Abstract
Background
Patients undergoing spinal surgery often experience chronic pain and functional limitations. These complications frequently lead to diminished quality of life, hope, and resilience. Inadequate family knowledge regarding home-based patient care exacerbates these challenges. This study investigates the effect of family support through education and follow-up after discharge on the quality of life, resilience, and hope in patients following spinal surgery.
Methods
This quasi-experimental study was conducted on 50 spinal surgery patients in Hamadan from November 2023 to October 2024. Participants were selected through convenience sampling and randomly allocated to either intervention or control groups. The intervention group received two visual training sessions before discharge and one month of follow-up, while the control group received only routine care. Data were collected using SF-36 (Quality of Life), MHS (Hope), and CD-RISC (Resilience) questionnaires. Finally, the data were analyzed using Independent and Paired t-tests, Chi-square, and ANCOVA at the significance level of P < 0.05 in SPSS 24.
Results
The groups showed no significant differences in baseline characteristics (P > 0.05). Post-intervention, the intervention group demonstrated significantly higher scores in quality of life (P = 0.013, Cohen’s d = 0.73, 95% CI [0.21–1.25]) and resilience (P = 0.01, d = 0.76, 95% CI [0.25–1.27]) compared to controls, indicating medium-to-large effect sizes. No significant difference was found for hope (P = 0.543, d = 0.17, 95% CI [-0.18, 0.52]).
Conclusion
Family education and post-discharge follow-up improved quality of life and resilience in spinal surgery patients, though no significant effect was observed on hope. These findings underscore the importance of structured family caregiving support and systematic follow-up in enhancing patient outcomes.
Keywords: Family caregivers, Quality of life, Resilience, Hope, Spinal fusion
Background
Degenerative spinal conditions, including degenerative changes, spinal stenosis, spondylolisthesis, and intervertebral disc herniation, represent the most prevalent spinal disorders [1]. Spinal surgery ranks as the second most common surgical procedure among individuals over 60 [2]. With rising degenerative spinal conditions in aging populations, the demand for spinal surgery is expected to grow. Nifert et al. forecast 13.3% and 19.3% increases in anterior/posterior cervical fusions by 2040 [3].
Postoperative patients frequently experience stress and anxiety due to physical changes and temporary activity restrictions, significant factors that impair quality of life (QoL). QoL encompasses multidimensional constructs including physical, cognitive, emotional, social, and spiritual domains [4]. Between one-fourth and one-eighth of patients undergoing lumbar spine surgery develop chronic pain or persistent disability, substantially impacting their QoL and functional capacity [5]. Spinal surgeries often disrupt daily activities, sleep patterns, and social/occupational functioning, consequently diminishing patients’ QoL - a primary outcome measure in spinal surgical care [6].
MRI structural abnormalities determine surgical outcomes, yet psychosocial factors significantly impact postoperative results and complications [7]. Resilience is an individual’s ability to utilize all favorable factors to maintain or restore mental and physical functioning in the face of adversities and stressful events [8]. DiSilvestro et al. identified resilience as a key modifiable factor enhancing early post-lumbar fusion pain relief and QoL [9]. Recent work by Meade et al. (2024) identified that “resilient recovery pathways” following neurological injuries yielded superior outcomes compared to low-resilience pathways [10]. By reducing stress and improving health, resilience training facilitates recovery after surgery. Patients’ families can overcome the stress of caring for their loved ones when they are resilient [11].
Hope represents a vital and dynamic force characterized by optimistic expectations for the future, which is reinforced and supported through relationships and resources [12]. Clinical studies have shown that hope is the most critical factor during the early stages of rehabilitation [13]. In a study examining the factors that enhance hope in patients with chronic illnesses, patients identified the support of their family members as the most significant reason for improving their sense of hope [14].
Concurrently with the rise in surgery rates, hospital stays have shortened due to healthcare cost constraints and system limitations. This transition creates a problematic disparity between clinical and home environments, leaving patients unprepared for post-discharge self-management [15, 16]. Post-operative limitations can diminish self-care abilities, making it essential to educate family caregivers who play a primary role in post-discharge care. However, a lack of knowledge among caregivers can undermine adherence to treatment protocols and increase the risk of patient readmission [15]. The study by Voss et al. (2011) revealed that only 25% of patient caregivers reported satisfaction with their Lives, while 29% experienced stress disorders, 82% suffered from sleep problems, and 69% exhibited chronic fatigue, highlighting the necessity for educational programs and follow-up support [17].
Recent work by Geeta Thapar et al. (2022) has emphasized the urgent need for comprehensive care strategies that address the complex physical, psychological, and functional challenges faced by post-spinal surgery patients [18].
Although family education in chronic diseases has been studied, its role in post-spinal surgery outcomes remains unclear. This study aimed to investigate the effects of family support through education and post-discharge follow-up on the quality of life, resilience, and hope in post-spinal surgery patients.
Methods
Study design
This study examined the effect of family caregiving support through education and post-discharge follow-up on the quality of life, resilience, and hope of post-spinal surgery patients. The present study is quasi-experimental because it aimed to determine the cause-and-effect relationship between independent and dependent variables. Still, due to existing limitations, simple random sampling in the population was not possible [19]. The research was conducted from November 2023 to October 2024 at Be’sat Hospital in Hamadan, Iran, using a convenience sampling method. Randomization in this study was based on the admission days of patients. A List of patient admission days during the study period was prepared and randomly assigned to either the intervention or control group using Random.org. Patients were then allocated to the corresponding group based on their admission date. For example, if October 5th was randomly assigned to the intervention group, all patients admitted on that day received the intervention. Conversely, if October 7th was assigned to the control group, patients admitted on that day were placed in the control group. A nurse not involved in the study managed the random list of days to ensure allocation concealment.
Inclusion and exclusion criteria
The inclusion criteria for the study were as follows: age between 30 and 70 years, planned spinal surgery, no previous spinal surgery, absence of concurrent chronic diseases, ability to communicate and adhere to study procedures, and availability of a caregiver who met al.l the following conditions: physical presence for home care, adequate literacy (at least a high school diploma), demonstrate adequate health literacy (score ≥ 75 on the TOFHLA) [20], access to and proficiency in using messaging applications, and absence of depression (PHQ-9 score ≤ 4) [21], or active psychological conditions. The exclusion criteria applied after enrollment included: withdrawal of consent, clinical deterioration that prevented continued participation, loss of caregiver support during the study, and non-compliance with the follow-up protocol.
Sample Size
To determine the required sample size, Formula 1 was used, which is an appropriate method for hypothesis testing in comparing the means of two populations [22]. Based on Sahar’s study (2018) results, the mean quality of life in the intervention group was 74.53 ± 13.35, and in the control group, it was 63.33 ± 10.24 [23]. Considering a confidence level of 95% and a power of 80%, the sample size for each group was calculated to be 18 participants. Considering the dropout rate of participants, the final sample size for each group was determined to be 25 participants.
 |
Formula 1: Sample size formula for comparing two independent means.
The CONSORT flowchart illustrates the participant allocation process (Fig. 1).
Fig. 1.
Flow diagram
Procedure
All patients underwent surgery by a single surgeon to maintain uniformity in surgical technique. All patients’ families received training on home care; however, the primary difference between the two groups lay in the educational methods and follow-up after discharge. Two educational sessions were conducted one day before discharge for the intervention group. The morning session (75 min) focused on physical care methods. It included 30 min of video training with explanations from the researcher, 30 min of practical exercises using medical mannequins, and 15 min for questions and answers. The afternoon session (45 min) was dedicated to teaching resilience and psychological coping skills through a 30-minute PowerPoint presentation, followed by 15 min for questions and answers. To facilitate learning and review, audio recordings of both sessions were provided to participants. Participants received audio files and educational materials (videos in CD format and PowerPoint files). The educational content was designed based on recommendations from clinical experts and the needs of patients, as identified in previous studies [15, 16, 24].
Table 1 compares the educational content and follow-up methods of the two groups. The content validity of the educational material was quantitatively assessed based on feedback from faculty members in the neurology and nursing departments, yielding a content validity ratio of 0.75. To evaluate the impact of the educational methods and follow-up after discharge on patients’ quality of life, resilience, and hope, pre-tests and post-tests (before training and one month after discharge) were utilized.
Table 1.
Comparison of educational methods and Follow-Up procedures between the intervention and control groups
| Component | Intervention Group | Control Group |
|---|---|---|
| Timing of Education | 1 day before discharge; 2 educational sessions (morning and evening) in the conference room | Before discharge, one short session |
| Educational Content |
Physical care (visual and video-based education): - How and when to change dressings, bathe, use lumbar belts, cervical collars, and compression stockings (morning sessions) - Duration of walking, suture removal, rest, and sleeping positions (morning sessions) - Psychological skills and problem-solving strategies for resilience and post-surgical challenges (evening sessions) |
General theoretical education: - General explanations about post-surgical care - General guidance on rest, nutrition, and daily activities - General warnings about danger signs (fever, redness, swelling, discharge) |
| Educational Materials |
Pamphlets: - Step-by-step instructions for post-surgical care Educational CDs: - Use videos and clear images for a better understanding of care steps |
Simple pamphlets: - General instructions about post-surgical care - No CDs or video content |
| Follow-Up |
Phone and online follow-up: - Reporting danger signs (fever, redness, swelling, discharge) via virtual messaging - Daily follow-up and quick responses to family questions |
Limited follow-up: - Phone calls only in case of problems - No regular follow-up or use of virtual messaging |
Outcome assessment
This study investigated three primary outcomes: between-group differences in quality of life, resilience, and hope. As secondary outcomes, within-group changes for each variable were evaluated in both groups.
The electronic questionnaire system automatically calculated all scores based on a predefined scoring method according to the questionnaire guidelines. This method completely blinded evaluators to the participants’ identities and allowed for group allocation and standardized scoring without human interpretation bias.
Instruments
The required data for this study were collected using the following four tools:
Demographic and Clinical Characteristics: Age, gender, and the type of surgery performed.
SF-36 Quality of Life Questionnaire: One of the most widely used questionnaires for assessing health-related quality of life, consisting of 36 questions. It includes eight dimensions summarized into two components: physical (PCS) and mental (MCS). The total scores of the eight dimensions are converted and reported (total score range: 0–100, with higher scores indicating better health). The original SF-36 was translated into Persian and culturally adapted by Montazeri et al., who reported acceptable validity and reliability. They found that, except for the vitality subscale, which had a Cronbach’s alpha of 0.65, the alpha values for the other SF-36 subscales ranged from 0.77 to 0.90 [25]. The reliability of this questionnaire in the present study was examined using the Cronbach’s alpha method, which was 0.86.
Connor-Davidson Resilience Scale (CD-RISC): Connor and Davidson (2003) developed the CD-RISC by reviewing resilience research literature. The creators believe this scale effectively distinguishes resilient individuals from non-resilient ones in clinical and non-clinical settings and can be utilized in research and clinical contexts. The scale consists of 25 items scored on a Likert scale from 0 (not actual) to 5 (always true). A score of 70 or higher indicates good mental resilience, while a score below 70 indicates poor mental resilience. In a 2023 study by Xiangxiang Tang et al. on predictors of activation in individuals with spinal cord injuries during hospitalization, the CD-RISC was widely used to measure resilience (α = 0.89). In this study, Cronbach’s alpha was 0.856 [26]. The reliability of this questionnaire in the present study was examined using the Cronbach’s alpha method, which was 0.79.
Miller Hope Scale: Developed by Miller and Powers (1988), this scale consists of 48 items. It aims to measure the level of hope in individuals, scored on a 5-point Likert scale from “strongly disagree” [1] to “strongly agree” [5]. Scores range from 48 to 240, with 48 indicating complete hopelessness and 240 indicating high hope. The reliability of this scale, as reported by Mohammad Khorrami using Cronbach’s alpha, was 0.83 [27]. The reliability of this questionnaire in the present study was examined using the Cronbach’s alpha method, which was 0.88.
Data analysis
Descriptive statistics were used to summarize the data. Mean, standard deviation, and percentages were used to report descriptive variables. Independent and paired t-tests were used to compare the quantitative variables (age, QoL, resilience, hope scores), and Chi-square tests were used to compare the qualitative variables (e.g., sex, education) between the two groups. Univariate covariance analysis (ANCOVA) was used to test the research hypotheses. Data analysis was performed using SPSS version 24.
Results
Table 2 examines the demographic variables of the study groups. The two groups were homogeneous regarding age, type of surgery, marital status, education level, and gender, with no significant differences (P > 0.05).
Table 2.
Demographic characteristics of the study groups
| Variable | Control Group | Intervention Group | P-Value | |
|---|---|---|---|---|
| Age (Mean ± SD) | 51.88 ± 11.52 | 47.48 ± 9.70 | 0.151* | |
| Gender n (%) | Female | (60%) 15 | (52%) 13 | 0.388 ** |
| Male | (40%) 10 | (48%) 12 | ||
| Education Level n (%) | Diploma | (%56) 14 | (%52) 13 | 0.77 ** |
| Bachelor’s | (%44) 11 | (%48) 12 | ||
| Marital Status n (%) | Single | (%28) 7 | 10 (%40) | 0.37** |
| Married | (%72) 18 | (%60) 15 | ||
| Type of Surgery n (%) | Laminectomy | 5 (%20) | (%28) 7 | 0.341** |
| PSF | 16 (%64) | 16 (%64) | ||
| ACDF | 4 (%16) | (8%) 2 | ||
*Independent T-test
**Chi-square
Table 3 presents the mean and standard deviation of pre- and post-test scores for the study variables in the control and intervention groups. As demonstrated in Table 3, intergroup comparisons revealed no statistically significant differences in baseline scores of the studied variables (p > 0.05). However, post-intervention comparisons showed significantly higher mean scores for resilience and quality of life in the intervention group than in controls (p < 0.05). At the same time, no significant difference was observed in hope scores between groups (p = 0.856).
Table 3.
Mean and standard deviation of Pre- and Post-Test scores for study variables by group
| Variable | Control Group | Intervention Group | P-value** | |
|---|---|---|---|---|
| Hope | Pre-Test | 68.25 ± 12.170 | 28.23 ± 4.169 | 0.918 |
| Post-Test | 08.27 ± 36.172 | 50.20 ± 6.173 | 0.856 | |
| P-value* | 0.353 | 0.053 | ||
| Resilience | Pre-Test | 94.19 ± 20.83 | 51.12 ± 64.87 | 0.350 |
| Post-Test | 70.16 ± 32.82 | 11.12 ± 76.91 | 0.027 | |
| P-value* | 0.690 | 0.008 | ||
| Quality of Life | Pre-Test | 78.16 ± 34.49 | 95.15 ± 49.50 | 0.805 |
| Post-Test | 22.15 ± 24.45 | 15.15 ± 12.55 | 0.026 | |
| P-value* | 0.287 | 0.018 | ||
*Paired T-test
** Independent T-test
Intragroup analysis indicated that control group participants showed no significant differences between baseline and post-intervention scores for any measured variables (p > 0.05). In contrast, the intervention group demonstrated significantly improved post-intervention scores for quality of life and resilience compared to baseline (p < 0.05). However, no significant improvement was observed in hope scores (p = 0.053).
The normality test results for the distribution of the resilience variable in both groups indicated that the assumption of normality was met. Specifically, the significance level of the Kolmogorov-Smirnov test for resilience in both groups was less than 0.05, confirming that the data did not follow a normal distribution. Additionally, the results of the homogeneity of regression slopes test showed that the calculated F-value was 0.969, with a significance level of 0.330 for the resilience variable. Since this value is more significant than 0.05, the assumption of homogeneity of regression slopes was also satisfied. Therefore, univariate analysis of covariance (ANCOVA) was the appropriate statistical approach for this variable.
Table 4 examines the effectiveness of the family caregiving support program in terms of resilience. The results indicate that the program significantly impacted resilience (P = 0.01). The calculated F-value for resilience exceeded the critical values, indicating a significant effect with 99% confidence.
Table 4.
Effectiveness of the family caregiving support program on resilience
| Source of Variation | Sum of Squares | Degrees of Freedom | Mean Square | F-Value | P-Value |
|---|---|---|---|---|---|
| Between Groups | 470.894 | 1 | 470.894 | 7.119 | 0.01 |
| Error | 3108.710 | 47 | 66.143 | ||
| Total | 390130.000 | 50 |
Table 5 shows the ANCOVA-adjusted means and between-group confidence intervals on the dependent variable of resilience.
Table 5.
ANCOVA-adjusted means and between-group confidence intervals of resilience
| Group | Mean | St. Error | 95% Confidence Interval | Cohen's d (95% CI) | |
|---|---|---|---|---|---|
| Lower Bound | Upper Bound | ||||
| Intervention | 90.137 | 1.634 | 86.850 | 93.424 | 0.76 (0.25-1.27) |
| Control | 83.943 | 1.634 | 80.656 | 87.230 | |
The normality of the results for the distribution of the quality of life variable in both groups indicated that the normality assumption was met. Specifically, the significance level of the Kolmogorov-Smirnov test for quality of life in both groups was less than 0.05, confirming that the data did not follow a normal distribution. Additionally, the results of the homogeneity of regression slopes test showed that the calculated F-value was 2.553, with a significance level of 0.085 for the quality of life variable. Since this value is more significant than 0.05, the assumption of homogeneity of regression slopes was also satisfied. Therefore, a univariate analysis of covariance (ANCOVA) was the appropriate statistical approach for this variable.
Table 6 examines the effectiveness of the family caregiving support program on quality of life. The results show that the program significantly impacted quality of life (P = 0.013). The calculated F-value for quality of life was greater than the critical values, indicating a substantial effect with 99% confidence.
Table 6.
Effectiveness of the family caregiving support program on quality of life
| Source of Variation | Sum of Squares | Degrees of Freedom | Mean Square | F-Value | P-Value |
|---|---|---|---|---|---|
| Between Groups | 1074.932 | 1 | 1074.932 | 6.675 | 0.013 |
| Error | 7568.720 | 47 | 161.037 | ||
| Total | 138229.774 | 50 |
Table 7 shows the ANCOVA-adjusted means and between-group confidence intervals on the Quality of Life dependent variable.
Table 7.
ANCOVA-adjusted means and between-group confidence intervals of quality of life
| Group | Mean | St. Error | 95% Confidence Interval | Cohen's d (95% CI) | |
|---|---|---|---|---|---|
| Lower Bound | Upper Bound | ||||
| Intervention | 54.825 | 2.539 | 49.718 | 59.933 | 0.73 (0.21-1.25) |
| Control | 45.546 | 2.539 | 40.439 | 50.653 | |
The normality test results for the distribution of the hope variable in both groups indicated that the normality assumption was met. Specifically, the significance level of the Kolmogorov-Smirnov test for hope in both groups was less than 0.05, confirming that the data did not follow a normal distribution. Additionally, the results of the homogeneity of regression slopes test showed that the calculated F-value was 1.318, with a significance level of 0.257 for the hope variable. Since this value is more significant than 0.05, the assumption of homogeneity of regression slopes was also satisfied. Therefore, a univariate covariance (ANCOVA) analysis was appropriate for this variable.
Table 8 examines the effectiveness of the family caregiving support program in terms of hope. The results indicate that the program did not significantly impact hope (P = 0.543). The calculated F-value for hope was smaller than the critical values, indicating no significant effect with 99% confidence.
Table 8.
Effectiveness of the family caregiving support program on hope
| Source of Variation | Sum of Squares | Degrees of Freedom | Mean Square | F-Value | P-Value |
|---|---|---|---|---|---|
| Between Groups | 43.792 | 1 | 43.792 | 0.375 | 0.543 |
| Error | 5487.242 | 47 | 116.750 | ||
| Total | 1523825.000 | 50 |
Table 9 shows the ANCOVA-adjusted means and between-group confidence intervals on the dependent variable of Hope.
Table 9.
ANCOVA-adjusted means and between-group confidence intervals on the dependent variable of hope
| Group | Mean | St. Error | 95% Confidence Interval | Upper Bound | Cohen's d (95% CI) |
|---|---|---|---|---|---|
| Lower Bound | |||||
| Intervention | 173.916 | 2.161 | 169.568 | 178.264 | 0.17 (-0.18, 0.52) |
| Control | 172.044 | 2.161 | 167.696 | 176.392 |
Discussion
This study examined the effect of family caregiving support through education and post-discharge follow-up on the quality of life, resilience, and hope of post-spinal surgery patients. Both groups demonstrated homogeneity in demographics and baseline measures (QoL, resilience, hope; p > 0.05). Post-intervention improvements in QoL (d = 0.73) and resilience (d = 0.76) showed medium-large effects, while hope showed minimal change (d = 0.17), confirming the intervention’s efficacy.
The present study demonstrated that educational sessions for patients’ families and daily post-discharge follow-up improved patients’ quality of life after surgery.
Indu Punia et al. (2022) found that providing educational packages to relatives of patients with spinal cord injuries significantly enhanced their quality of life, which is consistent with the findings of this study. Although the education delivery format in both studies was similar, we implemented a more extended follow-up period (one month vs. seven days), allowing for a more comprehensive evaluation. Additionally, utilizing online platforms enabled more precise tracking of patients by their families [28].
The results of this study align with Alimohammadi et al. (2021), which highlighted the positive impact of continuous care on quality of life in patients following disc surgery. Despite the shorter follow-up period in this study (one month vs. three months), the observed improvement in quality of life was nearly identical. This similarity could be attributed to integrating physical and psychological care education, whereas Alimohammadi et al. focused solely on physical care education. Consequently, this study highlights the importance of psychological care components, as they may lead to a faster recovery within a shorter timeframe. However, larger sample sizes and additional studies are needed in this field [4]. Wan-Ting Chu et al. (2020) concluded that utilizing simulation-based techniques for nursing care education in patients undergoing lumbar disc herniation surgery could enhance quality of life and boost their confidence in performing daily tasks, which aligns with our findings. Both studies highlight the importance of visual education methods, although Wan-Ting Chu et al. employed a more modern teaching approach. Nevertheless, both studies demonstrated superior effectiveness to traditional education methods [29]. Additionally, the survey by Zhongmin Fu et al. (2024) supports the present study’s findings. They determined that a multidisciplinary structured follow-up program had a more significant effect on self-efficacy and quality of life in patients who underwent cervical spondylosis surgery compared to routine follow-up. Reports indicate that over 30% of patients with cervical spine disorders experience depression and anxiety, highlighting the necessity of tailored follow-up programs to improve their quality of life [30].
Conversely, Davoodi et al. (2018) reported that self-care education did not significantly impact the quality of life in patients with gastric cancer following gastrectomy [31]. This finding contrasts with the results of our study, likely due to differences in disease response to care interventions. Moreover, their study focused on self-care programs, whereas the present study implemented family-centered education, which may establish a stronger support system.
Another significant finding of this study was the substantial improvement in resilience among patients following the enhancement of family education and continuous follow-up. Tayebeh Rakhshani et al. (2024) found that self-care training for patients with multiple sclerosis improved problem-solving abilities, thereby enhancing resilience [32]. Similarly, Zahra Molazem et al. (2017) observed that self-care education in patients undergoing coronary artery bypass surgery increased their resilience and overall happiness [33]. Mahnaz Seyedoshohadaee et al. (2015) reported that self-care training in heart failure patients contributed to higher resilience levels [34]. The results of these studies align with our research findings. However, for patients undergoing spinal surgery, immediate family-centered support post-discharge may be preferable, as these patients often face severe functional and psychological challenges, which might reduce their ability and motivation to engage in self-care programs. Given the limited evidence available, further research is necessary in this domain.
Hope was another variable examined in this study. Post-test scores showed improvements in both the intervention and control groups compared to pre-test scores; however, there was no significant difference between the post-test results. Li et al. (2014) in China found a positive correlation between hope and quality of life, demonstrating that higher levels of hope contribute to improved patient outcomes [35]. Similarly, Hekmatpou et al. (2013) and Kazemi et al. (2012) in Iran reported a direct relationship between hope and quality of life [36, 37]. Based on these studies, an association between hope and quality of life in the intervention group was expected; however, this finding contradicts the results of the present study. While post-test scores for quality of life and hope were higher than pre-test scores, the difference in hope levels was not statistically significant. Several factors may explain the findings of the study. First, the passage of time and gradual recovery after surgery may have increased hope levels in both groups. Second, the one-month follow-up may have been too short to reveal significant differences, as changes in hope typically require more time. Additionally, incorporating psychological counseling with the educational intervention could enhance outcomes. It’s important to note that the study population differs from those in previous research, as the cited studies focused on chronic patients less affected by biological factors like pain.
This study had limitations, including a small sample size and a short follow-up period, which may have affected the assessment of outcomes. One of the study’s strengths was the video-based education and the daily one-month follow-up. Another limitation of the study was the Hawthorne effect, which made it impossible to blind patients due to the nature of the educational intervention and the necessity for the patient’s family to be fully aware of the research process. Future research should utilize larger samples, extended follow-up periods, and objective performance measures while ethically controlling for observer effects.
Conclusion
This study showed that follow-up and education for the families of spinal surgery patients help increase their resilience and quality of life, but had no significant impact on life expectancy. Effective methods included educational programs tailored to their needs and monitoring through phone calls and messaging. These measures enable families to immediately contact service providers in case of questions or new patient conditions. It is recommended that these educational sessions and follow-ups be considered as complementary measures to improve patient health.
Acknowledgements
The authors sincerely thank the Vice Chancellor for Research and Technology of Hamadan University of Medical Sciences for their financial support and valuable assistance in conducting this research.
Authors’ contributions
MZ contributed to the study design, writing, final editing of the article, and data collection. AS designed the study and data collection, and wrote and finalized the article. BI supervised the study’s execution, reviewed the article, and approved the final version. SA conducted statistical data analysis and contributed to writing the methods section. AA participated in data collection and analysis. SZ designed the study, wrote and finalized the article, participated in data analysis, and reviewed the article.
Funding
This study was funded by the Vice Chancellor for Research and Technology of Hamadan University of Medical Sciences (Grant No. 140204273217).
Data availability
The de-identified datasets analyzed during this study are available from the corresponding author upon reasonable request. The survey instruments (SF-36, CD-RISC, and MHS) are publicly available through their respective copyright holders.
Declarations
Ethics approval and consent to participate
This study was approved by the Ethics Committee of Hamadan University of Medical Sciences (Ethics Code: IR.UMSHA.REC.1402.290). Literate participants provided written informed consent. In contrast, illiterate participants provided oral informed consent through a standardized process in which a neutral witness read the consent form aloud and signed it as confirmation of their complete understanding and voluntary participation. To ensure the rights of all participants, we adhered strictly to the Declaration of Helsinki at all stages of the study design and implementation, ensuring compliance with all ethical standards outlined in the declaration and its subsequent amendments.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Clinical trial number
Not applicable.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The de-identified datasets analyzed during this study are available from the corresponding author upon reasonable request. The survey instruments (SF-36, CD-RISC, and MHS) are publicly available through their respective copyright holders.