Energy expenditure estimation in beta-blocker medicated cardiac patients by

combining heart rate and body movement data

Jos J Kraala, Francesco Sartorb, Gabriele Papinib, Wim Stutb, Niels Peekc, Hareld M.C.

Kempsd, Alberto G Bonomib

a Department of Medical Informatics, Academic Medical Center Amsterdam, University

of Amsterdam, Amsterdam, The Netherlands

b Personal Health department, Philips Research, Eindhoven, The Netherlands

c Health eResearch Centre, Farr Institute for Health Informatics Research, University of

Manchester, Manchester, United Kingdom

d Department of Cardiology, Máxima Medical Center Veldhoven, Veldhoven, The

Netherlands

Word count: 5196

Corresponding author:

Jos J Kraal, Department of Medical Informatics, Academic Medical Center,

Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands

Email: j.kraal@mmc.nl

Abstract

Background: Accurate assessment of energy expenditure provides the opportunity to

monitor physical activity during cardiac rehabilitation. However, available assessment

methods, based on the combination of heart rate (HR) and body movement data, are not

applicable for patients using beta-blocker medication. Therefore, we developed an

energy expenditure prediction model for beta-blocker medicated cardiac rehabilitation

patients.

Methods: Sixteen male cardiac rehabilitation patients (age: 55.8±7.3 years, weight:

93.1±11.8 kg) underwent a physical activity protocol with 11 low to moderate intensity

common daily life activities. Energy expenditure was assessed using a portable indirect

calorimeter. HR and body movement data were recorded during the protocol using

unobtrusive wearable devices. In addition, patients underwent a symptom limited

exercise test and resting metabolic rate assessment. Energy expenditure estimation

models were developed using multivariate regression analyses based on HR, body

movement data and/or patient characteristics. In addition, a HR-flex model was

developed.

Results: The model combining HR, body movement and patient characteristics showed

the highest correlation and lowest error (R2=0.84, RMSE=0.834 kcal/min) with total

energy expenditure. The method based on individual calibration data (HR-flex) showed

lower accuracy (R2=0.83, RMSE=0.992 kcal/min).

Conclusions: Our results show that combining HR and body movement data improves

the accuracy of energy expenditure prediction models in cardiac patients, similar to

2

methods developed for healthy subjects. The proposed methodology does not require

individual calibration and is based on data available in clinical practice.

Word count: 228

Keywords: Accelerometry, heart rate monitoring, physical activity, energy expenditure,

cardiac rehabilitation, beta-blockers.

3

Introduction

Physical inactivity is an expanding problem and is related to several chronic diseases

including cardiovascular diseases, diabetes and obesity 1. Lack of physical activity may

lead to a chronic imbalance between energy intake and energy expenditure, which is

associated with all-cause and cardiovascular mortality 2,3. After a cardiac incident,

patients are recommended to participate in cardiac rehabilitation to improve their

physical fitness level and engage in a healthy lifestyle 4,5. Cardiac rehabilitation is a

multidisciplinary intervention to improve physical, psychological and social well being

of patients after a cardiac incident 6. However, the majority of the cardiac rehabilitation

programs focus on the improvement and enhancement exercise capacity and quality of

life, but do not target sedentary time and overall physical activity behavior in daily life.

This may be related to the fact that to date, no standardized methods exist to assess

physical activity accurately and unobtrusively in cardiac patients in daily life 7.

Physical activity is a multi-dimensional human behavior characterized by factors

such as type, pattern, duration and intensity of physical tasks and can be quantified by

determining energy expenditure 8. Gold standard methods for the assessment of energy

expenditure are indirect calorimetry and doubly labelled water. Although these methods

were shown to be accurate, they are costly and obtrusive, making them unsuitable for

population-based studies or integration in clinical practice 9.

Over the past decade, the use of wearable movement sensors for measuring

physical activity in free-living conditions has become widespread. Heart rate (HR)

monitors provide insight into the intensity of the aerobic work during physical activity.

During activities with moderate to vigorous intensity, HR and oxygen consumption

4

(VO2) are linearly related 10,11. Therefore energy expenditure, which is dependent on

VO212, can be predicted using HR recordings. However, in free-living conditions energy

expenditure assessment using HR has some important limitations13. First, the HR vs.

energy expenditure relation is not linear during rest and low-intensity activities and is

substantially influenced by internal and external factors (i.e. temperature, emotional

stress, caffeine) 14. In addition, inter-individual variation in HR is large, suggesting the

need for individual calibration processes to build a valid HR-based energy expenditure

model 15.

Yet, combining HR and accelerometer data may reduce these limitations 16. Tri-

axial accelerometers have been used to quantify body movement and predict energy

expenditure using linear models in healthy subjects and patients with a chronic disease

17–19. Accelerometers are small and minimally obtrusive, with battery life and storage

capacity enabling long-term monitoring from several days to weeks. Brage et al.

described a branched equation model in which a combination of HR and accelerometer

recordings was used to estimate physical-activity related energy expenditure in healthy

subjects 20. Other studies showed that models combining HR and accelerometer data

significantly improved energy expenditure estimation accuracy as compared to single-

input only models in children and healthy adults 21,22.

Although combining HR and accelerometer data for energy expenditure

assessment seems to lead to higher estimation accuracy in healthy adults, these

prediction models cannot be translated directly to cardiac patients. Beta-blocker therapy,

which aims at reducing myocardial oxygen demand by lowering heart rate and blood

pressure 23, plays an important role in the treatment of cardiovascular diseases and the

5

prevention of future cardiac events. The heart rate-lowering effect of beta-blockers,

which is highly variable between subjects, may impact the ability of HR to predict

energy expenditure. Therefore, the main objective of this study was to develop an

energy expenditure prediction model for beta-blocker medicated cardiac rehabilitation

patients based on both HR and accelerometer data. For this aim, multivariate linear

regression models were developed, and different combinations of features were

evaluated. In addition, a method based on a calibration protocol for model

personalization, called the flex-HR method 24, was evaluated as a benchmark solution.

6

Methods

Study population

Patients were eligible for participation when they were admitted to cardiac rehabilitation

two to four weeks after hospitalisation for myocardial infarction, unstable angina, or a

revascularization procedure (percutaneous coronary intervention or coronary artery

bypass grafting) at the Máxima Medical Center, Veldhoven (The Netherlands). High

risk of further events was an exclusion criterion (i.e. patients with symptomatic heart

failure, complex congenital heart disease, severe depression, arrhythmias or co-

morbidity limiting exercise performance). Recruitment was performed between

November 2013 and August 2014. Sixteen male patients that were participating in

exercise-based cardiac rehabilitation were recruited. Fourteen patients were taking beta-

blockers (metoprolol) at the time of the test. Each participant signed an informed

consent form. The study was approved by the local Medical Ethical Committee of

Máxima Medical Center, Veldhoven, The Netherlands.

Symptom limited exercise test

A symptom limited exercise test was carried out on a cycle ergometer in an upright-

seated position on an electromagnetically braked cycle ergometer (Lode Corrival, Lode

BV, Groningen), using an individualized ramp protocol aiming at a test duration of 8-12

minutes. Ventilatory parameters were measured breath-by-breath (Masterscreen CPX,

CareFusion, Hoechberg, Germany). The test was ended when the patient was not able to

maintain the required pedaling frequency. PeakVO2 was recorded as the final 30-second

averaged value of the test.

7

Physical activity protocol

The physical activity protocol, as described in Table 1, consisted of a randomly ordered

set of 11 daily living activities of low to moderate intensity. Resting HR (RHR) was

measured in the beginning of the test, and after each activity the patient had a recovery

break, which lasted until the HR reached the resting value.

Energy expenditure measurements

Oxygen uptake (VO2) and carbon dioxide production (VCO2) were measured for the

entire duration of the activity protocol by the Cosmed K4 b2 (Cosmed, Rome, Italy)

portable metabolic system, which is a breath-by-breath pulmonary gas exchange

measurement system consisting of a face mask, an analyzer unit, and a battery. Total

energy expenditure (TEE) was then obtained using the Weir equation from the breath-

by-breath measurements of O2 and CO2, averaged on a minute-by-minute basis and

divided by resting metabolic rate (RMR) to determine the physical activity level (PAL).

PAL is a popular parameter used to represent energy expenditure adjusted for RMR,

which allows to compare measurements of TEE for subjects with different body size

and composition. PAL can be used to determine activity intensity. Light, moderate, and

vigorous intensity activities are characterized by a PAL < 3, < 6, and ≥ 6, respectively

25. The average daily PAL for the healthy adult population is 1.7, a PAL of 1.2

represents the activity level of a bed-bound subjects 26, while an athlete participating in

the Tour de France can reach a PAL of 5 27.

8

RMR measurement

Prior to the RMR assessment, patients were instructed to fast for 12 hours and to avoid

sports activities for 24 hours. During the assessment, patients were asked to stay awake.

RMR was measured using the Cosmed metabolic equipment with the Canopy option

using the mean values of VO2 and VCO2 data collected over a five minutes stability

interval according to Matarese’s recommendations 28.

Physical activity and heart rate measurements

Physical activity was measured using an ActiGraph wGT3X+ activity monitor

positioned at the waist (ActiGraph, Pensacola, Florida), which is a tri-axial

accelerometer with dynamic range of ±6G and sampling frequency set to 40Hz.

Acceleration data were used to determine activity counts per minute (ACmin) according

to the following equation:

where, for each i-sample in a minute period (T) the vector magnitude signal (v) is

subtracted to the mean v over that minute to determine ACmin. The v signal is obtained

from the Euclidean norm of the x, y, z signal representing each sensing axis of the

accelerometer. HR was recorded on a second-by-second frequency using a

commercially available ECG chest belt (Garmin Ltd.) that wirelessly transmitted HR

data to the ActiGraph accelerometer unit for synchronous data storage. Minute-by-

9

minute average for HR and ACmin were computed to predict energy expenditure. HR

above rest (HRnet) was obtained by subtracting RHR from each value of the HR. Data

were visually inspected and annotated to determine start and stop for each activity over

the recorded acceleration signals. The recorded signals were processed and organized

using MatLab (Matlab, Mathworks, Cambridge, MA).

Energy expenditure modeling

TEE and PAL estimation models were developed using multivariate linear regression

analysis. This method allowed for automatically and iteratively selecting which

independent variables to include in the prediction models. Three different groups of

independent variables were considered: i) patient characteristics which included age,

body weight, height, BMI, RMR, Beta-blocker dose (i.e. metoprolol 0, 50, 75 or

100mg/day), and peakVO2; ii) Body movement as described by ACmin, and the

logarithm transform of ACmin; and iii) HR and HRnet information. Five different

energy expenditure prediction models were derived using the following combination of

independent variables: a) Body movement features; b) Body movement + patient

characteristics; c) HR features; d) HR features + patient characteristics; e) Body

movement + HR features + patient characteristics. Patient characteristics were included

in the modeling process to account for between-individual variability in the relationship

between HR or ACmin and energy expenditure (TEE or PAL) 29. HR and body

movement features were included to describe the within-individual variability in energy

expenditure.

10

Additionally, we used the HR-flex method as alternative solution to account for

between-patient variability in the energy expenditure prediction models 24. This method

deploys HR and energy expenditure measurements during a set of cycling activities at

increasing intensity (0W to 70W) to determine parameters of a linear prediction model

applicable to any other activity for HR values above the HR-flex. HR-flex is defined as

the lowest HR recorded during cycling at very low effort (0W). Below the HR-flex

energy expenditure is set to resting values. The HR-flex method leads to development of

an energy expenditure prediction equation by personalizing model parameters using

data from a cycling calibration protocol.

Statistics

Data are presented as mean standard deviation. Pearson correlation coefficient was

calculated to describe the association between ACmin, HR features and TEE or PAL for

each individual patient data or for the entire dataset. Student t-test was used to assess

significant differences in the estimation error of the energy expenditure prediction

models between the development and validation samples. Development of the energy

expenditure prediction model was carried out with data from 11 patients, while data

from the remaining 5 patients were used for validation. For the HR-flex model, patient

data from both groups (from the development and validation group) were used for

developing the patient-specific prediction model. Significance level was set to p < 0.05.

All statistical analyses were carried out using RStudio (version 0.98.507, R

Development Core Team, Free Software Foundation Boston, MA, USA).

11

12

Results

Sixteen patients agreed to participate. The dataset included a set of static variables

indicating the individual characteristics as well as a set of time-varying features like

TEE, PAL, HR and ACmin for an average of 64 minutes/patient (total 1027 data points

for each time-varying variables). Patients were divided in two groups for model

development and validation. Baseline characteristics are presented in Table 2. On the

entire study population, a significant association was found between peakVO2 and

resting VO2 (R = 0.65, p < 0.01) and between peakVO2 and RMR (R = 0.52, p < 0.05).

RHR was not significantly correlated with any of the patient characteristics. Max HR at

the symptom limited exercise test was negatively associated with age (R = - 0.57, p <

0.05) and positively associated with peakVO2 (R = 0.63, p < 0.01).

A significant correlation was found between HR or body movement data (HR,

HRnet, ACmin) and measures of energy expenditure (Table 3). On a group level HRnet

showed the strongest association with energy expenditure as compared to ACmin or

HR. On an individual level, the average correlation between HR and energy expenditure

sharply increased, indicating a strong between-subject difference in the relationship

between HR and TEE or PAL.

Results from the stepwise multivariate regression analysis are shown in Table 4

and Figure 1. Model e) including a combination of body movement, HR features, and

patient characteristics showed the lowest estimation error and the largest correlation

with both TEE and PAL (> 76% and > 83%). When HR features were omitted as input

to the prediction model (Models a and b) energy expenditure estimates showed larger

error (Table 4).

13

The HR-flex model showed the energy expenditure estimation accuracy was

comparable with some of the models based on a combination of HR and body

movement features (Table 4). However, accuracy was lower than the most accurate

prediction model based on HR, body movement and patient characteristics (Figure 2).

The HR-flex model accuracy was not possible to test using a hold-out group of patients

(like the validation group) since the method required individual calibration to set the

value of constituting parameters.

14

Discussion

In this study we developed an energy expenditure prediction model for cardiac

rehabilitation patients using beta-blockers, based on body movement and HR data. A

multivariate regression model shows the lowest estimation error in the estimation of

PAL and TEE when HR, body movement and patient characteristics are included.

Personalization of the prediction model using the HR-flex model with a cycling protocol

for calibration resulted in somewhat lower accuracy than the most accurate multivariate

model.

To our knowledge, this study is the first to validate energy expenditure

estimation models based on acceleration and HR in cardiac patients taking beta-

blockers. The observed correlations between body movement features and TEE as well

as HR features and TEE were comparable to studies in healthy subject 30,31. In addition,

a prediction model using individual HR features or body movement features showed

comparable coefficients with studies in healthy subjects 24,32. As discussed in previous

literature, accuracy of energy expenditure estimation can improve when HR and body

movement data are combined 20,33. A validation study in healthy subjects demonstrated a

lower RMSE and higher correlation in a HR-body movement model estimating free-

living PAEE (r2 = 0.78) as compared to a model using estimates from HR or body

movement alone (r2 = 0.59 and r2 = 0.61 respectively) 20. We observed similar results for

the individual HR-body movement regression models in cardiac patients (TEE: r2 = 0.56

and r2 = 0.64 respectively), and a similar improvement in r2 and RMSE in the regression

model when body movement, HR and patient characteristics were combined (r2 = 0.76

15

for TEE, r2 = 0.83 for PAL). Studies validating HR-body movement models in free-

living conditions showed comparable results 34,35.

Our results revealed that the accuracy of energy expenditure estimation improves

when specific patient characteristics are included in the model. Although HR and

oxygen consumption are linearly related, our results showed that variation in individual

patient characteristics (e.g. fitness, age and gender) cause a high between-subject

variation. Adding these patient characteristics and using individual calibration protocols

(e.g. step-test, cycling protocol) can capture these variations and improve the accuracy

of TEE/PAL estimation. In particular, the HR-Flex method, using HR data and a cycling

protocol for calibration proved to be more accurate in TEE and PAL estimation than the

models combining body movement and HR data only. These results are in line with the

literature, indicating the beneficial effects of individual calibration to capture between-

individual variances 34,36. However, individual calibration also requires resource-

demanding procedures, and is therefore unsuitable for large-scale studies and clinical

practice. In addition, branched equation models, used in several studies to distinguish

between low- and high intensity activities based on the transition between walking-

running 20, are unsuitable for cardiac patients with limited physical fitness levels.

Therefore, we aimed at developing an energy expenditure model that could be

applicable to patient data that are available at the start of a rehabilitation program to

successfully monitor physical activity in these patients in daily life. Currently, general

patient characteristics (i.e. age, body weight, BMI, medication use) are available in the

electronic patient record and a symptom limited exercise test is performed at the start of

each cardiac rehabilitation program to determine physical fitness (peakVO2). With this

16

data available, only RMR data are required before the most accurate multivariate

regression model described in our results (Table 4, model e) can be implemented.

However, RMR can be calculated using the Harris-Benedict equation 37, although RMR

assessment as performed in this study is probably more accurate.

Clinical interpretation and future directions

With the introduction of accurate energy expenditure models based on HR and body

movement for cardiac patients using beta-blockers, physical activity behavior can

become a more eminent topic in cardiac rehabilitation. First, objective feedback on

physical activity monitored during the intervention provides awareness concerning a

patient’s current physical activity behavior. With feedback and motivational coaching

based on the objective physical activity data, patients are able to develop self-

management skills and improve and/or maintain their physical activity behavior 38.

Second, accurate assessment of physical activity is essential for evaluating the

effectiveness of an intervention program and for studying the dose-response relation

between physical activity levels and health outcomes. Therefore, it is essential that

accurate energy expenditure models are developed, using data that are routinely

available at the start of cardiac rehabilitation without the need of additional procedures

and resources. Although a slight improvement in the TEE and PAL estimation error was

observed with individual calibration processes, generalizability is limited. With the

developments in wearable sensors, HR and body movement data collection will become

more accurate, comfortable and feasible in the home environment. Whereas this study

showed that TEE and PAL can be accurately predicted using HR and body movement

17

data in combination with patient characteristics and data derived from a symptom

limited exercise test, the inclusion of activity recognition and improvement of

technology may lead to an even further reduce the energy expenditure estimation error

39. Future studies can use our results as a roadmap for accurate physical activity

monitoring with wearable sensors. If the methodology is confirmed in a larger cardiac

rehabilitation population with mixed conditions, it will be more applicable in the cardiac

rehabilitation setting.

Strengths and limitations

In this study we developed energy expenditure estimation models using HR and body

movement in cardiac patients using beta-blocker medication. Nevertheless, generalizing

the results to the entire cardiac population (e.g. chronic heart failure) is not

straightforward considering the composition of the study population. Furthermore, our

results are based on laboratory investigation using an activity protocol including

treadmill walking and ergometer cycling as modes of activity. It is not known how well

the results apply to free-living conditions. The results of our study are based on a

limited number of cardiac rehabilitation patients. Therefore, the results cannot be

translated directly to a cardiac rehabilitation population with mixed cardiac conditions.

Conclusion

In conclusion, we developed an energy expenditure estimation model for beta-blocker

medicated cardiac rehabilitation patients that showed the highest accuracy when HR,

body movement data and patient characteristics were combined. Personalization of the

18

model using patient characteristics available at the start of the cardiac rehabilitation

program, results in an accurate and feasible model to estimate energy expenditure and

physical activity levels of beta-blocker medicated patients. Additional personalization

using a cycling protocol did not result in a substantial improvement in the estimation of

energy expenditure, indicating that this additional personalization protocol has little

benefit in clinical practice.

Acknowledgements

This study received funding from ZonMW, The Netherlands Organisation for Health

Research and Development (ZonMw project number 837001003) and was supported in

kind by Philips Research.

Conflict of interest

Authors FS, GP, WS and AB are employed by Philips Research.

Author contributions

All authors contributed to the conception and design of the work. JK, FS, GP and AB

contributed to the acquisition, analysis, or interpretation of data for the work. JK and

AB drafted the manuscript. All authors critically revised the manuscript, gave final

approval and agree to be accountable for all aspects of work ensuring integrity and

accuracy.

19

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25

Table 1. Routine of activity tasks randomly performed by the patients and their duration

Activity task Duration

(minutes)

Sitting at rest 5

Standing at rest 2

Walking 4 km/h 3

Walking 4 km/h, slope 5% 3

Walking 5.5 km/h 3

Cycling 0W 70 rpm 3

Cycling 40W 70 rpm 3

Cycling 70W 70 rpm 3

Cleaning a table 3

Vacuuming 3

Putting dishes in a dish washer 3

W, Watt; rpm, rounds per minute

26

Table 2. Baseline characteristics.

Development Group (n = 11)

Mean ± Sd Min - Max

Age, year 57.1 ± 6.6 45 - 64

Height, cm 182.8 ± 10.6 173 - 191

Weight, kg 95.2 ± 9.5 82 - 108

BMI, kg/m2 28.6 ± 3.5 23.8 - 33.7

RHR, bpm 54.4 ± 6.7 42 - 66

RMR, kcal/min 1.2 ± 0.1 1.0 - 1.4

Resting VO2, ml/min 324.7 ± 43.2 266.7 - 418.8

PeakVO2, ml/min 2326.8 ± 730.8 1429.3 - 3908.6

Max HR, bpm 138.5 ± 23.5 96.3 - 177.2

Beta-blocker dose, mg/day 48.3 ± 32.1 0 - 100

Validation Group (n = 5)

Mean ± Sd Min - Max

Age, y 53.124 ± 8.782 42 - 61

Height, m 175.5 ± 6.6 164 - 182

Weight, kg 94.8 ± 12.2 80 - 110

BMI, kg/m2 30.7 ± 3.1 25 - 33.2

RHR, bpm 59.1 ± 5.3 52 - 68

RMR, kcal/min 1.2 ± 0.2 1.0 - 1.4

Resting VO2, ml/min 351.8 ± 48.9 315.8 - 443.8

PeakVO2, ml/min 2369.3 ± 450.0 1839.4 - 3175.1

Max HR, bpm 151.7 ± 3.9 148.1 - 156.7

Beta-blocker dose, mg/day 80.3 ± 39.9 0 - 100

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BMI; body mass index; RHR, resting heart rate; bpm, beats per minute; RMR, resting metabolic rate;

VO2, oxygen consumption; PeakVO2, maximum oxygen consumption; max HR, maximum heart rate;

Table 3. Correlation between energy expenditure measures and body movement or HR

features for data from the entire study population (Group) or from each individual

patient (Individual).

Correlation TEE vs ACmin TEE vs HR TEE vs HRnet

p < 0.05 Mean Sd Mean Sd Mean Sd

Group 0.47 0.55 0.68

Individual 0.53 0.15 0.84 0.12 0.84 0.12

PAL vs ACmin PAL vs HR PAL vs HRnet

Mean Sd Mean Sd Mean Sd

Group 0.49 0.49 0.56

Individual 0.53 0.15 0.84 0.12 0.84 0.12

TEE, total energy expenditure; ACmin, activity counts per minute; HR, heart rate; HRnet, (heart rate –

resting heart rate); PAL, physical activity level

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Table 4. Accuracy of the energy expenditure prediction models.

TEE Development Validation

R2

RMSE

(kcal/

min)

Bias

(kcal/

min) R2

RMSE

(kcal/

min)

Bias

(kcal/

min)

Model a) Movement features 0.64 0.984 0 0.65 1.031 -0.135

Model b) Movement + Patient

features 0.63 0.99 0 0.57 1.156 -0.249

Model c) HR features 0.56 1.084 0 0.76 0.922 0.107

Model d) HR + Patient features 0.60 1.032 0 0.79 0.856 -0.079

Model e) Movement + HR +

Patient features 0.76 0.799 0 0.79 0.799 -0.126

HR-flex model 0.79 1.11 -0.22 - - -

PAL Development Validation

R2

RMSE

(kcal/

min)

Bias

(kcal/

min) R2

RMSE

(kcal/

min)

Bias

(kcal/

min)

Model a) Movement features 0.61 1.262 0 0.62 1.278 -0.076

Model b) Movement + Patient

features 0.67 1.161 0 0.68 1.178 0.008

Model c) HR features 0.74 1.026 0 0.72 1.123 0.22

Model d) HR + Patient features 0.75 1.006 0 0.79 0.986 0.145

Model e) Movement + HR +

Patient features 0.83 0.835 0 0.84 0.834 0.118

HR-flex model 0.80 0.846 -0.13 - - -

TEE, total energy expenditure; RMSE: root mean squared error; Bias: mean error; HR, heart rate; PAL,

physical activity level. The HR-flex model accuracy was not possible to test using a hold-out group of

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patients since the method required individual calibration to set the value of constituting parameters.

Results from the development group were showed to demonstrate the large generalization properties of

the models not affected by overfitting.

30

Figure 1. Correlation between measured and predicted TEE for all the developed

models based on a combination of HR, ACmin and patient characteristics.

31

Figure 2. Measured and predicted PAL from a representative patient according to the

HR-flex method and the multivariate model based on HR, body movement features and

patient characteristics.

Black line represents the measured PAL, while red and blue lines the predicted PAL

according to the prediction models.

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