Physical Assessment

Overview of Data Domain

Several measurements were recorded by the research coordinators during the study visit. The list of variables is included below along with an associated description for each.

Variables included in Data Domain

Height

Assessment of height (statue) is conducted by direct measurement of the length from the bottom of the feet to the highest point of the head.Height is recorded to the nearest tenth of a centimeter (cm).

Measurement_source_value: Height (cm)

Weight

Participants are asked to stand still on the scale with both feet even and flat. Weight is recorded to the nearest tenth of a kilogram (kg).

Measurement_source_value: Weight (kg)

Body Mass Index (BMI)

https://www.nhlbi.nih.gov/health/educational/lose_wt/BMI/bmicalc.htm

Body mass index (BMI) is a measure of body fat based on height and weight that applies to adults (20 years and older). BMI is a useful measure of overweight and obesity. The higher your BMI, the higher your risk for certain diseases such as heart disease, high blood pressure, type 2 diabetes, gallstones, breathing problems and certain cancers.

Although BMI can be used for most men and women, it does have some limits:

• It may overestimate body fat in athletes and others who have a muscular build
• It may underestimate body fat in older persons and others who have lost muscles.

BMI(metric) = weight in kilograms ÷ (2 x (height in meters))

BMI categories:

• Underweight = <18.5
• Normal weight = 18.5 - 24.9
• Overweight = 25 - 29.9
• Obesity = BMI of 30 or greater

Measurement_source_value: BMI

Waist Circumference (WC)

https://www.nhlbi.nih.gov/health/educational/lose_wt/risk.htm

Measuring waist circumference helps screen for possible health risks that come with overweight and obesity. If most of the fat is around the waist rather than at the hips, this increases risk for heart disease and type 2 diabetes. This risk goes up with a waist size that is greater than 35 inches for women or greater than 40 inches for men. To correctly measure the waist, our research coordinators will guide the participants to stand and place a tape measure around the middle, just above the hip bones. The waist will be measured just after you breathe out. Waist circumference is recorded to the nearest tenth of a cm.

Measurement_source_value: Waist Circumference (cm)

Hip Circumference (HC)

https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/hip-circumference

Measure hip circumference at the widest point, usually at the top of the hip bone. Hip circumference is recorded to the nearest tenth of a cm.

Measurement_source_value: Hip Circumference (cm)

Waist to Hip Ratio

https://www.healthline.com/health/waist-to-hip-ratio

The waist to hip ratio (waist circumference÷hip circumference) is an additional screening and monitoring parameter. A waist to hip ratio of less than 0.85 for men and less than 0.75 for women is considered excellent and is associated with low risk. A waist to hip ratio of 1 or higher is associated with increased risk.

Measurement_source_value: Waist to Hip Ratio (WHR)

Heart Rate

Heart rate assessment involves monitoring the frequency of heartbeats per minute, which is a key indicator of cardiac efficiency. In adults, the standard heart rate range is 60 to 100 beats per minute (bpm). This metric is recorded in beats per minute (bpm). The heart rate can be determined through manual palpation at a pulse site or by employing an electronic heart rate monitoring device. Interpreting heart rate requires consideration of the individual's age and physical fitness, as variations from the normal range may suggest cardiac anomalies. MayoClinic

Method of Collection: Automatic (Oscillometric) Device

Measurement_source_value: Heart Rate (bpm)

Blood Pressure

Blood pressure is measured as systolic over diastolic mm Hg. Normal blood pressure is below 120/80 mm Hg. Readings from 120-129/80 indicate elevated blood pressure, while 130-139/80-89 suggests stage 1 hypertension. Readings 140/90 mm Hg or higher indicate stage 2 hypertension. A measurement exceeding 180/120 mm Hg is a hypertensive crisis requiring emergency medical attention. Regular monitoring and lifestyle adjustments or medication may be necessary for managing blood pressure levels. MayoClinic

Method of Collection: Automatic (Oscillometric) Device

• Measurement_source_value: Systolic (mmHg)
• Measurement_source_value Diastolic (mmHg)

Data Processing

File Format

Data was exported from the RedCap Survey that was filled by clinical personnel into .csv format.

File organization is as follows:

pilot_data_root
└── clinical_data
    └── measurement.csv

Domain	Variable	Method	Data Capture	Data Standard/ File Extension	Open Source vs. Protected Database?
Physical Assessment	measurement.csv	device, then Azure	RedCap	.csv	Open source

Data Standards

OMOP

The OMOP (Observational Medical Outcomes Partnership) data standard, developed by the Observational Health Data Sciences and Informatics (OHDSI) program, is a standardized framework designed to improve the quality, reliability, and comparability of observational healthcare data. The main goal of the OMOP standard is to enable the aggregation and analysis of healthcare data from diverse sources, such as electronic health records (EHRs), insurance claims, and registries, in a consistent and interoperable manner.

Reference : https://ohdsi.github.io/CommonDataModel/

File Processing

The .csv file can be opened and processed using notebook application or any coding language example:( Python, R). Here is a python snippet code to extract height.

import pandas as pd

# Load the CSV file
df = pd.read_csv('measurement.csv')  # Ensure the file name is correctly specified

# Replace 'HEIGHT_IDENTIFIER' with the actual identifier for Height in your dataset
HEIGHT_IDENTIFIER = 'Height (cm)'  # This should be the actual identifier used in your CSV

# Filter the dataframe for rows where measurement_source_value is Height
height_df = df[df['measurement_source_value'] == HEIGHT_IDENTIFIER]

# Selecting only the necessary columns
# Assuming 'person_id' and 'value_as_number' represent the individual and their height
height_df = height_df[['person_id', 'value_as_number', 'measurement_source_value']]

# Save the filtered data to a new CSV file
height_df.to_csv('height_cm.csv', index=False)

print('Data saved to height_cm.csv')

Metadata and Example Outputs

Variable	Description	Example 1	Example 2
measurement_id	Unique identifier for the measurement	13	12
person_id	Unique identifier for the person	1029	1023
measurement_concept_id	Concept ID for the measurement	4245997	3025315
measurement_date	Date of the measurement	2023-08-28	2023-08-28
measurement_datetime	Date and time of the measurement	2023-08-28 00:00:00	2023-08-28 00:00:00
measurement_time	Time of the measurement	(can be empty)	(can be empty)
measurement_type_concept_id	Concept ID for the type of measurement	32862	32862
operator_concept_id	Concept ID for the operator of measurement	0	0
value_as_number	Numeric value of the measurement	48.2	150
value_as_concept_id	Concept ID for the value of measurement	0	0
unit_concept_id	Concept ID for the unit of measurement	0	0
range_low	Lower range of the measurement value	(can be empty)	(can be empty)
range_HIGH	Higher range of the measurement value	(can be empty)	(can be empty)
provider_id	Unique identifier for the provider	0	0
visit_occurrence_id	Unique identifier for the visit occurrence	0	0
visit_detail_id	Unique identifier for the visit detail	0	0
measurement_source_value	Source value of the measurement	BMI	Weight (Kg)
measurement_source_concept_id	Concept ID for the measurement source value	0	0
unit_source_value	Source unit of the measurement	(can be empty)	(can be empty)
unit_source_concept_id	Concept ID for the unit source	0	0
value_source_value	Source value for the measurement value	(can be empty)	(can be empty)
measurement_event_id	Unique identifier for the measurement event	0	0
meas_event_field_concept_id	Concept ID for the measurement event field	0	0