Smartphones for In-home Diagnostics in Telemedicine
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.
[1] Španiel, F., et al.: ITAREPS: Information Technology Aided Relapse
Prevention Programme in Schizophrenia. Schizophrenia Research Volume
98. Issues 1-3. 2008. 312-317.
[2] Španiel, 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.
[1] Španiel, F., et al.: ITAREPS: Information Technology Aided Relapse
Prevention Programme in Schizophrenia. Schizophrenia Research Volume
98. Issues 1-3. 2008. 312-317.
[2] Španiel, 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.
@article{"International Journal of Medical, Medicine and Health Sciences:62654", author = "Nálevka Petr", title = "Smartphones for In-home Diagnostics in Telemedicine", 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 = "Smartphones, Actigraphy, Telemedicine, In-home Diagnostics", volume = "6", number = "1", pages = "25-5", }