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. Author manuscript; available in PMC: 2023 Aug 1.
Published in final edited form as: J Gerontol Soc Work. 2021 Dec 22;65(6):618–634. doi: 10.1080/01634372.2021.2009079

Long-Distance Caregivers’ Use of Supportive Services

J Minahan Zucchetto 1, VR Cimarolli 2, MJ Wylie 3, F Falzarano 4, A Horowitz 5
PMCID: PMC9213565  NIHMSID: NIHMS1766943  PMID: 34933657

Abstract

While long-distance caregiving has received increased attention as a unique care experience, prior research has not explored the supportive services used by long-distance caregivers (LDCs) and the factors that predict their supportive service utilization. Using the Andersen Model of Health Care Utilization, the current study sought to: 1) describe the types of supportive services LDCs used and the frequency of usage and 2) identify predisposing, enabling, and need-related factors associated with supportive service use in a sample of LDCs. Participants were recruited from aging services organizations, a national participant registry, professional networks, participant referrals, and an existing pool of research participants. The sample included 304 LDCs who reported on the use of nine supportive services and completed measures assessing depression, caregiver burden, self-rated health, sociodemographic characteristics, and the condition of the care recipient (CR). Fifty percent of LDCs reported no service use. Multiple hierarchical regression analyses demonstrated that younger age, higher caregiver burden, greater depressive symptoms, more time spent helping the CR, and worse CR functional status were significantly related to greater total supportive service use by LDCs. The current study contributes to our understanding of the factors associated with LDCs’ supportive service utilization, highlighting the importance of need-related factors.

Keywords: Long-distance caregiving, supportive service use, Andersen model of health care utilization

According to the Caregiving in the U.S. 2020 report by the National Alliance for Caregiving (NAC) and AARP (2020), an estimated 41.8 million Americans (16.8%) served as unpaid caregivers for an older relative or friend (aged 50 years and older) in the prior 12 months, reflecting an increase of over 7 million since 2015. The report suggests that there are several factors contributing to the increased prevalence of individuals providing care for older adults, including 1) the increased aging and longevity of the baby boomer population, resulting in an increased need for care, 2) the insufficient availability of workers in the healthcare and in long-term services and supports (LTSS) sectors to adequately meet the care needs of the older adult population, 3) the increased push by states to offer home- and community-based care services as an LTSS option, and 4) an increase in individuals self-identifying as caregivers. Of these caregivers, approximately 12% of them are providing care for an older relative or friend from a distance of at least one hour away (NAC & AARP, 2015). On average, these long-distance caregivers (LDC) live approximately 450 miles away from their care recipient (CR), which equates to about seven hours of travel time (NAC & the MetLife Mature Market Institute [MMMI], 2004). Due to the increased distance between LDCs and their care recipients, LDCs face unique challenges in the caregiving experience compared to geographically proximate caregivers (Horowitz & Boerner, 2017).

Although LDC is a growing phenomenon, there is a paucity of research focusing on the nature and consequences of LDC. The limited research in this area suggests that, despite geographic distance, the types of assistance provided by LDCs are quite similar to the care activities carried out by geographically close caregivers (e.g., Roff et al., 2007; Vezina & Turcotte, 2010). Summarizing results from the NAC and the MMMI Miles Away (2004) report, Bevan and Sparks (2011) indicated that one-third of LDCs reported visiting CRs at least one day per week despite living at a distance. Based on the same report, Bevan and Sparks (2011) also noted that one-half of LDCs reported providing care for at least one full day per week; thus, although LDCs live at a distance from their CR, they still visit as well as perform caregiving tasks, which may be in person or remote. Long-distance caregivers engage in a wide variety of care-related activities to support their CR from a distance, including the provision of financial assistance, managing care, providing emotional support, and even performing practical and nursing tasks (Cagle & Munn, 2012; Douglas et al., 2016; Parker et al., 2006; Vezina & Turcotte, 2010). Additionally, LDCs are faced with distance as an additional challenge to the caregiving role, which can exacerbate care-related stress. Research has also indicated that LDCs report comparable or higher levels of emotional distress compared to proximate caregivers (Thompsell & Lovesone, 2002) and experience heightened levels of anxiety, helplessness, guilt, and uncertainty (Mazanec, 2012; Douglas et al., 2016; Harrigan & Koerin, 2007).

Although LDCs, like proximate caregivers, can be severely burdened because of their caregiving role, interventions designed to alleviate caregiver burden and stresses in LDCs that address the unique needs of this caregiving subgroup are non-existent. However, LDCs could use telehealth interventions and tools designed for proximate for caregivers that are delivered remotely (please see Chi & Demiris, 2015 for a review) or other internet or popular press resources (Douglas et al., 2016). These supportive services may be geared toward supporting the caregiver emotionally, such as support groups or through structured, time-limited psychosocial interventions (e.g., the Resources for Enhancing Alzheimer’s Caregiver Health [REACH] interventions; Burgio et al., 2003) that are delivered in-person (if available in the LDC’s area). These services were designed to alleviate stress and burden as well as improve mental health in geographically close caregivers. Additionally, supportive services can refer to services that help with instrumental care tasks that may benefit the CR, such as coordinating care and electronically monitoring the CR’s health, such as a geriatric care management service.

It is well documented that caregiver supportive services can have beneficial effects for caregiver well-being; however, this research has predominately focused on the effectiveness of improving well-being among proximate caregivers. Previous research demonstrates that caregiver support group programs that include educational components as well as intervention aspects that are geared specifically toward the needs of the caregiver, such as support group programs and/or individual counseling, are effective in decreasing caregiver burden and improving well-being among proximate family caregivers (Belle et al., 2006; Burgio et al., 2009; Chien et al., 2011; Czaja et al., 2018; Gaugler, et al., 2008).

In order to make beneficial services fully accessible to caregivers who are in need of support, past research has focused on identifying factors associated with service utilization. In addition, factors that may represent potential barriers for caregivers to access and utilize support services, such as lack of knowledge about services, have also been examined (e.g., Toseland et al., 2002). Andersen’s Model of Health Care Utilization (Andersen, 1995) represents an often-utilized conceptual framework for studying healthcare and social service use in family caregivers (Martindale-Adams et al., 2016). The model postulates that three types of variables are influential in health care services use: [1] predisposing variables encompassing propensities to use services (e.g., demographic and social structural variables), [2] enabling variables reflecting an individual’s ability to find and access services (e.g., community characteristics), and [3] need variables reflecting the severity of illness domain (e.g., subjective health status).

Prior research efforts have used this model to identify factors predicting use of services by caregivers for themselves (e.g., Martindale-Adam et al., 2016) and predicting use of services by CRs that are designed to provide some type of respite for caregivers (e.g., Shi et al., 2018). Our review of the literature specifically related to use of supportive services by caregivers for themselves indicated that all three umbrella categories of variables in Andersen’s model are associated with supportive service use in caregivers. In the predisposing variable domain, past research shows that increased age is associated with higher rates of supportive service use (Martindale-Adams et al., 2016). Furthermore, those caregivers who are more highly educated and married use more supportive services for themselves (Martindale-Adams et al., 2016; Toseland et al., 2002). In the enabling domain, being retired (Martindale-Adams et al., 2016), being employed part-time (Meyer et al., 2019), having a higher income (Martindale-Adams et al., 2016; Mavadandi et al., 2017), having easier access to services (Toseland et al., 2002), learning about services from a healthcare professional (Meyer et al., 2019), and having more social support (Robinson et al., 2005) predicted greater service use. In the need variables category, higher depression (Martindale-Adams et al., 2016), greater caregiver burden (Mavandadi et al., 2017; Toseland et al., 2002), worse caregiver physical health (Martindale-Adams et al., 2016; Toseland et al., 2002), and fewer hours on caregiver duty per day (Martindale-Adams et al., 2016) were associated with greater incidence of supportive service utilization among caregivers.

While there is a substantial amount of research in this area investigating the extent of and correlates of supportive services use for themselves in geographically close caregivers, this has not yet been investigated in LDCs. Hence, the purpose of this study was two-fold. First, we aimed to describe the types of supportive services used by LDCs and the extent of usage of these services. We considered services as supportive when they either provided emotional support for LDCs or helped with instrumental care tasks that may also benefit the CR. Second, we aimed to identify factors associated with supportive service use in a sample of LDCs. Utilizing Andersen’s Model of Health Care Utilization (Andersen, 1995) as a theoretical framework and guided by past research involving geographically proximate caregivers, we aimed to identify predisposing factors (i.e., age, gender, ethnicity/race, and relationship type to CR), enabling factors (i.e., CR’s living situation, LDCs’ income adequacy, employment status, instrumental support, and emotional support), and need-related factors (i.e., caregiver burden, depression, health status, distance from CR, time spent helping CR, as well as CR cognitive and functional status) that are associated with higher supportive service use in LDCs.

Method

Sample and Procedures

Long-distance caregivers for this cross-sectional study (N=304) were recruited from aging services organizations (45%), a large national database of persons interested in research participation (Research Match; 41%), and from professional networks, participant referrals, and an existing pool of research participants involved in an aging study (14%). This study took place in the United States, and New York and New Jersey were the most common states of residence for the LDCs. The most common states of CR residence were New York and Ohio. A small number of care recipients also resided in other countries with the most common country being Canada. Long-distance caregivers were defined as adults aged 21 years or older who live at least 2 hours travel distance from the CR (by usual means of transportation). Only caregivers who self-identified as primary (has main responsibility of care) or co-primary (has shared responsibility of care) caregivers were eligible to participate. The amount and/or type of care provided was not an eligibility criterion in order to represent the full range of LDC involvement. We were prepared to include both English- and Spanish-speaking caregivers, so we used a professional translation service and validated Spanish versions of scales whenever available. However, no LDCs chose to complete an interview in Spanish.

Recruitment letters describing the study and eligibility criteria were sent to family caregivers whose CR received services at a geriatric healthcare organization, and to members of the existing research panel. The study team contacted those potential participants by telephone after letters were sent. Those interested in the study from the Research Match database were provided information to contact the study team by telephone for eligibility screening. Telephone screening interviews were conducted with all potential study participants to determine if a) they had primary/shared responsibility for the care of an older relative or friend, b) the CR was functionally impaired (difficulty with ≥ 2 activities of daily living- e.g., bathing, dressing, etc., or unable to do at least 1 without assistance), and c) they live ≥ 2 hours travel from their CR. Fifty-five percent of eligible individuals from the aging service organizations participated in the study. Regarding the convenience samples, 85% of the eligible individuals from Research Match participated in the study, and 95% of the individuals from the professional network/referral group participated in the study. A recruitment flow chart is presented in Supplemental Figure 1. Data was collected through semi-structured telephone interviews conducted by trained research assistants.

Measures

Predisposing Variables

Single items were used to ascertain age, gender, ethnicity/race of the LDC and LDC’s relationship type to the CR (parent yes=1/no=0). For the current analysis, race/ethnicity was dichotomized to reflect non-Hispanic White (yes=1; 0=no).

Enabling Variables

Care recipients’ living situation was assessed with a single item asking if the CR currently lives in a residential facility (yes=1/no=0). Long-distance caregivers’ income adequacy was assessed with one item that asks participants to rate on a scale of 1–4 how well they feel they are able to manage on their income (ranging from 1=can’t make ends meet, 2 = just manage to get by, 3 = have enough money with a little extra, and 4=money is not a problem), with higher scores indicating greater perceived financial security (Cantor & Brennan 1993; Karpiak & Brennan, 2009). Similarly, LDCs’ employment status was assessed with one item, (e.g., “what is your current employment status?”). Responses were dichotomized to represent participants working full-time versus not full-time (yes=1/no=0). Long-distance caregivers’ instrumental and emotional support was conceptualized as perceived availability of instrumental and emotional social support and was measured with two items - one tapping instrumental social support (e.g., “If you needed extra help with tasks of everyday living, such as shopping, house cleaning, cooking, or giving you a ride, how often would you say you have family members or friends that you could count upon to help you?”) and one tapping emotional support availability (e.g., “If you needed emotional support such as someone to talk to or help you make a decision, how often do you have family members or friends that you could count upon to help you?”). Response options ranged from 1=not at all to 4=most of the time, with higher scores indicating greater levels of perceived support (Cantor & Brennan, 2000).

Need-related Variables

Caregiver burden and depression were assessed with the 12-item Zarit Caregiver Burden Scale (Bédard, et al., 2001; Cronbach’s α = .83 in the current study) and the Patient Health Questionnaire-9 (PHQ-9; Kroenke et al., 2001; Cronbach’s α = .82 in the current study), respectively. Long-distance caregivers’ self-reported physical health status was assessed via one-item (“How would you rate your overall physical health at the present time?”) with responses ranging from 1= very poor to 5=excellent. Distance from CR was calculated in miles based on addresses of both the LDC and CR reported by the caregiver. As an indicator of amount of time spent helping the CR, LDCs were asked to report how many hours they spend in a typical month doing things for or with the CR (including visiting time). We feel that total hours spent helping the CR provides a useful assessment of the amount of time LDCs dedicate to caring for their CRs both proximately and from a distance as opposed to frequency of visits. Care recipients’ cognitive status was assessed with a 6-item proxy checklist asking participants to indicate if the CR has any problems with memory, orientation, judgment, and function (Galvin et al., 2005), with response options including “No, not a problem” =0; “Yes, somewhat of a problem” =1; or “Yes, a major problem” =2. Items were summed as an indicator of the CR’s cognitive status, such that higher scores indicated greater cognitive impairment. Finally, to measure CR’s functional ability assessed via activities of daily living (ADL), LDCs were asked to rate the CR’s ability to carry out 7 ADL and 7 instrumental activities of daily living (IADL) tasks with no difficulty (1), little difficulty (2), a great deal of difficulty (3) or can’t do without help (4) (OARS: Center for the Study of Aging and Human Development, 1975; Horowitz et al., 2005; Cronbach’s α = .95 in the current study). Scores were summed to obtain an indicator of functional disability with higher scores indicating higher levels of disability.

Outcome Variable: Supportive Service Use

Participants were asked whether or not they were currently using nine supportive services which were designed to provide emotional or instrumental support - that is, help with caregiving tasks – for informal caregivers. The list included the following services supporting either LDC’s emotional well-being or instrumental care tasks that could benefit the CR: for LDC’s emotional support: [1] participation in an in-person support group for caregivers, [2] participation in a support group for caregivers over the telephone, [3] participation in a support group for caregivers that meets online/over the internet, [4] use of a Caregiver Mentor Matching Service – a service that matches caregivers with more experienced caregivers who could provide advice and support, [5] use of a private counselor/therapist, primarily to help with stresses of caregiving; for instrumental care that could also benefit the CR: [6] use of a Geriatric Care Management Service, [7] use of online or smartphone applications that assist in providing care to the CR, [8] use of a video phone system or an Internet-connected computer with a webcam to see the CR (e.g., any commercially available app on a smartphone or other device like FaceTime or Skype that allowed LDCs to see and communicate with the CR while not present), and [9] aide/home attendant or other professional caregiver using an electronic device, such as a tablet or smartphone, to convey CR’s health-related information and/or daily activities electronically to the LDC. Number of services used were summed to form a total service use score as the study’s outcome variable.

Data Analysis Plan

Frequency analyses were conducted for specific services used to describe the extent of service use among LDCs and to identify the most commonly used services. We then conducted bivariate analyses to explore multicollinearity among study variables and associations of study variables with the outcome (total number of services used). Finally, to determine associations among the predictor variables with the outcome, a multiple hierarchical regression was conducted. Consistent with Andersen’s Model of Healthcare Utilization, predisposing factors (i.e., age, gender, ethnicity/race, and relationship type to CR) were entered into the hierarchical regression model in step 1, enabling factors (i.e., CR’s living situation, LDCs’ income adequacy, employment status, instrumental support, and emotional support) were entered in step 2, and need-related factors (i.e., caregiver burden, depression, health status, distance from CR, time spent helping CR, as well as CR cognitive and functional status) were entered in the final step.

Results

Participant Characteristics

Table 1 presents descriptive statistics of participant demographic characteristics and variables used in the analyses. The sample in the current study consisted of 304 LDCs between the ages of 25–86 years (Mage=56.9, SD=12.5; Medianage=59), with 73% of the sample comprised of females. Overall, 212 participants (70.2%) reported to be Non-Hispanic White, 37 (12.3%) were Black, 35 (11.6%) were Hispanic, 6 (2%) were Asian/Pacific Islander, 1 (.3%) were Native American or Alaskan Native, and 11 participants (11.6%) reported their race as “other.” Seventy percent (n=213) of LDCs in the sample were caring for a parent (see Supplemental Table 1 for other CR-LDC relationships), and 45% (n=138) of CRs were living in a residential care setting. About half of LDCs (n=154) reported to be employed full-time.

Table 1.

Participant Characteristics (N=304)

Variable N (%) M (SD; Range)
Recruitment source a
Aging Service Organizations 138 (45.0)
Research Match 126 (41.0)
Professional Network/Referrals 40 (14.0)
Predisposing
Age 56.9 (12.5; 25–86)
Gender - Male 82 (27.0)
Non-Hispanic White - No 92 (30.3)
Relationship Type - Parent 213 (70.1)
Enabling
Residential Care - Yes 138 (45.4)
Income Adequacy 3.1 (.8; 1–4)
Employment Status – Full Time 154 (50.7)
Instrumental Support 3.4 (.9; 1–4)
Emotional Support 3.7 (.6; 1–4)
Need-related
Caregiver Burden 13.6 (8.2; 0–48)
Depression 4.0 (4.3; 0–27)
Health Status 4.2 (.7; 1–5)
Distance from CR (miles) 919.6 (1073.0; 11–8444)
Time Spent Helping CR (hours/month) 59.2 (108.8;0–720)
CR Cognitive Status 4.1 (2.0; 0–6)
CR Functional status 36.7 (12.5; 14–56)
Outcome
Number of Supportive Services .63 (.8; 0–9)
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Note.

a

recruitment source was not specifically included in hierarchical regression analysis; CR = Care recipient.

Frequency of Support Service Use

In this study, the number of services used by LDCs ranged from 0 to 3. A little over one-half of participants (n=169) reported not using any supportive services, about one-third (n=86) used one service, 11% of participants (n=32) used two services, and 4% reported using three services (n=13). Among LDCs who reported using supportive services, the average number of services used was 1.44 (SD = .67). The two most frequent types of supportive services used by LDCs were video phone/webcam systems to see the CR from afar (23%) and engaging with a therapist/counselor to talk about caregiving issues (15%).

Associations of Independent Variables with Total Supportive Service Use

The results of the bivariate correlational analyses are presented in Supplemental Table 2. Overall, a higher number of supportive services used was significantly associated with younger age (r=−.31), the CR living in the community (rather than residential care) (r=−.15), higher caregiver burden (r=.29), greater depressive symptoms (r=.23), and more time spent helping the CR (r=.23; see Supplemental Table 2).

Results of the hierarchical regression analysis (Table 2) revealed that in step 1, age was the only pre-disposing factor that significantly predicted service use and accounted for 9.8% of the variance (F(4, 278) = 7.59, p < .001). Age remained the only significant predictor following the inclusion of enabling factors in step 2 (F(9, 273) = 3.73, p < .001), and the change in R2 was not significant (p = .64). The inclusion of need-related factors in step 3 contributed significantly to the model (F(16, 266) = 4.94, p < .001), and the final regression model explained 22.9% (p < .001) of the variation in total service use. The final model indicated that younger age (β =−.26), higher caregiver burden (β =.15), greater depressive symptoms (β =.18), more time spent helping the CR (β =.18), and worse CR functional status (β =.15) all significantly predicted greater total supportive service use by LDCs.

Table 2.

Hierarchical Regression Analyses for Associations of Predisposing, Enabling and Need-related Factors with Total Number of Supportive Service Use

Step 1 Step 2 Step 3
Variable B SE (B) β t B SE (B) β t B SE (B) β t
Predisposing
Age −0.02*** .00 −.32 −5.26 −0.02*** .01 −.29 −4.23 −0.02*** .00 −.26 −3.97
Gender - Male −0.10 .11 −.05 −0.91 −0.09 .11 −.05 −0.80 −0.06 .11 −.03 −0.53
Non-Hispanic White - Yes 0.12 .11 .06 1.03 0.10 .12 .05 0.83 0.05 .11 .03 0.41
Relationship Type - Parent −0.00 .11 −.00 −0.03 −0.02 .11 −.01 −0.18 −0.10 .11 −.06 −0.97
Enabling
Residential Care - Yes −0.10 .10 −.06 −0.92 −0.11 .12 −.07 −0.96
Income Adequacy −0.04 .07 −.04 −0.58 0.05 .07 .05 0.73
Employment Status – FT 0.03 .11 .02 0.32 0.02 .10 .01 0.22
Instrumental Support −0.04 .06 −.04 −0.59 −0.03 .06 −.03 −0.44
Emotional Support 0.14 .10 .10 1.47 0.15 .09 .11 1.60
Need-related
Caregiver Burden 0.02* .01 .15 2.30
Depression 0.03* .01 .18 2.69
Health Status 0.12 .07 .10 1.63
Distance from CR 0.00 .00 .06 1.13
Time Spent Helping CR 0.00** .00 .18 3.12
CR Cognitive Status −0.02 .03 −.06 −0.92
CR Functional Status 0.01* .01 .15 2.15
R 2 .10 .11 .23
Adjusted R2 .09 .08 .18
F for change in R2 7.59*** 0.68 5.89***
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*

p < .05

**

p <.01

***

p < .001

FT = Full Time; CR = Care recipient

Discussion

To our knowledge, this is one of the first studies using Andersen’s Model of Health Care Utilization (Andersen, 1995) to specifically investigate supportive service utilization in LDCs’. Although LDC is receiving increased empirical attention, there is still a lack of research examining the nature and consequences of care provision in LDCs. Of the limited extant literature, it has become clear that geographical distance is an added challenge to the already often stressful caregiving experience. Bevan and Sparks (2011) provided some examples of the unique challenges of caring from a distance, stating that “assessing CRs’ needs and determining how and when they [LDCs] should get involved, locating services and monitoring the care, and the strain…on the distant caregiver’s proximal family relationships” are potential difficulties for LDCs (p. 27). As a result, there is a clear need for supportive services to assist LDCs in navigating and coping with the challenges of their unique care environment. However, the utilization of caregiver supportive services has not been widely examined among LDCs. Thus, our study aimed to: 1) describe the type of supportive services used for themselves and the extent of usage of these services by LDCs and 2) identify predisposing, enabling, and need-related factors associated with supportive service use in a sample of LDCs.

Results from the current study are generally consistent with the limited amount of previous literature on the topic. First, previous work has found a consistent pattern of a high need for services but low utilization among caregivers generally (Toseland et al., 2002). In the current study, we found relatively low utilization of supportive services, with LDCs reporting using about one support service, and over one-half reporting no service utilization. The most frequent type of supportive service used by LDCs was video phone/webcam systems to see the CR from afar, which is consistent with Benefield and Beck (2007)’s assertion that technology provides innovative solutions to some of the challenges experienced by LDCs. Furthermore, when considering predisposing, enabling, and need-related factors guided by Andersen’s Model of Health Care Utilization (Andersen, 1995), need-related factors were more prominently associated with supportive service use when compared to predisposing and enabling factors. Consistent with previous research on proximate caregivers, results indicated that higher caregiver burden and greater depressive symptoms were significantly related to greater total supportive service use by LDCs (Martindale-Adams et al., 2016; Mavandadi et al., 2017; Toseland et al., 2002). Interestingly, age was the only predisposing factor that was significantly related to total service use, but the relationship was in the opposite direction compared to findings from previous research in which increased age was found to predict increased likelihood of service use among proximate caregivers (Martindale-Adams et al., 2016). One explanation for this difference in findings could be the nature of the supportive services that were examined. Martindale-Adams and colleagues (2016) explored the following supportive services: “homemaker, home health aide, meals, transportation, visiting nurse, day care or senior day health program, support group, physician visit, mental health visit, emergency room visit, and inpatient care” (p. 1055). Martindale-Adams et al. (2016) examined supportive services for both the caregiver and the CR while the current study focused on LDCs’ use of supportive services. Additionally, since four of the nine supportive services examined in the current study (e.g., online support group, smartphone app, video phone system/webcam to see CR, electronic monitoring by professional CG) were technology-based, it is possible that younger individuals were more comfortable accessing these types of services. Also inconsistent with previous research on proximate caregivers was our finding that none of the enabling factors were related to total service use. Previous research had found that being retired (Martindale-Adams et al., 2016), being employed part-time (Meyer et al., 2019), having a higher income (Martindale-Adams et al., 2016; Mavadandi et al., 2017) and having more social support (Robinson et al., 2005) predicted greater service use. However, the current study did not replicate these findings. One potential explanation for the lack of replication is a difference in variable operationalization. Martindale-Adams and colleagues (2016) examined social support as measured by received support, negative interactions, and satisfaction while the current study examined the perceived availability of instrumental and emotional support for the LDC. Similarly, Martindale-Adams et al. (2016) included annual household income while the current study included income adequacy. It is possible that these differing operationalizations of the enabling variables contributed to the lack of replication. It is also possible that the enabling factors that enhance a proximate caregiver’s ability to access supportive services are not the same as those for LDCs. Future research should continue to try to identify significant enabling factors, such as having a supportive social network (e.g., Robinson et al., 2005) by applying the Andersen Model of Health Care Utilization (Andersen, 1995) for LDCs.

Limitations

Whereas the current study contributes to our understanding of the factors that are associated with supportive service utilization that LDCs access, the results should be interpreted within the context of the limitations of the study. One limitation of the current study is the inability to infer causal relationships between the predisposing, enabling, and need-related predictor variables and the outcome variable of total service use due to the cross-sectional design. Future studies may seek to implement longitudinal designs to elucidate the directionality of these variables. Likewise, while the use of a continuous dependent variable allowed us to capture the total supportive service use within the sample, we did not explore the patterns of utilization for each individual supportive service, which may be investigated in future research. Additionally, because the sample was comprised primarily of female caregivers, the generalizability of these findings to people with other gender identities is unclear. Interestingly, according to NAC and the MetLife Mature Market Institute (2004), the gender distribution among LDCs may differ from that among proximate caregivers, namely that males make up a greater proportion of LDCs than they do of proximate caregivers. Thus, the question of the generalizability of these findings to males is of particular significance to the larger LDC literature. Future research may implement strategies to recruit an increased number of male participants to examine whether the pattern of findings is consistent among both female and male LDCs. Another limitation of the current study is that the surveys were entirely self-reported. Thus, LDCs may have not accurately evaluated the level of their CR’s cognitive or functional impairment, and we did not have access to objective cognitive or functional data for the CR to corroborate LDCs’ evaluations. While it is possible that LDCs who experienced higher levels of depression or caregiver burden may have also rated their CR’s functioning worse, the correlations between these variables and functional status were nonsignificant and close to zero. Relatedly, we did not systematically collect data on the household composition of CRs living in the community, so there may have been variability in the presence of others in the CR’s home. One final limitation of the current study is related to the potential for sample selectivity. Specifically, the LDCs who were willing to participate in the current study may comprise a unique subsample of LDCs who are more interested and proactive in accessing research opportunities and caregiver resources. While sample selectivity may have been present, the rate of service utilization was similarly low to rates observed in previous research. Overall, while the study’s conclusions should be interpreted within the context of its limitations, there are important practical implications highlighted by the current study.

Implications and Future Directions

Despite some limitations of the current study, our findings carry valuable implications for future supportive service design and implementation. First, while younger age was associated with increased service utilization, it is possible younger adults felt more comfortable utilizing the technology-based services compared to older LDCs. Although not examined in the current study, unfamiliarity or discomfort utilizing the service because it is technology-based may be an important but modifiable barrier for older LDCs utilization of services. Thus, future studies may seek to explore barriers and facilitators for utilizing technology-based supportive services, specifically among older LDCs. Furthermore, in line with previous findings among proximate caregivers (e.g., Toseland et al., 2002), the current study emphasized the impact of need-related factors, such as high caregiver burden and depression, on supportive service utilization, suggesting that LDCs who are the most vulnerable are also the ones accessing the services. While it is promising to observe that the participants who are most in need of services are accessing most services, it is important to note that it is possible that LDCs who agreed to participate in this study are more able to access services than those you did not participate in this research. Future research should attempt to replicate these findings with larger, more diverse samples. Future research should also investigate whether these services are effective at reducing burden and improving well-being among LDCs. While previous research has found supportive services to be effective among proximate caregivers, there is a lack of research using samples of LDCs. Thus, future work may focus on longitudinal studies and intervention research to examine the prospective effectiveness of LDCs utilizing supportive services for themselves. Future research may also directly explore the differences in supportive service use among proximate caregivers and LDCs to better tailor the resources for these different caregiving populations.

Conclusions

In this cross-sectional study of 304 LDCs, we found generally low utilization of supportive services among LDCs, which is consistent with findings for geographically proximate caregivers (e.g., Toseland et al., 2002). We found that the most frequently utilized service was technology-based, specifically the use of a video phone system or an Internet-connected computer with a webcam to see their CR from afar. Finally, by applying Andersen’s Model of Health Care Utilization, we found that younger age, higher caregiver burden, greater depressive symptoms, more time spent helping the CR, and worse CR functional status were all significantly related to greater total supportive service use by LDCs, highlighting the importance of need-related factors compared to predisposing and enabling factors. These findings also indicate that some of the enabling factors that improve proximate caregivers’ access to supportive services may not enhance LDCs’ access to supportive services, so future research should continue to identify enabling factors to facilitate LDCs’ access and utilization of support services for themselves.

Supplementary Material

Supplemental Table 1
Supplemental Table 2
Supplemental Figure 1

Figure 1.

Figure 1

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Frequency of Support Service Use (N=304)

Note. CR = Care recipient; LDC = Long-distance caregiver

Funding

This work was supported by the National Institute on Aging (PI: Horowitz: R21-AG050018). Falzarano acknowledges support from a NIA-funded T32 training program grant (AG049666).

Footnotes

Disclosure Statement

No potential conflict of interest was reported by the authors.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Table 1
NIHMS1766943-supplement-Supplemental_Table_1.docx (12.9KB, docx)
Supplemental Table 2
NIHMS1766943-supplement-Supplemental_Table_2.docx (17.5KB, docx)
Supplemental Figure 1
NIHMS1766943-supplement-Supplemental_Figure_1.docx (19KB, docx)

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