METHOD AND SYSTEM FOR PREDICTING A VO2MAX MEASUREMENT

Abstract

A method, system and computer program for predicting a VO2max measurement for a subject, the method comprising: collecting a heart rate recovery value, measured over a first period starting at the end of a fitness test exercise completed by the subject, wherein the fitness test exercise comprises a plurality of phases, each phase having a predetermined duration and each phase being associated with a respective heart rate band, and during each phase, the subject adjusts their effort to maintain their heart rate in the respective heart rate band, and calculating a predicted VO2max measurement using the heart rate recovery value.

Claims

1. A method of predicting a VO2max measurement for a subject, the method comprising: collecting a heart rate recovery value, measured over a first period starting at the end of a fitness test exercise completed by the subject, wherein the fitness test exercise comprises a plurality of phases, each phase having a predetermined duration and each phase being associated with a respective heart rate band, and during each phase, the subject adjusts their effort to maintain their heart rate in the respective heart rate band, and calculating a predicted VO2max measurement using the heart rate recovery value.

2. The method according to claim 1, wherein the heart rate bands are defined by a range of percentage of maximum heart rate, and wherein the fitness test exercise comprises a low intensity phase and a high intensity phase, the low intensity phase having a lower heart rate range than the high intensity phase, and wherein the low intensity phase is the first phase to be performed in the fitness test exercise, and the high intensity phase is the last phase to be performed in the fitness test exercise.

3. The method according to claim 1, wherein the first period is at least 1 minute.

4. The method according to claim 3, wherein the first period is 3 minutes.

5. The method according to claim 1, wherein the method further comprises collecting a second heart rate recovery value measured over a second period starting at the end of the fitness test exercise, the second period being shorter than the first period, and the step of calculating the predicted VO2max measurement includes using the second heart rate recovery value.

6. The method according to claim 5, wherein the second period is 1 minute.

7. The method according to claim 1, wherein the method further comprises collecting a mass factor of the subject, and the step of calculating the predicted VO2max measurement includes using the mass factor.

8. The method according to claim 1, wherein the method further comprises collecting an ethnicity value, wherein the ethnicity value is a numerical value dependent on the ethnicity of the subject and the step of calculating the predicted VO2max measurement includes using the ethnicity value.

9. The method according to claim 7, wherein the step of calculating the predicted VO2max measurement comprises: applying a respective weighting to each of the collected values, and adding the weighted values.

10. The method according to claim 9, wherein the step of calculating the predicted VO2max measurement further comprises: adding a baseline value.

11. The method according to claim 1, wherein heart rate recovery value has a positive effect on predicted VO2max.

12. The method according to claim 5 or any of claims 6 to 11 when dependent on claim 5, wherein the second heart rate recovery value has a negative effect on the predicted VO2max.

13. The method according to claim 1, wherein the method further comprises, during each phase of the fitness test exercise, instructing the subject to adjust their efforts to maintain their heart rate in the heart rate band corresponding to the respective phase of the fitness test exercise.

14. The method according to claim 1, wherein the method further comprises, during each phase of the fitness test exercise, displaying the respective heart rate band.

15. The method according to claim 1, wherein the method further comprises, displaying the subject's heart rate during the fitness test exercise.

16. The method according to claim 1, wherein collecting the heart rate recovery value comprises the steps of: detecting the end of the fitness test exercise completed by the subject, automatically measuring heart rate data over the first period, and calculating the heart rate recovery value.

17. A computing device, the computing device configured to perform the method of claim 1.

18. A system for predicting a VO2max measurement for a subject, the system comprising a computing device and a heart rate monitor, the system configured to perform the method of any of claim 1.

19. A computer program for predicting a VO2max measurement for a subject, the computer program configured, when run on a computing device, to cause the computing device to perform the method of claim 1.

Description

SUMMARY OF THE FIGURES

[0051] Embodiments and experiments illustrating the principles of the invention will now be discussed with reference to the accompanying figures in which:

[0052] FIG. 1a shows an example of a system configured to predict a subject's VO2max.

[0053] FIG. 1b shows an example of a system configured to predict a subject's VO2max.

[0054] FIG. 2 shows an example heart rate graph for a subject performing a fitness test exercise and recovery period.

DETAILED DESCRIPTION OF THE INVENTION

[0055] Aspects and embodiments of the present invention will now be discussed with reference to the accompanying figures. Further aspects and embodiments will be apparent to those skilled in the art. All documents mentioned in this text are incorporated herein by reference.

[0056] Two example systems which may perform the method of the present invention are shown in FIGS. 1a and 1b. Each system includes a heart rate monitor 1, a computing device and a server 3. The system of FIG. 1a includes a smartphone 2 as the computing device and the system in FIG. 1b shows a computer 4 with a tower 4a and a display 4b. The tower may be integrated with the display, for example in a smart-TV screen. Other types of computing devices may be used in place of those shown without departing from the present invention.

[0057] A heart rate monitor 1 is provided to be worn by the subject. The heart rate monitor 1 may be a Myzone® chest belt monitor or another type of heart rate monitor which may be chest-based or wrist-based. The heart rate monitor 1 may have a memory for storing heart rate data.

[0058] The heart rate monitor 1 has a communication module configured to communicate with the computing device as shown by the arrows a. This may be by a wired connection or by wireless communication such as WiFi or Bluetooth. The heart rate monitor is configured to send heart rate information, such as heart rate in real time, stored heart rate data, or calculated heart rate recovery data to the computing device. Where the heart rate monitor sends HRR data to the computing device, the heart rate monitor calculates this from heart rate data measured over the first period starting at the end of a fitness test exercise completed by the subject.

[0059] Calculating the heart rate recovery value from the heart rate data may include analysing the heart rate data to determine the end of the fitness test exercise, determining the heart rate at the end of the fitness test exercise, determining the end of the first period and determining the heart rate at the end of the first period and subtracting the heart rate at the end of the first period from the heart rate at the end of the fitness test exercise to determine the heart rate recovery value.

[0060] The computing device has a processor, a memory, a display and a user input which may be a keyboard, mouse and/or touch screen. The computing device is configured to communicate with the heart rate monitor via the arrows a by a wired connection or by wireless communication such as WiFi or Bluetooth. The computing device is also configured to communicate with server 3 via arrows b. This connection may be a wired or wireless communication channel and may include communication over the internet.

[0061] The computing device has software installed which enables the system to collect factors and calculate the predicted VO2max. The software may be an application for smartphone 2 or a computer program for tower 4a. The software may enable the computing device to carry out all of the method or may enable the computing device, together with the server to carry out the method.

[0062] The computing device is configured to receive data from the heart rate monitor 1. Where the heart rate data includes the heart rate recovery value, this amounts to collection of the heart rate recovery value. Where raw heart rate data is received from the monitor, the computing device may calculate the heart rate recovery value from the heart rate data. In this case, calculating the heart rate recovery value constitutes collecting the heart rate recovery value.

[0063] The software may be configured such that the user may input factors directly via the user input(s), or it may allow a user to input information that the computing device can use to determine a factor. For example, when the mass factor is fat free mass, the user may input body fat percentage and weight and the computing device may calculate the fat free mass using this information.

[0064] The computing device may store information about factors/values, or factors themselves or any other information about the subject, including the predicted VO2max values in the memory of the computing device or at the server 3 so that the user need not enter them every time the method is to be performed.

[0065] In this case, collecting the factors may comprise retrieving the stored information from the memory or the server 3. The server may act as a store or back-up for information about the subject.

[0066] In some embodiments, the computing device may send factors or information from which factors can be determined to the server 3, and the server 3 will collect the factors and calculate the predicted VO2max. In this case, collecting the factors comprises the server receiving factors from the computing device, the server determining factors from information or the server retrieving stored factors.

[0067] The system in FIG. 1a has the advantage that it can be used by a single user without specialist equipment as the necessary software (e.g. an application) can simply be installed on their smartphone to enable the method of the present invention to be performed. The system in FIG. 1b may be used in a club environment where many subjects may carry out the fitness test exercise. The software required may be installed on the tower 4a.

[0068] The system may further comprise a piece of exercise equipment in communication with the computing device. In this case, the exercise equipment may send the computing device information about power attained during the exercise to enable the power factor to be determined. Alternatively, the exercise equipment may display this value and the user may enter the power factor manually into the computing device.

EXAMPLE

[0069] An example of performing the method of the present invention will now be described.

[0070] A subject wears heart rate monitor 1 while performing a fitness test exercise in which their heart rate is increased from 50-59% of their maximum HR at the beginning of the test to 80-85% of their maximum heart rate over 10 minutes. The smart phone 2 may display the target heart rate to instruct the user to attain the target heart rate. They then rest for the following five minutes.

[0071] The heart rate monitor 1 measures the subject's HR throughout the fitness test and during the recovery period. The heart rate monitor stores the heart rate data in its memory.

[0072] When a connection is present between the smartphone 2 and the heart rate monitor, the heart rate monitor sends the heart rate data to the smartphone. This may be in real time as the subject carries out the fitness test exercise, or after the exercise has been completed. The HR of the subject may be displayed by the smartphone in real time, optionally in addition to the target heart rate range, to aid the subject in attaining the target heart rate during the test exercise.

[0073] An app running on the smartphone 2 retrieves data stored about the subject from the smartphone memory or from the server 3. The app asks the subject to input their fat free mass in kg or body fat percentage and weight in kg and their ethnicity or retrieves previously stored values for these factors. Alternative units may be used, but the value should either be converted into the units given in the equation to be used or the weighting in the equation should be converted to match the units of the data input.

[0074] If body fat percentage and weight are entered, the fat free mass is calculated using the equation:

fat free mass=weight×(1−body fat percentage).

[0075] If the subject does not know their body fat percentage or fat free mass, they are asked to enter their weight and sex.

[0076] The app calculates the first HRR (HRR2) from the heart rate data by analysing the heart rate data to determine the end of the fitness test exercise, determining the heart rate at the end of the fitness test exercise, determining the heart rate at the end of the second minute of recovery and subtracting the heart rate at the end of the second minute of recovery from the heart rate at the end of the fitness test exercise to determine the first heart rate recovery value (HRR2).

[0077] The app calculates the second HRR (HRR1) from the heart rate data by analysing the heart rate data to determine the end of the fitness test exercise, determining the heart rate at the end of the fitness test exercise, determining the heart rate at the end of the first minute of recovery and subtracting the heart rate at the end of the first minute of recovery from the heart rate at the end of the fitness test exercise to determine the second heart rate recovery value (HRR1).

[0078] If the fat free mass was entered, retrieved or calculated, then formula 1 below is used to determine the predicted VO2max. If the fat free mass was not entered, retrieved or calculated, then formula 2 below is used to determine the predicted VO2max.

VO2 max=(fat free mass×0.066)−(Ethnicity×0.357)+(HRR2×0.019)−(HRR1×0.017)−0.729VO2 max=(Weight×0.025)−(Ethnicity×0.456)+(HRR2×0.029)−(HRR1×0.022)+(Sex×0.822)−0.330

[0079] The predicted VO2max is displayed to the user on the computing device and sent to the server for storage.

[0080] Experiments

[0081] The following examples show the accuracy of three methods according to the present invention. In the examples, a method according to the present invention is compared with a VO2max test to show that results achieved with a method according to the present invention are reliable predictions of the VO2max of a subject.

[0082] In each example, measures of height, weight, body fat percentage, fat free mass and fat mass were obtained. Measures of height were measured to the nearest 0.1 cm and measures of weight were obtained to the nearest 0.05 kg.

[0083] To measure VO2max, a computerised metabolic system (Cortex Metalyser 3B-R2, Cortex Biophysik GMBH, Leipzig, Germany) measured VO2 breath-by-breath with a flowmeter connected to a Hans-Rudolf mask. Before each test barometric pressure and ambient air was measured, and the gas analyser and inspiratory flowmeter were calibrated.

[0084] HR during the tests was measured using the MZ-3 Chest Heart Rate Monitor (Myzone Ltd, Isle of Man, UK), with a Myzone Receiver (MZ-R) used to log the HR data during the MZFitness test in one-second epochs.

Example 1

[0085] Cycling MZ-Fitness Test

[0086] All tests were performed in a laboratory environment in a thermo-neutral environment. For the cycling MZ-Fitness Test, a Wattbike Trainer (Wattbike, Nottingham, UK) was used. Participants were familiarised with the Wattbike, both in terms of the seat and handlebar adjustment and how to alter the resistance using airflow and magnetic resistance. The MZ-Fitness Test described below is a sub-maximal test which required the participants to exercise through different HR zones for 10 minutes (see Table below), by manipulating the Wattbike's resistance and/or their cadence to achieve a HR within the correct HR Zone for that period of the test. The HR zones are percentages of age predicted heart rate max (HRmax) using the HUNT study equation for predicting HRmax which is 211−0.64×age.

[0087] The HR zones are colour coded as shown in the table below.

TABLE-US-00001 Zone Heart Rate Time (mins) Grey 50-59% 1 Blue 60-69% 3 Green 70-79% 3 Purple 80-85% 3 Recovery HRR 5 Total time 15

[0088] A verbal explanation of the test was provided to the participants which was followed by a three-minute warm-up period. During this time, the participant was instructed to try and get themselves into the first HR Zone (50-59% of age predicted HRMax). This ensured patient safety as well as enabling familiarisation with the Myzone app, test screen on the app, and the practical use of the Wattbike. A washout period followed to enable participants to stretch, after which the MZ-Fitness Test began.

[0089] At the end of the test, the participant's HR was recorded for five minutes. Participants were asked to stop cycling entirely and not to “spin their legs” as may be the traditional instruction following a cycle MZ Fitness test validation ergometer fitness test. The first minute of the recovery period was conducted with the participant still seated on the Wattbike. After this initial minute, participants were asked to move to a chair and remain seated for a further four minutes. FIG. 2 displays exemplar data of a participants HR during the MZFitness test and subsequent recovery period.

[0090] HRR was determined as the absolute difference in final heart rate post submaximal test and after 1, 2, 3, 4, and 5 minutes. The HRR after n minutes will be referred to as HRRn to avoid confusion, for example, the HRR after one minutes will be referred to as HRR1 and so on.

[0091] Cycling VO2max Test

[0092] A maximal cycle ergometer ramp test on a Lode Sport Excalibur Cycle Ergometer (Lode BV Medical Technology, Groningen, Netherlands) was used to determine VO2max. After a short rest period after the MZ-Fitness Test, the cycling max test began. The test began with a two-minute period of resting whilst sitting on the ergometer, followed by a three-minute warm-up which led directly into the ramp test. The ramp test was determined using a previously validated prediction equation to estimate VO2max without the need for previous exercise data (see equation 1)

Predicted VO.sub.2max=34.142−0.005(Age.sup.2)+11.403(Gender)+1.463(PAS)+9.170(Height)−0.254(Body Mass)

Where Gender|(male=1,female=0);PAS,physical activity status(0−7)

[0093] Equation 1—Matthews et al non exercising VO2max prediction equation—see Matthews C E, Heil D P, Freedson P S, Pastides H. Classification of cardiorespiratory fitness without exercise testing. Med Sci Sports Exerc 1999; 31(3):486-493. PMID: 10188755.

[0094] Using the result of the above equation the cycling test ramp was determined using the ACSM cycling metabolic equations to calculate the cycle ergometer peak work rate (See Equations 2-4).

[00001]${\mathrm{Power}}_{\mathrm{final}}=\left(\frac{\left(\left(\mathrm{Predicted}{\mathrm{VO}}_{2\max }\times \mathrm{Body}\mathrm{Mass}\right)-7\right)}{1.8}\right)/6.12$

[0095] Equation 2—ACSM Cycling Metabolic Equations to calculation predicted final/peak power during a cycling ergometer test (see Cunha F A, Midgley A, Montenegro R, Vasconcellos F, Farinatti P. Utility of a Non-Exercise VO2max Prediction Model for Designing Ramp Test Protocols. Int J Sports Med 2015; 36(10):796-802. [doi: 10.1055/s-0034-1395590]).

[0096] Depending on the participants capabilities in the MZ-Fitness Test, participants began their ramp test at either 30 or 70 Watts (W). If a participant's final power during the MZ-Fitness was less than or equal to 90 W, participants began their maximal test at 30 W. This was to ensure that participants could complete the test and would not finish the test prematurely. This decision was made at the discretion of the investigators.

Power.sub.initial=30or 70W

[0097] Equation 3—Modified ACSM Initial Power

[0098] Therefore, the ramp during the test was determined as follows:

[00002]$\mathrm{Ramp}\mathrm{Ratio}=\frac{{\mathrm{Power}}_{\mathrm{Final}}-{\mathrm{Power}}_{\mathrm{Initial}}}{10\min }$

[0099] Equation 4—ACSM equation to determine the ramp protocol for the cycling ergometer max test (see Cunha F A, Midgley A, Montenegro R, Vasconcellos F, Farinatti P. Utility of a Non-Exercise VO2max Prediction Model for Designing Ramp Test Protocols. Int J Sports Med 2015; 36(10):796-802. [doi: 10.1055/s-0034-1395590]). Note: the equation was based on a predicted 10 minute test however, the test was open ended and finished based on the criteria described below.

[0100] During the ramp portion of the VO2max test, participants were instructed to cycle at a consistent cadence between 70-85 rpm for as long as they could. During each minute of the ramp, participants were asked to provide their Rating of Perceived Exertion (RPE) using the Borg10 Scale (see Borg G. An introduction to Borg's RPE-scale. Ithaca, N.Y. Mouv Publ. 1985. and Borg E, Kaijser L. A comparison between three rating scales for perceived exertion and two different work tests. Scand J Med Sci Sport 2006; 16(1):57-69. [doi: 10.1111/].1600-0838.2005.00448.4.

[0101] The test was terminated when one of the three conditions were met: [0102] The cadence dropped by 10 rpm for over 10 seconds, [0103] Participants volitionally ceased exercise or [0104] The researcher deemed termination was necessary due to health and safety reasons

[0105] At the end of the test, the same recovery protocol was followed as performed during the MZ-Fitness Test.

[0106] Statistical Analysis

[0107] Breath-by-breath VO2max data collected during each ramp test were time-averaged over 30 seconds. A participant was determined to meet VO2max if the following criteria were satisfied: [0108] Oxygen Uptake plateaued with an increase in workload [0109] HR plateau with an increase in heart rate [0110] Max HR was within ±15 BPM of age-predicted Max HR [0111] Respiratory Exchange Ratio of ≥1.1 [0112] Borg10≥7

[0113] There is no consensus on the number of criteria that should be met in order to call a test maximal, however, the inventors used the plateau in either heart rate or oxygen uptake as a criterion to determine whether max had been reached.

[0114] Prediction Equation Used:

VO2 max=(fat free mass×0.045)−(Ethnicity×0.236)+(HRR2×0.014)−(HRR1×0.016)+(Avg_watts×0.006)−0.243

[0115] Where: fat free mass measured in kg, ethnicity (white=1, non-white=0), HRR2 is the heart rate recovery after 2 minutes, HRR1 is the heart rate recovery after 1 minute and Avg_watts is the average watts in the final 30 seconds of the purple zone.

[0116] Equation 5. VO2max prediction equation that includes a power-based metric.

[0117] This equation explained a total of 93.6% of the variance in VO2max with a Standard error of estimate (SEE) of 0.22 L.min. This equation uses a participant's fat free mass, their ethnicity, their HRR after 1 and 2 minutes of the MZ-Fitness Test and the average watts in the final 30 seconds.

Example 2

[0118] In this example, the same fitness tests were performed as in Example 1. However, in this example, the power-based metric, or power value is omitted so as to make a prediction possible using a fitness test in conditions where it is not possible or it is difficult to measure or collect the power-based metric.

[0119] Prediction equation used:

VO2 max=(fat free mass×0.006)−(Ethnicity×0.357)+(HRR2×0.019)−(HRR1×0.017)−0.729

[0120] Where: fat free mass measured in kg, ethnicity (white=1, non-white=0), HRR2 is the heart rate recovery after 2 minutes, HRR1 is the heart rate recovery after 1 minute.

[0121] Equation 6. VO2max prediction equation without a power based metric.

[0122] This equation explains 88.8% of the variance in VO2max with a standard error of the estimate (SEE) of 0.29 L.min. This means that 95% of the time, an individual's predicted VO2max is accurate to ±0.29 L.min.

[0123] In comparison with the equation used in example 1, the removal of average watts in the final 30 seconds of the purple zone resulted in a decrease in explained variance of 4.8% from Equation 5. However, the variables present in Equation 6 are much more easily provided by the subject and therefore it is believed the decrease in explained variance is justified.

Example 3

[0124] In this example, the same fitness tests were performed as in Example 1. However, in this example, the power-based metric, or power value is omitted so as to make a prediction possible using a fitness test in conditions where it is not possible to measure the power-based metric. Further, the fat free mass is not used and instead, weight and sex are included. The reason for this is that it can be difficult for individuals to obtain a measurement of their fat free mass. Therefore, although using weight may provide a less accurate prediction, it allows a prediction to be given even if an individual does not yet have a measurement of their fat free mass.

VO2 max=(Weight×0.025)−(Ethnicity×0.456)+(HRR2×0.029)−(HRR1×0.022)+(Sex×0.822)−0.330

[0125] Where: weight is measured in kg, ethnicity (white=1, non-white=0), HRR2 is the heart rate recovery after 2 minutes, HRR1 is the heart rate recovery after 1 minute and self-reported sex (male=1, female=0). Equation 7. VO2max prediction equation if a subject does not know fat free mass.

[0126] In this equation, fat free mass is replaced by weight and sex (sex is coded as male=1, and female=0 in the equation) has also been reintroduced to the equation. This equation explained 82.3% of the variance in VO2max. This equation has a SEE of 0.36 L.min meaning 95% of the time the predicted VO2max will be within ±0.36 L.min of the measured VO2max. Sex has been introduced into the equation above. This can be explained as fat free mass explains the same variance as sex explains. However, weight does not explain this variance and therefore sex is reintroduced as a significant predictor into the equation.

[0127] Further Research

[0128] Treadmill MZ-Fitness Test

[0129] The treadmill test was performed on an H/P/Cosmos Quasar Treadmill (H/P/Cosmos Sports and Medical GMBH, Nussdoorf, Germany). The test was performed in exactly the same way as the test during the cycling test, except during the treadmill test participants were asked to adjust their running speed and incline as they saw fit to make sure that they were in the right HR Zones. Furthermore, at the end of the test, participants remained standing for the first minute of the recovery period, before moving to a chair for a further 4 minutes.

[0130] Treadmill VO2max Test

[0131] A maximal incline ramped treadmill test using the H/P/Cosmos Quasar Treadmill, was used to determine VO2max. This protocol was adapted from Sport and Exercise Physiology Testing Guidelines. The test used the final running speed from the MZ-Fitness test, and subtracted 2 km/h (or 1 km/h if subtracting 2 km/h meant the participant would be walking for the max test) to determine the starting speed for the max test. Participants also began the max test at 1% incline, the incline which most accurately represents outdoor running. Following the resting and warm-up phase, the ramp protocol began, which involved increasing the incline by 1% gradually every minute. The max test was terminated volitionally by the participant at exhaustion.

[0132] The results given by a sub group of the participants in examples 1 to 3 and analysis assessing the agreement of the outcome from the MZ-Fitness test across exercising modalities showed moderate to strong significant correlations between HRR1 and HRR2 across both the treadmill and the cycle modes of testing (r≤0.664, p≤0.05).

[0133] The VO2max prediction can be calculated in the same manner as for the examples above.

[0134] Comparison of VO2max with Normative Data

[0135] Once an individual is provided a prediction of their VO2max, it is helpful to provide them additional information about how this value compares to other individuals. To do this, normative data sets can be used. Normative data sets can be used to provide an individual a categorisation, and percentage value, based on their age and sex. For example, if an individual aged 25 had a predicted VO2max of 48 ml.kg.min, they could be informed that their fitness was “Good” and in the top 30% of individuals for their age and sex.

[0136] There are a wide range of normative data sets published in previous literature. One example is the American College of Sports Medicine Fitness Registry and the Importance of Exercise National Database (ACSM FRIEND).

[0137] Fitness Age

[0138] A Fitness Age score allows an individual to know whether their fitness is higher, or lower, than would be expected of an individual their age. To calculate this score, the ACSM FRIEND normative data set was used. Linear interpolation was performed between data points within this normative data set. This process involves predicting the data between two, or more, known data points by constructing a straight line between them. This allows a VO2max value to be assigned for each age point.

[0139] The database contains information for each 5% percentile of fitness for each age. So, the subject can choose to be compared with a certain percentile in the data. For example, someone with good fitness can be compared with data for those with good fitness. In this example, poor, fair and good categories were used. Good was assigned to the 75% percentile in the normative data, fair to the 50% data, poor to the 30% data.

[0140] The linear regression process was repeated for the poor, fair and good categories. This allows an individual to receive a Fitness Age score in comparison to a person who has poor, fair or good fitness. This allows a more meaningful comparison to be made as if an individual has a good fitness level comparing them to data relating to people with poor fitness may not be motivational. This allows the provision of a score that tells the subject whether they are more, or less, fit than someone with poor, fair or good fitness for their age. It can also provide an individual with what would be considered a good, fair or poor VO2max score for their age and sex.

[0141] For example, if a 25 year old male subject is compared with people with good fitness, and the average VO2max in the 75% percentile of the database for 25 year old males is 55 and the subject has a predicted VO2max of 55, then the fitness age score will be 25. If the same subject is compared with people with poor fitness, and the average VO2max in the 30% percentile of the database for 25 year old males is 42 and the subject has a predicted VO2max of 55, then the fitness age score will be younger than 25.

[0142] In another example, if a 55 year old female subject is compared with people with good fitness, and the average VO2max in the 75% percentile of the database for 55 year old females is 25 and the subject has a predicted VO2max of 25, then the fitness age score will be older than 55, for example, 60. If the same subject is compared with people with poor fitness, and the average VO2max in the 30% percentile of the database for 55 year old females is 21 and the subject has a predicted VO2max of 25, then the fitness age score may be younger than 55, for example, 42.

[0143] The systems and methods of the above embodiments may be implemented in a computer system (in particular in computer hardware or in computer software) in addition to the structural components and user interactions described.

[0144] The term “computer system” includes the hardware, software and data storage devices for embodying a system or carrying out a method according to the above described embodiments. For example, a computer system may comprise a central processing unit (CPU), input means, output means and data storage. Preferably the computer system has a monitor to provide a visual output display (for example in the design of the business process). The data storage may comprise RAM, disk drives or other computer readable media. The computer system may include a plurality of computing devices connected by a network and able to communicate with each other over that network.

[0145] The methods of the above embodiments may be provided as computer programs or as computer program products or computer readable media carrying a computer program which is arranged, when run on a computer, to perform the method(s) described above.

[0146] The term “computer readable media” includes, without limitation, any non-transitory medium or media which can be read and accessed directly by a computer or computer system. The media can include, but are not limited to, magnetic storage media such as floppy discs, hard disc storage media and magnetic tape; optical storage media such as optical discs or CD-ROMs; electrical storage media such as memory, including RAM, ROM and flash memory; and hybrids and combinations of the above such as magnetic/optical storage media.

[0147] The methods of the above embodiments may be provided as computer programs or as computer program products or computer readable media carrying a computer program which is arranged, when run on a computer, to perform the method(s) described above.

[0148] The term “computer readable media” includes, without limitation, any non-transitory medium or media which can be read and accessed directly by a computer or computer system. The media can include, but are not limited to, magnetic storage media such as floppy discs, hard disc storage media and magnetic tape; optical storage media such as optical discs or CD-ROMs; electrical storage media such as memory, including RAM, ROM and flash memory; and hybrids and combinations of the above such as magnetic/optical storage media.

[0149] The features disclosed in the foregoing description, or in the following claims, or in the accompanying drawings, expressed in their specific forms or in terms of a means for performing the disclosed function, or a method or process for obtaining the disclosed results, as appropriate, may, separately, or in any combination of such features, be utilised for realising the invention in diverse forms thereof.

[0150] While the invention has been described in conjunction with the exemplary embodiments described above, many equivalent modifications and variations will be apparent to those skilled in the art when given this disclosure. Accordingly, the exemplary embodiments of the invention set forth above are considered to be illustrative and not limiting. Various changes to the described embodiments may be made without departing from the spirit and scope of the invention.

[0151] For the avoidance of any doubt, any theoretical explanations provided herein are provided for the purposes of improving the understanding of a reader. The inventors do not wish to be bound by any of these theoretical explanations.

[0152] Any section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0153] Throughout this specification, including the claims which follow, unless the context requires otherwise, the word “comprise” and “include”, and variations such as “comprises”, “comprising”, and “including” will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps.

[0154] It must be noted that, as used in the specification and the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Ranges may be expressed herein as from “about” one particular value, and/or to “about” another particular value. When such a range is expressed, another embodiment includes from the one particular value and/or to the other particular value. Similarly, when values are expressed as approximations, by the use of the antecedent “about,” it will be understood that the particular value forms another embodiment. The term “about” in relation to a numerical value is optional and means, for example +/−10%.