Procedure
The study took place in the rehabilitation clinic Überruh in Isny, Germany. Fifteen patients wore both, the kmsMove-sensor on the hip and the portable indirect calorimeter MetaMax 3B (Cortex Biophysik, Leipzig, Germany) and completed their regular daily rehabilitation activities. The general physical activities were bicycle ergometer training, gymnastics, walking, and hiking. Besides these activities, the patients’ rehabilitation programs consisted of other kinds of activities like relaxation or psychological counseling. The measurements of all subjects started at 7:00 am and ended at 5:00 pm. Within that time span, there were breaks for breakfast and lunch and for changing clothes. The kmsMove-sensor recorded data without breaks while indirect calorimetry was paused for breaks. Afterwards, the test readings of the kmsMove-sensor and indirect calorimetry were synchronized. Due to a breakdown of one of the kmsMove-sensors and the disruption of the indirect calorimeters caused by cold weather conditions during outdoor activities, a loss of data occurred. These technical problems could not be solved in situ. Thus, the data for only nine patients could be analyzed, at least for a time period of 100 min. From these nine patients, the recordings of seven patients last for an average of 7 h.
Participants
The participants were patients of the rehabilitation clinic Überruh in Isny, Germany. They had been informed about the study in an oral presentation and provided informed consent on participating voluntarily. The remaining nine subjects of the original sample were all males at a mean age of 46.4 ± 10.9 years. They had a mean weight of 84.5 ± 9.2 kg and a mean height of 177.7 ± 8.1 cm. The subjects were all treated for back pain, received no medication, and were free of any cardiovascular complications.
Measurement instruments
kmsMove
The kmsMove-sensor consists of a 5.3 × 3 × 2-cm-sized body and can be fixed with a clip to the hip. Alternative fixation with a chest belt and wristband are also possible. During the measurements, the sensor was placed on the right side of the subjects’ hip. The sensor is a three-axial acceleration sensor with a range of ±8 g, a resolution of 12 bit and a sampling rate of 128 Hz. The recorded data from the sensor, including raw data from the acceleration sensor, can be displayed on a computer when connected to it via USB cable. While being connected, the sensor can also be charged and additionally configured with a special software that is optimized to manage scientific studies with large numbers of participants. After downloading data from the sensor, the energy expenditure is calculated and stored in a CSV file. Energy expenditure is displayed in steps of 1 s. Short time intervals allow monitoring spontaneous activities. The sensor offers an overall measuring time of 7 days. The calculation of energy expenditure is done in three steps: activity recognition, model selection, and calculation of energy expenditure. The recognition of different activities is based on the extraction of mathematical and statistical features of the raw acceleration signal. The features are calculated for each segment of 4 s. Calculated features are, amongst others, maximum frequency, step count, and the number of mean crossings. These features are the input information of a decision tree which classifies the activity of the person. Activities that can be detected are rest (combination of lying, sitting, and standing); bicycle or ergometer; going upstairs; walking (combination of jogging, going downstairs, walking slow, normal, and fast); and unknown activity. According to information from the manufacturer, the decision tree was generated using data of approximately 100 subjects who performed daily life activities. The accuracy of the activity recognition algorithm is discussed by Jatobá et al. [6]. According to the detected activity, one of five different models is selected. The formula for the models which are used to calculate energy expenditure is:
$$ {\hbox{EE}} = {b_0} + {b_1}{\hbox{EEAC}} + {b_2}{\hbox{Age}} \cdot {\hbox{EEAC}} + {b_3}{\hbox{Height}} \cdot {\hbox{EEAC}} + {b_4}{\hbox{Weight}} \cdot {\hbox{EEAC}} $$
For each activity, the model is built by a set of five coefficients (b
0–b
4). The coefficients were generated using data of indirect calorimetry of the above-mentioned 100 subjects. EEAC is derived from the zero-mean acceleration signal (a
x
, a
y
, a
z
) in a segment of 1 s which consists of N samples like:
$$ {\hbox{EEAC}} = {\hbox{smooth}}\;\left( {\frac{1}{N}\;\sum\limits_{{i = 1}}^N {\sqrt {{{a_{{xi}}}^2 + {a_{{yi}}}^2 + {a_{{zi}}}^2}} } } \right) $$
EEAC is smoothed using a moving average filter. Different models are used because there are activities with high overall acceleration (EEAC) but low energy expenditure (EE) and on the other hand, there are activities with low overall acceleration but high energy expenditure. The use of different models compensates such over- or underestimations. By the use of models that are dependent on subject-specific parameters like age, height, and weight, no subject-specific calibration of the sensor was necessary [6, 7].
Indirect calorimetry
Indirect calorimetry is a procedure, which is based on a certain relationship between the combustion of substrates, the consumption of oxygen, the expiration of carbon dioxide, and the production of energy. For the combustion of substrates, a certain amount of oxygen is required and a certain amount of carbon dioxide is accumulated. From the relationship of oxygen uptake and carbon dioxide expiration, the metabolic respiratory quotient can be calculated. For every metabolic respiratory quotient there is a certain caloric equivalent, which expresses the relationship between the combustion of a certain substrate, the required oxygen volume, and the amount of energy which is produced. If the caloric equivalent and the consumed oxygen volume are multiplied, one obtains the energy expenditure for that certain time period [8].
The portable indirect calorimeter MetaMax 3B uses the breath-by-breath technique and is able to run up to 20 h without being connected to a PC via wireless telemetry technology. This system is lightweight, comfortable, and offers good mobility for the user. For calculating energy expenditure, the MetaMax 3B uses an RQ-based equation with an assumed protein utilization part of total energy production (15%) according to Acheson [9].
Statistical methods
In order to investigate the agreement between the test readings of the kmsMove-sensor and indirect calorimetry, a two-way mixed, single measure, intraclass correlation (ICC (3,1)) was conducted. Therefore, the software SPSS Statistics 17.0 (SPSS Inc., Chicago, IL, USA) was applied. Furthermore, a Bland–Altman analysis was also performed to examine the agreement between the two devices. This has been done with Analyse-it for Microsoft Excel (Analyse-it Software, Ltd., Leeds, UK). The mean difference and the limits of agreement were calculated according to Bland and Altman [10]. The Bland–Altman analysis is a statistical procedure to check the agreement of two different measuring devices. Therefore, the measurement differences of both devices were plotted against their mean. Normally, these differences follow a normal distribution and so 95% of the differences usually lie between the limits of ±2 standard deviations around the mean difference (bias) of the two devices. Those limits are the limits of agreement. If the differences of the two devices lie between the limits of agreement, the two devices can be used interchangeably [10].