Students already report height, weight, age, and gender as part of the
spirometry and metabolism labs, so asking for this data was not
unique to the database. Since we are a small college, students were
not asked to identify ethnicity in the database, as even with the randomized participant numbers, this may have been identifying information. Once the database was built in a Google spreadsheet that
could be shared with participants, students were assigned a random
participant number and asked to enter their background information
into the database. To ensure privacy, I was the only one with a master
list matching student names to numbers. To ensure that students were
diligent in entering their information, a portion of their lab grade was
dependent on their database entries.
After students were introduced to the database, they were given
time to generate their research questions. During the second lab
period, students self-selected into small groups and were given
information regarding which experiments would take place during
the semester. Table 2 summarizes the physiology data students
would have access to over the course of the semester. Using information from Tables 1 and 2, they were given time to brainstorm
topics that would lead to generation of a research question. Students could ask any question of interest, provided that the question
could be examined using information from the database. To analyze their research question, each group needed to examine at least
four sets of physiological data; only two datasets could involve the
same body system. For example, if they examined heart rate and
blood pressure, their remaining datasets must come from two other
body systems.
Depending on their research question, students could divide the
database participants in a variety of ways. For example, students could
compare athletes versus nonathletes, females with different amounts
of sleep, or subjects with high, medium, and low stress across various
physiologic factors. During this brainstorming session, students
received guidance about their questions. Here are some examples of
questions and hypotheses:
• Does stress level affect resting heart rates, resting respiratory
rates, and reflex times between high- and low-stress groups?
• What differences are seen in reflex times (auditory, visual, and
patellar) and in resting and recovery rates of the cardiovascu-
lar and respiratory systems when comparing individuals who
primarily carry our aerobic exercise with those who primarily
carry out strength training?
• How do varying amounts of aerobic activity per week affect
lung volumes, resting heart rates, recovery blood glucose, and
patellar reflexes?
• Does getting less than six hours of sleep on average impact the
cardiovascular, nervous, and respiratory systems?
After generating their research question, students had to provide a
rationale justifying the body systems and measurements chosen.
To generate this rationale, students performed a literature search
and found peer-reviewed, reliable citations to justify their research
question. This initial justification and literature search helped form
the basis for the introduction of their group lab reports.
While students were working on their literature search and rationale, physiology data were being collected during standard lab sessions throughout the semester. The majority of data were collected
using i Worx IXTA physiology units. In previous years, the data collected were reported in individual lab packets and used primarily to
examine concepts of physiology specific to a particular body system.
For example, students analyzed an electrocardiogram in a resting subject and after the subject did three minutes of exercise. The changes
seen were used to discuss the regulation of the cardiac cycle. This sys-tem-specific learning still took place over the course of the semester to
reinforce lecture topics, but lab packet data were also entered into the
database. Sample data collected can be seen in Table 3.
The approach described here would be applicable to any physiology dataset regardless of the collection method. Several sets of data
(including blood glucose data, urine production rates, and urine specific gravity) were collected without the i Worx acquisition systems,
and numerous measurements such as blood pressure, heart rate,
breathing rate, and recovery rates don’t require sophisticated acquisition systems.
As various labs were completed during the semester and data were
added to the database, students were encouraged to write the methods
sections of their lab report and begin analyzing the data. Data analysis
required students to consider the best statistical comparison to use for
Table 1. Sample demographic and background
information included in the class database.
Age in years
Height in inches
Weight in pounds
Gender, M or F (genetic assignment)
Student athlete? (Yes or No)
Average hours of sleep per weekday
Average daily stress level on a scale of 1–10, with 10 being
high stress and 1 being low stress
Average hours of aerobic exercise per week
Average hours of strength training per week
Table 2. Body systems examined during the
semester and the associated data that were collected.
Body System Data Collected
Nervous system Auditory and visual reflex times
Muscular
system
Patellar tendon reflex time; grip strength;
minimum amplitude for finger twitch
Respiratory
system
Various lung volumes; FEV1; respiratory
rates at rest and recovery
Cardiovascular
system
Heart rate at rest and recovery; systolic
and diastolic blood pressure; various
electrocardiogram amplitudes and
intervals; blood type
Endocrine
system
Fasting blood glucose; oral glucose
tolerance test
Urinary system Urine production rate; urine specific
gravity
