Unit 4 Review Assignment Please write in complete sentences. You should work on this assignment as you go through the assigned readings and chapters in your textbook. Avoid copying directly from the text. Your responses should be in your own words. Chapter 8 1. Explain how listening helps to earn trust, to build collaboration and to negotiate resolutions to problems and conflicts. 1. Imagine you manage 100 employees. Some employees work in the home office, and others remotely in other cities. Identify and describe two ways you might encourage communication among these 100 people. 2. Describe two specific actions a leader might take to develop cooperative goals. 3. What is reciprocity? Why is reciprocity a successful approach for making daily decisions and negotiating differences among people? Chapter 9 6. Explain the relationship between leadership and control. 7. Explain what is meant by the phrase “in the flow.” 8. Identify and explain two methods or ways to increase skill and competency. 9. Explain the difference between training and coaching. Identify one way or technique you might use to coach a new employee. The Leadership Challenge Workbook 10. Your textbook focuses on the importance of building trust and recommends holding a one-on-one relationship building meeting to foster trust. On page 97 of your Workbook, you will see a list of questions that you might ask during such a meeting. Select three of these questions from the list and explain why you would consider these three the most important. Introduction Research relating to the effects of exercise on glycaemic control in people with type 1 diabetes has usu- ally been performed in laboratory environments.1–5 A recent literature review was performed to identify any related research where the replica- tion of laboratory based self-manage- ment research findings were applied into the real-life environment, and also to demonstrate any differences regarding the impact on glycaemic control between environments. It became evident that all research identified regarding self-manage- ment was based in a laboratory envi- ronment using either a treadmill or bicycle for exercise, and not applied into real-life situations.2–5 However, the knowledge generated from these laboratory based experiments under- pins current self-management rec- ommendations.2–4,6–11 From these original studies and literature review publications, a self-management algorithm for use when performing moderate intensity exercise before the evening meal was devised (see Table 1). The aim of this current study was to compare the glucose response in participants with type 1 diabetes, during and after a 40-minute exer- cise session at 70% VO2 max (mod- erate intensity exercise) while following the self-management algo- rithm, in the laboratory environ- ment using a treadmill, and while running in participants’ real-life environment. This was to evaluate the efficacy of using laboratory find ...

1. Unit 4 Review Assignment
Please write in complete sentences. You should work on this
assignment as you go through the assigned readings and
chapters in your textbook. Avoid copying directly from the text.
Your responses should be in your own words.
Chapter 8
1. Explain how listening helps to earn trust, to build
collaboration and to negotiate resolutions to problems and
conflicts.
1. Imagine you manage 100 employees. Some employees work
in the home office, and others remotely in other cities. Identify
and describe two ways you might encourage communication
among these 100 people.
2. Describe two specific actions a leader might take to develop
cooperative goals.
3. What is reciprocity? Why is reciprocity a successful approach
for making daily decisions and negotiating differences among
people?
Chapter 9
6. Explain the relationship between leadership and control.
7. Explain what is meant by the phrase “in the flow.”
8. Identify and explain two methods or ways to increase skill
and competency.
9. Explain the difference between training and coaching.

2. Identify one way or technique you might use to coach a new
employee.
The Leadership Challenge Workbook
10. Your textbook focuses on the importance of building trust
and recommends holding a one-on-one relationship building
meeting to foster trust. On page 97 of your Workbook, you
will see a list of questions that you might ask during such a
meeting. Select three of these questions from the list and
explain why you would consider these three the most important.
Introduction
Research relating to the effects of
exercise on glycaemic control in
people with type 1 diabetes has usu-
ally been performed in laboratory
environments.1–5 A recent literature
review was performed to identify any
related research where the replica-
tion of laboratory based self-manage-
ment research findings were applied
into the real-life environment, and
also to demonstrate any differences
regarding the impact on glycaemic
control between environments. It
became evident that all research
identified regarding self-manage-
ment was based in a laboratory envi-
ronment using either a treadmill or
bicycle for exercise, and not applied
into real-life situations.2–5 However,
the knowledge generated from these

3. laboratory based experiments under-
pins current self-management rec-
ommendations.2–4,6–11 From these
original studies and literature review
publications, a self-management
algorithm for use when performing
moderate intensity exercise before
the evening meal was devised (see
Table 1).
The aim of this current study was
to compare the glucose response in
participants with type 1 diabetes,
during and after a 40-minute exer-
cise session at 70% VO2 max (mod-
erate intensity exercise) while
following the self-management algo-
rithm, in the laboratory environ-
ment using a treadmill, and while
running in participants’ real-life
environment. This was to evaluate
the efficacy of using laboratory find-
ings, under controlled conditions, in
patient education for use in their
everyday life. The significance and
value of real-world data are becom-
ing an increasingly valuable source
of evidence for clinical practice.12
PRACTICAL DIABETES VOL. 32 NO. 6 COPYRIGHT © 2015
JOHN WILEY & SONS 217
Original article
Can laboratory based research regarding

4. type 1 diabetes and exercise be applied
into the real-life environment?
Abstract
The aim of this study was to determine whether results from
laboratory based research
examining glycaemic control during and after exercise can be
applied to a real-life
(non-laboratory) environment.
A comparative study of individuals with type 1 diabetes (n=9)
using basal bolus analogue
insulin regimens was undertaken. Glycaemic control before and
after two 40-minute runs at
70% VO2 max, in both laboratory and real-life environments,
was measured across 10
time-points during and up to 12 hours after exercise. Insulin
was adjusted in all participants
following a self-management algorithm.
Pooled mean glucose concentrations at each time-point were
compared. There was no
statistically significant difference (F[1, 8] = 1.489, p=0.257) in
overall mean glucose
concentrations between environments. Similarly, the exercise
environment or time-point of
measurement had no statistically significant effect on mean
glucose concentration (F[9, 72] =
0.499, p=0.871). However, during exercise, episodes of both
hypoglycaemia (<4.0mmol/L) and
hyperglycaemia (>9.0mmol/L) were more frequent in the
laboratory environment than in the
real-life environment: 5 vs 1 and 25 vs 19 episodes,
respectively; the frequency of acceptable
concentrations (4.0–9.0mmol/L) was greater in the real-life
environment (24 vs 34). In the 8–12

5. hours after exercise, hypoglycaemia occurred more frequently in
the real-life environment (3 vs 8)
with hyperglycaemia occurring more frequently in the
laboratory environment (22 vs 14); again,
there were slightly increased acceptable concentrations in the
real-life environment (29 vs 33).
The exercise environment does not appear to affect overall
mean blood glucose
concentrations. However, it may affect the timing and frequency
of hypoglycaemia and
hyperglycaemia. Copyright © 2015 John Wiley & Sons.
Practical Diabetes 2015; 32(6): 217–221
Key words
type 1 diabetes; moderate intensity exercise; laboratory and
real-life environments;
glycaemic control
Jacqui Charlton
MRes, BSc, PgCTLHE, RGN, Lecturer and Specialist
Nurse in Diabetes, Metabolic Unit, Western General
Hospital, Edinburgh; Edinburgh Napier University, UK
Lynn Kilbride
PhD, MSc, BA (Hons), PGCE, Head of School, Glasgow
Caledonian University, Glasgow, UK
Rory MacLean
PhD, MBPsS, FHEA, BSc (Hons), Lecturer in
Psychology, Sighthill Campus, Edinburgh Napier
University, Edinburgh, UK
Mark G Darlison
FSB, PhD, MSc, BSc, Professor of Neuroscience, and

6. Director of Research for the Faculty of Health, Life and
Social Sciences, Sighthill Campus, Edinburgh Napier
University, Edinburgh, UK
John McKnight
MB, BCh, MRCP, MD, FRCP, Consultant Physician,
Metabolic Unit, Western General Hospital,
Edinburgh, UK
Correspondence to:
Miss Jacqui Charlton, Room 4B16, Sighthill Campus,
Edinburgh Napier University, Edinburgh EH11 4BN,
UK; email: [email protected]
Received: 13 January 2015
Accepted in revised form: 6 March 2015
This supports the question of
whether data collected in a con-
trolled and possibly unrealistic
laboratory environment would be
replicated when performed in a real-
life environment.
Method
The inclusion criteria for partici-
pants were: people with type 1 dia-
betes of over two years’ duration;
aged 18–60 years old; HbA1c under
86mmol/mol (10.0%); using a basal
bolus insulin regimen; hypogly-
caemia awareness; and exercise
twice a week or more.
The exclusion criteria were: pre-

7. proliferative/proliferative retinopa-
thy; neuropathy/foot ulceration;
blood pressure >150/90; cardiovas-
cular disease/history of angina;
orthopaedic problems.
Study design
The study ran over a two-week
period. On days 1 and 3 of each
week, participants undertook 40
minutes of moderate intensity exer-
cise (days 1 and 8 in the laboratory,
and days 3 and 10 in real-life
environments). Days 2 and 9 were
rest days and participants were
instructed not to perform exercise.
All were instructed to follow the
self-management algorithm for
insulin and carbohydrate adjust-
ment (Table 1).
Data collection
The data collection methods for
glucose levels were:
• Before exercise until before
evening meal: participants per-
formed self-monitoring of blood glu-
cose (SMBG) using a TrueResult
meter (Nipro Diagnostics UK). This
meter was chosen due to ease of use
and small size for carrying in the
exercise sessions. SMBG was chosen
for this time-period as it was impor-
tant to establish any immediate
changes in blood glucose that may
require cessation of exercise.

8. • After the evening meal until 12
hours after: interstitial glucose
levels using the Minimed iPro
(Medtronic) continuous glucose
meter were used. This method was
used as this time-period was during
the night and performing SMBG
would disturb participants’ sleep.
The continuous glucose monitoring
218 PRACTICAL DIABETES VOL. 32 NO. 6 COPYRIGHT ©
2015 JOHN WILEY & SONS
Can laboratory based research regarding type 1 diabetes and
exercise be applied into the real-life environment?
Original article
Lunchtime insulin
• If exercising within 2 hours of eating a meal, reduce the
bolus/meal dose by 75%3,4
Blood glucose
• Aim for blood glucose of 8mmol/L immediately before
exercise
• If blood glucose over 12mmol/L, check for ketones and take a
correction dose
• If blood glucose over 17mmol/L do not exercise8–11
Food
• If blood glucose under 8mmol/L have the following
carbohydrate (CHO)1,14
Blood glucose prior to exercise (mmol/L) Amount of CHO (g)
Under 4 30

9. 4–6 20
6–8 10
8 or over 0
Bolus/meal insulin
• Eat within 2 hours of exercise and reduce the bolus/meal dose
by 30%3,9–11
• After 2 hours return to usual dose
Long-acting insulin
• Take usual Lantus or Levemir dose2
Blood glucose
• If blood glucose at 8mmol/L or under before bedtime have 10–
20g of CHO
Table 1. Algorithm for insulin and carbohydrate adjustment for
exercising at 70% VO2 max
Before exercise
After exercise
Figure 1. A comparison of mean blood glucose concentrations in
laboratory and real-life sessions. The
error bars represent standard deviations
Bl
oo
d
gl

10. uc
os
e
(m
m
ol
/L
)
18
16
14
12
10
8
6
4
2
0
Laboratory Real-life
Tim
e-p

11. oin
t
Be
for
e e
xe
rci
se
20
m
ins
du
rin
g
40
m
ins
at
en
d
Be
for
e e
ve
nin
g m
ea

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2 h
ou
rs
aft
er
6 h
ou
rs
aft
er
8 h
ou
rs
aft
er
10
ho
urs
af
ter
12
ho
urs
af
ter

13. data were only available by download
after the study period.
In order to ensure reliability of the
comparison of environments, it was
important that the exercise sessions
were consistent, i.e. performed at the
same time of day, following the same
self-management algorithm, and
exercising at the same intensity. In
the laboratory sessions, the treadmill
was used which was considered best
to replicate running in a real-life
environment. The intensity was con-
trolled by manipulating the speed
and grade to ensure that 70% VO2
max was achieved, which equates to
moderate intensity exercise.13 This
was pre-determined by a sub-maxi-
mal incremental walking test to
determine an individual’s 70% VO2
max. In the real-life sessions, partici-
pants undertook running outside or
in a sports hall in an area of their
choice. Participants were given an
individual training heart rate (THR)
with a minimum and maximum
range, which again was determined
by the sub-maximal incremental
walking test. They used a Polar wrist-
watch during the exercise sessions to
ensure their HR was within the target
range and this ensured the mainte-
nance of 70% VO2 max.
Analysis

14. Statistical. The four exercise sessions
were analysed using the mean blood
glucose concentrations and standard
deviations using a three-way repeated
measures test (ANOVA) to explore
any differences in blood glucose
measured in mmol/L between the
two environments (laboratory or real-
life), and in the two sessions (i.e.
week one and week two), across 10
time-points: before exercise, 20 min-
utes during, at 40 minutes [end of
session], before the evening meal,
and at 2, 4, 6, 8, 10 and 12 hours after.
Bonferroni adjustments were used to
control the probability of finding
type 1 errors. Statistical significance
was set at p<0.05. Data are presented
as means ± standard deviation.
Descriptive. A descriptive analysis was
also performed as the characteristics
of the statistical tests along with
the small participant numbers used
would not reveal any extreme glucose
episode outliers. Across the four
exercise sessions all data were
analysed for the occurrence of
participant glycaemic episodes in dif-
ferent glucose ranges: hypoglycaemia
(≤4.0mmol/L); acceptable range
(4–9mmol/L); and hyperglycaemia
(≥9.0mmol/L). The frequencies of
individual participant episodes for
each of these ranges were stated as

15. numbers. There were 18 individual
participant episodes (nine partici-
pants: two sessions [laboratory or real-
life]) for each time-point. This would
demonstrate differences between
time-points within environments.
Results
Nine individuals with type 1 diabetes
(five male, four female) completed
the study and performed four exer-
cise sessions (two laboratory and two
real-life). All used a basal bolus
analogue insulin regimen and exer-
cised regularly. The demographic
data are presented as means ±
standard deviation: age 39.3±10.5
years (range 24–56 years); BMI
24.8±1.7kg/m2; weight 75.6±6.2kg;
HbA1c 63mmol/mol (7.9±0.7%);
duration of diabetes 16.8±14.2 years
(range 4–46 years).
Mean glucose concentrations
The pooled data of mean glucose
concentrations taken at the specific
time-point with standard deviations
in the laboratory and real-life ses-
sions are shown in Figure 1, which
displays the comparison between
environments. The mean glucose
concentrations revealed similar
results in both environments.
However, the glucose concentrations
appeared to be more variable at the

16. real-life time-points, which were
recognised by the larger standard
deviations, compared with the labo-
ratory time-points.
Differences between
environments
With the overall mean glucose con-
centrations, the three-way ANOVA
verified that the environment did
not have a significant main effect
on the glycaemic control of partici-
pants (F[1, 8] = 1.489, p=0.257).
This referred to the comparison of
all mean glucose concentrations
for both environments, without
examining change in blood glucose
over time.
When investigating two-way inter-
actions regarding glucose control
between the variables, there were no
significant effects on environments
and times (F[9, 72] = 0.499, p=0.871).
PRACTICAL DIABETES VOL. 32 NO. 6 COPYRIGHT © 2015
JOHN WILEY & SONS 219
Can laboratory based research regarding type 1 diabetes and
exercise be applied into the real-life environment?
Original article
Table 2. A summary of episode percentages and numbers in
time periods for each glucose range

17. Environment Lab Real-life Lab Real-life Lab Real-life Lab
Real-life
Blood glucose 9.3% 1.9% 5.6% 0 9.3% 3.7% 5.6% 14.8%
under 4mmol/L (5) (1) (1) (0) (5*) (2*) (3) (8*)
Blood glucose 44.4% 63% 55.6% 50% 46.3% 46.3% 53.7%
61.1%
4–9mmol/L (24) (34) (10) (9) (25) (25) (29) (33)
Blood glucose 46.3% 35.2% 38.9% 50% 48.1% 51.9% 40.7%
25.9%
over 9mmol/L (25) (19) (7) (9) (26) (28) (22) (14)
*Additional hypoglycaemic episodes occurred in between the 2-
hourly time-points. In the 2–6 hours after period, 2 of these
occurred in the laboratory
environment and 1 during the real-life environment. In the 8–12
hours after period, 1 occurred in the real-life environment.
These occurrences have been
included in the episode numbers.
Baseline – 40 minutes Before evening meal 2–6 hours after 8–
12 hours after
Descriptive analysis
The episode percentage and num-
bers for each glucose range at the
different time periods are shown in
Table 2.
During exercise. When comparing
the laboratory vs real-life environ-
ment, episodes of both hypogly-

18. caemia (5 vs 1) and hyperglycaemia
(25 vs 19) during exercise in the
laboratory environment were more
frequent than during real-life exer-
cise, with greater acceptable concen-
trations in the real-life environment
(24 vs 34).
Up to 6 hours after exercise. During
the before evening meal time-point
and up until 6 hours after exercise,
increased hypoglycaemia occurred
in the laboratory environment (6 vs
2). Similar episode frequencies were
demonstrated in acceptable concen-
trations (35 vs 34), and hypergly-
caemia (33 vs 37).
8–12 hours after exercise. In the
8–12 hour time-period after exer-
cise, when comparing the laboratory
vs real-life environment, hypogly-
caemia occurred more frequently in
the real-life environment (3 vs 8)
with hyperglycaemia occurring
more frequently in the laboratory
environment (22 vs 14); again, there
were a slightly greater number of
acceptable concentrations in the
real-life environment (29 vs 33).
Discussion
The exercise session environments
were designed to mimic each
other, and the self-management
algorithm used was the same for

19. both environments. One might pre-
dict, therefore, that there would be
no difference between the glucose
responses in each environment.
Statistically, no difference was
found, but these findings were
based on only nine participants and
our study was therefore underpow-
ered to detect a difference, as 12
participants were required for an
80% chance of detecting a 0.5%
difference in glucose response.
The three-way ANOVA showed
no differences in glycaemic control
between the laboratory and real-
life environments. However, when
the descriptive analysis (which
highlighted extreme outliers) was
performed using episodes of gly-
caemic ranges – i.e. hypoglycaemia,
acceptable range of 4–9mmol/L,
and hyperglycaemia – important
differences in glucose concentra-
tions were apparent.
Hypoglycaemia episodes. At 20 min-
utes during exercise and at 40 min-
utes (end of exercise session) there
were higher hypoglycaemia episode
numbers in the laboratory environ-
ment. Whereas during the 8–12
hour period after the evening meal
and fast-acting analogue insulin
injection, there was an increase in

20. hypoglycaemia episodes in the real-
life environment.
Acceptable glucose range. Overall,
the patterns suggested that the
real-life sessions achieved greater
4–9mmol/L episodes compared
with the laboratory sessions, except
before the evening meal and 2 hours
after the exercise time-points. For
the whole of the experimental
period from baseline to 12 hours
post-exercise, the real-life environ-
ment showed more episodes within
the normal range (laboratory 88 vs
real-life 101). However, it must be
acknowledged that the mean glu-
cose results in the real-life environ-
ment did show higher standard
deviations which suggest greater
variability of glucose control.
Hyperglycaemic episodes. It is
appreciated that often people with
type 1 diabetes purposely aim for
higher blood glucose concentrations
before, during and after exercise, in
an attempt to prevent hypogly-
caemia. A high number of partici-
pants started with an elevated blood
glucose concentration, which did
not delay the start of exercise, and
no participant administered extra
fast-acting insulin at this point.
There was increased hyperglycaemia
during exercise in the laboratory,

21. which is interesting when also
considering the increased hypogly-
caemic episodes. This hypergly-
caemia increase was also apparent
during 8–12 hours after exercise. For
the real-life environment, the only
increased hyperglycaemia episodes
occurred after exercise until 4 hours
after the finish.
Study limitations
The addition of qualitative data
would have been useful in clarifying
the participants’ experiences of the
two environments, which may have
explained the outcomes. It could be
thought that participants may not
have exercised at 70% VO2 max dur-
ing the real-life sessions as they were
not observed by a researcher. This
would explain the increase in hypo-
glycaemic episodes during the labo-
ratory sessions and not in real-life.
However, if this was the case, the
participants would not have experi-
enced an increase in delayed hypo-
glycaemia after the real life sessions.
It may also be viewed as more stress-
ful running outside due to being
aware of the environment and safety
issues such as cars, whereas in the
laboratory there were no decisions
made regarding the route. Another
variable which was not accounted for
was that of temperature, as hot and
cold temperatures could affect gly-

22. caemic control.
As statistical analysis demon-
strated the environment did not
affect glycaemic control, this observa-
tion must provide reassurance that
previous laboratory based research
and subsequent findings can be used
in patient education to advise on self-
management strategies during exer-
cising and subsequently clarify the
reproducibility of clinical research in
everyday life. However, it must be
taken into consideration that the
sample size was a limitation of this
current study and, if correctly pow-
ered, results may have been different.
Another reason for caution with
extrapolating laboratory based statis-
tical data would be extreme outliers
of hypoglycaemia and hypergly-
caemia. In the current study, extreme
outliers were not highlighted in the
statistical analysis, but the descriptive
analysis demonstrated a difference
with the patterns of hypoglycaemia
and hyperglycaemia in each environ-
ment. This remains an important
issue for patients, but will require
further investigation using a larger
sample size.
Despite the lack of power in the
study, the self-management algo-
rithm was modified to reflect the
hypoglycaemia episodes; a slight

23. increase in the carbohydrate
amounts were introduced into the
220 PRACTICAL DIABETES VOL. 32 NO. 6 COPYRIGHT ©
2015 JOHN WILEY & SONS
Can laboratory based research regarding type 1 diabetes and
exercise be applied into the real-life environment?
Original article
before exercise section, and a carbo-
hydrate snack at bedtime was intro-
duced into the after exercise section.
The findings described cannot be
compared with other results in the
literature since no studies, as far as
these authors are aware, have been
published comparing exercise envi-
ronments in this manner. Despite
this, laboratory findings regarding
self-management strategies are used
by health care professionals to advise
patients on exercise management in
daily life, although they have not
been evaluated in that environment.
Conclusion
The initial aim of this study was to
determine whether results from lab-
oratory based research examining
glycaemic control during and after
exercise can be applied to a real-life

24. (non-laboratory) environment; stat -
istical analysis infers that this is
acceptable. However, the descriptive
analysis does suggest differences
within the laboratory environment
during exercise, and the delayed
risk of hypoglycaemia after the
real-life sessions 8–12 hours after
the post-exercise insulin dose, as it
would appear that there was a
difference between environments.
Hyper glycaemia frequency is also
increased in the laboratory environ-
ment during exercise and 8–12
hours after exercise, whereas in real-
life the increase was noticed after
exercise until 4 hours after the finish
of the session. When considering
acceptable glucose levels, the real-
life environment data do suggest
better glycaemic control; however,
the larger standard deviations would
imply greater variability. These find-
ings are essential for patient safety
and education, especially regarding
the prevention of hypoglycaemia.
Nevertheless, it is acknowledged that
these differences were observed
patterns and thus were not statisti-
cally analysed. A larger sample size
would be required to make further
interpretation and conclusions.
However, this does highlight issues
when applying laboratory research
findings into clinical care.

25. Acknowledgments
We thank Dr Martin Maxwell for his
on-going support, and Spencer
Fildes from Nipro Diagnostics UK
for providing the TrueResult blood
glucose meters.
Declaration of interests
There are no conflicts of interest
declared.
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PRACTICAL DIABETES VOL. 32 NO. 6 COPYRIGHT © 2015
JOHN WILEY & SONS 221
Can laboratory based research regarding type 1 diabetes and
exercise be applied into the real-life environment?
Original article
l Most exercise and type 1 diabetes research has been performed
in a laboratory
environment
l Apart from case studies, no publications have been found
regarding self-management
and glycaemic control observing patients in real-life
environments
l Statistical analysis did not show a difference on the effect of
glycaemic control in a
laboratory environment compared to real-life
l Descriptive analysis did show differences especially in
relation to hypoglycaemic episodes
during the exercise period and overnight
l No major conclusions can be made from these findings as the
study was underpowered,
but it does highlight issues to consider when using laboratory
data in clinical practice
Key points

28. Find out how non-diabetes drugs impact diabetes patients. Visit
the Practical Diabetes website and
click on drug notes
Drug notes
Aliskiren l Amlodipine l Bisoprolol l Bromocriptine l
Bumetanide l Carbamazepine l Cilostazol l Clopidogrel l
Colesevelam l Dabigatran l
Darbepoetin alfa l Diazoxide l Digoxin l Dipyridamole l
Domperidone l Doxazosin l Dronedarone l Duloxetine l
Eplerenone l Erythromycin
l Ezetimibe l Gabapentin l Indapamide l Ivabradine l Labetalol l
Lidocaine l Lorcaserin l Losartan l Methyldopa l
Metoclopramide l Nicorandil
l Nifedipine l Omacor l Orlistat l Prasugrel l Prolonged-release
nicotinic acid l Quinine sulphate l Ramipril l Ranolazine l
Rimonabant l
Rivaroxaban l Rosuvastatin l Sibutramine l Spironolactone l
Tadalafil l Testosterone l Torcetrapib
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