Application of an Effective Methodology forAnalysis of Fragility and Its Components inthe Elderly

  • J. L. C. Mello
  • D. M. T. Souza
  • C. M. Tamaki
  • V. A. C. Galhardo
  • D. F. Veiga
  • A. C. B. Ramos
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 738)


Fragility is a syndrome characterized by reduced physical and cognitive reserves making the elderly more vulnerable to adverse events, hospitalizations, falls, loss of independence, and death. Inertia sensors have been applied to quantify motion assessment in Timed Up and Go (TUG) test, accelerometers are used during balance assessment, and algorithms differentiate fragile, pre-fragile, and robust elderly people.

Objective: Developing a multifunctional sensor to evaluate fragility, based on marker phenotype and deficit accumulation index.

Methods: Primary, exploratory, interventional, analytical, and transversal study with a technological approach. The study will be developed, in partnership with researchers from the Federal University of Itajubá-MG using high-tech, multifunctional, and cost-effective sensor equipment in combination with a 3-axis gyroscope, a 3-axis accelerometer, electromyography and frequency meter, analysis of movement quality, energy expenditure, gait velocity, change in balance, heart rate variability during movement, and quality of quadriceps muscle contraction.

The data will be analyzed by software developed after the prototyping of the equipment. The fragility analysis procedure will not cause any damage or impairment to the health of the elderly participants, since the items used during the procedure will be the sensor, the measurement of the instruments, the Barthel Index, the Mental State Examination, and the Self-rated fragility assessment.

The validation of the sensor will not cause damage or impairment to the health of the participants.

Locations: Samuel Libânio Clinical Hospital, in the clinics of Health Clinic, Dementia, and Assistance Nucleus Nursing Education, and in the Basic Health Units of the municipality of Pouso Alegre-MG.

Casuistry: Convenience sample.

Eligibility criteria: 300 elderly people, 60 years of age or older, both sexes, signing the Free and Informed Consent Form (TCLE), and approval by the Research Ethics Committee of University of Vale do Sapucaí (UNIVÁS).

Criteria for non-inclusion: Elderly people with immobility or severe cognitive impairment that impedes understanding of the orientation towards the TUG.

Exclusion criteria: The waiving of continuing the study after the signing of the TCLE.


Healthcare information technology Frail elderly Medical device Wearable Accelerometry 


  1. 1.
    R.C. McDermid, H.T. Stelfox, S.M. Bagshaw, Frailty in the critically ill: a novel concept. Crit. Care 15, 301 (2011)CrossRefGoogle Scholar
  2. 2.
    Y. Schoon, K. Bongers, J. Van Kempen, R. Melis, R.M. Olde, Gait speed as a test for monitoring frailty in community-dwelling older people has the highest diagnostic value compared to step length and chair rise time. Eur. J. Phys. Rehabil. Med. 50(6), 693–701 (2014)Google Scholar
  3. 3.
    L.P. Fried et al., Frailty in older adults: evidence for a phenotype. J. Gerontol. A Biol. Sci. Med. Sci. 56(3), 146–156 (2001)MathSciNetCrossRefGoogle Scholar
  4. 4.
    M.E. Tinneti et al., A multifactorial intervention to reduce the risk of falling among elderly people living in the community. N. Engl. J. Med. 331(13), 821–827 (1994)CrossRefGoogle Scholar
  5. 5.
    S. Mathias, U.S.L. Nayak, B. Isaacs, Balance in elderly patients: the get up and go test. Arch. Phys. Med. Rehabil. 67, 387–389 (1986)Google Scholar
  6. 6.
    D. Podsiadlo, S. Richardson, The timed “Up & Go”: a test of basic functional mobility for frail elderly pearsons. J. Am. Geriatr. Soc. 39(2), 142–148 (1991)CrossRefGoogle Scholar
  7. 7.
    G.M. Savva, O.A. Donoghue, F. Horgan, C. O’Regan, H. Cronin, R.A. Kenny, Using timed up-and-go to identify frail members of the older population. J. Gerontol. A Biol. Sci. Med. Sci. 68, 441–446 (2013) CrossRefGoogle Scholar
  8. 8.
    B.R. Greene, E.P. Dohener, A. O’Halloran, R.A. Kenny, Frailty status can be accurately assessed using inertial sensors and the TUG test. Age Ageing 43, 406–411 (2014)CrossRefGoogle Scholar
  9. 9.
    D.L.B. Muniz; M.C. De Andrade. Biomechanical Analysis of the Phases of the March by Accelerometers (2011)Google Scholar
  10. 10.
    K. Aminian, B. Najafi, C. Bula, P.F. Leyvraz, P. Robert, Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. J. Biomech. 35, 689–699 (2002)CrossRefGoogle Scholar
  11. 11.
    B. Najafi, T. Khan, J. Wrobel, Laboratory in a box: wearable sensors and its advantages for gait analysis. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011, 6507–6510 (2011)Google Scholar
  12. 12.
    B. Najafi, D. Horn, S. Marclay, R.T. Crews, S. Wu, J.S. Wrobel, Assessing postural control and postural postural control strategy in diabetes patients using innovative and wearabletechnology. J. Diabetes Sci. Technol. 4, 780–791 (2010)CrossRefGoogle Scholar
  13. 13.
    M. Schwenk et al., Wearable sensor-based in-home assessment of gait, balance, and physical activity for discrimination of frailty status: baseline results of the Arizona frailty cohort study. Gerontology 61, 258–267 (2015)CrossRefGoogle Scholar
  14. 14.
    E. Innes, L. Straker, Validity of work-related assessments. Work 13(2), 125–152 (1999)Google Scholar
  15. 15.
    K.Y. Chen, D.R. Basset Jr., The technology of accelerometry-based activity monitors: current and future. Med. Sci. Sports Exerc. 37(Suppl 11), S490–S500 (2005)CrossRefGoogle Scholar
  16. 16.
    J. Vanhelst, L. Beghin, D. Truck, F. Gotrtrand, New validated thresholds for various intensities of physical activity in adolescents using the Actigraph accelerometer. Int. J. Rehabil. Res. 34(2), 175–177 (2011)CrossRefGoogle Scholar
  17. 17.
    H.A. Bischoff et al., Identifying a cut-off point for normal mobility: A comparison of the timed ‘up and go’ test in community-dwelling and institutionalised elderly women. Age Ageing 32(3), 315–320 (2003)CrossRefGoogle Scholar
  18. 18.
    F.I. Mahoney, D. Barthel, Functional evaluation: the Barthel index. Md. State Med. J. 14, 56–61 (1965)Google Scholar
  19. 19.
    M.F. Folstein, S.E. Folstein, P.R. McHugh, Mini-Mental State. J. Psychiatr. Res. 12, 189–198 (1985)CrossRefGoogle Scholar
  20. 20.
    D.P. Nunes, Y.A.O. Duarte, J.L.F. Santos, M.L. Lebrão, Screening for frailty in older adults using a self-reported instrument. Rev. Saúde Pública 49, 2 (2015)CrossRefGoogle Scholar
  21. 21.
    J.L. Fleg et al., Accelerated longitudinal decline of aerobic capacity in healthy older adults. Circulation 112(5), 674–682 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of Vale do SapucaíPouso AlegreBrazil
  2. 2.Federal University of ItajubáItajubáBrazil

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