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Monitoring functional capability of individuals with lower limb amputations using mobile phones.

Albert MV, McCarthy C, Valentin J, Herrmann M, Kording K, Jayaraman A - PLoS ONE (2013)

Bottom Line: To be effective, a prescribed prosthetic device must match the functional requirements and capabilities of each patient.Here, we quantify participant activity using mobile phones and relate activity measured during real world activity to the assigned K-levels.We observe a correlation between K-level and the proportion of moderate to high activity over the course of a week.

View Article: PubMed Central - PubMed

Affiliation: Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, Illinois, USA. markvalbert@gmail.com

ABSTRACT
To be effective, a prescribed prosthetic device must match the functional requirements and capabilities of each patient. These capabilities are usually assessed by a clinician and reported by the Medicare K-level designation of mobility. However, it is not clear how the K-level designation objectively relates to the use of prostheses outside of a clinical environment. Here, we quantify participant activity using mobile phones and relate activity measured during real world activity to the assigned K-levels. We observe a correlation between K-level and the proportion of moderate to high activity over the course of a week. This relationship suggests that accelerometry-based technologies such as mobile phones can be used to evaluate real world activity for mobility assessment. Quantifying everyday activity promises to improve assessment of real world prosthesis use, leading to a better matching of prostheses to individuals and enabling better evaluations of future prosthetic devices.

Show MeSH
The distribution of activity level for each subject.To aid interpretation, the participants have been ordered based on overall activity level (medium+high). The IDs correspond to the subject K-levels, and subscripts are given to match the description of subjects in Table 2. The gray transparency indicates the 95% confidence interval using bootstrapping over days recorded.
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pone-0065340-g003: The distribution of activity level for each subject.To aid interpretation, the participants have been ordered based on overall activity level (medium+high). The IDs correspond to the subject K-levels, and subscripts are given to match the description of subjects in Table 2. The gray transparency indicates the 95% confidence interval using bootstrapping over days recorded.

Mentions: To analyze the relationship between K-level and activity, we observe the fraction of time spent at the combined medium and high levels of activity for all participants (fig. 3). There was a tendency that participants with amputations had lower levels of activity than the 8 able-bodied participants (pā€Š=ā€Š0.08, one-tailed rank sums). More specifically, the K1 and K2 subjects are less active than any of the control subjects. Moreover, both K1 and K2 and two of the K3 subject showed less high-level activity than any of the healthy controls. Despite high inter-individual variations, even this small scale study showed trends that K levels co-vary with high level activity.


Monitoring functional capability of individuals with lower limb amputations using mobile phones.

Albert MV, McCarthy C, Valentin J, Herrmann M, Kording K, Jayaraman A - PLoS ONE (2013)

The distribution of activity level for each subject.To aid interpretation, the participants have been ordered based on overall activity level (medium+high). The IDs correspond to the subject K-levels, and subscripts are given to match the description of subjects in Table 2. The gray transparency indicates the 95% confidence interval using bootstrapping over days recorded.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3672103&req=5

pone-0065340-g003: The distribution of activity level for each subject.To aid interpretation, the participants have been ordered based on overall activity level (medium+high). The IDs correspond to the subject K-levels, and subscripts are given to match the description of subjects in Table 2. The gray transparency indicates the 95% confidence interval using bootstrapping over days recorded.
Mentions: To analyze the relationship between K-level and activity, we observe the fraction of time spent at the combined medium and high levels of activity for all participants (fig. 3). There was a tendency that participants with amputations had lower levels of activity than the 8 able-bodied participants (pā€Š=ā€Š0.08, one-tailed rank sums). More specifically, the K1 and K2 subjects are less active than any of the control subjects. Moreover, both K1 and K2 and two of the K3 subject showed less high-level activity than any of the healthy controls. Despite high inter-individual variations, even this small scale study showed trends that K levels co-vary with high level activity.

Bottom Line: To be effective, a prescribed prosthetic device must match the functional requirements and capabilities of each patient.Here, we quantify participant activity using mobile phones and relate activity measured during real world activity to the assigned K-levels.We observe a correlation between K-level and the proportion of moderate to high activity over the course of a week.

View Article: PubMed Central - PubMed

Affiliation: Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, Illinois, USA. markvalbert@gmail.com

ABSTRACT
To be effective, a prescribed prosthetic device must match the functional requirements and capabilities of each patient. These capabilities are usually assessed by a clinician and reported by the Medicare K-level designation of mobility. However, it is not clear how the K-level designation objectively relates to the use of prostheses outside of a clinical environment. Here, we quantify participant activity using mobile phones and relate activity measured during real world activity to the assigned K-levels. We observe a correlation between K-level and the proportion of moderate to high activity over the course of a week. This relationship suggests that accelerometry-based technologies such as mobile phones can be used to evaluate real world activity for mobility assessment. Quantifying everyday activity promises to improve assessment of real world prosthesis use, leading to a better matching of prostheses to individuals and enabling better evaluations of future prosthetic devices.

Show MeSH