Estimating Running Performance Combining Non-invasive Physiological Measurements and Training Patterns in Free-Living

Estimating Running Performance Combining
Non-invasive Physiological Measurements and
Training Patterns in Free-Living
40th International Conference of the IEEE Engineering in Medicine and Biology Society
HRV4Training.com
Marco Altini and Oliver Amft

2
ESTIMATING RUNNING PERFORMANCE
EMBC 2018 HRV4Training.com
While human performance in running has been analysed in
elite athletes as well as recreational ones, the scientific
community is still investigating different aspects behind the
limits of human performance
Being able to accurately estimate running performance can be
helpful at different levels:
• Provide individuals with better race pacing strategies
• Tailor training plans to individual abilities, reducing injury
risk

3
HOW DO WE ESTIMATE RUNNING PERFORMANCE?
EMBC 2018
Different anthropometrics, physiological and training
characteristics have been associated to running performance,
for example:
• Lower body fat
• Lower resting heart rate
• Higher heart rate variability
• Lab measurements of VO2max
• Training related variables: volume and intensity
HRV4Training.com

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LIMITATIONS OF CURRENT STUDIES
Most literature on running performance estimation is
constrained by small sample size and a rather
homogeneous sample (for example only men or a narrow
age or performance range).
Often variables are acquired in laboratory conditions or
supervised settings that greatly limit practical applicability.
Finally, parameters are analysed in isolation and typically
results are not cross-validated.

5
THE TIME IS RIGHT FOR A DIFFERENT APPROACH
In the past few years, we have witnessed fast technological
developments and integrations between different platforms
and services (e.g. via public APIs):
• Increased availability of multivariate data streams
acquired from mobile applications and wearable sensors
• Data is available at a greater scale with respect to
standard laboratory studies
We have the opportunity to provide additional insights on
the relation between physiology, training and performance

6
OUR CONTRIBUTION
In this work, we propose the first longitudinal, large scale
analysis of running performance with respect to a wide set of
variables either self-reported or acquired automatically
and non-invasively in free-living, without laboratory tests or
supervision
In particular, we used the HRV4Training app to collect data
from 2113 individuals of different fitness level, over a period
of 2 years and developed a model to highlight the relative
impact of different predictors on running performance
estimation, where reference was 10 km running time

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1. Users downloaded the
HRV4Training app from the
Apple store or Google Play.
The app allows a user to
measure resting physiology
(HR and HRV) using the
phone camera, and links to
other apps such as Strava or
TrainingPeaks to import
workouts data (e.g. heart
rate, pace, distance, etc.)
DATA ACQUISITION

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2. Inclusion criteria: measure resting physiology each
morning for at least 3 months, train with a heart rate
monitor
3. Reference data: the best 10 km time for each user over
the 2 years was used as reference. Then, the previous 3
months of data, were used to build the training set
(features related to training volume and patterns)
DATA ACQUISITION

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DEFINING PREDICTORS OF RUNNING PERFORMANCE
We computed features representative of different aspects that
may contribute to running performance:
• Ant: anthropometrics data
• Rest: resting physiological data (HR and HRV)
• Vol: training volume and speed
• TrPhy: physiological data during training, in particular the
speed to HR ratio, a feature representative of VO2max
• Pol: training polarisation, meaning training at different
intensities (often easy, a few times hard) wrt training mostly at
the same moderate intensity
• Performance: past running performance, so the second
best 10 km in the 3 months preceding our reference 10 km

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FEATURES EXAMPLE
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Quantiles of 10 km performance
BMI(kg/m^2)
a) BMI (Ant)
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c) Heart rate to speed ratio (TrPhys)
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d) 'Non−polarized' trainings (Pol)

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FEATURES EXAMPLE
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FEATURES EXAMPLE
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FEATURES EXAMPLE
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BMI(kg/m^2)
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Multiple linear regression models were validated using 10-
fold cross-validation. All users with at least 20 workouts were
used as training set
VALIDATION AND RESULTS

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16

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BLAND-ALTMAN FOR THE BEST MODEL
(PERFORMANCE)

18
FINDINGS AND CONSIDERATIONS
While no causal link can be established, it is of particular
interest to determine the impact of features representative of
training patterns derived from workouts, given the unknowns
linked to these aspects of training
According to our dataset and analysis, a more polarised
training regime, including a higher percentage of workouts
performed either at low or high speeds, as well as a lower
percentage go workouts performed at moderate HR intensity,
is associated with improved performance

19
CONCLUSIONS AND FUTURE WORK
In this work we used data acquired longitudinally in free
living on 2113 runners of all levels. We investigated the
relation between anthropometrics, resting physiology and
training related variables and performance, showing that
running performance can be estimated accurately
The proposed models do not require laboratory tests and
could be practically employed by the growing community of
recreational runners to estimate performance and tailor
training plans
The proposed model could be analyzed over longer periods
of time to determine the ability to track individual variability

Thank you
Marco Altini, PhD
altini.marco@gmail.com
40th International Conference of the IEEE Engineering in Medicine and Biology Society
HRV4Training.com

Estimating Running Performance Combining Non-invasive Physiological Measurements and Training Patterns in Free-Living

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