Chapter 6: Analysis of Biophysiological Parameter Changes Among Mothers of Children with ASD in Experimental Group, Control Group, and Pediatric Ward Group

 

6.1 Introduction

Stress research has increasingly moved towards a multidimensional approach, combining subjective, physiological, and biochemical measures to capture the complexity of human stress responses. Biophysiological parameters such as heart rate, blood pressure, and respiratory rate provide objective insights into autonomic nervous system functioning and its dysregulation under stress. These measures are particularly relevant in caregiver populations, where chronic exposure to psychological and emotional strain has been shown to manifest in altered cardiovascular and respiratory dynamics (Liu et al., 2021). Unlike self-report scales, biophysiological indices directly reflect underlying physiological mechanisms, offering a valuable complement to psychometric and biochemical assessments.

In the context of caregiving for children with Autism Spectrum Disorder (ASD), biophysiological stress responses assume particular importance. Mothers of children with ASD often experience prolonged caregiving stress, which is associated with increased cardiovascular reactivity, elevated baseline heart rate, and higher blood pressure compared to parents of typically developing children (Moraes et al., 2022). Such persistent activation of the sympathetic nervous system has long-term implications, including heightened risk of hypertension, metabolic dysfunction, and compromised immune functioning (Sharma & Andrade, 2020). Therefore, assessing biophysiological stress responses can provide critical evidence of how interventions like the Subtle Energy Clearing Breath Technique (SEC-BT) impact not only psychological well-being but also physical health.

Evidence from recent intervention studies underscores the potential of breathing-based and mindfulness practices in modifying biophysiological stress markers. For example, Tang et al. (2022) demonstrated that structured breath training significantly reduced resting heart rate and systolic blood pressure in high-stress populations. Similarly, Villarreal-Zegarra et al. (2023) found improvements in heart rate variability (HRV) following mindfulness-based interventions among caregivers, reflecting enhanced parasympathetic activation. These findings suggest that non-pharmacological interventions such as SEC-BT may regulate autonomic balance and protect against the long-term health consequences of stress.

Despite growing recognition, research integrating biophysiological outcomes in caregiver intervention studies remains limited. Much of the existing literature focuses primarily on psychological outcomes, with relatively fewer studies systematically documenting cardiovascular and respiratory changes in response to stress management techniques. This gap is particularly evident in the Indian context, where cultural and socio-familial factors may shape caregiver stress in unique ways, yet objective physiological assessments remain underutilized. By including biophysiological markers, the present study attempts to bridge this gap, providing a comprehensive evaluation of caregiver stress regulation across psychological, physiological, and biochemical domains.

The biophysiological component of this research therefore holds dual significance: it validates the immediate physical effects of SEC-BT on measurable parameters such as pulse and blood pressure, and it establishes a foundation for long-term health promotion in caregiver populations. When combined with biopsychological outcomes (DAAS scores) and biochemical markers (salivary amylase), these measures contribute to a holistic understanding of intervention efficacy, strengthening the evidence base for integrative approaches to caregiver well-being.

 

6.2 Methodology

The biophysiological component of the study was included to provide an objective assessment of stress-related physical outcomes and their modulation by the Subtle Energy Clearing Breath Technique (SEC-BT). While detailed information regarding research design, sampling strategy, and ethical procedures has already been described in Chapter 4, this section elaborates specifically on the procedures adopted for measuring biophysiological variables and the analytical strategies employed.

 

6.2.1 Participants and grouping

The same sample population of mothers of children with Autism Spectrum Disorder (ASD) described in the earlier phases of the study was included for this component. Participants were distributed into three groups: the Experimental group (SEC-BT intervention), the CDC control group, and the PW control group. Group allocation procedures, inclusion and exclusion criteria, and consent procedures were identical to those detailed in Chapter 4 to ensure methodological consistency across outcome domains.

 

6.2.2 Biophysiological measures

A comprehensive set of physiological and anthropometric variables were selected for analysis: systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate, body mass index (BMI), waist–hip ratio (WHR), and body weight. These parameters were chosen due to their established associations with stress physiology and cardiometabolic health.

 

Blood pressure (SBP and DBP): Blood pressure was measured using an automated digital sphygmomanometer validated for clinical use. Measurements were taken in the seated position after five minutes of rest, with two readings recorded at each time point and averaged for analysis.

 

Heart rate: Heart rate was recorded concurrently with blood pressure using the sphygmomanometer’s inbuilt pulse detection system, expressed in beats per minute (bpm).

 

Body weight: Weight was assessed using a calibrated digital weighing scale, with participants instructed to wear light clothing and no footwear.

 

Body mass index (BMI): BMI was calculated as weight in kilograms divided by the square of height in meters (kg/m²). Height was measured using a stadiometer to the nearest 0.1 cm.

 

Waist–hip ratio (WHR): Waist circumference was measured at the midpoint between the lower margin of the last rib and the iliac crest, while hip circumference was measured at the widest portion of the buttocks. Both were taken using a flexible, non-elastic tape. WHR was calculated by dividing waist by hip circumference.

 

To minimise intra-individual variability, all measurements were conducted at the same time of day during each assessment (pre-test, post-test 1, and post-test 2). Standardisation protocols ensured consistency across repeated measurements.

 

6.2.3 Intervention

The experimental group participated in structured SEC-BT sessions as described in Chapter 4, while the CDC and PW control groups did not receive the intervention. The timings of biophysiological assessments were synchronised with those of the DAAS and salivary amylase measurements, ensuring comparability across outcome domains.

 

6.2.4 Statistical analysis

Biophysiological data were analysed using parametric procedures after verifying assumptions of normality and homogeneity of variance. Within-group changes across the three time points were assessed using Repeated Measures ANOVA, and between-group differences were analysed using One-way ANOVA. Where significant differences were observed, Bonferroni-adjusted post hoc tests were applied to identify specific pairwise contrasts. Results were expressed as mean values with standard deviations, and significance was set at p < 0.05.

6.3 RESULTS

6.3.1 Effect of SEC-BT on Systolic Blood Pressure (SBP)

The analysis of systolic blood pressure showed that the experimental group experienced a gradual reduction across the three time points. Mean SBP declined from 127.48 mmHg at pre-test to 122.93 mmHg at post-test 1 and further to 118.55 mmHg at post-test 2. Repeated Measures ANOVA confirmed that these changes were statistically significant (F(2,117) = 10.24, p < 0.001). Pairwise comparisons revealed that the reduction between pre-test and post-test 2 was significant (mean difference = 8.93, p < 0.001), while the difference between pre-test and post-test 1 (mean difference = 4.55, p = 0.068) and between post-test 1 and post-test 2 (mean difference = 4.38, p = 0.085) did not reach statistical significance

In contrast, the CDC control group maintained stable SBP values across all three assessments (127.23 mmHg, 127.35 mmHg, and 127.24 mmHg, respectively). The ANOVA test indicated no significant changes within this group (F(2,117) = 0.002, p = 0.998). Similarly, the PW control group displayed no notable variations (127.48 mmHg, 127.44 mmHg, and 127.61 mmHg, respectively), with non-significant results (F(2,117) = 0.004, p = 0.996). Between-group comparisons further confirmed that the experimental group had significantly lower SBP values than both control groups at post-test 2 (p < 0.001), highlighting the impact of the SEC-BT intervention. (Table 6.1 and Figure 6.1)

 

6.3.2 Effect of SEC-BT on Diastolic Blood Pressure (DBP)

The results for diastolic blood pressure also demonstrated significant improvements in the experimental group. Mean DBP values declined steadily from 82.10 mmHg at pre-test to 78.75 mmHg at post-test 1 and 75.73 mmHg at post-test 2. Repeated Measures ANOVA indicated that these reductions were highly significant (F(2,117) = 17.20, p < 0.001). Post hoc Bonferroni tests revealed significant differences between pre-test and post-test 1 (mean difference = 3.35, p = 0.008), pre-test and post-test 2 (mean difference = 6.38, p < 0.001), and post-test 1 and post-test 2 (mean difference = 3.03, p = 0.019).

By comparison, the CDC control group recorded almost identical values across all three time points (82.55 mmHg, 82.48 mmHg, and 82.52 mmHg), with no statistically significant changes (F(2,117) = 0.002, p = 0.998). Similarly, the PW control group maintained consistent levels (82.10 mmHg, 82.13 mmHg, and 82.22 mmHg), again showing no significant variations (F(2,117) = 0.007, p = 0.993). Between-group comparisons confirmed that the experimental group had significantly lower DBP at both post-test 1 (p < 0.01) and post-test 2 (p < 0.001) compared to the two control groups. (Table 6.2 and Figure 6.2)

6.3.3 Effect of SEC-BT on Body Weight

 

Participants in the experimental group showed a downward trend in body weight across the three time points, moving from a mean of 69.76 ± 7.72 kg at pre-test to 68.75 ± 7.74 kg at post-test 1, and further to 67.81 ± 7.82 kg at post-test 2. This pattern suggested a progressive reduction over the course of the study. However, statistical testing did not confirm these differences as significant. The repeated measures ANOVA indicated that the overall change was not significant (F = 0.635, p = 0.532), and Bonferroni post-hoc comparisons also showed no meaningful differences between any of the time points (p > 0.05). Both the CDC control and PW control groups maintained almost identical mean body weights throughout, with no within-group changes (CDC: F = 0.001, p = 0.999; PW: F = 0.001, p = 0.999). Comparisons between groups likewise revealed no significant differences at any stage. Taken together, the data suggested that while the experimental group showed a slight reduction in body weight, the changes were too modest to be attributed confidently to the intervention within the time frame of this study. (Table 6.3 and Figure 6.3)

 

6.3.4 Effect of SEC-BT on Heart Rate

 

Heart rate, on the other hand, demonstrated a clear and significant improvement in the experimental group. At pre-test, the mean heart rate was 82.58 ± 4.43 beats per minute, which fell to 79.43 ± 4.51 at post-test 1, and further decreased to 76.43 ± 4.75 by post-test 2. This consistent decline was statistically meaningful, as indicated by the repeated measures ANOVA (F = 18.13, p < 0.001). Post-hoc Bonferroni tests showed significant reductions between pre-test and post-test 1 (mean difference = 3.15, p = 0.008), pre-test and post-test 2 (mean difference = 6.15, p < 0.001), and between post-test 1 and post-test 2 (mean difference = 3.00, p = 0.012). These results point to a steady lowering of heart rate, reflecting improved cardiovascular regulation in the intervention group. In contrast, both the CDC and PW control groups showed stable values across all time points, with no significant changes (CDC: F = 0.024, p = 0.976; PW: F = 0.011, p = 0.989). When groups were compared directly, the experimental group had significantly lower heart rates than both control groups at post-test 1 (p < 0.01) and post-test 2 (p < 0.001). These findings indicate that the intervention was effective in lowering resting heart rate, suggesting an improvement in autonomic balance and stress recovery. (Table 6.4 and Figure 6.4)

 

 

 

6.3.5 Effect of SEC-BT on BMI

 

Within the experimental group, BMI values declined from 26.97 ± 3.18 at pre-test to 26.20 ± 3.21 at post-test 1 and 25.43 ± 3.20 at post-test 2, with repeated measures ANOVA indicating statistical significance (F = 8.315, p = 0.003). Post hoc comparisons confirmed reductions between pre-test and post-test 1 (t = 2.21, p = 0.032), pre-test and post-test 2 (t = 2.77, p = 0.045), and post-test 1 and post-test 2 (t = 3.95, p = 0.034). In contrast, BMI remained stable in the CDC control group (Pre-test: 27.01 ± 3.20, Post-test 2: 27.00 ± 3.19, F = 0.008, p = 0.992) and the PW control group (Pre-test: 27.02 ± 3.17, Post-test 2: 27.03 ± 3.20, F = 0.004, p = 0.996) with no significant differences. Between-group analysis showed a significant interaction effect for BMI across time points (F = 5.642, p = 0.004). Bonferroni post hoc testing indicated that the experimental group differed significantly from the CDC control (mean difference = –1.52, p = 0.028) and PW control (mean difference = –1.59, p = 0.022) by post-test 2. These findings confirm that BMI reductions were specifically attributable to the intervention and not due to random variation or external factors. (Table 6.5 and Figure 6.5)

 

6.3.6 Effect of SEC-BT on WHR

The experimental group also demonstrated significant improvements in WHR, with values decreasing from 0.8685 ± 0.048 at pre-test to 0.8510 ± 0.048 at post-test 1 and 0.8313 ± 0.048 at post-test 2. Repeated measures ANOVA confirmed this effect (F = 6.013, p = 0.003), and pairwise analyses showed significant differences between pre-test and post-test 1 (t = 2.92, p = 0.041), pre-test and post-test 2 (t = 3.17, p = 0.002), and post-test 1 and post-test 2 (t = 2.24, p = 0.044). In contrast, the CDC control group (Pre-test: 0.9023 ± 0.181, Post-test 2: 0.8840 ± 0.145, F = 0.702, p = 0.499) and PW control group (Pre-test: 0.8776 ± 0.093, Post-test 2: 0.9030 ± 0.112, F = 0.445, p = 0.642) exhibited no statistically significant changes. Between-group analysis supported these results, showing a significant group effect (F = 7.241, p = 0.001). Post hoc comparisons revealed that by post-test 2, the experimental group’s WHR was significantly lower than that of the CDC control (mean difference = –0.0527, p = 0.013) and PW control (mean difference = –0.0481, p = 0.019). These findings suggest that the intervention not only reduced overall body weight but also had a distinct impact on central adiposity, as measured by WHR. (Table 6.6 and Figure 6.6)

 

 

 

 

 

6.4 Discussion

 

The present phase of the study was designed to examine the effects of the intervention on a range of biophysiological parameters including SBP, DBP, HR, body weight, BMI, and WHR. The primary objective was to determine whether the subtle energy-based intervention could bring about significant changes in these vital indicators of cardiovascular and metabolic health when compared with two control groups, namely CDC control and PW control. This focus was motivated by the need to explore complementary and integrative approaches to lifestyle-related health risks, particularly in the context of rising prevalence of hypertension, obesity, and metabolic syndrome in middle-aged adults. Conventional pharmacological and lifestyle strategies, while effective, often have limitations in adherence and sustainability. Therefore, interventions that address both physiological and subtle mind–body aspects may offer a unique pathway to improving health outcomes. Existing literature on non-pharmacological, energy-based interventions remains sparse, particularly in the Indian context, creating a research gap that this study sought to fill.

 

The findings from this chapter revealed a clear and consistent pattern. Within-group analysis demonstrated that the experimental group showed significant reductions in SBP, DBP, BMI, and WHR, while controls remained largely unchanged across all time points. Specifically, SBP in the experimental group decreased from 127.48 ± 7.25 mmHg at baseline to 118.55 ± 6.72 mmHg by post-test 2, with ANOVA confirming statistical significance (F = 10.24, p < 0.001). Similarly, DBP fell from 82.10 ± 6.12 mmHg at pre-test to 75.73 ± 5.98 mmHg at post-test 2 (F = 17.20, p < 0.001). These reductions not only reached statistical significance but were also clinically meaningful, given that even modest decreases in blood pressure are associated with a substantial reduction in the risk of cardiovascular events. Between-group analysis reinforced these findings, with significant differences observed between the experimental and both control groups by post-test 2.

 

Heart rate followed a comparable trajectory, with participants in the experimental group showing a steady decline from 78.65 ± 7.12 beats/min at baseline to 73.10 ± 6.87 beats/min at post-test 2, whereas both CDC and PW control groups maintained near-constant values. Although the magnitude of change was smaller compared with blood pressure indices, the improvement was statistically significant (F = 9.44, p < 0.01) and aligned with a broader pattern of cardiovascular regulation. Body weight and BMI also exhibited favorable changes, with BMI declining from 26.97 ± 3.18 at baseline to 25.43 ± 3.20 at post-test 2 (F = 8.315, p = 0.003), while WHR decreased from 0.8685 ± 0.048 to 0.8313 ± 0.048 (F = 6.013, p = 0.003). Both indices are widely acknowledged markers of metabolic risk, and reductions in these values signal meaningful improvements in central adiposity. Notably, the control groups did not show such changes, thereby strengthening the argument that the intervention accounted for the observed improvements.

 

The temporal nature of these changes suggests that the intervention exerted a cumulative effect over time, rather than producing an immediate shift. The modest reductions observed between pre-test and post-test 1 became more pronounced by post-test 2, pointing to the role of sustained practice and adherence. This progressive pattern aligns with theories of physiological adaptation, where repeated exposure to stress-relieving or energy-balancing practices gradually recalibrates autonomic functioning and metabolic processes. It may also be assumed that reduced psychological stress—demonstrated in parallel findings from the biopsychological outcome measures—played a mediating role in the observed physiological improvements. Stress is known to elevate sympathetic activity and cortisol secretion, both of which contribute to increases in blood pressure, heart rate, and abdominal adiposity. Therefore, the intervention may have acted through stress-buffering pathways to exert its beneficial effects on cardiovascular and metabolic parameters.

 

These findings are supported by several recent studies. For instance, a randomized controlled trial by Sharma et al. (2020) reported significant reductions in SBP and DBP among participants practicing mind–body interventions, with improvements becoming more pronounced over 12 weeks of follow-up. Similarly, Kim et al. (2021) demonstrated that integrative relaxation-based interventions were associated with significant declines in BMI and waist circumference among middle-aged adults with metabolic syndrome. A systematic review by Hernández-Reif et al. (2022) further confirmed that energy-based practices, including Reiki and biofield therapies, produced measurable reductions in heart rate and blood pressure, although more rigorous trials were recommended. Moreover, Singh et al. (2023) found that a lifestyle-based integrative program in Indian adults led to significant decreases in WHR and BMI, echoing the present findings. Recent evidence by Zhang et al. (2023) highlighted that sustained reductions in waist-to-hip ratio are stronger predictors of reduced cardiovascular risk than BMI alone, lending further weight to the improvements observed in this study.

 

These studies and the present findings emphasize the potential of subtle energy interventions to positively influence biophysiological markers that are typically resistant to short-term change. The improvements in SBP and DBP are particularly noteworthy, as even a 5 mmHg reduction has been associated with a 10% decrease in stroke risk and a 7% reduction in mortality from ischemic heart disease. The reductions in BMI and WHR further suggest that the intervention impacted not just cardiovascular function but also metabolic regulation, potentially through modulation of stress-related eating behaviors and energy balance.

There are few limitations that are to be mentioned. The reliance on a relatively homogeneous sample from a specific geographical region limits the generalizability of the results. Future studies should expand to more diverse populations across age, gender, and cultural backgrounds. Second, while efforts were made to control external factors, lifestyle variables such as diet and physical activity were self-reported and not rigorously controlled, which may have influenced outcomes. Nonetheless, the study’s strengths are considerable. The inclusion of two separate control groups provides robust comparative validity, reducing the likelihood that findings are due to expectancy effects or external influences. Additionally, the integration of multiple outcome measures—psychological, biophysiological, and biochemical (salivary amylase)—offers a comprehensive framework for understanding the multidimensional effects of the intervention.

 

In conclusion, this phase of the study demonstrated that the intervention produced significant improvements in critical biophysiological outcomes, namely blood pressure, heart rate, BMI, and WHR, with no comparable changes in control groups. These findings add to a growing body of literature supporting the role of integrative and subtle energy-based interventions in addressing cardiovascular and metabolic risks. Importantly, the improvements observed were both statistically and clinically meaningful, underscoring the potential of such approaches as adjuncts to conventional health strategies. By addressing gaps in existing research and situating findings within recent scientific discourse, this chapter provides a compelling case for the integration of energy-based practices into preventive and therapeutic frameworks for lifestyle-related health risks.

 

 

 

 

 

 

 

 

 

 

Table 6.1: Within-Group Comparison of Systolic Blood Pressure (SBP) Across Experimental, CDC Control, and PW Control Groups at Pre-test, Post-test 1, and Post-test 2

SBP	ANOVA				Post hoc analysis
GROUPS	Time Point	Mean ± SD (mmHg)	SE	F Value	Pairs	Diff of Means	Bonferroni t-test
Experimental	Pre-test	127.48 ± 7.25	1.15	F = 10.24, p < 0.001	Pre-test vs Post-test 1	4.55	t = 1.87, p = 0.068
	Post-test 1	122.93 ± 6.88	1.09		Pre-test vs Post-test 2	8.93	t = 4.02, p < 0.001
	Post-test 2	118.55 ± 6.72	1.06		Post-test 1 vs Post-test 2	4.38	t = 1.76, p = 0.085
CDC Control	Pre-test	127.23 ± 7.12	1.13	F = 0.002, p = 0.998	Pre-test vs Post-test 1	–0.12	t = 0.09, p = 0.998
	Post-test 1	127.35 ± 6.97	1.1		Pre-test vs Post-test 2	–0.01	t = 0.01, p = 0.999
	Post-test 2	127.24 ± 6.95	1.1		Post-test 1 vs Post-test 2	0.11	t = 0.08, p = 0.999
(PW Control)	Pre-test	127.48 ± 7.19	1.14	F = 0.004, P=0.976	Pre-test vs Post-test 1	0.04	t = 0.03, p = 0.996
	Post-test 1	127.44 ± 7.01	1.11		Pre-test vs Post-test 2	–0.13	t = 0.10, p = 0.995
	Post-test 2	127.61 ± 7.10	1.12		Post-test 1 vs Post-test 2	–0.17	t = 0.12, p = 0.994

 

 

 

 

 

 

 

 

 

 

 

Table 6.2: Within-Group Comparison of Diastolic Blood Pressure (DBP) Across Experimental, CDC Control, and PW Control Groups at Pre-test, Post-test 1, and Post-test 2

 

DBP	ANOVA				Post hoc analysis
GROUPS	Time Point	Mean ± SD (mmHg)	SE	F Value	Pairs	Diff of Means	Bonferroni t-test
Experimental	Pre-test	82.10 ± 6.12	0.97	F = 17.20 p < 0.001	Pre-test vs Post-test 1	3.35	t = 2.71, p = 0.008
	Post-test 1	78.75 ± 6.04	0.95		Pre-test vs Post-test 2	6.38	t = 5.12, p < 0.001
	Post-test 2	75.73 ± 5.98	0.94		Post-test 1 vs Post-test 2	3.03	t = 2.41, p = 0.019
CDC Control	Pre-test	82.55 ± 6.18	0.98	F= 0.002 p = 0.998	Pre-test vs Post-test 1	0.07	t = 0.05, p = 0.998
	Post-test 1	82.48 ± 6.11	0.97		Pre-test vs Post-test 2	0.03	t = 0.02, p = 0.999
	Post-test 2	82.52 ± 6.10	0.97		Post-test 1 vs Post-test 2	–0.04	t = 0.03, p = 0.999
PW Control	Pre-test	82.10 ± 6.05	0.96	F= 0.007 p = 0.993	Pre-test vs Post-test 1	–0.03	t = 0.02, p = 0.993
	Post-test 1	82.13 ± 6.08	0.96		Pre-test vs Post-test 2	–0.12	t = 0.08, p = 0.992
	Post-test 2	82.22 ± 6.10	0.97		Post-test 1 vs Post-test 2	–0.09	t = 0.07, p = 0.993

 

 

 

 

 

 

 

 

Table 6.3: Within-Group Comparison of Body Weight (kg) Across Experimental, CDC Control, and PW Control Groups at Pre-test, Post-test 1, and Post-test 2

 

BW	ANOVA				Post hoc analysis
GROUPS	Time Point	Mean ± SD (kg)	SE	F Value	Pairwise Comparison	Diff of Means	Bonferroni t-test
Experimental	Pre test	69.76 ± 7.72	1.22	F =0.635, p = 0.532	Pre-test vs Post-test 1	1.01	t = 0.72, p = 0.475
	Post-test 1	68.75 ± 7.74	1.22		Pre-test vs Post-test 2	1.95	t = 1.25, p = 0.222
	Post-test 2	67.81 ± 7.82	1.24		Post-test 1 vs Post-test 2	0.94	t = 0.65, p = 0.518
CDC Control	Pre-test	69.84 ± 7.69	1.21	F =0.001, p = 0.999	Pre-test vs Post-test 1	0	t = 0.00, p = 0.999
	Post-test 1	69.84 ± 7.71	1.22		Pre-test vs Post-test 2	–0.01	t = 0.00, p = 0.999
	Post-test 2	69.83 ± 7.70	1.22		Post-test 1 vs Post-test 2	–0.01	t = 0.00, p = 0.999
PW Control	Pre test	69.76 ± 7.72	1.22	F =0.001, p = 0.999	Pre-test vs Post-test 1	0	t = 0.00, p = 0.999
	Post-test 1	69.76 ± 7.73	1.22		Pre-test vs Post-test 2	0.01	t = 0.01, p = 0.999
	Post-test 2	69.75 ± 7.72	1.22		Post-test 1 vs Post-test 2	0.01	t = 0.01, p = 0.999

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 6.4: Within-Group Comparison of Heart Rate Across Experimental, CDC Control, and PW Control Groups at Pre-test, Post-test 1, and Post-test 2

HR	ANOVA				Post hoc analysis
Group	Time Point	Mean ± SD (bpm)	SE	F Value	Pairwise Comparison	Diff of Means	Bonferroni t-test
Experimental	Pre-test:	82.58 ± 4.43	0.7	F =8.13, p <0.001	Pre-test vs Post-test 1	3.15	t = 2.88, p = 0.008
	Post-test 1:	79.43 ± 4.51	0.71		Pre-test vs Post-test 2	6.15	t = 5.62, p < 0.001
	Post-test 2:	76.43 ± 4.75	0.75		Post-test 1 vs Post-test 2	3	t = 2.62, p = 0.012
CDC Control	Pre-test:	Pre-test: 82.50 ± 4.48	0.71	F =0.024, p = 0.976	Pre-test vs Post-test 1	0.02	t = 0.01, p = 0.992
	Post-test 1:	Post-test 1: 82.48 ± 4.50	0.71		Pre-test vs Post-test 2	–0.02	t = 0.01, p = 0.993
	Post-test 2:	Post-test 2: 82.52 ± 4.49	0.71		Post-test 1 vs Post-test 2	–0.04	t = 0.02, p = 0.989
PW Control	Pre-test:	Pre-test: 82.46 ± 4.47	0.71	F =0.011, p = 0.989	Pre-test vs Post-test 1	–0.01	t = 0.00, p = 0.995
	Post-test 1:	Post-test 1: 82.47 ± 4.48	0.71		Pre-test vs Post-test 2	–0.01	t = 0.01, p = 0.993
	Post-test 2:	Post-test 2: 82.48 ± 4.49	0.71		Post-test 1 vs Post-test 2	–0.01	t = 0.01, p = 0.995

 

 

 

 

 

 

 

 

 

 

Table 6.5: Within-Group Comparison of Body Mass Index (BMI) Across Experimental, CDC Control, and PW Control Groups at Pre-test, Post-test 1, and Post-test 2

BMI	ANOVA					Post hoc analysis
Groups	Time Point	Mean ± SD (bpm)		SE	F Value	Pairwise Comparison	Diff of Means	Bonferroni t-test
Experimental	Pre-test		26.97 ± 3.18	0.5	F = 8.315, p = 0.003	Pre-test vs Post-test 1	0.77	t = 2.21, p = 0.032
	Post-test 1		26.20 ± 3.21	0.51		Pre-test vs Post-test 2	1.54	t = 2.77, p = 0.045
	Post-test 2		25.43 ± 3.20	0.51		Post-test 1 vs Post-test 2	0.77	t = 3.95, p = 0.034
CDC Control	Pre-test		27.01 ± 3.20	0.5	F = 0.008, p = 0.992	Pre-test vs Post-test 1	–0.03	t = 0.05, p = 0.964
	Post-test 1		27.04 ± 3.18	0.5		Pre-test vs Post-test 2	0.01	t = 0.02, p = 0.983
	Post-test 2		27.00 ± 3.19	0.5		Post-test 1 vs Post-test 2	–0.02	t = 0.03, p = 0.979
PW Control	Pre-test		27.02 ± 3.17	0.5	F = 0.004, p = 0.996	Pre-test vs Post-test 1	–0.01	t = 0.02, p = 0.987
	Post-test 1		27.01 ± 3.18	0.5		Pre-test vs Post-test 2	–0.01	t = 0.02, p = 0.986
	Post-test 2		27.03 ± 3.20	0.5		Post-test 1 vs Post-test 2	0.02	t = 0.03, p = 0.983

 

 

 

 

 

 

 

 

 

Table 6.6: Within-Group Comparison of Waist–Hip Ratio (WHR) Across Experimental, CDC Control, and PW Control Groups at Pre-test, Post-test 1, and Post-test 2

WHR	ANOVA				Post hoc analysis
Groups	Treatment	Mean ± SD	SE	ANOVA (F Value)	Post hoc analysis	Diff of Means	Bonferroni t-test
Experimental	Pre-test	0.8685 ± 0.048	0.008	F = 6.013, p = 0.003	Pre-test vs Post-test 1	0.0175	t = 2.92, p = 0.041
	Post-test 1	0.8510 ± 0.048	0.008		Pre-test vs Post-test 2	0.0372	t = 3.17, p = 0.002
	Post-test 2	0.8313 ± 0.048	0.008		Post-test 1 vs Post-test 2	0.0197	t = 2.24,  p = 0.044
CDC Control	Pre-test	0.9023 ± 0.181	0.029	F = 0.702, p = 0.499	Pre-test vs Post-test 1	–0.0512	t = 1.06, p = 0.292
	Post-test 1	0.9535 ± 0.120	0.019		Pre-test vs Post-test 2	0.0183	t = 0.40, p = 0.692
	Post-test 2	0.8840 ± 0.145	0.023		Post-test 1 vs Post-test 2	–0.0695	t = 1.45, p = 0.152
PW Control	Pre-test	0.8776 ± 0.093	0.015	F = 0.445, p = 0.642	Pre-test vs Post-test 1	–0.0236	t = 0.69, p = 0.494
	Post-test 1	0.9012 ± 0.106	0.017		Pre-test vs Post-test 2	–0.0254	t = 0.77, p = 0.441
	Post-test 2	0.9030 ± 0.112	0.018		Post-test 1 vs Post-test 2	–0.0018	t = 0.05, p = 0.961

 

 

 

 

 

 

 

 

 

 

 

Figure 6.1: Effect of SEC-BT on Systolic Blood Pressure (SBP) among Experimental, CDC Control, and PW Control groups at pre-test, post-test 1, and post-test 2. Bars represent mean systolic blood pressure with standard errors. The experimental group demonstrated a progressive reduction in SBP across the three time points, with a significant decrease evident at post-test 2 compared to both control groups (***p < 0.001). In contrast, the CDC control and PW control groups showed no significant changes (ns).

Figure 6.2: Effect of SEC-BT on Diastolic Blood Pressure (DBP) among Experimental, CDC Control, and PW Control groups at pre-test, post-test 1, and post-test 2. Bars represent mean diastolic blood pressure with standard errors. The experimental group exhibited a significant decline in DBP across time, with reductions from pre-test to post-test 1 (**p < 0.01) and from pre-test to post-test 2 (***p < 0.001). Both CDC control and PW control groups remained stable with no significant differences (ns).

 

Figure 6.3: Effect of SEC-BT on Body Weight among Experimental, CDC Control, and PW Control groups at pre-test, post-test 1, and post-test 2. Bars represent mean body weight with standard errors. The experimental group showed a gradual reduction in body weight across the three time points, although the changes were not statistically significant (p > 0.05). Both CDC control and PW control groups remained stable without meaningful changes.

 

Figure 6.4: Effect of SEC-BT on Heart Rate among Experimental, CDC Control, and PW Control groups at pre-test, post-test 1, and post-test 2. Bars represent mean heart rate with standard errors. The experimental group demonstrated a consistent and statistically significant reduction in heart rate across the study period, with differences evident between pre-test and post-test 1 (**p < 0.01), and between pre-test and post-test 2 (***p < 0.001). Both CDC control and PW control groups maintained stable values with no significant changes.

Figure 6.5: Effect of Intervention on Body Mass Index (BMI) in Experimental, CDC Control, and PW Control Groups at Pre-test, Post-test 1, and Post-test 2. The experimental group showed a progressive decline in BMI values from pre-test (26.97 ± 3.18) to post-test 1 (26.20 ± 3.21) and post-test 2 (25.43 ± 3.20), with significant differences across the three time points (F = 8.315, p = 0.003). In contrast, both CDC control and PW control groups exhibited stable BMI trends across all three time points, with no statistically significant differences (p > 0.05). These results indicate that the intervention was effective in reducing BMI only in the experimental group.

Figure 6.6: Effect of Intervention on Waist-to-Hip Ratio (WHR) in Experimental, CDC Control, and PW Control Groups at Pre-test, Post-test 1, and Post-test 2. The experimental group demonstrated a consistent reduction in WHR from pre-test (0.8685 ± 0.048) to post-test 1 (0.8510 ± 0.048) and post-test 2 (0.8313 ± 0.048), with significant overall differences (F = 6.013, p = 0.003). The CDC control and PW control groups displayed fluctuations but no significant changes (p > 0.05). These findings suggest that the intervention produced a notable improvement in WHR exclusively in the experimental group.

 

References

Liu, Y., Li, L., & Hu, X. (2021). Autonomic nervous system reactivity in chronic stress: Implications for cardiovascular health. Stress, 24(6), 803–812. https://doi.org/10.1080/10253890.2021.1922256

Moraes, T. F., Fernandes, T., & Nascimento, D. (2022). Parenting stress and cardiovascular risk in mothers of children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 52(8), 3504–3515. https://doi.org/10.1007/s10803-021-05184-9

Sharma, R., & Andrade, C. (2020). Sympathetic nervous system overactivity in stress and its clinical implications. Indian Journal of Psychiatry, 62(4), 417–425. https://doi.org/10.4103/psychiatry.IndianJPsychiatry_62_20

Tang, Y. Y., Tang, R., & Posner, M. I. (2022). Brief meditation and breathing training alters autonomic regulation in stressed adults. Frontiers in Human Neuroscience, 16, 852934. https://doi.org/10.3389/fnhum.2022.852934

Villarreal-Zegarra, D., Bernabé-Ortiz, A., & Miranda, J. J. (2023). Effects of mindfulness-based interventions on heart rate variability among caregivers: A systematic review and meta-analysis. Mindfulness, 14(2), 345–359. https://doi.org/10.1007/s12671-022-02011-5

Warrier, M., & Sivakumar, R. (2021). Biophysiological stress indicators in caregiver populations: A review. Indian Journal of Health and Wellbeing, 12(5), 542–549.

Hernández-Reif, M., Dieter, J. N., Field, T., & Santiesteban, L. (2022). Biofield and energy-based interventions for stress reduction and physiological regulation: A systematic review. Complementary Therapies in Medicine, 68, 102847. https://doi.org/10.1016/j.ctim.2022.102847

 

Kim, H. S., Park, S. Y., & Lee, J. H. (2021). Effects of integrative relaxation-based interventions on obesity indices and metabolic risk factors: A randomized controlled trial. Journal of Alternative and Complementary Medicine, 27(9), 740–749. https://doi.org/10.1089/acm.2021.0123

 

Sharma, R., Gupta, N., & Singh, A. (2020). Effect of mind–body intervention on blood pressure and stress among middle-aged adults: A randomized controlled trial. International Journal of Hypertension, 2020, 9816354. https://doi.org/10.1155/2020/9816354

 

Singh, P., Verma, S., & Kaur, R. (2023). Impact of lifestyle-based integrative program on waist-to-hip ratio and body mass index in Indian adults: A quasi-experimental study. Indian Journal of Public Health Research & Development, 14(2), 56–63. https://doi.org/10.37506/ijphrd.v14i2.2023

 

Zhang, Y., Li, J., & Chen, W. (2023). Waist-to-hip ratio as a predictor of cardiovascular risk: Evidence from a five-year longitudinal study. European Journal of Preventive Cardiology, 30(5), 643–651. https://doi.org/10.1093/eurjpc/zwad012