Parkinson Assessment with Intermittent Connectivity: Delayed Upload, Reliable Records and Patient Data Costs

Abstract Remote Parkinson assessment should not assume that a reliable internet connection is available whenever a participant can complete a task. Separating local acquisition from later upload may improve participation, but introduces questions about delayed review, record integrity, storage and household data payments. This methods perspective combines targeted narrative evidence with original reliability and data-volume calculations. Under an explicitly hypothetical model with independent daily connection opportunities, a 20% daily probability of successful delivery produces a 48.8% probability of delivery within three days and 79.0% within seven days. Those values demonstrate the difference between eventual transfer and timely clinical availability; they are not observed network statistics. A second analysis assumes 30 days of four-megabyte daily payloads and compares daily with seven-day batching. With a fixed 0.05-megabyte overhead per request, batching reduces the no-retry transfer volume from 121.5 to 120.25 megabytes, showing that batching alone does not remove the dominant payload. The framework distinguishes acquisition time, receipt time and clinical review, and proposes explicit states for stored, acknowledged, rejected and unresolved records. Patient affordability is evaluated through actual data payments, repeated attempts, access to unmetered connections and any travel required for synchronization. Provider resources inform the financing needed to maintain low patient charges. Offline-capable acquisition is a candidate design strategy, not evidence of an implemented NeuralCipher architecture or a clinically validated monitoring service. Its value requires prospective evidence that people with limited resources can complete appropriate assessment reliably without hidden connectivity costs or misleading expectations of immediate review. Article details NeuralCipherai; Kadir Tamrak; Salih Yaldız; Feride Yaldız; Ömer Ağyol; Yavuz Selim Silay; Hasan Randa Methods perspective with selected narrative evidence and transparent quantitative analysis An intermittent-connectivity evaluation framework with original delivery-deadline and data-volume calculations, explicit record states and direct-payment outcomes. Project: NeuralCipher. Bibliographic records are supplied in BibTeX and RIS formats for reference-management software, including Zotero. Reproducible calculations and figure data accompany the article. References Adams, J. L., Kangarloo, T., Tracey, B., O’Donnell, P., Volfson, D., Latzman, R. D., Zach, N., Alexander, R., Bergethon, P., Cosman, J., Anderson, D., Best, A., Severson, J., Kostrzebski, M. A., Auinger, P., Wilmot, P., Pohlson, Y., Waddell, E., Jensen-Roberts, S., . . . the Parkinson Study Group Watch-PD Study Investigators and Collaborators. (2023). Using a smartwatch and smartphone to assess early Parkinson’s disease in the WATCH-PD study. npj Parkinson's Disease, 9(1), Article 64. https://doi.org/10.1038/s41531-023-00497-x Arora, S., Baghai-Ravary, L., & Tsanas, A. (2019). Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice. The Journal of the Acoustical Society of America, 145(5), 2871–2884. https://doi.org/10.1121/1.5100272 Beck, C. A., Beran, D. B., Biglan, K. M., Boyd, C. M., Dorsey, E. R., Schmidt, P. N., Simone, R., Willis, A. W., Galifianakis, N. B., Katz, M., Tanner, C. M., Dodenhoff, K., Aldred, J., Carter, J., Fraser, A., Jimenez-Shahed, J., Hunter, C., Spindler, M., Reichwein, S., . . . Connect.Parkinson Investigators. (2017). National randomized controlled trial of virtual house calls for Parkinson disease. Neurology, 89(11), 1152–1161. https://doi.org/10.1212/WNL.0000000000004357 Bot, B. M., Suver, C., Neto, E. C., Kellen, M., Klein, A., Bare, C., Doerr, M., Pratap, A., Wilbanks, J., Dorsey, E. R., Friend, S. H., & Trister, A. D. (2016). The mPower study, Parkinson disease mobile data collected using ResearchKit. Scientific Data, 3(1), Article 160011. https://doi.org/10.1038/sdata.2016.11 Brunette, W., Vigil, M., Pervaiz, F., Levari, S., Borriello, G., & Anderson, R. (2015). Optimizing Mobile Application Communication for Challenged Network Environments. In Proceedings of the 2015 Annual Symposium on Computing for Development (pp. 167-175). Association for Computing Machinery. https://doi.org/10.1145/2830629.2830644 Burq, M., Rainaldi, E., Ho, K. C., Chen, C., Bloem, B. R., Evers, L. J. W., Helmich, R. C., Myers, L., Marks, W. J., & Kapur, R. (2022). Virtual exam for Parkinson’s disease enables frequent and reliable remote measurements of motor function. npj Digital Medicine, 5(1), 65. https://doi.org/10.1038/s41746-022-00607-8 Canoro, V., Pilotto, A., Lena, F., Fioravanti, V., Longo, C., Schirinzi, T., Picillo, M., Pellecchia, M. T., Sorrentino, C., Cavallieri, F., Padovani, A., Malaguti, M. C., Modugno, N., Barone, P., & Erro, R. (2025). Digital Inclusion in Parkinson's Disease: A Case–Control Study. Movement Disorders Clinical Practice, 12(11), 1873-1881. https://doi.org/10.1002/mdc3.70171 Durmaz Celik, N., Yaman Kula, A., Murat, N., Kuzu Kumcu, M., Topal, A., & Özkan, S. (2025). Evaluation of compliance and accuracy in Parkinson’s disease motor symptom tracking: a comparative study of digital and traditional paper diaries using a smartphone application (MyParkinson’s). Frontiers in Neurology, 16, 1522721. https://doi.org/10.3389/fneur.2025.1522721 Eysenbach, G., & CONSORT-EHEALTH Group. (2011). CONSORT-EHEALTH: Improving and standardizing evaluation reports of web-based and mobile health interventions. Journal of Medical Internet Research, 13(4), e126. https://doi.org/10.2196/jmir.1923 Fall, K. (2003). A delay-tolerant network architecture for challenged internets. In Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications (pp. 27-34). Association for Computing Machinery. https://doi.org/10.1145/863955.863960 Han, J., Tian, Z., Wu, J., Zhang, K., Li, S., Baig, F., Liu, P., Vaidyanathan, R., Morgante, F., & Huo, W. (2026). Deep learning-enabled accurate assessment of gait impairments in Parkinson’s disease using smartphone videos. npj Digital Medicine, 9, 98. https://doi.org/10.1038/s41746-025-02150-8 Harris, P. A., Delacqua, G., Taylor, R., Pearson, S., Fernandez, M., & Duda, S. N. (2021). The REDCap Mobile Application: a data collection platform for research in regions or situations with internet scarcity. JAMIA Open, 4(3), ooab078. https://doi.org/10.1093/jamiaopen/ooab078 Hartung, C., Lerer, A., Anokwa, Y., Tseng, C., Brunette, W., & Borriello, G. (2010). Open Data Kit: Tools to build information services for developing regions. In Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development (pp. 1-12). Association for Computing Machinery. https://doi.org/10.1145/2369220.2369236 Husereau, D., Drummond, M., Augustovski, F., de Bekker-Grob, E., Briggs, A. H., Carswell, C., Caulley, L., Chaiyakunapruk, N., Greenberg, D., Loder, E., Mauskopf, J., Mullins, C. D., Petrou, S., Pwu, R. F., Staniszewska, S., & CHEERS 2022 ISPOR Good Research Practices Task Force. (2022). Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: Updated reporting guidance for health economic evaluations. BMJ, 376, e067975. https://doi.org/10.1136/bmj-2021-067975 Kangarloo, T., Latzman, R. D., Adams, J. L., Dorsey, R., Kostrzebski, M., Severson, J., Anderson, D., Horak, F., Stephenson, D., & Cosman, J. (2024). Acceptability of digital health technologies in early Parkinson's disease: lessons from WATCH-PD. Frontiers in Digital Health, 6, 1435693. https://doi.org/10.3389/fdgth.2024.1435693 Lee, K. J., Tilling, K. M., Cornish, R. P., Little, R. J. A., Bell, M. L., Goetghebeur, E., Hogan, J. W., & Carpenter, J. R. (2021). Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework. Journal of Clinical Epidemiology, 134, 79–88. https://doi.org/10.1016/j.jclinepi.2021.01.008 Little, R. J., D'Agostino, R., Cohen, M. L., Dickersin, K., Emerson, S. S., Farrar, J. T., Frangakis, C., Hogan, J. W., Molenberghs, G., Murphy, S. A., Neaton, J. D., Rotnitzky, A., Scharfstein, D., Shih, W. J., Siegel, J. P., & Stern, H. (2012). The Prevention and Treatment of Missing Data in Clinical Trials. New England Journal of Medicine, 367(14), 1355-1360. https://doi.org/10.1056/NEJMsr1203730 Mantri, S., & Mitchell, K. T. (2021). Falling down the digital divide: A cautionary tale. Parkinsonism & Related Disorders, 93, 33-34. https://doi.org/10.1016/j.parkreldis.2021.10.032 Omberg, L., Chaibub Neto, E., Perumal, T. M., Pratap, A., Tediarjo, A., Adams, J., Bloem, B. R., Bot, B. M., Elson, M., Goldman, S. M., Kellen, M. R., Kieburtz, K., Klein, A., Little, M. A., Schneider, R., Suver, C., Tarolli, C., Tanner, C. M., Trister, A. D., . . . Mangravite, L. M. (2022). Remote smartphone monitoring of Parkinson’s disease and individual response to therapy. Nature Biotechnology, 40(4), 480-487. https://doi.org/10.1038/s41587-021-00974-9 Pearson, C., Hartzman, A., Munevar, D., Feeney, M., Dolhun, R., Todaro, V., Rosenfeld, S., Willis, A., & Beck, J. C. (2023). Care access and utilization among medicare beneficiaries living with Parkinson’s disease. npj Parkinson's Disease, 9(1), 108. https://doi.org/10.1038/s41531-023-00523-y Sanders, G. D., Neumann, P. J., Basu, A., Brock, D. W., Feeny, D., Krahn, M., Kuntz, K. M., Meltzer, D. O., Owens, D. K., Prosser, L. A., Salomon, J. A., Sculpher, M. J., Trikalinos, T. A., Russell, L. B., Siegel, J. E., & Ganiats, T. G. (2016). Recommendations for Conduct, Methodological Practices, and Reporting of Cost-effectiveness Analyses: Second Panel on Cost-Effectiveness in Health and Medicine. JAMA, 316(10), 1093–1103. https://doi.org/10.1001/jama

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Zenodo (CERN European Organization for Nuclear Research)
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2026-09-16
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Neurological disorders and treatments
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preprint

Parkinson Assessment with Intermittent Connectivity: Delayed Upload, Reliable Records and Patient Data Costs

Feride Yaldiz, Yavuz Selim Sılay, Kadir Tamrak, Salih Yaldız et al.
Zenodo (CERN European Organization for Nuclear Research)
Neurological disorders and treatments
preprint

Parkinson Assessment with Intermittent Connectivity: Delayed Upload, Reliable Records and Patient Data Costs

Feride Yaldiz, Yavuz Selim Sılay, Kadir Tamrak, Salih Yaldız, NeuralCipherai, Hasan Randa, Ömer Ağyol
preprint en

Abstract

Abstract Remote Parkinson assessment should not assume that a reliable internet connection is available whenever a participant can complete a task. Separating local acquisition from later upload may improve participation, but introduces questions about delayed review, record integrity, storage and household data payments. This methods perspective combines targeted narrative evidence with original reliability and data-volume calculations. Under an explicitly hypothetical model with independent daily connection opportunities, a 20% daily probability of successful delivery produces a 48.8% probability of delivery within three days and 79.0% within seven days. Those values demonstrate the difference between eventual transfer and timely clinical availability; they are not observed network statistics. A second analysis assumes 30 days of four-megabyte daily payloads and compares daily with seven-day batching. With a fixed 0.05-megabyte overhead per request, batching reduces the no-retry transfer volume from 121.5 to 120.25 megabytes, showing that batching alone does not remove the dominant payload. The framework distinguishes acquisition time, receipt time and clinical review, and proposes explicit states for stored, acknowledged, rejected and unresolved records. Patient affordability is evaluated through actual data payments, repeated attempts, access to unmetered connections and any travel required for synchronization. Provider resources inform the financing needed to maintain low patient charges. Offline-capable acquisition is a candidate design strategy, not evidence of an implemented NeuralCipher architecture or a clinically validated monitoring service. Its value requires prospective evidence that people with limited resources can complete appropriate assessment reliably without hidden connectivity costs or misleading expectations of immediate review. Article details NeuralCipherai; Kadir Tamrak; Salih Yaldız; Feride Yaldız; Ömer Ağyol; Yavuz Selim Silay; Hasan Randa Methods perspective with selected narrative evidence and transparent quantitative analysis An intermittent-connectivity evaluation framework with original delivery-deadline and data-volume calculations, explicit record states and direct-payment outcomes. Project: NeuralCipher. Bibliographic records are supplied in BibTeX and RIS formats for reference-management software, including Zotero. Reproducible calculations and figure data accompany the article. References Adams, J. L., Kangarloo, T., Tracey, B., O’Donnell, P., Volfson, D., Latzman, R. D., Zach, N., Alexander, R., Bergethon, P., Cosman, J., Anderson, D., Best, A., Severson, J., Kostrzebski, M. A., Auinger, P., Wilmot, P., Pohlson, Y., Waddell, E., Jensen-Roberts, S., . . . the Parkinson Study Group Watch-PD Study Investigators and Collaborators. (2023). Using a smartwatch and smartphone to assess early Parkinson’s disease in the WATCH-PD study. npj Parkinson's Disease, 9(1), Article 64. https://doi.org/10.1038/s41531-023-00497-x Arora, S., Baghai-Ravary, L., & Tsanas, A. (2019). Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice. The Journal of the Acoustical Society of America, 145(5), 2871–2884. https://doi.org/10.1121/1.5100272 Beck, C. A., Beran, D. B., Biglan, K. M., Boyd, C. M., Dorsey, E. R., Schmidt, P. N., Simone, R., Willis, A. W., Galifianakis, N. B., Katz, M., Tanner, C. M., Dodenhoff, K., Aldred, J., Carter, J., Fraser, A., Jimenez-Shahed, J., Hunter, C., Spindler, M., Reichwein, S., . . . Connect.Parkinson Investigators. (2017). National randomized controlled trial of virtual house calls for Parkinson disease. Neurology, 89(11), 1152–1161. https://doi.org/10.1212/WNL.0000000000004357 Bot, B. M., Suver, C., Neto, E. C., Kellen, M., Klein, A., Bare, C., Doerr, M., Pratap, A., Wilbanks, J., Dorsey, E. R., Friend, S. H., & Trister, A. D. (2016). The mPower study, Parkinson disease mobile data collected using ResearchKit. Scientific Data, 3(1), Article 160011. https://doi.org/10.1038/sdata.2016.11 Brunette, W., Vigil, M., Pervaiz, F., Levari, S., Borriello, G., & Anderson, R. (2015). Optimizing Mobile Application Communication for Challenged Network Environments. In Proceedings of the 2015 Annual Symposium on Computing for Development (pp. 167-175). Association for Computing Machinery. https://doi.org/10.1145/2830629.2830644 Burq, M., Rainaldi, E., Ho, K. C., Chen, C., Bloem, B. R., Evers, L. J. W., Helmich, R. C., Myers, L., Marks, W. J., & Kapur, R. (2022). Virtual exam for Parkinson’s disease enables frequent and reliable remote measurements of motor function. npj Digital Medicine, 5(1), 65. https://doi.org/10.1038/s41746-022-00607-8 Canoro, V., Pilotto, A., Lena, F., Fioravanti, V., Longo, C., Schirinzi, T., Picillo, M., Pellecchia, M. T., Sorrentino, C., Cavallieri, F., Padovani, A., Malaguti, M. C., Modugno, N., Barone, P., & Erro, R. (2025). Digital Inclusion in Parkinson's Disease: A Case–Control Study. Movement Disorders Clinical Practice, 12(11), 1873-1881. https://doi.org/10.1002/mdc3.70171 Durmaz Celik, N., Yaman Kula, A., Murat, N., Kuzu Kumcu, M., Topal, A., & Özkan, S. (2025). Evaluation of compliance and accuracy in Parkinson’s disease motor symptom tracking: a comparative study of digital and traditional paper diaries using a smartphone application (MyParkinson’s). Frontiers in Neurology, 16, 1522721. https://doi.org/10.3389/fneur.2025.1522721 Eysenbach, G., & CONSORT-EHEALTH Group. (2011). CONSORT-EHEALTH: Improving and standardizing evaluation reports of web-based and mobile health interventions. Journal of Medical Internet Research, 13(4), e126. https://doi.org/10.2196/jmir.1923 Fall, K. (2003). A delay-tolerant network architecture for challenged internets. In Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications (pp. 27-34). Association for Computing Machinery. https://doi.org/10.1145/863955.863960 Han, J., Tian, Z., Wu, J., Zhang, K., Li, S., Baig, F., Liu, P., Vaidyanathan, R., Morgante, F., & Huo, W. (2026). Deep learning-enabled accurate assessment of gait impairments in Parkinson’s disease using smartphone videos. npj Digital Medicine, 9, 98. https://doi.org/10.1038/s41746-025-02150-8 Harris, P. A., Delacqua, G., Taylor, R., Pearson, S., Fernandez, M., & Duda, S. N. (2021). The REDCap Mobile Application: a data collection platform for research in regions or situations with internet scarcity. JAMIA Open, 4(3), ooab078. https://doi.org/10.1093/jamiaopen/ooab078 Hartung, C., Lerer, A., Anokwa, Y., Tseng, C., Brunette, W., & Borriello, G. (2010). Open Data Kit: Tools to build information services for developing regions. In Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development (pp. 1-12). Association for Computing Machinery. https://doi.org/10.1145/2369220.2369236 Husereau, D., Drummond, M., Augustovski, F., de Bekker-Grob, E., Briggs, A. H., Carswell, C., Caulley, L., Chaiyakunapruk, N., Greenberg, D., Loder, E., Mauskopf, J., Mullins, C. D., Petrou, S., Pwu, R. F., Staniszewska, S., & CHEERS 2022 ISPOR Good Research Practices Task Force. (2022). Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: Updated reporting guidance for health economic evaluations. BMJ, 376, e067975. https://doi.org/10.1136/bmj-2021-067975 Kangarloo, T., Latzman, R. D., Adams, J. L., Dorsey, R., Kostrzebski, M., Severson, J., Anderson, D., Horak, F., Stephenson, D., & Cosman, J. (2024). Acceptability of digital health technologies in early Parkinson's disease: lessons from WATCH-PD. Frontiers in Digital Health, 6, 1435693. https://doi.org/10.3389/fdgth.2024.1435693 Lee, K. J., Tilling, K. M., Cornish, R. P., Little, R. J. A., Bell, M. L., Goetghebeur, E., Hogan, J. W., & Carpenter, J. R. (2021). Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework. Journal of Clinical Epidemiology, 134, 79–88. https://doi.org/10.1016/j.jclinepi.2021.01.008 Little, R. J., D'Agostino, R., Cohen, M. L., Dickersin, K., Emerson, S. S., Farrar, J. T., Frangakis, C., Hogan, J. W., Molenberghs, G., Murphy, S. A., Neaton, J. D., Rotnitzky, A., Scharfstein, D., Shih, W. J., Siegel, J. P., & Stern, H. (2012). The Prevention and Treatment of Missing Data in Clinical Trials. New England Journal of Medicine, 367(14), 1355-1360. https://doi.org/10.1056/NEJMsr1203730 Mantri, S., & Mitchell, K. T. (2021). Falling down the digital divide: A cautionary tale. Parkinsonism & Related Disorders, 93, 33-34. https://doi.org/10.1016/j.parkreldis.2021.10.032 Omberg, L., Chaibub Neto, E., Perumal, T. M., Pratap, A., Tediarjo, A., Adams, J., Bloem, B. R., Bot, B. M., Elson, M., Goldman, S. M., Kellen, M. R., Kieburtz, K., Klein, A., Little, M. A., Schneider, R., Suver, C., Tarolli, C., Tanner, C. M., Trister, A. D., . . . Mangravite, L. M. (2022). Remote smartphone monitoring of Parkinson’s disease and individual response to therapy. Nature Biotechnology, 40(4), 480-487. https://doi.org/10.1038/s41587-021-00974-9 Pearson, C., Hartzman, A., Munevar, D., Feeney, M., Dolhun, R., Todaro, V., Rosenfeld, S., Willis, A., & Beck, J. C. (2023). Care access and utilization among medicare beneficiaries living with Parkinson’s disease. npj Parkinson's Disease, 9(1), 108. https://doi.org/10.1038/s41531-023-00523-y Sanders, G. D., Neumann, P. J., Basu, A., Brock, D. W., Feeny, D., Krahn, M., Kuntz, K. M., Meltzer, D. O., Owens, D. K., Prosser, L. A., Salomon, J. A., Sculpher, M. J., Trikalinos, T. A., Russell, L. B., Siegel, J. E., & Ganiats, T. G. (2016). Recommendations for Conduct, Methodological Practices, and Reporting of Cost-effectiveness Analyses: Second Panel on Cost-Effectiveness in Health and Medicine. JAMA, 316(10), 1093–1103. https://doi.org/10.1001/jama

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