This study aims to examine the classification accuracy of screening older people with and without frailty based on physical activity and gait analysis in their daily life environment. A classification model will be created based on physical activity…
ID
Source
Brief title
Condition
- Other condition
Synonym
Health condition
Frailty screening op basis van fysieke activiteit
Research involving
Sponsors and support
Intervention
Outcome measures
Primary outcome
The primary endpoint is the classification accuracy, sensitivity, and
specificity of frailty obtained by different machine learning methods.
Different models will be generated based on gait variables collected by
wearables sensors during daily life.
Secondary outcome
The secondary endpoint is the comparison of classification models of gait
variables collected by wearables sensors with the standard clinical
assessments.
Background summary
Frailty, an age-related clinical syndrome, is closely related to adverse
outcomes such as disability and incapacitation. Early intervention can delay or
reverse deterioration progress. Therefore, early screening and assessment could
be greatly beneficial. Many measurement instruments are available to evaluate
frailty by examining different aspects of it. However, most of these
instruments only evaluate frailty during clinical visits or in a controlled
laboratory environment where physical tests are performed. Examining frailty in
the context of daily life functioning may better reflect real capacity and
function. Walking ability has been reported to be closely linked to frailty.
However, there are few studies that address gait in daily life and examine the
association with frailty. Examining walking during daily life could provide a
screening for the presence of frailty and be used for early diagnosis of
frailty. This could support subsequent personalized guided interventions
Study objective
This study aims to examine the classification accuracy of screening older
people with and without frailty based on physical activity and gait analysis in
their daily life environment. A classification model will be created based on
physical activity patterns, gait outcomes, clinical assessment methods, and a
combination of both. The classification performance will be validated using
Area Under the Curve, and the accuracy, sensitivity, and specificity will be
calculated of the models.
Study design
Observational study.
Study burden and risks
All assessments are non-invasive and will be performed according to established
guidelines. Assessment instruments are part of care-as-usual. All assessments
will be done during the first home visit . For (pre)frail older participants
from the outpatient those not yet been assessed during clinical consultation
will be completed. The sensor is lightweight and will be attached to the thigh
with special skin-sensitive tape. There is no need for charging or removing the
device. Participants can perform all their usual activities. Therefore, we
consider the risks of the current study negligible, and the burden for the
participants minimal. This approach could enable early screening of frailty and
support personalized, guided interventions. It aligns with current trends in
care that emphasize the importance of providing assessments and support in
one's own environment.
Ant. Deusinglaan 1
Groningen 9713 AV
NL
Ant. Deusinglaan 1
Groningen 9713 AV
NL
Listed location countries
Age
Inclusion criteria
- Be able to walk independently without or with a walking aid other than a
walker, for at least 3 minutes continuously inside and/or outside.
- Capacity to consent
- Evaluated as being frail or pre-frail or frail by the clinical assessment
[for frail/prefrail group]
Exclusion criteria
- A history of central neurological problems (e.g., cerebral vascular accident,
acquired brain damage, or Parkinson*s Disease).
- The participant is terminally ill (i.e., life expectancy < 3 months according
to the attending physician).
physician).
Inability to communicate (e.g., severe hearing, vision and language judged by
clinicians).
- Severe cognitive impairment defined by MMSE (0-17) or indicated by clinical
diagnosis.
non-frail particpiants:
One or more of the Fried criteria: weakness; self-reported exhaustion; slow
walking speed; and low physical activity.
Design
Recruitment
Followed up by the following (possibly more current) registration
No registrations found.
Other (possibly less up-to-date) registrations in this register
No registrations found.
In other registers
| Register | ID |
|---|---|
| CCMO | NL84887.042.23 |