Hierarchy of individual calibration levels for heart rate and accelerometry to measure physical activity.

Combining accelerometry with heart rate (HR) monitoring may improve precision of physical activity measurement. Considerable variation exists in the relationships between physical activity intensity (PAI) and HR and accelerometry, which may be reduced by individual calibration. However, individual calibration limits feasibility of these techniques in population studies, and less burdensome, yet valid, methods of calibration are required. We aimed to evaluate the precision of different individual calibration procedures against a reference calibration procedure: a ramped treadmill walking-running test with continuous measurement of PAI by indirect calorimetry in 26 women and 25 men [mean (SD): 35 (9) yr, 1.69 (0.10) m, 70 (14) kg]. Acceleration (along the longitudinal axis of the trunk) and HR were measured simultaneously. Alternative calibration procedures included treadmill testing without calorimetry, submaximal step and walk tests with and without calorimetry, and nonexercise calibration using sleeping HR and gender. Reference accelerometry and HR models explained >95% of the between-individual variance in PAI (P < 0.001). This fraction dropped to 73 and 81%, respectively, for accelerometry and HR models calibrated with treadmill tests without calorimetry. Step-test calibration captured 62-64% (accelerometry) and 68% (HR) of the variance between individuals. Corresponding values were 63-76% and 59-61% for walk-test calibration. There was only little benefit of including calorimetry during step and walk calibration for HR models. Nonexercise calibration procedures explained 54% (accelerometry) and 30% (HR) of the between-individual variance. In conclusion, a substantial proportion of the between-individual variance in relationships between PAI, accelerometry, and HR is captured with simple calibration procedures, feasible for use in epidemiological studies.

Effect of combined movement and heart rate monitor placement on physical activity estimates during treadmill locomotion and free-living.

A placement effect on activity measures from movement sensors has been reported during treadmill and free-living activity. Positioning of electrodes may impact on movement artifact susceptibility as well as surface ECG waveform amplitudes and thus potentially on the precision by which heart rate (HR) is ascertained from such ECG traces. The purpose of this study was to examine the extent to which placement of the combined HR and movement sensor, Actiheart, influences measurement of HR and movement, and estimates of energy expenditure. A total of 24 participants (20-39 years, 45-109 kg, 1.54-2.05 m, 19-29 kg m(-2)) were recruited. Whilst wearing two monitors, one placed at the level of the third intercostal space (upper position) and one just below the apex of the sternum (lower position), study participants performed level walking, incline walking, and level running on treadmill, and completed at least one day of free-living monitoring. Placement differences in HR data quality, movement counts, and energy expenditure (estimated from combined HR and movement) were analyzed with regression techniques. Quality of HR data was generally higher when monitors were placed in the lower position. This effect was more pronounced in men during both treadmill activity (relative risk, RR [95% CI] of noisy HR data in upper vs. lower position, RR=1.3[0.3; 5.6] in women, RR=174[14; 2,156] in men) and during free-living (RR=1.2[0.4; 3.3] in women, RR=25[9.6; 67] in men). There were minor placement differences (< or =8%) in movement counts only in women during incline walking and running. During free-living, no placement effect on counts was observed. In all test scenarios, estimates of energy expenditure from the two positions were not significantly different. Positioning the Actiheart at the level below the sternum may yield cleaner HR data. Regardless of which position is used, this has little or no effect on movement counts and energy expenditure estimates, which is encouraging for studies where research participants may have to position the monitors themselves.

Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure.

The combination of heart rate (HR) monitoring and movement registration may improve measurement precision of physical activity energy expenditure (PAEE). Previous attempts have used either regression methods, which do not take full advantage of synchronized data, or have not used movement data quantitatively. The objective of the study was to assess the precision of branched model estimates of PAEE by utilizing either individual calibration (IC) of HR and accelerometry or corresponding mean group calibration (GC) equations. In 12 men (20.6-25.2 kg/m2), IC and GC equations for physical activity intensity (PAI) were derived during treadmill walking and running for both HR (Polar) and hipacceleration [Computer Science and Applications (CSA)]. HR and CSA were recorded minute by minute during 22 h of whole body calorimetry and converted into PAI in four different weightings (P1-4) of the HR vs. the CSA (1-P1-4) relationships: if CSA > x, we used the P1 weighting if HR > y, otherwise P2. Similarly, if CSA < or = x, we used P3 if HR > z, otherwise P4. PAEE was calculated for a 12.5-h nonsleeping period as the time integral of PAI. A priori, we assumed P1 = 1, P2 = P3 = 0.5, P4 = 0, x = 5 counts/min, y = walking/running transition HR, and z = flex HR. These parameters were also estimated post hoc. Means +/- SD estimation errors of a priori models were -4.4 +/- 29 and 3.5 +/- 20% for IC and GC, respectively. Corresponding post hoc model errors were -1.5 +/- 13 and 0.1 +/- 9.8%, respectively. All branched models had lower errors (P < or = 0.035) than single-measure estimates of CSA (less than or equal to -45%) and HR (> or =39%), as well as their nonbranched combination (> or =25.7%). In conclusion, combining HR and CSA by branched modeling improves estimates of PAEE. IC may be less crucial with this modeling technique.