Development of a cognitive-based smartphone application for Malaysian Parkinson's disease patients: Exploring the possibility?
DOI:
https://doi.org/10.31117/neuroscirn.v5i1.126Keywords:
telemedicine, non-motor symptoms, PD, dementia, MalayAbstract
The COVID-19 pandemic has accelerated the digital health system. Healthcare organizations want to give medical treatment to individuals who live a great distance away. As a result, they are emphasizing the creation of bespoke telemedicine apps. The number of individuals using telemedicine apps is increasing significantly. Increasing technology gives patients healthcare resources. This has been made feasible via a new telemedicine system and by developing a telemedicine app. Patients can use several technologies to communicate with healthcare professionals. For comfort and privacy, you can employ live visual media. The creation of telemedicine apps is the most attractive and practical investment. With the growing availability and usage of technology in PD, the focus of these technologies is gradually turning toward the disease's vast spectrum of Non-Motor Symptoms (NMS). The nature of NMS makes them difficult to objectively measure, further development and building on experience gained in other conditions may still result in NMS capture that is feasible. Although it is impossible to offer recommendations for the use of digital technology outcomes for NMS in clinical practise based on currently available data, evidence for these devices is evolving, and such guidance may become accessible in the not-too-distant future. To our knowledge, this is the first telemedicine method of its sort to address cognition as one of the NMS in Malay PD patients. The project will be done on two consecutive phases (1 year each); Phase1 aims to develop the Dementia Coach Mobile App, and Phase2 aims to validation of this app by using PD patients sample from SASMEC. Therefore, we hypothesize that developing a friendly mobile app to assess dementia for PD patients is highly beneficial and could be used for diagnosis of NMS in PD patients.
References
Aghanavesi, S., Nyholm, D., Senek, M., Bergquist, F., & Memedi, M. (2017). A smartphone-based system to quantify dexterity in Parkinson's disease patients. Informatics in Medicine Unlocked, 9, 11-17. https://doi.org/10.1016/j.imu.2017.05.005
Aghanavesi, S., Memedi, M., Dougherty, M., Nyholm, D., & Westin, J. (2017). Verification of a method for measuring Parkinson’s disease related temporal irregularity in spiral drawings. Sensors, 17(10), 2341. https://doi.org/10.3390/s17102341
Antonini, A., Stoessl, A. J., Kleinman, L. S., Skalicky, A. M., Marshall, T. S., Sail, K. R., ... & Odin, P. L. A. (2018). Developing consensus among movement disorder specialists on clinical indicators for identification and management of advanced Parkinson’s disease: a multi-country Delphi-panel approach. Current Medical Research and Opinion, 34(12), 2063-2073. https://doi.org/10.1080/03007995.2018.1502165
Athilingam, P., Visovsky, C., Elliott, A. F., & Rogal, P. J. (2015). Cognitive screening in persons with chronic diseases in primary care: Challenges and recommendations for practice. American Journal of Alzheimer's Disease & Other Dementias®, 30(6), 547-558. https://doi.org/10.1177/1533317515577127
Bhidayasiri, R., & Martinez-Martin, P. (2017). Clinical assessments in Parkinson's disease: scales and monitoring. International Review of Neurobiology, 132, 129-182. https://doi.org/10.1016/bs.irn.2017.01.001
Boulos, M. N. K., Wheeler, S., Tavares, C., & Jones, R. (2011). How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX. Biomedical Engineering Online, 10(1), 1-14. https://doi.org/10.1186/1475-925X-10-24
Chen, K. H., Lin, P. C., Yang, B. S., & Chen, Y. J. (2018). The difference in visuomotor feedback velocity control during spiral drawing between Parkinson’s disease and essential tremor. Neurological Sciences, 39(6), 1057-1063. https://doi.org/10.1007/s10072-018-3331-4
Clifford, G. D., & Clifton, D. (2012). Wireless technology in disease management and medicine. Annual Review of Medicine, 63, 479-492. https://doi.org/10.1146/annurev-med-051210-114650
Doherty, S. T., & Oh, P. (2012). A multi-sensor monitoring system of human physiology and daily activities. Telemedicine and e-Health, 18(3), 185-192. https://doi.org/10.1089/tmj.2011.0138
Dufau, S., Duñabeitia, J. A., Moret-Tatay, C., McGonigal, A., Peeters, D., Alario, F. X., ... & Grainger, J. (2011). Smart phone, smart science: how the use of smartphones can revolutionize research in cognitive science. PloS One, 6(9), e24974. https://doi.org/10.1371/journal.pone.0024974
Fortney, J. C., Burgess, J. F., Bosworth, H. B., Booth, B. M., & Kaboli, P. J. (2011). A re-conceptualization of access for 21st century healthcare. Journal of General Internal Medicine, 26(2), 639-647. https://doi.org/10.1007/s11606-011-1806-6
Gill, P. S., Kamath, A., & Gill, T. S. (2012). Distraction: an assessment of smartphone usage in health care work settings. Risk Management and Healthcare Policy, 5, 105. https://doi.org/10.2147/RMHP.S34813
Hindle, J. V. (2010). Ageing, neurodegeneration and Parkinson’s disease. Age and Ageing, 39(2), 156-161. https://doi.org/10.1093/ageing/afp223
Klasnja, P., & Pratt, W. (2012). Healthcare in the pocket: mapping the space of mobile-phone health interventions. Journal of Biomedical Informatics, 45(1), 184-198. https://doi.org/10.1016/j.jbi.2011.08.017
Koch, S. (2006). Home telehealth—current state and future trends. International Journal of Medical Informatics, 75(8), 565-576. https://doi.org/10.1016/j.ijmedinf.2005.09.002
Kruizinga, M. D., Stuurman, F. E., Exadaktylos, V., Doll, R. J., Stephenson, D. T., Groeneveld, G. J., ... & Cohen, A. F. (2020). Development of novel, value-based, digital endpoints for clinical trials: a structured approach toward fit-for-purpose validation. Pharmacological Reviews, 72(4), 899-909. https://doi.org/10.1124/pr.120.000028
Levine, S. R., & Gorman, M. (1999). “Telestroke” the application of telemedicine for stroke. Stroke, 30(2), 464-469. https://doi.org/10.1161/01.str.30.2.464
Lin, P. C., Chen, K. H., Yang, B. S., & Chen, Y. J. (2018). A digital assessment system for evaluating kinetic tremor in essential tremor and Parkinson’s disease. BMC Neurology, 18(1), 1-8. https://doi.org/10.1186/s12883-018-1027-2
Lopez-de-Ipina, K., Solé-Casals, J., Faúndez-Zanuy, M., Calvo, P. M., Sesa, E., Roure, J., ... & Bergareche, A. (2018). Automatic analysis of Archimedes’ spiral for characterization of genetic essential tremor based on Shannon’s entropy and fractal dimension. Entropy, 20(7), 531. https://doi.org/10.3390/e20070531
Louis, E. D. (2009). Essential tremors: a family of neurodegenerative disorders?. Archives of Neurology, 66(10), 1202-1208. https://doi.org/10.1001/archneurol.2009.217
Ludlow, C. L., Connor, N. P., & Bassich, C. J. (1987). Speech timing in Parkinson's and Huntington's disease. Brain and Language, 32(2), 195-214. https://doi.org/10.1016/0093-934x(87)90124-6
Mack, J., & Marsh, L. (2017). Parkinson’s disease: cognitive impairment. Focus, 15(1), 42-54. https://doi.org/10.1176/appi.focus.20160043
Maglione, J. E., Liu, L., Neikrug, A. B., Poon, T., Natarajan, L., Calderon, J., ... & Ancoli-Israel, S. (2013). Actigraphy for the assessment of sleep measures in Parkinson's disease. Sleep, 36(8), 1209-1217.. https://doi.org/10.5665/sleep.2888
Marder, K., Zhao, H., Eberly, S., Tanner, C. M., Oakes, D., & Shoulson, I. (2009). Dietary intake in adults at risk for Huntington disease: analysis of PHAROS research participants. Neurology, 73(5), 385-392. https://doi.org/10.1212/WNL.0b013e3181b04aa2
Martin, C. K., Correa, J. B., Han, H., Allen, H. R., Rood, J. C., Champagne, C. M., ... & Bray, G. A. (2012). Validity of the Remote Food Photography Method (RFPM) for estimating energy and nutrient intake in near real‐time. Obesity, 20(4), 891-899. https://doi.org/10.1038/oby.2011.344
Mellone, S., Tacconi, C., Schwickert, L., Klenk, J., Becker, C., & Chiari, L. (2012). Smartphone-based solutions for fall detection and prevention: the FARSEEING approach. Zeitschrift für Gerontologie und Geriatrie, 45(8), 722-727. https://doi.org/10.1007/s00391-012-0404-5
Mellone, S., Tacconi, C., & Chiari, L. (2012). Validity of a Smartphone-based instrumented Timed Up and Go. Gait & Posture, 36(1), 163-165. https://doi.org/10.1016/j.gaitpost.2012.02.006
Miller, D. B., & O’Callaghan, J. P. (2015). Biomarkers of Parkinson’s disease: present and future. Metabolism, 64(3), S40-S46. https://doi.org/10.1016/j.metabol.2014.10.030
Olshansky, S. J., Goldman, D. P., Zheng, Y., & Rowe, J. W. (2009). Aging in America in the twenty‐first century: demographic forecasts from the MacArthur Foundation Research Network on an aging society. The Milbank Quarterly, 87(4), 842-862. https://doi.org/10.1111/j.1468-0009.2009.00581.x
Owens, A. P., Hinds, C., Manyakov, N. V., Stavropoulos, T. G., Lavelle, G., Gove, D., ... & Aarsland, D. (2020). Selecting remote measurement technologies to optimize assessment of function in early Alzheimer's disease: a case study. Frontiers in Psychiatry, 1163. https://doi.org/10.3389/fpsyt.2020.582207
Panayides, A., Pattichis, M. S., Pattichis, C. S., Schizas, C. N., Spanias, A., & Kyriacou, E. (2010). An overview of recent end-to-end wireless medical video telemedicine systems using 3G. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology (pp. 1045-1048). IEEE. https://doi.org/10.1109/IEMBS.2010.5628076
Pereira, C. R., Pereira, D. R., Weber, S. A., Hook, C., De Albuquerque, V. H. C., & Papa, J. P. (2019). A survey on computer-assisted Parkinson's disease diagnosis. Artificial Intelligence in Medicine, 95, 48-63. https://doi.org/10.1016/j.artmed.2018.08.007
Rajan, R. (2012). The promise of wireless: an overview of a device-to-cloud mHealth solution. Biomedical Instrumentation & Technology, 46(2), 26. https://doi.org/10.2345/0899-8205-46.s2.26
Rosser, J. C., Gabriel, N., Herman, B. A., & Murayama, M. (2001). Telementoring and teleproctoring. World Journal of Surgery, 25(11), 1438-1448. https://doi.org/10.1007/s00268-001-0129-x
Rogante, M., Grigioni, M., Cordella, D., & Giacomozzi, C. (2010). Ten years of telerehabilitation: A literature overview of technologies and clinical applications. NeuroRehabilitation, 27(4), 287-304. https://doi.org/10.3233/NRE-2010-0612
San Luciano, M., Wang, C., Ortega, R. A., Yu, Q., Boschung, S., Soto-Valencia, J., ... & Saunders-Pullman, R. (2016). Digitized spiral drawing: A possible biomarker for early Parkinson’s disease. PloS One, 11(10), e0162799. https://doi.org/10.1371/journal.pone.0162799
Scharre, D. W., Chang, S. I., Murden, R. A., Lamb, J., Beversdorf, D. Q., Kataki, M., ... & Bornstein, R. A. (2010). Self-administered Gerocognitive Examination (SAGE): a brief cognitive assessment Instrument for mild cognitive impairment (MCI) and early dementia. Alzheimer Disease & Associated Disorders, 24(1), 64-71. https://doi.org/10.1097/WAD.0b013e3181b03277
Sisti, J. A., Christophe, B., Seville, A. R., Garton, A. L., Gupta, V. P., Bandin, A. J., ... & Pullman, S. L. (2017). Computerized spiral analysis using the iPad. Journal of Neuroscience Methods, 275, 50-54. https://doi.org/10.1016/j.jneumeth.2016.11.004
Solé-Casals, J., Anchustegui-Echearte, I., Marti-Puig, P., Calvo, P. M., Bergareche, A., Sánchez-Méndez, J. I., & Lopez-de-Ipina, K. (2019). Discrete cosine transform for the analysis of essential tremor. Frontiers in Physiology, 9, 1947. https://doi.org/10.3389/fphys.2018.01947
van Wamelen, D. J., Martinez‐Martin, P., Weintraub, D., Schrag, A., Antonini, A., Falup‐Pecurariu, C., ... & International Parkinson and Movement Disorder Society Parkinson's Disease Non‐Motor Study Group. (2021). The Non‐Motor Symptoms Scale in Parkinson’s disease: Validation and use. Acta Neurologica Scandinavica, 143(1), 3-12.. https://doi.org/10.1111/ane.13336
Worringham, C., Rojek, A., & Stewart, I. (2011). Development and feasibility of a smartphone, ECG and GPS based system for remotely monitoring exercise in cardiac rehabilitation. PloS One, 6(2), e14669.. https://doi.org/10.1371/journal.pone.0014669
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2022 Wael Mohamed, Shahedah Koya Kutty, Akram Khedher, Indranath Chatterjee
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The observations and associated materials published or posted by NeurosciRN are licensed by the authors for use and distribution in accord with the Creative Commons Attribution license CC BY-NC 4.0 international, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
