Relationship between caregiving burden and alterations in circadian rhythms among spousal caregivers of individuals with cognitive impairment

Abstract

Background

Caring for individuals with cognitive impairment is demanding and may impact caregiver well-being. This study examined whether caregiving burden is linked to alterations in circadian rhythm of spousal caregivers (SCGs), using both objective and subjective measures.

Methods

A total of 104 SCGs were enrolled, of which 54 wore Fitbit devices to collect objective data on sleep-wake cycles and circadian heart rate rhythm (CHR). Subjective sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI). Multiple regression analyses were conducted to examine the association between caregiving burden, as measured by the Zarit Burden Interview (ZBI), and circadian rhythm variables.

Results

Higher caregiving burden was related to lower goodness of fit (β = − 0.306, t = − 2.144, p = 0.037) and greater subjective sleep disturbance (β = 0.203, t = 2.021, p = 0.046). Although not statistically significant, a trend toward earlier awakening was observed as the caregiving burden increased, particularly in the SCG of individuals with dementia. No significant associations were found for other variables.

Conclusions

Caregiving burden may negatively influence circadian health in SCGs, especially rhythm regularity. These findings suggest a potential connection between caregiving stress and some aspects of circadian function, emphasizing the need for further research on caregiver burden and circadian function.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-025-06316-7.

Introduction

Caring for individuals with dementia can be highly demanding and have adverse effects on caregivers’ physical or mental well-being [1–4]. Caregiving burden is known to cause health issues in caregivers, including depression [5, 6], cognitive decline [7–10], cardiovascular disease [10, 11], and sleep disturbance [11–14]. These effects can be devastating because the primary caregivers of individuals with dementia are often a spouse caregiver (SCG) [15].

Although most of existing literature has focused on sleep disturbances such as reduced sleep duration or poor sleep quality in caregivers [11–14], there is also interest in alteration in circadian rhythm such as sleep-wake cycle [16] and rest-activity rhythm (RAR) [17, 18], which may provide a more comprehensive understanding of caregivers’ behavioral or physiological functions. Disruption of circadian rhythms was also linked to other health problems. For example, changes in RARs, such as lower amplitude, less robust rhythms, and delayed peak activity times, have been associated with transition to mild cognitive impairment (MCI) or dementia in older adults [19], and delayed phases in RARs have been related to depression in older adults [20]. Additionally, it has been reported that the risk of cardiovascular disease was associated with changes in the sleep-wake cycle [21]. Thus, there is a correlation between negative health outcomes due to caregiving burden and circadian rhythm disruption. Therefore, it is essential to understand the alterations of circadian rhythm that arise from SCGs’ caregiving burden.

Most studies on circadian rhythm changes in SCGs have focused on changes in the sleep-wake cycle and have been limited to subjective assessments using self-report questionnaires, such as the Pittsburgh Sleep Quality Index (PSQI) [22]. These studies’ methods rely on self-reported assessments that are influenced by factors such as emotional state and subjective experience, thereby may limit the ability to determine circadian rhythm changes’ exact pattern [23, 24].

Objective measures of circadian rhythms can use biological markers such as core body temperature [25] or melatonin [26], or behavioral indicators such as sleep-wake cycles and rest-activity rhythms (RARs) using polysomnography or actigraphy. Among these, actigraphy is easier to use than other methods and has been used in many studies on circadian rhythms [27, 28]. For example, Fitbit devices objectively estimate sleep patterns [29, 30], and Heart rate data, including circadian rhythms of heart rate (CHR), can be obtained using photoplethysmography sensors embedded in the device [31, 32]. CHR could complement behavioral indicators of circadian rhythms by providing insight into autonomic nervous systems function [33]. Although different from the traditional RAR, CHR is derived from physiological rhythms and serves as an indirect behavioral indicator of the circadian rhythm system, reflecting physical activity and rest patterns [34, 35]. Despite these advantages, there are limited studies on SCGs using actigraphy such as Fitbit to measure their circadian rhythms.

Based on existing evidence linking caregiver burden with circadian rhythm dysregulation, we investigated whether increased caregiving burden would be associated with alterations in circadian rhythm among SCGs. Specifically, we assessed whether caregiving burden was associated with changes in sleep-wake cycles and CHR. Sleep-wake cycles were measured using both Fitbit (objective) and PSQI (subjective), while CHR was derived solely from Fitbit data. Given the limited prior research using objective tools to examine sleep-wake cycles and CHR in SCGs, we broadly examined multiple circadian rhythm parameters to identify those most sensitive to higher caregiving burden.

Given the limited prior research using objective tools for these circadian rhythms of SCG, rather than focusing on a single marker, we examined multiple circadian rhythm parameters to identify which features may be particularly sensitive to higher caregiving burden. In addition, considering previous studies showing sex-based [1] and care recipient diagnosis [36] differences in caregiving burden, we further assessed whether these variables moderated the observed association. By identifying these unexplored areas, this study aims to deepen our understanding of circadian health in SCGs and inform future interventions to support their well-being.

Methods

Study design and participants’ characteristics

This study enrolled the SCGs of individuals who visited the geriatric psychiatry clinic at Chungnam National University Hospital (in South Korea) from May 2020 to August 2023. The inclusion criteria for the study participants were as follows: (1) age between 55 and 90 years; (2) serving as the primary caregiver for the spouse; (3) capable of independent functioning; and (4) no diagnosis of dementia. A total of 104 SCGs were recruited for the study. Of these, 54 caregivers voluntarily agreed to wear a Fitbit device. Participation in Fitbit monitoring for more than two weeks was entirely voluntary. The remaining participants declined to wear Fitbit device due to discomfort with wearing the device, scheduling conflicts, or lack of access to a compatible smartphone for data syncing. Of the 54 SCGs, 30 were caring for spouses diagnosed with dementia, 19 were caring for spouses with MCI, and 5 were caring for spouses with normal cognitive function (CN).

Dementia was diagnosed using the DSM-IV criteria, while MCI met the core clinical criteria recommended by the National Institute on Aging and Alzheimer’s Association guidelines [37]. CN was defined as having a CDR score of 0 and a Mini-Mental State Examination (MMSE) score of 27 or higher [38] and was treated for anxiety disorder or insomnia. The SCGs underwent a comprehensive clinical assessment by experienced neuropsychologists and research nurses. This study did not involve any clinical trials. Clinical trial number: not applicable.

Circadian rhythm assessment

Sleep-wake cycle variable

Participants were instructed to wear the device daily for a minimum of two consecutive weeks. The following objective sleep-wake cycle parameters were obtained from wearable Fitbit: sleep duration, sleep efficiency, and sleep onset and offset times (with onset and offset times expressed in minutes from midnight).

The Korean version of the PSQI [39] was used to determine participants’ self-assessed sleep-wake cycle [22]. In the PSQI, each question is assigned a score ranging from 0 to 3, resulting in a total score between 0 and 21. Participants with scores above 5 were considered to have poor sleep quality. Component scores were derived for subjective sleep quality, sleep latency (i.e., time taken to fall asleep), sleep duration, habitual sleep efficiency (i.e., the ratio of time a person actually sleeps to the total time they spend in bed), sleep disturbances, use of sleep medications, and daytime dysfunction.

Circadian rhythms of heart rate parameters

Heart rate data were collected via photoplethysmography sensors embedded in the Fitbit device, summarized every 15 min, and averaged over the next valid day to generate participant-level estimates. If less than 75% of heart rate data was collected during the analysis time, the analysis was excluded. Data were retrieved using a custom Python script via the Fitbit Web API. The utility and feasibility of using Fitbit in clinical research has been previously reported and clinical outcomes have been documented in several studies [29, 31].

To evaluate CHR, we applied cosinor analysis via CosinorPy using the 2-day continuous heart rate data. Cosinor analysis can measure the following key circadian rhythm parameters: amplitude (half the difference between peak and trough of the fitted curve), Midline Estimating Statistic of Rhythm (MESOR; the rhythm-adjusted mean heart rate), acrophase(the timing of the peak in the fitted rhythm, expressed in hours), and goodness of fit (GoF). GoF was calculated as an R-squared value, indicating how well the observed data fit the cosinor model. Values closer to 1 indicate better model fit and stronger circadian rhythmicity.

Clinical assessments

Caregiving burden

Caregiving burden was assessed using the Korean version [40] of 22-item Zarit Caregiver Burden Interview (ZBI). Each item is scored on a five-point Likert scale, ranging from 0 (never) to 4 (nearly always), yielding a total score between 0 and 88 [41].

Other clinical variable assessments

Global cognition of each participant was assessed using the Korean version of the MMSE [38]. To evaluate the severity of depressive symptoms, we used the Korean version of the Geriatric Depression Scale [42]. Further, we used the Korean version of the International Physical Activity Questionnaire (IPAQ) [43], which utilizes the Metabolic Equivalent Task (MET) variable to determine physical activity categories. The total minutes spent engaged in physical activity of various intensities over the past seven days were identified, and responses were transformed into MET-minutes per week (MET-min/week) according to the IPAQ scoring protocol [44]. Average MET scores were calculated for each activity type. The following MET values were used: walking = 3.3 MET, moderate-intensity activity = 4.0 MET, vigorous-intensity activity = 8.0 MET. Total physical activity was calculated as the sum of the MET-min/week values derived from walking, moderate-intensity activity, and vigorous-intensity activity.

Statistical analysis

To investigate whether there were differences in the characteristics of the full sample (n = 104) and the Fitbit-wearing subgroup (n = 54, of whom 52 also completed the PSQI), Mann–Whitney U tests were used for continuous variables, and chi-square tests were used for categorical variables.

Multiple regression analyses were then conducted to examine the association between caregiving burden, as measured by the ZBI, and circadian rhythm related variables. Analyses were performed sequentially for: (1) Fitbit-derived sleep-wake cycle parameters (sleep duration, sleep efficiency, sleep onset time, and offset time); (2) PSQI scores (total and subdomains); and (3) CHR parameters (amplitude, MESOR, acrophase, and GoF). In all models, SCG’s age and sex, as well as the care recipient’s cognitive status (dementia vs. non-dementia), were included as covariates.

Additional moderation analyses were conducted to examine whether SCG sex or care recipient cognitive status moderated the relationship between caregiving burden and circadian rhythm outcomes. Care recipient cognitive status was categorized as dementia versus non-dementia for these analyses. To limit the number of exploratory tests, these were only performed on sleep or circadian variables that showed a trend-level association (p < 0.1) with caregiver ZBI score in the initial regression model. The interaction term ([moderator] × [ZBI]) was used as an independent variable; sex of the SCG, as well as the cognitive status of care recipients, were treated as covariates when appropriate, while circadian rhythm variables were treated as dependent variables.

All analyses were performed using SPSS version 21.0 (SPSS Inc., Chicago, IL). Statistical significance was set at p < 0.05.

Results

Characteristics of the study population

Table 1 summarizes the characteristics of the full sample (n = 104) and the Fitbit subgroup (n = 54) who wore the Fitbit.

Table 1.

Sample characteristics

Variable	Full sample	Fitbit	p-value
	n = 104	n = 54
SCG factors
Age, y	72.88 ± 7.07	71.77 ± 6.47	0.254
Sex, F (%)	33.70	33.3	0.968
Education, y	8.93 ± 4.49	9.32 ± 4.34	0.776
Household, n	1.16 ± 0.36	1.08 ± 0.38	0.140
BMI	24.68 ± 3.45	24.44 ± 2.92	0.853
Other disease (%)
DM	23.10	20.8	0.718
HTN	52.90	41.5	0.160
Stroke	3.80	3.8	0.973
Hyperlipidemia	39.40	49.1	0.269
TIA	-	-	-
Coronary diseases	8.70	5.7	0.494
APOE4 positive	15.40	21.3	0.756
MMSE	24.95 ± 2.91	25.77 ± 2.24	0.172
GDS	12.90 ± 7.07	11.98 ± 6.40	0.273
ZBI	34.99 ± 19.75	33.75 ± 18.13	0.562
IPAQ total	2439.74 ± 3159.75	1807.35 ± 2429.22	0.561
PSQI total	8.25 ± 3.56	7.84 ± 3.42	0.476
Subjective sleep quality	1.21 ± 0.63	1.23 ± 0.47	0.854
Sleep latency	2.52 ± 0.71	2.52 ± 0.61	0.684
Sleep duration	1.10 ± 1.05	0.96 ± 0.97	0.462
Habitual sleep efficiency	1.19 ± 1.30	1.00 ± 1.10	0.492
Sleep disturbance	1.42 ± 0.62	1.29 ± 0.50	0.135
Use of sleep medicine	0.63 ± 0.10	0.52 ± 1.02	0.521
Daytime dysfunction	0.95 ± 1.46	0.59 ± 0.60	0.571
Care-recipient factor
MMSE	19.46 ± 5.90	20.21 ± 5.35	0.779
CDR	0.82 ± 0.55	0.77 ± 0.44	0.482
NPI	20.98 ± 19.68	15.96 ± 16.64	0.095
Cognitive status of care-recipients (%)
Dementia	59.6	55.6	0.624
Non-dementia	40.4	44.4	0.624

Values are given as means ± standard deviations

Abbreviations: BMI Body mass index, DM Diabetes Mellitus, HTN Hypertension, TIA Transient ischemic attack, APOE4 Apolipoprotein ε4, MMSE Mini-Mental State Examination, GDS Geriatric Depression Scale, ZBI Zarit Burden Interview, IPAQ International Physical Activity Questionnaire, PSQI Pittsburgh Sleep Quality Index, CDR Clinical Dementia Rating, NPI Neuropsychiatric Inventory

All SCGs included in the study were living with the care recipient at the time of participation. For the Fitbit group (n = 54), participants wore actigraphy for an average of 12.02 days (SD = 4.92). 87% of participants provided at least 7 valid days of data, ensuring adequate data quality for circadian rhythm analysis. No statistically significant differences in demographic characteristics were observed between the two groups. Both groups had total PSQI scores exceeding the clinical cut-off point of 5, with respective scores of 8.25 ± 3.56 and 7.84 ± 3.42. Similarly, although the ZBI is usually considered a continuous scale, the observed total scores in both groups (34.99 ± 19.75 and 33.75 ± 18.13) suggest relatively high levels of caregiver burden compared to previous studies [45].

We also performed a comparative analysis of Fitbit-derived sleep-wake parameters, PSQI, and CHR parameters categorized by care recipients’ cognitive status (dementia vs. non-dementia) within the Fitbit group, but no significant differences were found (Additional file 2).

Association between caregiving burden and fitbit-derived sleep-wake parameters

Although no statistically significant associations were found between caregiving burden and Fitbit-derived sleep-wake cycle parameters, there was a trend decreasing offset time as caregiving burden increased (β = − 0.254, t = − 1.725, p = 0.091) (Table 2).

Table 2.

Association between caregiving burden and Fitbit-derived sleep-wake parameters among SCGs (n = 54)

	ZBI of SCGs
Sleep-wake cycle variable	β	SE	t	p a
Sleep duration(min)	−0.193	0.519	−1.307	0.197
Sleep efficiency(%)	0.040	0.000	0.266	0.791
Onset time(min)	−0.107	0.938	−0.708	0.483
Offset time(min)	−0.254	0.778	−1.725	0.091

Covariates of the linear regression model included SCGs’ age and sex, as well as care recipients’ cognitive status

Abbreviations: SCG Spousal caregiver, ZBI Zarit Burden Interview

Association between caregiving burden and subjective sleep outcomes (PSQI)

To better understand overall trends in caregivers’ subjective sleep outcomes, we examined the relationship between PSQI and caregiving burden using the full sample (n = 104). As the caregiving burden increased, sleep disturbance worsened significantly (β = 0.203, t = 2.021, p = 0.046), while sleep latency exhibited a trend-level association that did not reach the conventional threshold for statistical significance (β = 0.196, t = 1.953, p = 0.054) (Table 3).

Table 3.

Association between caregiving burden and PSQI of SCGs (n = 104)

	ZBI of SCGs
PSQI score	β	SE	T	p
PSQI total	0.136	0.018	1.351	0.180
Subjective sleep quality	0.089	0.003	0.875	0.384
Sleep latency	0.196	0.003	1.953	0.054
Sleep duration	−0.057	0.005	−0.569	0.570
Habitual sleep efficiency	0.100	0.007	0.996	0.322
Sleep disturbance	0.203	0.003	2.021	0.046*
Use of sleep medicine	−0.122	0.006	−1.106	0.271
Daytime dysfunction	0.160	0.007	1.598	0.113

Covariates of the linear regression model included age and sex of SCGs, as well as the cognitive status of care-recipients

Abbreviations: SCG Spousal caregiver, ZBI Zarit Burden Interview, PSQI Pittsburgh Sleep Quality Index

*p < 0.05

However, no statistically significant associations were found between caregiving burden and PSQI total or subdomain scores among Fitbit wearers (n = 54) (Additional File 1).

Association between caregiving burden and circadian heart rate rhythm (CHR)

An association was observed between increased caregiving burden and lower GoF values (β = − 0.306, t = − 2.144, p = 0.037; Table 4), indicating that caregivers’ CHRs became less regular as burden increased (Fig. 1).

Table 4.

Association of caregiving burden and CHR parameters (n = 54)

	ZBI of SCGs
CHR parameters	β	SE	t	pa
Amplitude	−0.180	0.020	−1.255	0.215
MESOR	0.038	0.057	0.265	0.792
GoF	−0.306	0.001	−2.144	0.037*
Acrophase	−0.004	0.011	−0.027	0.978

Covariates of the linear regression model included age and sex of SCGs, as well as the cognitive status of care-recipients

Abbreviations: SCG Spousal caregiver, ZBI Zarit Burden Interview, CHR Circadian rhythm of heart rate, MESOR Midline Estimating Statistic of Rhythm, GoF Goodness of fit

*p < 0.05

Fig. 1.

Association between caregiving burden and SCGs’ GoF. Notes: Covariates of the linear regression model include age and sex of SCGs and the cognitive level of care-recipients. Abbreviations: SCG, spousal caregiver; ZBI, Zarit Burden Interview; GoF, goodness of fit

Other parameters related to the CHRs did not exhibit a significant association with the ZBI score.

Moderating effects of SCG sex and care recipient cognitive status on offset time and GoF

To further explore potential moderator effects of SCGs’ sex and care recipients’ cognitive status, we conducted additional interaction analyses exclusively for variables with a p-value below 0.1 (off-set time and GoF) in the primary regression models. The care recipients’ cognitive status significantly moderated the effect of caregiving burden on offset time (β = −4.239, t = −2.924, p = 0.005) (Table 5). Further subgroup analyses revealed a significant correlation between heightened caregiver burden and earlier awakening among SCGs of individuals with dementia (β[SE] = −3.441[1.020], t = −3.375, p = 0.001), whereas no such association was observed among SCGs of individuals without dementia (β[SE] = 0.798[1.030], t = 0.775, p = 0.442) (Fig. 2).

Table 5.

Moderating effect of sex of SCGs and cognitive status of care recipients on the association between caregiving burden versus offset time and GoF of SCGs

Dependent variable	Moderator	Independent variable	B (LLCI-ULCI)	t	p
Offset time
	Sex	ZBI	−0.877 (−2.659–0.906)	−0.989	0.328
		Sex	52.499 (−68.888–173.885)	0.870	0.389
		ZBI x Sex	−1.993 (−5.676–1.691)	−1.088	0.282
	Cognitive status	ZBI	0.798 (−1.272–2.868)	0.775	0.442
		Cognitive level	164.708 (55.188–274.227)	3.024	0.004
		ZBI x cognitive level	−4.239 (−7.154–−1.325)	−2.924	0.005*
GoF
	Sex	ZBI	−0.001 (−0.003–0.001)	−1.090	0.281
		Sex	0.136 (−0.010–0.282)	1.874	0.067
		ZBI x Sex	−0.004 (−0.008–0.001)	−1.711	0.094
	Cognitive status	ZBI	−0.002 (−0.004–0.001)	−1.238	0.222
		Cognitive status	0.018 (−0.0128–0.163)	0.247	0.806
		ZBI x cognitive status	−0.001(−0.005–0.003)	−0.362	0.719

Moderate for sex effect adjusted for age of SCGs and cognitive level of care-recipient

Moderate for cognitive level of care recipients adjusted for age and sex of SCGs

Abbreviations: SCG Spousal caregiver, ZBI Zarit Burden Interview, GoF Goodness of fit

Fig. 2.

Moderating effect of cognitive level of care recipients and SCGs’ GoF. Notes: Covariates include SCGs’ age and sex. Abbreviations: GoF, goodness of fit; SCG, spousal caregiver; ZBI, Zarit Burden Interview

Discussion

While prior studies have examined circadian rhythm disruption caregivers, relatively few have explored alterations in circadian rhythm using objective metrics such as sleep-wake cycle and CHR, and even fewer have focused specifically on SCGs. We found that greater caregiving burden was significantly associated with reduced circadian rhythm regularity of heart rate, as reflected by lower GoF in CHR. In addition, caregiving burden was related to greater subjective sleep disturbance and, although not statistically significant, showed a tendency toward earlier awakening, particularly among SCGs of individuals with dementia.

Among the investigated circadian rhythm parameters, GoF was the only indicator significantly associated with caregiving burden. GoF reflects the overall regularity of circadian rhythms, with lower values indicating a more disorganized or unstable rhythm pattern [46]. This finding suggests that caregiving stress may disrupt the general organization of physiological rhythms rather than affecting specific parameters such as acrophase or amplitude. This is consistent with prior RAR-based studies, which reported that caregivers who are unable to leave individuals with dementia alone often experience frequent nighttime awakenings, potentially contributing to rhythm fragmentation [18]. However, unlike those findings—where MESOR was also affected—our results suggest that caregiving burden may primarily influence rhythm regularity. This difference may reflect the nature of CHR, which captures not only behavioral rhythms, but also internal physiological stress responses mediated by autonomic regulation [33–35].

Although offset time was not significantly associated with caregiving burden, a trend toward earlier awakening was observed, particularly among SCGs of individuals with dementia. This may reflect the tendency of these caregivers to wake up early and struggle to return to sleep due to care recipients’ nocturnal behaviors [47]. This multifactorial interaction may help explain why the trend toward earlier awakening was more pronounced in this subgroup. However, prior studies suggest that such disruptions are not solely attributable to the care recipient but also caregivers’ factors [12, 47–49]. In our study, SCGs reported poorer subjective sleep quality despite sufficient sleep duration, as indicated by higher PSQI global and subdomain scores (excluding sleep duration) compared to cognitively healthy older adults in Korea [50], as well as a positive association between caregiving burden and subjective sleep disturbance. This finding contrasts with that of Ryuno et al. [14], who reported a significant association between caregiving burden and reduced sleep duration. One possible explanation for this discrepancy is the use of different devices: the ActiGraph GT9X used in Ryuno’s study is known to underestimate sleep duration, whereas Fitbit has shown greater concordance with PSG in detecting sleep parameters [51]. Additionally, the caregivers in our study were older (mean age: 70.2 vs. 66.9 years) and had higher PSQI scores (8.25 ± 3.56 vs. 5.2 ± 3.6), suggesting greater baseline sleep disturbance that may have masked any association between caregiving burden and sleep duration. Although neither result reached statistical significance, Fitbit data showed a trend toward earlier awakening, and PSQI responses indicated longer sleep latency with increasing caregiving burden. These discrepancies may reflect differences in assessment tools, or the varied ways in which caregiving stress manifests in individual sleep patterns. For instance, caregivers often manage dual responsibilities (e.g., medication schedules, hospital visits), which may delay sleep onset [12], while nighttime vigilance or anticipatory anxiety may lead to earlier awakening [47].

Taken together, these findings imply that increased caregiving burden may lead to changes in the sleep-wake cycle—not necessarily through reduced sleep duration, but via shifts in sleep timing that may be either advanced or delayed, depending on individual adaptation. More critically, it is not the specific timing (e.g., waking early or sleeping late) that may matter most, but rather the disruption in regularity. Additionally, such disruptions may persist even after care recipients’ nocturnal behaviors improve, implying that alterations in caregivers’ circadian regulation may become chronic [47]. Therefore, when assessing SCGs’ sleep problems, it is crucial to consider other aspects of sleep quality beyond sleep duration. Specifically, given our results, examining the robustness of circadian rhythms in caregivers with a high caregiving burden may be particularly beneficial.

Alterations in circadian rhythms have been associated with depression [18] and cognitive decline in caregivers [19]. Disruptions to the circadian system can impact cardiovascular health independently of behavioral and environmental cycles [11, 21]. These disruptions are important because they can cause serious practical problems for caregivers, such as caregivers failing to recognize new symptoms, forgetting to administer medication, being unable to multitask, and struggling to make rational decisions [12]. Studies have shown that circadian rhythm alterations in caregivers can be improved with appropriate sleep interventions, thus highlighting the importance of early intervention [12, 14].

Strengths and limitations

This study has several strengths, including its use of objective measurements to examine circadian rhythms and its specific focus on SCGs. While CHR is distinct from traditional RAR, it provides unique physiological insights into circadian regulation by primarily reflecting autonomic nervous system activity, thereby complementing behavioral indicators [33–35]. Moreover, we assessed multiple circadian rhythm variables, including those derived from both sleep-wake cycles and CHR. Although not all variables showed statistically significant associations, the consistent directionality and conceptual coherence of the findings support the relevance of the selected domains to caregiving burden.

Despite these strengths, several limitations should be acknowledged. Although vascular risk factors were assessed and statistically controlled for, we did not collect detailed information on medications that may affect heart rate, such as beta-blockers, which remains a clear limitation. Furthermore, the absence of heart rate variability (HRV) analysis is notable. The Fitbit device used in this study does not provide access to raw inter-beat interval data through its API, offering only summary-level HRV metrics during sleep periods [52]. This restricted our ability to examine autonomic nervous system activity in more depth. Future studies employing devices capable of high-resolution inter-beat interval recording could allow for a more comprehensive assessment of caregivers’ autonomic and circadian profiles. Additionally, we did not account for non-circadian factors that may influence circadian rhythms, such as voluntary physical activity or lifestyle habits. The relatively small sample size and the inclusion of SCGs of individuals who were CN may also limit the generalizability of the findings. For the interaction analysis, these participants were grouped with non-dementia care recipients group. But the subgroup of SCGs of individuals with CN was particularly small (n = 5). While this grouping supported statistical feasibility, comparisons between SCGs of individuals with dementia and without dementia should be interpreted with caution. Lastly, the study may be subject to selection bias, as participants were recruited from a single geriatric psychiatric clinic. This may limit the applicability of the findings to other settings or more diverse caregiving populations.

Conclusion

Among the circadian rhythm parameters examined, only GoF—a marker of rhythm regularity—was significantly associated with caregiving burden. This suggests that caregiving stress may contribute to disruptions in the stability of physiological rhythms. Higher caregiving burden was also associated with greater subjective sleep disturbance. While offset time showed a non-significant trend, especially among SCGs of individuals with dementia, no other circadian parameters demonstrated notable associations. These findings underscore the potential value of GoF as an objective indicator of circadian rhythm disruption in caregivers. Further research is needed to validate these findings in larger samples and to explore interventions aimed at maintaining circadian health in this population.

Abbreviations

CHR

Circadian rhythms of heart rate

CN

Cognitively normal

GoF

Goodness of fit

HRV

Heart rate variability

IPAQ

International physical activity questionnaire

MCI

Mild cognitive impairment

MET

Metabolic equivalent of task

MMSE

Mini-mental state examination

MESOR

Midline estimating statistic of rhythm

PSQI

Pittsburgh sleep quality index

SCG

Spousal caregiver

ZBI

Zarit caregiver burden interview

Authors’ contributions

SYP and SYJ made substantial contributions to the conception and design of work. SYP, JBL, TL, HYJ and SYK made substantial contributions to data analysis and interpretation of data. SYJ made substantial contributions to data curation, funding acquisition, and project administration. SYP have drafted the work, while JBL, TL, HYJ, SYK and SYJ have substantively revised it. All authors read and approved the final manuscript.

Funding

This work was supported by the Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Education (grant number RS-2023-00210380), and Chungnam National University Hospital.

Data availability

The data generated and analyzed during the current study are not publicly available due to privacy restrictions but are available on reasonable request from the corresponding author.

Declarations

Ethics approval and consent to participate

The work described was carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans and was approved by the Institutional Review Board of Chungnam National University Hospital (reference number 2020-05-002). Each participant provided their informed consent prior to participation.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.