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
|
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. |
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