This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.
Clinical frailty syndrome is a common geriatric syndrome, which is characterized by physiological reserve decreases and increased vulnerability. The changes associated to ageing and frailties are associated to changes in gait characteristics and the basic functional capacities. Traditional clinical evaluation of Sit-to-Stand (Si-St) and Stand-to-Sit (St-Si) transition is based on visual observation of joint angle motion to describe alterations in coordination and movement pattern. The latest generation smartphones often include inertial sensors with subunits such as accelerometers and gyroscopes, which can detect acceleration.
Firstly, to describe the variability of the accelerations, angular velocity, and displacement of the trunk during the Sit-to-Stand and Stand-to-Sit transitions in two groups of frail and physically active elderly persons, through instrumentation with the iPhone 4 smartphone. Secondly, we want to analyze the differences between the two study groups.
A cross-sectional study that involved 30 subjects over 65 years, 14 frail and 16 fit subjects. The participants were classified with frail syndrome by the Fried criteria. Linear acceleration was measured along three orthogonal axes using the iPhone 4 accelerometer. Each subject performed up to three successive Si-St and St-Si postural transitions using a standard chair with armrest.
Significant differences were found between the two groups of frail and fit elderly persons in the accelerometry and angular displacement variables obtained in the kinematic readings of the trunk during both transitions.
The inertial sensor fitted in the iPhone 4 is able to study and analyze the kinematics of the Si-St and St-Si transitions in frail and physically active elderly persons. The accelerometry values for the frail elderly are lower than for the physically active elderly, while variability in the readings for the frail elderly is also lower than for the control group.
Clinical frailty syndrome is a common geriatric syndrome which is characterized by physiological reserve decreases and increased vulnerability and which may, in the event of unexpected intercurrent processes, result in falls, hospitalization, institutionalization, or even death [
A previous study in the frailty detection [
The changes associated to ageing and frailties are associated to changes in gait characteristics and the basic functional capacities of the individual [
As people get older, the ability to rise from a chair, usually labeled as the sit-to-stand (Si-St) and stand-to-sit (St-Si) postural transition, becomes a more demanding functional daily task [
A previous study concludes that inertial sensors can offer an accurate and reliable method to study human motion [
There are two goals in the present study. First to describe the variability of the accelerations, angular velocity, and displacement of the trunk during the Si-St and St-Si transitions in two groups of frail and physically active elderly persons, through instrumentation with the iPhone 4 smartphone; and second, to analyze the differences and performance of the variance between the study groups (frail and healthy).
A cross-sectional study that involved 30 subjects over 65 years, 14 frail and 16 fit subjects. The participants were classified with frail syndrome by the Fried criteria (unintentional weight loss, self-reported exhaustion, weakness, slow walking speed, and low physical activity) [
Fit participants were recruited through advertisements in the Sport and Health Center in Torremolinos, Spain. Frail participants were recruited through advertisements in the Geriatrics Centers in Torremolinos and Benalmadena, Spain. Written informed consent was obtained from each individual. The ethics committee of the Faculty of Medicine at the University of Malaga, Spain approved the study.
Analysis was performed with SPSS version 15 for Windows while the data collection phase used inferential analysis between variables by type and normal. The nonparametric test Mann-Whitney [
Characteristics of sample (N=30).
|
Mean | Standard deviation | ||
|
Frail (n=14) | Fit (n=16) | Frail (n=14) | Fit (n=16) |
Age (years) | 83.71 | 70.25 | 6.37 | 3.32 |
Weight (kg)a | 56.21 | 71.03 | 9.64 | 13.11 |
Height (cm)b | 155.79 | 159.44 | 7.81 | 10.61 |
Body mass index (kg/m2)c | 23.36 | 27.87 | 3.48 | 3.79 |
akg=kilograms
bcm=centimeters
cm=meters
Acceleration-based values from the Si-St and St-Si transitions (N=30).
|
Mean | Standard deviation |
|
|
|||||||
Frail (n=14) | Fit (n=16) | Frail (n=14) | Fit (n=16) | Ua |
|
||||||
|
|||||||||||
|
|
2.004 | 3.353 | 0.761 |
1.475 | 49.50 | .009 | ||||
|
|
-1.443 | -3.136 | 1.211 | 1.198 | 30.50 | <.001 | ||||
|
|
3.069 | 6.248 | 1.240 | 1.913 | 15.00 | <.001 | ||||
|
|
-1.471 | -6.182 | 0.788 | 2.415 | 0.000 | <.001 | ||||
|
|
0.668 | 0.018 | 0.513 | 0.690 | 45.00 | .005 | ||||
|
RVi.acc.max | 7.065 | 8.972 | 2.233 | 2.506 | 58.00 | .025 | ||||
|
RV.acc.mean | 2.975 | 4.215 | 1.063 | 0.964 | 44.00 | .005 | ||||
|
|||||||||||
|
|
3.567 | 6.200 | 2.028 | 1.752 | 26.50 | <.001 | ||||
|
|
-2.950 | -9.003 | 2.441 | 4.334 | 14.00 | <.001 | ||||
|
|
5.830 | 3.834 | 2.170 | 2.682 | 62.00 | .038 | ||||
|
|
-3.770 | -6.645 | 1.928 | 2.374 | 35.00 | <.001 | ||||
|
|
0.874 | -1.611 | 1.672 | 1.701 | 36.00 | .002 | ||||
|
RV.acc.max | 7.213 | 10.652 | 2.566 | 3.501 | 41.00 | .003 | ||||
|
RV.acc.min | 0.364 | 0.808 | 0.255 | 0.479 | 38.00 | .002 | ||||
|
RV.acc.mean | 3.188 | 4.263 | 0.708 | 1.048 | 45.00 | .005 |
aU=U-Mann-Whitney
bm=meters
cs=second
d
eacc=acceleration
fmax=maximum
gmin=minimum
h
iRV=resultant vector
j
Gyroscope-based values from the Si-St and St-Si transitions (N=30).
|
Mean | Standard deviation |
|
|
|||||||||
Frail (n=14) | Fit (n=16) | Frail (n=14) | Fit |
Ua |
|
||||||||
|
|||||||||||||
|
roll.rotation.maxb(deg)c | 102.920 | 196.544 | 98.755 | 109.519 | 35.00 | .001 | ||||||
|
roll.rotation.mean (deg) | -24.754 | 83.837 | 58.165 | 150.560 | 61.00 | .034 | ||||||
|
rated.yaw.mine(deg/s)f | -47.813 | -26.131 | 17.501 | 25.998 | 42.00 | .004 | ||||||
|
rate.pitch.max (deg/s) | 27.414 | 123.404 | 15.552 | 141.318 | 28.00 | <.001 | ||||||
|
rate.roll.max (deg/s) | 18.924 | 165.437 | 8.843 | 120.989 | 0.000 | <.001 | ||||||
|
rate.roll.min (deg/s) | -19.796 | -62.597 | 10.956 | 39.321 | 56.00 | .020 | ||||||
|
rate.roll.mean (deg/s) | 0.459 | 49.993 | 1.289 | 82.129 | 59,00 | .028 | ||||||
|
|||||||||||||
|
roll.rotation.min (deg) | -163.264 | -61.157 | 38.955 | 108.358 | 60.00 | .031 | ||||||
|
roll.rotation.mean (deg) | -15.487 | 83.102 | 40.876 | 142.182 | 62.00 | .038 | ||||||
|
rate.yaw.max (deg/s) | 41.309 | 130.470 | 11.316 | 138.379 | 57.00 | .022 | ||||||
|
rate.yaw.min (deg/s) | -67.449 | -37.077 | 21.053 | 30.776 | 49.00 | .009 | ||||||
|
rate.roll.max (deg/s) | 38.146 | 145.150 | 18.918 | 129.161 | 13.00 | <.001 | ||||||
|
rate.roll.min (deg/s) | -25.596 | -70.275 | 16.433 | 50.714 | 58.00 | .025 |
aU=U-Mann-Whitney
bmax=maximum
cdeg=degrees
drate=angular velocity
emin=minimum
fs=second
The present study has described and examined the identification, analysis, and differentiation in the performance of kinematic variables using the inertial sensor fitted in the iPhone 4 during Si-St and St-Si transitions in healthy and frail elderly people. Significant differences were found between the groups of elderly people in the accelerometry and angular displacement variables obtained in the kinematic readings of the trunk during the both transitions.
The results obtained in this study show a series of weakness in the frail elderly population group. The most significant deficits found in the Si-St and St-Si transitions corresponded to accelerometry (see
As far as we are aware, this is the first study that has used iPhone 4 technology to analyze and study the kinematics of healthy and frail persons aged over 65 years during the Si-St and St-Si transitions. Moreover, it is the first study that has shown the possibility of differentiating kinematic patterns in both transitions. The instrumented kinematic analysis of the Si-St and St-Si transitions was analyzed previously [
It should be noted that frailty is defined as a clinical syndrome in which three or more of the following criteria should be present: unintentional weight loss, self-referred exhaustion, muscular weakness, low walking speed, and low physical activity levels [
There are two recent studies [
Another recent study which has worked on the instrumentalization of the Timed Get Up and Go [
From a clinical perspective, the present study demonstrates that these new accelerometry parameters play an important role in differentiating between subjects with different functional states. These results provide new knowledge, extending existing knowledge on the isolated study of Si-St and St-Si transitions in frail and physically active elderly persons [
With regards to analysis of the data obtained in the present study, the differences between the frail and the physically active elderly show a series of deficits in the group of frail persons in both transitions. It is notable that the most significant deficit for the frail elderly in the Si-St and St-Si corresponded to accelerometry, with the frail elderly obtaining much lower minimum and maximum accelerations than the physically active elderly in the y-axis (see
The present findings motivate future investigations along these lines, but this study presents some limitations. First, men and women have different characteristics and it would be very interesting to provide the differences between them after the St-Si/Si-St exercise. A new study is needed to compare between genders. Moreover, prospective studies are needed to determine if acceleration-derived measures, perhaps in combination with other metrics and previously described measures of fall risk, can predict. Additional work is also needed to explore other properties of accelerometer-derived measures of the Si-St/St-Si. Note that here we specifically focused on timing of transition and a subset of the properties of the signal; further analysis of the complete waveform and other time points may provide additional utility. Indeed, it appears that the proposed approach not only may offer a more refined scale for assessing older adults, but it may also help to pinpoint specific problems that give rise to an abnormal performance of functional tasks (eg, Si-St/St-Si transitions). In the meantime, the present results demonstrate the potential of using an accelerometer to measure Si-St/St-Si performance, while maintaining simplicity and requiring no additional time to acquire the data.
The inertial sensor fitted in the iPhone 4 is able to study and analyze the kinematics of the Si-St and St-Si transitions in frail and physically active elderly persons. The accelerometry values for the frail elderly are lower than for the physically active elderly, while variability in the readings for the frail elderly is also lower than for the control group. This suggests that the frail elderly carry out the test in a more careful, restricted way during the functional tasks, which make up the transitions, possibly showing their reduced ability to regulate movement when performing these tasks and transitions. The compensation mechanisms also play an important role. These results indicate that the additional, relevant information for future discriminant analysis comes mainly from the acceleration signal during the Si-St and St-Si transitions.
roll, pitch, and yaw
resultant vector
standard deviations
sit-to-stand
stand-to-sit
vertical axis
Authors' Contributions: ACV conceived of the study, participated in its design and coordination, and drafted the manuscript. ACV also had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Both authors performed the statistical analysis and provided critical content revision of the manuscript. Both authors read and approved the final manuscript.
Conflicts of Interest: None declared.