Supporting the supporter

Abstract

The social, economic, and physical costs associated with providing long-term care for a child with disabilities can be overwhelming, and it is not uncommon for caregivers to experience burnout, emotional distress, and significant health ailments as a result of their commitment to their child. Social support can be a key resource to combat these negative effects, as ample research has shown that social support can act as a buffer to the negative effects of stress. The current study explored whether short-term supportive interactions between parents of children with disabilities and members of their supportive network (n = 40 dyads) influenced the physiological stress responses (as measured by salivary cortisol) of both interactants. Results indicated that receiving support in a short interaction resulted in parents experiencing decreases to their physiological stress, though the quality of the support played a key role in determining how beneficial the interaction was in this context. These results suggest the importance of considering support quality when examining the influence of social support on physical outcomes for support recipients.

Keywords

Caregiving children with disabilities salivary cortisol social support stress verbal person-centeredness

Mental and physical disabilities affect millions of people in the U.S., and managing disabilities is associated with significant costs for family caregivers. It is not uncommon for caregivers to experience negative outcomes stemming from their responsibilities (Centers for Disease Control and Prevention [CDC], 2010). Social support, however, may help caregivers better manage their demands, as previous research suggests that social support can protect individuals from the deleterious effects of stress (Cohen & Willis, 1985). There is still much to learn, however, about when support works best and how the quality of support might influence individuals’ stress.

This study has two primary goals. First, it explores how engagement in a supportive interaction affects the physiological stress of both parents caring for a child with disabilities (PCDs) as well as the support provider. Understanding the implications of social support for PCDs is an important step in promoting caregiver health. Exploring the experiences of relationship partners supporting PCDs is also important, as provider experiences have received less empirical attention and carry implications for the health of the provider and the relationship they have with PCDs. Second, this study seeks to advance the literature outlining the associations between social support and health by exploring how support quality influences short-term experiences of physiological stress. Additionally, this study examines more natural conversations between relationship partners when considering support quality, contributing to the growing list of methods for exploring the connections between social support and health.

The community costs of disabilities in the U.S.

According to CDC (2012), the disability prevalence in the U.S. has grown 17.1% in the past decade. Having a child with disabilities is linked to numerous significant challenges, such as an increased risk for falling below the poverty level (Emerson, 2003), experiencing depression, a higher incidence of health complaints, and an increased risk for all-cause mortality (Schulz & Beach, 1999). A 2006 study by the National Alliance for Caregiving found that all of the 528 caregivers surveyed reported declines in their health and increases in their stress as a result of caregiving activities (National Alliance for Caregiving, 2006).

Caregiving parents face some unique challenges that can exacerbate these outcomes (Faw & Leustek, 2015). Disabled children can engage in problematic behavior that can disrupt family life and strain relationships (De Andrés-García, Moya-Albiol, & González-Bono, 2012). Similarly, PCDs must navigate stigma associated with disabilities and the grief that comes with realizing a child will never achieve certain milestones (Ha, Hong, Mailick Seltzer, & Greenberg, 2008). In addition to care providers, the costs of caregiving can influence the health of the person receiving care (CDC, 2010). Declining caregiver health is associated with premature institutionalization of disabled individuals (Carretero, Garcés, Ródenas, & Sanjosé, 2009), underscoring the importance of caregiver health in families of children with disabilities.

Social support and its role in caregiving

Because caregiving carries serious health implications, practitioners have pinpointed social support as a resource key to reducing caregiver strain (Lovell, Moss, & Wetherell, 2012). Social support is “verbal and nonverbal communication between recipients and providers that reduces uncertainty about the situation […] and functions to enhance a perception of personal control in one’s life experience” (Albrecht & Adelman, 1987, p. 19). Many types of support exist, though emotional support, which includes acts of caring and expressions of love, has received the most empirical attention (Cutrona & Russell, 1990), as it is most strongly associated with enhanced well-being. This is true for PCDs, as previous research has shown strong associations between emotional support and parents’ subjective well-being and psychological adjustment (White & Hastings, 2004). Indeed, PCDs often cite challenges centered on feelings of social isolation and desiring emotional connection as key difficulties they face (Faw & Leustek, 2015).

Social support, stress, and health

The associations between support and health are well established, with research indicating that higher levels of perceived support positively influence health outcomes (MacGeorge, Feng, & Burleson, 2011). Because of these findings, scholars have investigated specific mechanisms to explain this relationship. One theory, the stress buffering hypothesis, argues that support serves a protective function by helping distressed individuals cope with their problems (Cohen & Willis, 1985). According to this hypothesis, support is most essential when people experience difficulties, as this is when stress is most likely to increase.

Studies based on the stress buffering hypothesis have shown that heightened levels of support result in many positive outcomes, including lower levels of psychological distress and fewer health complaints (Cohen & Willis, 1985). While the stress buffering hypothesis originally measured social support as a perception, research has since demonstrated the associations between communicated support and positive outcomes (e.g., Afifi, Granger, Denes, Joseph, & Aldeis, 2011). Additionally, research has highlighted the importance of considering how specific types of support, and especially emotional support, influence health (Cohen, 2004). Along with calls for considering the effects of communicated support, researchers have also challenged an assumption underlying much of the stress buffing hypothesis research: that more support is always better, as this assumption minimizes the fact that support varies in its effectiveness (MacGeorge et al., 2011). Finally, researchers continue to explore the biological mechanisms that explain the connections between social support, stress, and health (Uchino, Bowen, Carlisle, & Birmingham, 2012).

This study investigates how emotional support of differing quality might buffer a person’s physiological stress as manifested by salivary cortisol. Scholars have increasingly focused on cortisol to elucidate the connections between social phenomena and stress (Floyd et al., 2007; Priem & Solomon, 2015). Cortisol is a glucocorticoid hormone with many functions including stress response. When a person experiences stress, cortisol is secreted by the adrenal glands through a series of chain reactions in the hypothalamic–pituitary–adrenal axis. After stressor exposure, cortisol is secreted into the blood stream and passively diffuses into saliva (Hellhammer, Wust, & Kudielka, 2009). When exposed to a stressor, increases in serum cortisol happen quickly, whereas increases in salivary cortisol appear after about 10 min and return to their basal level after about 30 min (Afifi et al., 2011; Floyd et al., 2007).

Cortisol has been used previously to explore general associations between social support and stress. For example, a 2008 study by Floyd and Riforgiate found that spouses who received more supportive affection from their partner experienced diurnal cortisol profiles indicative of lower stress levels. This finding is echoed among PCDs in a 2012 study by Lovell and colleagues, which found that PCDs who reported higher levels of perceived support experienced a cortisol awakening response indicative of enhanced well-being.

These particular studies focused on the physiological responses of participants over several hours or even a full day. Studies like these, while valuable, are limited in that they present aggregate information and cannot speak to specific interactions or to the immediate effects of a single interaction (Priem & Solomon, 2015). Investigating responses to a single supportive encounter provides an opportunity for uncovering what might make support more effective. Additionally, understanding a short-term interaction’s influence on physiological stress is valuable, as interactions can take place anytime and with anyone. Thus, knowledge about these short-term effects provides one potential avenue for offering care to PCDs.

In an effort to answer some of the lingering questions about the short-term effects of social support on stress, recent studies have examined how salivary cortisol levels change when receiving support immediately following a stressful task (Priem & Solomon, 2009, 2011, 2015). In these studies, the researchers found that receiving support after a stressful task resulted in salivary cortisol decreases. In their 2011 study, participants who received hurtful messages, rather than supportive ones, did not experience these same decreases, suggesting that receiving support does influence short-term physiological stress. These studies provide a foundation for understanding the relationship between support and stress, but they are limited in their ability to address questions of support quality. This study contributes to the ongoing research exploring the role of support quality on salivary cortisol using a hierarchy of support message quality: message verbal person-centeredness (VPC).

Communicated social support and support quality

Message VPC (Burleson, 2003) is defined as “the extent to which messages explicitly acknowledge, legitimize, and contextualize the feelings and perspectives of a distressed other” (Bodie et al., 2011, p. 231). Many studies have explored message VPC with results from a meta-analysis indicating a strong, positive relationship (r = .61) between message VPC and support effectiveness (High & Dillard, 2012). In its original conception, coding for message VPC consisted of nine levels with each increasing level indicating a step up in support quality (Burlseon & Samter, 1985). However, most research exploring message VPC simplifies the scheme by coding support based on three broader categories, and research does not find evidence to support that the nine levels are truly distinct from one another (High & Dillard, 2012).

According to the simplified coding scheme (High & Dillard, 2012), the first level of support features messages labeled as low person-centered (LPC) support. These messages ignore or deny the distressed individual’s feelings and criticize their emotions. The next level in the hierarchy, moderate person-centered (MPC) support, recognizes the other person’s feelings but does not fully legitimize them. These messages often include sympathy or attempts at distraction. The final level includes messages high in person-centeredness (HPC). HPC messages fully acknowledge the other’s feelings and legitimize their emotions. Evidence suggests that HPC support helps distressed people gain insight into their circumstances, allowing them to engage in cognitive reappraisals (Jones & Wirtz, 2006). Many studies have explored the effects of message VPC, consistently finding HPC messages to be the most sensitive and the most likely to encourage reappraisals (High & Dillard, 2012; Jones & Wirtz, 2006).

Although ample evidence supports the associations between positive outcomes and HPC support, researchers have only recently begun to examine the physiological outcomes associated with receiving support of differing quality. In one study, participants with high levels of communication apprehension were offered comforting messages before they had to give a speech. The researchers found that distracting messages (messages likely classified as MPC) produced significant reductions in salivary cortisol. The same was not true, however, for messages designed to be more sensitive (Priem & Solomon, 2009). This is interesting, as research would generally suggest that HPC support should produce the most positive outcomes (High & Dillard, 2012). Other studies have manipulated support quality by coaching some study participants to provide supportive messages while others are instructed to provide hurtful (Priem & Solomon, 2011) or impartial (Priem & Solomon, 2015) messages. Results from both of these studies indicated positive outcomes for individuals who received support.

When interpreting these results, it is important to note that all of these participants were exposed to an acute stressor and then received comfort from either someone with whom they were not close or a close individual who had received specific instructions about how to act. These conditions are very different from people’s day-to-day interactions and do not fully explore the influence of support quality on physiological stress. The large amount of evidence demonstrating the effectiveness of HPC messages, taken with evidence from research that found that PCDs’ ratings of friends’ and family’s support were the most consistent predictor of parent well-being (White & Hastings, 2004), suggests that better-quality support should produce positive, short-term outcomes for PCDs. As such, I hypothesize that PCDs who receive HPC support in a short-term interaction will experience greater reductions in physiological stress compared to their baseline stress levels than PCDs receiving MPC or LPC support (Hypothesis 1). Similarly, I predict that PCDs who receive MPC support in a short-term interaction will experience greater stress reductions compared to their baseline levels than PCDs receiving LPC support (Hypothesis 2).

Social support and the support provider

Because support is dyadic by nature, it is important to consider the support provider when assessing the potential outcomes of a supportive interaction. Evidence on support provision’s effects is mixed. In some cases, those sought for support might feel their autonomy is threatened or resent the distressed person (Lu, 1997). Providing support has also been linked with emotional strain, as is the case with PCDs (Carretero et al., 2009). There are also benefits to providing support, including increased positive affect and feelings of satisfaction (Lu & Argyle, 1992). Studies have found that support provision is associated with enhanced feelings of trust, a greater sense of purpose, and a reduced risk of mortality (Brown, Nesse, Vinokur, & Smith, 2003).

Research exploring the benefits of expressing affection highlights the potential benefits of support provision. This research has consistently found positive outcomes, such as lower physiological stress, associated with expressing affection (Floyd et al., 2007). While providing support is not the same as communicating affection, the two behaviors are associated (Floyd & Riforgiate, 2008). This is especially true for the communication of emotional support (Cutrona & Russell, 1990). In one study, participants who reported higher levels of affectionate expression experienced lower salivary cortisol levels even when controlling for the affection they received (Floyd, 2006). This implies that the communication of affection is salubrious, and it is likely that the same effect exists for communicating support.

In these interactions, it is possible that the quality of the support offered influences the outcomes experienced by the support provider. Researchers have posited that matching support to meet support seekers’ needs is essential to creating a successful interaction (Cutrona & Russell, 1990), and it is possible that individuals providing high-quality support (e.g., HPC messages) derive satisfaction from offering support that is thoughtful and engaged. On the other hand, providing HPC messages requires higher levels of cognitive complexity, and it is possible that producing HPC messages requires significant effort and emotional resources that might leave a provider feeling drained (Burleson, 2010). Presently, little research has investigated what the effects of offering support, let alone of offering different quality support, might have on providers. As such, I pose the following research question: Does the quality of the support provided affect the physiological stress response of the support provider (Research Question 1)?

Method

Participants

Participants (n = 40 dyads) were recruited through snowball sampling. Each dyad consisted of at least one PCD and one conversation partner. Conversation partners were identified by parents as people they had previously approached for support. Parents were almost exclusively mothers (97.5%) and ranged from 25 to 77 years old (M = 48.65, SD = 10.62). Conversation partners (female: 40%) ranged from 27 to 80 years old (M = 49.30, SD = 10.04). Participants were predominately white (93.8%). Conversation partners consisted of spouses or romantic partners (60%), friends (17.5%), parents (12.5%), or adult children of the parent (10%). These dyads had usually known one another for more than 7 years (90% of dyads).

Exclusion criteria

Interested individuals completed a screening questionnaire. Because of the nature of cortisol, all participants could not currently use any steroids, prescription hormones (excluding contraceptives), thyroid medication, or tobacco products. Pregnant or breastfeeding women were also excluded from participation (Floyd et al., 2007).

Appointment procedures

Appointments lasted about 2 hr. All participants refrained from eating anything, drinking anything but water, exercising vigorously, brushing their teeth, or chewing gum for at least 2 hr prior to their appointment as these activities are known to affect salivary cortisol (Gordis, Granger, Susman, & Trickett, 2006). At the onset of the appointment, participants provided baseline saliva specimens using a passive drool method (Granger et al., 2007). Under this method, participants rinsed their mouths out with water. They then provided approximately 2 mL of saliva in a polypropylene centrifuge tube by drooling or spitting into the tube.

Next, participants completed the study’s conversation portion. Conversations lasted 10 min and were recorded. In the conversation, the PCD was instructed to share challenges they experienced when caring for their child. In response, partners were asked to respond in helpful, appropriate ways. This direction was intended to prime the partner to respond in generally supportive ways. Both participants were told that the goal was for the conversation to feel natural. After the interaction, participants engaged in a 10-min rest period before providing another saliva specimen. They then completed a 20-min rest period followed by a third specimen. During these rest periods, participants were under supervision of the researcher. They were not allowed to talk with one another or engage in any activity other than reading a magazine from a preapproved selection. After providing the third saliva sample, they were given access to a survey with several scales. Twenty minutes after their third specimen, participants provided a final specimen. At the study’s end, each participant received US$25.

Measurements

Salivary cortisol assay

Saliva samples were kept on ice when transported from the appointment and then frozen at −5°C until they were transferred to a −20°C freezer in the laboratory. Specimens remained frozen until analysis, when they were thawed and centrifuged at 2,800 r/min for 20 min. The aqueous layer of the saliva was separated into aliquots and stored at −20°C. Specimens were assayed using a competitive microtiter plate enzyme immunoassay. All specimens were run in duplicate and at a variety of dilutions (from 1:1 to 1:10). Specimens that fell outside the assay limits of detection or had unacceptable coefficients of variation (CVs) were re-assayed. Acceptable measurements for all samples were achieved resulting in no missing data. The specimen intra-assay CVs (n = 14 plates) ranged from 9% to 15%, and inter-assay CVs ranged from 8% to 14% across the three controls.

Covariates and control variables

Because salivary cortisol serves multiple functions and is sensitive to changes, certain covariates were included in analysis. Cortisol operates on a diurnal pattern, with levels peaking approximately 30–45 min after waking and slowly declining throughout the day (Edwards, Evans, Hucklebridge, & Clow, 2001). As such, the appointment start time and participants’ time of waking were included as covariates. Cortisol levels are also known to fluctuate based on sleep quality (Lasikiewicz, Hendrickx, Talbot, & Dye, 2008). Participants rated the quality of their sleep the night before their appointment on a scale of 0 = not well at all to 4 = very well (M = 2.56, SD = 0.90). Finally, participants indicated their experiences of stress leading up to the appointment on a scale of 0 = not stressful at all to 4 = extremely stressful (M = 0.85, SD = 0.93).

In addition, data was also collected in the survey administered toward the end of the appointment (during the rest period between the Time 3 and Time 4 saliva sample collection). Parents were asked to evaluate the appropriateness of their partner’s behavior (Bodie et al., 2011), using a 1 = strongly disagree to 5 = strongly agree scale. Sample statements included “my conversation partner behaved correctly” and “my feelings toward my conversation partner became more positive a result of this conversation.” Items indicating negative perceptions were reverse coded so that high scores represented more appropriate conversations (M = 4.25, SD = 0.56). Parents also responded to 5 items assessing the normalcy of their partner’s behavior (e.g., “my partner behaved as he/she normally would”; M = 4.40, SD = 0.67). Partners also responded to these items, though they were adjusted to reflect partners’ perceptions of their own behavior, with higher scores indicating the belief that they had behaved appropriately (M = 4.08, SD = 0.79) and normally (M = 4.14, SD = 0.59).

Finally, parent participants also completed the Global Perceived Stress Scale (GPSS; Cohen, Karmarck, & Mermelstein, 1983). This 14-item scale asked parents to indicate how often they perceived events in their life to be overwhelming on a scale of 0 = never/almost never to 4 = more than once per week. Six scale items were reverse coded so that higher scores represented higher levels of perceived stress (M = 1.95, SD = 0.63).

Coding interactions

All conversations between dyads were coded to examine the quality of support provided. First, all conversations were transcribed. Second, two coders analyzed the conversation using the transcripts. Coders received training on the definition of emotional support as well as common message features that constitute emotional support. Coders rated the interaction’s overall quality of support using Burleson’s original (1982) coding scheme for message VPC, which classified messages on a 1–9 scale (for a description of this coding scheme, see High & Dillard, 2012). These scores were then used to classify the support provided in the interaction as LPC, MPC, or HPC support. Conversations coded with a score of 1–3 were coded as LPC messages; conversations with scores of 4–6 were labeled as MPC messages; and conversations with scores from 7–9 as HPC messages.

Before coding began, the coders engaged in 3 hr of training to familiarize themselves with the definition of emotional support, the coding scheme, and the codebook instructions. The coders carefully studied Burleson’s (1982) coding scheme as well as the coding scheme outlined in High and Dillard’s (2012) meta-analysis on message VPC. Additionally, the researcher provided examples of messages varying in VPC based on the literature. The coders discussed at length what could be considered emotional support and how to evaluate emotional support using the coding scheme. To complete the coding, coders were instructed to first read through the transcript. Then, during a second reading of the transcript, coders were asked to assess the quality of support provided during the interaction by considering all expressions of emotional support. Coders then identified which level of message VPC was most prevalent in the exchanges. Although message VPC has traditionally been studied at a message level (rather than a conversation level), recent research has expanded examinations of message VPC by examining entire conversations (Bodie, Jones, Vickery, Hatcher, & Cannava, 2014; High & Solomon, 2014). As such, coders rated each conversation as a whole rather than breaking them down into smaller units.

Intercoder reliability was tested at several points. Both coders first independently coded five random transcripts using the 1–9 VPC scale. From this information, each conversation was coded as LPC, MPC, or HPC support. Both the 1–9 coding score and the LPC/MPC/HPC coding scores were then compared, and intercoder reliability was assessed using Scott’s π. The coders then came together, reconciled their differences, and clarified the coding scheme. They then coded an additional five cases, assessed the reliability, and reconciled their differences. At this point, coders had engaged approximately 12 hr of training, coding, and discussion. The coders had also established acceptable intercoder reliability (reliability on the 1–9 message VPC coding scheme: Scott’s π = .74; 84.6% agreement; reliability on the 1– 3 message VPC coding scheme: Scott’s π = 1.0; 100% agreement). The remaining cases were then randomly divided among the coders and coded for message VPC. Among study participants, there were 11 cases of LPC support, 14 cases of MPC support, and 15 cases of HPC support.

Results

The first step of analysis was to examine the cortisol specimens for deviations from normality. Because the cortisol data were not normally distributed, a natural log transformation was used to address the skew of the data (average skew: 1.04, SE = .37; post-transformation skew: −.17, SE = .37; Keene, 1995). The cortisol data was also assessed for outliers, and none were found. Next, a correlation matrix was used to assess relationships between the variables of interest for each group of participants (for PCDs, see Table 1; for partners, see Table 2).

Table 1.

Descriptive statistics for the primary variables of analysis for parents of children with disabilities.

	M	SD	n	1	2	3	4	5	6	7	8	9
1. Baseline cortisol	3.12	0.27	40	–
2. Cortisol T2	3.10	0.26	40	.84**	–
3. Cortisol T3	3.04	0.26	40	.79**	.85**	–
4. Cortisol T4	3.02	0.24	40	.74**	.80**	.85**	–
5. Prestudy stress	0.85	0.53	40	−.29	−.23	−.22	−.31	–
6. Waking	6:37	1:11	40	.12	.05	.13	.28	.01	–
7. Sleep quality	2.56	0.90	40	−.02	−.11	.01	.10	−.18	−.02*	–
8. Appointment time	14:07	3:50	40	−.53**	−.49**	−.57**	−.51 **	.42**	−.01	.00	–
9. Coded support	5.45	2.38	40	.52**	.31*	.29	.17	.11	−.03	−.13	−.23	–

Note. Analysis represents values for parents of children with disabilities only. T2 = Time 2; T3 = Time 3; T4 = Time 4.

*p < .05; **p < .01.

Table 2.

Descriptive statistics for the primary variables of analysis for conversation partners.

	M	SD	n	1	2	3	4	5	6	7	8	9
1. Baseline cortisol	3.20	0.27	40	–
2. Cortisol T2	3.16	0.24	40	.82**	–
3. Cortisol T3	3.14	0.24	40	.76**	.83**	–
4. Cortisol T4	3.11	0.24	40	.87**	.85**	.88**	–
5. Prestudy stress	0.80	0.82	40	−.03	−.08	−.04	−.13	–
6. Waking	6:23	1:15	39	.42**	.44**	.25	.37*	−.10	–
7. Sleep quality	2.63	0.81	40	.04	−.01	−.03	−.02	−.27	−.06	–
8. Appointment time	14:07	3:50	40	−.46**	−.35*	−.32*	−.42**	.19	−.38*	−.13	–
9. Coded support	5.45	2.38	40	.11	−.07	−.18	−.10	.02	−.02	.32*	−.23	–

Note. Analysis represents values for conversation partners only. T2 = Time 2; T3 = Time 3; T4 = Time 4.

*p < .05; **p < .01.

Support quality and physiological stress among parent caregivers

The first set of hypotheses explored the relationship between the support provided during the study conversation and parents’ physiological outcomes. Before analysis, steps were taken to assess whether parents who received LPC, MPC, or HPC support significantly differed in potentially confounding ways. Using one-way analyses of variance (ANOVAs), groups were evaluated for differences in their caregiver burden and the functioning of their disabled child (as measured using survey data). Neither of these variables significantly differed between groups, caregiver burden: F(2, 37) = 2.52, ns; child functioning: F(2, 37) = .50, ns. Groups also did not differ in their reports of stress leading up to the appointment, F(2, 37) = .11, ns. Similarly, the groups were assessed for differences in the perceived conversational appropriateness, F(2, 37) = 1.97, ns, and normalcy, F(2, 37) = .35, ns. Finally, groups were assessed for differences in their waking time and appointment time. Results indicated no differences for time of waking, F(2, 37) = .10, ns, or appointment time, F(2, 37) = 1.24, ns.

H1 and H2 predicted that parents who received HPC support would experience greater cortisol reductions when compared with parents who received LPC or MPC support (H1), and that MPC support recipients would experience greater cortisol reductions when compared with LPC support recipients (H2). To test these hypotheses, a one-way repeated measures analysis of covariance (ANCOVA) was used. The four transformed cortisol values (baseline and samples taken at 10, 30, and 50 min postinteraction) were entered as the within-subject (dependent) variables, and support quality (LPC, MPC, or HPC) was entered as the between-subject (independent) variable. Time of waking, quality of sleep, the day’s stress, and the appointment start time were included as control variables. Results indicated that cortisol levels significantly changed during the appointment regardless of the quality of support received, F(3, 99) = 3.64, p < .05, partial η² = .10. Analysis also revealed a significant effect of the quality of support on cortisol, F(6, 99) = 5.59, p < .01, partial η² = .25 (see Table 3 and Figure 1).

Table 3.

Repeated measures ANCOVA summary table for changes in cortisol by quality of support provided.

Source	SS	df	MS	F	p	η²
Cortisol	.11	3	.037	3.64	.02	.10
Cortisol × Appointment Time	.05	3	.02	1.56	ns	.05
Cortisol × Stress	.05	3	.02	1.64	ns	.05
Cortisol × Wake	.07	3	.02	2.43	.07	.07
Cortisol × Sleep	.06	3	.02	2.07	ns	.06
Cortisol × Support Quality	.34	6	.06	5.59	.001	.25
Error	.996	99	.01

Note. n = 40. ANCOVA = analysis of covariance; SS = sum of squared; df = degree of freedom; MS = mean squared.

Figure 1.

Changes on cortisol by support quality. Note. LPC (n = 11); MPC (n = 14); HPC (n = 15). Error bars represent standard error. LPC = low person centered; MPC = moderate person centered; HPC = high person centered.

To further explore these differences, change scores were computed by subtracting each of the postinteraction cortisol levels from the baseline cortisol measure, resulting in three scores representing the change in cortisol from baseline to Time 2, baseline to Time 3, and baseline to Time 4. One-way ANCOVAs were used to test for differences between each level of support at each time point. Each subsequent ANCOVA included time of waking, quality of sleep, the day’s stress, and appointment start time as covariates. The first one-way ANCOVA (with level of support as the independent variable and the change in cortisol from baseline to Time 2 as the dependent variable) revealed statistically significant differences in participants’ cortisol levels, F(2, 37) = 4.38, p < .05, partial η² = .19. Pairwise comparisons using Bonferroni’s correction revealed that participants who received HPC support experienced cortisol reductions (average reduction = .10, SE = .03) that were significantly greater than those by MPC support recipients (M = −0.05, SE = .04, p < .05). The difference in cortisol reductions between HPC support recipients and LPC support recipients (M = −0.03, SE = .04) approached significance (p < .10). There were no significant differences between MPC and LPC message recipients.

The second ANCOVA, testing if participants in each level of support (the independent variable) experienced differences in the changes to their cortisol levels from baseline to Time 3 (the dependent variable), was also significant, F(2, 37) = 7.69, p < .01, partial η² = .29. Again, pairwise comparisons using Bonferroni’s correction indicated that HPC support recipients (M = 0.17, SE = .04) experienced greater cortisol reductions than MPC support recipients only (M = 0−.05, SE = .04, p < .01). Differences between HPC support recipients and LPC support recipients (M = 0.06, SE = .05) were not significant, and differences between LPC support recipients and MPC support recipients were also nonsignificant. A third and final ANCOVA tested for differences in cortisol changes between baseline and Time 4. Results revealed a statistically significant effect for level of support on cortisol changes between baseline and Time 4, F(2, 37) = 11.14, p < .01, partial η² = .38. Pairwise comparisons using Bonferroni’s correction demonstrated that HPC support recipients (M = 0.24, SE = .04) experienced significantly larger cortisol reductions when compared with MPC support recipients (M = −0.01, SE = .04, p < .01) and LPC support recipients (M = 0.03, SE = .05, p < .01). Again, no significant differences emerged between LPC support recipients and MPC support recipients.

These results demonstrated that HPC support recipients experienced cortisol reductions at all three post-baseline time points, and that these differences were significantly greater than those experienced by MPC support recipients at all time points and those experienced by LPC support recipients at Time 4 (with differences that approached significance at Time 2). Thus, H1, which stated that HPC support recipients would experience the greatest cortisol reductions, was supported. However, H2, which stated that MPC support recipients would experience sharper declines in salivary cortisol when compared with LPC support recipients, was not supported, as the changes in MPC support recipients’ cortisol levels did not differ from the changes of LPC support recipients’ cortisol levels at any time point.

Post hoc analyses examined whether the level of support was associated with participants’ perceived stress. A one-way ANOVA with participants’ scores on the GPSS (Cohen et al., 1983) as the dependent variable and participant’s support level (HPC, MPC, or LPC) as the independent variable was conducted. Results were significant, F(2, 37) = 3.42, p < .05, partial η² = .16. Pairwise comparison using Bonferroni’s correction indicated that HPC support recipients had significantly lower perceived stress (M = 1.69, SE = .15) than MPC support recipients (M = 2.27, SE = .16, p < .05). However, similar to the cortisol results, LPC support recipients’ perceived stress levels (M = 1.91, SE = .18) were not significantly different than MPC or HPC recipients.

Providing support and physiological stress for conversation partners

This study also explored how support provision might affect the provider by examining whether the quality of support provided influences providers’ physiological stress during the interaction (RQ1). A repeated measures ANCOVA was used with the four transformed cortisol values as the within-subjects (dependent) variables and the quality of support provided (LPC, MPC, or HPC) as the between-subjects (independent) variable. Time of waking, the day’s stress, quality of sleep, and the appointment time were included as covariates. Results indicated no significant effect of support quality on partners’ cortisol levels, F(2, 37) = .61, ns.

Discussion

Much evidence demonstrates the strong associations between social support and positive health outcomes, leading scholars to pinpoint support as a key resource for long-term caregivers (Lovell et al., 2012). This study explored whether short-term supportive interactions between PCDs and members of their supportive network influenced the physiological stress responses of both interactants. Results indicated that communicated support significantly influenced PCDs’ physiological stress levels. Specifically, receiving support in an interaction resulted in recipients experiencing decreases to their physiological stress, though the quality of the support played a key role in determining how beneficial the interaction was in this context.

Support and support recipients

Results from H1 demonstrated that PCDs experienced significant reductions to their salivary cortisol levels as a result of engaging in a short-term supportive interaction with a supportive partner. Other research examining communication and cortisol has argued that short-term cortisol reductions indicate a health benefit (Afifi et al., 2011; Floyd et al., 2007). Thus, these findings provide some empirical support for the stress buffering hypothesis (Cohen & Willis, 1985), which states that support positively influences health by protecting recipients from heightened stress that is known to cause physical deterioration.

This study also sought to expand the stress buffering hypothesis by exploring the role of support quality in influencing physiological stress. Analyses revealed that participants receiving high-quality support in the interaction (messages coded as HPC support) experienced the most pronounced reductions in their salivary cortisol. Results indicated that HPC message recipients experienced cortisol reductions at all time points relative to their baseline levels. As HPC messages are commonly identified as the most helpful, effective form of emotional support (High & Dillard, 2012), this finding aligns with and expands previous research, suggesting that HPC support can be effective at helping individuals manage their physiological stress.

While the results demonstrated significant reductions in cortisol levels among HPC message recipients, it is interesting to note that HPC message recipients experienced higher salivary cortisol levels at the onset of the appointment when compared with MPC or LPC message recipients. While preliminary analysis revealed that HPC message recipients did not significantly differ in their overall caregiver burden, the functioning of their child, their self-reported stress on the day of the appointment, their time of waking, or appointment time, it is possible that something about how HPC support recipients behaved during the interaction as a result of their higher baseline cortisol levels incited the partner to respond with better support. For example, if the partner was aware that the parent usually experiences high levels of stress (even when the parent herself is less aware of this), the partner might approach the interaction differently with the parent’s needs in mind. That is, in situations where providers perceive higher emotional stakes, they might seek to respond with their best, most helpful support.

Research examining the interplay between interactants has found evidence for this notion, demonstrating that, when support providers perceive recipients to experience heightened distress, they are more likely to provide solace support behaviors (Barbee, Rowatt, & Cunningham, 1998). Research suggests that highly distressed people may feel ambivalent or uncomfortable seeking support (Barbee & Cunningham, 1995). In these situations, highly stressed participants might benefit from indirectly signaling their need for support without having to solicit it. Thus, it is possible that HPC message recipients, as a result of their higher cortisol levels, signaled a need that caused their partner to respond with better support. Future research should further explore the behaviors that might signal to providers that they should up their game as well as how providers interpret and respond to these signals.

Unlike the results for HPC messages, the results for recipients of both MPC and LPC messages contradict previous research. LPC message recipients in this study experienced positive changes in their salivary cortisol (i.e., they experienced a reduction in salivary cortisol from baseline to the end of the appointment). These changes, however, were not always significantly different from those experienced by HPC message recipients, and they were never significantly different from MPC message recipients. This would suggest that participants receiving LPC support experienced physiological outcomes that were, in some instances, no different from those who received HPC or MPC messages.

As with LPC support recipients, MPC support recipients did not experience the hypothesized changes to their cortisol levels. In fact, MPC support recipients experienced increases in their salivary cortisol from baseline to Time 2 and Time 3. MPC support recipients’ cortisol levels did not recover below the baseline, suggesting a trend that MPC support recipients experienced higher cortisol at the end of the appointment. This contradicts what past research would suggest (High & Dillard, 2012; Priem & Solomon, 2009). It is possible that MPC recipients in this study experience a variety of supportive messages in their day-to-day interactions. If these participants have received HPC support in the past, then receiving MPC support might fail to meet their expectations, resulting in the awareness that their needs are not being met. Previous research has shown that, when support fails to meet expectations, the potential benefits recipients experience are minimized (Priem & Solomon, 2015).

Another possible explanation for why the results for LPC and MPC message recipients deviated from expectations could be explained by considering participants’ message processing. Recent research using a dual-process framework suggests that support recipients process messages in two ways, either dedicating significant effort to fully scrutinize the messages they receive or instead focusing on environmental cues and heuristics to evaluate the messages (Bodie et al., 2011; Burleson, 2010). According to this research, in situations where people rely on simple cues or heuristics, they are less likely to critically evaluate the messages they receive. On the other hand, individuals who exert the effort to fully scrutinize messages are likely to weigh the message content and quality, with evidence suggesting that they should respond most positively to HPC messages (Bodie et al., 2011). Whether individuals exert the effort to process the message or simply rely on heuristics is determined by their motivation and capability to process the message (Bodie & Burleson, 2008).

In this study, it is possible that message processing played a role in how PCDs responded to the supportive interaction. If MPC message recipients were motivated and capable of processing the support they received, then the dual-process model purports that they would critically scrutinize the support messages they received. If they engaged in full and effortful processing, they could conclude that the MPC support they received was inferior or unhelpful (Bodie & Burleson, 2008; Bodie et al., 2011), potentially resulting in the physiological stress reactions documented. On the other hand, if LPC message recipients lacked either motivation or capability to process the support, the dual-process framework suggests that they would instead rely on heuristic cues, potentially focusing on the relationship they have with their supportive partner to understand the interaction. In this sense, they would essential offer their partner the benefit of the doubt regarding their supportive efforts (Burleson, 2010). If this occurred, LPC recipients could be satisfied with the support they received and experience some benefit even though previous research and independent coding would label this support as ineffective (High & Dillard, 2012). Because no measures of motivation or capability were included in this study, this possible explanation remains speculation. Future research should continue to explore the role motivation and capability in supportive interactions, linking these observations with physiological measures to obtain greater understanding of the role supportive message processing plays in influencing physiological stress outcomes.

The findings in this study emphasize the need for continued research on support quality and its effects on physiological stress and health. Although research has consistently found HPC support to be most effective at reducing reports of distress, the majority of these studies have used contrived interactions relying on trained confederates, asked participants to respond to hypothetical scenarios, or required participants to rate researcher-created messages (High & Dillard, 2012). A limited number of studies have sought to apply message VPC and VPC coding schemes to more natural interactions between preexisting relationship partners or examined VPC at the conversation level (for exceptions, see Bodie et al., 2014; High & Solomon, 2014).

In more natural interactions (such as those in the current study), the outcomes of supportive communication are contingent upon factors aside from message VPC. In this study, parents participated with a key source of support, and it is reasonable to assume that participants had expectations for the interaction. These expectations probably look quite different from those experienced by people entering a contrived interaction or even those held by a person without the same stressors as those experienced by PCDs. It is also important to note that when receiving contrived support, participants are aware that the support and its implications end with the research. In more natural interactions, any conversational antecedents, consequences, and evaluations can carry relational information that does not cease with the study. Thus, in some ways, the relational stakes might be higher in interactions occurring between existing partners, as this support could filter into future interactions. Awareness of the larger relational and supportive trajectory could result in partners evaluating messages in more complex ways than contrived research designs can accurately measure.

With this in mind, future research should explore how expectations, anticipation of future interactions, and message processing influence perceptions of support. In doing so, researchers can tease apart when MPC support can be more effective (as in Priem & Solomon, 2009) versus the situations where it is least effective (as in the results of this study). Research should also seek to identify when LPC support might be most effective, as it is possible that these situations exist (Burleson, 2010). Future research should also explore the mechanisms underlying what makes certain support more effective than others. Researchers have suggested that HPC messages are more effective because they encourage cognitive reappraisals (Jones & Wirtz, 2006), and future work should continue to explore the role that cognitive reappraisals and other potential mechanisms play in determining the effectiveness of social support.

Support and support providers

This study also explored how offering support might influence the physiological stress of support providers. Results did not illuminate any significant associations between expressed support and support quality on providers’ physiological stress. It is possible that participant characteristics may have limited the ability to detect differences. More than half of the partners (n = 23) were the parent’s spouse or romantic partner. As such, it is possible that the challenges discussed by these dyads featured problems that directly affected the partner as well. Thus, partners may have been forced to respond to challenges present in their own lives, potentially limiting the positive effects of providing support. This could also lead to corumination, which negatively influences stress (Byrd-Craven, Granger, & Auer, 2010). If these differences exist, they could be diluted when combined with data from nonromantic conversation partners.

It is also possible that other partner characteristics might have made it difficult to detect differences in their physiological responses. In this study’s sample, all but one support recipient were female, whereas approximately 60% of partners were male. Research has previously established that men are less likely to provide highly sensitive support than women and tend to feel less comfortable doing so (Burleson et al., 2005; MacGeorge, Graves, Feng, Gillihan, & Burleson, 2004). Research has also shown that men and women experience different physiological changes in response to their emotional environment, with men less susceptible to both positive and negative interactions with their partner (Crockett & Neff, 2012). It is possible that any physiological reactions among partners became muddled by the study’s mixed-gender sample of providers.

Another possible explanation for the lack of findings is the length of the supportive interaction. It is likely that providers are not experiencing the same levels of distress as recipients and thus might be less susceptible to short-term physiological changes. This could be especially true for providers responding to the needs of PCDs, as the challenges they express might ongoing and familiar. Most research exploring the outcomes of support provision takes a long-term perspective (see Brown et al., 2003). Scholars should examine the long- and short-term physiological outcomes associated with providing support to better illuminate the relationship between offering support and overall well-being.

Study limitations

Although this study points to interesting conclusions, it is not without limitations. First, the study includes a small sample size. Previous research using physiological markers has demonstrated that small sample sizes can be sufficient in identifying meaningful difference (see Floyd et al., 2007). It is possible, however, that the sample was insufficient to detect small but meaningful differences. Additionally, this sample is not representative. The majority of parents were married (82.5%) and white (97.5%), and approximately 50% reported annual household incomes greater than US$60,000. Parents also had comparatively low levels of perceived stress (M = 1.95 of 4). It is possible that single parents, those experiencing lower socioeconomic standing, those with higher perceived stress, or a combination of the three, could benefit from support differently than the present sample. Future research should attempt to feature a more diverse sample so that results may more closely resemble the general population. Seeking out these groups also serves to benefit those PCDs likely experiencing more severe needs.

Additionally, nearly all of the parents were mothers. While these women identified as the primary caregiver, it is possible that fathers experience different responses to the stressors associated with having a disabled child. Future research should seek to evaluate the experiences of fathers as support recipients in supportive interactions. It is also important to note that 60% of participants were romantic couples, including co-parents of the child with disabilities. It is possible that experiences of these parent-partners differed from partners who were not a part of the family unit. Research should seek explore what differences in support provision exist in such dyads when compared with non-co-parenting dyads.

Another limitation of this study was the limited control exerted over participants’ conversations. This openness was designed to induce conversations mirroring natural interactions as much as possible. It became clear in analysis, however, that not providing more structure resulted in topics ranging greatly in their severity. Although all conversations involved the discussion of a problem and the provision of emotional support, the wide range of interactions might account for some of the differences seen among recipients as well as the support provided. In the future, allowing partners to interact naturally while also utilizing a more structured prompt could better balance the need for study control with the desire to analyze more natural interactions. Finally, this study focused solely on verbal aspects of support. Ample evidence highlights that nonverbal aspects of support are important considerations when assessing support quality (Bodie & Jones, 2012). Future research should continue to investigate both verbal and nonverbal conversational elements and how the two create more sensitive, effective messages.

Conclusion

This study takes an initial step in exploring the effects of short-term supportive interactions on the physiological stress of both support recipients and providers. For scholars, the findings of this study carry important implications. First, it reinforces the long-held belief that social support is an important communication process associated with well-being. Second, it provides evidence that variations in support quality can result in different outcomes for recipients. This expands the stress buffering hypothesis in demonstrating that support quality is an important factor to consider when examining the relationship between support and stress. Third, it raises questions about support quality and where existing assessment tools might fall short. The results of this study did not cleanly map onto the suggested support hierarchy as proposed by message VPC scholarship. This is significant, as much of the current literature surrounding questions of support quality rests on this model. If this way of evaluating support does not function as expected, scholars should pause to consider what else might account for differences in outcomes and how future research could explore these ideas.

The results of this study also carry implications for caregivers as it found that a short, 10-min conversation can affect cortisol levels. PCDs might be encouraged to engage in supportive interactions more frequently knowing that brief conversations can influence their physiological stress. Organizations promoting parents’ well-being might use this information to encourage engagement in support groups or other interactions, emphasizing that even short conversations can achieve some benefit. Similarly, practitioners could use the knowledge that HPC support influenced physiological stress positively to train others to provide better support, resulting in the greatest chance for enhanced well-being among caregivers and their families.

Footnotes

Author’s note

Portions of this article were presented at the National Communication Association convention in Las Vegas, Nevada.

Acknowledgements

I thank Dr. Malcolm R. Parks, Dr. Kathleen O’Connor, Dr. Valerie Manusov, Dr. Hendrika Meischke, Dr. Jacquelyn Harvey, Elizabeth Parks, and the anonymous reviewers for their helpful feedback and encouragement.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the Peter Clarke Graduate Research Fund at the University of Washington.

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