J Can Chiropr Assoc 2013; 57(3) 243 ISSN 0008-3194 (p)/ISSN 1715-6181 (e)/2013/243–250/$2.00/©JCCA 2013 Association between heart rate variability and manual pulse rate John Hart, DC, MHSc* Introduction: One model for neurological assessment in chiropractic pertains to autonomic variability, tested commonly with heart rate variability (HRV). Since HRV may not be convenient to use on all patient visits, more user-friendly methods may help fill-in the gaps. Accordingly, this study tests the association between manual pulse rate and heart rate variability. The manual rates were also compared to the heart rate derived from HRV. Methods: Forty-eight chiropractic students were examined with heart rate variability (SDNN and mean heart rate) and two manual radial pulse rate measurements. Inclusion criteria consisted of participants being chiropractic students. Exclusion criteria for 46 of the participants consisted of a body mass index being greater than 30, age greater than 35, and history of: a) dizziness upon standing, b) treatment of psychiatric disorders, and c) diabetes. No exclusion criteria were applied to the remaining two participants Assistant Director of Research Sherman College of Chiropractic P.O. Box 1452 Spartanburg, SC 29304 Phone: 864-578-8770, Ext. 232 Email: [email protected] ©JCCA2013 Introduction : Un des modèles d’évaluation neurologique en chiropratique est lié à la variabilité autonome, testée habituellement avec la variabilité de la fréquence cardiaque (VFC). Puisque l’usage de la VFC n’est pas toujours convenable à toutes les visites médicales, d’autres méthodes plus conviviales peuvent aider à combler les lacunes. Alors, cette étude examine la relation entre la prise de pouls manuelle et la variabilité de la fréquence cardiaque. Les rythmes manuels ont aussi été comparés au rythme cardiaque dérivé de la VFC. Méthodologie : Quarante-huit étudiants en chiropratique ont été examinés par la mesure de la variabilité de la fréquence cardiaque (SDNN et fréquence cardiaque moyenne) et par deux mesures manuelles du pouls radial. Les critères d’admissibilité étaient le fait d’être des étudiants en chiropratique. Un participant n’était pas admissible s’il avait un indice de masse corporelle supérieur à 30, s’il était âgé de plus de 35 ans, et s’il avait des antécédents : a) d’étourdissements en position debout, b) de traitement pour des troubles psychiatriques, et c) de diabète. Aucun critère d’inadmissibilité n’a été retenu contre les deux participants restants qui étaient aussi des bénévoles servant d’échantillon de commodité. Les rapports 244 J Can Chiropr Assoc 2013; 57(3) Association between heart rate variability and manual pulse rate who were also convenience sample volunteers. Linear associations between the manual pulse rate methods and the two heart rate variability measures (SDNN and mean heart) were tested with Pearson’s correlation and simple linear regression. ...

1. J Can Chiropr Assoc 2013; 57(3) 243
ISSN 0008-3194 (p)/ISSN 1715-6181 (e)/2013/243–
250/$2.00/©JCCA 2013
Association between heart rate variability
and manual pulse rate
John Hart, DC, MHSc*
Introduction: One model for neurological assessment
in chiropractic pertains to autonomic variability, tested
commonly with heart rate variability (HRV). Since
HRV may not be convenient to use on all patient visits,
more user-friendly methods may help fill-in the gaps.
Accordingly, this study tests the association between
manual pulse rate and heart rate variability. The manual
rates were also compared to the heart rate derived from
HRV.
Methods: Forty-eight chiropractic students were
examined with heart rate variability (SDNN and
mean heart rate) and two manual radial pulse
rate measurements. Inclusion criteria consisted of
participants being chiropractic students. Exclusion
criteria for 46 of the participants consisted of a body
mass index being greater than 30, age greater than 35,
and history of: a) dizziness upon standing, b) treatment
of psychiatric disorders, and c) diabetes. No exclusion
criteria were applied to the remaining two participants
Assistant Director of Research
Sherman College of Chiropractic
P.O. Box 1452

2. Spartanburg, SC 29304
Phone: 864-578-8770, Ext. 232
Email: [email protected]
©JCCA2013
Introduction : Un des modèles d’évaluation neurologique
en chiropratique est lié à la variabilité autonome,
testée habituellement avec la variabilité de la fréquence
cardiaque (VFC). Puisque l’usage de la VFC n’est
pas toujours convenable à toutes les visites médicales,
d’autres méthodes plus conviviales peuvent aider à
combler les lacunes. Alors, cette étude examine la
relation entre la prise de pouls manuelle et la variabilité
de la fréquence cardiaque. Les rythmes manuels ont
aussi été comparés au rythme cardiaque dérivé de la
VFC.
Méthodologie : Quarante-huit étudiants en
chiropratique ont été examinés par la mesure de
la variabilité de la fréquence cardiaque (SDNN et
fréquence cardiaque moyenne) et par deux mesures
manuelles du pouls radial. Les critères d’admissibilité
étaient le fait d’être des étudiants en chiropratique.
Un participant n’était pas admissible s’il avait un
indice de masse corporelle supérieur à 30, s’il était
âgé de plus de 35 ans, et s’il avait des antécédents : a)
d’étourdissements en position debout, b) de traitement
pour des troubles psychiatriques, et c) de diabète. Aucun
critère d’inadmissibilité n’a été retenu contre les deux
participants restants qui étaient aussi des bénévoles
servant d’échantillon de commodité. Les rapports
244 J Can Chiropr Assoc 2013; 57(3)
Association between heart rate variability and manual pulse rate

3. who were also convenience sample volunteers. Linear
associations between the manual pulse rate methods and
the two heart rate variability measures (SDNN and mean
heart) were tested with Pearson’s correlation and simple
linear regression.
Results: Moderate strength inverse (expected)
correlations were observed between both manual pulse
rate methods and SDNN (r = -0.640, 95% CI -0.781,
-0.435; r = -0.632, 95% CI -0.776, -0.425). Strong direct
(expected) relationships were observed between the
manual pulse rate methods and heart rate derived from
HRV technology (r = 0.934, 95% CI 0.885, 0.962; r =
0.941, 95% CI 0.897, 0.966).
Conclusion: Manual pulse rates may be a useful
option for assessing autonomic variability. Furthermore,
this study showed a strong relationship between manual
pulse rates and heart rate derived from HRV technology.
k e y w o r d s : heart rate, chiropractic, pulse rate,
adjustment, manipulation
linéaires entre les méthodes de mesure de pouls manuelle
et les deux mesures de variabilité de la fréquence
cardiaque (SDNN et fréquence moyenne) ont été testés
à l’aide d’une analyse de corrélation de Pearson et de
régression linéaire simple.
Résultats : Des corrélations inverses de niveau
modéré (prévues) ont été observées entre les deux
méthodes de mesure de pouls manuelle et la SDNN (r
= -0,640, 95 % CI -0,781, -0,435; r = -0,632, 95 % CI
-0,776, -0,425). Des relations directes de niveau élevé
(prévues) ont été observées entre les méthodes de mesure
de pouls manuelle et le rythme cardiaque dérivé de la

4. technique de VFC (r = 0,934, 95 % CI 0,885, 0,962; r =
0,941, 95 % CI 0,897, 0,966).
Conclusion : La prise de pouls manuelle peut se
présenter comme une option pratique dans l’évaluation
de la variabilité autonome. De plus, cette étude
démontre une relation importante entre le pouls manuel
et le rythme cardiaque dérivé de la technique de VFC.
m o t s c l é s : fréquence cardiaque, rythme cardiaque,
chiropratique, pouls, ajustement, manipulation
Introduction
One approach in chiropractic care of patients pertains to
the analysis and adjustment of vertebral subluxation, a
condition with various theoretical underpinnings. Others
may prefer to call the target of chiropractic intervention
a “functional articular lesion,” where the purpose of the
intervention is to “produce (a) beneficial neurologic
ef-
fect.”1 In either case, a measurable neurological outcome
of some type is presupposed. For purposes of this study,
the “adjustable lesion” is referred to as vertebral subluxa-
tion since the author considers this to be a more familiar
term within the profession. Briefly, vertebral
subluxation
is theorized to consist of some type of minor biomechan-
ical aberrancy between two vertebrae, resulting in some
type of (and yet still-to-be defined) neurological disturb-
ance. The present study focuses on a potentially useful
neurological predictor, if not also a useful outcome vari-
able that may be related to putative subluxation.
One aspect of subluxation theory involves the potential
effect of subluxation on the autonomic nervous system
(ANS), the health of which can be assessed in terms of
“autonomic variability” measures.2

5. R.W. Stephenson advanced the idea that subluxation
interferes with the body’s ability to adapt.3 In current day
terminology, neurological adaptability, particularly in re-
gard to the ANS is described by the complexity model as it
is known in medicine.4 In chiropractic, neuro-adaptabilty
is typically analyzed with pattern analysis.5 Briefly, the
concept is that variation in certain autonomic functions,
such as heart rate, is considered to represent a healthy
nervous system. A higher amount of heart rate variability
is neurologically healthier than lower heart rate variabil-
ity in terms of various cardiological and noncardiological
diseases.2 There are exceptions to this concept. For ex-
ample, higher variation in blood pressure has been cor-
related with atherosclerosis and diabetic nephropathy in
patients with Type 2 Diabetes.6
Many chiropractors who focus on vertebral subluxa-
J Can Chiropr Assoc 2013; 57(3) 245
J Hart
tion may wish to choose from a variety of options for as-
sessing ANS adaptability/variability. The number of these
options is currently limited. Thus, additional evidence-
based options would seem helpful to increase feasibility
in chiropractic practice for assessing ANS adaptability.
One way to test a potentially useful option for assess-
ing autonomic variability is to compare it to a gold stan-
dard for autonomic variability such as heart rate variabil-
ity (HRV). One of the main measures in HRV is the stan-
dard deviation of normal-to-normal beats (SDNN),7 hav-
ing a unit in milliseconds (ms), representing the amount
of variability of the heart rate. A higher SDNN value is
considered healthier than a lower SDNN value.2 Another

6. main measure in HRV is mean heart rate. Both of these
measures (SDNN and mean heart rate) are considered as
ANS markers.
Sessions for HRV testing are typically either 5 min-
utes or 24 hours. The shorter time frame is an approach
commonly used in chiropractic research, in regard to: a)
before and after care findings for HRV and pain,8 b)
cor-
relation with health perception,9 and c) correlation with
area of the spine that was adjusted.10
One medical study that used the 5 minute approach for
HRV found a moderate strength, statistically
significant
inverse correlation between SDNN and heart rate that
was derived from a 10 second electrocardiogram (ECG)
recording.11 While that study used a technology-based
method to obtain the resting heart rate (10 second ECG
recording), the authors commented on the practical ap-
peal of employing manual methods of heart (pulse) rate
for autonomic assessment in routine clinical practice.11
Other studies using heart rate variability have also shown
the inverse relationship between resting heart rate and
heart rate variability,12-15 again using technology-based
methods for the derivation of the heart rate. The inverse
relationship between SDNN and heart rate means that as
heart variability increases (considered a neurologically
healthy occurrence), pulse rate decreases (also considered
a neurologically healthy occurrence).
Manual pulse rate as obtained with, say, radial artery
palpation, is used for a variety of purposes, including
the assessment of “autonomic nervous system tone.”16
A lower pulse rate is considered healthier than a higher
pulse rate.17 One previous study compared the average
of four 15 second pulse readings taken manually to HRV
(SDNN) and found a moderate strength, statistically sig-

7. nificant inverse (expected) correlation between
SDNN
and the manual pulse rates.18 The present study further
tests this correlation with: a) a different sample of partici-
pants, and b) different methods for obtaining the manual
pulse readings (two 15 second times instead of four).
The present study further builds on the aforementioned
study18 by comparing: a) SDNN to the mean heart rate de-
rived from the HRV session itself and b) mean heart rate
derived in HRV to the manual pulse rates. The manual
pulse rate has been shown to be strongly correlated with
heart rate derived from technology.19-20
The manual pulse rate times used in the present study
were 15 seconds. Although 30 seconds is a more common
time frame for manual pulse measurement in a health care
setting, the differences between pulse rates taken with
15-, 30-, or 60-second time frames have not been found to
be statistically significant.21
The aim of the present study is to determine what, if
any, relationship exists between manual pulse rate and
HRV. In particular, pulse rate is compared to the HRV
values of SDNN and heart rate (derived from the HRV
recording).
Research hypotheses
An inverse relationship was expected between SDNN and
pulse rate since lower heart rate is considered neurologic-
ally healthier than a higher pulse rate, and a higher SDNN
value is considered neurologically healthier than a lower
SDNN value. A direct relationship was expected in the
secondary analysis comparing the different methods of
heart rate measurement.
Methods
Sample characteristics

8. The study was approved by the Institutional Review
Board at Sherman College of Chiropractic. The recruit-
ment of participants at the College consisted of a com-
bination of global emails to all students, along with invi-
tations in the classrooms from the author. Most of the par-
ticipants within the sample (n = 46) ended up being part
of another study on subclinical orthostatic hypotension,
the exclusion criteria for which consisted of: a) body mass
index greater than 30, b) dizziness upon standing, c) past
treatment of psychiatric disorders, d) history of diabetes,
and e) age greater than 35 years. No formal exclusion cri-
246 J Can Chiropr Assoc 2013; 57(3)
Association between heart rate variability and manual pulse rate
teria were applied to the two additional participants. All
participants were chiropractic students who participated
on strictly a voluntary basis.
Examination
The two examination procedures consisted of: 1) A 5
minute HRV exam using a Biopac Heart Rhythm Scan-
ner (Version 1, Clinical Edition, Biocom Technologies,
Poulsbo, WA); and 2) Two manually-palpated radial pulse
measurements, each taken over a 15-second interval, 15
seconds apart. The readings were timed with a digital
timer with the first pulse count beginning on
the first tar-
get second number on the timer (i.e., starting with beat
#1 on the zero second mark). The 15 second results were
multiplied by 4 to obtain a beats per minute (BPM) meas-
urement.
After a minimum of 5 minutes rest in the seated pos-

9. ition, the two tests (HRV and manual pulse) were per-
formed with the participant continuing to be seated. For
pulse rate, the first pulse rate (Pulse 1), as
well as the mean
of Pulse 1 and the second pulse rate (“mean of Pulse1 and
Pulse2”) were used in the analysis. From the HRV data,
SDNN and mean heart rate (“mean heart rate in HRV”)
were used.
Data analysis
Pearson’s r was used to test for a linear association be-
tween SDNN and each of the following heart rate meth-
ods:
1) Mean heart rate, derived from the 5 minute
HRV session (the gold standard measurement
of resting heart rate in the present study);
2) Pulse 1;
3) Mean of Pulse1 and Pulse2.
Patient characteristic were also measured. Spearman’s
correlation coefficient was used to assess for
nonlinear,
but still monotonically trending, associations between
body mass index (BMI) and age. An association between
SDNN and sex was examined using a t-test for independ-
ent samples. BMI was calculated using the formula cited
by the Centers for Disease Control and Prevention based
on height, weight, and a conversion factor. 22 In addition,
simple linear regression (rather than multiple linear re-
gression, which showed problems with collinearity) was
used to test the linear relationship between dependent
variable heart rate derived from HRV and the two manual
pulse rate methods and to examine the magnitude of the

10. difference in HRV-derived heart rate for every one-unit
change in manually assessed pulse rate. Since HRV and
pulse rates typically are different for male and female,23
correlations were also performed by sex.
Analyses were performed in Stata IC 12.1 (StataCorp,
College Station, TX). Confidence intervals for
correlation
coefficients were obtained, and comparisons of correla-
tion coefficients between sexes were performedusing
an
online calculator.24 Two tailed p-values less than or equal
to the traditional alpha level 0.05 were considered statis-
tically significant.
Results
Data were collected from a total of 48 chiropractic stu-
dent volunteers (19 female, 29 male; 39.6% and 60.4%
respectively), each of whom underwent both HRV and
manual radial pulse rate assessments during a single visit.
The mean age of the participants was 26.4 years (SD 4.3),
with a mean BMI of 24.7 (SD 3.0; Table 1).
Correlation of SDNN and patient characteristics
BMI and age exhibited nonlinear relationships with
SDNN according to scatter plot inspection (Figures 1 and
Table 1.
Summary statistics, including patient characteristics.
BMI = body mass index. SDNN = standard deviation
of normal-to-normal beats in HRV. Pulse 1 and
mean Pulse1 Pulse2 are manual methods of pulse
measurement.
Variable n mean SD Min Max

11. Age 48 26.4 4.3 20.0 34.0
BMI 48 24.7 3.0 18.8 31.4
SDNN (ms) 48 62.2 31.8 11.9 155.0
Mean HR in HRV 48 71.5 12.3 50.2 106.2
Pulse 1 48 71.9 12.6 48.0 112.0
Mean Pulse1 Pulse2 48 71.7 12.2 50.0 112.0
J Can Chiropr Assoc 2013; 57(3) 247
J Hart
Figure 1.
Scatter plot for SDNN and age.
Figure 2.
Scatter plot for SDNN and BMI.
Figure 3.
SDNN and Pulse 1.
As manual pulse rate increases (horizontal axis),
SDNN decreases (vertical axis),
as expected.
Figure 4.
Mean heart rate (HR) in HRV versus Pulse1 manual

12. pulse rate. As manual pulse increases (horizontal axis),
so too does mean heart rate derived from technology in
HRV vertical axis), as expected.
248 J Can Chiropr Assoc 2013; 57(3)
Association between heart rate variability and manual pulse rate
2). Correlation coefficients are provided in Table
2. The
correlations of age and BMI with SDNN were not statis-
tically significant (p > 0.05;Table 2). Mean
SDNN for
females was 52.8 (95% CI 42.1, 63.4) compared to 68.4
(95% CI 54.8, 82.0) for males, a difference that was not
statistically significant (p = 0.0678) but
potentially clinic-
ally important given that the mean difference was 15.6.
Correlation of SDNN and pulse rate
Mean SDNN was 62.2 milliseconds (SD 31.8; Table 1).
The different pulse rate measurements showed essen-
tially the same correlations with SDNN. These associa-
tions were statistically significant and reflected
moderate-
strength inverse (expected) relationships between SDNN
and the following variables: Mean heart rate in HRV (r
= -0.661, p < 0.0001); Pulse 1 (r = -0.640, p < 0.0001);
Mean Pulse1 and Pulse2 (r = -0.632, p < 0.0001). The
scatter plot in Figure 3 shows, graphically, the relation-
ship between SDNN and Pulse 1.
Since the correlations of the manual methods were so
similar, Pulse 1 was arbitrarily selected as the manual

13. pulse method to be correlated with SDNN,
stratified by
sex. Here, correlation with SDNN revealed similar correl-
ations: r = -0.676, p = 0.0015 for females; and r = -0.630,
p = 0.0003 for males. The difference between these two
correlation coefficients was not statistically
significant (p
= 0.8026).
Relationships between mean heart rate in HRV and
manual pulse rate
Mean heart rates for the three methods studied were as
follows: a) mean heart rate in HRV: 71.5 BPM (SD 12.3);
b) Pulse 1: 71.9 BPM (SD 12.6); and c) mean of Pulse1
and Pulse2: 71.7 BPM (SD 12.2). Very strong and direct
correlations were observed between mean heart rate in
HRV and both Pulse 1 (r = 0.934, p < 0.0001; Figure 4)
and the mean of Pulse1 and Pulse2 (r = 0.941, p < 0.0001;
Table 3). Since the correlation coefficients
were similar
for both manual methods, Pulse 1 was again used for cor-
relations by sex with mean heart rate in HRV. Here, simi-
lar correlations were found between sexes: r = 0.950, p <
0.0001 for females; and r = 0.919, p < 0.0001 for males.
The difference between these two correlations was not
statistically significant (p = 0.4354).
In linear regression analyses using mean heart rate in
HRV as the dependent variable, the R-squared value was
0.877 for Pulse 1 and 0.887 for the mean of Pulse1 and
Pulse 2. The regressioncoefficient was 0.91 for
Pulse 1 (p
< 0.005; 95% CI 0.8, 1.0) and 0.95 for the mean of Pulse1
and Pulse2 (p < 0.005; 95% CI 0.86, 1.05). This means
that for every 1 BPM change in manual pulse rate, the
mean heart rate in HRV would also expected to change in
the same direction by approximately 1 BPM.

14. Discussion
In regard to SDNN, the heart rates (mean heart rate in
HRV, Pulse 1, and mean Pulse1 and Pulse2) revealed the
Table 2.
Testing SDNN against three pulse predictors and three patient
characteristic variables. Pearson correlation is used for
continuous variables exhibiting a linear relationship in their
scatter plots (#s 1-3 in list) while Spearman is used for
correlations where nonlinear relationships were observed
(variables 4-5). CI = confidence interval
Variable n r 95% CI for r p
1) Mean HR in HRV 48 –0.661 –0.795, –0.465 < 0.0001
2) Pulse 1 48 –0.640 –0.781, –0.435 < 0.0001
3) Mean Pulse1Pulse2 48 –0.632 –0.776, –0.425 < 0.0001
4) Age 48 –0.199 –0.457, 0.090 0.1761
5) BMI 48 0.103 –0.186, 0.376 0.4845
J Can Chiropr Assoc 2013; 57(3) 249
J Hart
expected (inverse) relationships with SDNN. That is, a
lower pulse (considered neurologically healthier than a
higher pulse) is related to higher heart rate variability (con-
sidered neurologically healthier than lower HRV). Age,

15. sex, and BMI did not have associations with SDNN that
were statistically significant, although therewas a
nearly
statistically significant difference in SDNN
between males
and females. This nearly significant finding is
consistent
with findings in another study that used a 24
hour monitor-
ing protocol.23 There, a significant mean
difference of 35
milliseconds SDNN was observed between males and fe-
males aged 10-29 years, and a smaller mean difference of
17 milliseconds SDNN was observed between males and
females aged 30-49 years.23 In the present study, the analy-
sis was not stratified by age group, however a
difference
of 15.6 milliseconds SDNN was observed between sexes.
Interestingly, other research using the same HRV technol-
ogy used in the present study did not find a
statistically
significant mean difference in SDNN between
sexes.25 In
any event, the present study did not show that sex had an
effect on the strength or significance of the
correlations
between manual pulse rate with the HRV findings
(SDNN
and mean heart rate in HRV).
The present study revealed statistically
significant cor-
relations between manual and technology based pulse
rate measurements, which may in turn be useful proxy
measures of autonomic variability, and potential changes
in autonomic variability after vertebral adjustment. Even
aside from its correlation with heart variability, manu-

16. ally assessed pulse rate stands on its own as a marker for
autonomic health in other studies. Correlations between
the manual pulse rate methods and mean heart rate in
HRV were very strong (and statistically significant) as
ex-
pected.
One of the strengths of the current study is that the
count method for the manual pulse reading began with
“1” instead of “zero” on the zero second mark. In this
regard, pulse rate measurement using the former method
(starting with “1” count on the zero second mark) has
been shown to be more strongly associated with heart rate
derived from ECG.21
Admittedly, a formal sample size calculation was not
conducted in advance of the study. However, a posterior-
ly, it was determined that in order to detect a statistically
significant, moderate-strength correlation (e.g.,
absolute
value of r between 0.400 and 0.700), a sample size of 24
would be needed.26 Consequently, for at least a moderate
strength correlation, the sample size in the present study
appeared to be adequate.
In linear regression, the average change in pulse rates
was essentially a 1:1 ratio between mean heart rate in
HRV and either of the manual pulse rate methods. How-
ever, mean Pulse1 Pulse2 showed a slightly stronger as-
sociation with the presumed gold standard for heart rate
in this study (i.e., heart rate derived from HRV), which
suggests that the average of two pulse rate measurements
may be the preferred method over any single determina-
tion in future studies.
Limitations to the study are that the participants com-
prised a convenience sample and were relatively healthy,
making the generalizability of these findings to
other

17. patient populations limited. Additionally, p-values were
not adjusted for multiple hypothesis tests. However even
if multiple testing had been adjusted for, these
findings
Table 3.
Testing mean heart rate in HRV against the two manual pulse
methods using Pearson correlations (r, p for r)
and linear regression (coefficient, p for regression coefficient).
MP1P2 = mean pulse 1 pulse 2. P1 = Pulse 1.
CI = confidence interval.
Pearson Linear regression
Covariable n r 95% CI for Pearson r p Coefficient 95% CI for
regression coefficient p
MP1P2 48 0.941 0.897, 0.966 < 0.0001 0.95 0.86, 1.0 < 0.001
P1 48 0.934 0.885, 0.962 < 0.0001 0.91 0.82, 1.0 < 0.001
250 J Can Chiropr Assoc 2013; 57(3)
Association between heart rate variability and manual pulse rate
would remain statistically significant due to
the already-
existing very low p-values in correlation and regression
results.
Conclusion
In this study of relatively healthy chiropractic students,
manual pulse rates showed: a) a moderate inverse correla-

18. tion with the SDNN value in heart rate variability, and b) a
strong direct correlation with heart rate derived from HRV
technology. Manual pulse rate determinations may be a
useful proxy measure for chiropractors and chiropractic
researchers seeking to assess the global neurological ef-
fect of vertebral adjustment on putatively diagnosed ver-
tebral subluxation. Additional research involving more
representative patient populations are needed to verify the
findings derived from the current study. Further
studies to
assess the association between manual pulse rate and both
health status and clinically significant changes in
health
status following vertebral adjustment are also needed.
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12. Carney RM, Rich MW, teVelde A, Saini J, Clark K,
Freedland KR. The relationship between heart rate, heart
rate variability and depression in patients with coronary
artery disease. J Psychosom Res. 1988; 32(2):159-164.
13. Van Hoogenhuyze D, et al. Reproducibility and relation to
mean heart rate of heart rate variability in normal subjects
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14. Ramaekers D, Ector H, Aubert AE, Rubens A, Van de
Werf F. Heart rate variability and heart rate in healthy
volunteers. Eur Heart J. 1998; 19: 1334-1341.
15. Soliman EZ, Elsala, MA, Li Y. The relationship between
high resting heart rate and ventricular arrhythmogenesis in
patients referred to ambulatory 24 h electrocardiographic
recording. Europace. 2010; 12: 261-265.
16. Hsia J, et al. Resting heart rate as a low tech predictor
of coronary events in women: prospective cohort study.
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17. Verrier RL, Tan A. Heart rate, autonomic markers, and
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Running Head: Fatih Sultan Mehmet Bridge
1
Fatih Sultan Mehmet Bridge

22. 6
Introduction to Fatih Sultan Mehmet Bridge
The Fatih Sultan Mehmet Bridge is also known as the Second
Bosphorus Bridge. It is located in Istanbul, Turkey. When it was
built it was the 5th longest bridge in the world but now it is at
19th rank. This bridge was completed in 1988. The name was
given to this bridge after 15th century on the name of conqueror
Ottoman Sultan Mehmet who was famous due to his win against
Byzantine capital in 1453. It carries Asian Highway 1, Asian
Highway 5, European route E80 and Otoyol 2
highways(Brownjohn, Dumanoglu, & Severn, 2009).
This bridge exists between the Kavacik (Asian side) and
Hisarustu (European side). It has a specialty that it is made of
steel pylons and vertical hangers. So that is why it is called as
gravity- anchored suspension bridge. It has 1510 meters long
width of 39 meters. The distance between the towers (which are
main span) is 1090 meters and the height of 105 meters. it has
64 meters clearance from the sea level.
Body and Evolution
Structure of the bridge:
This bridge is designed by Freeman Fox & his partners and
BOTEK Bosphorus technical corporation. This company has
also designed the first Bosphorus Bridge. It is an independent
consulting firm that provides multi-disciplinary Civil
Engineering service. It was established in 1975, that gained
very extensive experiences by most of its projects regarding
quality, design, supervision, cost and scheduling in the fields of
bridges, environmental engineering, motorways, tunnels,
railways, industrial facilities, residential buildings and many
others, which are awarded by municipalities, governments and
other private enterprises(Picozzi et al., 2010).
Many companies like (Mitsubishi heavy industries, IHI
corporation, one Italian firm Impregilo and one Turkish
company SFTA has jointly done the construction work of this
bridge. The bridge was fully completed on July, 3 1988. Prime

23. Minister Turgut Ozal has inaugurated it by driving his official
car as becoming a first driver of this bridge. The cost if this
bridge is SU$ 130 million.
Daily Transportation:
This bridge is located on Trans-European motorway between
Ankara and Edirne. This highway bridge has four lanes for
traffic and it also has one emergency lane in case of emergency
in each direction of the bridge. On the weekday morning,
Commercial traffic mostly flows from westbound to the
European part, so it can be said that five of the eight lanes move
towards westbound and three lanes move towards eastbound.
In contrast with weekday evenings, five of the eight lanes move
towards eastbound and rest of the three move in the
westbound(Apaydin, 2002). If we talk about the pedestrians
they are not allowed to travel on the bridge. Now a day, almost
150,000 vehicles pass daily in both of directions; which
includes 65 % automobiles.
Collection of Toll payment:
Fatih Sultan Mehmet Bridge is a toll bridge. It has a specific
payment mode which only for the passangers who travelled
from Europe to Asia and there is no payment for the passengers
who travelled from Asia to Europe. This is rule is also
applicable on first Bosphorus Bridge. But the important thing is
that since 2008 to up till now the payment for toll is not made
in cash. There is a special electronic method of being collecting
the payments through remote payment system. In spite of that
OGS, contactless smart sticker which is also known as HGS
system is in use (Ubertini, 2010). The HGS sticker and OGS
device can also be obtained at different stations before reaching
the bridges and highways. So, the rule of deducting the payment
from Europe to Asia is implemented on the both bridges (first &
second Bosphorus Bridge).
GPS Monitoring of Fatih Sultan Mehmet Bridge:
The second suspension bridge that connects Asia to Europe is
called Fatih Sultan Mehmet Bridge. This bridge is monitored
through by using the GPS technique. The specialty of this

24. system is that it takes an observation in 0.1 second interval that
are recorded for weeks.This system also collects some other
data like the weather conditions and information about traffic
volume as well. At the first step a time series of that respective
point is taken which is constructed and combines the data of all
information like time, weather conditions and traffic volume.
Then after that a single comparison of each observation of the
days was investigated(Gunaydin, Adanur, Altunisik, & Sevim,
2012). Therefore, monitoring is the best tool of big engineering
structures like highways and bridges which also informs about
major disasters management and risk analysis and it is very
helpful in keeping the lives safe of the travelers.
An artificial methodology was also adopted that belongs to soft
computing methods as a predictor of the earthquakes. The need
of this technology was raised after the 17th August earthquake
in North Anatolian Fault Zone (NAFZ) since the new
earthquakes were expected. So, this technology implemented on
the Fatih Sultan Mehmet Bridge was considered as an efficient
for complex behaviors of the objects that causes factors
specifically in case of continuous monitoring system(Dost,
Apaydın, Dedeoğlu, MacKenzie, & Akkol, 2013).
Wireless Technologies used in Fatih Sultan Mehmet Bridge:
With the fast improvements in the telecommunication that
decrease the communication costs and provides convenient
communication is quite famous. In this work, design of the low
cost sensing unit able the monitoring system in collecting,
storing, analyzing, communicating and estimating the
parameters.
Suitability of the network of this low cost sensing unit is
through wireless instruments which monitors the dynamic
properties and vibration characteristics during the ambient
vibration recording field test on the Fatih Sultan Mehmet Bridge
in Istanbul, Turkey (Picozzi, et al., 2010).The main advantage
of using this wireless sensing unit is decentralization of the data
analysis, decrease in the installation cost and possibilities that

25. were considered fruitful as functional capabilities with the
exploitation at the same time and place of different sensors.
Seismic Analysis of the Faith Sultan Mehmet Bridge:
Based on the separate analysis of the asynchronous response of
bridge regarding earthquake predictions and free vibration data,
the third from the three long and modern long bridges of Europe
it was investigated and reviewed that there is a strong
relationship between the seismic technology and prediction of
the earthquake.The main conclusion of the study is that as the
seismic response is a valid consideration but, some
asynchronous elements must also be given a proper attention in
this regard(Ubertini, 2010).
Conclusion
The Fatih Sultan Mehmet Bridge is also known as the Second
Bosphorus Bridge. It is located in Istanbul, Turkey. This bridge
was completed in 1988. It has 1510meter long width of 39 m.
the distance between the towers (which are main span) is 1090
m and the height of 105 m. it has 64 m clearance from the sea
level. Many analyses have been made regarding the monitoring
system of the bridge which includes GPS monitoring system,
wireless system and seismic analysis.
All these analysis were conducted about to know that how these
systems manage and control the monitoring systems. All the
analysis suggests that monitoring system is quite efficient and
working properly in managing the traffic. The cost of this
bridge is SU$ 130 million. Now days, almost 150,000 vehicles
pass daily in both of directions; which includes 65 %
automobiles. Fatih Sultan Mehmet Bridge is a toll bridge. It has
a specific payment mode which only for the pass angers who
travelled from Europe to Asia and there is no payment for the
passengers who travelled from Asia to Europe.

26. References:
1. Apaydin, N. (2002). Seismic analysis of Fatih Sultan Mehmet
suspension bridge. Ph. D. Thesis, Department of Earthquake
Engineering, Bogazici University, Istanbul, Turkey.
2. Brownjohn, J., Dumanoglu, A., & Severn, R. (2009). Ambient
vibration survey of the Fatih Sultan Mehmet (Second Bosporus)
suspension bridge. Earthquake engineering & structural
dynamics, 21(10), 907-924.
3. Dost, Y., Apaydın, N., Dedeoğlu, E., MacKenzie, D., &
Akkol, O. (2013). Non-destructive testing of Bosphorus bridges
Nondestructive Testing of Materials and Structures (pp. 819-
825): Springer.
4. Gunaydin, M., Adanur, S., Altunisik, A. C., & Sevim, B.
(2012). Construction stage analysis of fatih sultan mehmet
suspension bridge. Structural Engineering and Mechanics,
42(4), 489-505.
5. Picozzi, M., Milkereit, C., Zulfikar, C., Fleming, K.,
Ditommaso, R., Erdik, M., . . . Özel, O. (2010). Wireless
technologies for the monitoring of strategic civil
infrastructures: an ambient vibration test on the Fatih Sultan
Mehmet Suspension Bridge in Istanbul, Turkey. Bulletin of
Earthquake Engineering, 8(3), 671-691.
6. Ubertini, F. (2010). Prevention of suspension bridge flutter
using multiple tuned mass dampers. Wind and Structures, 13(3),
235-256.
Appendix
An Arial View of bridge:

27. Original
Article. . . . . . . . . . . . . .
Preterm Infant Thermal Responses to Caregiving Differ
by Incubator Control Mode
Karen A. Thomas, PhD, RN
OBJECTIVE:
To determine the influence of caregiving on preterm infant and
incubator
temperature and to investigate incubator control mode in
thermal responses
to caregiving.
STUDY DESIGN:
The intensive within-subject design involved continuous
recording of
infant and incubator temperature and videotaping throughout a
24-hour
period in 40 hospitalized preterm infants. Temperature at care
onset was
compared with care offset, and 5, 10, 15, and 20 minutes
following care
offset using ANOVA-RM.

28. RESULTS:
Following caregiving, infant and incubator temperature differed
significantly over time by incubator control mode. In air servo-
control,
infant temperature tended to decrease after caregiving, while in
skin
servo-control infant temperature remained relatively stable.
With
caregiving, incubator temperature remained consistent in air
servo-
control and increased in skin servo-control.
CONCLUSIONS:
The temperature effects of caregiving should be considered
relative to
maintenance of thermoneutrality and unintentional thermal
stimulation.
Journal of Perinatology (2003) 23, 640 – 645.
doi:10.1038/sj.jp.7211002
INTRODUCTION
The purpose of this study was to determine the influence of
caregiving activities on preterm infant and incubator
temperature.
Specific research questions included: (1) What is the effect of

29. caregiving, defined as care clusters, on infant and incubator
temperature at care offset, and at 5, 10, 15, and 20 minutes
following care offset compared with temperature at care onset?
(2) Does incubator control mode effect infant and/or incubator
temperature responses to caregiving?
Thermal care has been termed the cornerstone of neonatal
care.
1 Simulation models have demonstrated infants’ profound
capacity to exchange body heat with the environment.2
Provision of
a supportive thermal environment minimizes metabolic
requirements, a basic objective of neonatal care.3 Because
thermal
capability is exponentially correlated with gestational age,4
management of the thermal environment requires close attention
in preterm infants who are particularly prone to both hyper- and
hypothermia. Temperature management is related to infant
outcomes. In one study of six neonatal intensive-care units
hypothermia (body temperature <961F) was statistically
significant factor increasing neonatal illness severity.5
Incubators provide a micro-environment suited to the infant’s
thermoregulatory abilities. Incubators are operated in skin
servo-
or air servo-control modes and these operation modes
produce differing patterns of incubator and infant temperature.6
While incubator control appears straightforward, the regulation
of
incubator and infant temperature is complex. External thermal
perturbations, such as initiation of phototherapy, produce large,
sustained changes in incubator thermal environment requiring
up

30. to 3 hours to reach new equilibrium.7 Aside from the thermal
control of the ambient room temperature, three control systems
are
in effect: the infant’s thermoregulatory control, the incubator’s
set
point, and the caregiver making adjustments to the incubator set
point. The ongoing interaction of these control systems is
further
complicated by one extremely important condition: the
incubator is
not a closed system. Caregiving involves opening the incubator,
potentially disrupting incubator temperature. In addition to
shifting infant temperature, alterations in incubator temperature
also produce wide-ranging responses. In a study of the effects
of
cool exposure in preterm infants (34±2 weeks gestational age,
22±1 days postnatal age), air temperature 1.51C less than
thermoneutrality resulted in state change.
8
Neonatal care providers have struggled with balancing needed
caregiving against the physiologic costs of disrupting infants.
Caregiving has been shown to produce a variety of
physiological
responses including hypoxia, heart and respiratory changes,
Address correspondence and reprint requests to Karen A.
Thomas, PhD, RN, Department of
Family and Child Nursing, University of Washington, Box
357262, Seattle, WA 98195-7262, USA.
Research supported by a grant awarded from the National
Center for Nursing Research, R29

31. NR02420.
Statistical consultant: Robert Burr, PhD, MSEE, Department of
Biobehavioral Nursing and Health
Systems, University of Washington.
Department of Family and Child Nursing, University of
Washington, Seattle, WA, USA.
Journal of Perinatology 2003; 23:640 – 645
r 2003 Nature Publishing Group All rights reserved. 0743-
8346/03 $25
www.nature.com/jp640
distress cues, and sleep – wake state disturbance.9,10 – 15 A
considerable portion of the infant’s day is spent in caregiving
activities and the pattern of caregiving frequently involves
repetitive
interruption. In one of the earliest studies of neonatal
caregiving,
nursing interventions were the most frequent source of infant
contact, with an average of 1.68 interventions occurring per
hour.
16 The pattern of caregiving has been documented in
hospitalized preterm infants (mean gestational age 31.7 weeks)
using 24-hour video recording.17 In total, 70% of care occurred
in
clusters (three or more activities), 11% in paired occurrences
(two
activities), and 19% in single activities. The mean duration of
clustered care was 8.31 minutes while the mean duration of

32. single
care activities was 0.48 minutes. In another study of low-risk
preterm infants (31 to 36 weeks gestational age, postnatal age 3
to
16 days), caregiving was found to be the predominant factor,
producing cyclic influences based on pattern of caregiving.
18
Although caregiving procedures differ across nurseries, in
general
studies of care patterns have demonstrated significant amounts
of
disruption as well as changeable pattern of caregiving. Although
the thermal disruption produced by caregiving is clinically
recognized, there have been few studies of the effect of
caregiving
on infant and incubator and infant temperature.
The consequences of caregiving on temperature involve two
general areas of concern. The first concern is the direct effect of
caregiving on incubator and/or infant temperature. In preterm
infants less than 1500 g, decreases in both peripheral (sole of
foot,
mean 1.31C, range 0.2 to 3.0) and central (abdominal skin,
mean
0.71C, range 0.0 to 1.71C) temperature occurred with
caregiving
episodes that lasted 15 to 45 minutes; temperature recovery
required up to a 2-hour period.19 The decline in body
temperature
was negatively correlated with body weight. Using an
intervention
protocol to improve thermal environment stability in fragile
ELBW
infants (23 to 29 weeks gestational age) during the first days of

33. life,
one nurse investigator demonstrated less variability in ambient
temperature, less peripheral vasoconstriction (heel temperature
<35.21C), and decreased gradient between heel and abdominal
skin temperature.
20 In this experimental study, approximately 10 to
11 caregiving disruptions occurred in a 10-hour period.20
The second area of concern related to temperature effects of
caregiving is related to sensory stimulation. Although not
widely
studied in preterm infants, thermal stimulation is a powerful
form
of sensory input. In a study of temperature stimulation, heart
rate
variability was entrained by differing frequencies of skin (palm)
temperature; this entrainment varied with age.21 Thus, preterm
infants are sensitive to thermal stimulation and this stimulation
potentially affects multiple functions.
MATERIALS AND METHODS
Infant and incubator temperature were recorded continuously
over
a 24-hour period in 40 hospitalized preterm infants using an
intensive within-subject design. Selection criteria included:
gestational age 26 to 33 weeks and postnatal age 14 to
28 days, housed in an incubator, weight average for gestational
age, absence of major congenital anomalies and surgical
interventions, intraventricular hemorrhage rGrade II, and not
receiving phototherapy at the time of study. Infants receiving
>28
days of ventilatory assistance were excluded from the study.
Given the selection criteria, infants were medically stable,

34. experiencing the typical problems of prematurity. The study was
conducted in the NICU of a large Northwestern metropolitan
hospital in which average nursery ambient temperature
was 231C.
Infant abdominal skin and incubator temperature were recorded
at minute intervals using a battery-operated monitor (Vitalog
PMS8, Redwood, CA). A skin probe (YSI427, Yellow Springs,
OH)
was adhered to the infant’s right lateral abdomen, at the point of
intersection of perpendicular lines drawn from the anterior
axilla
and the umbilicus, using a reflective probe cover (Accutemp
Plus,
Kentec Medical, Irvine, CA) which was then covered with an air
and moisture permeable tape (Tegaderm, 3M, St. Paul, MN). An
ambient air temperature probe (YSI405, Yellow Springs, OH)
was
suspended 10 cm from the center of the incubator ceiling. The
recorded air temperatures reflected the central portion of the
incubator where the infant was positioned. Temperature probes
were calibrated against a certified thermometer to assess
accuracy
(r>o.99).
Infants were videotaped throughout the 24-hour data collection
period using a lapse time video recording system with 12:1
reduction (Panasonic AG-6030). A mini-camera (Toshiba) was
suspended from the incubator ceiling, providing a view of the
infant and incubator interior. Caregiving was defined as all
health-
related entrances to the incubator. Caregiving was coded from
the
videotapes in 1-minute epochs using a dichotomous code
(absent,
present). Type of caregiving was not recorded. Inter-rater

35. reliability
of coding, determined by percent agreement, was maintained at
>85%. The time stamp on the video recording was synchronized
with the computer used in programming the temperature
monitor;
the time difference between the two systems was <5 seconds
over a
24-hour period.
Care clusters were determined from the caregiving code and
care onset and offset were then determined. As caregiving was
coded yes/no and care of preterm infants often involves multiple
incubator entrances, a care cluster was operationally defined as
an
episode of care lasting at least 1 minute and preceded by an
interval of greater than 20 minutes without caregiving. This
definition was based on visual analysis of tapes as well as
consultation with neonatal nursing experts. Duration of care
clusters and the interval between clusters were calculated.
Infant
and incubator temperature at care onset, offset, and at 5, 10, 15,
and 20 minutes following care offset were then identified.
Differences in temperature from care onset to offset and the
time
following as well as the effect of incubator control mode were
Temperature and Caregiving Thomas
Journal of Perinatology 2003; 23:640 – 645 641
analyzed using analysis of variance for repeated measures
(ANOVA-RM).
Informed consent for participation was obtained from parents.

36. Following application of temperature recording and video
equipment, infants received typical nursery care. Selection of
incubator operation mode as well as set point for air servo- or
skin
servo-control was based on unit procedures and nurses’ clinical
judgment. Incubators were the same model (Air Shields C-200).
Study procedures were approved by human subjects review
committees at both the investigator’s home institution and the
study clinical setting.
RESULTS
Subject characteristics and temperature-descriptive statistics are
provided in Tables 1 and 2. Mean gestational age of the 40
subjects
was 30.3 (SD 2.5) weeks and mean postnatal age at time of
study
was 16.6 (SD 4.8) days. The sample distribution of race and
ethnicity was Black, 2; Hispanic, 3; Asian, 2; White, 33. The
description of caregiving clusters is provided in Table 3. Infants
received caregiving during 11 to 46% of the 24 hour recording
period, mean 23%. The mean number of clusters per 24 hours
was
12.6 and mean interval between care was 87.9 minutes. The
mean
duration of care clusters was 25.6 minutes; however, the
caregiving
mode was 1. For 16.7% of all care clusters, care duration was 1
minute, and care duration of 1 to 2 minutes accounted for 25%
of
all care clusters. Thus, there was considerable variability both
within and across subjects, with frequent very short-term care
as
well as long bouts of care.
There were no significant differences (t-test) between incubator

37. control mode in mean cluster duration or interval, number of
care
clusters, percent of time in caregiving, and Apgar scores at 1
and 5
minutes. There was no association (w2) between incubator
control
mode and gender, oxygen therapy, treatment of sepsis,
continuous
or intermittent gavage feeding, and receipt of antibiotics or
caffeine. No infants were receiving analgesics or propranalol.
Five
infants in skin servo-control incubators were receiving
lorazapam.
Infants in skin servo-control incubators differed from those in
air
servo-control (t-test, 38 df) by gestational age (28.12 vs 30.30
weeks, p ¼ 0.002), postconceptional age (30.18 vs 32.67 weeks,
p<0.000), and study weight (1060.53 vs 1545.17 g, p<0.000).
Using w2, there was an association between incubator control
mode
and IV therapy (skin, 13; air, 6, p ¼ 0.002) and ventilatory
support
(skin, 8; air, 4, p ¼ 0.053). Mean abdominal skin temperature
for
all subjects at onset of care cluster was 36.401C (SD 0.726) and
mean incubator air temperature was 30.721C (SD 2.45).
Changes
in temperature following caregiving were highly variable, with
increase and/or decrease in both infant and incubator
temperature
following care. Duration of care cluster did not correlate with
temperature changes following care (r<0.10). As the set points
for
incubator control differ based on type of control, control mode
was

38. examined in relationship to temperature changes in infant and
incubator following care (Table 4, Figure 1).
Table 1 Sample Characteristics by Incubator Control Mode
Variable Air servo-control
(n=23)
Skin servo-control
(n=17)
Total
(N=40)
fr (%) Fr (%) fr (%)
Gender
Male 15 (65.2) 8 (47.1) 23 (57.5)
Female 8 (34.8) 9 (52.9) 17 (42.5)
Oxygen 7 (30.4) 10 (58.8) 17 (42.5)
Ventilation* 4 (17.4) 8 (47.1) 12 (30)
IV fluids 6 (26.1) 13 (76.5) 19 (47.5)
IVH 1 (4.3) 2 (11.8) 3 (7.5)
Sepsis 5 (21.7) 6 (35.3) 11 (27.5)
Intermittent gavage 14 (60.9) 6 (35.3) 20 (50)

39. Continuous gavage 7 (30.4) 10 (58.8) 17 (42.5)
Nipple fed 6 (35.3) 0 6 (15)
Antibiotics 3 (13.0) 3 (17.6) 6 (15)
Bronchodilator 4 (17.4) 1 (5.9) 5 (12.5)
Caffeine 8 (34.8) 8 (47.1) 16 (40)
Lorazepam 0 5 (29.4) 5 (12.5)
Steriods 4 (17.4) 5 (29.4) 9 (22.5)
Note: IV=intravenous, IVH=intraventricular hemorrhage.
*
Ventilation includes continuous positive airway pressure.
Table 2 Descriptive statistics by incubator control mode
Air servo-control (n=23) Skin servo-control (n=17) Total
(N=40)
Variable Mean (SD) Min/Max Mean (SD) Min/Max Mean (SD)
Min/Max
Gestational age (weeks) 30.3 (2.5) 26/30 28.1 (1.6) 26/31 29.4
(2.4) 26/33
Birth weight (g) 1506.0 (409.1) 910/2220 1098.5 (237.2)
566/1500 1332.8 (398.9) 566/2220
Apgar 1 minute 5.6 (2.4) 2/9 4.7 (2.3) 1/9 5.2 (2.3) 1/9
Apgar 5 minute 7.3 (1.5) 4/9 7.3 (1.2) 5/9 7.3 (1.3) 4/9

40. PCA (weeks) 32.7 (2.1) 29.1/35.3 30.2 (1.4) 28.1/32.8 31.6
(2.2) 28.1/35.3
PNA (day) 16.6 (4.8) 13/29 14.5 (2.1) 11/18 15.7 (4.0) 11/29
Study weight (g) 1545.2 (414.5) 906/2300 1060.5 (220.1)
625/1390 1339.2 (419.1) 625/2300
Thomas Temperature and Caregiving
642 Journal of Perinatology 2003; 23:640 – 645
Infant and incubator temperature across time were analyzed
separately using ANOVA-RM (Table 5) and the Greenhouse –
Geisser epsilon correction for F test degrees of freedom. For
infant
temperature there was a significant time and time � control
main
effect. Within subject contrasts demonstrated significant
polynomial
time and time � control effects. ANOVA-RM for incubator
temperature also revealed significant main effects for time and
time � control with significant polynomial components for time
and time � control. Using t-test comparisons, abdominal
temperature at care onset did not differ between control modes,
although mean air temperature in skin servo-control was
slightly
higher than in air servo-control (36.44 vs 36.371C). In air
servo-
control incubators, mean infant temperature tended to decrease
during care, continued to decrease after care offset, and then
rose

41. slightly, however at 20 minutes following care offset precare
temperature was not re-established. In skin servo-control, mean
infant temperature tended to increase slightly throughout care
and after.
Summarizing infant temperature data using the mean does not
portray clinically significant changes occurring within infants.
Change in infant temperature following caregiving was further
examined in those infants whose abdominal skin temperature at
care onset was within normal ranged, defined as 36.2 to 36.81C.
Infant temperature at care onset was compared with temperature
at
20 minutes following care offset. In air servo-control
incubators, of
the 71 care clusters (representing 23 infants) that started with
infant temperature within normal range, temperature was below
normal range in 21 (30%) and above normal range in 14 (20%),
20 minutes following care offset. In skin servo-control
incubators,
there were 90 care clusters (representing 17 infants), initiated
while
infant temperature was within normal range, that resulted in
temperature below normal range in 10 instances (11%) and
above
Table 3 Description of Daily Caregiving (N=503 Care Clusters,
40 Infants)
Variable Mean SD Minimum Maximum
Clusters/infant 12.6 2.6 9 17
Cluster duration (min) 25.6 27.3 1 224
Cluster interval (min) 87.9 53.2 10 316

42. Caregiving (%) 23 8 11 46
Table 4 Effect of Caregiving on Infant and Incubator
Temperatures by Incubator Control Mode (N=40)
Air servo-control (n=23) Skin servo-control (n=17)
Infant Incubator Infant Incubator
Time T1C D T1C D T1C D T1C D
Onset
Mean (SD) 36.37 (0.77) 30.25 (1.60) 36.44 (0.67) 31.31 (3.14)
Min/max 33.63/38.25 27.10/33.50 33.17/38.75 22.51/37.25
Offset
Mean (SD) 36.29 (0.75) 0.08 (0.57) 30.37 (1.55) �0.11 (0.43)
36.46 (0.61) �0.02 (0.52) 31.76 (3.38) �0.45 (1.79)
Min/max 33.47/37.99 �1.89/2.72 27.10/34.24 �2.11/1.59
34.60/37.85 �2.62/2.47 22.73/38.57 �7.78/4.32
5 minutes
Mean (SD) 36.26 (0.76) 0.12 (0.59) 30.33 (1.57) �0.08 (0.39)
36.45 (0.65) �0.01 (0.52) 31.92 (3.55) �61 (2.01)
Min/max 34.17/37.99 �1.79/2.75 27.10/34.13 �1.54/1.50
34.04/37.85 �2.54/2.29 22.37/39.84 �8.75/4.70
10 minutes
Mean (SD) 36.26 (0.75) 0.12 (0.59) 30.27 (1.59) �0.02 (0.35)
36.46 (0.65) �0.03 (0.54) 31.99 (3.64) �0.70 (2.20)

43. Min/max 34.28/38.07 �1.79/1.96 27.10/33.60 �1.56/1.35
33.85/37.85 �2.75/2.35 22.23/40.35 �9.65/5.09
15 minutes
Mean (SD) 36.27 (0.77) 0.04 (0.61) 30.25 (1.60) 0.01 (0.33)
36.47 (0.63) �0.04 (0.56) 31.96 (3.70) �0.68 (2.34)
Min/max 34.17/38.17 �1.79/1.92 27.03/34.13 �1.46/1.29
33.78/37.85 �2.94/2.50 22.80/40.49 �10.24/5.38
20 minutes
Mean (SD) 36.29 (0.77) 0.08 (0.62) 30.24 1.62 0.02 (0.35)
36.49 (0.63) �0.06 (0.56) 31.95 (3.64) �0.63 (2.40)
Min/max 33.97/38.17 �1.83/1.83 27.03/34.34 �2.38/1.43
33.89/37.82 �3.08/2.64 22.73/40.49 �10.47/5.66
Note: T1C=temperature; D=temperature difference: onset –
offset, and offset-value at 5, 10, 15, and 20 minutes,
respectively.
Positive value=temperature decrease after care, negative
value=temperature increases after care.
Temperature and Caregiving Thomas
Journal of Perinatology 2003; 23:640 – 645 643
normal range in 21 instances (23%). For those care clusters that
began when infants’ temperature was within normal range, the
minimum and maximum temperature change following care was
�1.07 to 1.711C in air servo-control mode and �1.35 to 0.961C
in
skin servo-control mode.

44. Incubator temperature differed between control mode at all time
points and skin servo-control demonstrated greater variability.
Air
servo-control incubators showed little change in air temperature
during care and following. In skin servo incubators, air
temperature increased with care giving and remained elevated
20
minutes following care offset.
Infants in servo-control were less than 33 weeks
postconceptional age. To examine the possible confounding of
postconceptional age and incubator control mode, the sample
was
divided into two subgroups, <33 and Z33 weeks
postconceptional
age and compared. There were no statistically significant
differences by age group in abdominal temperature at care onset
or
offset. Air temperatures were generally warmer in infants less
than
33 weeks PCA compared to those 33 weeks and older (31.19 vs
29.461C, p ¼ 0.018) and remained warmer after care offset.
Owing
to subject numbers and limited statistical power,
postconceptional
age could not be entered as a covariate in the ANOVA-RM
reported
above. A modified model, using only infants less than 33 weeks
(n ¼ 27) and comparing temperature at care onset, care offset,
and 15 and 20 minutes following care offset also demonstrated a
significant main effect of control (3, 23 df, p ¼ 0.048) and
significant within subject time � control effect.
DISCUSSION
The findings illustrate that caregiving is associated with

45. changes in
infant and incubator temperature and incubator control mode is
a
factor governing these changes. Synthesizing the results of five
research articles in which either (1) oxygen consumption was
used
to define TNZ, or (2) skin temperature was recorded when core
body temperature was carefully controlled within normal range,
abdominal skin temperature between 36.2 and 36.81C is used to
define TNZ.
22 – 26 While the magnitude of mean change in infant
temperature is relatively small, decreasing temperature
following
care in air servo-control incubators results in unacceptably low
temperatures in some infants. Caregiving resulted in significant
change in incubator temperature, with particularly large
variation
in skin servo-control incubators. The higher skin servo-control
air
temperatures following caregiving indicate the ambient
temperature required to sustain infant temperature following
this
disruption. Again, the mean values do not adequately portray
the
experiences of some infants with extremely large changes in
incubator temperature following caregiving. While the changes
in
incubator temperature support maintenance of infant
temperature,
they also reflect considerable thermal stimulation. The effect of
thermal stimulation in preterm infants is incompletely
understood;
however, temperature has been related to respiratory control.

46. 27
Table 5 Analysis of Variance for Repeated Measures for Infant
and
Incubator Temperature at Care Onset, Offset, and 5, 10, 15, and
20
Minutes Following Offset (n=40)
Source F df p
Infant
Multivariate test Time 3.483 5, 34 0.012
Time � control 3.012 5, 34 0.023
Within subjects contrast Time 11.092 1, 38 0.002
Time � control 9.039 1, 38 0.005
Incubator
Multivariate test Time 7.903 5, 34 0.000
Time � control 7.792 5, 34 0.000
Within subjects contrast Time 13.863 1, 38 0.001
Time � control 25.927 1, 38 0.000
Incubator Control Method
Skin servoAir servo
In
cu

47. b
a
to
r
°C
33.0
32.5
32.0
31.5
31.0
30.5
30.0
29.5
onset
offset
5 min after offset
10 min after offset
15 min after offset
20 min after offset

48. Incubator Control Method
Skin servoAir servo
In
fa
n
t
°C
36.7
36.6
36.5
36.4
36.3
36.2
36.1
onset
offset
5 min after offset
10 min after offset
15 min after offset

49. 20 min after offset
Figure 1. Incubator and infant temperature following caregiving
(95% confidence intervals).
Thomas Temperature and Caregiving
644 Journal of Perinatology 2003; 23:640 – 645
In a previous study, differing thermal environments were
produced by skin and air servo-incubator control modes.6 Skin
servo-control resulted in more variable air but more stable
infant
temperature, while air servo-control resulted in more stable air
and
more variable infant temperature.6 In a Cochrane review of
incubator control mode, skin servo-control reduced neonatal
death
rate among low-birth-weight infants, particularly among VLBW
(relative risk 0.72, 95% CI 0.54 to 0.97).28 The current study
shows
that the thermal effects of caregiving also differ by incubator
control mode. The study was not designed to determine
superiority
of incubator control mode. Rather, results highlight the need for
further study of incubator control.
Videotaping may have influenced caregiving pattern and hence
the thermal responses observed. Only the hands of caregivers
were
visible and caregivers were not identifiable. The camera was
unobtrusive and did not physically interfere with the infants
care.
Nursery staff were accepting the research study and did not

50. express
discomfort with the videotaping procedures.
In summary, preterm infant caregiving activities altered both
infant and incubator temperature. These changes in temperature
were dependent on mode of incubator control. In general, air
servo-control resulted in reduction of mean infant temperature
with caregiving, while in skin servo-control mean infant
temperature tended to rise with caregiving. Within infants,
however, caregiving produced variable effects, with body
temperature both increasing and decreasing following
caregiving.
Incubator regulation in response to caregiving produces wide
variability in air temperature. Further investigation of the effect
of
this thermal stimulation is needed.
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8. Bach V, Telliez F, Zoccoli G, Lenzi P, Leke A, Libert JP.
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12. Evans JC, Vogelpohl DG, Bourguignon CM, Morcott CS.
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16. Duxbury ML, Henly SJ, Broz LJ, Armstrong GD, Wachdorf
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Temperature and Caregiving Thomas
Journal of Perinatology 2003; 23:640 – 645 645
STA 614 Cumulative Assessment #3 - FINAL (Fall 2015)
The final cumulative assignment is comprised of 21 short-
answer questions posted on Blackboard and is worth 225 points.
For some of the questions you will need refer to posted articles
and for others you may need to use StatCrunch (these things are
indicated clearly in bold/underlined text either within questions
or prior to a group of questions, so please read carefully!). You
may copy tables directly from StatCrunch into the Blackboard
answer box as needed, however it is preferred that you would
simply incorporate only those numbers that are absolutely
necessary into your written response. Note that there is no
formal StatCrunch report for this assignment.
Please put all of your answers into Blackboard and submit your
final assignment as soon as you can get it done. While the
official deadline is Friday December 11, we would like to be
able to grade assignments as they are completed throughout the
week since final course grades are due on December 14. Please
note that this assignment covers all aspects of the course with a
slight focus toward the last four weeks.Also note: if you need
your grade submitted to SAP so that you can print paperwork to
meet a reimbursement deadline, please send me an email
immediately after submitting your final with a request for
expedited grading.
Collaboration Policy and Grading
Collaboration Policy (Please please please do not do anything
that would force me to apply this!!!)

57. Absolutely no collaboration or communication of any kind with
other students or friends to aid in the completion of these
assignments is allowed. This includes, but is not limited to,
verbal or email communication, text messaging, etc. If for any
reason I deem that you have violated this policy, then you will
at minimum receive a 0 for the assignment and most likely a
failing grade in the course.
Please note: It is acceptable to email Dr. Nolan for
clarifications related to the CUA – but please don’t expect
answers to questions of a statistical nature about specific parts
of this assignment. It is also important to remember that you
may and should feel free to ask of me any general questions or
questions about other assignments/feedback from the course and
I will gladly answer those.
Please please please do not do anything that would cause me to
need to enforce this policy! Thanks!
Grading Rubric: Answers will receive…

58. Full Credit: Question is answered completely and correctly
80-95% Credit: Answer contains 1-2 minor statistical errors
60-80% Credit: Answer contains 3+ minor statistical errors or
any major statistical error
0-60% Credit: Answer is substantially incomplete, contains
multiple major statistical errors, or does not address the
question that was asked.
Reminder: Expected answer lengths are given for many
questions and reflect roughly the length of answers in my
solution key. Please note that the expected length of answer
will have nothing to do with the grading – however it is also
true that the inclusion of irrelevant information (especially if it
is incorrect) may result in deductions.
Questions 1-6 refer to Article 1, entitled Preterm Infant Thermal
Responses to Caregiving Differ by Incubator Control Mode,
found in the final cumulative assessment folder on Blackboard.
#1 (25 points) In Table 2, they provide means and standard
deviations for birth weight for the two samples (air/skin). First
explain why the two samples are independent. Then use
StatCrunch to conduct an appropriate test to determine if the
mean birth weights differ between the two groups. Indicate
validity conditions for the test and assess whether they are
satisfied. Then give the appropriate output from StatCrunch
and a sentence describing the results of the test. Also produce
and interpret a confidence interval in an appropriate manner.
Finally, indicate how this all might impact the authors analysis.
(Expected length of answer: SC Output + 150-250 words)
Hint: To conduct this test you should provide StatCrunch with
“summary statistics”. You should not need to put anything into
the data table.
#2 (12 points) In Table 5 a repeated measures, two-way
ANOVA is conducted. Explain WHY repeated measures is
appropriate in this case. Then identify the four factors involved
in the study and their levels. (Expected length of answer: 100-

59. 200 words)
#3 (12 points) The means given in Table 4 can be placed into
the interaction plot shown below. Assuming that the larger
differences in means that are observed in this plot are in fact
significant, carefully interpret the information contained in the
plot. In particular, what are the most important effects? The
interaction effect tested significant in Table 5. What does the
plot tell you about its importance? (Expected length of answer:
100-200 words) Note: The vertical axis is temperature on the
Celsius scale. **Uses Article 2 I-plot.jpg**
#4 (12 points) In the discussion on page 643, the authors state:
“Using t-test comparisons, abdominal temperature at care onset
did not differ between control modes, although the mean air
temperature in skin servo-control was slightly higher than in air
servo-control (36.44 vs 36.37°C).” What three serious
statistical mistakes have the authors committed in making this
conclusion? (Expected Length of Answer: 50-100 words)
#5 (10 points) Based on the discussion section, identify two
specific limitations of this study. Describe how a future study
might avoid these limitations. (Expected Length of Answer:
100-200 words)
#6 (10 points) In a follow-up study, you wish to determine
whether Apgar scores can predict the need for incubation.
Write a brief introduction to a report in which you will analyze
these. Make sure to include the statistical method you will use
and any appropriate references.
Questions 7-11 refer to Article 2, entitled Association between
heart rate variability and manual pulse rate, found in the final
cumulative assessment folder on Blackboard.
#7 (10 points) The authors state in the caption of Table 2 (page

60. 248) that they’ve used Pearson correlation for the first three
items and Spearman correlation for Age and BMI. Based on all
of the information they provide, do you agree with these
choices? What additional items (that were not provided) should
be reviewed to support these decisions? (Expected Length of
Answer: 50-150 words)
#8 (10 points) Table 2 in a somewhat unusual manner provides
95% CI’s for the correlation. Consider the first line relating to
the correlation between SDNN and Mean HR in HRV. Interpret
the CI (or if you prefer, the squares). (Expected Length of
Answer: 50-100 words)
#9 (10 points) On page 248 alone, the authors have reported no
less than twenty p-values and CI’s. How should this fact affect
their analysis? (Expected Length of Answer: <50 words)
#10 (12 points) In their analysis, the authors examined pairwise
correlations and used simple linear regression. They likely
should be using multiple regression. Explain how multiple
regression might be used here and briefly discuss additional
issues that might result from its use. Also indicate how such
issues may have already affected their use of correlation and
SLR. (Expected Length of Answer 50-100 words).
#11 (10 points) Near the end of page 248, the authors produced
a 95% CI for the slope related to Pulse1 (0.80 to 1.00) in a
model to estimate Mean HR in HRV as the dependent variable.
Give the statistical interpretation of this interval. (Expected
Length of Answer: <50 words)
Questions 12-15 refer to the StatCrunch dataset: 1601 Blood
Gas Values
The goal based on these 150 observations is to predict arterial
blood gas values (ABG, mm Hg) based on central venous blood
gas values (VBG, mm Hg). You should use StatCrunch in
coming up with your answers to these questions.

61. #12 (10 points) Assume that a regression is appropriate. Use
StatCrunch to provide the estimate of R2 for the regression
relationship and explain what the value means. (Expected
length of answer: <25 words)
#13 (10 points) Assume that regression is appropriate. Obtain
and interpret a 95% confidence interval for the slope. (Expected
length of answer: <50 words words)
#14 (10 points) Assume that regression is appropriate. Use
StatCrunch to predict with 90% confidence the ABG for a
patient having VBG = 40 mg Hg. Provide a proper statistical
statement from your prediction and then assess its clinical
value. (Expected length of answer: 50-75 words)
#15 (10 points) Assess all assumptions of the regression model
as best you can based on the data provided. If you use a graphic
for your assessment, do not attempt to copy that graphic into
Blackboard, but rather simply refer to it. (Expected length of
answer: 50-75 words)
Questions 16-21 are unrelated to any particular article or
dataset.
#16 (8 points) Elisabeth wishes to compare average heights
across three different groups of people. She plans to collect
data for 30 people chosen from each group. Provide Elisabeth
with a brief indication of what procedures she ought to use in
her analysis. (expected length of answer: <25 words)
#17 (9 points) Explain why the following statement is false:
When we lack evidence against our null hypothesis, this in
general means that we should collect a larger sample in order to
find such evidence (expected length of answer: 25-50 words).
#18 (9 points) The primary purpose of a manuscript I am
reading is to present lots and lots of different correlations. Will

62. I be able to make clinical use of the results? Why or why not?
(expected length of answer: 25-50 words)
#19 (9 points) Which are more valuable in assessing the clinical
importance of results: hypothesis testing or confidence
intervals? Explain your answer. (expected length of answer:
25-50 words)
#20 (9 points) An author uses two-factor ANOVA to examine
height as the response. He finds that there is an interaction
between his two factors, gender and ethnicity. Explain, in basic
terms, what it means to have such an interaction. You may use
examples as appropriate (expected length of answer: 25-50
words).
#21 (8 points) A study is submitted for IRB approval that plans
to compare the efficacy of a new drug, when used in addition to
current standards of care, in treating a particular form of cancer.
The study will be placebo-controlled, and investigators will
follow subjects until their deaths or until they choose to
withdraw from the study. Identify the appropriate response
variable and explain what analysis techniques might be used in
this scenario. (expected length of answer: 25-75 words)