ABSTRACT
Background:
Midlife health poses stress to women. Perceived Stress Scale (PSS) score allows subjective assessment of perceived stress whereas heart rate variability (HRV) gives electrocardiogram (ECG)-based quantification of cardiac autonomic function, but the relation between these two is scarcely studied. Hence, the association between PSS and HRV was studied in middle-aged women.
Methodology:
A cross-sectional study was done in midlife premenopausal (n = 51), perimenopausal (n = 59), and postmenopausal (n = 82) women. Perceived stress was interviewed by PSS score. ECG-based 5 min HRV was done by Variowin HR Software for frequency domain, time domain, and geometric HRV parameters. PSS and HRV were further compared or associated in subgroups, taking P < 0.05 as statistically significant.
Results:
HRV was reduced in all three groups which had a prevalence of PSS grade 0, 1, 2 44%, 53%, and 3%, respectively, without intergroup difference. Diabetic and/or hypertensive subgroups exhibited reduced HRV than nondiabetic nonhypertensive subgroups. No significant correlation was found between PSS score and HRV parameters in either group. HRV did not associate with PSS grade-based subgroups in pre-, peri-, or postmenopausal stage.
Conclusion:
Lack of HRV to perceived subjective stress association was observed in 40–55-year-old women with mild confounding by diabetes and/or hypertension. We suggest HRV total power and low-frequency: high-frequency ratio as best HRV parameters and consideration of age, reproductive health stage, and presence of comorbidities as a weak confounder for both HRV and PSS studies of middle-aged women. Further studies are warranted to consolidate the utility of HRV and PSS for the assessment of these two different types of stress.
KEYWORDS: Autonomic, cardiac, menopause, middle-aged, psychological stress
INTRODUCTION
Females pass through stress during menopausal transition, adversely affecting mental health.[1] Subjective stress level can be assessed by subjective questionnaires like perceived stress scale (PSS).[2] It can reveal perceived stress by array of validated questions. A gradual decline of estrogen support can adversely increase stress level in midlife women.[3] Psychological stress is known to alter cardiac autonomic balance which manifests as reduced heart rate variability (HRV).[4] HRV is beat-to-beat variability of RR interval with lesser value indicating cardiac autonomic stress.[5] HRV studies are lacking in middle-aged women population, and HRV to subjective stress association has not been tested. Whether psychological stress leads to cardiac autonomic imbalance largely remains unknown. Even in most studies of middle-aged women, pre-, peri-, or postmenopausal state is neither considered nor compared mostly. Separate consideration of prevalent diabetes and/or hypertension is also warranted while studying these parameters. In midlife women, stress, menopause, and cardiac dysautonomia all add to the age-induced negative impact on cardiac health. Hence, it looks fruitful to study stress as perceived by PSS score subjectively and its association with cardiac autonomic activity as revealed by 5 min HRV tool. In light of these, we studied subjective stress among women with 40–55 years and its association with short-term HRV, if any.
METHODOLOGY
Study approval and study setting
For this cross-sectional study, prior permissions were sought from physiology and psychiatry departments, and the research protocol was prospectively approved by the institutional review board of our institution. It was prospectively registered in Clinical Trials Registry - India, and participants were enrolled after getting signed informed consent. The study was undertaken in heterogeneous sample of the general population in 5 different areas of 2 cities.
Study participants
By simple, convenient sampling of general population, we chose women aged 40–55 years, with or without menopause, ready to give written informed consent. Participants were screened for point-of-care random plasma glucose (RPG) ≥200 mg/dL and brachial blood pressure in hypertensive range as per the Joint National Committee (JNC) 8 criteria. We excluded participants having body mass index (BMI) ≥32.5, self-reported thyroid disorder, any substance abuse, chronic infection, history of cardiovascular disease or intervention, use of drug-altering cardiac autonomic function, polycystic ovarian disease, and polycystic ovary syndrome. One enrolled participant was excluded after HRV measurement due to abnormal electrocardiogram (ECG).
Study groups
Based on Stages of Reproductive Aging Workshop (STRAW + 10),[6] study participants were divided into three subgroups: premenopausal, perimenopausal, and postmenopausal. Participants were further divided into nondiabetic nonhypertensive, and diabetic and/or hypertensive based on RPG and blood pressure assessment or self-reported diabetes and/or hypertension. Data were collected between July 2023 and December 2023.
Participant assessment
For each enrolled participant, initial assessment was carried out for personal history, reproductive history, medical history, and physical activity. RPG was assessed by glucometer, and office blood pressure was measured by mercury sphygmomanometer in the supine position using JNC-8 criteria. Barefoot height was measured by stadiometer, and weight was measured by a weighing scale to derive BMI. Ten minute of rest was given before accomplishing HRV measurement.
Heart rate variability measurements
HRV measurement was done with a validated instrument using a standard protocol.[7] Beat-to-beat variability in sino-atrial nodal discharge was recorded by ECG and was computed and analyzed by the software Variowin HR to determine the indices of HRV. The assessment of HRV was accomplished between 9.00 am and 12.00 noon in an isolated room in at given community setting. Participants were asked to refrain from coffee, tea, and cola drinks before the procedure. Five minutes lead II ECG was taken for the analysis of beat-to-beat HRV after supine rest for 10 min while the subject was in the supine position and breathing freely. The ECG recorded from the precordial leads was transferred online to a microcomputer for the analysis of HRV. Only stationary time series of approximately 5-min duration free of arrhythmia and artifact were used with a sampling frequency 500 Hz.
Heart rate variability parameters
HRV was studied in detail with respect to frequency domain, time domain, and poincare plot parameters.[7]
Time-domain analysis of HRV parameters encompassed RR interval, standard deviation of all RR intervals (SDNN), the square root of the mean of the sum of the squares of differences between adjacent RR intervals (RMSSD), standard deviation (SD) of successive differences, and pNN50, which is the percentage of consecutive RR intervals that differ by >50 ms.
Frequency-domain analysis of HRV included the power of high-frequency (HF), (0.15–0.40 Hz); low-frequency (LF), (0.04–0.15 Hz); and very LF (VLF), (below 0.04 Hz) power ranges. LF and HF were also presented in normalized units and as an LF-to-HF ratio.
Poincare plot analysis consisted of SD1 and SD2, which are SD of RR interval along major and minor axis, respectively. The scatter index is expressed as ratio of SD1 to SD2, reflecting the nonlinear HRV.
Statistical analysis
We entered data into Excel spreadsheets and sorted for groups and subgroups. Quantitative data was expressed as mean and SD, while categorical data were expressed as frequency or number. We used Graph Pad software for statistical analysis. Parametric or nonparametric distribution was checked before selecting the statistical test. Two groups were compared by Mann–Whitney U test or t-test for quantitative data, and for three groups, quantitative data were compared by analysis of variance test. Chi-square test was used for comparing the distribution of categorical data. For each test, P < 0.05 was taken as statistically significant.
RESULTS
This cross-sectional study was done on 192 middle-aged women divided into premenopausal (n = 51, mean age 42 years), perimenopausal (n = 59, mean age 44.58 years), and postmenopausal (n = 82, mean age 48.78 years) groups which were further divided into nondiabetic nonhypertensive or diabetic and/or hypertensive subgroups.
Nondiabetic nonhypertensive premenopausal, perimenopausal, and postmenopausal groups had comparable baseline parameters except for higher mean age in postmenopausal group. Both quantitative and qualitative PSS scores were not significantly different among three groups. Among HRV parameters, there was no general pattern of distribution across pre- to postmenopausal group, and all differences were insignificant except heart rate, LF power, and mode value. The perimenopausal group exhibited significantly lower (better) heart rate and higher LF power and mode value than other 2 groups [Table 1].
Table 1.
Comparison of study parameters in nondiabetics nonhypertensive group divided into premenopausal, perimenopausal and postmenopausal groups
| Group parameters | Premenopause (n=51) | Perimenopause (n=59) | Postmenopause (n=82) | P |
|---|---|---|---|---|
| Age (years) | 42.00±2.03 | 44.58±3.49 | 48.78±4.47 | <0.001* |
| BMI (kg/m2) | 27.85±4.50 | 26.80±4.13 | 26.23±4.91 | 0.14 |
| Right systolic bBP (mmHg) | 121.20±10.33 | 123.17±9.79 | 119.24±12.05 | 0.12 |
| Right diastolic bBP (mmHg) | 75.04±7.47 | 75.68±7.45 | 73.12±7.13 | 0.10 |
| PSS score | 14.12±5.57 | 15.58±5.46 | 13.78±6.01 | 0.16 |
| PSS grade 0/1/2 | 24/25/2 | 22/35/2 | 38/42/2 | 0,79 |
| Heart rate | 82.47±12.28 | 75.78±8.70 | 78.94+13.69 | 0.01* |
| VLF power | 692.75±538.58 | 701.93±626.53 | 692.17±672.26 | 0.7 |
| LF power | 400.13±251.42 | 531.64±407.33 | 383.24±383.64 | 0.01* |
| LFnu | 1.72±8.18 | 0.60±0.16 | 0.56±0.19 | 0.54 |
| Max LF | 0.08±0.09 | 0.07±0.03 | 0.07±0.03 | 0.89 |
| HF power | 449.84±709.22 | 468.23±722.12 | 413.89±618.52 | 0.28 |
| HFnu | 0.44±0.18 | 0.55±1.17 | 0.43±0.19 | 0.68 |
| Maximum HF | 0.27±0.09 | 0.27±0.08 | 0.28±0.09 | 0.64 |
| LF: HF | 1.81±1.59 | 2.00±1.44 | 1.97±1.89 | 0.47 |
| SD1 | 17.17±11.84 | 18.11±15.03 | 18.17±15.60 | 0.75 |
| SD2 | 61.47±186.14 | 38.82±15.18 | 35.10±17.27 | 0.19 |
| SD1/SD2 | 0.48±0.24 | 0.44±0.18 | 0.49±0.24 | 0.52 |
| Mode value | 720.65±139.51 | 791.29±89.07 | 765.54±116.44 | 0.01* |
| Triangular HRV index | 8.05±2.89 | 7.99±2.84 | 7.14±2.72 | 0.06 |
| SDNN | 36.56±36.67 | 34.58±16.02 | 32.03±16.64 | 0.39 |
| RMSSD | 27.57±18.47 | 29.23±20.62 | 28.29±21.37 | 0.58 |
| SDSD | 26.25±19.07 | 28.11±21.01 | 26.99±21.94 | 0.57 |
| NN50 | 28.57±49.67 | 27.81±37.3 | 24.04±38.39 | 0.74 |
| pNN50 | 8.25±13.54 | 8.32±11.35 | 7.24±11.75 | 0.8 |
*Statistical significance. BMI: Body mass index, PSS: Perceived Stress Scale, LF: Low-frequency, VLF: Very LF, HF: High-frequency, SD: Standard deviation, HRV: Heart rate variability, SDNN: Standard deviation of all RR interval, RMSSD: Root mean square of successive differences between normal heartbeats, SDSD: Standard deviation of successive differences
Diabetic and/or hypertensive premenopausal, perimenopausal, and postmenopausal groups have comparable baseline parameters except for higher mean age than the postmenopausal group. PSS score was insignificantly different among 3 groups both quantitatively and qualitatively. HRV parameters did not exhibit a distinct pattern of distribution across pre- to postmenopausal group, and all differences were statistically insignificant except HF power and LF/HF ratio. There was a trend of increasing LF/HF ratio from pre- to postmenopausal group with statistical significance. HF power was significantly higher in perimenopausal than the other two groups [Table 2].
Table 2.
Comparison of study parameters in diabetic and/or hypertensive group divided into premenopausal, perimenopausal and postmenopausal groups
| Parameter | Premenopause (n=24) | Perimenopause (n=28) | Postmenopause (n=47) | P |
|---|---|---|---|---|
| Age (years) | 43.13±2.42 | 46.14±3.38 | 51.43±2.89 | <0.001* |
| BMI (kg/m2) | 29.53±3.96 | 30.10±4.37 | 28.02±4.89 | 0.06 |
| Right systolic bBP (mmHg) | 145.83±14.49 | 141.11±13.15 | 144.04±19.14 | 0.67 |
| Right diastolic bBP (mmHg) | 86.63±12.06 | 87.25±7.35 | 84.17±10.73 | 0.41 |
| PSS score | 14.21±5.44 | 17.71±6.83 | 16.79±6.47 | 0.12 |
| PSS grade 0/1/2 | 11/12/0 | 08/18/2 | 15/28/4 | - |
| Heart rate | 85.46±17.77 | 80.25±11.74 | 78.36+10.71 | 0.26 |
| VLF power | 843.16±793.33 | 526.40±409.09 | 600.65±534.64 | 0.34 |
| LF power | 419.35±448.67 | 505.77±826.54 | 358.73±376.22 | 0.39 |
| LFnu | 0.57±0.13 | 0.57±0.17 | 0.63±0.20 | 0.09 |
| Maximum LF | 0.07±0.03 | 0.06±0.02 | 0.07±0.03 | 0.23 |
| HF power | 410.65±640.48 | 487.41±922.97 | 309.06±514.01 | 0.04* |
| HFnu | 0.43±0.13 | 0.43±0.17 | 0.36±0.18 | 0.07 |
| Maximum HF | 0.26±0.10 | 0.28±0.09 | 0.27±0.09 | 0.77 |
| LF: HF | 1.59±1.06 | 1.87±1.91 | 2.70±2.22 | 0.04* |
| SD1 | 17.88±16.94 | 19.98±16.66 | 15.90±12.95 | 0.16 |
| SD2 | 34.83±18.79 | 33.72±16.69 | 31.48±15.47 | 0.64 |
| SD1/SD2 | 0.50±0.25 | 0.53±0.22 | 0.48±0.23 | 0.37 |
| Mode value | 719.84±136.91 | 750.70±108.36 | 768.67±99.66 | 0.20 |
| Triangular HRV index | 7.61±3.22 | 7.01±2.65 | 7.38±3.13 | 0.90 |
| SDNN | 31.90±18.41 | 31.72±17.40 | 28.55±15.02 | 0.53 |
| RMSSD | 26.79±22.42 | 31.87±25.82 | 23.83±18.00 | 0.10 |
| SDSD | 25.37±22.95 | 30.89±26.57 | 22.32±18.62 | 0.09 |
| NN50 | 20.33±34.55 | 29.68±46.34 | 18.13±33.01 | 0.36 |
| pNN50 | 6.49±11.78 | 10.48±16.97 | 5.36±10.40 | 0.37 |
*Statistical significance. BMI: Body mass index, PSS: Perceived Stress Scale, LF: Low-frequency, VLF: Very LF, HF: High-frequency, SD: Standard deviation, HRV: Heart rate variability, SDNN: SD of all RR interval, RMSSD: Root mean square of successive differences between normal heartbeats, SDSD: SD of successive differences
Comparison was done between nondiabetic nonhypertensive, and diabetic and/or hypertensive subgroups separately in pair for each pre-, peri- and postmenopausal stage groups.
Most confounders were comparable in premenopausal group but differed significantly between former and later group in peri- and postmenopausal participants. Blood pressure exhibited higher values in the diseased group than in the nondiseased group. In all three sets of comparisons, the diseased group exhibited higher PSS scores than nondiseased group, but this difference was significant only in postmenopausal subgroups. In two subgroups of premenopausal group, all HRV parameters were distributed with insignificant differences. In perimenopausal group, except for higher VLF and LF power and lower SD1/SD2 ratio, all HRV parameters were comparable. In postmenopausal group, HFnu and HF power was significantly higher in nondiseased group, while LFnu and LF/HF ratio was significantly higher in diseased group [Table 3].
Table 3.
Comparison of study parameters in nondiabetics nonhypertensive group and diabetic and/or hypertensive group in premenopausal, perimenopausal and postmenopausal groups
| Parameter | Groups | ||||||||
|---|---|---|---|---|---|---|---|---|---|
|
| |||||||||
| Premenopausal | P | Peri-menopausal | P | Postmenopausal | P | ||||
|
|
|
|
|||||||
| NDNH (n=51) | DH (n=24) | NDNH (n=59) | DH (n=28) | NDNH (n=82) | DH (n=51) | ||||
| Age (years) | 42.00±2.03 | 43.13±2.42 | 0.05 | 44.58±3.49 | 46.14±3.38 | 0.05 | 48.78±4.47 | 51.43±2.89 | 0.001* |
| Total score | 14.12±5.57 | 14.21±5.44 | 0.93 | 15.58±5.46 | 17.71±6.83 | 0.14 | 13.78±6.01 | 16.79±6.47 | 0.01* |
| BMI (kg/m2) | 27.85±4.50 | 29.53±3.96 | 0.08 | 26.80±4.13 | 30.10±4.37 | 0.003* | 26.23±4.91 | 28.02±4.89 | 0.03* |
| SBP (mmHg) | 121.20±10.33 | 145.83±14.49 | <0.001* | 123.17±9.79 | 141.11±13.15 | <0.001* | 119.24±12.05 | 144.04±19.14 | <0.001* |
| DBP (mmHg) | 75.04±7.47 | 86.63±12.06 | <0.001* | 75.68±7.45 | 87.25±7.35 | <0.001* | 73.12±7.13 | 84.17±10.73 | <0.001* |
| Heart rate | 82.47±12.28 | 85.46±17.77 | 0.72 | 75.78±8.70 | 80.25±11.74 | 0.10 | 78.94±13.69 | 78.36±10.71 | 0.97 |
| VLF power | 692.75±538.58 | 843.16±793.33 | 0.87 | 701.93±626.53 | 526.40±409.09 | 0.01* | 692.17±672.26 | 600.65±534.64 | 0.42 |
| LF power | 400.13±251.42 | 419.35±448.67 | 0.51 | 531.64±407.33 | 505.77±826.54 | 0.05* | 383.24±383.64 | 358.73±376.22 | 0.30 |
| LFnu | 1.72±8.18 | 0.57±0.13 | 0.82 | 0.60±0.16 | 0.57±0.17 | 0.42 | 0.56±0.19 | 0.63±0.20 | 0.04* |
| Maximum LF | 0.08±0.09 | 0.07±0.03 | 0.56 | 0.07±0.03 | 0.06±0.02 | 0.09 | 0.07±0.03 | 0.07±0.03 | 0.84 |
| HF power | 449.84±709.22 | 410.65±640.48 | 0.53 | 468.23±722.12 | 487.41±922.97 | 0.28 | 413.89±618.52 | 309.06±514.01 | 0.03* |
| HFnu | 0.44±0.18 | 0.43±0.13 | 0.88 | 0.55±1.17 | 0.43±0.17 | 0.55 | 0.43±0.19 | 0.36±0.18 | 0.04* |
| Maximum HF | 0.27±0.09 | 0.26±0.10 | 0.71 | 0.27±0.08 | 0.28±0.09 | 0.59 | 0.28±0.09 | 0.27±0.09 | 0.85 |
| LF: HF | 1.81±1.59 | 1.59±1.06 | 0.96 | 2.00±1.44 | 1.87±1.91 | 0.40 | 1.97±1.89 | 2.70±2.22 | 0.03* |
| SD1 | 17.17±11.84 | 17.88±16.94 | 0.53 | 18.11±15.03 | 19.98±16.66 | 0.76 | 18.17±15.60 | 15.90±12.95 | 0.26 |
| SD2 | 61.47±186.14 | 34.83±18.79 | 0.24 | 38.82±15.18 | 33.72±16.69 | 0.06 | 35.10±17.27 | 31.48±11.47 | 0.17 |
| SD1/SD2 | 0.48±0.24 | 0.50±0.25 | 0.62 | 0.44±0.18 | 0.53±0.22 | 0.04* | 0.49±0.24 | 0.48±0.23 | 0.67 |
| Mode value | 720.65±139.51 | 719.84±136.91 | 0.66 | 791.29±89.07 | 750.70±108.36 | 0.06 | 765.54±116.44 | 768.67±99.66 | 0.98 |
| Triangular HRV index | 8.05±2.89 | 7.61±3.22 | 0.31 | 7.99±2.84 | 7.01±2.65 | 0.10 | 7.14±2.72 | 7.38±3.13 | 0.72 |
| SDNN | 36.56±36.67 | 31.90±18.41 | 0.38 | 34.58±16.02 | 31.72±17.40 | 0.15 | 32.03±16.64 | 28.55±15.02 | 0.20 |
| RMSSD | 27.57±18.47 | 26.79±22.42 | 0.47 | 29.23±20.62 | 31.87±25.82 | 0.97 | 28.29±21.37 | 23.83±18.00 | 0.12 |
| SDSD | 26.25±19.07 | 25.37±22.95 | 0.47 | 28.11±21.01 | 30.89±26.57 | 1.00 | 26.99±21.94 | 22.32±18.62 | 0.12 |
| NN50 | 28.57±49.67 | 20.33±34.55 | 0.58 | 27.81±37.3 | 29.68±46.34 | 0.75 | 24.04±38.39 | 18.13±33.01 | 0.14 |
| pNN50 | 8.25±13.54 | 6.49±11.78 | 0.62 | 8.32±11.35 | 10.48±16.97 | 0.82 | 7.24±11.75 | 5.36±10.40 | 0.13 |
*Statistical significance. NDNH: Nondiabetic nonhypertensive, DH: Diabetic and/or hypertensive, BMI: Body mass index, SBP: Systolic blood pressure, DBP: Diastolic blood pressure, LF: Low-frequency, VLF: Very LF, HF: High-frequency, SD: Standard deviation, HRV: Heart rate variability, SDNN: Standard deviation of all RR interval, RMSSD: Root mean square of successive differences between normal heartbeats, SDSD: Standard deviation of successive differences
Spearman’s correlations were done between PSS score and HRV parameters of six subgroups based on diseased or nondiseased and pre-, peri- or postmenopausal status. Most correlations were negative and statistically insignificant. PSS score showed a significant correlation with SD2 in nondiabetic nonhypertensive premenopausal group, maximum LF and LF/HF ratio in diabetic and/or hypertensive premenopausal group, and maximum HF in diabetic and/or hypertensive postmenopausal group [Table 4].
Table 4.
Correlation between perceived Stress Scale score and heart rate variability parameters in non-diabetics non-hypertensive group and diabetic and/or hypertensive group in premenopausal, perimenopausal and postmenopausal groups
| Parameter | Group | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
| ||||||||||||
| Premenopausal | Peri-menopausal | Postmenopausal | ||||||||||
|
| ||||||||||||
| Subgroup | ||||||||||||
|
| ||||||||||||
| NDNH (n=51) |
DH (n=24) |
NDNH (n=59) |
DH (n=28) |
NDNH (n=82) |
DH (n=47) |
|||||||
| r | P | r | P | r | P | r | P | r | P | r | P | |
| Age (years) | −0.13 | 0.35 | 0.18 | 0.40 | 0.24 | 0.07 | 0.11 | 0.57 | −0.07 | 0.51 | −0.02 | 0.88 |
| BMI (kg/m2) | 0.19 | 0.19 | −0.40 | 0.06 | 0.11 | 0.43 | −0.31 | 0.10 | 0.04 | 0.71 | −0.05 | 0.72 |
| SBP (mmHg) | 0.12 | 0.38 | −0.03 | 0.89 | 0.01 | 0.95 | −0.14 | 0.47 | −0.11 | 0.35 | 0.10 | 0.50 |
| DBP (mmHg) | −0.06 | 0.66 | −0.18 | 0.40 | −0.08 | 0.57 | 0.37 | 0.06 | −0.16 | 0.15 | 0.13 | 0.38 |
| Heart rate | 0.01 | 0.95 | 0.19 | 0.38 | −0.01 | 0.97 | 0.13 | 0.52 | 0.08 | 0.46 | −0.04 | 0.81 |
| VLF power | −0.01 | 0.95 | 0.08 | 0.72 | 0.04 | 0.76 | −0.15 | 0.44 | −0.12 | 0.28 | −0.02 | 0.90 |
| LF power | −0.12 | 0.39 | 0.14 | 0.51 | −0.18 | 0.19 | 0.01 | 0.98 | −0.18 | 0.10 | −0.01 | 0.98 |
| LFnu | 0.07 | 0.64 | −0.39 | 0.06 | −0.01 | 0.97 | 0.25 | 0.21 | −0.02 | 0.83 | 0.10 | 0.49 |
| Maximum LF | −0.12 | 0.41 | −0.44 | 0.03* | −0.01 | 0.96 | 0.30 | 0.13 | −0.07 | 0.54 | −0.06 | 0.71 |
| HF power | −0.24 | 0.1 | 0.30 | 0.15 | −0.08 | 0.55 | −0.20 | 0.30 | −0.13 | 0.25 | −0.10 | 0.52 |
| HFnu | −0.09 | 0.52 | 0.39 | 0.06 | 0.06 | 0.66 | −0.25 | 0.21 | −0.01 | 0.92 | −0.10 | 0.53 |
| Max HF | −0.04 | 0.78 | 0.08 | 0.70 | 0.25 | 0.06 | −0.05 | 0.81 | 0.03 | 0.78 | −0.34 | 0.02* |
| LF: HF | 0.16 | 0.25 | −0.40 | 0.05* | −0.003 | 0.98 | 0.25 | 0.20 | −0.02 | 0.83 | 0.10 | 0.50 |
| SD1 | −0.07 | 0.60 | 0.01 | 0.97 | −0.04 | 0.76 | −0.15 | 0.45 | −0.16 | 0.14 | 0.09 | 0.56 |
| SD2 | −0.28 | 0.05* | 0.16 | 0.44 | −0.07 | 0.62 | −0.17 | 0.40 | −0.15 | 0.17 | −0.02 | 0.91 |
| SD1/SD2 | −0.06 | 0.70 | −0.05 | 0.84 | 0.04 | 0.75 | −0.01 | 1.00 | −0.08 | 0.49 | 0.06 | 0.71 |
| Mode value | 0.004 | 0.98 | −0.18 | 0.39 | 0.02 | 0.89 | −0.10 | 0.60 | −0.07 | 0.56 | 0.03 | 0.84 |
| Triangular HRV index | −0.05 | 0.73 | −0.03 | 0.89 | −0.18 | 0.18 | −0.16 | 0.42 | −0.17 | 0.14 | −0.01 | 0.95 |
| SDNN | −0.11 | 0.42 | 0.11 | 0.60 | −0.07 | 0.61 | −0.18 | 0.35 | −0.19 | 0.10 | −0.03 | 0.86 |
| RMSSD | −0.10 | 0.48 | 0.08 | 0.71 | −0.06 | 0.65 | −0.26 | 0.18 | −0.17 | 0.14 | 0.08 | 0.58 |
| SDSD | −0.11 | 0.46 | 0.08 | 0.71 | −0.06 | 0.65 | −0.27 | 0.17 | −0.16 | 0.14 | 0.08 | 0.58 |
| NN50 | −0.10 | 0.47 | 0.21 | 0.32 | −0.12 | 0.38 | −0.14 | 0.48 | −0.17 | 0.13 | 0.06 | 0.67 |
| pNN50 | −0.11 | 0.45 | 0.15 | 0.48 | −0.11 | 0.41 | −0.16 | 0.41 | −0.15 | 0.17 | 0.05 | 0.73 |
*Statistical significance. NDNH: Nondiabetic nonhypertensive, DH: Diabetic and/or hypertensive, BMI: Body mass index, SBP: Systolic blood pressure, DBP: Diastolic blood pressure, LF: Low-frequency, VLF: Very LF, HF: High-frequency, SD: Standard deviation, HRV: Heart rate variability, SDNN: SD of all RR interval, RMSSD: Root mean square of successive differences between normal heartbeats, SDSD: SD of successive differences
A comparison was done between subgroups based on PSS score severity (grade 0 vs. grade 1/2) in pre-, peri- and postmenopausal groups who were nondiabetic nonhypertensive. Most HRV parameters did not follow any specific pattern and were statistically insignificant in all groups except SDNN in postmenopausal group. VLF power, LF power, HF power, SD2, and Triangular HRV index were higher in subgroups with PSS grade 0 than the subgroup with PSS grade 1/2. VLF power, LF power, SD2, and Triangular HRV index were statistically significant in postmenopausal group only, while HF power was statistically significant in premenopausal group. LF/HF ratio was higher in subgroups with PSS grade 1/2 than in subgroups with PSS grade 0, but it was statistically significant only in premenopause group [Table 5].
Table 5.
Comparison of heart rate variability parameters between subgroups with perceived stress scale score grade 0 and grade 1 or 2 in premenopausal, perimenopausal and postmenopausal groups in non-diabetics non-hypertensive group
| Parameter | Group | ||||||||
|---|---|---|---|---|---|---|---|---|---|
|
| |||||||||
| Premenopausal | Peri-menopausal | Postmenopausal | |||||||
|
| |||||||||
| Subgroup | |||||||||
|
| |||||||||
| PSS grade 0 (n=24) | PSS grade 1/2 (n=27) | P | PSS grade 0 (n=22) | PSS grade 1/2 (n=37) | P | PSS grade 0 (n=38) | PSS grade 1/2 (n=44) | P | |
| Age | 42.21±2.09 | 41.81±2.00 | 0.41 | 43.41±2.70 | 45.27±3.74 | 0.07 | 48.92±4.67 | 48.66±4.34 | 0.70 |
| Total score | 9.67±2.90 | 18.07±4.21 | <0.001 | 10.18±2.34 | 18.78±4.06 | <0.001 | 8.76±2.96 | 18.11±4.35 | <0.001 |
| BMI | 27.42±4.56 | 28.24±4.51 | 0.49 | 26.55±4.28 | 26.95±4.08 | 0.53 | 25.99±4.53 | 26.45±5.26 | 0.81 |
| SBP | 119.42±10.76 | 122.78±9.86 | 0.26 | 122.64±10.49 | 123.49±9.49 | 0.86 | 120.97±11.30 | 117.75±12.60 | 0.32 |
| DBP | 75.17±8.61 | 74.93±6.46 | 0.76 | 75.82±8.40 | 75.59±6.95 | 0.72 | 74.03±6.84 | 72.34±7.36 | 0.28 |
| Heart rate | 83.83±15.19 | 81.26±9.09 | 0.96 | 75.59±9.88 | 75.89±8.06 | 0.91 | 77.03±10.74 | 80.59±15.74 | 0.27 |
| VLF power | 737.68±663.35 | 652.80±406.26 | 0.82 | 738.73±611.54 | 680.05±642.61 | 0.88 | 853.14±826.39 | 553.16±470.13 | 0.03 |
| LF power | 424.11±245.76 | 378.81±259.10 | 0.38 | 595.96±396.57 | 493.40±414.17 | 0.11 | 458.81±425.46 | 317.98±334.80 | 0.01 |
| LFnu | 2.96±11.94 | 0.61±0.18 | 0.24 | 0.60±0.17 | 0.60±0.15 | 0.83 | 0.58±0.17 | 0.55±0.20 | 0.46 |
| Maximum LF | 0.10±0.13 | 0.06±0.02 | 0.28 | 0.07±0.02 | 0.07±0.03 | 0.64 | 0.07±0.03 | 0.07±0.03 | 0.47 |
| HF power | 641.53±963.33 | 279.46±286.86 | 0.03 | 554.57±946.39 | 416.89±557.13 | 0.52 | 428.56±522.42 | 401.21±696.74 | 0.15 |
| HFnu | 0.47±0.17 | 0.42±0.20 | 0.31 | 0.40±0.17 | 0.64±1.47 | 0.64 | 0.42±0.17 | 0.44±0.20 | 0.62 |
| Maximum HF | 0.28±0.08 | 0.25±0.09 | 0.26 | 0.25±0.08 | 0.28±0.08 | 0.18 | 0.28±0.09 | 0.29±0.09 | 0.51 |
| LF: HF | 1.27±0.82 | 2.29±1.94 | 0.05 | 1.98±1.26 | 2.01±1.56 | 0.81 | 1.97±1.77 | 1.98±2.01 | 0.47 |
| SD1 | 18.48±12.44 | 16.00±11.37 | 0.42 | 19.33±18.41 | 17.39±12.83 | 0.95 | 20.67±17.03 | 16.01±14.10 | 0.10 |
| SD2 | 93.26±270.56 | 33.21±10.54 | 0.10 | 40.87±14.47 | 37.60±15.66 | 0.41 | 39.35±18.30 | 31.43±15.63 | 0.02 |
| SD1:SD2 | 0.51±0.26 | 0.46±0.22 | 0.29 | 0.44±0.24 | 0.44±0.14 | 0.31 | 0.50±0.27 | 0.49±0.21 | 0.93 |
| Mode value | 723.02±119.44 | 718.55±157.47 | 0.99 | 793.17±101.77 | 790.17±82.08 | 0.94 | 784.46±105.59 | 749.20±123.91 | 0.22 |
| Triangular HRV index | 8.19±2.74 | 7.93±3.06 | 0.69 | 8.76±3.22 | 7.53±2.53 | 0.18 | 7.87±2.93 | 6.51±2.38 | 0.04 |
| SDNN | 33.80±14.09 | 39.02±48.97 | 0.40 | 36.93±17.49 | 33.18±15.16 | 0.49 | 35.94±17.21 | 28.66±15.54 | 0.02 |
| RMSSD | 30.84±21.71 | 24.67±14.86 | 0.40 | 31.63±26.34 | 27.79±16.58 | 0.84 | 30.90±21.47 | 26.04±21.28 | 0.13 |
| SDSD | 29.71±22.17 | 23.17±15.60 | 0.38 | 30.52±26.75 | 26.68±16.98 | 0.84 | 29.61±22.12 | 24.73±21.79 | 0.14 |
| NN50 | 37.46±65.43 | 20.67±28.64 | 0.29 | 33.23±46.35 | 24.59±30.98 | 0.50 | 26.87±38.05 | 21.59±38.96 | 0.06 |
| PNN50 | 11.00±17.39 | 5.81±8.50 | 0.25 | 9.99±14.00 | 7.34±9.52 | 0.57 | 8.04±11.25 | 6.55±12.26 | 0.09 |
BMI: Body mass index, SBP: Systolic blood pressure, DBP: Diastolic blood pressure, LF: Low-frequency, VLF: Very LF, HF: High-frequency, SD: Standard deviation, HRV: Heart rate variability, SDNN: Standard deviation of all RR interval, RMSSD: Root mean square of successive differences between normal heartbeats, SDSD: SD of successive differences, PSS: Perceived Stress Scale
A comparison was done between subgroups based on PSS score severity (grade 0 vs. grade 1/2) in pre-, peri- and postmenopausal groups who were diabetic and/or hypertensive. Most of the HRV parameters do not follow any specific pattern and were statistically insignificant in all groups except Max LF, which was statistically significant in premenopausal group and was higher in subgroups with PSS grade 0 than the subgroup with PSS grade 1–2 while Max HF was statistically significant in postmenopausal group and was higher in subgroups with PSS grade 0 than the subgroup with PSS grade 1–2 [Table 6].
Table 6.
Comparison of heart rate variability parameters between subgroups with perceived stress scale score grade 0 and grade 1 or 2 in premenopausal, perimenopausal and postmenopausal groups in diabetic and/or hypertensive group
| Parameter | Group | ||||||||
|---|---|---|---|---|---|---|---|---|---|
|
| |||||||||
| Premenopausal | Peri-menopausal | Postmenopausal | |||||||
|
| |||||||||
| Subgroup | |||||||||
|
| |||||||||
| PSS grade 0 (n=11) | PSS grade 1/2 (n=13) | P | PSS grade 0 (n=8) | PSS grade 1/2 (n=20) | P | PSS grade 0 (n=15) | PSS grade 1/2 (n=32) | P | |
| Age | 42.82±2.48 | 43.38±2.43 | 0.61 | 45.38±3.62 | 46.45±3.33 | 0.44 | 52.07±2.46 | 51.12±3.06 | 0.34 |
| Total score | 9.55±2.58 | 18.15±3.81 | <0.001 | 9.38±2.45 | 21.05±4.81 | <0.001 | 9.27±2.52 | 20.31±4.36 | <0.001 |
| BMI | 30.78±3.86 | 28.48±3.87 | 0.13 | 32.39±4.04 | 29.19±4.24 | 0.11 | 29.29±5.32 | 27.43±4.64 | 0.16 |
| SBP | 145.45±10.35 | 146.15±17.70 | 0.78 | 141.75±11.49 | 140.85±13.99 | 0.64 | 140.20±14.40 | 145.84±20.96 | 0.37 |
| DBP | 88.64±8.26 | 84.92±14.67 | 0.53 | 83.50±5.04 | 88.75±7.68 | 0.09 | 81.27±9.89 | 85.53±10.99 | 0.25 |
| Heart rate | 81.27±17.09 | 89.00±18.23 | 0.19 | 78.13±7.68 | 81.10±13.09 | 0.67 | 77.53±5.91 | 78.75±12.40 | 0.85 |
| VLF power | 784.93±778.98 | 892.44±833.60 | 0.53 | 842.06±580.99 | 400.13±233.92 | 0.03 | 588.86±485.70 | 606.18±563.48 | 0.87 |
| LF power | 445.85±549.00 | 396.93±365.35 | 0.65 | 493.21±466.25 | 510.80±943.74 | 0.71 | 299.15±270.93 | 386.66±417.56 | 0.70 |
| LFnu | 0.60±0.15 | 0.55±0.11 | 0.33 | 0.56±0.23 | 0.57±0.14 | 1.00 | 0.64±0.21 | 0.63±0.20 | 0.79 |
| MaxLF | 0.09±0.04 | 0.06±0.02 | 0.03 | 0.05±0.01 | 0.06±0.02 | 0.14 | 0.06±0.01 | 0.07±0.03 | 0.57 |
| HF power | 341.14±490.54 | 469.47±759.97 | 0.42 | 550.70±688.90 | 462.10±1016.54 | 0.64 | 236.30±346.33 | 343.17±578.03 | 0.87 |
| HFnu | 0.40±0.15 | 0.45±0.11 | 0.33 | 0.44±0.23 | 0.43±0.14 | 1.00 | 0.36±0.22 | 0.36±0.16 | 0.84 |
| Maximum HF | 0.25±0.10 | 0.27±0.10 | 0.42 | 0.29±0.10 | 0.27±0.09 | 0.90 | 0.32±0.08 | 0.25±0.08 | 0.01 |
| LF: HF | 1.92±1.39 | 1.31±0.60 | 0.30 | 2.43±3.25 | 1.64±1.05 | 1.00 | 3.06±2.88 | 2.53±1.87 | 0.91 |
| SD1 | 17.66±14.25 | 18.08±19.51 | 0.49 | 24.40±22.65 | 18.22±13.93 | 0.64 | 13.02±8.74 | 17.25±14.44 | 0.29 |
| SD2 | 36.42±24.50 | 33.49±13.12 | 0.49 | 41.55±22.53 | 30.58±13.14 | 0.24 | 29.89±11.47 | 32.22±17.14 | 1.00 |
| SD1:SD2 | 0.51±0.22 | 0.49±0.28 | 0.28 | 0.45±0.25 | 0.57±0.21 | 0.26 | 0.44±0.23 | 0.51±0.23 | 0.17 |
| Mode value | 755.29±149.43 | 689.84±123.25 | 0.23 | 762.62±80.90 | 745.93±73.13 | 0.75 | 766.24±63.43 | 769.81±113.65 | 0.77 |
| Triangular HRV index | 7.86±3.84 | 7.41±2.72 | 1.00 | 7.89±2.75 | 6.65±2.59 | 0.33 | 7.10±2.10 | 7.51±3.54 | 1.00 |
| SDNN | 33.05±22.11 | 30.92±15.49 | 0.65 | 38.01±21.50 | 29.21±15.38 | 0.35 | 26.25±10.48 | 29.62±16.78 | 0.93 |
| RMSSD | 27.13±19.89 | 26.50±25.17 | 0.57 | 38.51±30.04 | 29.21±24.28 | 0.33 | 20.10±11.35 | 25.58±20.32 | 0.30 |
| SDSD | 25.85±20.37 | 24.97±25.75 | 0.57 | 37.48±30.57 | 28.26±25.17 | 0.33 | 18.35±12.13 | 24.17±20.90 | 0.32 |
| NN50 | 19.91±35.06 | 20.69±35.54 | 0.69 | 48.38±63.62 | 22.20±36.80 | 0.60 | 11.60±21.34 | 21.19±37.17 | 0.44 |
| PNN50 | 6.83±11.97 | 6.19±12.09 | 1.00 | 13.75±18.00 | 9.18±16.84 | 0.71 | 3.02±5.39 | 6.46±11.98 | 0.49 |
BMI: Body mass index, SBP: Systolic blood pressure, DBP: Diastolic blood pressure, LF: Low-frequency, VLF: Very LF, HF: High-frequency, SD: Standard deviation, HRV: Heart rate variability, SDNN: SD of all RR interval, RMSSD: Root mean square of successive differences between normal heartbeats, SDSD: SD of successive differences, PSS: Perceived Stress Scale
DISCUSSION
Subjective stress and menopausal transition can adversely affect cardiovascular health in middle-aged women, and it can be studied as an incident event like hypertension. However, one endpoint is cardiac dysautonomia which can be studied by cardiac autonomic function tests. Among these tests, HRV is gaining popularity as it simultaneously measures the balancing of cardiac activity by two divisions of the autonomic nervous system. Reduced HRV is the measure of cardiac autonomic imbalance, which is a forerunner of adverse cardiovascular event. HRV is utilized in our study owing to its objectivity, validity, simplicity, and wide spectrum of analysis in time domain, frequency domain as well as geometric method. In the midlife strata, from pre- to postmenopause, psychological stress based on PSS score and cardiac autonomic stress based on 5 min HRV were studied for association, if any. While comparing pre- versus postmenopasual group, confounding effect of age[8,9] has to be reduced. For that, we kept the age group 40–55 years, excluding 30–40 years premenopausal females and postmenopasual females with age more than 55 years as same is missing in most other studies. Midlife women were divided into three groups so that reproductive health could not stay as a confounder.[10,11] Further grouping was done with reference to the presence of diabetes and/or hypertension that affects the stress[12,13] under study.
Reduced HRV was observed in all premenopausal, peri-menopausal, and postmenopausal groups that did not differ significantly from pre- to postmenopausal groups. Reduced HRV was reflected as low total power, reduced HF power, increased LF power, and high SD1:SD2 ratio. The measure of sympathovagal balance LF: HF ratio was on the higher side in all three groups, indicating cardiac autonomic stress. Negative affect is found to be associated with reduced parasympathetic activity measured throuh HRV as seen in middle-aged study participants.[8] Reduced HRV may be considered a potential biomarker for affective disorders.[14] Hence, we tested its association with PSS-based perceived stress.
PSS score revealed a prevalence of perceived stress in almost 56% midlife women under study, of which high-grade stress was present in 3% of participants only. Grade 1 mild stress was predominant in most study participants (53%). This is lesser than prevalence as reported in another Indian population study.[15] The pattern of prevalent stress grade was 1, 0, 2 in ascending in all three groups under study. The lower prevalence of high stress can be due to the urban sample, age < 55 years, and familial support that reduces stress, as evident in all participants.[16] HRV and PSS reflect two different types of stress that we tested for association.
Testing of PSS to HRV association underscored: (1) lack of differences in HRV parameters between groups, (2) no differences in PSS score between groups, (3) lack of correlation between PSS and HRV in all groups, (4) slightly better correlation of PSS with HRV in diabetic and/or hypertensive than normal, and (5) LF: HF ratio as best HRV parameter.
The lack of correlation between HRV and SPS indicates lack of association between psychological stress and cardiac autonomic stress. The reasons can be: (1) female subjects are more likely to present HRV nonlinear behavior regardless of the age group,[17] (2) participants were of middle age, and stress is more evident in young and old age; alike HRV is also affected by age which was 40–55 years showing lesser effect of age,[18] (3) HRV itself is antropy that may not be correlate with stress,[19] (4) the cardiac autonomic status and psychological stress might not have a direct, rather a complex relationship that is hard to delineate,[20] (5) HRV and PSS can be two different aspects of neurological status, (6) subjective nature of PSS[21] versus objective nature of ECG-based HRV, (7) only 3% women had grade 2 PSS score (high-stress level) so that exact effect of such stress could not be studied on HRV,[22] and (8) 24-h HRV can better delineate any circadian variability.[23]
The lack of HRV to PSS correlation was also evident when groups were divided further into pre-, peri-, or postmenopausal as per STRAW criteria indicating no effect of transition towards attainment of menopause on HRV and stress relationship. PSS score grade based comparison also revealed no HRV differences, and there was no correlation between PSS score and HRV. However, the HRV was higher with participants with diabetes and/or hypertension compared to normal, but the same was absent for PSS score. Diabetes and hypertension are known to affect HRV in line with previous observations,[7,24] but it was not associated with perceived stress. It indicates a greater effect of these diseases on organic changes in cardiac autonomic nerves but no significant effect on subjective stress as perceived. A review on HRV and psychological stress has highlighted that there is impact of psychological stress on sympathovagal balance that is closely reflected by HRV metrics and suggesting that HRV can be used as an informative marker of the physiological effects of psychological stressors in healthy adult populations.[4] However, it also noted that most of the reviewed studies had been performed under laboratory conditions instead of natural working life settings.[4] The same was seen in our participants where HRV – psychological stress relation was not evident in a natural scenario after subject was given a 10 min rest prior HRV testing.
HRV is one out of many cardiac autonomic function tests[25] and PSS is one of the tools used to assess stress levels of a person. HRV is known to be affected by stress, anxiety, and depression, but it was not evident. The reason can be either a lack of correlation between subjective stress and cardiac autonomic stress or limitation of the methods used herewith. It can be further studies in terms of EEG,[26] cortisol level,[27] or other stress tests. Alike, HRV for 24 h can be insinuated to find the association, if any, over the full circadian cycle.
Limitations
Our study was limited by moderate sample size, short-term rather than long-term HRV, lack of cortisol level, the subjectivity of PSS score, and cross-sectional nature of measurement.
CONCLUSION
Women aged 40–55 years women show a lack of association of HRV with perceived subjective stress that is further confounded by diabetes and/or hypertension. The best HRV parameter suggested are HRV total power and LF: HF ratio. Consideration of age, reproductive health group staging, and presence of diabetes and hypertension are also suggested as weak confounders for both HRV and PSS studies of middle-aged women. We also suggest the utility of PSS score for assessing subjective stress and HRV to quantify cardiac autonomic functioning with further vertical and interventional studies for its consolidation.
Ethical approval
Research protocol was prospectively approved from Institutional Review Board of our medical college.
Footnote
Declaration Regarding the Use of Generative Artificial Intelligence (AI): No AI tools were used for data analysis and manuscript preparation. Authors assume full responsibility for the entire content of the manuscript.
Data availability statement
The data of the current research can be obtained from the corresponding author on making a reasonable request.
Conflicts of interest
There are no conflicts of interest.
Funding Statement
Nil.
REFERENCES
- 1.Kuck MJ, Hogervorst E. Stress, depression, and anxiety: Psychological complaints across menopausal stages. Front Psychiatry. 2024;15:1323743. doi: 10.3389/fpsyt.2024.1323743. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Brimienė I, Šiaudinytė M, Burokas A, Grikšienė R. Exploration of the association between menopausal symptoms, gastrointestinal symptoms, and perceived stress: Survey-based analysis. Menopause. 2023;30:1124–31. doi: 10.1097/GME.0000000000002259. [DOI] [PubMed] [Google Scholar]
- 3.Iqbal J, Huang GD, Xue YX, Yang M, Jia XJ. Role of estrogen in sex differences in memory, emotion and neuropsychiatric disorders. Mol Biol Rep. 2024;51:415. doi: 10.1007/s11033-024-09374-z. [DOI] [PubMed] [Google Scholar]
- 4.Immanuel S, Teferra MN, Baumert M, Bidargaddi N. Heart rate variability for evaluating psychological stress changes in healthy adults: A scoping review. Neuropsychobiology. 2023;82:187–202. doi: 10.1159/000530376. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Soules MR, Sherman S, Parrott E, Rebar R, Santoro N, Utian W, et al. Stages of reproductive aging workshop (STRAW) J Womens Health Gend Based Med. 2001;10:843–8. doi: 10.1089/152460901753285732. [DOI] [PubMed] [Google Scholar]
- 6.Georgieva-Tsaneva G, Gospodinova E, Cheshmedzhiev K. Examination of cardiac activity with ECG monitoring using heart rate variability methods. Diagnostics (Basel) 2024;14:926. doi: 10.3390/diagnostics14090926. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Solanki JD, Hirani CN, Vohra AS, Panjwani SJ, Senta VM, Rudani DK. A comparative cross-sectional study of cardiac autonomic status by five minute heart rate variability among type 2 diabetics, hypertensives and normotensive-nondiabetics. Niger Med J. 2023;64:373–81. [PMC free article] [PubMed] [Google Scholar]
- 8.Accardo A, Merlo M, Silveri G, Del Popolo L, Dalla Libera L, Restivo L, et al. Influence of ageing on circadian rhythm of heart rate variability in healthy subjects. J Cardiovasc Med (Hagerstown) 2021;22:405–13. doi: 10.2459/JCM.0000000000001048. [DOI] [PubMed] [Google Scholar]
- 9.Sarkar SM, Dhar BK, Crowley SS, Ayittey FK, Gazi MA. Psychological adjustment and guidance for ageing urban women. Ageing Int. 2023;48:222–30. doi: 10.1007/s12126-021-09467-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Ramesh S, James MT, Holroyd-Leduc JM, Wilton SB, Sola DY, Ahmed SB. Heart rate variability as a function of menopausal status, menstrual cycle phase, and estradiol level. Physiol Rep. 2022;10:e15298. doi: 10.14814/phy2.15298. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Haritha YD, Bilquis B. Stress among women during menopause-impact on mental health. J Adv Biol Biotechnol. 2024;27:776–81. [Google Scholar]
- 12.Ryczkowska K, Adach W, Janikowski K, Banach M, Bielecka-Dabrowa A. Menopause and women's cardiovascular health: Is it really an obvious relationship? Arch Med Sci. 2023;19:458–66. doi: 10.5114/aoms/157308. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Philbois SV, Facioli TP, De Lucca I, Veiga AC, Chinellato N, Simões MV, et al. What do we know about the role of menopause in cardiovascular autonomic regulation in hypertensive women? Menopause. 2024;31:408–14. doi: 10.1097/GME.0000000000002348. [DOI] [PubMed] [Google Scholar]
- 14.Gullett N, Zajkowska Z, Walsh A, Harper R, Mondelli V. Heart rate variability (HRV) as a way to understand associations between the autonomic nervous system (ANS) and affective states: A critical review of the literature. Int J Psychophysiol. 2023;192:35–42. doi: 10.1016/j.ijpsycho.2023.08.001. [DOI] [PubMed] [Google Scholar]
- 15.Satpathy M, Lenka C. Stress a hidden problem in the life of menopausal women. Int J Indian Psychol. 2020;8:938–47. [Google Scholar]
- 16.Jain M, Solanki S, Khare D. Determinants of mid-life health among rural women of Central India: A cross-sectional study. People. 2021;14:08. [Google Scholar]
- 17.Calderón-Juárez M, González-Gómez GH, Echeverría JC, Lerma C. Revisiting nonlinearity of heart rate variability in healthy aging. Sci Rep. 2023;13:13185. doi: 10.1038/s41598-023-40385-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Sarveswaran G, Jayaseelan V, Krishnamoorthy Y, Sakthivel M, Arivarasan Y, Vijayakumar K, et al. Perceived stress and its determinants among postmenopausal women in urban Puducherry. J Midlife Health. 2021;12:33–8. doi: 10.4103/jmh.JMH_127_20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Lombardi F, Huikuri H, Schmidt G, Malik M E-Rhythm Study Group of European Heart Rhythm Association. Short-term heart rate variability: Easy to measure, difficult to interpret. Heart Rhythm. 2018;15:1559–60. doi: 10.1016/j.hrthm.2018.05.023. [DOI] [PubMed] [Google Scholar]
- 20.O'Riordan A, Howard S, Gallagher S. Blunted cardiovascular reactivity to psychological stress and prospective health: A systematic review. Health Psychol Rev. 2023;17:121–47. doi: 10.1080/17437199.2022.2068639. [DOI] [PubMed] [Google Scholar]
- 21.Harris KM, Gaffey AE, Schwartz JE, Krantz DS, Burg MM. The perceived stress scale as a measure of stress: Decomposing score variance in longitudinal behavioral medicine studies. Ann Behav Med. 2023;57:846–54. doi: 10.1093/abm/kaad015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Farhan A, Lyazidi A, Labakoum B, Rattal M, Mouhsen A. Impact of heart rate variability on physiological stress: Systematic review. Biomed Pharmacol J. 2023;16:997–1010. [Google Scholar]
- 23.Hayano J, Yuda E. Assessment of autonomic function by long-term heart rate variability: Beyond the classical framework of LF and HF measurements. J Physiol Anthropol. 2021;40:21. doi: 10.1186/s40101-021-00272-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Solanki JD, Basida SD, Mehta HB, Panjwani SJ, Gadhavi BP. Comparative study of cardiac autonomic status by heart rate variability between under-treatment normotensive and hypertensive known type 2 diabetics. Indian Heart J. 2017;69:52–6. doi: 10.1016/j.ihj.2016.07.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Cheshire WP, Freeman R, Gibbons CH, Cortelli P, Wenning GK, Hilz MJ, et al. Electrodiagnostic assessment of the autonomic nervous system: A consensus statement endorsed by the American Autonomic Society, American Academy of Neurology, and the International Federation of Clinical Neurophysiology. Clin Neurophysiol. 2021;132:666–82. doi: 10.1016/j.clinph.2020.11.024. [DOI] [PubMed] [Google Scholar]
- 26.Attar ET, Balasubramanian V, Subasi E, Kaya M. Stress analysis based on simultaneous heart rate variability and EEG monitoring. IEEE J Transl Eng Health Med. 2021;9:2700607. doi: 10.1109/JTEHM.2021.3106803. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Ding Y, Tan K, Sheng L, Ren H, Su Z, Yang H, et al. Integrated mental stress smartwatch based on sweat cortisol and HRV sensors. Biosens Bioelectron. 2024;265:116691. doi: 10.1016/j.bios.2024.116691. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data of the current research can be obtained from the corresponding author on making a reasonable request.