HEALTH

In this context, health is defined as user's activity profiles which are compared to those with diabetics or cardiovascular deseases patients.

Similarities of the activity profiles are estimated by the following terms:

Nil-Low-Moderate-High

Cardiovascular profile is affacted by gender, age and total daily duration of physical activities consisting of 30s - 10min activity bouts.

The more physical activity, the less similarity between the profiles.

Diabetes profile is affected by the gender, age and body mass index (BMI).

For women the prodile it is also affected by daily standing and physical activity time, and the number of long physical activity bouts.

For men the prodile it is also affected by the number of long sitting and physical activity bouts.

SEDENTARY BEHAVIOUR

Sedentary behaviour (SB) consists of lying, sitting and standing.

The Exsed -service have validated method to recognise sitting and standind positions. The service provides online information about SB and number of breaks of sitting sessions.

In case the time spent on continuous sitting exseeds 90 min, the application sents a notification to encourage to break the prolonged sitting by stand up and taking few steps.

SLEEP

When going to bed, the sensor is removed from the clip and inserted into the wristband. This enables to identify sleeping from the time being awake.

When attached to the wristband the sensor can measure total duration of sleeping and detect periods of sleep classified as restless and restful.

PHYSICAL ACTIVITY

In Exsed -service physcal activities are classified according to the intensities of the activities in METs (metabolic equivalent or basic metabolic rate while sitting still). One MET equals to 3.5 ml/kg/min of oxygen consumption.

Physical activities which are below 3 METs are classified as light physical activity (LPA).

LPA does not cause breathless or considerable increase in heart rate. Walking slowly shortish distances is an everyday example of light physical activity.

Physical activities which are above 3 METs are classified as moderate to vigorous physical activity (MVPA).

MVPA causes breathless and a significant increase in heart rate. Brisk walking, jogging or running are good examples of MVPA.

Daily physical activities are also described by number of steps by walking or running, and by duration of LPA and MVPA.

The Exsed -service identifies correctly the duration and intensity of the most weight bearing physical activities done by foot. The duration of cycling is also identified adequately.

The activities that are done purely by upper body large muscle groups and are not weight bearing activities may not be recognised accurately.

Scientific background behind the measurement of physical activity and sedentary behavior

Elementary physics is behind the measurement.  Every movement creates forces that can be measured with an accelerometer attached to the body and presented as multiples of Earth’s gravity. Acceleration simply denotes a change in velocity over time. The faster you move, the higher the acceleration.  In the case of no movement of the body, there is no acceleration either, but the orientation of the accelerometer can be determined because the direction of Earth’s gravity is established and measurable with the accelerometer.

The UKK Institute has developed and validated an analysis algorithm for mean amplitude deviation (MAD) of the acceleration signal. With the MAD algorithm, both time and volume of physical activity at different intensity levels can be reliably determined.  For the analysis of sedentary behavior, the UKK Institute has developed and validated an algorithm that calculates the angle for posture estimation (APE) of the body. With the APE algorithm, it is possible to determine with high reliability whether, when and how long the person is sitting/lying or standing.  Both physical activity at different intensity levels and sedentary behavior can be recognized at about 90% accuracy by analyzing the data from a hip-worn accelerometer with the MAD-APE algorithms.

Detailed information from the MAD-APE algorithms permits creating a personal profile of physical activity, standing and sedentary behavior and presenting these values on hour-by-hour basis 24/7. The general feature of the MAD-APE algorithms is that they can be consistently used to analyze the acceleration data measured with a hip-worn device in all target groups from children to older adults or irrespective of the accelerometer brand  – provided that the measured data is triaxial and raw.

The MAD-APE algorithms have been employed in the analysis of the accelerometer data from population-based Health 2011 survey and in the recent KunnonKartta 2017 population-based study of Finnish adults. Several statistical analyses are being made from these data in order to explore associations of physical activity and sedentariness patterns with various health outcomes, e.g. cardiovascular diseases.

References

Aittasalo M, Vähä-Ypyä H, Vasankari T, Husu P, Jussila AM, Sievänen H. Mean amplitude deviation calculated from raw acceleration data: a novel method for classifying the intensity of adolescents' physical activity irrespective of accelerometer brand. BMC Sports Sci Med Rehabil. 2015;7:18.

Husu P, Suni J, Vähä-Ypyä H, Sievänen H, Tokola K, Valkeinen H, Mäki-Opas T, Vasankari T. Objectively measured sedentary behavior and physical activity in a sample of Finnish adults: a cross-sectional study. BMC Public Health. 2016;16:920

Husu P, Vähä-Ypyä H, Vasankari T. Objectively measured sedentary behavior and physical activity of Finnish 7- to 14-year-old children- associations with perceived health status: a cross-sectional study. BMC Public Health. 2016;16:338.

Sievänen H, Kujala U. Accelerometry – Simple but challenging. Editorial. Scand J Med Sci Sports 2017;27:574-578.

Vasankari V, Husu P, Vähä-Ypyä H, Suni J, Tokola K, Halonen J, Hartikainen J, Sievänen H, Vasankari T. Association of objectively measured sedentary behaviour and physical activity with cardiovascular disease risk. Eur J Prev Cardiol. 2017;24:1311-1318.

Vähä-Ypyä H, Husu P, Suni J, Vasankari T, Sievänen H. Reliable recognition of lying, sitting and standing with a hip-worn accelerometer. Scand J Med Sci Sports. 2018;28:1092 – 1102.

Vähä-Ypyä H, Vasankari T, Husu P, Mänttäri A, Vuorimaa T, Suni J, Sievänen H. Validation of Cut-Points for Evaluating the Intensity of Physical Activity with Accelerometry-Based Mean Amplitude Deviation (MAD). PLoS One. 2015;10:e0134813.

Vähä-Ypyä H, Vasankari T, Husu P, Suni J, Sievänen H. A universal, accurate intensity-based classification of different physical activities using raw data of accelerometer. Clin Physiol Funct Imaging. 2015;35:64-70