Heart Rate Variability as a Biomarker for Posttraumatic Growth: A Comparative Study of Brain Tumor Patients and Caregivers

ABSTRACT

Objective

To explore the levels and group differences of posttraumatic growth and heart rate variability in a sample of brain tumor patients and their caregivers.

Methods

Convenience sampling method was adopted, and general information questionnaire, posttraumatic growth scale, and heart rate variability test were applied to survey and test a total of 55 dyads of brain tumor patients and their caregivers (110 participants) who were hospitalized in a tertiary‐level hospital in Guangzhou City.

Results

The posttraumatic growth scores of brain tumor patients and their caregivers were 59.00 (44.00, 71.00) and 65.00 (56.00, 73.00), respectively. Analysis of variance showed that total power [TP], high frequency power [HF], and low frequency power [LF] were higher in caregivers than in brain tumor patients, and the results were significantly different (t = −4.424 to −2.194, p _ln total = 0.030, p _ln LF < 0.001, p _ln HF < 0.001); In addition to the above frequency domain metrics, individuals “individuals” with PTG (vs. without PTG) showed significantly higher SDNN, RMSSD (F = 4.300 to 42.275, p = < 0.001 to 0.041).

Conclusions

Significant differences in HRV metrics across populations (patients vs. caregivers) and individuals with different levels of posttraumatic growth suggest that HRV may serve as a biomarker for assessing posttraumatic growth.

1. Introduction

The high disability and mortality rates of brain tumors may cause severe traumatic events for both patients and caregivers [1]. The diagnosis and treatment of brain tumors not only present physical challenges, but also have a significant impact on the mental health of patients and their caregivers [2]. Brain tumor patients may experience prolonged periods of physical discomfort, uncertainty, and social isolation during the course of treatment, while caregivers face ongoing caregiving stress and emotional distress.

Yet, individuals who struggle with events or situations that are traumatic in nature may also experience positive psychological changes, i.e., posttraumatic growth (PTG) [3]. Posttraumatic growth (PTG) refers to the process by which an individual, after experiencing a major trauma, obtains the enhancement of life meaning, emotional maturity, and self‐actualization through positive psychological adaptation and reflection [4]. However, the predominant reliance on self‐report scales [3] is increasingly criticized. Such measures are susceptible to social desirability and self‐deception, potentially reflecting an “illusory” form of PTG rather than constructive growth [5, 6], thus exploring more objective measurements has also become a pressing need in this area of research. Currently, there is a trend to look for objective indicators of psychological states [7]. This pursuit is part of a broader scientific effort to identify reliable biomarkers for psychological stress and resilience [8, 9]. The goal is to find objective, physiological indicators that can complement and enrich our understanding of subjective psychological processes.

In this context, Heart Rate Variability (HRV) has emerged as a promising candidate for such an objective biomarker [9], which is the degree of fluctuation in the time interval between consecutive heartbeats and is often considered a biomarker of autonomic nervous system regulation [10, 11]. It has been shown to play an important role in assessing the state of psychological well‐being and the stress response [8, 12], and a low HRV index often represents a poor physical and mental state. In the field of cancer research, HRV is thought to indirectly reflect an individual's tumor growth rate as well as prognostic status [13]. Despite the widespread interest in HRV in oncology and mental health‐related fields, no systematic conclusions have been drawn about the relationship between HRV and PTG.

Some scholars have explored the possible relationship between HRV and PTG in a study of the Tianjin explosion incident [14], and found that during positive emotional picture stimulation, the low and high frequency indices of HRV were significantly higher in the PTG group than in the control group. However, it is not known whether these findings generalize to other populations, such as brain tumor patients and caregivers. This study is among the first to explore HRV parameters in this specific population dyad. Therefore, the present study explored whether there were significant differences in HRV parameters between different populations (brain tumor patients and caregivers) and individuals with different levels of PTG, with the aim of providing initial insight on the study of the psychophysiological status of HRV and exploring the possibility of providing an objective method of testing for the assessment of PTG. Given this shared traumatic context, this study explores PTG levels and their group difference with HRV in brain tumor patients and their caregivers, aiming to provide new ideas for the discovery of neurobiological links of PTG.

2. Materials and Methods

2.1. Participants

58 dyads of brain tumor patients and their caregivers (116 participants) attending a certain Guangzhou City tertiary‐level hospital were selected as survey respondents from June to October 2024 using the convenience sampling method, in which 3 dyads of patients and their caregivers refused to participate in the survey, and 55 dyads were finally included. The inclusion criteria for patients were: (1) diagnosed with brain tumor by pathological examination and aware of their condition; (2) literate and able to comprehend and complete the questionnaires independently; (3) age: 18–70. Inclusion criteria for caregivers: (1) literate and able to comprehend and complete the questionnaires independently; (2) identified as the primary informal caregiver, who was a family member or relative (e.g., spouse, parent, adult child, sibling) and provided care without financial compensation; (3) age: 18–70. Exclusion criteria for both patients and caregivers were a history of mental illness and other serious physical illnesses.

2.2. Measurement

2.2.1. General Information

A questionnaire was developed by the researcher and included demographic information (age, gender, marital status, work status, literacy, relationship with the patient, etc.) and disease information (length of illness, length of caregiving, knowledge of the disease, etc.).

2.2.2. Posttraumatic Growth

Posttraumatic growth was measured using the culturally adapted Chinese Posttraumatic Growth Inventory (C‐PTGI) [15]. This 20‐item version excluded an original item on religious faith due to cultural inapplicability. Although the five‐factor structure was retained, the specific items comprising each subscale were reconfigured during the validation process to better fit the Chinese context. The factor structure and the redistribution of some items to different subscales were confirmed through validation studies to achieve optimal model fit within the cultural context [15]. The resulting subscales are: Relationships with Others (3 items), New Possibilities (4 items), Personal Strength (3 items), Self‐Transformation (4 items), and Life Lessons (6 items). The scale demonstrates good reliability in cancer populations and uses a 6‐point Likert scale (total score: 0–100). The same criterion as Xu et al. [16]—60% of the total score—was applied to determine the PTG cut‐off, which corresponded to 60 points in this study.

2.2.3. Heart Rate Variability

The heart rate variability analysis system used in the experiment was the CM500 model produced by Sichuan Kreider. The HRV frequency domain metrics: [low‐frequency power (LF: 0.05–0.15 Hz), total power (TP), high‐frequency power (HF: 0.15–0.40 Hz), and low‐frequency power/high‐frequency power values (LF/HF)] and the time domain metrics: [standard deviation of the R‐R intervals (SDNN), root‐mean‐square of the difference of neighboring R‐R intervals (RMSSD)] could be recorded. The current study usually performs a natural logarithmic transformation of the HRV Indicators [14] to normalize their skewed distributions. Electrodes were placed on cleaned wrist/ankle skin (alcohol swabbed), with room temperature maintained at 22°C–25°C and humidity below 60% to minimize artifacts.

2.3. Methods of Data Collection

General questionnaires and scales: after obtaining informed consent, a general information questionnaire and a posttraumatic growth scale were administered by the investigator to brain tumor patients and their caregivers during their hospitalization, and the questionnaires were filled out independently, respectively, in a non‐intrusive environment. Questions that were not clear to the respondents were explained by the investigator on the spot. The questionnaires were uniformly returned by the investigator after verification that there were no errors or omissions.

Heart rate variability data collection: after informed consent was obtained, data were collected by the same subjects who had been professionally trained in a sitting position in a quiet room under the direction of the investigator. Before starting, the skin of the subject's wrists and ankles was wiped with alcohol to ensure sensitivity of the electrode placement position. After the system transferred the subject's ECG information to the software, heart rate variability analysis was performed, with the recording window period set to 5 min and the sampling rate set to 500 Hz.

2.4. Sample Size Calculation

In a previous study [17] comparing the effect of handshaking on heart rate variability in cancer patients and their caregivers, it was found that the mean SDNN at baseline levels for the patient and caregiver groups were μ ₁ = 24.7 ms (millisecond) and μ ₂ = 33.3 ms, with standard deviations of σ ₁ = 13.6 ms and σ ₂ = 15.5 ms, respectively. Based on these results, according to the sample size estimation formula:

$$\mathit{n}=\frac{{\left({\mathit{z}}_{1-\alpha /2}+{\mathit{z}}_{1-\mathit{\beta }}\right)}^2·\left({\sigma }_1^2+{\sigma }_2^2\right)}{{\left({\mathit{\mu }}_1-{\mathit{\mu }}_2\right)}^2}$$

setting a two‐sided significance level α of 5% and a statistical power β of 0.8, it was calculated that 45 participants were needed for each group in this study. Considering a 15% dropout rate, the sample size was set at 53 participants per group, and 55 participants per group were actually included.

2.5. Statistical Analysis

SPSS 26.0 statistical software was used for data processing. Count data were described by frequency and percentage, and measurement data conformed to normal distribution and were described by mean ± standard deviation. The Mann–Whitney U test was used to compare PTGI scores between patients and caregivers. Independent samples t‐test was applied to compare HRV indices between the patient and caregiver groups. Two‐way ANOVA was employed to examine the main effects of population (patient vs. caregiver) and PTG level (growth vs. non‐growth), as well as their interaction, on HRV parameters. Differences were considered statistically significant at p < 0.05.

3. Results

3.1. Demographic and Clinical Variables

A total of 116 questionnaires were distributed (58 to patients and 58 to caregivers) and 110 valid questionnaires were collected (55 to patients and 55 to caregivers), with a valid recovery rate of 94.8%. Details are provided in Table 1.

TABLE 1.

Demographic and clinical characteristics of the participants.

Characters	Patients		Caregivers		p
	Case (n = 55)	Percentage (%)	Case (n = 55)	Percentage (%)
Age
Mean ± SD	45.73 ± 13.72		42.44 ± 10.54		0.161
< 25	6	10.9	2	3.6	0.180
25–50	24	43.6	43	78.2
> 50	25	45.5	10	18.2
Gender
Male	27	49.1	31	56.4	0.450
Female	28	51.0	24	43.6
Education
Primary and below	16	29.1	8	14.5	0.174
Junior high school	12	21.8	17	30.9
High school (including secondary school)	14	25.5	12	21.8
University (including college and graduate school)	13	23.6	18	32.7
Job
Full time job	20	36.4	30	54.5	0.005*
Part time job	5	9.1	12	21.8
Jobless (including retirement)	30	54.5	13	23.6
Marriage
Marriage	46	83.6	50	90.9	0.625
Unmarried (including single, divorced, widowed)	9	16.4	5	9.1
Knowledge about the disease
Not at all.	28	50.9	26	47.3	0.117
Partially understand	23	41.8	26	47.3
Fully understand	4	7.3	4	7.3
Type of disease
Meningioma	19	34.5
Glioma	17	30.9
Pituitary tumor	11	20.0
Others	8	14.5
Duration of illness (months)
< 1 months	5	9.1
1–3 months	12	21.8
3–6 months	7	12.7
6–12 months	5	9.1
> 12 months	26	47.3
Relation
Spouse			22	40.0
Parents			20	36.4
Sons or daughters			6	10.9
Siblings			2	3.6
Others			5	9.09
Daily time spent caring
< 4 h			9	16.4
4–8 h			16	29.1
> 8 h			30	54.5

Note: Data are presented as n (%), Mean ± SD, p‐values for age (continuous variable) were derived from independent samples t‐test. p‐values for categorical variables (e.g., Gender, Education) were derived from the Chi‐square test.* P < 0.05.

3.2. Posttraumatic Growth and Heart Rate Variability Indicators

The C‐PTGI scale scores in this study were tested by the Kolmogorov–Smirnov test, p < 0.05, did not obey normal distribution, and were statistically described using quartiles M (p ₂₅, p ₇₅). The Mann–Whitney U test showed a statistically significant difference between patient and caregiver scores only in the “self‐transformation” and “life lessons” dimensions (p < 0.05), and a comparative analysis of median scores revealed that caregivers had higher median scores in these two dimensions. In addition, although there was no statistically significant difference, the caregiver's total scale median score was numerically higher than the patient's, and the personal strength dimension median score was slightly lower than the patient's. See Table 2 for details.

TABLE 2.

PTGI (Total score and subscales).

	Patients (n = 55) M (p 25, p 75)	Caregivers (n = 55) M (p 25, p 75)	Z	p
PTGI scale total score (0–100)	59.00 (44.00, 71.00)	65.00 (56.00, 73.00)	−1.567	0.117
Relationship with others (0–15)	9.00 (7.00, 11.00)	9.00 (7.00, 11.00)	−0.063	0.950
New possibilities (0–20)	12.00 (9.00, 15.00)	12.00 (10.00, 14.00)	−1.002	0.316
Personal strength (0–15)	11.00 (9.00, 12.00)	10.00 (9.00, 12.00)	−0.181	0.856
Self‐transformation (0–20)	10.00 (6.00, 12.00)	12.00 (9.00, 13.00)	−2.251	0.024*
Life lessons (0–30)	20.00 (13.00, 23.00)	22.00 (18.00, 26.00)	−2.084	0.037*

Note: * P < 0.05.

Independent samples t‐test was taken to compare the differences in HRV indexes between brain tumor patients and their caregivers. The results showed that the total power, low‐frequency power and high‐frequency power of brain tumor patients were significantly lower than those of their caregivers, and the differences were statistically significant (t = −4.424 ~ −2.194, p _{ln TP} = 0.030, p _{ln LF} < 0.001, p _{ln HF} < 0.001). Detailed information was showed in Table 3.

TABLE 3.

Comparison of heart rate variability indicators in patients with brain tumors and their caregivers.

	Patients (n = 55)	Caregivers (n = 55)	T	p
Variables
ln SDNN	3.41 ± 0.72	3.44 ± 0.59	−0.275	0.784
ln RMSSD	3.09 ± 1.01	3.07 ± 0.79	0.133	0.894
ln TP	7.11 ± 1.10	7.57 ± 1.07	−2.194	0.030*
ln LF	7.16 ± 0.58	7.56 ± 0.44	−4.096	< 0.001*
ln HF	6.68 ± 0.61	7.24 ± 0.49	−4.424	< 0.001*
ln LF/HF	0.38 ± 0.26	0.32 ± 0.24	1.273	0.206

Note: * P < 0.05.

Brain tumor patients and their caregivers were divided into a “growth group” (≥ 60 points) and a “non‐growth group” (< 60 points) with a cut‐off point of 60 points on the Posttraumatic Growth Scale (PTSG), and a two‐way ANOVA was used to compare the groups. The results showed that there was a statistically significant effect of PTG level on ln TP, ln _SDNN, and ln _RMSSD (F _{ln SDNN} = 4.300, p _{Ln SDNN} = 0.041; F _{ln RMSSD} = 5.614, p _{ln RMSSD} = 0.020; F _{ln TP} = 12.210, p _{ln SDNN} < 0.001) while different populations and their interaction with PTG levels were not statistically significant. Main effects of population were significant for ln _TP (F = 12.210, p < 0.001), ln _LF (F = 34.872, p < 0.001), and ln _HF (F = 33.520, p < 0.001). PTG level similarly affected all three metrics (Fs = 22.814–42.275, Ps < 0.001), yet no interaction effect was found to have a significant effect on them. Finally, neither the population, PTG level, nor its interaction effect had a significant effect on Ln _LF/HF. See Table 4 for details.

TABLE 4.

Two way ANOVA of population and level of posttraumatic growth on indices of heart rate variability.

Variables/sources of variation	Square sum	df	MS	F	p
ln SDNN
Population	0.075	1	0.075	0.179	0.673
PTG	4.300	1	4.300	4.300	0.041*
Population × PTG	0.498	1	0.498	1.181	0.280
ln RMSSD
Population	0.545	1	0.545	0.683	0.410
PTG	4.478	1	4.478	5.614	0.020*
Population × PTG	0.457	1	0.457	0.573	0.451
ln TP
Population	12.138	1	12.138	12.210	< 0.001*
PTG	22.680	1	22.680	22.814	< 0.001*
Population × PTG	0.234	1	0.234	0.235	0.629
ln LF
Population	6.572	1	6.572	34.872	< 0.001*
PTG	7.968	1	7.968	42.275	< 0.001*
Population × PTG	0.368	1	0.368	1.955	0.165
ln HF
Population	7.908	1	7.908	33.520	< 0.001*
PTG	6.883	1	6.883	29.173	< 0.001*
Population × PTG	0.456	1	0.456	1.933	0.167
ln LF/HF
Population	0.060	1	0.060	0.932	0.337
PTG	0.040	1	0.040	0.614	0.435
Population × PTG	0.005	1	0.005	0.079	0.779

Note: N = 110. Population = patient vs. caregiver; PTG = posttraumatic growth; ln _RMSSD = natural logarithm transformed; ln _SDNN = natural logarithm transformed standard deviation of the R‐R intervals; ln _TP = natural logarithm transformed total power. ln _HF = natural logarithm transformed high frequency power; ln _LF = natural logarithm transformed low frequency power; ln _LF/HF = natural logarithm transformed low to high frequency ratio. * P < 0.05.

4. Discussion

The results of this study showed that the posttraumatic growth scores of brain tumor patients and their caregivers were skewed and the median was close to or higher than the posttraumatic growth cut‐off point (60), indicating that posttraumatic growth occurred in the majority of the patients and caregivers at a moderately high level, but it was still lower than the results of the study conducted by Lin et al. [18] on caregivers of neurological critical illnesses.

Furthermore, the results suggest that brain tumors may not only produce profound changes for the patient, but their effects also radiate to the entire family. In terms of specific dimensions, caregivers scored significantly higher than patients on the dimensions of “self‐transformation” and “life lessons”, which is consistent with the findings of Carolina et al. [19]. This may be due to the fact that the caregivers felt the fragility of life after witnessing a fatal threat to their relative and deepened their reflections on their own life during this process. Cultural factors may also play a role: traditional Chinese culture is family‐centered, and caregivers' dedication and responsibility are often seen as noble and great [20], a perception that may reinforce their feelings of self‐growth.

It has also been argued that self‐reported high scores on PTG do not necessarily reflect an individual's true situation: Maercker and Zoellner [6] developed the Janus‐Face model, which posits that what appears as PTG can be categorized into two distinct types: “constructive” and “illusory.” Individuals with constructive PTG genuinely integrate the traumatic experience and achieve sustainable personal growth. In contrast, those with illusory PTG may report growth primarily as a form of self‐deceptive coping to mitigate ongoing psychological distress. Adriel [5] also argued that self‐reported PTG scores are often too inflated, and suggested that an important basis for distinguishing between “genuine” and “illusory” PTG is to explore the physiological mechanisms underlying the development of PTG, in order to minimize the bias of PTG findings, and to better develop interventions to promote genuine PTG. This will reduce the bias of PTG research results and lead to better development of interventions to promote genuine PTG.

The Janus‐Face model provides a theoretical lens for interpreting our physiological findings. While our study design does not permit a direct quantification of the proportion of constructive versus illusory PTG in our cohort, the significant main effects of PTG level on HRV metrics (e.g., higher SDNN, RMSSD in the high‐PTG group) offer a potential physiological criterion for making such a distinction in future research. According to the model, “constructive” PTG should be underpinned by genuine psychological adaptation, which is likely reflected in better physiological regulation (i.e., higher HRV), whereas ‘illusory’ PTG might not show such concordance. The fact that individuals in the ‘growth group’ exhibited elevated HRV is consistent with the profile of “constructive” growth. Therefore, our findings, while not quantifying the prevalence of each type, may inform future research. They suggest that HRV merits exploration as a potential adjunct to self‐report, possibly aiding in the refinement and operationalization of the “constructive” versus “illusory” PTG framework.

The results of independent samples t‐test showed that HRV indicators including total power [TP], low‐frequency power [LF], and high‐frequency power [HF] were significantly higher in caregivers than in patients with brain tumors. HRV is a result of the interaction between the sympathetic and parasympathetic nervous systems.

HRV plays a key role in health status and that individuals with low HRV tend to be associated with negative physical and mental states [10]. The presence of a tumor may affect the functional performance of the autonomic nervous system through direct neurological compression, metabolic disturbances, or therapeutic side effects (e.g., post‐surgical traumatic reactions, cardiovascular toxicity of chemotherapeutic agents), which can lead to a decrease in TP [21]. Also, patients' lower LF and LF power may be associated with impaired sympathetic‐parasympathetic regulation. In contrast, caregivers typically have a healthier physiologic state and therefore exhibit significantly higher HRV Indicators.

Emotional and psychological stress are also important factors influencing HRV [12]. Although caregivers face a certain amount of psychological stress when assuming caregiving responsibilities, their perception of stress and emotional regulation may be superior, enabling them to maintain relatively higher levels of HRV through positive coping strategies.

Additionally, the lifestyles of brain tumor patients are often limited by their disease and treatment. For example, patients may be bedridden for long periods of time and lack the necessary physical activity due to their condition or lack of physical strength. Sedentary or low‐activity state is associated with a decrease in HRV Indicators [22, 23], especially LF and TP. Caregivers, on the other hand, have nursing responsibilities and may have higher levels of daily activity, which may have a protective effect on the maintenance of their HRV levels. Future studies could further explore the specific pathways of action of these mechanisms and validate the differences in HRV dynamics between caregivers and patients and their health significance through a longitudinal design.

We compared the differences in HRV Indicators between different populations (brain tumor patients vs. caregivers) and different PTG levels (growing vs. non‐growing), and found that both factors had a significant effect on SDNN, RMSSD, TP, LF, and LF metrics: compared to the non‐growing group, the growing group had higher SDNN, RMSSD, TP, LF, and LF metrics, which is consistent with the findings of Laurel et al. [24] and Wei [14] et al. With the exception of LF and the significance of the LF/HF representation, which is still controversial [25], the other metrics included in the present study are usually considered to respond to parasympathetic activity [26, 27].

The present study analyzed the relationship between HRV and PTG in a population of brain tumor patient‐caregiver dichotomy and found that those with high PTG levels also possessed higher HRV indicators, which may imply that individuals with high levels of PTG have lower levels of stress, a result that is different from the hypothesis of the study by AnnMarie et al. [28]. According to Stephen's affective‐cognitive model [29], stress and PTG have a U‐shaped relationship, i.e., moderate stress can be used as an engine to drive an individual to occur or experience PTG, but too much stress may cause a complete breakdown. Therefore, caregivers may have undergone better emotional regulation during the stressful event of witnessing and caring for a relative with a brain tumor, reducing their own stress and thus increasing their PTG levels.

Our findings provide a novel perspective by examining PTG within the patient–caregiver dyad sharing a common trauma. The observed HRV differences between these roles suggest that autonomic regulation may be modulated by one's position within a stressful context, thereby adding nuance to the psychophysiology of PTG, providing initial support for exploring HRV as a potential objective indicator. A key finding was that individuals in the high‐PTG group, regardless of being a patient or caregiver, generally exhibited more adaptive HRV. This consistency, though preliminary, suggests that HRV might capture a physiological signal that coincides with reported psychological growth. Therefore, our results encourage future research to consider HRV as a candidate for a complementary assessment tool, which may 1 day help to triangulate findings from self‐report measures.

In conclusion, caregivers not only had significantly higher PTG levels than brain tumor patients, but also exhibited higher HRV indicators. Moreover, the finding that the high‐PTG group had significantly elevated HRV across both populations strengthens the evidence for a link between the psychological experience of growth and autonomic nervous system function. Therefore, in the future, HRV can be considered as an objective indicator for assessing individual PTG, and interventions such as HRV biofeedback [30] can be implemented to promote PTG levels in brain tumor patients and their caregivers.

This study adds to the literature on HRV and PTG by focusing on brain tumor patients and their caregivers. Our findings indicate that patients and caregivers differ in their experiences of both psychological growth and autonomic regulation. Furthermore, the observation that higher PTG levels were linked to more adaptive HRV profiles across these groups offers initial evidence worthy of further investigation.

5. Conclusion

This study examined the relationship between posttraumatic growth and heart rate variability metrics in brain tumor patients and their caregivers. The results showed significant differences in HRV Indicators between different populations (patients and caregivers) and between individuals with different levels of posttraumatic growth, suggesting that HRV has the potential to be a biomarker for assessing posttraumatic growth.

6. Clinical Implications

The significant consistent difference between elevated HRV metrics (SDNN, RMSSD, TP, HF) and higher PTG levels underscores the potential clinical utility of heart rate variability as an objective biomarker for assessing posttraumatic growth. Clinicians could leverage non‐invasive HRV monitoring during routine follow‐ups to complement self‐reported PTG scales, thereby identifying individuals with discordant physiological and psychological profiles—such as those exhibiting “illusory PTG” (high self‐report scores but low HRV) who may require targeted interventions For brain tumor patients, whose suppressed autonomic function likely constrains PTG capacity, interventions should prioritize autonomic nervous system restoration through HRV biofeedback training [30] or graded physical activity [31] to counteract treatment‐related sedentarism. Conversely, caregivers' preserved HRV suggests they may benefit more from resilience‐building strategies (e.g., mindfulness or social support) to sustain existing PTG. Critically, the difference in PTG and autonomic regulation between patients and caregivers highlights the need for dyad‐centered care models [32], such as joint biofeedback sessions or family psychoeducation to harmonize growth trajectories within the caregiving unit.

7. Study Limitations

This study has several limitations warranting consideration. First, the cross‐sectional design precludes causal inference regarding the relationship between PTG development and autonomic nervous system adaptation; longitudinal tracking across disease stages is essential to elucidate temporal dynamics. Second, unmeasured confounders—including medications, comorbidities, and lifestyle factors—may have influenced HRV metrics, potentially biasing associations with PTG. Third, recruitment from a single urban tertiary hospital limits generalizability to rural populations or cultures with divergent caregiver norms. Additionally, the 5‐min HRV recording protocol may not capture diurnal autonomic variability, suggesting future studies should employ 24‐h ambulatory monitoring for ecological validity. Another limitation of this study is that PTG and HRV were assessed during active treatment, a period of ongoing trauma, not solely after adversity. However, this timing aligns with the view of PTG as a dynamic process that can initiate during coping [33]. Nevertheless, future longitudinal studies tracking participants from diagnosis through survivorship are needed to understand how this relationship evolves. Finally, although our sample showed no statistically significant differences between patients and caregivers in terms of mean age (p = 0.161), gender distribution (p = 0.450), or the distribution of self‐reported disease knowledge (p = 0.117) at the group level (see Table 1), these factors could still act as confounders at the individual level. For instance, individual variations in age and disease understanding are known to influence autonomic nervous system activity and psychological adaptation independently of group status. Future studies would benefit from employing more granular, validated measures of disease understanding and incorporating them as covariates in models to disentangle their specific influence on the PTG‐HRV relationship.

Author Contributions

Tenggang Shen: conceptualization, methodology, software, data curation, writing – original draft, investigation, formal analysis. Linxin Xie: formal analysis, data curation, software.

Funding

The authors have nothing to report.

Ethics Statement

The study was reviewed by the Hospital Ethics Committee (NZLLKZ2024038), and the respondents participated voluntarily.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.