A 2-minute Fitness Test for Lifestyle Applications: The PhysioFit

Task and Its Analysis based on Heart Rate

Neide Simões-Capela

1,2

, Jan Cornelis

, Giuseppina Schiavone

and Chris Van Hoof

1,2

ESAT, KU Leuven, Kasteelpark Arenberg 10, Heverlee, Belgium

IMEC, Kapeldreef 75, Heverlee, Belgium

IMEC-NL, High Tech Campus 31, Eindhoven, The Netherlands

Keywords: Health Related Fitness, Cardio-respiratory Fitness, Submaximal Fitness Test, Ruffier-Dickson Task,

PhysioFit Task.

Abstract: Cardio-respiratory fitness (CRF) denotes the health of cardiorespiratory and musculoskeletal systems, thus

being important to evaluate effects of (un)healthy lifestyles. Non-exhaustive submaximal fitness tests enable

simple, fast, and inexpensive CRF assessment, in situations with low accuracy requirements. An example is

the Ruffier-Dickson task (RD), consisting of 30 squats executed within 45 seconds, it estimates a CRF score

from heart rate (HR) during the task. Squats, however, are not straightforward for subjects with poor fitness.

To overcome this limitation, we developed the PhysioFit task (PF). It entails two minutes of stationary

pedaling and employs HR for CRF estimation. PF outcomes were analyzed using RD as benchmark, according

to HR changes during the task; CRF scores estimated with methods based on HR; correlation of CRF scores

to body composition. The analysis relied on data from 28 subjects who executed both tasks. Although, HR

variations during PF were lower relative to RD, PF produced significant changes in HR during pedaling and

allowed for significant recovery after one minute. Significant agreement was found between tasks for two

CRF scores, and both presented strong negative and positive correlations with fat and muscle percentage,

respectively. Preliminary results show that PF is promising towards fast fitness assessments.

1 INTRODUCTION

Physical fitness describes how readily physical

activities can be performed, something that can be

defined in relation to health targets (i.e. health

related) or to a specific athletic skill (i.e. skill related).

The concept of health related fitness is the most

pertinent for the general population as it quantifies

diverse health aspects, namely body composition

(BComp); cardiorespiratory endurance; muscular

strength and endurance; and flexibility (McArdle,

Katch, & Katch, 2015). Nonetheless, accurate fitness

assessment is complex, expensive and has varied

health contraindications, being mainly limited to

athletes and specialized research. Simple and

affordable alternatives exist that can fit a wide range

of individuals, if a suboptimal accuracy is tolerated.

The work hereby presented is aimed at evaluating

the potential of a 2-min pedaling task, the PhysioFit

(PF), towards physical fitness assessment. The PF is

compared to the Ruffier-Dickson (RD) task, a simple

fitness test that relies on the execution of 30 squats to

attain a fitness evaluation, based on heart rate (HR)

during the exercise. The PF task offers an alternative

for situations in which the former is not feasible (e.g.

subjects with low weight or poor fitness). The

analysis methods developed for the RD task are

applied on PF task data, to test whether relevant

fitness information can be extracted. The article is

organized as follows. Chapter 1 introduces relevant

concepts related to fitness and summarizes the state-

of-the-art in data analysis. Chapter 2 details our

methods for data collection and analysis. Chapter 3, 4

and 5 present results, discussion, and conclusion,

respectively.

1.1 Body Composition

Body mass index (BMI) assesses the normalcy of a

person’s weight in relation to height, as in eq.1

(WHO, 2004).

BMI  weight/height



)

Analyzing BComp, a domain of health related

fitness, provides a more comprehensive assessment,

Simões-Capela, N., Cornelis, J., Schiavone, G. and Van Hoof, C.

A 2-minute Fitness Test for Lifestyle Applications: The PhysioFit Task and Its Analysis based on Heart Rate.

DOI: 10.5220/0010234503770385

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF, pages 377-385

ISBN: 978-989-758-490-9

377

quantifying relative amounts of fat, muscle, and bone

in the body. The most convenient measurement

method uses bioimpedance analysis. Normal ranges

for each BComp component vary with age, gender,

ethnicity and measuring device (McArdle et al.,

2015).

1.2 Cardiorespiratory Fitness

Cardiorespiratory fitness (CRF) reflects the health of

the cardiovascular, respiratory, and musculoskeletal

systems. Thus, highly influencing the level to which

everyday aerobic activities can be performed (Arena

et al., 2007). Tough, its major interest lies on the

inverse correlation to morbidity and mortality

(Kodama et al., 2009). Several testing methodologies

and descriptors are available for CRF assessment, and

they are summarized next.

CRF testing comprises maximal and submaximal

fitness tests. In maximal tests the exercise workload

is incrementally increased until the test subject

achieves volitional exhaustion. These tests provide

the most accurate assessments. Though, they have

limited applicability on the general population related

to health contraindications (Thompson, Arena, Riebe,

and Pescatello, 2013). Their widespread use is also

limited by the requirements for specialized medical

supervision, on-site emergency equipment, specific

training, and elaborated protocols requiring

expensive acquisition setups. Submaximal tests, on

the other hand, do not require subjects to reach

exhaustion. With less contraindications, they are

suited for a wider range of individuals (e.g. children,

elderly), and the varied acquisition protocols

available meet different user requirements. Overall,

submaximal tests present a convenient alternative to

the maximal counterparts in situations with low CRF

accuracy requirements (e.g. home, primary care,

general research).

The golden standard for CRF assessment is the

maximal oxygen uptake (VO

2max

), achieved when

consumed oxygen reaches a plateau, despite an

increase in exercise load (i.e. when reaching

exhaustion). It can be directly accessed by ventilatory

gas analysis (Fletcher et al., 2013). CRF categorical

classifications (e.g. very poor, poor, fair...) based on

2max

are provided by the American College of

Sports and Medicine (ACSM) (ACSM, 2014).

Gender, age, height, body size/composition, training

status and type of testing protocol all influence the

2max

value (Fletcher et al., 2013). Height is

especially important when the center of mass is

displaced during the test protocol (McArdle, Katch,

and Katch, 2015).

One CRF correlate that is easier to assess is HR.

HR at rest (HR

rest

) is a general indicator of wellness,

while a decline in the HR response to submaximal

exercise represents an enhancement in endurance.

Also, the HR recovery pattern is a mortality predictor

(ACSM, 2014). HR reaches its maximum (HR

max

)

approximately when VO

2max

is achieved. For an

increasing exercise load, HR increases linearly with

oxygen consumption (VO

). The relation holds

during light to moderate workloads but may degrade

for high workloads as VO

accelerates. The linearity

of the HR-VO

relation has been used to predict

2max

in submaximal tasks, by applying a linear

regression to known points and extrapolating the

relation up to a theoretical HR

max

. Due to the

assumptions put into this prediction, the estimated

value is usually

within 10-20% of the actual VO

2max

Some authors refer that this accuracy level is

unacceptable for research, but can still be valuable in

lifestyle applications (e.g. screening at the gym)

(McArdle et al., 2015). Likewise, we argue that for

non-fitness specific research, such estimates can be

useful.

2max

has been derived from HR

rest

, HR

max

and

weight as in eq.2 (N. Uth, 2005). The conversion to

relative units (mL/min.kg) is required for comparison

with guidelines and among subjects, which is

achieved by multiplying by 1000/weight. In eq.2 the

proportional factor (pf) takes different values for

women and men: 14.5x10

-3

l/min.kg and 15.3x10

-3

l/min.kg, respectively. Theoretical HR

max

(HR

max,th

)

can be estimated from eq.3 (Tanaka, Monahan, &

Seals, 2001). Variations to this formulation are

available, though no agreement exists on which is

generally preferable (ACSM, 2014).

,



weight  pf  H









)





 208  0.7  age

)

1.2.1 Ruffier-Dickson Task

The RD task is one of the simplest submaximal tasks

found in the literature. It consists on resting for 5 min,

performing 30 squats over a period of 45 s, and

recovering for 5 min (Figure 2 (b)). The basic setup

requires a stopwatch and a mat to lay down during

rest and recovery. Bilateral squatting, involved in the

task, primarily activates lower body musculature, but

spinal and abdominal muscles are also engaged

(Eliassen, Saeterbakken, & van den Tillaar, 2018).

While early literature on the design of this task is not

accessible online, recent studies compared the RD

task results against maximal fitness tests and

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378

formulated predictive VO

2max

models.

The RD task analysis traditionally relies on three

discrete HR values: at rest (P0), right after exercise

(P1) and 1 min into recovery (P2). A reference for the

expected values is presented in Table 1. Such values

are employed in calculating numerical fitness scores,

such as the Ruffier index (Ri, eq.4) or the latter

Ruffier-Dickson index (RDi, eq.5). The choice of

factors in Ri and RDi is explained by De Mondernard

et al. (De Mondenard, 1987), showing an ad-hoc

process with weak empirical validation. Numerical

outputs of Ri and RDi can be translated to fitness

categories (e.g. excellent, good, fair...) as described in

previous literature (De Mondenard, 1987)(Dah,

1991)(Sartor et al., 2016), though the classification

ranges vary. The interest of Ri and RDi scores in

fitness evaluation was re-evaluated in recent studies,

that examined their relation to VO

2max

measured

during maximal tasks in healthy individuals.

Table 1: RD task: expected HR at rest, maximum steady

state during exercise and 1 min into recovery (De

Mondenard, 1987).

P0: Rest P1: Ada

tion P2: Recover

P0<50 bpm: good

Basal endurance.

P0>80 bpm: poor

asal endurance.

P1<2P0: good condition.

P1>2P0: insufficient

training.

P2≤P0: very good/good

endurance.

P2>P0+20: insufficient

trainin

Ri  P0  P1  P2  200 10

⁄

(4)

RDi  



P1  70



2P2P0 10

⁄

(5)

Sartor et al. (Sartor et al., 2016) shown that RDi alone

should not be used to classify CRF levels in healthy

subjects: the index shown low agreement

(kappa=0.29) to ACSM CRF categorical levels and

explained only 15% of the variability (adjusted

=0.15, sensitivity for good and fair=61%,

specificity for poor=49%). Including RDi, age,

gender (0=female, 1=male) and height on a

multivariate model (eq.6), enhanced the agreement

with ACSM levels (kappa=0.39) and the explained

variability to 53% (adj. r2=0.53, sensitivity for good

and fair=62%, specificity for poor=63%). These

authors developed other models using HR values

other than P0, P1 and P2. In our view, the increase in

complexity did not justify the performance

enhancement (adj. r

=0.59, sensitivity for good and

fair=64%, specificity for poor=62%, kappa=0.42),

thus, those models are not detailed here. The models

were developed on data from 81 healthy subjects (18

F, 63 M), with age ∈[18, 67] years old, height ∈[1.61,

1.88] m, weight ∈[52.5, 100] kg, and BMI ∈[18.8,

33.6] kg/m

,

 3.79  0.56gender

 0.03age  4.53heigh

 0.09RDi

)

Guo et al. (Guo et al., 2018) developed three models

to predict VO

2max

, respectively based on Ri, RDi and

HR values (P0, P1, P2). Neither Ri (p= 0.06) nor RDi

(p= 0.32) were significant predictors of VO

2max

. The

best model (eq.7) was found using P0, P1, P2, age,

gender (0=female, 1=male,) and height (adj. r

=0.64,

sensitivity for good and fair=79%, specificity for

poor=56%, kappa = 0.6). The models were developed

on 40 healthy subjects (22 F, 18 M), age ∈[19, 60]

years old, height ∈[1.57, 1.93] m, weight ∈[49.9,

121.6] kg, BMI ∈[18.6, 41.2] kg/m

, P0 ∈[49, 98]

beats per minute (bpm), P1 ∈[101, 184] bpm, and P2

∈[56, 152] bpm.

,

 3.014  1.16gender

0.03

height

 118.76

P1  P2

age



)

To calculate their performance metrics, both

Sartor et al. (Sartor et al., 2016) and Guo et al. (Guo

et al., 2018) used three CRF classes (i.e. poor, fair and

good), adapted from ACSM’s classification for

2max

during the Balke treadmill protocol (ACSM,

2014).

1.2.2 PhysioFit Task

The PF task is a submaximal task comprising 5 min

of rest while sitting, 2 min pedaling and 5 min of sited

recovery (Figure 1(a)). Pedaling is performed in

upright seated position on a stationary bike with a

fixed gear, with the objective of attaining and

maintaining 35 km/h.

The setup requires a chair, a

stopwatch, and a stationary minibike (i.e. without

upper limb support). The pedaling activity primarily

activates lower body musculature and secondarily

arm, abdominal and back muscles when upper limbs

are used for support (So, Ng, and Ng, 2005). The

muscle activation is a close match to the RD task, an

important factor when comparing both tasks, as

2max

values predicted from upper and lower body

exercises have low correlation (McArdle et al., 2015).

The PF task was thought to cater for varied levels

of fitness, accounting for some individuals being

unable to perform or repeat complex movements (e.g.

squats, step-up/down); while keeping the setup

portable (i.e. excluding treadmills or a bicycle

ergometers) and affordable (minibike prices range

from 20-200 euros, depending on brand); and

excluding tasks requiring the test subject to leave the

A 2-minute Fitness Test for Lifestyle Applications: The PhysioFit Task and Its Analysis based on Heart Rate

379

controlled experimental environment to perform

them (e.g. field walk, run tests). This task was first

employed in psychophysiological research related to

eating disorders, as a physical stressor (Simões-

Capela, Schiavone, De Raedt, Vrieze, & Van Hoof,

2019). The aim was to weight the effect of physical

activity on bio signals, when primarily studying the

effects of mental stress on the body.

2 METHODS

2.1 Data

The PF and RD tasks were compared based on data

from two studies, in which volunteers with varied

levels of fitness completed both tasks.

Dataset 1 results from a pilot study designed to

compare both tasks, following standard task

protocols. The study was reviewed and approved by

the medical ethics committee of Ziekenhuis Oost-

Limburg. The study sample consisted of 13 subjects

(7M, 6F) from a working population with

age=30.4±7.2 years and BMI=22.3±4.3 kg/m

(mean

± s. dev.). All agreed to voluntarily participate and

consented to the data collection after an explanation

of the study procedures. All subjects were older than

18 years and working on a day desk job (i.e. excluding

physical exertive jobs and shift works). The following

constituted exclusion criteria: sensitive skin or known

allergy to Ag/AgCl electrodes; inability to perform

the protocol (e.g. limited mobility, respiratory illness,

cardiovascular illness); acute illness (e.g. flu);

pregnancy; and carrying implanted devices. The

participants interfaced with three devices: 1) Health

Patch (imec/Biotelemetry), a sensing node attached to

an adhesive chest patch, used to continuously capture

ECG; 2) HBF-516 (Omron), a full BComp monitor,

to measure weight and estimate fat and muscle

percentages based on bio-impedance analysis; 3) low-

cost uncalibrated minibike (crivit, LIDL), used during

the workout. The minibike was compared to a

calibrated device (deskcycle, 3Dinnovations) to attest

the accuracy of its displayed velocity, and an error of

10km/h was found (i.e. 35km/h displayed as

3.5km/h). This was taken in consideration during data

collection. The tasks (Figure 1) were conducted in a

dedicated study room under the supervision of trained

researchers. The tasks were performed at the same

time (between 3h and 5h pm) on consecutive days to

avoid circadian changes. The task order was

randomized. At the first contact the admission criteria

were verified and background information (i.e. age,

gender, height, weight, and BComp parameters) was

collected. After applying the wearable sensors, the

tasks took place as depicted in Figure 1, while a timed

slideshow presentation with directions was shown on

screen for reference. A screen recording was

captured, to document any time diversions and

account for them in the analysis.

Dataset 2 was originally dedicated to test the

effect of diverse activities on bio-signal quality, and

its methods include slight variations from Dataset 1.

It was incorporated here to extend the study sample.

The study was reviewed and approved by the medical

ethics committee of Universiteit Ziekenhuis Leuven.

The study sample consists of 15 subjects (5 M, 10 F)

from a working population, with age=34.2±10.3

years and BMI=22.4±3.1 kg/m

(mean ± s. dev.). The

admission criteria, the ECG acquisition device and

bike setup were the same as in Dataset 1. The body

analyzer was not employed. All procedures (Figure 2,

with relevant tasks highlighted) were performed on a

single 90 min study session between 8h and 12h am.

In contrast to Dataset 1, there was no randomization

of the task order. Weight and height were self-

reported.

Figure 1: Task protocol for Dataset 1: (a) PF task and (b)

RD task.

Figure 2: Task protocol for Dataset 2: (a) PF task and (b)

RD task. Highlighted time slots were considered in the

analysis.

2.2 Analysis

The analysis entailed: 1) pre-processing; 2)

investigation of HR at P0, P1 and P2; 3) obtention of

CRF scores using HR based models found in previous

literature for the RD task; and 4) comparison of CRF

HEALTHINF 2021 - 14th International Conference on Health Informatics

380

scores to BComp. The previous steps were applied on

data from both task, and results were compared within

and between tasks. The analysis was carried out using

MATLAB 2019b. All correlations were analyzed

considering Cohen’s guidelines (low correlation:

|ρ|≤0.3; moderate correlation: 0.3<|ρ|<0.5; strong

correlation: |ρ|≥0.5).

ECG signals from each subject were truncated to

the interval from start to end of each task. The start of

each task was identified by the acceleration signature

produced by the calibration procedure. Remaining

phases were annotated based on the timings from the

screen recordings. The R-peaks were identified in the

ECG signal using an automatic beat detector

(Romero, Grundlehner, & Penders, 2009). HR was

calculated based on R-R intervals and converted to

bpm. Each 1-min window was assessed for outliers

(i.e. values outside the interval of mean HR ± 2.5 s.

dev.) and these points were excluded.

Each of the relevant HR values was obtained by

calculating the median of 15 seconds following the

P0, P1 and P2 time points (cf. Figur

1 and Figure 2).

Median HR values were used to reduce the effect of

outliers. In both tasks it was investigated if changes

in HR from rest to adaption, from adaption to

recovery and from rest to recovery were significant.

Among tasks, the HR values at each phase and the

absolute HR variation from phase to phase were

compared.

For both tasks, CRF scores were estimated based

on 6 indices: Ri (eq.4), RDi (eq.5), VO

2max,sartor

(eq.6),

2max,guo

(eq.7), VO

2max,uth

(eq.2) and VO

2max,uth,th

(eq.2, eq.3). In VO

2max,uth

, the HR

max

was substituted

by P1, in the expectation it would produce a

proportional estimation. All outputs in units of L/min

were translated to relative units of ml/kg.min. In both

tasks the agreement between each pair of CRF indices

was studied. Among tasks, the agreement among CRF

values was tested.

The correlations of HR and CRF scores to BComp

and BMI were investigated. Since only Dataset 1

includes information on BComp this analysis was

limited to those 13 subjects.

3 RESULTS

This section includes results from the comparative

analysis of HR and CRF scores within each task and

among tasks. For most part of the analysis, Dataset 1

and 2 were treated as a single dataset, after visually

verifying that both had a similar HR behavior (Figure

3). Background information of the study sample is

summarized in Table 2.

Table 2: Study sample: demographics and anthropometrics

(mean ± s. dev. [max, min]).

Male (N=12) Female (N=16) All (N=28)

ge, years

32.3 ± 5.6 [25, 44] 32.5 ± 11.2 [18, 52]

32.4 ± 9.1

[18, 52]

eight, m

1.83 ± 0.12 [1.69, 2.05] 1.65 ± 0.05[1.57,1.76]

1.73 ± 0.12

[1.57, 2.05]

eight, kg

78.6 ± 19.0 [54.6,117.5] 59.7 ± 11.0 [43, 92]

67.8 ± 17.4

[43, 117.5]

MI, kg/m

23.2 ± 4.3 [18.4, 35.5] 21.7 ± 3.0 [16.4,29.7]

22.4 ± 3.6

[16.4, 35.5]

3.1 HR Intra and Inter-task

The distributions of P0, P1 and P2 are depicted in

Figure 3, for both tasks. Based on the Kolmogorov-

Smirnov (KS) normality test, all HR distributions are

right-skewed, for such we used non-parametric

statistics in the analysis. Data was not transformed to

a normal distribution in order not to omit outliers. The

Wilcoxon signed-rank test for dependent variables

was employed to find significant differences. For

non-significant differences, the presence of a linear

relation was investigated based on Spearman’s test.

In both tasks, there are significant differences

between rest and adaption (z=-4.6, p<<0.01 for PF

and z=-4.6, p<<0.01 for RD) and between adaption

and recovery (z=4.6, p<<0.01 for PF and z=4.6

p<<0.01 for RD). Differences between rest and

recovery are significant for the PF task (z=-2.9,

p<0.01), but not for RD (z=1.36, p=0.17), in which

case a significant moderate correlation is found

(ρ=0.4, p=0.01). The absolute variations in HR

median

from rest to adaptation are 27.6 bpm and 42.5 bpm,

and from adaption to recovery are 23.0 bpm and 44.4

bpm, respectively for PF and RD task.

Figure 3: HR at rest (P0), adaption (P1) and recovery (P2):

(a) PF task, (b) RD task, (c) statistics. Significant

differences indicated with * (p<0.01) or ** (0.01≤p-

value<0.05), otherwise Spearman’s ρ and p-value are

shown. Ci: 95% confidence interval.

A 2-minute Fitness Test for Lifestyle Applications: The PhysioFit Task and Its Analysis based on Heart Rate

381

Across tasks, the HR is significantly different during

rest (z=-2.4, p=0.02) and adaption (z=-4.6, p<<0.01),

but not during recovery (z=1.0, p=0.3), presenting a

significant moderate correlation in this case (ρ=0.4,

p=0.03).

3.2 CRF Intra and Inter-task

All CRF scores’ distributions are right skewed

according to the KS normality test, hence non-

parametric statistics were used in the analysis. The 6

CRF indices have different scales, and vary

differently with an increasing level of fitness: Ri and

RDi tend to -∞, while VO

2max,sartor

2max,guo

2max,uth

and

2max,uththeory

tend to +∞. In this case a

test to compare medians is not appropriate. Thus, the

relation among CRF indices’ output was investigated

using regression.

The linear regression between each pair of CRF

indices (i.e. Ri-RDi, Ri-VO

2max,sartor

, Ri-VO

2max,guo

...)

was calculated (Figure 4). For the PF task, four pairs

of algorithms presented significant strong correla-

tions: Ri-RDi (ρ=0.81, p>>0.01), Ri-VO

2max,uththeory

(ρ=-0.88, p>>0.01), RDi-VO

2max,uththeory

(ρ=-0.54,

p>>0.01) and VO

2max,sartor

-VO

2max,guo

(ρ=0.59,

p>>0.01). For the same task, a moderate correlation

was found for RDi-VO

2max,sartor

(ρ=-0.48, p=0.01). For

the RD task, a significant strong correlation was

found for five pairs: Ri-RDi (ρ=0.81, p>>0.01), Ri-

2max,uththeory

(ρ=-0.5, p=0.01), VO

2max,sartor

2max,uththeory

(ρ=0.49, p=0.01), VO

2max,sartor

2max,guo

(ρ=0.67, p>>0.01) and VO

2max,uth

2max,uththeory

(ρ=-0.86, p>>0.01). For the same task,

Figure 4: Linear relations between CRF indices for (a) PF task; (b) RD task. rho: Spearman’s ρ, p: p-value, s. dev: standard

deviation, Ci: 95% confidence interval. Significant correlations marked in green (p<0.01) or blue (0.01≤p-value<0.05).

Figure 5: Linear regression of CRF scores from PF task against RD task and differences’ plot for: (a)Ri, (b) RDi, (c)

2max,sartor

, (d)VO

2max,guo

, (e)VO

2max,uth

and (f)VO

2max,uth theory

. n: number of points, y: regression formula, r

: coef. of

determination, p: Pearson’s correlation p-value, rho (p): Spearman’s ρ and p-value, RMSE: root mean squared error, SD: s.

dev., LOA: limits of agreement (±1.96 s. dev.), presented as dashed lines, CV: coef. of variation (s. dev./mean in %), KS p-

value: p-value for Kolmogorov-Smirnov test.

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a moderate correlation was found for three pairs: Ri-

2max,sartor

(ρ=-0.40, p=0.33), VO

2max,guo

2max,uththeory

(ρ=-0.43, p=0.02) and VO

2max,guo

2max,uththeory

(ρ=0.42, p=0.03). Only three pairs of

algorithms correlate well in both PF and RD tasks.

Regression analysis was used to investigate

proportionality among CRF scores from different

tasks. To understand if the relations found were

significant the analysis of differences (Bland &

Altman, 1999) was performed on the residuals

(Figure 5), which is more robust to compare different

acquisition methods than simple correlation. Three

CRF indices shown significant agreement between

tasks: Ri with moderate correlation (ρ=0.4, p=0.01);

2max,sartor

(ρ=0.9, p<<0.01) and VO

2max,guo

(ρ=0.9,

p<<0.01) with strong correlation. All residuals

arenormal (KS p-value>0.05), hence the results from

regression are trustworthy. There is a systematic error

between PF and RD for 5 CRF indices, as illustrated

by the significant proportional bias on Ri (bias=2.6,

p>>0.01), RDi (bias=2.3, p>>0.01), VO

2max,sartor

(bias=-2.9, p>>0.01), VO

2max,uth

(bias=3.0, p=0.01),

and VO

2max,uththeory

(bias=-3.6, p=0.01). As for

2max,guo

(bias=-0.1, p=0.91) the bias is not

significant. Ri and RDi have similar limits of

agreement, both indicating a wide variability of the

residuals. The VO

2max,sartor

(in comparison to

2max,guo,

2max,uth

and VO

2max,uththeory

) presented

the narrowest limits of agreement for the differences

between tasks (LOA=7.4). The models with least

dispersion of the residuals are VO

2max,sartor

(CV=8.7%) followed by VO

2max,guo

(CV=10%).

3.3 BMI and Body Composition

For Dataset 1 (N=13), correlations of P

0,1,2

and CRF

scores to BMI, muscle and fat percentages were

investigated based on Spearman’s correlation test.

Significant correlations are highlighted in Table 3.

Table 3: Correlation among CRF indices and

anthropometrics for: (a) PF task, (b) RD task. Significant

correlations marked with *(p<0.01) or **(0.01≤p-

value<0.05).

(a) PF task (b) RD task

BMI Muscle

Fat % BMI

Muscle

Fat %

0 0.29 -.12 0.12 0.20 -.63** 0.48

P1 0.27 -.57** 0.46 -.35 -.02 -.15

P2 0.42 -.19 0.24 0.52 -.38 0.59

Ri 0.28 -.36 0.27 0.30 -.59** 0.54

RDi 0.31 -.48 0.43 0.07 -.18 0.20

2max,sartor

-.56 0.92* -.92* -0.52 0.90* -.90*

2max,guo

-.66** 0.64** -.84* -.60** 0.66* -.83*

2max,uth

0.08 -.05 0.03 -.24 0.43 -.39

2max,uththeory

-.22 0.26 -.19 -.16 0.70* -.51

4 DISCUSSION

All HR distributions are right-skewed, which is a

common find in HR literature, occurring whenever

the sample as a subgroup of tachycardic subjects

(Palatini, 1999).

For the intra-task comparison, we found that both

tasks produce statistically and physiologically

significant changes in HR from rest to adaption and

both show significant recovery after adaption. In the

RD task, HR at rest and HR during recovery have a

significant moderate correlation. As for the PF task

HR at rest and during recovery are significantly

different.

For the HR inter-task comparison only HR during

recovery agrees across tasks, presenting a significant

moderate correlation. HR at rest can naturally change

across measurements related to diet and activity prior

to the measurement and acute changes in emotional

state. Nonetheless, HR at rest is systematically higher

for the RD task. In the current work, it is difficult to

evaluate if body position (McArdle et al., 2015) is the

reason for the difference, as Dataset 1 and Dataset 2

present varied resting positions. It is also possible that

recovery is insufficient as the resting phase in Dataset

2 takes place after other activities. During adaption it

is noted that pedaling leads to a significantly lower

peak HR (less 22.1 bpm) than the squats, each

producing a median variation in HR from rest to

adaption of 27.6 bpm and 42.5 bpm, respectively.

Such difference is unlikely due to body position, as

this can only account for HR in PF (sitting) being

systematically lower than RD (standing) by 1 bpm

(McArdle et al., 2015) (Figure 3). During recovery

there is a significant moderate correlation in HR

across tasks, which could mean that the HR braking

system acts to bring HR back to a baseline level,

independently of the intensity of the physical stressor.

Related to different body positions during recovery,

HR in PF (sitting) should be systematically higher

than RD (laying down). This should not invalidate the

correlation found, though the ~1 bpm systematic error

was not accounted in the calculation.

In the CRF intra-task comparison, we verified that

not all models are consistent in the CRF score

obtained for the same task (

Figure 4). This does not

appear to be a problem specific of the PF task: only 5

and 8 out 15 pairs of models agree for PF and RD,

respectively. The discrepancies can be attributed to

the different variables and their weight in each model.

For CRF inter-task comparison, two models

shown to be consistent across tasks: VO

2max,guo

with

no significant bias and VO

2max,sartor

systematically

estimating higher fitness levels for the PF task (

Figure

A 2-minute Fitness Test for Lifestyle Applications: The PhysioFit Task and Its Analysis based on Heart Rate

383

5). Unsurprisingly Ri and RDi shown poor agreement,

as expected from their low rating as CRF predictors

in previous literature (Sartor et al., 2016)(Guo et al.,

2018). Finally, we verified that outputs from both

2max,guo

and VO

2max,sartor

agreed with other fitness

indicator. With both presenting strong positive

correlation with muscle percentage, and strong

negative correlation with fat percentage (Table 3).

As the two different fitness tasks agree on the

CRF scores obtained from two models that have been

independently developed, and those scores agree with

other fitness indicator (BComp), we illustrate the

potential of our task for rough CRF estimation.

Nonetheless, these are preliminary results and we are

aware of the limitations of the current work. Dataset

2 presents design flaws related to the objectives this

investigation, such as the incongruence of body

positions with Dataset 1. We compare our task results

to another submaximal task, while the correct

approach towards validation is the comparison

against a golden standard. Submaximal tests are

especially useful for intra-subject comparison, over

repeated measurements, which excludes

reproducibility issues that are present across subjects.

Our datasets present cross-sectional designs,

preventing this analysis. Also, test-retest variability

was not addressed. These limitations constitute points

for further investigation.

5 CONCLUSIONS

We propose the PhysioFit, a simple 2-min pedaling

task for fitness assessment, suited for subjects with

low fitness level. We show that it induces a

significant change in HR. We identify two models

from previous literature (Sartor et al., 2016) (Guo et

al., 2018) that can be used to analyze it, and obtain

fitness scores based on HR during the task. CRF

scores obtained from both models shown strong

agreement with body composition indices. We reckon

that this task is no match for settings requiring high

accuracy assessments. Though, it has potential for

rough fitness indexation in lifestyle and wellbeing

applications (e.g. routine health checkups, tracking

training progress or diet) or in non-fitness specific

research studying human physiology (e.g.

psychophysiology). With this work we intend to

inspire the periodical monitoring of fitness levels in

individuals who only casually engage in physical

activity, be it in research studies, in the general

practitioner’s office, at home or in the work

environment.

ACKNOWLEDGEMENTS

The authors acknowledge their gratitude to Emma

Laporte for a preliminary literature review on fitness

tasks; Erika Lutin and Christophe Smeets for

reviewing the study materials; Luc Hons and Pieter

Vandervoort for clinical supervision; and Leen

Tordeurs for data management.

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