics is unknown. This resulted in a total of 340 respondents.

ANALYSIS

Associations between caregiver race and ethnicity and each outcome variable were each examined using the chi-square test. Proportional odds logistic regression models were used to determine which groups exhibited a statistically significant difference to the referent group (white, non-Hispanic caregivers) when controlling for other caregiver demographic variables (age, gender, education, rurality, and relationship to care recipient) at a predefined alpha level of 0.05.

Results

DEMOGRAPHICS

Respondents (N = 340) were mostly male (58.3%, n = 198) and non-Hispanic white (59.7%, n = 203) and had a mean age of 40.3 years (standard deviation 13.9). Most caregivers cared for a parent/grandparent (57.4%, n = 195) or a spouse/partner (30.5%, n = 103). Respondent demographic information can be found in Table 1.

Table 1.

Participant Characteristics

	TOTAL ( N = 340)
Age
Mean (SD)	40.3 (13.9)
Range	18–85
Gender
Male	199 (58%)
Female	141 (42%)
Race/ethnicity
White	203 (59.7%)
Asian	42 (12.4%)
Black or African American	42 (12.4%)
Other	12 (3.5%)
Hispanic	41 (12.1%)
Education
College degree	129 (37.9%)
Less than college	69 (20.3%)
More than college	142 (41.8%)
Income
$100,000 or more	127 (37.4%)
<$50,000	91 (26.8%)
$50,000 to $99,999	122 (35.9%)
Rurality
Rural	54 (15.9%)
Urban	286 (84.1%)
Care for >1 condition
No	210 (61.7%)
Yes	130 (38.2%)

SD, standard deviation.

STATISTICAL RESULTS

Full statistical analysis can be found in Table 2.

Table 2.

Regression Analysis

PREDICTORS	COMPUTER COMFORT			SMARTPHONE COMFORT			TABLET COMFORT
PREDICTORS	ORs	CI	p	ORs	CI	p	ORs	CI	p
Don't use \| not very comfortable	0	0.00–0.04	<0.001	0.02	0.00–0.07	<0.001	0.12	0.04–0.37	<0.001
Not very comfortable \| comfortable	0.07	0.02–0.24	<0.001	0.05	0.01–0.16	<0.001	0.28	0.10–0.81	0.019
Comfortable \| very comfortable	0.83	0.28–2.52	0.747	0.4	0.12–1.30	0.127	2.17	0.76–6.17	0.145
Age
Younger than 35 (reference)
35–64	1.96	1.14–3.38	0.016	0.63	0.34–1.15	0.138	1.3	0.78–2.16	0.318
65+	1.52	0.57–4.33	0.411	0.33	0.12–0.99	0.044	0.91	0.34–2.48	0.846
Female gender	0.9	0.54–1.51	0.689	1.9	1.08–3.39	0.028	0.76	0.47–1.24	0.271
Race/ethnicity
White (reference)
Asian	3.14	1.36–8.01	0.011	3.67	1.31–13.16	0.025	4.64	2.05–11.69	0.001
Black or African American	0.73	0.35–1.50	0.385	0.46	0.21–0.98	0.043	0.99	0.49–2.00	0.969
Hispanic	1.25	0.59–2.78	0.563	0.36	0.17–0.79	0.011	1.79	0.86–3.89	0.129
Education
College degree (reference)
Less than college	0.81	0.42–1.56	0.524	0.79	0.40–1.59	0.512	1.44	0.76–2.75	0.265
More than college	1.15	0.63–2.07	0.653	1.42	0.76–2.68	0.273	1.44	0.83–2.52	0.194
Urban location	0.9	0.47–1.69	0.749	0.92	0.45–1.80	0.818	1.57	0.85–2.88	0.143
Relationship to patient
Spouse or partner (reference)
Other	1.01	0.47–2.12	0.972	1.19	0.52–2.60	0.677	1.36	0.65–2.79	0.406
Parent or grandparent	1.64	0.68–3.87	0.265	1.45	0.58–3.54	0.42	1.18	0.52–2.66	0.686

Bold: p < 0.05.

CI, confidence interval; OR, odds ratio.

COMPUTER COMFORT

Most Asian (n = 34/42, 81%), non-Hispanic white (n = 139/203), 68%, and Hispanic (n = 27/41, 66%) caregivers expressed comfort using computers. About half of the black (n = 21/42, 50%) caregivers expressed comfort using computers. When adjusting for demographic covariates, compared with non-Hispanic white caregivers, Asian caregivers had higher odds (odds ratio [OR] 3.14; 95% confidence interval [CI] 1.36, 8.02; p = 0.01) of expressing comfort using computers.

Smartphone comfort

Most caregivers who identified as Asian caregivers (n = 38/42, 90%) or non-Hispanic white (n = 150/203, 74%) were comfortable using smartphones. About half of the black (n = 24/42, 57%) or Hispanic (n = 23/41, 56%) caregivers expressed comfort using smartphones. When adjusting for demographic covariates, compared with non-Hispanic white caregivers, black caregivers (OR 0.46; 95% CI 0.21, 0.98; p = 0.04) and Hispanic caregivers (OR 0.36; 95% CI 0.17, 0.79; p = 0.01) expressed lower odds of comfort using smartphones, while Asian caregivers (OR 3.67; 95% CI 1.31, 13.16; p = 0.025) expressed higher odds of comfort using smartphones.

Tablet comfort

Most Asian caregivers identifying as Asian (n = 34/42, 81%) or Hispanic (n = 27/41, 66%) expressed comfort using tablets. About half of the non-Hispanic white (n = 110/203, 54%), black or African American (n = 21/42, 50%) caregivers expressed comfort using tablets. When adjusting for demographic covariates, compared with non-Hispanic white caregivers, Asian caregivers had higher odds (OR 4.64; 95% CI 2.05, 11.69; p = 0.001) of expressing comfort using tablets.

Discussion

PRIMARY RESULTS

We found that, compared with non-Hispanic white caregivers, black caregivers reported lower odds of being comfortable using smartphones, while Hispanic caregivers reported lower odds of being comfortable with smartphones. Asian caregivers reported higher odds of being comfortable with computers and tablets.

COMPARISON WITH PRIOR LITERATURE

In our sample, the differing levels of technological comfort in using computers, smartphones, and tablets by different racial and ethnic groups imply that the adoption of telehealth may not be equitably accessible to all caregivers. A scoping review of 33 studies examining the implementation of technologies and their influence on health care inequities found that patients of minority racial and ethnic groups are often less likely to adopt, and therefore benefit from, innovative health technologies.¹⁷ Further work could consider whether caregiver comfort using these technologies is a factor in their adoption.

A cross-sectional study of 197,076 patient visits during the COVID-19 pandemic found that black/African American and Hispanic patients are less likely to both have video visits and utilize telehealth.¹¹ Another cross-sectional study of 873 telehealth visits during the first wave of the COVID-19 pandemic found that 14% of patients who did not access telehealth visits had no caregiver to assist them.⁸ Family caregiver comfort using telehealth could be an important factor in accessing telehealth; lower caregiver comfort using telehealth technologies could result in lower patient utilization, although more work is needed to understand whether an association between caregiver technological comfort and telehealth utilization exists.

Other work has found that training and coaching can be an effective strategy to improve caregiver comfort using technological interventions.^18,19 Our results highlight disparities in technological comfort using computers, smartphones, and tablets by race and ethnicity; future interventions could be developed to include culturally sensitive trainings on how to use and access these technologies to facilitate telehealth visits.^20
–22 In particular, the strategies identified by Shaw et al. of early stakeholder involvement by members of marginalized communities in the design of training materials could be applied to address the observed disparities in technological comfort.²²

While the TECH model places technological comfort as a factor influencing individual moderators, but separate from race and ethnicity, which influence sociodemographic moderators,⁶ our results indicate an association between technological comfort by race and ethnicity. As further work develops the relationship between these factors, it will be important to determine whether and how they should be linked in our conceptual models. Our results support the conclusions of the authors of the TECH model that caregiver health technologies must fundamentally integrate the perspectives of marginalized communities to advance equitable access to health and health care.⁶

STRENGTHS AND LIMITATIONS

This study occurred during the summer of 2020, after many health systems had transitioned to telehealth. While this sample approximates the demographic characteristics of a well-established national survey using probability-based sampling,¹⁵ there is the potential for sampling bias as we recruited over the internet, despite studies showing comparable health-related estimates between probability-based and internet-based sampling.²³ While no sample weights were applied, respondents' racial and ethnic demographics approximated those of the Caregiving in the U.S. (CGUS) 2020 report, and thus, the study cannot be considered nationally representative. Each racial and ethnic demographic group in our sample was within 1% point of the demographics of CGUS 2020; for instance, the CGUS 2020 sample was 12.6% black or African American, while our sample was 12.4% black or African American.

The overall sample size of the non-Hispanic white respondents was limited relative to the overall sample size. While we have provided 95% CIs to highlight the upper and lower estimates based on our data, these estimates could be over- or underestimating the true population value due to our limited sample size. Our survey was also only conducted in English, thus more work is needed to determine whether our results generalize to Spanish-speaking populations. While we excluded participants who failed quality checks and it has been demonstrated that quality checks only rarely alter marginal distributions, the possibility exists that some of the removed responses were valid responses that should have been included in the analysis, and which could have influenced our estimates.²⁴

We also asked participants about their overall level of comfort using technologies common to telehealth. While this provides useful information about self-reported comfort, we did not use a validated scale nor did we ask about components of comfort, including access to the devices, internet connectivity, privacy concerns, or cost. Future work should consider examining what components of comfort using these technologies may be driving the differences we observed.

Conclusions

The level of technological comfort in family caregivers could be a factor in understanding differences in patient access to telehealth. Health systems should consider caregiver-focused, culturally responsive training materials on how to use telehealth technologies as a strategy to ensure equitable access to health care services. Disparities exist between caregivers of different racial and ethnic groups in their comfort using technologies required for telehealth. Understanding the drivers of these disparities will help develop interventions to ensure equitable access to health systems.

Footnotes

Authors' Contributions

R.W.R.B.: conceptualization, methodology, software, formal analysis, investigation, and writing—original draft. W.O.: investigation and writing—review and editing. F.P.: methodology and writing—review and editing. J.N.D.-O.: methodology, formal analysis, and writing—review and editing. P.J.B.: conceptualization, methodology, formal analysis, supervision, and writing—review and editing.

Disclosure Statement

No competing financial interests exist.

Funding Information

This work was supported by a Sayles Research Grant from Dartmouth College (grant no. 02012020).

Supplementary Material

Supplementary Appendix SA1

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Supplementary Material

Please find the following supplemental material available below.

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