Physical activity assessment – Heart rate monitoring

Heart rate monitoring is a measure of a direct physiological response to physical activity. The development of minute by minute heart rate monitors with internal capacity for multiple days’ storage without displaying heart rate has increased the feasibility of this objective measure of physical activity. A heart rate monitor is commonly configured as a chest strap which is wirelessly connected to a data logger hidden in a watch. The use of electrodes provides an alternative way to obtain heart rate as it can improve compliance, but is sometimes considered less feasible for the individual. The theoretical basis of this measure is the linear relationship that exists between heart rate and energy expenditure (EE) in steady state exercise involving large muscle groups. The method has shown to have high reproducibility within subjects (Strath et al, 2000). The primary outcome measure is heart rate and thereby identify the time spent at different intensity levels using absolute heart rate values or heart rate indices. A secondary outcome measure is physical activity energy expenditure estimated using regression equations derived from individual or group calibrations.

The slope and y-intercept of this linear relationship varies within and between individuals (Li et al, 1993); factors such as age, gender, weight and fitness level modulate this relationship (Dugas et al, 2005) as do ambient temperature, body posture and emotional state such as anxiety or stress. Additionally, heart rate response tends lag after changes in movement and remain elevated after movement stops; this means that heart rate may mask sporadic activity; this is of particular relevance in children (Trost, 2008). Assessment of physical activity using heart rate is problematic at low levels of activity as its linear relationship with physical activity is more reliable during higher intensities of activity.

A number of techniques have been devised to overcome the limitations of heart rate monitoring including individual calibration, and heart rate indices.

Absolute heart rate values
Absolute heart rate values have been used to distinguish between activity intensities (Sirard & Pate, 2001). The method is based on using a percentage maximum heart rate and an intensity of ≥140 beats per minutes. It has been suggested as an approximate measure of moderate vigorous physical activity in children (Simons-Morton et al, 1994). In sedentary adults average 24-hour heart rate does not rise much above resting levels of 60-100 beats per minute.

Heart rate indices
Three of the most popular heart rate indices are:

Activity heart rate index (AHR) – mean of the recorded heart rate minus resting heart rate
Physical activity (PAHR)-25 – the percentage of heart rate 25% above resting heart rate
PAHR-50 - the percentage of heart rate 50% above resting heart rate

(Trost et al, 2001)

All three measures depend on the measurement of resting heart rate (Trost, 2008). The profound impact of differences in the definition of resting heart rate and the protocol to measure it on the estimation of physical activity has been demonstrated (Logan et al, 2000). A consensus on these issues is required before heart rate indices can be used effectively (Trost, 2008) Face validity has recently been demonstrated in a cohort where intensity of physical activity measured by heart rate using the individual calibration, FLEX HR was found to be associated with insulin level, a marker of insulin resistance (Assah et al, 2008).

Calibration
To estimate energy expenditure, information is required on the relationship between an individual’s heart rate and oxygen consumption (VO₂₎ so that energy expenditure can be predicted for each given heart rate value. Due to inter-individual variability of resting heart rate, maximal heart rate and an individual’s heart rate response to activity, individual calibration is required to increase the accuracy of heart rate data to estimate energy expenditure (Rennie et al, 2001).

The individual heart rate-VO₂ relationship is usually derived in a laboratory setting and then applied to predict energy expenditure in free-living situations. Determining whether to calibrate is a matter of weighing up the likely increased validity of the measurement versus the feasibility of the calibration method. There are different options for calibration. The most intensive methods are likely to produce more precise data but are relatively invasive, take time for the participant to undertake the test in the laboratory setting and are therefore not always feasible for large-scale studies. Examples of the levels of calibration in descending order of intensity are:

1. Treadmill with VO₂ measurement
2. Treadmill
3. Step test with VO₂ measurement
4. Step test
5. 3-minute walk with VO₂ measurement
6. 3-minute walk
7. No dynamic calibration

The choice of activities in the calibration protocol and in particular their intensity will affect the accuracy of the prediction of VO₂ for each heart rate level (Corder et al, 2008). Simplified methods, namely step test and sleeping heart rate have been suggested accepting the reduction in precision (Corder et al, 2005; Corder et al, 2007; Brage et al, 2007).

An 8-minute ramped step test with a 2-minute recovery has been investigated (Brage et al, 2007). Simple methods such as this are portable, therefore making it feasible for field studies involving larger numbers of people or where facilities are minimal. Sleeping heart rate is an alternative easy calibration method and measurements taken overnight may explain substantial variation in the heart rate and PAEE relationship without the need for long laboratory procedures (Brage et al, 2007).

FLEX heart rate
The FLEX heart rate method (Spurr et al, 1988) uses the data from the individual calibration to estimate energy expenditure, allowing for the correcting of inter-individual variation in heart rate data (Leonard, 2003). It has been validated against free-living measurements of TEE and PAEE by DLW in children, adults and athletes and provides valid data at a group level (Ceesay et al, 1989; Livingstone et al, 1990; Ekelund et al, 2002). It assumes that above a given threshold (the FLEX point) there is a linear relationship between heart rate and oxygen consumption; below this threshold the relationship is variable. Energy expenditure above the threshold or FLEX point is estimated by a linear prediction.

FLEX heart rate has been described as an individually determined heart rate, measured in conjunction with VO₂ that can be used to distinguish between resting energy expenditure (REE) and activity EE (Livingstone et al, 1992). Thus, when HR drops below the FLEX point, resting energy expenditure is assumed, and TEE from heart rate may be estimated as follows:

TEE = EE _sleep + EE _rest+ EE _activity
EE _sleep = BMR
EE _rest = REE (HR < FLEX HR)
EE _activity = EE from regression equation (HR ≥ FLEX HR)

Heart rate FLEX point is defined empirically as the average of the lowest heart rate during exercise and the highest during rest (Ceesey et al, 1989). Livingstone et al (1992) evaluated the accuracy of the FLEX heart rate method to estimate TEE in 36 free-living children using DLW. Individual differences ranged from -16.7% to 18.8%; mean group differences ranged from -9.2% ± 4.5% to 3.5% ± 6.6%. The method has also been validated in various adult populations comparing energy expenditure estimates with those measured by whole body calorimetry and DLW (Ceesay et al, 1989; Livingstone et al, 1990; Ekelund et al, 2002).

Limitations of FLEX heart rate
The key limitations of the FLEX heart rate method are that it depends on the assumption that the measured heart rate - VO₂ relationship is reliable in free-living daily activities and that it reflects cardiovascular responses to activity and not other physiological responses to external stimuli e.g. caffeine (Livingstone et al, 2000). The individual calibration should be undertaken as close to the heart rate monitoring period as possible since the estimation of energy expenditure is based on the assumption that the measured heart rate - VO₂ relationship has remained constant. This relationship may change as a result of improved cardiovascular fitness, changes in body composition or certain conditions/diseases (e.g.anaemia).

Predictive equations
The feasibility of using heart rate monitoring without individual calibration to estimate EE using prediction parameters has been investigated and found suitable for ranking individuals in epidemiological studies (Rennie et al, 2001). Other studies have also shown that it is possible to estimate energy expenditure from heart rate using multivariate predictive equations derived from group data in adults (Strath et al, 2000; Hiilloskorpi et al, 2003; Dugas et al, 2005; Keytel et al, 2005) and children (Livingstone et al, 2000). Variation in applying different prediction equations has been investigated and individual differences found; group level analysis was found to be satisfactory for some population groups (Iannotti et al, 2004). It is important that prediction equations are derived in studies with adequate numbers (>100) and are representative of the population to be studied.

Another approach to analysing heart rate data has been to adjust for fitness and age to increase the validity of using heart rate to predict EE (Strath et al, 2000).