Authors: Nálevka Petr

Abstract:

Many contemporary telemedical applications rely on regular consultations over the phone or video conferencing which consumes valuable resources such as the time of the doctors. Some applications or treatments allow automated diagnostics on the patient side which only notifies the doctors in case a significant worsening of patient’s condition is measured. Such programs can save valuable resources but an important implementation issue is how to ensure effective and cheap diagnostics on the patient side. First, specific diagnostic devices on patient side are expensive and second, they need to be user-˜friendly to encourage patient’s cooperation and reduce errors in usage which may cause noise in diagnostic data. This article proposes the use of modern smartphones and various build-in or attachable sensors as universal diagnostic devices applicable in a wider range of telemedical programs and demonstrates their application on a case-study – a program for schizophrenic relapse prevention.

Keywords: Telemedicine, smartphones, actigraphy, In-home Diagnostics

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1081499

Downloads 1490

References:

[1] ┼ápaniel, F., et al.: ITAREPS: Information Technology Aided Relapse Prevention Programme in Schizophrenia. Schizophrenia Research Volume 98. Issues 1-3. 2008. 312-317.[2] ┼ápaniel, F., et al.: The Information Technology Aided Relapse Prevention Programme in Schizo- phrenia: an extension of a mirror-design followup, International Journal of Clinical Practice Volume 62, Issue 12. 2008. 1943-1946.[3] Nalevka P.: Predicting Relapse of Schizophrenia. Proceedings of the 23th International Congress of the European Federation for Medical Informatics. Oslo, 2011.[4] Hrdlicka J., Klema J.: Schizophrenia prediction with the adaboost algorithm. Proceedings of the 23th International Congress of the European Federation for Medical Informatics. Oslo, 2011.[5] Subotnik, K., Nuechterlein, K.: Prodromal signs and symptoms of schizophrenic relapse. Journal of Abnormal Psychology, Vol 97(4), 1988, 405-312.[6] Haug, HJ., Wirz-Justice A., Rössler W.: Actigraphy to measure day structure as a therapeutic variable in the treatment of schizophrenic patients. Acta Psychiatrica Scandinavica. Munksgaard Inter- national Publishers. 2000.[7] Wulff K., Joyce E., Middleton B., Dijk DJ., Foster R.: The suitability of actigraphy, diary data, and urinary melatonin profiles for quantitative assessment of sleep disturbances in schizophrenia. A case report, Vol. 23, No. 1-2 , 2006. 485-495.[8] Harvey A.: Sleep and Circadian Rhythms in Bipolar Disorder: Seeking Synchrony, Harmony, and Regulation. Am J Psychiatry. 2008. 165:820- 829.[9] Srinivasan R., Chen C., Cook D.: Activity Recognition using Actigraph Sensor. Proceedings of the International workshop on Knowledge Discovery from Sensor Data, 2010.[10] Prociow P., Crowe J.: Development of mobile psychiatry for bipolar disorder patients. Conference Proceedings of the International Conference of IEEE Engineering in Medicine and Biology Society. 2010. 5484-5487[11] Ancoli-Israel S., Cole R., Alessi C. et al.: The role of actigraphy in the study of sleep and circadian rhythms. American Academy of Sleep Medicine Review Paper. 2003. 26(3):342-92.[12] Srinivasan R., Chen C., Cook D.: Activity Recognition using Actigraph Sensor. Proceedings of the International workshop on Knowledge Discovery from Sensor Data, 2010.[13] Herz MI, Melville C.: Relapse in schizophrenia. American Journal of Psychiatry. 1980. 137(7):801-5.