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Mobile Romberg test assessment (mRomberg).

Alejandro Galán-Mercant, Antonio I Cuesta-Vargas1

  • 1Departamento de Psiquiatría y Fisioterapia, Facultad de Ciencias de la Salud, Universidad de Málaga, Andalucia Tech, Cátedra de Fisioterapia y Discapacidad, Instituto de Biomedicina de Málaga (IBIMA), Grupo de Clinimetria (AE-14) Málaga, Malaga, Spain. acuesta.var@gmail.com.

BMC Research Notes
|September 14, 2014
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Summary

Instrumenting the Romberg test with an iPhone accelerometer can differentiate between frail and non-frail elderly individuals. Accelerometer data revealed significant differences in balance control, highlighting potential for objective frailty assessment.

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Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Kinesiology

Background:

  • Early detection of physical frailty is crucial for mitigating its severity in elderly populations.
  • Classical functional tests like the Romberg test can be instrumented for objective assessment.
  • Utilizing smartphone accelerometers offers a potential alternative for frailty diagnosis.

Purpose of the Study:

  • To measure and describe accelerometry values during the Romberg test in frail and non-frail elderly individuals using an iPhone 4.
  • To analyze performance differences in the Romberg test between frail and non-frail groups.
  • To characterize accelerometer responses to increasing balance challenges within each group.

Main Methods:

  • A cross-sectional study involving 18 elderly subjects (9 frail, 9 non-frail) aged over 70.
  • Data collected using an iPhone 4 accelerometer during modified Romberg tests.
  • Non-parametric statistical tests (Mann-Whitney U, Wilcoxon Signed-Rank) were used for analysis.

Main Results:

  • Significant differences in lateral axis and resultant vector accelerations were observed between frail and non-frail groups, particularly with eyes closed.
  • Accelerometer values showed increased differences as the Romberg test's difficulty (eyes open/closed, feet parallel/tandem) increased.
  • Statistically significant differences (p < 0.01) were found across various accelerometer metrics and test conditions.

Conclusions:

  • An iPhone 4 accelerometer can effectively analyze Romberg test kinematics to differentiate between frail and non-frail elderly individuals.
  • Accelerometer data revealed significant kinematic differences correlating with frailty status.
  • The study demonstrates the potential of smartphone-based accelerometry for objective frailty assessment.