Perceived partner support and post-traumatic symptoms after an acute cardiac event:A longitudinal study

Abstract

Objective

Cardiac disease induced post-traumatic stress symptoms (CDI-PTSS) have been associated with negative consequences for patients’ mental and physical health. Identifying risk factors as well as potential buffers is necessary for understanding the development and maintenance of CDI-PTSS. The current study focused on the mediating and moderating role played by patients’ perceptions of their partners’ ways of providing support (active engagement, overprotection, and protective buffering) in the development and stabilization of CDI-PTSS levels over time.

Method

Male patients (N = 106) were recruited at hospitalization (T₁) and completed the study’s questionnaires at two time points: approximately four months after hospital discharge (T₂) and approximately eight months after discharge (T₃).

Results

Structual equation modeling was used to test the study hypotheses. All three forms of T₂ perceived partner support were positively associated with T₂ CDI-PTSS levels which, in turn, were positively associated with T₃ CDI-PTSS levels. The linear association between T₂ and T₃ CDI-PTSS was positive but decreased as perceived partner protective buffering levels increased.

Conclusions

In the context of CDI-PTSS, perceived partner support seems to have a different effect than it has in non-traumatic illness contexts. Interventions for couples coping with CDI-PTSS should be designed accordingly.

Keywords

Acute coronary syndrome cardiac disease induced post-traumatic stress symptoms couples Perceived support Partners

Being exposed to a traumatic event might lead to the emergence of post-traumatic stress symptoms (PTSS), the severity and prevalence of which may even amount to a post-traumatic stress disorder (PTSD) diagnosis (Bryant, 2019). Post-traumatic stress symptoms are commonly comorbid with other conditions such as depression, substance use, and anxiety disorders, and are associated with significant mental and physical impairments (Carlson & Weiner, 2020; Kearns et al., 2021).

Post-traumatic stress symptoms have mostly been investigated in the context of traumatic events such as military combat, natural disasters, and sexual assault (Bryant, 2019). Yet there is growing scientific interest in the development of PTSS following a life-threatening medical condition (Hatch et al., 2018). According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; APA, 2013) post-traumatic stress disorder (PTSD) is a mental disorder which may develop in individuals who have been exposed to a traumatic event, including experiences involving threatened death or serious injury. As cardiovascular diseases (CVDs), especially in their acute clinical expression (i.e., acute coronary syndrome/ACS), are extremely frightening as well as the leading cause of death and disability worldwide (World Health Organization, 2021), it is now acceptable, both conceptually and empirically, to suggest that they can potentially lead to cardiac disease induced PTSS (CDI-PTSS; Agarwal et al., 2019; Edmondson et al., 2012; Vilchinsky et al., 2017; Vilchinsky, 2021).

Studies indicate that the prevalence of a PTSD diagnosis following a cardiac event ranges from 4–17% on average (Jacquet-Smailovic et al., 2021) and is approximately 30% for CDI-PTSS (Agarwal, et al., 2019). Patients who develop CDI-PTSS are at higher risk for negative mental and physical consequences (Jacquet-Smailovic et al., 2021) and even for mortality in the three years following the cardiac event (Edmondson et al., 2012). Several personal factors are recognized as increasing the risk for developing CDI-PTSS, such as perceived severity of the illness, distress during hospitalization, exposure to critical care, fear of illness progression, and previous vulnerability to distress (Vilchinsky et al., 2017). Despite their well-documented contribution to patients’ outcomes (King & Reis, 2012; Rapelli et al., 2021; Wiesmaierova et al., 2019), only recently have interpersonal factors, such as low quality of interpersonal relationships and lack of social support, received scientific attention as putative contributors to or buffers of CDI-PTSS (Jacquet-Smailovic et al., 2021).

Social support is an established interpersonal factor associated with low risk for PTSS (e.g., Hansford & Jobson, 2021; Simon et al., 2019). For most adult cardiac patients, the main source of social support is their intimate partner (Rapelli, et al., 2021). A recent study revealed that partner support tended to buffer the negative psychological effects of a cardiac event (Wiesmaierova et al., 2019). Yet in the abovementioned study only a unidimensional measure of support was used (focusing only on the positive aspects of support). Other studies that conceptualized support as a multidimensional construct consisting of both positive and negative aspects found that some kinds of partner support (e.g., compulsive caregiving, protective buffering, and overprotection) could be associated with enhanced levels of psychological distress (George-Levi et al., 2020; Vilchinsky et al., 2011). This inconsistency is partly explained by the kind of support provided (Nurullah, 2012). Whereas supporting the patient by being sensitive and engaging seems to be beneficial, overprotection and protective buffering have been found to increase patients' distress (O’Bertos et al., 2020; Rapelli et al., 2021; Zniva et al., 2017). In addition, perceived support as opposed to actual provided support has usually been found to have a stronger effect on patients’ outcomes (Uchino et al., 2018).

Understanding how different kinds of perceived support contribute to the trajectory over time of CDI-PTSS may be valuable for practitioners working with patients and partners. However, no study to date has unfolded the role played by specific ways of support as perceived by patients in this context. Thus, the current study applied a longitudinal prospective design to explore the contribution of different kinds of perceived partner support (active engagement, overprotection, and protective buffering) to the emergence and stabilization of patients’ CDI-PTSS over time.

Perceived partner support

Social support is a multidimensional concept, and many conceptualizations have been offered to define its different dimensions. One main differentiation is between provided support (the actual supportive behaviors provided by the partner according to their self-reports) and perceived support (one’s subjective perception of receiving said support) (Uchino, 2009). The perception that the partner is supportive seems to have a greater positive effect on partner outcomes than do actual supportive acts (Uchino et al., 2018). It has been suggested that the belief that support is available provides a sense of personal security, whereas actual provided support may increase feelings of helplessness, fears of rejection, or dependency on the provider (Thoits, 2011). Finally, not every type of support is helpful, and it is crucial to identify the kind of perceived support most beneficial for the patient (George-Levi et al., 2016; 2020; O’Bertos et al., 2020).

In the field of health psychology, a well-accepted conceptualization of partner support is the one suggested by Coyne and Smith (1991, 1994), who differentiated between three ways of giving support (WOGS): active engagement, overprotection, and protective buffering. Subsequently, partners’ ways of providing support and the parallel ways of perceiving such support have been studied among varied medical and stressful conditions such as cancer, heart disease, and military trauma, and have been found to predict patients’ outcomes (e.g., Dekel et al., 2018; Kuijer et al., 2000; Perndorfer et al., 2019; Vilchinsky et al., 2011).

Perceived active engagement represents patients’ acknowledgment of their partners' efforts to provide support by talking openly with them, and by using a constructive problem-solving approach. This kind of support, especially when measured from the recipient’s point of view, has been found to be associated with several positive outcomes (Falconier et al., 2015). For example, whereas perceived active engagement has been found to be positively associated with relationship satisfaction in patients as well as in partners (Hagedoorn et al., 2011), provided active engagement (as reported by the providers) has not been found to be related to patients’ outcomes (Butner-Kozimor & Savla, 2020). Moreover, in a study focusing specifically on cardiac patients and their partners, it was found that provided active engagement (as reported by the partner providing the support) was positively associated with patients' smoking cessation but only when patients' own perceptions of partners' active engagement were high as well (Vilchinsky et al., 2011).

Perceived overprotection represents patients’ perceptions of being underestimated by their partners, who, consequently, provide excessive support or limit patient activity. Despite partners’ best intentions, perceptions of this way of giving support are usually unhelpful for patients (Niemann-Mirmehdi et al., 2019; Vilchinsky et al., 2011).

Finally, perceived protective buffering refers to patients’ perceptions that their partners are hiding worries/difficulties from them to minimize conflict or distress. Previous studies have shown that patients who perceive their partner as using protective buffering tend to experience worse outcomes in terms of mental health and relationship satisfaction (Hagedoorn et al., 2011; Langer et al., 2009). Interestingly, a recent dyadic daily diary study on patients following hematopoietic stem cell transplantation (HSCT) and their partners showed that when both partners reported using this way of coping, daily provided protective buffering was beneficial in terms of patients' adjustment (Kroemeke & Sobczyk-Kruszelnicka, 2019).

In sum, despite the important role played by perceived support in moderating traumatic symptoms (e.g., Hansford & Jobson, 2021; Simon et al., 2019), and the importance of perceived social support for cardiac patients’ outcomes (Vilchinsky et al., 2011; Wiesmaierova et al., 2019), very little is known about the effect of patients’ perceptions of their partners’ ways of providing support in the context of CDI-PTSS.

The current study

The current study focused on the contribution of perceived partner support (active engagement, overprotection, and protective buffering) to CDI-PTSS as assessed approximately four and eight months after patients’ hospitalization for an acute cardiac event, representing early manifestations of and chronic CDI-PTSS, respectively. The findings from previous studies that were described above suggest that perceived partner support may play a complex role in the trajectory of CDI-PTSS development over time. One possibility is that perceived partner support is a pre-morbid resource and is thus associated with early CDI-PTSS levels, either minimizing or enhancing them, and therefore also associated with CDI-PTSS levels over time. Yet it is also possible to suggest that perceived support acts as a moderator of the association between early CDI-PTSS and chronic CDI-PTSS. Patients might detect partners’ initial reactions to their emerging signs of CDI-PTSS, a perception which could either calm or exacerbate their chronic-stage symptoms. To better understand the intricate role played by perceived support in the unique context of CDI-PTSS, we assessed both mediation and moderation models.

Accordingly, we hypothesized that patients’ perceptions of partner support would predict their CDI-PTSS development over time. Using a mediation model, we assessed whether perceived partner support would be associated with patients’ early CDI-PTSS which, in turn, would associate with patients’ CDI-PTSS over time. We hypothesized that the perception of receiving high levels of active engagement would be negatively associated with CDI- PTSS, whereas perceiving overprotection would be positively associated with CDI-PTSS. As for perceived protective buffering, most studies have documented the costly nature of this perceived way of giving support. Nevertheless, perceived protective buffering may have a beneficial effect in the unique context of CDI-PTSS due to the preference on the part of cardiac patients with PTSS to avoid reminders of their distress (Ellis, 2010). Although patients overall differ in such tendencies (i.e., in terms of monitoring their illness closely or blunting any reminders of it) (Plamann et al., 2021; Shiloh & Orgler‐Shoob, 2006), cardiac patients coping with PTSS do indeed have a tendency to avoid any reminder of the illness (Edmondson et al., 2012). For these patients, perceiving their partners as supportive via protective buffering may align with their own avoidance tendency. Our hypothesis regarding perceived protective buffering was therefore exploratory.

Second, we assessed a moderation model in which perceived partner support buffers or facilitates the development of CDI-PTSS over time, from an early to a chronic manifestation overtime. More specifically, we hypothesized that the positive association between levels of early and chronic CDI-PTSS would be weaker when perceived active engagement levels were high versus low. We also hypothesized that this association would be stronger when perceived overprotection levels were high versus low. As protective buffering is usually conceived as a negative form of giving support, but in the current context was suspected to align with patients’ tendency toward avoidance, thus protecting them from experiencing more symptoms, our hypothesis in this case was again exploratory.

According to Heart Disease and Stroke Statistics (Tsao et al., 2022), males have a higher prevalence (approximately 34% higher) of undergoing a cardiac event than do females in almost all age groups. Females who experience an acute cardiac event tend to be older and have more medical complications, and are more likely to be widowed, than men (Tsao et al., 2022). Therefore, as the likelihood of recruiting female cardiac patients who also received social support from a living partner was rather low, in the current study we focused on male patients and their female partners.

Method

Participants and procedure

The present study was part of a large-scale longitudinal study conducted on CDI-PTSS among patients diagnosed with acute coronary syndrome/ACS (myocardial infarction or unstable angina) and their partners. Data were collected from February 2015-March 2018 from the intensive cardiac care unit (ICCU) of Sheba Medical Center, the largest medical center in Israel. The target population included all patients diagnosed with ACS who were in a significant intimate relationship. Exclusion criteria included a cardiac diagnosis other than ACS; elective hospitalization; patients' cognitive, physical, or language difficulties which precluded participation in interviews; coronary artery bypass graft (CABG) surgery during hospitalization; patients over the age of 85; patients who had died during hospitalization; tourists; and patients under guardianship.

Upon approval of Sheba Medical Center’s institutional review board, eligible patients were approached by the research team during their hospitalization in the ICCU (T1). Patients who agreed to participate provided signed consent and were asked to complete T1 questionnaires regarding their demographic data during hospitalization (either by themselves or with the help of research assistants). Approximately four months after hospital discharge (T₂), a research assistant re-contacted participants by phone and arranged an appointment with them (usually at their homes) to complete T2 questionnaires. Approximately eight months after hospital discharge (T3), patients were re-contacted to participate in a follow-up interview over the phone and asked to complete T3 questionnaires. Participation in the study was voluntary, and patients received no compensation.

Recruitment process

Figure 1 presents the recruitment process flowchart. During the study period (February 2015-March 2018), 1902 patients were hospitalized in the ICCU of Sheba Medical Center, Tel Hashomer, Israel. Of them, 461 (24.2%) met all the inclusion criteria. Of these patients 342 were eligible for the study. Of these eligible patients 156 patients agreed to participate and were recruited at Time 1. Only 13 patients were famales and consisted of female-patient/male-partner pairs, whereas the remaining 143 patients were males and consisted of male-patient/female-partner pairs. Given that there were too few female patients to unveil significant gender-by-role interactions, we did not include these 13 couples in further analyses.¹ The final sample thus consisted of 143 male patients. Of these remaining 143 patients, 106 patients completed full data at T₂, and 95 patients provided complete data at all three measurement time points.

Figure 1.

Flowchart of the study recruitment process. Note. ICCU - Intensive cardiac care unit; The present study was part of a large-scale longitudinal study conducted on CDI-PTSS among patients diagnosed with ACS and their partners. The target population included all patients diagnosed with ACS who were in a significant intimate relationship.

Sample characteristics

As can be seen in Table 1 patients (N = 106) mean age was 60.5 years (SD = 10.7). All participants were married (88.7%) or cohabiting with an intimate partner. The average relationship duration was 32.80 years (SD = 14.3). Patients completed on average 13.70 years of education (SD = 3.5), and the majority (64%) reported their income as being equal to or above the average income in (country masked for review) that year. Illness severity indicators showed that, overall, illness severity for most patients was mild to moderate according to both angiogram (M = 2.19, SD = 1.25) and echocardiogram (M = 2.12, SD = 1.12) scores.

Table 1.

Means, SDs, and bivariate correlations of all variables tested (N = 106).

	M	SD	Range	T₂ CDI-PTSS n = 106	T₃ CDI-PTSS n = 98	T₂ perceived partner AE	T₂ perceived partner PB	T₂ perceived partner OP
Age	60.5	10.7	33–85	−.06	−.31**	−.17	−.04	.05
Relation dur	32.8	14.3	2–59	−.08	−.31**	−.18	.10	−.02
Education	13.7	3.5	4–23	−.13	−.19	.06	−.22*	−.20*
Income	3.6	1.0	1–5	−.13	−.33**	−.02	−.36**	−.17
Illness sev EF	2.1	1.1	1–5	.12	.06	−.08	.06	−.03
Illness sev AN	2.2	1.2	1–5	−.04	.07	−.06	.13	.07
Patients' T₂ CDI-PTSS	6.8	10.1	0–60	—	.47***	.17	.35***	.30**
Patients' T₃ CDI-PTSS	5.5	6.8	0–60	—	—	.18	.26*	.21*
T₂ perceived partner AE	3.8	.85	1–5	—	—	—	.30**	.38**
T₂ perceived partner PB	2.5	.82	1–5	—	—	—	—	.56**
T₂ perceived partner OP	2.3	.92	1–5	—	—	—	—	—

Note. CDI-PTSS = Cardiac disease induced post-traumatic stress symptoms; Perceived partner. AE = Perceived partner active engagement; Perceived partner PB = Perceived partner protective buffering; Perceived partner OP = Perceived partner overprotection; Relation Dur = relationship duration; Education = Education level; Income = Income level; Illness sev EF = Illness severity echocardiogram; Illness sev AN = Illness severity angiogram.

***p <.001, **p <.01, *p <.05.

Measures

CDI-PTSS

Cardiac disease induced post-traumatic stress symptoms (CDI-PTSS) were measured at T₂ and T_3, using the Hebrew version of the PTSD Scale Self-Report for the DSM-5 (PSS-SR5; Foa et al., 2016). The PSS-SR5 is based on the Post-Traumatic Stress Diagnostic Scale with added items to reflect the changes made to the PTSD diagnostic criteria in the DSM-5. The PSS-SR5 consists of 20 items, all tapping criteria B, C, D, and E of the DSM-5: re-experiencing, avoidance, negative alterations in cognitions and mood, and arousal symptoms. Symptoms are measured on a scale ranging from 0 (not at all) to 4 (six or more times a week). To generate a continuous measure for CDI-PTSS, symptom item scores are summed, so that a higher score indicates a more severe level of symptoms. Cronbach’s α was 0.92 for T₂ and 0.94 for T₃.

Perceived partner support

Patients’ perception of the support they received from their partners was measured at T₂ (approximately four months after discharge), using the validated Hebrew version (Vilchinsky et al., 2011) of the Ways of Giving Support (WOGS; Buunk et al., 1996) questionnaire (the perceived version) which consists of 19 items: five active engagement items (e.g., “My partner tries to discuss it with me openly”), six overprotection items (e.g., “My partner treats me like a baby”), and eight protective buffering items (e.g., “My partner tries to hide her worries about me”). Items are measured on a 5-point scale ranging from 1 (never) to 5 (very often). The Cronbach’s alpha values were .82, .79 and .69 for active engagement, overprotection, and protective buffering, respectively.

Patients’ demographic characteristics

Demographic data were collected at T₁ (during hospitalization), and included age, relationship duration, education level, and income level (below-average, average, or above-average).

Patients’ illness severity

Patients’ cardiac illness severity was assessed by a senior cardiologist based on echocardiography to examine cardiac function and by coronary angiography to examine severity of coronary artery disease, both performed during index hospitalization. Echocardiogram and angiogram scores were both assessed on a scale ranging from 1 (mild) to 4 (most severe).

Statistical analysis

Missing data were treated by using full information maximum likelihood (FIML; Enders, 2010), a preliminary test of the missing values yielding p = .770. We thus assumed these data were missing completely at random (MCAR) (Little, 1988). Descriptive statistics were used to describe the demographic details of the sample and the study’s measures. Bivariate Pearson correlations were applied to evaluate the associations between the study’s different variables. Due to the high correlations between the three perceived partner support subscales (all correlations were significant and ranged between .30–.56), we ran three independent path analysis models using structural equation modeling (SEM) with Mplus to test the study hypotheses.

First, we assessed a mediation model, taking the three perceived partner support subscales (active engagement, overprotection, and protective buffering) as measured at T₂ as a point of departure to understand the development of T₂ CDI-PTSS and T₃CDI-PTSS. Doing so created a causal model, by which we assessed the direct effect of T₂ perceived partner support on T₃ CDI-PTSS levels and the indirect effect via T₂ CDI-PTSS levels. The CDI-PTSS levels were pseudo-indicators based on the means across relevant items but corrected by the internal consistency, as suggested by Brown (2015). Second, we assessed moderation models in which the putative buffering or facilitating effect of support as measured at T₂ on the development of T₃ CDI-PTSS was tested. We applied the PROCESS procedure (Hayes & Preacher, 2013; Model 1) to assess how each way of T₂ perceived support moderated the effect of T₂ CDI-PTSS on T₃ CDI-PTSS. A lower moderation effect represents the range from lowest to mean less 1sd; moderate represents the mean ± 1sd; and high represents mean plus 1sd to highest.

Power analysis

To calculate model power, we looked at the structural model as a hierarchical model where step one included background variables and the three perceived partner support subscales as measured at T₂, and step two included the T₂ CDI-PTSS levels. Results showed a power (1-b) of 0.89, given a sample size of 100 respondents and an effect size of 0.15 (Soper, 2006).

Results

To explore the associations between measures, as well as the possible effect of covariates, Pearson correlations were computed for patients’ CDI-PTSS levels with the following demographic variables: age, relationship duration, education level, income level, and patients’ objective measures of illness severity. As Table 1 shows, relationship duration, education level, and income level correlated negatively with patients’ T3 CDI-PTSS. Thus, these variables were controlled for in the primary analyses.

Table 1 also presents the means and standard deviations of patients' T₂ and T₃ CDI-PTSS levels and patients' T₂ perceived partner support as well as the bivariate correlations among them. Patients reported overall mild CDI-PTSS levels at both time points. Significant positive correlations were detected between T₂ perceived partner overprotection, T₂ perceived partner protective buffering, and patients' CDI-PTSS at T₂ and T_3. That is, higher levels of T₂ perceived partner overprotection and T₂ perceived partner protective buffering were accompanied by higher levels of patients’ T₂ and T₃ CDI-PTSS. Moreover, the three subscales of T₂ perceived partner support were correlated. A significant positive association was found between T₂ active engagement and T₂ overprotection, between T₂ active engagement and T₂ protective buffering, and between T₂ overprotection and T₂ protective buffering.

The correlations indicated that the subscales, though moderately related, were not identical.

Yet due to the high correlations detected between the three types of T₂ support, we also assessed the partial correlations among each type of T₂ support and the outcome measures (T₂ CDI-PTSS and T₃ CDI-PTSS), while controlling for the other two types of T₂ support (results are presented in Table 2). From Table 2 it can be seen that only the correlation between T₂ perceived partner protective buffering and T₂ CDI-PTSS remained significant when conducting partial correlations. Thus, the primary analysis in which we assessed three models, one for each T₂ perceived way of giving support, was followed by a sensitivity analysis in which we ran each model again controlling for the two other types of T₂ support.

Table 2.

Partial correlations between T₂ active engagement, T₂ overprotection, and T₂ protective buffering and T₂ CDI-PTSS and T₃ CDI-PTSS (N=106).

	T₂ CDI-PTSS n = 106	T₃ CDI-PTSS n = 98
T₂ perceived partner active engagement^a	.06	.07
T₂ perceived partner overprotection^b T₂ perceived partner protective buffering^c	.12 .20*	.07 .15

Note. CDI-PTSS = Cardiac disease induced post-traumatic stress symptoms.

^aControlling for T₂ perceived parner overprotection and T₂ perceived partner protective buffering.

^bControlling for T₂ perceived parner active engagement and T₂ perceived partner protective buffering.

^cControlling for T₂ perceived parner active engagement and T₂ perceived partner protective buffering.

*p < .05.

Testing the path analysis models

To examine the contribution of T₂ perceived partner support (active engagement, overprotection, and protective buffering) to T₂ and T₃ CDI-PTSS levels, three integrative path analysis models, with multiple time points, were developed. Relationship duration, education level, and income level were significantly correlated with CDI-PTSS, as described above, and thus were also factored into each model. Indirect paths were calculated as the product of the two components: from the independent variable (T₂ perceived partner support) to the mediator (T₂ CDI-PTSS) and from the mediator to T₃ CDI-PTSS. Table 3 consists of all paths in the tested mediations, and the indirect effects are followed by a confidence interval as a result of bootstrapping (n = 1000 repeats). Figure 2 (a,b,c) present the paths from background variables to T3 CDI-PTSS via T₂ perceived partner support and T2 CDI-PTSS, for each model.

Table 3.

Path analysis model results: standardized regression coefficients and confidence intervals of indirect effects (N=106).

Independent	Mediator	Dependent variable	Independent → mediator	Mediator → dependent	Independent → dependent	Indirect	95% CI
Indirect effect: Support → T₂ CDI-PTSS → T₃ CDI-PTSS
Model 1
T2 active engagement	T₂ CDI-PTSS	T₃ CDI-PTSS	.207* (.099)	.499*** (.156)	.036 (.099)	.103* (.061)	[.007,.253]
Model 2
T2 overprotection	T₂ CDI-PTSS	T₃ CDI-PTSS	.369*** (.103)	.497** (.178)	.025 (.149)	.183* (.108)	[.053,.439]
Model 3
T2 protective buffering	T₂ CDI-PTSS	T₃ CDI-PTSS	.447*** (.093)	.502** (.171)	−.006 (.157)	.224* (.100)	[.094,.473]

Note. CDI-PTSS=Cardiac disease induced post-traumatic stress symptoms. CI= confidence interval.

***p < .001, **p < .01, *p < .05.

Figure 2.

(a, b, c) Path analysis models results. Note. Path model with parameter estimates. Numbers printed in lines correspond to standardized significant regression weights. a- Path analysis model for Model 1. b- Path analysis model for Model 2. c- Path analysis model for Model 3. CDI-PTSS – Cardiac disease induced post-traumatic stress symptoms. ***p < .001, **p < .01, *p < .05.

In the first model, T₂ perceived active engagement served as the independent variable, T₃ CDI-PTSS as the dependent variable, and T₂ CDI-PTSS as the mediating variable. The model showed good fit indices: CFI = 1.00, TLI = 1.00, χ ² = 3.74, df = 5, p = .59, RMSEA = .000, SRMR = .046. T₂ CDI-PTSS and T₃ CDI-PTSS were positively associated with each other (b = .50, p <.01). Higher levels of T₂ perceived active engagement were associated with higher levels of T₂ CDI-PTSS (b = .21, p < .05). As presented in Table 3 and Figure 2(a), the results of this model reveal that T₂ perceived active engagement was not directly associated with T₃ CDI-PTSS, but rather indirectly via T₂ CDI-PTSS (indirect = .103, p < .05).

In the second model, T₂ perceived overprotection served as the independent variable, T₃ CDI-PTSS as the dependent variable, and T₂ CDI-PTSS as the mediating variable. The model showed good fit indices: CFI = 1.00, TLI = 1.00, χ ² = 1.88, df =5, p = .87, RMSEA = .000, SRMR=.027. Higher levels of T₂ perceived overprotection were accompanied by higher levels of T₂ CDI-PTSS (b = .37, p < .001). As evident in Table 3 and Figure 2(b), T₂ perceived overprotection was not associated directly with T₃ CDI-PTSS, but rather indirectly via T₂ CDI-PTSS (indirect =.224, p < .05).

In the third model, T₂ perceived protective buffering served as the independent variable, T₃ CDI-PTSS as the dependent variable, and T₂ CDI-PTSS as the mediating variable. The model showed good fit indices: CFI = 1.00, TLI = 1.00, χ ² = 1.92, df = 5, p = .86, RMSEA = .000, SRMR = .023. Higher levels of T₂ perceived protective buffering were associated with higher levels of T₂ CDI-PTSS (b = .45, p < .001). As evident in Table 3 and Figure 2(c), T₂ perceived protective buffering was not associated directly with T₃ CDI-PTSS, but rather indirectly via T₂ CDI-PTSS (indirect = .183, p < .05).

Sensitivity analysis: We ran each of the three mediation models again while controlling in each model for the two other types of support. Again, the same significant patterns of direct and indirect effects emerged. Results of the sensitivity analysis are presented in Electronic Supplement 1.

Moderation analysis: Testing potential interaction effects on T₃ CDI-PTSS

In the second stage of analysis, we applied the PROCESS procedure (Hayes & Preacher, 2013; Model 1) to assess the putative buffering or facilitating effects of different T₂ perceived ways of giving support on the association between T₂ CDI-PTSS and T3 CDI-PTSS. Our analysis revealed a significant interaction effect only for T₂ perceived protective buffering: interaction = −0.246, p = .008, 95% (CI - 0.427, −0.067). We plotted simple slopes with respect to the moderator levels, and present in Figure 3 an illustration of the different linear associations for low, moderate, and high values of protective buffering. The linear association between T₂ and T₃ CDI-PTSS was positive but decreased as T₂ perceived protective buffering levels increased. Specifically, when T₂ perceived protective buffering exceeded the value of 3.60 (on the 5-point scale), the T_2-T₃ CDI-PTSS association was no longer significant.

Figure 3.

Simple slopes and linear prediction for Model 2 interaction analysis. Note. CDI-PTSS=Cardiac disease induced post-traumatic stress symptoms; PB= protective buffering ***p < .001, **p < .01, *p < .05.

Discussion

The current study focused on the role played by perceived partner support in the development of CDI-PTSS over time among cardiac patients. The unique nature of CDI-PTSS, which includes the tendency to disengage from any reminder of the traumatic event, including partners’ supportive acts, makes pinpointing the beneficial and detrimental kinds of perceived partner support in this context, an important focus for scientific attention.

In accordance with our hypotheses, when patients perceived their partners as providing higher levels of overprotection, and even more so protective buffering, they reported more post-traumatic symptoms shortly after the event, which in turn predicted more symptoms over time. Indeed, former studies focusing on these two types of support found them to be ineffective and even harmful for patients’ outcomes, as both types tend to minimize patients’ ability to cope with distress (Coyne & Smith, 1994; O’Bertos et al., 2020; Perndorfer et al., 2019; Vilchinsky et al., 2011). Our study adds to the literature by showing that patients’ perceptions of being treated overprotectively or via protective buffering is associated with higher levels of CDI-PTSS.

Contrary to our hypothesis, however, the same pattern was also detected for perceived active engagement. It is possible that patients interpret their partners’ engagement as an alarming signal and therefore feel more symptoms. This costly effect has been recognized in previous studies and has been attributed to the fact that despite partners’ best intentions, perceived support might increase feelings of helplessness (George-Levi et al., 2020; Thoits, 2011). Thus, it seems that perceived partner support, whether manifested in engagement, buffering, or overprotection, may be interpreted by patients as a reminder of the trauma they wish to avoid. Consequently, these perceptions may activate feelings of distress, alertness, and avoidance (i.e., symptoms of PTSD).

Yet, as the associations between perceived support and early CDI-PTSS were measured cross-sectionally in the current study, it is also possible that partners provided more support, of all kinds, as a response to patients’ CDI-PTSS levels and not vice versa. In other words, as patients show more debilitating symptoms, their partners may provide more of whatever behavior they consider to be supportive, and consequently, the patients perceive the reception of higher levels of support of any kind. Thus, perceived support may also be the consequence of patients’ CDI-PTSS and not the other way around. Regardless of the sequence of events, it seems that any perception of partners’ attempts to alleviate patients’ symptoms are futile, at least when measured approximately four months after hospital discharge.

However, when observing the dynamic between CDI- PTSS and perceived support over time, perceived protective buffering emerge as a moderator, such that the significant association found between early and chronic CDI-PTSS diminished as patients perceived higher levels of protective buffering. Thus, perceived protective buffering, which has previously been found in several studies to be a detrimental way of giving support (Niemann-Mirmehdi et al., 2019; Vilchinsky et al., 2011), seems to buffer CDI-PTSS from becoming chronic. It seems reasonable to suggest that the more CDI-PTSS the patient shows, the more their partner intuitively avoids discussing the traumatic event, leading the patient to perceive higher levels of protective buffering. Regardless, this chain of events evidently buffers patients’ CDI-PTSS from crystalizing and becoming chronic.

The current study had several limitations. First, the participation rate was 45%, potentially limiting the findings’ generalizability. As one of the dominant PTSS symptoms is the tendency to avoid trauma reminders (DSM-5, 2013), it may be that patients who refused to participate in a study that included questions that could trigger the trauma were those patients who experienced the highest PTSS levels. Second, the data were collected from a single source (i.e., self-report questionnaires), potentially raising concerns about common method variance (CMV) and social desirability bias. Yet subjective perceptions were the focus of this research, as studies have indicated that social support perceptions have a greater effect on adjustment than actual support (Chen et al., 2021). Moreover, the current study did not include data regarding partners’ perception of the support they provided. Future studies would do well to examine both partners’ perceptions, as coping with an illness is a dyadic process (Vilchinsky & Dekel, 2018). Third, this study focused on male patients; it is therefore not possible to differentiate between the effect of gender and the effect of social role (partner-patient).

Despite these limitations, the current study, being longitudinal and based on a relatively large clinical sample, adds to the notion that perceived support types that are generally harmful may be beneficial under unique circumstances, such as CDI-PTSS. Clinicians are therefore advised to focus on the specific support needs of cardiac patients who experience high levels of CDI-PTSS. Cardiac disease induced PTSS has a unique manifestation which calls for tailored interventions to help patients and partners better cope, not only with the concrete medical and behavioral demands of the illness, but also with its specific emotional ramifications.

Supplemental Material

Supplemental Material - Perceived partner support and post-traumatic symptoms after an acute cardiac event: A longitudinal study

Supplemental Material for Perceived partner support and post-traumatic symptoms after an acute cardiac event: A longitudinal study by Sivan George-Levi, Keren Fait, Hanoch Hod, Shlomi Matezky, and Noa Vilchinsky in Journal of Social and Personal Relationships.

Footnotes

Acknowledgements

This study is partially based on the first and second authors' dissertation studies carried out at Bar-Ilan University, Ramat-Gan, Israel.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by a grant from the Schnitzer Foundation for Research on the Israeli Economy and Society, Bar-Ilan University.

Data Availability

The data set is available via OSF repository. The link is:

ORCID iDs

Sivan George-Levi

Shlomi Matezky

Note

Supplemental Material

Supplemental material for this article is available online.

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Supplementary Material

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Perceived partner support and post-traumatic symptoms after an acute cardiac event:A longitudinal study

Abstract

Objective

Method

Results

Conclusions

Keywords

Perceived partner support

The current study

Method

Participants and procedure

Recruitment process

Sample characteristics

Measures

CDI-PTSS

Perceived partner support

Patients’ demographic characteristics

Patients’ illness severity

Statistical analysis

Power analysis

Results

Testing the path analysis models

Moderation analysis: Testing potential interaction effects on T3 CDI-PTSS

Discussion

Supplemental Material

Supplemental Material - Perceived partner support and post-traumatic symptoms after an acute cardiac event: A longitudinal study

Footnotes

Acknowledgements

Declaration of Conflicting Interests

Funding

Data Availability

ORCID iDs

Note

Supplemental Material

References

Supplementary Material

Moderation analysis: Testing potential interaction effects on T₃ CDI-PTSS