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Relation Between Estimated Cardiorespiratory Fitness and Running Performance in Free-Living: an Analysis of HRV4Training Data

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Abstract

In this work, we propose to use anthropometrics and physiological data to estimate cardiorespiratory fitness (CRF) in free-living and analyze the relation between estimated CRF and running performance. In particular, we use the ratio between running speed and heart rate (HR) as predictor for CRF estimation in free-living. The ratio is representative of fitness as lower HR at a given speed is expected for more fit individuals. Then, we analyze the relation between estimated CRF and running performance for 10 km, half marathon and full marathon runs. CRF estimation models were developed using lab-based V O2max measurements. CRF estimates were obtained from data collected in unsupervised free-living in a sample of 532 runners for a period ranging between 1 and 8 months using the HRV4Training app. During the same period, running performance was determined for all runners. We show that the speed to HR ratio provides higher accuracy in CRF estimation compared to resting HR or no-physiological data (15% to 18% reduction in RMSE for person-independent models). Secondly, we found moderate to strong correlations between CRF estimated from free-living data and running performance (Pearson's r = 0.56 − 0.61). We conclude that estimating CRF in free-living using mobile technology and data integration can be a valuable tool to support individualized training plans and to track fitness and performance outside laboratory settings.
Relation Between Estimated Cardiorespiratory Fitness and Running
Performance in Free-Living: an Analysis of HRV4Training Data
Marco Altini1, Chris Van Hoof2and Oliver Amft1
Abstract In this work, we propose to use anthropometrics
and physiological data to estimate cardiorespiratory fitness
(CRF) in free-living and analyze the relation between estimated
CRF and running performance. In particular, we use the ratio
between running speed and heart rate (HR) as predictor for
CRF estimation in free-living. The ratio is representative of
fitness as lower HR at a given speed is expected for more fit
individuals. Then, we analyze the relation between estimated
CRF and running performance for 10 km, half marathon and
full marathon runs. CRF estimation models were developed
using lab-based V O2max measurements. CRF estimates were
obtained from data collected in unsupervised free-living in
a sample of 532 runners for a period ranging between 1
and 8 months using the HRV4Training app. During the same
period, running performance was determined for all runners.
We show that the speed to HR ratio provides higher accuracy
in CRF estimation compared to resting HR or no-physiological
data (15% to 18% reduction in RMSE for person-independent
models). Secondly, we found moderate to strong correlations
between CRF estimated from free-living data and running
performance (Pearson’s r= 0.56 0.61). We conclude that
estimating CRF in free-living using mobile technology and data
integration can be a valuable tool to support individualized
training plans and to track fitness and performance outside
laboratory settings.
I. INTRODUCTION AND RELATED WORK
In the past few years, ubiquitous sensing technologies
showed unprecedented insights into the relation between
physical activity, health and performance [1]. A multitude
of wearable devices and mobile applications have been
developed to support recreational and professional athletes in
tracking their workouts. Physiological data, including heart
rate (HR) and heart rate variability (HRV) have been used
to monitor athletes fitness levels as well as recovery from
previous workouts [2].
Due to fast paced technological developments and integra-
tions between different platforms and services (e.g. public
APIs), increased availability of multivariate data streams
acquired from mobile applications and wearable sensors (e.g.
GPS, accelerometer, physiological data), new applications
and techniques have been developed. Smartphone-based
measurements have become popular [3], as smartphone-
integrated sensors could be used, e.g. GPS to track distance
and photoplethysmography to track physiological data using
the phone’s camera and flash light as light source [4], [5].
While several mobile applications and wearable sensors
have been released on the market in the recent past, typically
providing users with estimates of calories burnt, steps taken
1M. Altini and O. Amft are with ACTLab, University of Passau, DE
altini.marco@gmail.com
2C. Van Hoof is with imec, Leuven, BE
(e.g. Fitbit) and workouts data such as distance, time, speed,
heart rate, etc. (e.g. Garmin), all important metrics reflecting
individual behavior, limited work has been carried out to
provide insights on the individual’s actual health and per-
formance status outside of laboratory settings. In particular,
cardiorespiratory fitness (CRF) can potentially provide more
information for both health and sports applications, as it is a
key health parameter [6], [7], [8] and performance indicator
in endurance sports [9], [10].
Current gold standard and practice for CRF measurement
is direct measurement of oxygen volume (V O2in ml/min)
during maximal exercise (i.e.V O2max). However, V O2max
tests are affected by multiple limitations. Medical supervision
is required and the test can be risky for individuals in non-
optimal health conditions. Sub-maximal tests have also been
developed [11], typically requiring to measure HR while
running at a certain speed or biking at a certain intensity.
HR while performing a specific activity in laboratory set-
tings, is discriminative of CRF levels due to the inverse
relation between HR and CRF [12], with more fit individuals
typically showing lower HR at a given workout intensity.
Commercial devices, for example some sport watches paired
to HR monitors [13] (e.g. Polar devices), provide CRF
estimation using a regression model including HR at a
predefined running speed as predictor. A few methods have
been recently proposed to estimate CRF using wearable
sensor data acquired in free-living [7], [8], [14], [15], without
the need for laboratory tests. Using wearable sensors in free-
living to estimate V O2max is a novel approach that could
be applied to a larger population compared to maximal or
sub-maximal laboratory tests.
The relation between V O2max and running performance
has been investigated several times in laboratory settings,
sometimes with conflicting results [9], [10]. V O2max es-
timation in free-living to track performance over a broad
population with different fitness levels has never been investi-
gated before. Technological advances make it finally possible
to monitor longitudinally physiological data in free-living
with minimal burden on the user, enabling new opportunities
beyond laboratory settings. Monitoring training progress
using free-living estimates, without the need for additional
protocols, could provide individuals with an effective tool
for individualized training and motivation.
In this work, we first analyzed different CRF estimation
models relying on anthropometrics data only, anthropomet-
rics and physiological data at rest as well as anthropometrics
and physiological data during exercise, proposing the speed
to HR ratio as predictor able to minimize estimation error.
Then, we used the HRV4Training app [5] to collect resting
HR and HRV data, training summaries (HR during exercise,
distance, duration, elevation gain for each workout) and a
user anthropometrics (age, weight, height and gender) for
a total of 532 runners. We used HRV4Training data to
derive running performance over 1to 8months as well as
to estimate V O2max according to the lab-validated CRF
estimation models. Finally, we compared estimated V O2max
and running performance by integrating data from additional
services. Our contribution is therefore twofold:
We show that CRF estimation error can be reduced by
15% and 18% when using the speed to HR ratio as
predictor, with respect to anthropometrics data only and
resting physiological data respectively.
We show moderate to strong correlations between free-
living CRF estimates and running time (Pearson’s r=
0.56 0.61), consistently for distances between the 10
km and the full marathon, highlighting how estimated
V O2max can potentially be used to track individual
performance outside laboratory settings.
II. METHODS AND DATA COLLECTION
We used lab data and acquired data in free-living settings.
Lab data included measurements of HR at sub-maximal
intensities (rest and running at different speeds), used as
predictors for V O2max estimation, as well as a V O2max
test. Free-living data included training summaries (HR,
speed, distance, time), resting physiological measurements
(HR and HRV) and runner anthropometrics (age, gender,
height and weight). V O2max estimation models developed
in the lab were used to estimate V O2max from free-living
data and investigate the relation with running performance,
also derived from training summaries in free-living.
A. Laboratory data: V O2max modeling
Participants for laboratory studies were 48 (22 male, 26
female), age 25.0±6.2years, weight 67.8±10.4kg, height
173.3±9.1cm, BMI 22.5±2.3kg/m2and V O2max
44.8±7.2ml/min. Written informed consent was obtained,
and the study was approved by the ethics committee of
Maastricht University. HR data were acquired using the
ECG Necklace, a platform configured to acquire one lead
ECG data at 256 Hz, and three-axial accelerometer data
at 32 Hz. Reference CRF was determined as V O2max, by
means of an incremental test on a cycle ergometer [16] using
a indirect calorimeter that analyzed O2consumption and
CO2production. Two laboratory protocols were performed.
The first protocol included simulated activities performed
while wearing a portable indirect calorimeter and the ECG
Necklace. Activities included in this study were: lying down
and running (treadmill flat at 7,8,9,10 km/h). Activities
were carried out for a period of at least 4 minutes. The
second protocol was a V O2max test providing reference data
for CRF.
B. Free-living data: V O2max estimations
Runners were not recruited but downloaded the
HRV4Training application from the Apple Store and
agreed to provide collected measurements for research
purposes via a consent form embedded in the application.
Instructions were provided to reproduce conditions similar
to measurements at rest in laboratory settings. HR and
HRV duration was configurable between 1and 5minutes.
The HRV4Training app is used for morning measurements
at rest, and integrates with other commonly used services
to retrieve actual workouts data and training summaries.
Thus, training summaries including HR, speed, time,
distance and elevation gain were acquired using Strava’s
public APIs. Each user voluntarily linked HRV4Training to
Strava to retrieve training summaries in the HRV4Training
application.
We included in this analysis all runners that recorded train-
ings for at least 1 month. We included only runner trainings
when a HR rate monitor was used, as HR data during exercise
is required for our V O2max estimation. Finally, we included
in this analysis only users that ran one of the following
events: 10 km, half marathon (21.1 km) or full marathon
(42.2 km). The inclusion criteria yielded 532 runners (493
male, 34 female), 88581 physiological measurements at rest,
and 24712 trainings including HR and GPS data, i.e 46
trainings and 70 physiological measurements per user on
average. Mean age was 39.9±8.0years, mean weight was
72.5±9.1kg, mean height was 178.0±7.3cm and mean
BMI was 22.8±2.1kg/m2.
III. DATA ANALYS IS
A. Laboratory data: V O2max modeling
CRF was estimated using multiple linear regression mod-
els and different sets of predictors collected in laboratory set-
tings (see Sec. II-A). We compared the following three cases:
anth, including anthropometrics data only (BMI, age and
gender), resting including anthropometrics and physiological
data acquired at rest, i.e. morning HR and HRV, training
including anthropometrics, resting physiological data and
the speed to HR ratio. The speed to HR ratio was used
as it could be derived from free-living training summaries
regardless of a runner HR and preferred running speed and
is representative of fitness as lower HR at a given speed is
expected for more fit individuals.
B. Free-living data: V O2max estimations
Resting physiological data were acquired in free-living
using HRV4Training’s photoplethysmographic (PPG) mea-
surement. PPG is an unobtrusive technique for detecting
blood volume changes during a cardiac cycle and is often
measured using reflection by illuminating the skin using
a LED (e.g. the phone’s flash) and detecting the amount
of light that is reflected by a photodetector or a camera
located next to the light source. Details on this method can
be found in [5]. HR was computed as the mean HR over
the measurement window. As HRV feature we used rMSSD
as it was shown to be a clear marker of parasympathetic
activity and often used to determine physiological stress due
to training load [17]. rMSSD was computed as the square
root of the mean squared difference between PPG peak to
peak intervals.
Running performance was determined for each runner as
the fastest time over distances between the 10 km and full
marathon for the measurement period. Runners were also
split into performance categories based on their best times. In
particular, we created three categories: slow runners (10 km
above 47.5 minutes, half marathon time above 1 hour and 45
minutes, full marathon time above 4 hours and 15 minutes),
fast runners (10 km below 40 minutes, half marathon time
below 1 hour and 30 minutes, full marathon time below
3 hours and 15 minutes) and average runners, including
all remaining ones. Training summaries data were used to
determine running performance as well as running HR and
speed, used as predictors for V O2max estimates. The speed
to HR ratio was computed from training summaries and used
as predictor for V O2max estimation models.
IV. RESULTS AND DISCUSSION
A. Laboratory data: V O2max modeling
Results for leave one participant out cross-validation of
V O2max estimation models are shown in Fig. 1. Root mean
square error (RMSE) for anth was 4.2±3.0ml/kg/min, while
for resting was 4.1±3.1ml/kg/min and for training was
3.5±2.8ml/kg/min. Results are consistent with previous
research on V O2max estimation using sub-maximal HR,
confirming that physiological responses to more intense
exercise (e.g. the speed to HR ratio during running) consis-
tently improve V O2max estimation accuracy. In particular,
participant-independent RMSE is reduced by 15% and 18%
on our dataset when using training compared to resting and
anth respectively.
B. Free-living data: Running performance and V O2max
estimations
Running performance derived from training summaries
acquired in free-living was 48.8±6.0minutes for 10 km
runs, 108.0±15.9minutes for half marathon runs and 225.0±
36.6minutes for full marathons. Fig. 2 shows the relation
between V O2max estimated using the training model, i.e.
the best performing V O2max estimation model described in
Sec. IV-A, and running performance. A moderate to strong
inverse relation is visible for all running distances (Pearson’s
rranging between 0.56 and 0.61). Finally, Fig. 2.d
shows boxplots of the relation between estimated V O2max
and running category as defined in Sec. III-B, highlighting
consistent differences between groups.
Reported results are consistent with previous small-scale
studies showing moderate to strong correlations between
V O2max and running performance [9], [10]. While it is clear
that there is more to running performance than V O2max, and
other variables might serve as more accurate predictors in
laboratory settings (for example lactate threshold and running
efficiency [18], [9]), measuring these parameters requires
laboratory infrastructure, expensive equipment and dedicate
R=0.72
30
40
50
60
30 40 50 60
Predicted VO2max
Reference VO2max
Gender
Female
Male
Anth − VO2max (ml/kg/min)
R=0.72
30
40
50
60
30 40 50 60
Predicted VO2max
Reference VO2max
Resting − VO2max (ml/kg/min)
R=0.8
30
40
50
60
30 40 50 60
Predicted VO2max
Reference VO2max
Training − VO2max (ml/kg/min)
−20
0
20
30 35 40 45 50 55
Mean, (Reference + Fitted)/2
Residuals
Bland−Altman − Anth − VO2max (ml/kg/min)
−20
0
20
30 35 40 45 50 55
Mean, (Reference + Fitted)/2
Residuals
Bland−Altman − Resting − VO2max (ml/kg/min)
−20
0
20
30 40 50
Mean, (Reference + Fitted)/2
Residuals
Bland−Altman − Training − VO2max (ml/kg/min)
Fig. 1. Relation between estimated and measured V O2max for the different
models implemented based on the lab data. Anth refers to anthropometrics
characteristics only, Resting includes anthropometrics, resting HR and
rMSSD, training includes anthropometrics and the speed to HR ratio during
running activities as predictors. Correlation between actual and estimated
V O2max is also reported for each analysis.
personnel. On the other hand, we showed that V O2max can
be estimated with good accuracy using the speed to HR
ratio as predictor, which can be easily acquired using today’s
smartwatches and mobile phone applications. Additionally,
we found the inverse relation between running performance
and estimated V O2max to be consistent across different
running distances. We believe that the availability of metrics
representative of running performance such as the proposed
V O2max estimate could help individuals keep track of their
fitness level, effectively closing the loop between training
and objective estimates of physical fitness and performance.
R=−0.6
30
40
50
60
70
0.6 0.8 1.0
<− Faster −−− Time (hours) −−− Slower −>
Estimated VO2max
Category
Fast
Average
Slow
Gender
Female
Male
a) Best 10km time in relation to VO2max
R=−0.56
30
40
50
60
70
1.5 2.0 2.5
<− Faster −−− Time (hours) −−− Slower −>
Estimated VO2max
b) Best half marathon time in relation to VO2max
R=−0.61
30
40
50
60
70
3 4 5
<− Faster −−− Time (hours) −−− Slower −>
Estimated VO2max
c) Best marathon time in relation to VO2max
30
40
50
60
70
Fast Average Slow
Estimated VO2max
d) Runner category and VO2max (all users)
Fig. 2. Relation between running performance (racing duration for
distances between the 10 km and the full marathon) and estimated V O2max
for data collected using the HRV4Training application in unsupervised
free-living settings. A moderate to strong inverse relationship is shown
independently of running distance. Distributions of V O2max values and
running performance are also shown.
V. CONCLUSIONS
In this paper we investigated the relation between es-
timated V O2max and running performance in free-living.
First, we used data acquired under laboratory settings to build
and validate V O2max estimation models and proposed the
speed to HR ratio as predictor that can be easily computed
from running workouts. We showed that V O2max estimation
error can be reduced by 15% and 18% with respect to anthro-
pometrics data only and resting physiological data (HR and
HRV) respectively. Then, we acquired free-living workouts
data from 532 individuals over a period of up to 8months
using the HRV4Training application. Trainings were used
to estimate V O2max and running performance. We found a
moderate to strong negative correlation between estimated
V O2max and running performance (r= 0.56 0.61), for
all distances between the 10 km and the full marathon.
Given the greater sample size compared to typical studies,
we could provide confirmative insights on the feasibility of
using sub-maximal HR to estimate fitness level in free-living,
and use such estimated fitness level as a metric representative
of running performance. Estimated V O2max can potentially
be used to track individual performance outside laboratory
settings, driving motivation and helping athletes of all lev-
els keep track of progress as well as adopt individualized
training plans based on a person’s physiological response
to training. Our approach confirms the potential of mobile
technology and data integration to provide relevant insights
in free-living performance on the population level. Further
work is needed to investigate individual variance.
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... Table 1. The results of the scientific literature's search revealed that there were five studies [44][45][46][47][48] published in peer-reviewed journals, of which only three apps were available to be downloaded on commercial platforms (HRV4Training, InterWalk app, and TOHRC Walk Test). HRV4Training [44] was the only app stored in both app markets (Google Play and App Store). ...
... The results of the scientific literature's search revealed that there were five studies [44][45][46][47][48] published in peer-reviewed journals, of which only three apps were available to be downloaded on commercial platforms (HRV4Training, InterWalk app, and TOHRC Walk Test). HRV4Training [44] was the only app stored in both app markets (Google Play and App Store). All the included apps were available in English, except for the InterWalk app, which was only available in Danish [46]. ...
... All the included apps were available in English, except for the InterWalk app, which was only available in Danish [46]. Four studies [45][46][47][48] included < 20 participants, and one study [44] included 48 individuals. Three studies included healthy adults [44,47,48], whereas Brinkløv et al. [46] included participants with type 2 diabetes mellitus and Brooks et al. [45] included those with congestive heart failure and pulmonary hypertension. ...
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Background Cardiorespiratory fitness (CRF) assessment provides key information regarding general health status that has high clinical utility. In addition, in the sports setting, CRF testing is needed to establish a baseline level, prescribe an individualized training program and monitor improvement in athletic performance. As such, the assessment of CRF has both clinical and sports utility. Technological advancements have led to increased digitization within healthcare and athletics. Nevertheless, further investigation is needed to enhance the validity and reliability of existing fitness apps for CRF assessment in both contexts. Objectives The present review aimed to (1) systematically review the scientific literature, examining the validity and reliability of apps designed for CRF assessment; and (2) systematically review and qualitatively score available fitness apps in the two main app markets. Lastly, this systematic review outlines evidence-based practical recommendations for developing future apps that measure CRF. Data Sources The following sources were searched for relevant studies: PubMed, Web of Science®, ScopusTM, and SPORTDiscus, and data was also found within app markets (Google Play and the App Store). Study Eligibility Criteria Eligible scientific studies examined the validity and/or reliability of apps for assessing CRF through a field-based fitness test. Criteria for the app markets involved apps that estimated CRF. Study Appraisal and Synthesis Methods The scientific literature search included four major electronic databases and the timeframe was set between 01 January 2000 and 31 October 2018. A total of 2796 articles were identified using a set of fitness-related terms, of which five articles were finally selected and included in this review. The app market search was undertaken by introducing keywords into the search engine of each app market without specified search categories. A total of 691 apps were identified using a set of fitness-related terms, of which 88 apps were finally included in the quantitative and qualitative synthesis. Results Five studies focused on the scientific validity of fitness tests with apps, while only two of these focused on reliability. Four studies used a sub-maximal fitness test via apps. Out of the scientific apps reviewed, the SA-6MWTapp showed the best validity against a criterion measure (r = 0.88), whilst the InterWalk app showed the highest test–retest reliability (ICC range 0.85–0.86). Limitations Levels of evidence based on scientific validity/reliability of apps and on commercial apps could not be robustly determined due to the limited number of studies identified in the literature and the low-to-moderate quality of commercial apps. Conclusions The results from this scientific review showed that few apps have been empirically tested, and among those that have, not all were valid or reliable. In addition, commercial apps were of low-to-moderate quality, suggesting that their potential for assessing CRF has yet to be realized. Lastly, this manuscript has identified evidence-based practical recommendations that apps might potentially offer to objectively and remotely assess CRF as a complementary tool to traditional methods in the clinical and sports settings.
... Erlangen-Nurnberg (DE) email: altini.marco@gmail.com between the 10 km and the marathon [4]. Training related variables, such as training volume (distance per week), as well as average training speed have been associated to improved running performance too. ...
... We computed the speed to HR ratio, a feature that relies on the fact that a more fit (faster) runner would maintain a lower HR while running at a certain speed, with respect to less fit (slower) runners. This parameter is the main predictor behind V O 2 max estimation models relying on sub-maximal tests or workouts data [4], [11]. • Pol: training polarization. ...
... The estimation models developed in this work do not require laboratory tests, and could be practically employed by the growing community of recreational runners to estimate performance and tailor training plans. Results for different feature sets are consistent with previous results from smaller studies [3], [7], [5], [4], showing a positive correlation between higher estimates of V O 2 max, higher HRV, lower HR, higher training volume, higher training speed, a more polarized training regime and running performance. ...
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In this work, we use data acquired longitudinally, in free-living, to provide accurate estimates of running performance. In particular, we used the HRV4Training app and integrated APIs (e.g. Strava and TrainingPeaks) to acquire different sets of parameters, either via user input, morning measurements of resting physiology, or running workouts to estimate running 10 km running time. Our unique dataset comprises data on 2113 individuals, from world class triathletes to individuals just getting started with running, and it spans over 2 years. Analyzed predictors of running performance include anthropometrics, resting heart rate (HR) and heart rate variability (HRV), training physiology (heart rate during exercise), training volume, training patterns (training intensity distribution over multiple workouts, or training polarization) and previous performance. We build multiple linear regression models and highlight the relative impact of different predictors as well as trade-offs between the amount of data required for features extraction and the models accuracy in estimating running performance (10 km time). Cross-validated root mean square error (RMSE) for 10 km running time estimation was 2.6 minutes (4% mean average error, MAE, 0.87 R^2), an improvement of 58% with respect to estimation models using anthropometrics data only as predictors. Finally, we provide insights on the relationship between training and performance, including further evidence of the importance of training volume and a polarized training approach to improve performance.
... Even though the double labeled water method is considered the most general technique to estimate energy expenditure due to physical activity [1], it is commonly accepted that using the VO2max is a valid measure of functional capacity for patients with cardiopulmonary diseases. Many studies have been conducted to estimate the VO2max values based on the cycle exercise test, treadmill test, walk tests [1][2][3][4] and, on the other hand, running performance [5]. However, there is also evidence that submaximal exercise testing can be inaccurate for determining VO2max which cannot be estimated by prediction equations in patients with stable COPD [6]. ...
... It is well known that an LS fit to data can be assessed with various model selection criteria, see e.g. https://en.wikipedia.org/wiki/Model_selection. In our case, we choose a popular model validation technology which is cross-validation (CV) [53,54]], in particular Leave-One-Out (LOO) CV regarding its VO2max-related modeling applications [5,55]. LOO CV uses (N − 1) input/output data points for training and the remaining single input/output data point for validation, that is a prediction is made for that point. ...
... As seen from Table 2, the values of the performance measures are quite close to those RMSEs obtained above in this Section. Also, Figs 7 and 8 present a very nice prediction quality of the considered models, which translates to very good properties of the residuals (not illustrated here), compare [5,[53][54][55]. Additionally, the Pearson correlation coefficients R are very high for both cases. ...
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Background. The six-minute walk test (6MWT) is considered to be a simple and inexpensive tool for the assessment of functional tolerance of submaximal effort. The aim of this work was 1) to background the nonlinear nature of the energy expenditure process due to physical activity, 2) to compare the results/scores of the submaximal treadmill exercise test and those of 6MWT in pulmonary patients and 3) to develop nonlinear mathematical models relating the two. Methods. The study group included patients with the COPD. All patients were subjected to a submaximal exercise test and a 6MWT. To develop an optimal mathematical solution and compare the results of the exercise test and the 6MWT, the least squares and genetic algorithms were employed to estimate parameters of polynomial expansion and piecewise linear models. Results. Mathematical analysis enabled to construct nonlinear models for estimating the MET result of submaximal exercise test based on average walk velocity (or distance) in the 6MWT. Conclusions. Submaximal effort tolerance in COPD patients can be effectively estimated from new, rehabilitation-oriented, nonlinear models based on the generalized MET concept and the 6MWT.
... Testing CameraHRV Photoplethysmography (PPG)-measured HRV data will be obtained from CameraHRV (Marco Altini, Amsterdam, Netherlands)-an Android App which has been utilized in multiple clinical trials [74][75][76] and validated with both the Polar H7 device and the golden standard electrocardiography (ECG) [77]. PPGmeasured HRV is a reliable means of computing HRV [78] and will be used to assess heart rate as well as timebased (AVNN, standard deviation of NN intervals (SDNN), root mean square of successive differences (rMSSD), percentage of successive normal sinus RR intervals more than 50 ms (pNN50)) and frequency-based (LF, HF) resting HRV. ...
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Background: Chronic widespread pain (CWP), including fibromyalgia (FM), affects one in every ten adults and is one of the leading causes of sick leave and emotional distress. Due to an unclear etiology and a complex pathophysiology, FM is a condition with few, if any, effective and safe treatments. However, current research within the field of vagal nerve innervation suggests psychophysiological and electrical means by which FM may be treated. This study will investigate the efficacy of two different noninvasive vagal nerve stimulation techniques for the treatment of FM. Methods: The study will use a randomized, single-blind, sham-controlled design to investigate the treatment efficacy of motivational nondirective resonance breathing (MNRB™) and transcutaneous vagus nerve stimulation (Nemos® tVNS) on patients diagnosed with FM. Consenting FM patients (N = 112) who are referred to the Department of Pain Management and Research at Oslo University Hospital, in Oslo, Norway, will be randomized into one of four independent groups. Half of these participants (N = 56) will be randomized to either an experimental tVNS group or a sham tVNS group. The other half (N = 56) will be randomized to either an experimental MNRB group or a sham MNRB group. Both active and sham treatment interventions will be delivered twice per day at home, 15 min/morning and 15 min/evening, for a total duration of 2 weeks (14 days). Participants are invited to the clinic twice, once for pre- and once for post-intervention data collection. The primary outcome is changes in photoplethysmography-measured heart rate variability. Secondary outcomes include self-reported pain intensity on a numeric rating scale, changes in pain detection threshold, pain tolerance threshold, and pressure pain limit determined by computerized pressure cuff algometry, blood pressure, and health-related quality of life. Discussion: The described randomized controlled trial aims to compare the efficacy of two vagal nerve innervation interventions, MNRB and tVNS, on heart rate variability and pain intensity in patients suffering from FM. This project tests a new and potentially effective means of treating a major public and global health concern where prevalence is high, disability is severe, and treatment options are limited. Trial registration: ClinicalTrials.gov NCT03180554 . Registered on August 06, 2017.
... Photoplethysmography (PPG)-measured HRV data will be obtained from CameraHRV (Marco Altini, Amsterdam, Netherlands)-an Android App which has been utilized in multiple clinical trials [53,54,55] and validated with both the Polar H7 device and the golden standard electrocardiography (ECG) [56]. ...
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Background Chronic widespread pain (CWP), including fibromyalgia (FM), affects one in every ten adults and is one of the leading causes of sick leave and emotional distress. Due to an unclear etiology and a complex pathophysiology, FM is a condition with few, if any, effective and safe treatments. However, current research within the field of vagal nerve innervation suggests psychophysiological and electrical means by which FM may be treated. This study will investigate the efficacy of two different non-invasive vagal nerve stimulation techniques for the treatment of FM. Methods The study will use a randomized, single blind, sham-controlled design to investigate the treatment efficacy of motivational nondirective resonance breathing (MNRBTM) and transcutaneous vagus nerve stimulation (Nemos® tVNS) on patients diagnosed with FM. Consenting FM patients (N=112) who are referred to the Department of Pain Management and Research at Oslo University Hospital, in Oslo, Norway, will be randomized into one of four independent groups. Half of these participants (N=56) will be randomized to either an experimental tVNS group or a sham tVNS group. The other half (N=56) will be randomized to either an experimental MNRB group or a sham MNRB group. Both active and sham treatment interventions will be delivered twice per day at home, 15 min/morning and 15 min/evening, for a total duration of 2 weeks (14 days). Participants are invited to the clinic twice, once for pre- and once for post-intervention data collection. The primary outcome is changes in photoplethysmography measured heart rate variability. Secondary outcomes include self-reported pain intensity on a numeric rating scale, changes in pain detection threshold, pain tolerance threshold, and pain-pressure limit determined by computerized pressure cuff algometry, blood pressure, and health related quality of life. Discussion The described randomized controlled trial aims to compare the efficacy of two vagal nerve innervation interventions, MNRB and tVNS, on heart rate variability and pain intensity in patients suffering from FM. This project tests a new and potentially effective means of treating a major public and global health concern where prevalence is high, disability is severe, and treatment options are limited. Trial registration ClinicalTrials.gov Identifier: NCT03180554; Date of Registration: 08/06/2017
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The aim of this study was to verify the power of VO2max, peak treadmill running velocity (PTV), and running economy (RE), unadjusted or allometrically adjusted, in predicting 10 km running performance. Eighteen male endurance runners performed: 1) an incremental test to exhaustion to determine VO2max and PTV; 2) a constant submaximal run at 12 km·h-1 on an outdoor track for RE determination; and 3) a 10 km running race. Unadjusted (VO2max, PTV and RE) and adjusted variables (VO2max 0.72 , PTV 0.72 and RE 0.60) were investigated through independent multiple regression models to predict 10 km running race time. There were no significant correlations between 10 km running time and either the adjusted or unadjusted VO2max. Significant correlations (p < 0.01) were found between 10 km running time and adjusted and unadjusted RE and PTV, providing models with effect size > 0.84 and power > 0.88. The allometrically adjusted predictive model was composed of PTV 0.72 and RE 0.60 and explained 83% of the variance in 10 km running time with a standard error of the estimate (SEE) of 1.5 min. The unadjusted model composed of a single PVT accounted for 72% of the variance in 10 km running time (SEE of 1.9 min). Both regression models provided powerful estimates of 10 km running time; however, the unadjusted PTV may provide an uncomplicated estimation.
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We describe an approach to support athletes at various fitness levels in their training load analysis using heart rate (HR) and heart rate variability (HRV). A smartphone-based application (HRV4Training) was developed that captures heart activity over one to five minutes using photoplethysmog-raphy (PPG) and derives HR and HRV features. HRV4Training integrated a guide for an early morning spot measurement protocol and a questionnaire to capture self-reported training activity. The smartphone application was made publicly available for interested users to quantify training effect. Here we analyze data acquired over a period of 3 weeks to 5 months, including 797 users, breaking down results by gender and age group. Our results suggest a strong relation between HR, HRV and self-reported training load independent of gender and age group. HRV changes due to training were larger than those of HR. We conclude that smartphone-based training monitoring is feasible and a can be used as a practical tool to support large populations outside controlled laboratory environments.
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In this work, we propose to use pattern recognition methods to determine submaximal heart rate (HR) during specific contexts, such as walking at a certain speed, using wearable sensors in free-living, and use context-specific HR to estimate cardiorespiratory fitness (CRF). CRF of 51 participants was assessed by a maximal exertion test (VO2max). Participants wore a combined accelerometer and HR monitor during a laboratory based simulation of activities of daily living and for two weeks in free-living. Anthropometrics, HR while lying down and walking at predefined speeds in laboratory settings were used to estimate CRF. Explained variance (R(2)) was 0.64 for anthropometrics, and increased up to 0.74 for context-specific HR (0.73 to 0.78 when including fat-free mass). Then, we developed activity recognition and walking speed estimation algorithms to determine the same contexts (i.e. lying down and walking) in free-living. Context-specific HR in free-living was highly correlated with laboratory measurements (Pearson's r = 0.71-0.75). R(2) for CRF estimation was 0.65 when anthropometrics were used as predictors, and increased up to 0.77 when including free-living context-specific HR (i.e. HR while walking at 5.5 km/h). R(2) varied between 0.73 and 0.80 when including fat-free mass among the predictors. RMSE was reduced from 354.7 ml/min to 281.0 ml/min by the inclusion of context-specific HR parameters (21% error reduction). We conclude that pattern recognition techniques can be used to contextualize HR in free-living and estimated CRF with accuracy comparable to what can be obtained with laboratory measurements of HR response to walking.
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Objective: In this paper we propose artificial intelligence methods to estimate cardiorespiratory fitness (CRF) in free-living using wearable sensor data. Methods: Our methods rely on a computational framework able to contextualize heart rate (HR) in free-living, and use context-specific HR as predictor of CRF without need for laboratory tests. In particular, we propose three estimation steps. Initially, we recognize activity primitives using accelerometer and location data. Using topic models, we group activity primitives and derive activities composites. We subsequently rank activity composites, and analyze the relation between ranked activity composites and CRF across individuals. Finally, HR data in specific activity primitives and composites is used as predictor in a hierarchical Bayesian regression model to estimate CRF level from the participant's habitual behavior in free-living. Results: We show that by combining activity primitives and activity composites the proposed framework can adapt to the user and context, and outperforms other CRF estimation models, reducing estimation error between 10.3% and 22.6% on a study population of 46 participants. Conclusions: Our investigation showed that HR can be contextualized in free-living using activity primitives and activity composites and robust CRF estimation in free-living is feasible.
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There is growing interest in the role of sedentary behavior as a risk factor for poor health, independent of physical activity (PA). To guide the spectrum of descriptive, analytic, and intervention studies on sedentary behavior, the authors advocate a behavioral epidemiology framework. This 5-phase framework is useful because it outlines a series of sequential stages important for developing, evaluating, and diffusing interventions to reduce sedentary behavior and improve population health. Studies of sedentary behavior and health outcomes (phase I) have found consistent evidence that excessive use of screen-based media is linked to overweight and obesity in children, and there is some evidence among adults that overall sedentary time is associated with risk factors for cardiometabolic disease, some cancers, and mortality. Biological mechanisms to explain possible relationships have started to emerge but are mostly based on animal models. Obtaining valid and reliable measurements of sedentary behavior (phase II) remains a research priority because self-reports are prone to recall bias, and it appears that sedentary habits do not appear to be well represented by measures of individual behaviors such as TV viewing. Studies have identified few modifiable correlates of sedentary behavior (phase III), although research appears to be limited to studies of TV viewing or to scenarios in which sedentary behavior is defined as an absence of PA. Rigorous intervention research (phase IV) has focused almost exclusively on reducing self-reported TV viewing among children and adolescents, and there is consistent evidence that these interventions are efficacious. There appear to be no interventions focused exclusively on reducing sedentary behavior of adults. Translation studies (phase V) are absent because the underlying evidence is still emerging. Future research should focus on examining causal associations between sedentary behavior and health, developing objective measures of domain-specific sitting time, and identifying modifiable correlates of sedentary behavior that can be used as leverage points for behavioral interventions.
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The measurement of heart rate variability (HRV) is often considered a convenient non-invasive assessment tool for monitoring individual adaptation to training. Decreases and increases in vagal-derived indices of HRV have been suggested to indicate negative and positive adaptations, respectively, to endurance training regimens. However, much of the research in this area has involved recreational and well-trained athletes, with the small number of studies conducted in elite athletes revealing equivocal outcomes. For example, in elite athletes, studies have revealed both increases and decreases in HRV to be associated with negative adaptation. Additionally, signs of positive adaptation, such as increases in cardiorespiratory fitness, have been observed with atypical concomitant decreases in HRV. As such, practical ways by which HRV can be used to monitor training status in elites are yet to be established. This article addresses the current literature that has assessed changes in HRV in response to training loads and the likely positive and negative adaptations shown. We reveal limitations with respect to how the measurement of HRV has been interpreted to assess positive and negative adaptation to endurance training regimens and subsequent physical performance. We offer solutions to some of the methodological issues associated with using HRV as a day-to-day monitoring tool. These include the use of appropriate averaging techniques, and the use of specific HRV indices to overcome the issue of HRV saturation in elite athletes (i.e., reductions in HRV despite decreases in resting heart rate). Finally, we provide examples in Olympic and World Champion athletes showing how these indices can be practically applied to assess training status and readiness to perform in the period leading up to a pinnacle event. The paper reveals how longitudinal HRV monitoring in elites is required to understand their unique individual HRV fingerprint. For the first time, we demonstrate how increases and decreases in HRV relate to changes in fitness and freshness, respectively, in elite athletes.
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Assessment of the functional capacity of the cardiovascular system is essential in sports medicine. For athletes, the maximal oxygen uptake [Formula: see text] provides valuable information about their aerobic power. In the clinical setting, the [Formula: see text] provides important diagnostic and prognostic information in several clinical populations, such as patients with coronary artery disease or heart failure. Likewise, [Formula: see text] assessment can be very important to evaluate fitness in asymptomatic adults. Although direct determination of [Formula: see text] is the most accurate method, it requires a maximal level of exertion, which brings a higher risk of adverse events in individuals with an intermediate to high risk of cardiovascular problems. Estimation of [Formula: see text] during submaximal exercise testing can offer a precious alternative. Over the past decades, many protocols have been developed for this purpose. The present review gives an overview of these submaximal protocols and aims to facilitate appropriate test selection in sports, clinical, and home settings. Several factors must be considered when selecting a protocol: (i) The population being tested and its specific needs in terms of safety, supervision, and accuracy and repeatability of the [Formula: see text] estimation. (ii) The parameters upon which the prediction is based (e.g. heart rate, power output, rating of perceived exertion [RPE]), as well as the need for additional clinically relevant parameters (e.g. blood pressure, ECG). (iii) The appropriate test modality that should meet the above-mentioned requirements should also be in line with the functional mobility of the target population, and depends on the available equipment. In the sports setting, high repeatability is crucial to track training-induced seasonal changes. In the clinical setting, special attention must be paid to the test modality, because multiple physiological parameters often need to be measured during test execution. When estimating [Formula: see text], one has to be aware of the effects of medication on heart rate-based submaximal protocols. In the home setting, the submaximal protocols need to be accessible to users with a broad range of characteristics in terms of age, equipment, time available, and an absence of supervision. In this setting, the smart use of sensors such as accelerometers and heart rate monitors will result in protocol-free [Formula: see text] assessments. In conclusion, the need for a low-risk, low-cost, low-supervision, and objective evaluation of [Formula: see text] has brought about the development and the validation of a large number of submaximal exercise tests. It is of paramount importance to use these tests in the right context (sports, clinical, home), to consider the population in which they were developed, and to be aware of their limitations.
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Advancements in technology have always had major impacts in medicine. The smartphone is one of the most ubiquitous and dynamic trends in communication, in which one's mobile phone can also be used for communicating via email, performing Internet searches, and using specific applications. The smartphone is one of the fastest growing sectors in the technology industry, and its impact in medicine has already been significant. To provide a comprehensive and up-to-date summary of the role of the smartphone in medicine by highlighting the ways in which it can enhance continuing medical education, patient care, and communication. We also examine the evidence base for this technology. We conducted a review of all published uses of the smartphone that could be applicable to the field of medicine and medical education with the exclusion of only surgical-related uses. In the 60 studies that were identified, we found many uses for the smartphone in medicine; however, we also found that very few high-quality studies exist to help us understand how best to use this technology. While the smartphone's role in medicine and education appears promising and exciting, more high-quality studies are needed to better understand the role it will have in this field. We recommend popular smartphone applications for physicians that are lacking in evidence and discuss future studies to support their use.
Conference Paper
We describe an approach to support athletes at various fitness levels in their training load analysis using heart rate (HR) and heart rate variability (HRV). A smartphone-based application (HRV4Training) was developed that captures heart activity over one to five minutes using photoplethysmography (PPG) and derives HR and HRV features. HRV4Training integrated a guide for an early morning spot measurement protocol and a questionnaire to capture self-reported training activity. The smartphone application was made publicly available for interested users to quantify training effect. Here we analyze data acquired over a period of 3 weeks to 5 months, including 797 users, breaking down results by gender and age group. Our results suggest a strong relation between HR, HRV and self-reported training load independent of gender and age group. HRV changes due to training were larger than those of HR. We conclude that smartphone-based training monitoring is feasible and a can be used as a practical tool to support large populations outside controlled laboratory environments.