DOI: 10.5281/zenodo.2846014
title: Accurate capture of real-world gait speed in frail, older adults: insights into performance and behavior from a longitudinal clinical trial (Data for interventional clinical trial)

description: Digital technologies and advanced analytics have drastically improved our ability to capture and interpret health relevant data from patients. However, to date, limited data and results have been published detailing real-world patient compliance, demonstrating accuracy in target indications or examining what novel insights and clinical value can be derived. Here we present novel, digital mobility data from two studies: an independent, non-interventional validation study with elderly, naturally slow walking subjects, and a global, multi-site phase IIb clinical trial involving patients with age-related muscle loss and slow walking speed (sarcopenia). Based on these data, we validate the accuracy of a novel algorithm for capturing in-clinic and real-world gait speed in frail, slow-walking adults. We demonstrate the feasibility of continuous monitoring with a wearable inertial sensor in elderly adults in real-world settings, and propose minimum thresholds for compliance required for robust capture of gait behaviors in this population. We also show how simple, inferred contextual information, describing the length of a given walking bout, can explain some of the variation in real-world gait speed, and use this information to demonstrate for the first time a relationship between in-clinic performance and real-world gait speed behavior. This work lays a foundation for exploration of the clinical relevance and value of such measures and is a first step in building a more complete chain of evidence between standardized physical performance assessment, real-world behavior, and subjective perceptions of mobility, independence and health.

This dataset contains data collected during the interventional clinical trial: derived data from raw accelerometry data, and summary performance data.

The full dataset, including raw accelerometry data, is available here: https://s3.console.aws.amazon.com/s3/buckets/mueller-et-al-2019
description: Contact/correspondence for data set: Arne Mueller (arne.mueller@novartis.com)
Access/URLs:
•	Independent validation study
o	Derived data and metadata: 10.5281/zenodo.2841298

•	Interventional clinical trial
o	Derived and metadata: 10.5281/zenodo.2846014

•	Both studies
o	Full datast: https://s3.console.aws.amazon.com/s3/buckets/mueller-et-al-2019

DOI for publication: TBC

General notes:
Note, the device records into one or more data files, but these files belong to the same recording. Also note, that patient usually has two belts (one leather belt and one comfortable flex belt) with one device each and decides which belt he/she wants to wear when. If a belt is open (belt buckle not closed) the belt records a belt open flag. However, a belt might be stored unworn but in a closed state. 
Note, in actibelt (when worn correctly) x, y and z values correspond to vertical, lateral and back-forward acceleration. 

Derived data descriptions
meta.csv: meta data to map patients to recoding files
1.	belt.id: serial number of the device (repeated if the device was re-used)
2.	start: timestamp in UTC when the recording was started
3.	end: timestamp in UTC when the recording was ended
4.	duration: duration of the recording in seconds 
5.	stopcode: stop code at the end of a recording (vendor specific)
6.	format: version of the proprietary data format
7.	file: name of a raw file (several files can be present for one UUID)
8.	uuid: unique ID per belt, patient and recording period. Note one UUID can have several files
9.	SUBJID:  anonymized patient identifier
10.	gender: gender of patient
11.	patient_timezone: time zone of the patient to derive local time for start and end of recording
12.	range_of_birth: a range of 5 years for the patient's year of birth (is left inclusive and right exclusive, e.g. (1930-1935] is &gt;= 1930 and &lt; 1935) 
clin_acti_walk_tests.csv: Clinical walk test during which the actibelt was worn
1.	SUBJID: anonymized patient id
2.	VISITNUM: visit number (coding according to internal use)
3.	repeat_in_visit: test repetition within visit
4.	test_date: date when the test was performed 
5.	watch_speed: walking speed calculated by stop watch and distance
6.	acti_speed: estimated speed by actibelt
7.	uuid: UUId of actibelt measurement
8.	file: actibelt filename 
9.	patient_timezone: time zone of  measurement
10.	test_start_time: start timestamp of test in UTC 
11.	test_stop_time: end timestamp of test in UTC
12.	test: The walk test conducted (4mWT: 4 meter,   6MWT: six minute,  400mWT: 400 meter)

daily-summaries.csv: daily step, wear time and activity summaries
1.	SUBJID: aanonymized patient id
2.	day: day/date for this summary 
3.	VISITNUM: internal visit number coding
4.	daytime: daytime of the day (night &gt;20h to 4h, from or daytime &gt;4h to 20h)
5.	isvisit: logical, TRUE is this day was a clinical visit day
6.	deltaday: the number of days to the nearest visit
7.	total_weartime: hours of wearing during this time
8.	max_activity: the maximum hourly activity count for this time
9.	total_activity: sum of activity count for this time
10.	total_steps: number of steps in this time

weartime.csv: wear time per patient within each day and hour of day
bodyheight-femur.csv: patient height and length of femur in cm
steps.h5: Steps for each patient in HDF5 format
1.	SUBJID: anonymized patient id
2.	time: time of heel strike (UTC)
3.	running: was this a running step (logical)?
4.	speed:  estimated step speed in m/sec
5.	duration: estimated duration of the step in sec.
6.	distance: estimated distance of the step in meters
7.	side: left or right foot (estimated)?
8.	bout: the bout identifier this step is in
9.	freq: windowed step frequency
10.	walk.ratio: windowed walking ratio
11.	belt: the belt index this step was estimated from (for internal use)
12.	tz: time zone of this step (patient time zone)
13.	daytime: categorized daytime of the time stamp
bout-summaries.h5: steps summarized into bouts per hour and 
1.	SUBJID: anonymized patient id
2.	VISITNUM: internal visit number coding
3.	bout: bout identifier
4.	hour: hour in day
5.	days: 
6.	day: day/date
7.	daytime: categorized daytime
8.	deltaday: number of days to the nearest visit
9.	Steps: number of steps in bout
10.	avg_duration: mean step duration in bout in sec.
11.	avg_distance: mean step distance in bout in meters
12.	total_distance: total distance covered with this bout (meters)
13.	total_duration: total duration of this bout in sec.
14.	med_speed: median step speed in this bout in m/sec
15.	avg_speed: mean step speed in this bout in m/sec
16.	min_speed: minimal step speed in this bout in m/sec
17.	max_speed:  maximum step speed in this bout in m/sec
18.	q25_speed: 25th percentil step speed in this bout in m/sec
19.	q75_speed: 75th step speed in this bout in m/sec
20.	sd_speed: step speed standard deviation this bout
21.	iqr_speed: step speed inter-quartile range of this bout
22.	avg_walkratio0: mean walk ratio of steps in this bout
23.	avg_freq: average windowed step frequency in this bout as reported by step detection software
24.	Nsteplength: body hight-normalised step length (mean(distance*1000)/height*mean_height)
25.	Ncadence: mean(60/duration)*sqrt(height/mean_height)
26.	avg_walkratio: nsteplength/ncadence
27.	isvisit: is this day a visit (logical)?
28.	total_weartime: total daytime weartime
29.	bucket: categorized bout length in number of steps

Raw data
Each UUID (see meta.csv) is an HDF5 file with each file recording for a dataset for which the x, y and z acceleration is stored as well as the KSS with 100 Hz. To get acceleration gravity units the x, y or z value needs to be divided by 336. The KSS indicates whether the belt buckle was closed (1) or open (0).
Raw data is available here: https://s3.console.aws.amazon.com/s3/buckets/mueller-et-al-2019