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Towards a minimized unsupervised technical assessment of physical performance in domestic environments

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Published:23 May 2017Publication History

ABSTRACT

Early detection of changes in mobility associated with functional decline can increase the therapeutic success by prolonging self-determined living. To get an unbiased and high frequently status of the physical performance of the persons at risk, unsupervised assessments of their functional abilities should ideally take place in their homes.

Thus, we have developed a minimized unsupervised technical assessment of physical performance in domestic environments. By conducting an exploratory factor analysis, based on the results of 79 study participants with a minimum age of 70 years, we could clarify that common assessment items mainly represent three key parameters of functional performance "mobility and endurance", "strength" and "balance". Consequently, we identified a minimal set of assessment items that is suitable for home-assessments and that, since covering all three parameters, is able to generate clinical meaningful and relevant insights about the functional status. Regarding the parameter mobility, we developed a technical assessment of physical performance for domestic environments, which utilizes short distance walk times assessed via ambient presence sensors as an indicator for potential functional decline. In a field trial over ten months with 20 participants with a mean age of 84.25 years, we could confirm the general feasibility of our approach and the proposed system.

References

  1. Bean, J. F., Kiely, D. K., LaRose, S., Alian, J., and Frontera, W. R. Is stair climb power a clinically relevant measure of leg power impairments in at-risk older adults? Archives of physical medicine and rehabilitation 88, 5 (2007), 604--609.Google ScholarGoogle Scholar
  2. Beswick, A. D., Rees, K., Dieppe, P., Ayis, S., Gooberman-Hill, R., Horwood, J., and Ebrahim, S. Complex interventions to improve physical function and maintain independent living in elderly people: a systematic review and meta-analysis. The Lancet 371, 9614 (2008), 725--735.Google ScholarGoogle ScholarCross RefCross Ref
  3. Clegg, A., Young, J., Iliffe, S., Rikkert, M. O., and Rockwood, K. Frailty in elderly people. The Lancet 381, 9868 (2013), 752--762.Google ScholarGoogle ScholarCross RefCross Ref
  4. Cooper, R., Kuh, D., Cooper, C., Gale, C. R., Lawlor, D. A., Matthews, F., Hardy, R., et al. Objective measures of physical capability and subsequent health: a systematic review. Age and ageing 40, 1 (2011), 14--23.Google ScholarGoogle Scholar
  5. Costello, A. B., and Osborne, J. W. Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation 10 (2005), 173--178.Google ScholarGoogle Scholar
  6. Dasenbrock, L., Heinks, A., Schwenk, M., and Bauer, J. Technology-based measurements for screening, monitoring and preventing frailty. Zeitschrift für Gerontologie und Geriatrie 49, 7 (2016), 581--595.Google ScholarGoogle ScholarCross RefCross Ref
  7. de Morton, N. A., and Lane, K. Validity and reliability of the de morton mobility index in the subacute hospital setting in a geriatric evaluation and management population. Journal of rehabilitation medicine 42, 10 (2010), 956--961.Google ScholarGoogle Scholar
  8. Ejupi, A., Gschwind, Y. J., Valenzuela, T., Lord, S. R., and Delbaere, K. A kinect and inertial sensor-based system for the self-assessment of fall risk: A home-based study in older people. Human-Computer Interaction 31, 3--4 (2016), 261--293. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Elsawy, B., and Higgins, K. E. The geriatric assessment. Am Fam Physician 83, 1 (2011), 48--56.Google ScholarGoogle Scholar
  10. Frenken, T., Frenken, M., Gövercin, M., Kiselev, J., Meyer, J., Wegel, S., and Hein, A. A novel ict approach to the assessment of mobility in diverse health care environments. In CEWIT-TZI-acatech Workshop" ICT meets Medicine and Health" (ICTMH 2013) (2013).Google ScholarGoogle Scholar
  11. Frenken, T., Gövercin, M., Mersmann, S., and Hein, A. Precise assessment of self-selected gait velocity in domestic environments. In Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2010 4th International Conference on Pervasive Computing Technologies for Healthcare, IEEE (2010), 1--8.Google ScholarGoogle ScholarCross RefCross Ref
  12. Frenken, T., Lipprandt, M., Brell, M., Gövercin, M., Wegel, S., Steinhagen-Thiessen, E., and Hein, A. Novel approach to unsupervised mobility assessment tests: Field trial for atug. In Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2012 6th International Conference on Pervasive Computing Technologies for Healthcare, IEEE (2012), 131--138.Google ScholarGoogle ScholarCross RefCross Ref
  13. Giannouli, E., Bock, O., Mellone, S., and Zijlstra, W. Mobility in old age: Capacity is not performance. BioMed Research International 2016 (2016).Google ScholarGoogle Scholar
  14. Greene, B. R., Doheny, E. P., Kenny, R. A., and Caulfield, B. Classification of frailty and falls history using a combination of sensor-based mobility assessments. Physiological measurement 35, 10 (2014), 2053.Google ScholarGoogle Scholar
  15. Guralnik, J. M., Simonsick, E. M., Ferrucci, L., Glynn, R. J., Berkman, L. F., Blazer, D. G., Scherr, P. A., and Wallace, R. B. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. Journal of gerontology 49, 2 (1994), M85--M94.Google ScholarGoogle Scholar
  16. Hellmers, S., Fudickar, S., Büse, C., Dasenbrock, L., Heinks, A., Bauer, J. M., and Hein, A. Technology supported geriatric assessment. In Ambient Assisted Living. Springer, 2017, 85--100.Google ScholarGoogle Scholar
  17. Helmer, A., Lipprandt, M., Frenken, T., Eichelberg, M., and Hein, A. 3 dlc: a comprehensive model for personal health records supporting new types of medical applications. Journal of Healthcare Engineering 2, 3 (2011), 321--336.Google ScholarGoogle ScholarCross RefCross Ref
  18. Isken, M., Frenken, T., Frenken, M., and Hein, A. Towards pervasive mobility assessments in clinical and domestic environments. In Smart Health. Springer, 2015, 71--98.Google ScholarGoogle Scholar
  19. Lawton, M. P., and Brody, E. M. Assessment of older people: self-maintaining and instrumental activities of daily living. The Gerontologist 9, 3 (1969), 179--186.Google ScholarGoogle ScholarCross RefCross Ref
  20. Leopold, T., and Skopek, J. Retirement and changes in housework: A panel study of dual earner couples. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences (2016).Google ScholarGoogle Scholar
  21. Liu, L., Stroulia, E., Nikolaidis, I., Miguel-Cruz, A., and Rincon, A. R. Smart homes and home health monitoring technologies for older adults: A systematic review. International journal of medical informatics 91 (2016), 44--59.Google ScholarGoogle Scholar
  22. Müller, S. M., Steen, E.-E., and Hein, A. Inferring multi-person presence in home sensor networks. In Ambient Assisted Living. Springer, 2016, 47--56.Google ScholarGoogle Scholar
  23. Pavel, M., Hayes, T., Tsay, I., Erdogmus, D., Paul, A., Larimer, N., Jimison, H., and Nutt, J. Continuous assessment of gait velocity in parkinson's disease from unobtrusive measurements. In Neural Engineering, 2007. CNE'07. 3rd International IEEE/EMBS Conference on, IEEE (2007), 700--703.Google ScholarGoogle ScholarCross RefCross Ref
  24. Pavel, M., Hayes, T. L., Adami, A., Jimison, H., and Kaye, J. Unobtrusive assessment of mobility. In Engineering in Medicine and Biology Society, 2006. EMBS'06. 28th Annual International Conference of the IEEE, IEEE (2006), 6277--6280.Google ScholarGoogle ScholarCross RefCross Ref
  25. Podsiadlo, D., and Richardson, S. The timed up & go: a test of basic functional mobility for frail elderly persons. Journal of the American geriatrics Society 39, 2 (1991), 142--148.Google ScholarGoogle Scholar
  26. Rittweger, J., Schiessl, H., Felsenberg, D., and Runge, M. Reproducibility of the jumping mechanography as a test of mechanical power output in physically competent adult and elderly subjects. Journal of the American Geriatrics Society 52, 1 (2004), 128--131.Google ScholarGoogle ScholarCross RefCross Ref
  27. Scanaill, C. N., Carew, S., Barralon, P., Noury, N., Lyons, D., and Lyons, G. M. A review of approaches to mobility telemonitoring of the elderly in their living environment. Annals of biomedical engineering 34, 4 (2006), 547--563.Google ScholarGoogle Scholar
  28. Schwenk, M., Howe, C., Saleh, A., Mohler, J., Grewal, G., Armstrong, D., and Najafi, B. Frailty and technology: a systematic review of gait analysis in those with frailty. Gerontology 60, 1 (2013), 79--89.Google ScholarGoogle Scholar
  29. Sprint, G., Cook, D. J., and Weeks, D. L. Toward automating clinical assessments: A survey of the timed up and go. IEEE reviews in biomedical engineering 8 (2015), 64--77.Google ScholarGoogle Scholar
  30. Steen, E.-E., Frenken, T., Eichelberg, M., Frenken, M., and Hein, A. Modeling individual healthy behavior using home automation sensor data: Results from a field trial. Journal of Ambient Intelligence and Smart Environments 5, 5 (2013), 503--523. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Troosters, T., Gosselink, R., and Decramer, M. Six minute walking distance in healthy elderly subjects. European Respiratory Journal 14, 2 (1999), 270--274.Google ScholarGoogle ScholarCross RefCross Ref

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  • Published in

    cover image ACM Other conferences
    PervasiveHealth '17: Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare
    May 2017
    503 pages
    ISBN:9781450363631
    DOI:10.1145/3154862

    Copyright © 2017 ACM

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    Publication History

    • Published: 23 May 2017

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