TECHNOLOGY-BASED EVALUATION IN PARKINSONISM

Selvin Balki; Emine Nacar; Rabia Seva Özkan; Merve Karakurt

doi:10.7197/cmj.1512299

Derleme

BibTex

RIS

Kaynak Göster

PARKİNSONDA TEKNOLOJİK TABANLI DEĞERLENDİRME

Yıl 2024, , 156 - 163, 30.09.2024

Selvin Balki , Emine Nacar , Rabia Seva Özkan , Merve Karakurt

https://doi.org/10.7197/cmj.1512299

Öz

Parkinson hastalığı, motor ve motor olmayan semptomlarla karakterize nörodejeneratif bir hastalıktır ve bu semptomlar zaman içinde kötüleşir. Günümüzde hastalığın ilerleyişini izlemek ve tedavi yanıtlarını değerlendirmek için geleneksel klinik değerlendirme yöntemleri kullanılmaktadır. Ancak bu yöntemler subjektiftir ve belirli durumları ölçmede yetersiz kalabilir. Son yıllarda, giyilebilir teknolojiler, akıllı sensörler ve veri analizindeki ilerlemeler sayesinde parkinson hastalarının değerlendirilmesinde teknolojik tabanlı yaklaşımlar daha fazla dikkat çekmektedir. Bu teknolojiler sayesinde hastaların günlük aktiviteleri, motor fonksiyonları ve semptomları izlenilerek objektif veriler elde edilebilir. Tremor şiddeti, rijidite, bradikinezi, postüral instabiliteler, donma fenomeni ve konuşma bozukluğunun motor parametreleri gibi motor semptomlar bu teknolojiler aracılığıyla objektif olarak ölçülebilir. Ayrıca, bu verilerin uzaktan aktarılabilmesi, hastaların kendi evlerinde değerlendirilebilmesine olanak tanır ve sağlık uzmanlarına sürekli geri bildirim sağlar. Bu derleme, parkinson hastalarında teknolojik tabanlı değerlendirme yöntemlerinin önemini ve potansiyelini vurgulamakta ve gelecekteki araştırmalara rehberlik etmeyi amaçlamaktadır.

Anahtar Kelimeler

Parkinson, teknolojik ölçüm, giyilebilir teknolojiler, akıllı sensörler

Kaynakça

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3. Güler, S., Caylan, A., Turan, F. N., & Dağdeviren, N. (2022). Prevalence and Clinical Features of Idiopathic Parkinson’s Disease in Western Turkey. Archives of Neuropsychiatry, 59(2), 98. doi: 10.29399/npa.27486.
4. Pringsheim T, Jette N, Frolkis A, Steeves TD. The prevalence of Parkinson's disease: a systematic review and meta‐analysis. Movement disorders. 2014;29(13):1583-90. doi: 10.1002/ mds.25945.
5. Dorsey ER, Bloem BR. The Parkinson pandemic—a call to action. JAMA neurology. 2018;75(1):9-10. doi: 10.1001/ jamaneurol.2017.3299.
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9. Hoehn MM. Commentary: Parkinsonism: Onset, progression, and mortality. Neurology. 1998;50:318-. doi: 10.1212/wnl. 50.2.318.
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13. Rovini E, Maremmani C, Cavallo F. How wearable sensors can support Parkinson's disease diagnosis and treatment: a systematic review. Frontiers in neuroscience. 2017;11:555. doi: 10.3389/fnins.2017.00555.
14. Di Lazzaro G, Ricci M, Al-Wardat M, Schirinzi T, Scalise S, Giannini F, et al. Technology-based objective measures detect subclinical axial signs in untreated, de novo Parkinson’s disease. Journal of Parkinson's disease. 2020;10(1):113-22. doi: 10.3233/JPD-191758
15. Godinho C, Domingos J, Cunha G, Santos AT, Fernandes RM, Abreu D, et al. A systematic review of the characteristics and validity of monitoring technologies to assess Parkinson’s disease. Journal of neuroengineering and rehabilitation. 2016;13:1-10. doi: 10.1186/s12984-016-0136-7.
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17. Moreau C, Rouaud T, Grabli D, Benatru I, Remy P, Marques A-R, et al. Overview on wearable sensors for the management of Parkinson’s disease. NPJ Parkinson's Disease. 2023;9(1):153. doi: 10.1038/s41531-023-00585-y.
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20. Delrobaei M, Tran S, Gilmore G, McIsaac K, Jog M. Characterization of multi-joint upper limb movements in a single task to assess bradykinesia. Journal of the neurological sciences. 2016;368:337-42. doi: 10.1016/j.jns.2016.07.056.
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TECHNOLOGY-BASED EVALUATION IN PARKINSONISM

Yıl 2024, , 156 - 163, 30.09.2024

Selvin Balki , Emine Nacar , Rabia Seva Özkan , Merve Karakurt

https://doi.org/10.7197/cmj.1512299

Öz

Parkinson's disease is a neurodegenerative disorder characterized by motor and non-motor symptoms that worsen over time. Today, traditional clinical assessment methods are used to monitor disease progression and evaluate treatment responses. However, these methods are subjective and may fail to measure specific conditions. In recent years, thanks to advances in wearable technologies, smart sensors, and data analysis, technology-based approaches to the assessment of patients with Parkinson's disease have gained more attention. With these technologies, objective data can be obtained by monitoring patients' daily activities, motor functions, and symptoms. Motor symptoms such as tremor severity, rigidity, bradykinesia, postural instabilities, freezing phenomenon, and motor parameters of speech impairment can be objectively measured through these technologies. Furthermore, the ability to remotely transmit these data allows patients to be assessed in their own homes and provides continuous feedback to healthcare professionals. This review highlights the importance and potential of technology-based assessment methods in Parkinson's patients and aims to guide future research.

Anahtar Kelimeler

Parkinson, technological measurement, wearable technologies, intelligent sensors

Kaynakça

1. Fahn S. Unified Parkinson's disease rating scale. Recent developments in Parkinson's disease. 1987:153-63.
2. Balestrino R, Schapira A. Parkinson disease. European journal of neurology. 2020;27(1):27-42. doi: 10.1111/ene.14108.
3. Güler, S., Caylan, A., Turan, F. N., & Dağdeviren, N. (2022). Prevalence and Clinical Features of Idiopathic Parkinson’s Disease in Western Turkey. Archives of Neuropsychiatry, 59(2), 98. doi: 10.29399/npa.27486.
4. Pringsheim T, Jette N, Frolkis A, Steeves TD. The prevalence of Parkinson's disease: a systematic review and meta‐analysis. Movement disorders. 2014;29(13):1583-90. doi: 10.1002/ mds.25945.
5. Dorsey ER, Bloem BR. The Parkinson pandemic—a call to action. JAMA neurology. 2018;75(1):9-10. doi: 10.1001/ jamaneurol.2017.3299.
6. Dorsey ER, Elbaz A, Nichols E, Abbasi N, Abd-Allah F, Abdelalim A, et al. Global, regional, and national burden of Parkinson's disease, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet Neurology. 2018;17(11):939-53. doi: 10.1016/S1474-4422(18)30295-3.
7. Jankovic J, Tan EK. Parkinson’s disease: etiopathogenesis and treatment. Journal of Neurology, Neurosurgery & Psychiatry. 2020;91(8):795-808. doi: 10.1136/jnnp-2019-322338.
8. Wolters EC. Variability in the clinical expression of Parkinson's disease. Journal of the neurological sciences. 2008;266(1-2):197-203. doi: 10.1016/j.jns.2007.08.016.
9. Hoehn MM. Commentary: Parkinsonism: Onset, progression, and mortality. Neurology. 1998;50:318-. doi: 10.1212/wnl. 50.2.318.
10. Ozanne A, Johansson D, Hällgren Graneheim U, Malmgren K, Bergquist F, Alt Murphy M. Wearables in epilepsy and Parkinson's disease—a focus group study. Acta Neurologica Scandinavica. 2018;137(2):188-94. doi: 10.1111/ane.12798.
11. Qu Y, Zhang T, Duo Y, Chen L, Li X. Identification and quantitative assessment of motor complications in Parkinson’s disease using the Parkinson’s KinetiGraph™. Frontiers in Aging Neuroscience. 2023;15. doi: 10.3389/fnagi.2023.1142268.
12. Espay AJ, Bonato P, Nahab FB, Maetzler W, Dean JM, Klucken J, et al. Technology in Parkinson's disease: challenges and opportunities. Movement Disorders. 2016;31(9):1272-82. doi: 10.1002/mds.26642.
13. Rovini E, Maremmani C, Cavallo F. How wearable sensors can support Parkinson's disease diagnosis and treatment: a systematic review. Frontiers in neuroscience. 2017;11:555. doi: 10.3389/fnins.2017.00555.
14. Di Lazzaro G, Ricci M, Al-Wardat M, Schirinzi T, Scalise S, Giannini F, et al. Technology-based objective measures detect subclinical axial signs in untreated, de novo Parkinson’s disease. Journal of Parkinson's disease. 2020;10(1):113-22. doi: 10.3233/JPD-191758
15. Godinho C, Domingos J, Cunha G, Santos AT, Fernandes RM, Abreu D, et al. A systematic review of the characteristics and validity of monitoring technologies to assess Parkinson’s disease. Journal of neuroengineering and rehabilitation. 2016;13:1-10. doi: 10.1186/s12984-016-0136-7.
16. Özen D, Karakaya MG, Yenişehir S, Çıtak İ. Parkinsonlu Hastalarda Teknoloji Temelli Yürüyüş Değerlendirmelerinin Literatür Analizi Literature Analysis Of Technology-Based Gait Assessment In Patients With Parkinson Disease. Full Text Book.56.
17. Moreau C, Rouaud T, Grabli D, Benatru I, Remy P, Marques A-R, et al. Overview on wearable sensors for the management of Parkinson’s disease. NPJ Parkinson's Disease. 2023;9(1):153. doi: 10.1038/s41531-023-00585-y.
18. Suppa A, Kita A, Leodori G, Zampogna A, Nicolini E, Lorenzi P, et al. L-DOPA and freezing of gait in Parkinson’s disease: Objective assessment through a wearable wireless system. Frontiers in neurology. 2017;8:406. doi: 10.3389/fneur. 2017.00406.
19. Bove F, Di Lazzaro G, Mulas D, Cocciolillo F, Di Giuda D, Bentivoglio AR. A role for accelerometry in the differential diagnosis of tremor syndromes. Functional neurology. 2018;33(1):45. doi: 10.11138/fneur/2018.33.1.045
20. Delrobaei M, Tran S, Gilmore G, McIsaac K, Jog M. Characterization of multi-joint upper limb movements in a single task to assess bradykinesia. Journal of the neurological sciences. 2016;368:337-42. doi: 10.1016/j.jns.2016.07.056.
21. Ricci M, Giannini F, Saggio G, Cenci C, Di Lazzaro G, Pisani A, editors. A novel analytical approach to assess dyskinesia in patients with Parkinson disease. 2018 IEEE international symposium on medical measurements and applications (MeMeA). 2018; (pp. 1-5), IEEE. doi: 10.1109/MeMeA.2018.8438666.
22. Kaya D, Soyukibar TE. Parkinson’s Disease and Parkinsonism. The Journal of Turkish Family Physician. 2022;13(4):182-92. doi: 10.15511/tjtfp.22.00482
23. Jankovic J. Parkinson’s disease: clinical features and diagnosis. Journal of neurology, neurosurgery & psychiatry. 2008;79(4):368-76. doi: 10.1136/jnnp.2007.131045.
24. Kraus P, Lemke M, Reichmann H. Kinetic tremor in Parkinson’s disease–an underrated symptom. Journal of neural transmission. 2006;113:845-53. doi: 10.1007/s00702-005-0354-9.
25. Fleischman DA, Wilson RS, Schneider JA, Bienias JL, Bennett DA. Parkinsonian signs and functional disability in old age. Experimental aging research. 2007;33(1):59-76. doi: 10.1080/03610730601006370.
26. Rigas G, Tzallas AT, Tsipouras MG, Bougia P, Tripoliti EE, Baga D, et al. Assessment of tremor activity in the Parkinson’s disease using a set of wearable sensors. IEEE Transactions on Information Technology in Biomedicine. 2012;16(3):478-87. doi: 10.1109/TITB.2011.2182616.
27. Heldman DA, Jankovic J, Vaillancourt DE, Prodoehl J, Elble RJ, Giuffrida JP. Essential tremor quantification during activities of daily living. Parkinsonism & related disorders. 2011;17(7):537-42. doi: 10.1016/j.parkreldis.2011.04.017.
28. Delrobaei M, Memar S, Pieterman M, Stratton TW, McIsaac K, Jog M. Towards remote monitoring of Parkinson’s disease tremor using wearable motion capture systems. Journal of the neurological sciences. 2018;384:38-45. doi: 10.1016/ j.jns.2017.11.004.
29. Grimaldi G, Manto M-U, Manto M. Tremor: from pathogenesis to treatment: Morgan & Claypool Publishers; 2008.
30. Rahimi F, Bee C, South A, Debicki D, Jog M, editors. Variability of hand tremor in rest and in posture—A pilot study. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2011: IEEE. doi: 10.1109/IEMBS.2011.6090067.
31. Braybrook M, O’Connor S, Churchward P, Perera T, Farzanehfar P, Horne M. An ambulatory tremor score for Parkinson’s disease. Journal of Parkinson's disease. 2016;6(4):723-31. doi: 10.3233/JPD-160898.
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Toplam 66 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	İngilizce
Konular	Birinci Basamak Sağlık Hizmetleri, Sağlık ve Toplum Hizmetleri
Bölüm	Derlemeler
Yazarlar	Selvin Balki 0000-0003-4903-6349 Emine Nacar 0000-0002-1172-1837 Rabia Seva Özkan 0000-0001-9892-5949 Merve Karakurt 0000-0002-3370-7526
Yayımlanma Tarihi	30 Eylül 2024
Gönderilme Tarihi	8 Temmuz 2024
Kabul Tarihi	10 Ağustos 2024
Yayımlandığı Sayı	Yıl 2024

Kaynak Göster

AMA	Balki S, Nacar E, Özkan RS, Karakurt M. TECHNOLOGY-BASED EVALUATION IN PARKINSONISM. CMJ. Eylül 2024;46(3):156-163. doi:10.7197/cmj.1512299

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