006-hand_grip

Hand grip strength dataset

Hand grip strength (HGS) is an effective marker of general health in middle age and older adulthood. Grip strength is measured by squeezing a type of muscle strength testing equipment, known as a dynamometer.

HGS is a measure of the maximum force that an individual can exert with their hand. It is a widely used measure of muscular strength. HGS is an easy and quick measure to obtain.

In a clinical setting, HGS is often used as a measure to assess changes in muscle strength over time. For example, in patients with conditions such as osteoarthritis, rheumatoid arthritis, or sarcopenia, Additionally, HGS can be used to assess the effectiveness of rehabilitation programs, such as those used to recover from a stroke or other neurological condition.

HGS is measured using a Jamar Hydraulic Hand Dynamometer. The measurement is a measure of the static force (without movement) that the hand can exert when squeezing around the dynamometer.

Data availability:

The information is stored in 1 parquet file: hand_grip.parquet

from pheno_utils import PhenoLoader

pl = PhenoLoader('hand_grip')
pl

PhenoLoader for hand_grip with
10 fields
2 tables: ['hand_grip', 'age_sex']

Data dictionary

pl.dict

	field_string	description_string	folder_id	feature_set	field_type	strata	data_coding	array	pandas_dtype	bulk_file_extension	relative_location	units	bulk_dictionary	sampling_rate	transformation	list_of_tags	stability	sexed	debut	completed
tabular_field_name
collection_date	Collection date (YYYY-MM-DD)	Collection date (YYYY-MM-DD)	6	hand_grip	Date	Collection time	NaN	Single	datetime64[ns]	NaN	hand_grip/hand_grip.parquet	Time	NaN	NaN	NaN	NaN	Accruing	Both sexes	2018-10-18	NaN
collection_timestamp	Collection timestamp	Collection timestamp	6	hand_grip	Datetime	Collection time	NaN	Single	datetime64[ns, Asia/Jerusalem]	NaN	hand_grip/hand_grip.parquet	Time	NaN	NaN	NaN	NaN	Accruing	Both sexes	2018-10-18	2019-11-13
finger_grip_strength_left	Finger grip strength (left)	Finger grip strength (left)	6	hand_grip	Continuous	Primary	NaN	Single	float	NaN	hand_grip/hand_grip.parquet	kg	NaN	NaN	NaN	NaN	Complete	Both sexes	2018-10-18	2019-11-13
finger_grip_strength_right	Finger grip strength (right)	Finger grip strength (right)	6	hand_grip	Continuous	Primary	NaN	Single	float	NaN	hand_grip/hand_grip.parquet	kg	NaN	NaN	NaN	NaN	Complete	Both sexes	2018-10-18	NaN
dominant_hand	Dominant hand	Hand preference for fine motor tasks	6	hand_grip	Categorical (single)	Primary	007_01	Single	category	NaN	hand_grip/hand_grip.parquet	NaN	NaN	NaN	NaN	NaN	Accruing	Both sexes	2018-10-18	NaN
hand_grip_strength_left	Hand grip strength (left)	Hand grip strength (left)	6	hand_grip	Continuous	Primary	NaN	Single	float	NaN	hand_grip/hand_grip.parquet	kg	NaN	NaN	NaN	NaN	Accruing	Both sexes	2018-10-18	NaN
hand_grip_strength_right	Hand grip strength (right)	Hand grip strength (right)	6	hand_grip	Continuous	Primary	NaN	Single	float	NaN	hand_grip/hand_grip.parquet	kg	NaN	NaN	NaN	NaN	Accruing	Both sexes	2018-10-18	NaN
timezone	Timezone	Timezone	6	hand_grip	Categorical (single)	Collection time	NaN	Single	string	NaN	hand_grip/hand_grip.parquet	NaN	NaN	NaN	NaN	NaN	Accruing	Both sexes	2018-10-18	NaN

Plot histogram and ecdf for hand_grip_strength_left at baseline visit

from pheno_utils.basic_plots import hist_ecdf_plots

col = "hand_grip_strength_left"
df = pl[[col] + ["age", "sex", "collection_date"]].loc[:,:,"00_00_visit",0,:]

# plot histogram and ecdf
hist_ecdf_plots(df.dropna(subset=[col,"sex", "age"]), col, gender_col="sex")

# stats
display(df[col].describe().to_frame().T)

	count	mean	std	min	25%	50%	75%	max
hand_grip_strength_left	10336.0	34.177962	11.432727	2.358683	25.083688	31.638105	43.091326	79.061244