Abstract
Introduction
While surgery is considered a standard treatment for patients diagnosed with early-stage lung cancer, it often brings significant challenges for patients including functional limitations and post-operative symptoms. Family caregivers play a critical role in care coordination, post-operative management, and addressing emotional distress often with limited preparation and support. Despite their importance, few studies have assessed the interrelationship between patient and caregiver outcomes during the peri-operative period.
Methods
This study is a secondary analysis of data from a randomized controlled trial involving 190 patient-caregiver dyads who participated in a dyadic multimedia self-management intervention. Structural equation modeling (SEM) was used to evaluate the impact of various sociodemographic variables and psychosocial mediators on dyadic outcomes.
Results
Self-efficacy was a consistent predictor, with positive effects on preparedness and quality of life (QOL) and negative effects on psychological distress. While activation and knowledge were positively associated with self-efficacy and preparedness, they showed minimal effects on caregiver burden and distress. When evaluating the interdependent relationship, higher patient QOL was associated with improved caregiver preparedness and reduced burden. Caregiver distress significantly predicted greater patient distress and lower patient QOL.
Conclusions
The results of this study support the interdependent, bi-directional associations of patient and caregiver well-being. Further multi-media interventions in the perioperative period focused on self-efficacy and promoting emotional resilience among caregivers may substantially improve post-operative outcomes for both members of the dyad.
Keywords: Lung cancer surgery, dyadic outcomes, family caregivers, self-efficacy, psychological distress, quality of life, structural equation modeling
Introduction
Lung cancer is the third most common cancer in the United States, accounting for 12% of new cancer cases, yet it is the leading cause of cancer-related deaths, responsible for approximately one in five cancer fatalities.1,2,3 The majority of patients diagnosed with lung cancer are over the age of 65, with an average age of 70.3
For non-metastatic lung cancer, pulmonary resection is the standard treatment. These surgeries can lead to significant post-operative pain, reduced pulmonary function, and persistent dyspnea.4 Chronic pain may last more than 12 months, and mental health issues including depression and anxiety are common, exacerbating complications and reducing patient self-efficacy.5,6,7 Post-operative complications such as air leakage, respiratory infections, and arrhythmias may occur, which are often linked to pre-operative risk factors such as age, smoking, and underlying cardiovascular conditions.8 Despite employing minimally invasive techniques that reduce the length of the hospital stay, readmission rates and complications such as pneumonia, pneumothorax, and dysrhythmias remain prevalent.9 Considering this, family caregivers face significant strain in supporting and caring for their loved ones during the post-surgical recovery period.
Family caregivers, representing approximately one in five Americans, provide a wide range of services including feeding, dressing, coordinating medications, and managing financial matters. They dedicate upwards of 30 hours a week to caregiving tasks and face considerable challenges including high rates of depression, work-related difficulties, and financial strain.10 For lung cancer family caregivers, the burden is compounded by uncertainty regarding prognosis, functional decline, and managing the patient’s emotional distress. Despite these challenges, caregivers are rarely asked about their own needs. Currently, patients and caregivers are responsible for navigating the post-discharge recovery process, with limited support from healthcare providers during this transitional period. Fragmented communication among healthcare teams further intensifies the burden on caregivers who are often left to coordinate care and manage the patient’s physical and psychological distress.11
Self-management has been explored as a model of cancer care that empowers patients to set goals for their recovery, focusing on skills such as decision-making, resource utilization, and partnership with providers. An important aspect of self-management is self-efficacy, defined as the family caregiver and patient’s perceived capacity to successfully complete tasks. Though fewer studies have examined the effects of self-efficacy in the lung cancer population, evidence shows increased levels of patient self-efficacy correlated with less lung cancer symptoms and functional impairments. Similarly, caregivers with high self-efficacy for helping their loved one experience lower overall mood disturbances. When assessing dyad self-efficacy, patients in “patient high-caregiver high” dyads report significantly less symptom burden than patients in both “patient low-caregiver high” and “patient low-caregiver low” dyads.12 Several studies have provided evidence to support this relationship in the cancer population.13,14,15 Cancer patient self-efficacy has been correlated with family caregiver outcome variables of benefit finding, anxiety, and depression, suggesting an important bi-directional relationship where the self-efficacy of the patient directly influences the well-being and emotional outcomes of the family caregiver.16
Given the substantial impacts on QOL for both patients and caregivers in the context of lung cancer surgery recovery, there is growing need to understand the potential interrelationships between patient and caregiver outcomes in the lung cancer surgery setting. This is particularly important given that postoperative hospital stays are shorter, and the burden of care and postoperative recovery increasingly falls on family caregivers. The purpose of this study was to describe the interrelationships between patient and caregiver outcomes, with a focus on psychological distress, QOL, caregiver burden, caregiver preparedness, self-efficacy, and activation.
Methods
This study was a secondary data analysis of a randomized controlled trial of a multimedia dyadic self-management intervention among patients scheduled for lung cancer surgery and their family caregivers. The overall trial design was described previously.17 Briefly, the randomized trial enrolled adult patients (≥18 years or older) with stage I-III resectable primary lung cancer, able to read and understand English or Chinese, and had an identified family caregiver were eligible. Eligible caregivers included an adult family member or friend identified by the patient as being the primary caregiver before and after surgery. The study design and protocol were approved by the Institutional Review Board (IRB), and all participants (patients and caregivers) provided written informed consent prior to study participation. Following informed consent, the patient/caregiver dyads were randomized to either the intervention arm or the attention control arm.
The intervention was based on the Chronic Care Self-Management Model (CCM). It was delivered by trained registered nurses (RNs) over five sessions before surgery, before discharge, and at day +2, +7 and 2-months post-discharge. Self-management coaching skills included goal setting, problem solving, and post-operative recovery/caregiving strategies. Participants also viewed a 15-minute video that provided information on what to expect before and after lung cancer surgery. The video was available to participants through a secure website accessed with computers, smartphones or tablets. Handbooks with corresponding content were given to both the patient and family caregivers and were used during the intervention sessions.
For the attention control arm, dyads watched a video from the American Cancer Society on clinical trials and were given print materials on clinical trials. Participants in the attention control arm also received five sessions delivered by a Clinical Research Assistant (CRA) that mirrored the frequency of the intervention arm.
Outcome Measures
Dyadic psychological distress was assessed using the Distress Thermometer (DT). The DT is an efficient, low subject burden measure to evaluate psychological distress based on a scale of 0 (no distress) to 10 (extremely distressed).18 Dyadic self-efficacy was assessed using the Self-Efficacy Scale, an adapted 8-item tool evaluating family caregiver and patient confidence in self-management.19 Dyadic post-operative recovery knowledge was assessed using the Surgery Related Knowledge Tool developed by the investigators based on specific content within the MSM intervention.
Patient QOL was assessed using the Functional Assessment of Cancer Therapy-Lung (FACT-L). The FACT-L is a lung cancer-specific version of the FACIT System, including the FACT-G scales (physical, social/family, emotional, and functional well-being) and a 10-item disease-specific symptom index (LCS). Respondents rated each item on a 5-point Likert scale (0=not at all; 4=very much) based on the past 7 days. A 2–3-point change in scores is considered the minimal clinically important difference.20,21 Patient activation was assessed using the Patient Activation Measure (PAM). This measured patient “activation”, which uses a Guttman-like scale that reflects a patient’s level of engagement and empowerment in their healthcare.22
Family caregiver burden was assessed using the Montgomery Borgatta Caregiver Burden Scale (MBCBS). The 14-item caregiver burden tool assessed three dimensions: objective burden, subjective demand, and subjective stress. Scores were measured on a 1-5 ordinal scale, with high burden indicated by scores of ≥23 for objective burden, ≥15 for subjective demand, and ≥13.5 for subjective stress.23 The 8-item Preparedness for Caregiving Scale, part of the Family Care Inventory, measured caregiving preparedness, with scores ranging from 0 to 4 and higher scores indicating better preparedness.24 Family caregiver QOL was assessed using the City of Hope-Quality of Life–Family (COH-QOL-Family), a 37-item instrument measuring caregiver quality of life across physical, psychological, social, and spiritual domains. Using a 0-10 ordinal scale, higher scores indicated better QOL.25,26 Finally, caregiver activation was assessed using the FCG Activation in Transitions Tool (FCAT), a 10-item survey designed to assess the family caregiver’s level of engagement and preparedness to care for patients during transitions.27
Statistical Analysis
Descriptive statistics were used to describe demographic and clinical characteristics, as well as outcome measures for both patients and family caregivers. Structural equation modeling (SEM), using covariances and maximum likelihood estimation was used to assess interdependent associations between patient and caregiver outcomes. The proposed theoretical model had several observed predictors (age, sex, marital status, caregiver employment status, and comorbidities), three observed mediators for both patient and family caregivers (activation, self-efficacy, and knowledge), and patient and family caregiver outcomes (patient distress and quality of life; family caregiver distress, burden, preparedness, and quality of life). We examined a model where patient predictors influence patient distress and quality of life and caregiver predictors influence caregiver distress, burden, preparedness, and quality of life. In addition, we assessed, in a bi-directional manner, how caregiver outcomes influenced patient outcomes, and conversely, how patient outcomes influenced caregiver outcomes.
All patient and family caregiver outcomes were evaluated within the same 3-month post-operative time window. Thus, this analytic approach does not establish temporal precedence between patient and caregiver variables, nor does it allow for inference regarding causal or dynamic reciprocal effects.
The model was evaluated by testing the significance of all direct and indirect effects specified in the model, using a p-value threshold of <0.05 to determine statistical significance. This approach was employed to identify factors that had direct/indirect influence on the various outcomes under study. All SEM analyses were conducted with the CALIS procedure in SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).
Results
Sociodemographic and Clinical Characteristics
Sociodemographic and clinical characteristics of dyads are presented in Table 1. The final participant sample included a total of 190 patient-caregiver dyads (N= 380). The median patient age was 68 years (interquartile range [IQR]: 62-75), while caregivers were younger, with a median age of 62.5 years (IQR: 49-71). Most patients and caregivers identified as female (60% and 59% respectively), and most participants identified as White (63% of patients and 65% of caregivers). Approximately half of both the caregiver and patient groups reported having attended or completed college, and nearly 30% in each group reported receiving a graduate level education. Most patients were married or partnered, and caregivers were predominantly spouses and partners, followed by daughters. While most patients were retired (56%), caregivers exhibited a wider range of employment statuses, with 40% employed full-time.
Table 1.
Patient and FCG Sociodemographic and Clinical Characteristics
| Overall N = 3801 | Patient N = 1901 | Caregiver N = 1901 | |
|---|---|---|---|
| Age, median (IQR), years | 66.0 (56.0, 74.0) | 68.0 (62.0, 75.0) | 62.5 (49.0, 71.0) |
| Age group, n (%) | |||
| Less than 65 years | 160 (42%) | 60 (32%) | 100 (53%) |
| 65 years and older | 216 (57%) | 130 (68%) | 86 (45%) |
| Sex, n (%) | |||
| Female | 226 (59%) | 114 (60%) | 112 (59%) |
| Male | 153 (40%) | 76 (40%) | 77 (41%) |
| Race, n (%) | |||
| White | 244 (64%) | 120 (63%) | 124 (65%) |
| Black/African American | 12 (3%) | 6 (3%) | 6 (3%) |
| American Indian/Alaska Native | 1 (0%) | 1 (1%) | 0 (0%) |
| Asian | 85 (22%) | 44 (23%) | 41 (22%) |
| Native Hawaiian/other Pacific Islander | 2 (1%) | 1 (1%) | 1 (1%) |
| More than one race | 7 (2%) | 3 (2%) | 4 (2%) |
| Other | 20 (5%) | 10 (5%) | 10 (5%) |
| Ethnicity, n (%) | |||
| Hispanic or Latino | 48 (13%) | 22 (12%) | 26 (14%) |
| Non-Hispanic | 319 (84%) | 162 (85%) | 157 (83%) |
| Refuse to Answer | 11 (3%) | 6 (3%) | 5 (3%) |
| Marital status, n (%) | |||
| Single | 40 (11%) | 12 (6%) | 28 (15%) |
| Separated/Divorced | 33 (9%) | 20 (11%) | 13 (7%) |
| Married/Partnered | 279 (73%) | 137 (72%) | 142 (75%) |
| Widowed | 18 (5%) | 16 (8%) | 2 (1%) |
| Other | 1 (0%) | 0 (0%) | 1 (1%) |
| Education, n (%) | |||
| Did not complete high school | 17 (4%) | 13 (7%) | 4 (2%) |
| Completed high school/GED | 61 (16%) | 35 (18%) | 26 (14%) |
| Vocational School | 12 (3%) | 3 (2%) | 9 (5%) |
| Some college/Graduated college | 188 (49%) | 89 (47%) | 99 (52%) |
| Some graduate school/Completed graduate school | 99 (26%) | 48 (25%) | 51 (27%) |
| Employment status, n (%) | |||
| Part-time | 30 (8%) | 17 (9%) | 13 (7%) |
| Full-time | 121 (32%) | 45 (24%) | 76 (40%) |
| Unemployed | 25 (7%) | 9 (5%) | 16 (8%) |
| Retired | 181 (48%) | 106 (56%) | 75 (39%) |
| Other | 21 (6%) | 13 (7%) | 8 (4%) |
| Smoking status, n (%) | |||
| Currently smoking | 16 (4%) | 10 (5%) | 6 (3%) |
| Quit less than 6 months ago | 19 (5%) | 15 (8%) | 4 (2%) |
| Quit more than 6 months ago | 133 (35%) | 80 (42%) | 53 (28%) |
| Never smoked | 211 (56%) | 85 (45%) | 126 (66%) |
| Zubrod Score, n (%) | |||
| Normal activity, no symptoms | 141 (74%) | 141 (74%) | - |
| Symptoms, fully ambulatory | 41 (22%) | 41 (22%) | - |
| Symptoms, in bed <50% of time | 4 (2%) | 4 (2%) | - |
| Surgical method, n (%) | |||
| MIS | 147 (77%) | 147 (77%) | - |
| Open | 27 (14%) | 27 (14%) | - |
| Cancelled/aborted surgery | 16 (8%) | 16 (8%) | - |
| Pathologic stage, n (%) | |||
| I | 109 (57%) | 109 (57%) | - |
| II | 36 (19%) | 36 (19%) | - |
| III | 28 (15%) | 28 (15%) | - |
| IV | 1 (1%) | 1 (1%) | - |
| Relationship to patient, n (%) | |||
| Spouse/Partner | 120 (63%) | - | 120 (63%) |
| Parent | 1 (1%) | - | 1 (1%) |
| Daughter | 32 (17%) | - | 32 (17%) |
| Son | 10 (5%) | - | 10 (5%) |
| Other | 25 (13%) | - | 25 (13%) |
Median (Q1, Q3); n (%)
Abbreviations: IQR, inter-quartile range
N = 174
Regarding smoking status, 50% of patients previously smoked compared to 30% of caregivers. At the time of enrollment, 5% of patients were currently smoking. Caregivers reported overall good functional status, with 82% reporting no limitations in daily functioning. Similarly, 59% of patients reported normal functional status despite their lung cancer diagnosis, while 35% reported minor limitations to daily living. As expected, based on the standard of care at City of Hope, most patients (77%) underwent minimally invasive surgical resection including robot-assisted and video-assisted thoracoscopy. Over half of patients were diagnosed with stage I lung cancer, and the median length of hospital stay was 3 days (IQR: 2-4).
Baseline and Follow-Up Descriptive Outcomes
At baseline, patients and caregivers both reported moderate distress with median scores of 4.0 (patients, IQR: 2-7; caregivers, IQR: 2-6) based on the Distress Thermometer. At three months post-discharge, median distress scores decreased to 3.0 for both groups (patients, IQR:1-6; caregivers, IQR: 2-6). FACT-L scores for patient quality of life declined slightly from a median of 107.7 (IQR: 93-119) at baseline to 102.9 (IQR: 92.7-119) at three months. Caregiver burden also decreased slightly from 44.0 (IQR: 42-48) to 42.0 (38.5-46). While caregiver preparedness improved slightly over time, quality of life remained stable from baseline to follow-up (Table 2).
Table 2.
Descriptive Data on Outcomes
| Baseline | 3 months post-discharge | |||||
|---|---|---|---|---|---|---|
|
|
||||||
| Overall | Patient | Caregiver | Overall | Patient | Caregiver | |
| N = 3801 | N = 1901 | N = 1901 | N = 3441 | N = 1861 | N = 1581 | |
| Distress (higher score means worse health) | 4.0 (2.0, 7.0) | 4.0 (2.0, 7.0) | 4.0 (2.0, 6.0) | 3.0 (1.0, 6.0) | 3.0 (1.0, 6.0) | 3.0 (2.0, 6.0) |
| Missing | 2 | 0 | 2 | 39 | 30 | 9 |
| FACT-L (higher score means better QoL) | 107.7 (93.0, 119.0) | 107.7 (93.0, 119.0) | - | 102.9 (92.7, 119.0) | 102.9 (92.7, 119.0) | - |
| Missing | 3 | 3 | 30 | 30 | ||
| Caregiver Burden (higher score means worse health) | 44.0 (42.0, 48.0) | - | 44.0 (42.0, 48.0) | 42.0 (38.5, 46.0) | - | 42.0 (38.5, 46.0) |
| Missing | 2 | 2 | 6 | 6 | ||
| Caregiver Preparedness (higher score means better preparedness) | 2.7 (2.0, 3.1) | - | 2.7 (2.0, 3.1) | 3.0 (2.4, 3.3) | - | 3.0 (2.4, 3.3) |
| Missing | 2 | 2 | 7 | 7 | ||
| Caregiver QOL (higher score means better QoL) | 27.2 (23.1, 31.3) | - | 27.2 (23.1, 31.3) | 27.2 (23.2, 31.4) | - | 27.2 (23.2, 31.4) |
| Missing | 3 | 3 | 7 | 7 | ||
Median (Q1,Q3)
Predictors of Patient Outcomes
SEM identified several direct and indirect predictors of patient outcomes, mediated through activation, self-efficacy and knowledge. A theoretical model describing these relationships is presented in Figure 1. Older and male patients demonstrated lower levels of activation while patients with diabetes reported higher activation. Patients who were married or partnered had higher self-efficacy and knowledge. Subsequently, patients with higher self-efficacy experienced less psychological distress. Higher self-efficacy and activation were also associated with better higher QOL, whereas knowledge was not significantly associated with either patients’ psychological distress or QOL (Figure 2).
Figure 1.
Theoretical model of factors affecting patient and family caregivers’ quality of life using Structural Equation Modeling
Figure 2. Relationship between patient variables.
*P < .05
The values in this figure indicate the standardized coefficients
Predictors of Caregiver Outcomes
Male family caregivers demonstrated lower levels of activation, while married or partnered caregivers exhibited lower self-efficacy. Employed caregivers had lower knowledge. Higher self-efficacy was associated with lower psychological distress, as well as higher preparedness and QOL. In contrast, higher activation was associated with lower QOL. Notably, both activation and knowledge showed positive, but non-significant, direct effects on caregiver burden, preparedness and psychological distress (Figure 3). This may suggest a nuanced dynamic between preparedness and emotional strain.
Figure 3. Relationship between FCG variables.
*P < .05
The values in this figure indicate the standardized coefficients
Interdependent Associations Between Patient and Caregiver Outcomes
Additionally, SEM evaluated the interdependent associations between family caregiver and patient outcomes (Figure 4). Higher caregiver distress was associated with higher patient psychological distress and lower patient QOL while higher patient psychological distress was associated with increased caregiver distress. Additionally, higher patient QOL was associated with reduced caregiver distress and burden, as well as increased caregiver preparedness and QOL. These findings emphasize the interconnected nature of patient and caregiver well-being and support the idea that dyadic interventions targeting one member of the pair can meaningfully improve outcomes for the other.
Figure 4. Relationship between patient and FCG outcomes.
*P < .05
The values in this figure indicate the standardized coefficients
Discussion
This study provides valuable insights into the interplay between patient and family caregiver outcomes after lung cancer surgery. While most caregivers were spouses or partners, a substantial portion identified as daughters, suggesting the role of adult children in cancer care and recovery. These findings illuminate the need for multigenerational support strategies for both spousal and non-spousal caregivers. A significant portion of caregivers had received a graduate degree and reported working full time during this study, suggesting a population that has many responsibilities outside of their caregiving role. Marital/partnered status and sex were significant predictors of both patient and caregiver outcomes, primarily through their impact on self-efficacy and knowledge.
Findings revealed that sociodemographic factors including marital status, age and sex were associated with various psychosocial mediators such as activation, self-efficacy, and knowledge. These mediators, in turn, significantly influenced psychological distress for both patients and caregivers. Among these, self-efficacy had the strongest and most consistent associations with outcomes across both groups. Family caregivers with greater self-efficacy experienced less distress and burden and significantly higher quality of life. Similarly for patients, higher self-efficacy was associated with lower distress and improved quality of life. Self-efficacy has been evaluated for its potential utility in numerous health outcome studies. Self-Efficacy Theory focuses on the idea that individuals with high confidence in their ability to complete courses of action are more likely to attain their goals.28 Using a measure of self-efficacy, the Cancer Behavior Inventory (CBI), multiple studies have demonstrated a correlation between self-efficacy and both quality of life and psychological distress.29 While previous studies have been primarily focused on patient self-efficacy and its effect on outcomes, our findings demonstrate that caregiver self-efficacy is equally vital. Interventions aimed at strengthening both the caregiver’s and patient’s ability to manage illness-related changes may meaningfully enhance dyadic recovery and well-being.
Interestingly, knowledge and activation, while positively associated with self-efficacy, did not exhibit strong protective effects on caregiver outcomes as anticipated. In fact, knowledge showed a small positive direct effect on both caregiver burden and psychological distress. As theorized in the Stress Process Model of Caregiving, stress may not arise from the demands of the care itself, but rather the caregiver’s perception of role strain and resource overload.30 This is underscored by studies highlighting similar perceived caregiver burden despite varying levels of caregiver knowledge and technical skills.31 Both activation and knowledge had positive mediating effects on caregiver preparedness, but their non-significant or negative impacts on both burden and QOL suggest a disconnect between “knowing what to do” and “feeling capable of doing it.” Future research should explore this distinction and identify various contextual factors such as health literacy, multi-tasking, resource availability etc. that may influence caregiver outcomes. These results suggest the need to refine self-management interventions to reduce information overload and increase confidence-building among caregivers.
When analyzing the dyadic relationship, family caregiver distress played an influential role as a significant predictor of increased patient psychological distress and lower patient QOL. While both patients and caregivers reported similar levels of baseline distress, caregiver distress had a greater impact on dyadic outcomes. These findings should be interpreted as evidence of interdependence within dyads rather than true reciprocity, as significant associations were observed without establishing temporal ordering. Establishing true reciprocity would require longitudinal modeling with explicit temporal precedence, which was beyond the scope of the present analysis. This phenomenon could be explained by the various sources of distress dependent on phase of illness. Following diagnosis, caregivers must support patients to cope with complex emotions. Prior research suggests that even close family caregivers feel unprepared to handle the emotions arising from a new diagnosis.32 During the treatment, sources of distress may shift towards worrying about whether the patient will tolerate the treatment and results will be effective. This is coupled with caregiver responsibilities to manage and monitor the physical and emotional health of the patient while juggling other activities of daily life.
During the survivorship phase, caregivers may experience more fears of cancer recurrence than patients. It is possible that in this patient population, with predominantly stage I lung cancer, individuals are more likely to feel a sense of optimism after surviving potentially curative surgery. Family caregivers, however, may not experience this to the same degree. Prior work assessing psychological distress over time in GI and lung cancer dyads demonstrated that baseline scores on the Distress Thermometer were higher in caregivers than in patients.33 Similar outcomes were identified in a study specific to lung cancer surgery, with caregivers exhibiting elevated distress levels throughout follow-up.34
Implications
Overall, the results of this study support the use of structured, multi-modal support interventions in perioperative care models, especially those targeting self-efficacy and dyadic interactions. As evidence of the limited impact of activation and knowledge on distress and burden reduction among caregivers, future efforts should go beyond information delivery to include promotion of emotional resilience and perceived caregiving competence. The observed interdependence between caregiver distress and patient outcomes highlights the need for consistent assessment of caregiver well-being during all phases of recovery. As essential members of the team, caregivers should be included in preoperative education, discharge planning and follow-up care by healthcare teams.
Strengths
There were several strengths of this study including use of a randomized controlled trial and large sample size. This trial employed a dyadic framework which highlights the complex and interdependent nature of recovery following lung cancer surgery. Focused on a Chronic Care Self-Management Model, this study integrated evidence-based coaching strategies pre- and postoperatively. Structural Equation Modeling allowed for exploration of nuanced relationships between sociodemographic factors, psychosocial mediators and dyadic outcomes.
Limitations
There are limitations of this study to consider. The sample was demographically homogenous with most patients identifying as White, well educated, and over one third of the population reporting annual income exceeding $100,000. This may underrepresent the experiences of lower-income and racial/ethnic minority dyads, limiting generalizability. All outcomes were self-reported, which may introduce potential recall bias. Two time points were used to evaluate outcomes over time. While this timeframe captures the acute recovery period, it is possible that longer follow-up times are needed to assess the impact of the each studied predictor and mediator on dyadic outcomes.
Conclusion
This study highlights the interconnected nature of patient and caregiver outcomes following lung cancer surgery. Self-efficacy was identified as a key psychosocial mediator, playing a central role in shaping outcomes including psychological distress, caregiver burden, and QOL. Our findings highlight that increased knowledge and activation among caregivers may not necessarily translate into improved outcomes. This suggests the need for interventions that prioritize confidence building and emphasize emotional resilience. Caregiver distress was a significant predictor of patient outcomes, reinforcing the necessity of perioperative care tailored to family caregivers. As evidence of this multimedia self-management intervention, support during the high-stress transitional phases of lung cancer surgery has the potential to improve recovery and overall well-being for both members of the dyad.
Acknowledgements:
Research reported in this publication is supported by the National Cancer Institute of the National Institutes of Health under award number R01CA217841. It also includes work performed in the Biostatistics and Mathematical Oncology Core - Shared Resource (BMOC-SR) supported by the National Cancer Institute of the National Institutes of Health under award number P30CA033572. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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