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Objective characterization of daily living transitions in patients with Parkinson’s disease using a single body-fixed sensor

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Abstract

Body-fixed sensors (BFS), e.g., accelerometers worn for several days, can be used to augment the traditional clinical assessment. Long-term recordings obtained with BFS have been applied to study tremor, postural control, freezing of gait, turning abilities, motor response fluctuations and fall risk among older adults and patients with Parkinson’s disease (PD). We aimed to test whether BFS-derived measures of transitions differ between patients with PD and healthy controls, and to evaluate whether there are differences among patients with mild PD, compared to more severe patients, and to controls. We also explored the added value of the metrics extracted from the sensor as compared to traditional testing in the lab. Ninety-nine patients with PD and 38 healthy older adults (HOA) participated in this study and wore a body-fixed sensor for 3 days. Walk-to-sit (n = 3286) and Sit-to-walk (n = 2858) transitions were analyzed and a machine learning algorithm was applied to distinguish between the groups. Significant differences in transitions were observed between PD patients and HOA, between mild and severe PD, and between mild PD and HOA, both in temporal and distribution features. The machine learning algorithm discriminated patients from HOA (accuracy = 92.3 %), mild from severe patients (accuracy = 89.8 %), and mild patients from HOA (accuracy = 85.9 %). These initial results suggest that body-fixed sensor-derived metrics of everyday transitions can characterize disease severity and differentiate mild PD patients from healthy older adults. Perhaps this approach can help with the integration of BFS into clinical care and the tracking of disease progression and the response to therapy.

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References

  1. Horak FB, Mancini M (2013) Objective biomarkers of balance and gait for Parkinson’s disease using body-worn sensors. Mov Disord 28:1544–1551

    Article  PubMed  PubMed Central  Google Scholar 

  2. Maetzler W, Rochester L (2015) Body-worn sensors-the brave new world of clinical measurement? Mov Disord 30:1203–1205

  3. Mirelman A, Giladi N, Hausdorff JM (2015) Body-fixed sensors for Parkinson disease. JAMA 314:873–874

    Article  CAS  PubMed  Google Scholar 

  4. Becker C, Schwickert L, Mellone S, Bagala F, Chiari L, Helbostad JL, Zijlstra W, Aminian K, Bourke A, Todd C, Bandinelli S, Kerse N, Klenk J (2012) Proposal for a multiphase fall model based on real-world fall recordings with body-fixed sensors. Z Gerontol Geriatr 45:707–715

    Article  CAS  PubMed  Google Scholar 

  5. Weiss A, Brozgol M, Dorfman M, Herman T, Shema S, Giladi N, Hausdorff JM (2013) Does the evaluation of gait quality during daily life provide insight Into fall risk?: a novel approach using 3-Day accelerometer recordings. Neurorehabil Neural Repair 27:742–752

    Article  PubMed  Google Scholar 

  6. Zijlstra W, Bisseling RW, Schlumbohm S, Baldus H (2010) A body-fixed-sensor-based analysis of power during sit-to-stand movements. Gait Posture 31:272–278

    Article  PubMed  Google Scholar 

  7. van Schooten KS, Pijnappels M, Rispens SM, Elders PJ, Lips P, van Dieen JH (2015) Ambulatory fall-risk assessment: amount and quality of daily-life gait predict falls in older adults. J Gerontol A Biol Sci Med Sci 70:608–615

    Article  PubMed  Google Scholar 

  8. Demonceau M, Donneau AF, Croisier JL, Skawiniak E, Boutaayamou M, Maquet D, Garraux G (2015) Contribution of a trunk accelerometer system to the characterization of gait in patients with mild to moderate Parkinson’s disease. IEEE J Biomed Health Inform 19:1803–1808

  9. Weiss A, Herman T, Giladi N, Hausdorff JM (2014) Objective assessment of fall risk in Parkinson’s disease using a body-fixed sensor worn for 3 days. PLoS One 9:e96675

    Article  PubMed  PubMed Central  Google Scholar 

  10. El-Gohary M, Pearson S, McNames J, Mancini M, Horak F, Mellone S, Chiari L (2013) Continuous monitoring of turning in patients with movement disability. Sensors (Basel) 14:356–369

    Article  Google Scholar 

  11. Mancini M, Priest KC, Nutt JG, Horak FB (2012) Quantifying freezing of gait in Parkinson’s disease during the instrumented timed up and go test. Conf Proc IEEE Eng Med Biol Soc 2012:1198–1201

    PubMed  PubMed Central  Google Scholar 

  12. Mancini M, Chiari L, Holmstrom L, Salarian A, Horak FB (2015) Validity and reliability of an IMU-based method to detect APAs prior to gait initiation. Gait Posture 43:125–131

  13. Mancini M, El-Gohary M, Pearson S, McNames J, Schlueter H, Nutt JG, King LA, Horak FB (2015) Continuous monitoring of turning in Parkinson’s disease: rehabilitation potential. NeuroRehabilitation 37:3–10

    Article  PubMed  PubMed Central  Google Scholar 

  14. Heldman DA, Espay AJ, LeWitt PA, Giuffrida JP (2014) Clinician versus machine: reliability and responsiveness of motor endpoints in Parkinson’s disease. Parkinsonism Relat Disord 20:590–595

    Article  PubMed  PubMed Central  Google Scholar 

  15. Mazilu S, Calatroni A, Gazit E, Mirelman A, Hausdorff JM, Troester G (2015) Prediction of Freezing of Gait in Parkinson’s from Physiological Wearables: An Exploratory Study. IEEE J Biomed Health Inform 19:1843–1854

  16. Rocchi L, Palmerini L, Weiss A, Herman T, Hausdorff JM (2014) Balance testing with inertial sensors in patients with Parkinson’s disease: assessment of motor subtypes. IEEE Trans Neural Syst Rehabil Eng 22:1064–1071

    Article  PubMed  Google Scholar 

  17. Roy SH, Cole BT, Gilmore LD, De Luca CJ, Thomas CA, Saint-Hilaire MM, Nawab SH (2013) High-resolution tracking of motor disorders in Parkinson’s disease during unconstrained activity. Mov Disord 28:1080–1087

    Article  PubMed  Google Scholar 

  18. Klucken J, Friedl K, Eskoffer BM, Hausdorff JM (2015) Enabling technologies for Parkinson’s disease management. IEEE J Biomed Health Inform 1:1775–1776

    Article  Google Scholar 

  19. Maetzler W, Domingos J, Srulijes K, Ferreira JJ, Bloem BR (2013) Quantitative wearable sensors for objective assessment of Parkinson’s disease. Mov Disord 28:1628–1637

    Article  PubMed  Google Scholar 

  20. Papapetropoulos S, Mitsi G, Espay AJ (2015) Digital health revolution: is it time for affordable remote monitoring for Parkinson’s disease? Front Neurol 6:34

    Article  PubMed  PubMed Central  Google Scholar 

  21. Goetz CG, Fahn S, Martinez-Martin P, Poewe W, Sampaio C, Stebbins GT, Stern MB, Tilley BC, Dodel R, Dubois B, Holloway R, Jankovic J, Kulisevsky J, Lang AE, Lees A, Leurgans S, LeWitt PA, Nyenhuis D, Olanow CW, Rascol O, Schrag A, Teresi JA, Van Hilten JJ, LaPelle N (2007) Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): process, format, and clinimetric testing plan. Mov Disord 22:41–47

    Article  PubMed  Google Scholar 

  22. Lomaglio MJ, Eng JJ (2005) Muscle strength and weight-bearing symmetry relate to sit-to-stand performance in individuals with stroke. Gait Posture 22:126–131

    Article  PubMed  PubMed Central  Google Scholar 

  23. Podsiadlo D, Richardson S (1991) The timed “Up & Go”: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc 39:142–148

    Article  CAS  PubMed  Google Scholar 

  24. Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, Scherr PA, Wallace RB (1994) A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol 49:M85–M94

    Article  CAS  PubMed  Google Scholar 

  25. Pahor M, Guralnik JM, Ambrosius WT, Blair S, Bonds DE, Church TS, Espeland MA, Fielding RA, Gill TM, Groessl EJ, King AC, Kritchevsky SB, Manini TM, McDermott MM, Miller ME, Newman AB, Rejeski WJ, Sink KM, Williamson JD (2014) Effect of structured physical activity on prevention of major mobility disability in older adults: the LIFE study randomized clinical trial. JAMA 311:2387–2396

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Weaver TB, Robinovitch SN, Laing AC, Yang Y (2016) Falls and Parkinson’s Disease: evidence from video recordings of actual fall events. J Am Geriatr Soc 64:96–101

    Article  PubMed  Google Scholar 

  27. Herman T, Weiss A, Brozgol M, Giladi N, Hausdorff JM (2014) Identifying axial and cognitive correlates in patients with Parkinson’s disease motor subtype using the instrumented Timed Up and Go. Exp Brain Res 232:713–721

    Article  PubMed  Google Scholar 

  28. Weiss A, Herman T, Plotnik M, Brozgol M, Maidan I, Giladi N, Gurevich T, Hausdorff JM (2010) Can an accelerometer enhance the utility of the Timed Up & Go Test when evaluating patients with Parkinson’s disease? Med Eng Phys 32:119–125

    Article  PubMed  Google Scholar 

  29. Bagala F, Klenk J, Cappello A, Chiari L, Becker C, Lindemann U (2013) Quantitative description of the lie-to-sit-to-stand-to-walk transfer by a single body-fixed sensor. IEEE Trans Neural Syst Rehabil Eng 21:624–633

    Article  PubMed  Google Scholar 

  30. Schwenk M, Gogulla S, Englert S, Czempik A, Hauer K (2012) Test-retest reliability and minimal detectable change of repeated sit-to-stand analysis using one body fixed sensor in geriatric patients. Physiol Meas 33:1931–1946

    Article  CAS  PubMed  Google Scholar 

  31. van Lummel RC, Ainsworth E, Hausdorff JM, Lindemann U, Beek PJ, van Dieen JH (2012) Validation of seat-off and seat-on in repeated sit-to-stand movements using a single-body-fixed sensor. Physiol Meas 33:1855–1867

    Article  PubMed  Google Scholar 

  32. Van Lummel RC, Ainsworth E, Lindemann U, Zijlstra W, Chiari L, Van CP, Hausdorff JM (2013) Automated approach for quantifying the repeated sit-to-stand using one body fixed sensor in young and older adults. Gait Posture 38:153–156

    Article  PubMed  Google Scholar 

  33. Benzinger P, Lindemann U, Becker C, Aminian K, Jamour M, Flick SE (2014) Geriatric rehabilitation after hip fracture. Role of body-fixed sensor measurements of physical activity. Z Gerontol Geriatr 47:236–242

    Article  CAS  PubMed  Google Scholar 

  34. Iluz T, Weiss A, Gazit E, Tankus A, Brozgol M, Dorfman M, Mirelman A, Giladi N, Hausdorff JM (2015) Can a body-fixed sensor reduce Heisenberg’s uncertainty when it comes to the evaluation of mobility? Effects of aging and fall risk on transitions in daily living. J Gerontol A Biol Sci Med Sci. doi:10.1093/gerona/glv049

  35. Herman T, Rosenberg-Katz K, Jacob Y, Auriel E, Gurevich T, Giladi N, Hausdorff JM (2013) White matter hyperintensities in Parkinson’s disease: do they explain the disparity between the postural instability gait difficulty and tremor dominant subtypes? PLoS One 8:e55193

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Rosenberg-Katz K, Herman T, Jacob Y, Giladi N, Hendler T, Hausdorff JM (2013) Gray matter atrophy distinguishes between Parkinson disease motor subtypes. Neurology 80:1476–1484

    Article  PubMed  PubMed Central  Google Scholar 

  37. Hoehn MM, Yahr MD (2001) Parkinsonism: onset, progression, and mortality. 1967. Neurology 57:S11–S26

    CAS  PubMed  Google Scholar 

  38. Curtze C, Nutt JG, Carlson-Kuhta P, Mancini M, Horak FB (2015) Levodopa is a double-edged sword for balance and gait in people with Parkinson’s disease. Mov Disord 30:1361–1370

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Folstein MF, Folstein SE, McHugh PR (1975) “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189–198

    Article  CAS  PubMed  Google Scholar 

  40. Whitney SL, Hudak MT, Marchetti GF (2000) The dynamic gait index relates to self-reported fall history in individuals with vestibular dysfunction. J Vestib Res 10:99–105

    CAS  PubMed  Google Scholar 

  41. Berg KO, Wood-Dauphinee SL, Williams JI, Maki B (1992) Measuring balance in the elderly: validation of an instrument. Can J Public Health 83(Suppl 2):S7–11

    PubMed  Google Scholar 

  42. Beauchet O, Annweiler C, Allali G, Berrut G, Herrmann FR, Dubost V (2008) Recurrent falls and dual task-related decrease in walking speed: is there a relationship? J Am Geriatr Soc 56:1265–1269

    Article  PubMed  Google Scholar 

  43. Montero-Odasso M, Verghese J, Beauchet O, Hausdorff JM (2012) Gait and cognition: a complementary approach to understanding brain function and the risk of falling. J Am Geriatr Soc 60:2127–2136

    Article  PubMed  PubMed Central  Google Scholar 

  44. Yogev-Seligmann G, Hausdorff JM, Giladi N (2008) The role of executive function and attention in gait. Mov Disord 23:329–342

    Article  PubMed  Google Scholar 

  45. Rochester L, Nieuwboer A, Baker K, Hetherington V, Willems AM, Kwakkel G, Van WE, Lim I, Jones D (2008) Walking speed during single and dual tasks in Parkinson’s disease: which characteristics are important? Mov Disord 23:2312–2318

    Article  PubMed  Google Scholar 

  46. Muir-Hunter SW, Wittwer JE (2015) Dual-task testing to predict falls in community-dwelling older adults: a systematic review. Physiotherapy 102(1):29–40

    Article  PubMed  Google Scholar 

  47. van Hees VT, van Lummel RC, Westerterp KR (2009) Estimating activity-related energy expenditure under sedentary conditions using a tri-axial seismic accelerometer. Obesity (Silver Spring) 17:1287–1292

    Google Scholar 

  48. Weiss A, Mirelman A, Buchman AS, Bennett DA, Hausdorff JM (2013) Using a body-fixed sensor to identify subclinical gait difficulties in older adults with IADL disability: maximizing the output of the timed up and go. PLoS One 8:e68885

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Weiss A, Herman T, Plotnik M, Brozgol M, Giladi N, Hausdorff JM (2011) An instrumented timed up and go: the added value of an accelerometer for identifying fall risk in idiopathic fallers. Physiol Meas 32:2003–2018

    Article  CAS  PubMed  Google Scholar 

  50. Moor BC (1981) Principle component analysis in linear system. IEEE Transactions on Automatic Control 26:17–32

  51. Herman T, Weiss A, Brozgol M, Giladi N, Hausdorff JM (2014) Gait and balance in Parkinson’s disease subtypes: objective measures and classification considerations. J Neurol 261:2401–2410

    Article  PubMed  Google Scholar 

  52. Milman U, Atias H, Weiss A, Mirelman A, Hausdorff JM (2014) Can cognitive remediation improve mobility in patients with Parkinson’s disease? Findings from a 12 week pilot study. J Parkinsons Dis 4:37–44

    PubMed  Google Scholar 

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Acknowledgments

This work was minded in part by the Michael J. Fox Foundation for Parkinson’s Research. We thank the subjects for their participation.

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Authors and Affiliations

  1. Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, 6 Weizman Street, 6423906, Tel Aviv, Israel

    Hagar Bernad-Elazari, Talia Herman, Anat Mirelman, Eran Gazit & Jeffrey M. Hausdorff

  2. Department of Neurology and Neurosurgery, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Nir Giladi

  3. Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel

    Nir Giladi

  4. Sieratzki Chair in Neurology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Nir Giladi

  5. Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel

    Anat Mirelman, Nir Giladi & Jeffrey M. Hausdorff

  6. Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Jeffrey M. Hausdorff

Authors
  1. Hagar Bernad-Elazari
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  2. Talia Herman
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  3. Anat Mirelman
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  4. Eran Gazit
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  5. Nir Giladi
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  6. Jeffrey M. Hausdorff
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Correspondence to Jeffrey M. Hausdorff.

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Conflicts of interest

Drs. Mirelman, Giladi, and Hausdorff submitted a patent application on the use of body-fixed sensors in Parkinson’s disease. The intellectual property rights for this patent application are owned and managed by the Tel Aviv Sourasky Medical Center. The other authors declare that they have no conflict of interest.

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Bernad-Elazari, H., Herman, T., Mirelman, A. et al. Objective characterization of daily living transitions in patients with Parkinson’s disease using a single body-fixed sensor. J Neurol 263, 1544–1551 (2016). https://doi.org/10.1007/s00415-016-8164-6

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  • DOI: https://doi.org/10.1007/s00415-016-8164-6

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