Age differences in emotional support buffering on the relationship between physical disability and psychological distress

Abstract

Greater levels of physical disability are often accompanied by greater levels of psychological distress. Stress Buffering Theory (Cohen & Wills, 1985; Thoits, 2011) posits that the level of social support buffers this relationship. Life Stage Theory proposes the previously untested hypothesis that the salience of the buffering offered by social support may decrease with age – i.e., that as individuals age, emotional support offers a weaker buffer of the effect of disability on psychological distress (Segrin, 2003). This research tested a unified Stress Buffering/Life Stage model of psychological distress. We hypothesized that the buffering effect of emotional support is greater for younger adults than for older adults. Study data were drawn from 293 adults who self-identified as having a physical disability. Respondents’ age, emotional support, psychological distress, and disability were assessed. A saturated three-way interaction analysis of covariance model was used to evaluate the association of disability and psychological distress, as buffered by emotional support, for older versus younger people. Results were consistent with a unified Stress Buffering Life Stage model. Greater levels of emotional support attenuated the relationship between disability and psychological distress for younger, but not for older, study participants. Self-reported measures and the cross-sectional design precludes making causal inferences. Emotional support may be particularly salient for younger people in buffering the effects of disability on psychological distress.

Keywords

Disability distress mental health social support adulthood age differences stress buffering theory life stage theory

Introduction

Psychological distress is a major health condition in the United States, with 45 million (i.e., approximately 18%) of adults at risk for moderate to serious levels of distress (Lee & Singh, 2020). Even at low and moderate levels, psychological distress (i.e., non-specific symptoms of stress, anxiety, and depression) significantly increases the risk of negative outcomes. Those outcomes include subjective well-being and quality of life (Uddin et al., 2017), medical outcomes including incident arthritis, cardiovascular disease, and chronic obstructive pulmonary disease (McLachlan & Gale, 2018), and premature mortality (Russ et al., 2012). This is particularly concerning, given that approximately 23% of adults in the United States have experienced some level of psychological distress in recent years (Daly, 2022).

Disability and psychological distress

People with disabilities are at increased risk of poor mental health (He et al., 2019; Stickley et al., 2021), including suicide acceptability (Lund et al., 2016; Lutz & Fiske, 2018). Studies have shown that symptoms of depression may be 2 to 10 times more common in individuals with disabilities or chronic illnesses, and depression is one of the most common secondary conditions associated with disability and chronic illness (Thompson, 2002). Adults with disabilities report experiencing frequent mental distress almost 5 times as often as adults without disabilities (Cree et al., 2020), and are at substantially elevated risk for anxiety, depressive symptomatology, and major depressive disorder, with those who have more severe impairments exhibiting significantly higher levels of psychological distress (Turner et al., 2006; Turner & McLean, 1989). This may be due to stressors related to stigma, accessibility challenges, and/or financial difficulties (Kuper, 2021; Noh et al., 2016)

A putative buffer: Emotional social support

While the fundamental disability-psychological distress link has been well established, factors that buffer this relationship remain largely underexplored. One potentially promising buffer is social support. Of the four primary social support components defined by (Weinert & Brandt, 1987), informational, instrumental, emotional, and appraisal, emotional support (i.e., the provision of caring, empathy, love, and trust) in particular has been shown in studies to be associated with fewer symptoms of psychological distress (Kessler et al., 1992; Muñoz-Laboy et al., 2014; Patrick et al., 2001; Wethington & Kessler, 1986). Many studies have focused on emotional support because emotional support has been shown to be an important predictor across multiple disciplines, including education (Malecki & Demaray, 2003), health conditions (Littlefield et al., 1990; Neugebauer & Katz, 2004), and job stress (LaRocco et al., 1980). In this study, we used perceived emotional support in view of research showing that the perception of emotional support is the most effective type of social support when reducing distress, or mental-health-related outcomes (Berkman, 1995; Cohen & Wills, 1985; Stroebe & Stroebe, 1996).

The stress-buffering model (Cohen & Wills, 1985; Thoits, 2011) posits that social support, particularly perceived emotional support, mitigates or buffers the negative impact of life stress on psychological well-being (Vangelisti, 2009). According to this model, social support decreases the negative effects of stress by reducing both the appraised threat of stress and the stress response that follows threat (Cohen & Wills, 1985).

Although social support has been found to mediate the relationship between disability and depression (Zhao et al., 2022), research focused on the moderating effect of support on the relationship between disability and negative outcomes is limited. Nonetheless, there is some evidence that emotional social support buffers the relationship between physical disability and psychological distress. In a study of older adults, Haron, et al. (2018) found that emotional social support buffered the association between disability and life satisfaction (a negative correlate of psychological distress), such that the negative relationship between disability and life satisfaction was weaker for individuals with higher levels of emotional support. This supports the theoretical expectation that individuals who report greater difficulty functioning due to their disability require greater emotional support to overcome the concomitant high levels of psychological distress compared to individuals with less severe levels of physical disability.

Extending the buffering model

Life stage theory originates from developmental psychology and proposes that human development continues throughout the lifespan, is marked by stage-related challenges, and is dependent on the social environment (Erikson, 1963). Thus, the importance of social relationships varies across life stages (Umberson & Karas Montez, 2010) and the buffering effect of social support may not be uniformly salient across the lifespan. In fact, it may decline as the individual ages (Segrin, 2003), given a typical shrinking of social networks due to normative changes such as divorce, retirement, death, etc. Studies that have found that social support was more strongly related to life satisfaction (George et al., 1985) and mental health (Lutz & Fiske, 2018; Milner et al., 2016; Segrin, 2003) for younger adults compared to middle-aged or older adults. Given these differences, (Shin & Gyeong, 2023) recommend that researchers examine relationships between social support and well-being by age as shown in Figure 1.

Figure 1.

Stress buffering model, stage model, and stress buffering stage model.

There have been several studies suggesting that age does moderate the association between social support and depression with the strongest association at earlier stages in the lifespan. These findings support the life stage model that the meaning of social support varies across ages and may matter less in some life stages than others. For example, (George et al., 1985) found that social support was more strongly related to well-being for younger adults compared to middle-aged or older adults. It is possible that as people age they develop additional coping skills, and may have less need for social support or have coping skills that supersede social support (Jensen et al., 2014).

In a large sample of individuals with disabling medical conditions (Terrill et al., 2016), resilience was lowest among participants who were middle-aged or younger. This suggests the possibility that, with lowered resilience, younger individuals with high levels of physical disability and low social support may be more negatively impacted than older individuals with high levels of physical disability and low social support. Perhaps older individuals with disabilities who in some cases have had a disability for a longer time than younger individuals, have developed greater resilience resulting in less need for emotional support. It is also possible that older individuals may have developed additional coping mechanisms that are more helpful, or eclipse the role of social support. In addition, younger adults with disabilities may have more difficulty maintaining close social ties and may be excluded from normative social roles that can result in emotional support (Allen et al., 2000).

Because the Haron, et al. (2018) study focused only on older adults with disabilities, there remains the unaddressed question of whether the putative buffering effect of emotional support on the disability-psychological distress relationship prevails across a broader adult age range. The current research tested this question in the form of a unified Stress Buffering/Life Stage model. Specifically, based on a sample of 296 adults with physical disabilities, we tested whether (1) emotional support buffers the disability-psychological distress link, and (2) if so, whether the strength of this buffering effect dissipates with age. In addition to bolstering our understanding of environmental conditions underlying psychological distress, should such a moderated buffering effect exist, targeted emotional social support could provide a fertile ground for interventions designed to decrease the link between physical disability and distress.

Methods

Participants

Various organizations and individuals involved in serving adults with physical disabilities were contacted to assist with participant recruitment nationwide. Individuals were eligible for participation if they: (1) were 18 years or older, (2) self-identified as someone with a physical disability, and (3) resided in the United States. Data were collected from 296 participants representing most states in the US; states and territories with the highest representation included Colorado (9%), Illinois (9%), California (8%), North Carolina (8%), Kansas (5%), Pennsylvania (5%), Virginia (5%), Oregon (5%), and Washington, DC (4%).

Demographic characteristics of the sample are presented in Table 1. Nearly two-thirds of the sample (64%) was female. The sample was majority White (84%) with the rest of the sample reporting their race as African American/Black (8%), Biracial or Multiracial (4%), Hispanic or Latino (<2%), Asian or Asian American (<2%), and Alaska Native or Native American (<2%). At the time of the survey, most of the sample was single (61%) and 38 percent of the sample lived alone. Approximately equal percentages lived in urban (39%) and suburban (44%) areas, while 17% lived in rural areas. Forty seven percent reported owning their home. Slightly less than half (44%) were a parent. Approximately 7% of the sample completed their education with a high school degree; 6% went to trade school or vocational school; 10% held an Associate’s degree; 26% held a Bachelor’s degree; and 36% had at least some graduate education, with 20% holding a Master’s degree and 7% holding a Doctoral degree. The average age was 46.30 (SD = 14.05).

Table 1.

Demographic characteristics.

	N	%
Gender
Male	104	35.1
Female	189	63.9
Other	3	1.0
Marital status
Single	182	61.5
Married/Living with partner	114	38.5
City density
Urban	114	38.5
Suburban	131	44.3
Rural	51	17.2
Own or Rent
Own	139	47.0
Rent	157	53.0
Household composition
Live alone	112	37.8
Live with others	184	62.2
Parental status
Parent	131	44.3
Not a parent	165	55.7
Education
Some high school	1	0.3
High school diploma	20	6.8
Some college	42	14.2
Trade school/vocational school	17	5.7
Associate’s degree	28	9.5
Bachelor’s degree	76	25.7
Some graduate school	27	9.1
Master’s degree	60	20.3
Doctoral degree (Ph.D., J.D., etc.)	21	7.1
Other	4	1.4
Income
$7,499 or less	36	12.2
$7,500 to $14,999	51	17.2
$15,000 to $24,999	53	17.9
$25,000 to $34,999	49	16.6
$35,000 to $49,999	49	16.6
$50,000 to $74,999	26	8.8
$75,000 to $99,999	18	6.1
$100,000 or over	14	4.7
Children under 18 in household
0	222	75.0
1	37	12.5
2	27	9.1
3	8	2.7
4	2	0.7
Race
Black	24	8.1
White	249	84.1
Other	23	7.8

Participant disability severity was measured by a self-report of difficulties due to health conditions including physical disabilities (WHODAS 2.0), which varied from 32 to 143 (M = 72.82, SD = 21.59). Additionally, participants reported the number of days per month difficulties were present (M = 15.09, SD = 12.56), that they were totally unable to carry out usual activities (M = 5.92, SD = 9.62), and that they needed to cut back usual activities due to their physical disability (M = 9.29, SD = 10.33). Approximately 60% of participants described their disability as acquired (i.e., developed during childhood or adulthood) and the rest as lifelong (since birth).

Procedure

Approval for this study was obtained from our University Institutional Review Board. As noted previously, individuals were eligible for participation if they were over 18, self-identified as having a physical disability, and lived in the United States. Organizations interested in assisting with recruitment received a copy of an electronic flier including a short description of the study with eligibility and participation information. Additionally, an electronic copy of the recruitment flyer was periodically posted on various social media and networking sites (e.g., Instagram, LinkedIn) that focused on individuals with disabilities. Over 50 state and national organizations participated, with United Cerebral Palsy as the top recruiter (over 100 participants). Potential participants were asked to email the study coordinator for more information about participating in the study; in response, the coordinator emphasized eligibility criteria, asked the potential participants to confirm that they met these criteria, and asked if they were interested in either the online or telephone survey format. If responders acknowledged meeting the study criteria, the coordinator emailed the individual a unique Internet link or arranged for a telephone interview at a later time. Participants were compensated with a $10 gift card for their time completing the survey. Data collection occurred between January and October 2015.

Individuals who did not meet study admission criteria (i.e. denied identifying as having a physical disability) in the online data collection were excluded, along with those who did not provide an address within the United States.

Measures

Psychological distress

Psychological distress was measured using the six-item Kessler Psychological Distress Scale (K6) (Kessler et al., 2002). The K6 measures psychological distress, including depression and anxiety symptoms, with six items answered on a five-point Likert scale ranging from 0 (none of the time) to 4 (all of the time). Higher scores on the K6 reflect a higher level of psychological distress. The K6 has been successfully used in a range of population and community surveys around the world (Kessler et al., 2002). The internal reliability of the K6 was good (α = .87). Because a preliminary analysis revealed a positive skew, the K6 was transformed using the square root transformation.

Disability

The World Health Organization Disability Assessment Schedule 36-item version, WHODAS 2.0 (Üstün et al., 2010) is self-administered and assesses participants’ difficulties due to health conditions, including physical disabilities. This measure is widely used for measuring disability across multiple populations, including the general population and patients with mental disorders (e.g., Guilera et al., 2015). The 36 items are measured on a scale from 0 to 4. It includes major life domains such as activity and participation. Items include “In the past 30 days, how much difficulty did you have in: Standing for long periods such as 30 minutes? Moving around inside your home?” Previous research has shown this measure to have high internal consistency (α = .98) and to be valid across multiple populations (see the WHODAS 2.0 manual for a summary: Üstün, 2010) . Internal consistency in the current sample was .94. Because 40% of the current sample noted that they did not work or go to school, the four items referencing work/school were omitted, resulting in a 32-item scale.

Emotional social support

The PROMIS emotional support scale (PROMIS Short Form - Emotional Support 8a - Version 2.0, n.d.) was used to capture the availability of others with whom to talk and feeling appreciated by others. The PROMIS emotional support item bank specifically aims to assess perceived feelings of being cared for and valued as a person. Participants were presented with the following items: ‘‘I have someone who will listen to me when I need to talk’’; ‘‘I have someone to confide in or talk to about myself or my problems’’; ‘‘I have someone who makes me feel appreciated’’ and ‘‘I have someone to talk with when I have a bad day.’’ Each item was followed by a Likert-type response scale with possible responses of Never (0), Rarely (1), Sometimes (2), Often (3), and Always (4). We calculated a raw summary score ranging from 0 to 16. The internal reliability was good (α = .93).

Age

Each study participant was asked to provide their age on the date they completed the questionnaire. As previously noted, the mean for age was 46.30 (SD = 14.05) with a range from 18 to 84.

Control variables

Address possible spurious effects, the multivariate analysis initially controlled for gender, marital status, population density, home ownership, household composition, parental status, education, income, number of children at home, and ethnicity. However, because none of these variables substantially altered the results, the results are presented here without controlling for these variables in order to simplify the presentation.

Statistical analysis

The distributions of the sociodemographic variables were first examined, followed by calculations of descriptive statistics and bivariate correlations of age, disability level, and emotional support. A multivariable analysis of covariance was conducted by regressing psychological distress on all main effects, two-way interactions, and the three-way interaction of age, disability level, and emotional support. Parameter estimates and associated statistical significance of simple effects and simple effect differences were calculated. All analyses were conducted using SAS Version 9.4 (Cary, NC).

Results

Univariate characteristics and bivariate relationships among variables

Bivariate relationships among the central variables employed in the study are presented in Table 2. As expected, disability was strongly correlated with psychological distress and moderately negatively correlated with emotional support. Of particular note, the correlation between disability and psychological distress was r = .53 (p < .001), reflecting challenges accompanying physical disabilities.

Table 2.

Means, standard deviations, and correlations.

	Age	Disability	Emotional support	Psychological distress
Disability	.06
Emotional support	−.16**	−.37***
Psychological distress	−.05	.53***	−.38***
Mean	46.30	72.82	11.88	2.39
Std.Dev	14.05	21.59	3.65	.90

Note. *p < .05; **p < .01; ***p < .001.

Multivariable analysis results

In order to test for a differential buffering effect of emotional support on the relationship between disability and psychological distress, psychological distress was regressed on all main effects, two way interactions, and the three-way interaction of age, disability level, and emotional support. The resulting parameter estimates are presented in Table 3; Figures 2-4 summarize these results at three age levels (25, 40, and 55).

Table 3.

Parameter estimates and associated t- and p-values.

Parameter	Estimate	t	p
Intercept	.1236	.06	.950
Disability	.0628	2.60	.010
Emotional support	.1719	1.13	.260
Disability x Emotional support	−.0047	−2.44	.015
Age	.0234	.61	.542
Disability x age	−.0008	−1.67	.095
Emotional support x age	−.0041	−1.34	.181
Disability x Emotional support x age	.0001	2.31	.022

Figure 2.

The relationship between expected psychological distress and disability, by emotional support: Age 25.

Figure 3.

The relationship between expected psychological distress and disability, by emotional support: Age 40.

Figure 4.

The relationship between expected psychological distress and disability, by emotional support: Age 55.

The difference in the buffering effect of emotional support across the lifespan is indicated by the significant three-way interaction (b = .0001, p < .05) and by comparing Figures 2-4. Figure 2 shows a significant difference in slopes between 25-year-olds reporting high emotional support and those reporting low emotional support. In Figure 3, among 40-year-olds, the slopes are more similar, indicating a reduction in the moderating effect of emotional support. Finally, among 55-year-olds (Figure 4), the slopes are nearly identical, indicating a still greater reduction in the moderating effect of emotional support. It should be noted that age was employed as a continuous variable in this multivariate analysis; the three age levels were selected for these figures as representative ages for younger, middle-aged, and older adults.

Discussion

The stress-buffering model asserts that social support moderates the detrimental effects of a stressor (e.g., physical disability) on mental health, with buffering effects being greatest under high stress when an individual perceives that support will be provided when needed (Cohen & Wills, 1985). The life-stage model asserts that the impact of social support on psychosocial problems decreases with age (Segrin, 2003). The present research proposes a unified model which integrates these two paradigms. Specifically, we tested the hypothesis that the buffering by social support on the relationship between a stressor (disability level) and the response to the stressor (psychological distress) is attenuated by age, i.e., that the buffering is stronger for younger adults than for older adults (see Figures 1-3).

The results of our non-additive multivariable analysis of covariance on 296 adults who self-identified as having a physical disability support the proposed integrated model. For younger adults, emotional social support significantly moderated the relationship between level of physical disability and psychological distress, as predicted by the stress-buffering model. For older adults, the observed level of buffering dropped to non-significance.

The moderation of a buffering effect suggests an important nuance in our understanding of the stress-response relationship. Under a classical stress-buffering model, the buffering effect is assumed to be unconditional. The integrated stress-buffering stage model proposed here suggests an important limiting condition: viz., that the buffering effect declines with age. In this regard, emotional support intervention strategies targeted toward older adults may be of limited value, while similar efforts targeted at younger adults may meet with greater success.

These results should not be interpreted as diminishing the importance of emotional social support for older adults: social support provides health and survival benefits (among other benefits) to older as well as younger adults by strengthening coping and recovery when ill or via biological mechanisms that protect against illness (White et al., 2009). Rather, the results presented here suggest that the buffering offered by social support, which was clearly observed among younger adults, may not be as salient for older adults. As Figure 2 and in particular, Figure 3 demonstrate, older adults with a greater disability were found to report significantly greater levels of psychological distress regardless of their level of emotional social support. In contrast. younger adults with greater disability and higher levels of social support were not found to report significantly greater levels of psychological distress as shown in Figure 1.

There are several possible explanations for the diminution of the buffering effect as individuals grow older; for example, the use of alternative coping skills and/or the decreased salience of social support. In addition, optimism has been linked to lower levels of depression, fewer somatic symptoms, and fewer symptoms of psychological distress (Jahanara, 2017) and age has been found to be negatively related to optimism (Paúl et al., 2007). As individuals grow older, it may become increasingly difficult for social support to overcome the decreased optimism and the concomitant increased psychological distress. Similarly, weathering, or cumulative exposure to adversity (Geronimus, 2023), could decrease the buffering effect of social support. It is also possible that as individuals with physical disabilities age, they have accepted their disability, and support from others does not impact their distress.

Finally, there may also be important differences in the types and numbers of individuals available to provide emotional support. Older individuals have reported smaller networks and perhaps less intense relationships which may result in differences in emotional support given the way it is traditionally assessed (Cicirelli, 2010).

Of additional consequence, this study found that, among younger adults, higher levels of emotional social support fully buffered the otherwise-harmful effect of disability severity on psychological distress. If these results are replicated elsewhere, it suggests the potential promise of emotional social support intervention strategies targeted toward younger adults.

Interpretation of these results should be approached with caution. The cross-sectional nature of the study design limits causal attributions. It is possible, for example, that greater levels of psychological distress could lead to the elicitation of greater levels of social support. In addition, these results are based exclusively on self-reported data. The primary outcome variable employed in this study provided a general measure of psychological distress, and it is entirely possible that different outcomes would be observed for specific disorders (i.e., depression and anxiety), or for positive outcomes such as well-being. The sample was majority White, highly educated, and lived in the US, and may not be comparable to other samples of individuals with physical disabilities. Finally, regarding demographic information of our sample, we did not ask about comprehensive gender categories (e.g., cis-gender and transgender) or sexual orientation; future research should collect these potentially informative participant characteristics. Future research could examine how younger versus older individuals with a disability are likely to differ in the specific disabling conditions that they have and how long they have been living with those conditions. These factors could interact with distress and emotional support.

Conclusion

These limitations notwithstanding, the results presented here suggest a promising avenue for understanding the complex relationship between disability and psychological distress. The results of this study suggest the importance of emotional social support in buffering the effects of disability on psychological distress among younger adults. However, these results also underscore the limited buffering effect offered by social support among older adults. It remains for future study whether the conditional restriction imposed by age is mediated by other coping mechanisms, less value of social support, or some other mitigating factor. In summary, these results underscore the importance of social support as a possible intervention for younger adults, and the importance of identifying equivalent mitigating interventions for older adults.

Footnotes

Acknowledgments

The authors thank Briana Altman and Caroline Moore for their assistance with data collection.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Data collection was supported by the Society for Community Research and Action; The American Psychological Association, the Society for the Psychological Study of Social Issues, and George Washington University.

Presentations

This research has not been previously presented at any professional meetings.

Open research statement

As part of IARR’s encouragement of open research practices, the authors have provided the following information: This research was not pre-registered. The data used in the research are available upon request by emailing: pww@gwu.edu. The materials used in the research are available upon request. by emailing: rohrbeck@gwu.edu.

ORCID iD

Cynthia A. Rohrbeck

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