“Mobile Nurse” Platform for Ubiquitous Medicine

Z. R. Struzik; K. Yoshiuchi; M. Sone; T. Ishikawa; H. Kikuchi; H. Kumano; T. Watsuji; B. H. Natelson; Y. Yamamoto

doi:10.1055/s-0038-1625391

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2007; 46(02): 130-134
DOI: 10.1055/s-0038-1625391

Original Article

Schattauer GmbH

“Mobile Nurse” Platform for Ubiquitous Medicine

Authors

Z. R. Struzik

¹Educational Physiology Laboratory, Graduate School of Education, The University of Tokyo, Tokyo, Japan
K. Yoshiuchi

²Department of Psychosomatic Medicine, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
M. Sone

³Corporate Research and Development Group, Sharp Corporation, Nara, Japan
T. Ishikawa

³Corporate Research and Development Group, Sharp Corporation, Nara, Japan
H. Kikuchi

²Department of Psychosomatic Medicine, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
H. Kumano

²Department of Psychosomatic Medicine, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
T. Watsuji

³Corporate Research and Development Group, Sharp Corporation, Nara, Japan
B. H. Natelson

⁴Department of Neurosciences, UMDNJ-New Jersey Medical School, East Orange, NJ, USA
Y. Yamamoto

¹Educational Physiology Laboratory, Graduate School of Education, The University of Tokyo, Tokyo, Japan

Abstract

Summary

Objectives : We introduce “Mobile Nurse" (MN) - an emerging platform for the practice of ubiquitous medicine.

Methods : By implementing in a dynamic setting of daily life the patient care traditionally provided by the clinical nurses on duty, MN aims at integral data collection and shortening the response time to the patient. MN is also capable of intelligent interaction with the patient and is able to learn from the patient's behavior and disease sign evaluation for improved personalized treatment.

Results : In this paper, we outline the most essential concepts around the hardware, software and methodological designs of MN. We provide an example of the implementation, and elaborate on the possible future impact on medical practice and biomedical science research.

Conclusions : The main innovation of MN, setting it apart from current tele-medicine systems, is the ability to integrate the patient's signs and symptoms on site, providing medical professionals with powerfultools to elucidate disease mechanisms, to make proper diagnoses and to prescribe treatment.

Keywords

Ubiquitous computing - ecological momentary assessment (EMA) - real-time analysis - wearable sensors - wireless technologies

Full Text

References

References
1. Istepanian R, Jovanov E, Zhang Y. Guest editorial introduction to the special section on m-health: beyond seamless mobility and global wireless health-care connectivity. IEEE Trans Inform Technol Biomed 2004; 4: 405-414.
2. Stone A, Shiffman S. Ecological momentary assessment (ema) in a behavioral medicine. Ann Behav Med 1994; 16: 199-202.
3. McMair D, Lorr M, Dropplemann L. Profile of Mood States (POMS). Revised ed.San Diego: Educational and Industrial Testing Service; 1992
4. Stone A, Broderick J, Shiftman S, Schwartz J. Understanding recall of weekly pain from a momentary assessment perspective: absolute agreement, between- and within-person consistency, and judged change in weekly pain. Pain 2004; 107: 61-69.
5. Stone A, Shiffman S, Schwartz J, Broderick J, Hufford M. Patient non-compliance with paper diaries. BMJ 1994; 324: 1193-1194.
6. Teicher M. Actigraphy and motion analysis - new tools for psychiatry, Harvard Rev Psychiatry. 1995 3. 18-35
7. Wolff EI, Putnam F, Post R. Motor activity and affective illness: the relationship of amplitude and temporal distribution to changes in affective state. Arch Gen Psychiatry 1985; 42: 288-294.
8. Ohashi K, Yamamoto Y, Teicher MH. Locomotor microactivities associated with therapeutic responses in patients with seasonal affective disorders. To appear; 2006
9. Ohashi K, Amaral LN, Natelson BH, Yamamoto Y. Asymmetrical singularities in real-world signals. Phys Rev E 2003; 68 065 204 (R)- 1-4.
10. Lawrie S, MacHale S, Cavanagh J, O’Carroll R, Goodwin G. The difference in patterns of motor and cognitive function in chronic fatigue syndrome and severe depressive illness. Psychol Med 2000; 30: 433-442.
11. DeLuca J, Johnson S, Ellis S, Natelson B. Cognitive functioning is impaired in patients with chronic fatigue syndrome devoid of psychiatric disease. J Neurol Neurosurg Psychiatry 1997; 62: 151-155.
12. Riccio C, Reynolds C, Lowe P. Clinical applications of continuous performance tests:. Measuring attention and impulsive responding in children and adults, New York: Wiley; 2001
13. Stone A, Turkkan J, Bachrach CA, Jobe J, Kurtzman H, Cain V. The Science of Self-Report: Implications for Research and Practice. 1st ed. Mahwah: Lawrence ErlbaumAssociates; 2000
14. Amaral LN, Ivanov PC, Aoyagi N, Hidaka I, Tomono S, Goldberger AL, Stanley HE, Yamamoto Y. Behavioral-independent features of complex heartbeat dynamics. Phys Rev Lett 2001; 86: 6026-6029.
15. Aoyagi N, Ohashi K, Yamamoto Y. Frequency characteristics of long-term heart rate variability during constant routine protocol. Am J Physiol 2003; 285: R171-176.
16. Goldberger AL, Amaral LN, Glass L, Havlin S, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000; 101: e215-220.
17. Kiyono K, Struzik ZR, Aoyagi N, Sakata S, Hayano J, Yamamoto Y. Multiscale probability density function analysis: Non-Gaussian and scale-invariant fluctuations of healthy human heart rate, IEEE Transactions on Biomedical Engineering. Special Issue on Heart Rate Variability 2006; 53 (01) 95-102.
18. Kiyono K, Struzik ZR, Aoyagi N, Sakata S, Hayano J, Yamamoto Y. Critical scale-invariance in healthy human heart rate. Phys Rev Lett 2004; 93: 178103.
19. Struzik ZR, Hayano J, Sakata S, Kwak S, Yamamoto Y. Scaling in heart rate requires antagonistic autonomic control. Phys Rev E 2004; 70: 050901 (R).
20. Struzik ZR, Hayano J, Sakata S, Kwak S, Yamamoto Y. Aging of complex heart rate dynamics, IEEE Transactions on Biomedical Engineering. Special Issue on Heart Rate Variability 2006; 53 (01) 89-94.
21. Cristianini S-TJ, Kernel N. Methods for Pattem Analysis. Cambridge University Press; 2004
22. Markou M, Singh S. Novelty detection: a review-part 1: statistical approaches. Signal Processing 2003; 83: 2481-2497.
23. Headache classification subcommittee of the international headache society The international classification of headache disorders. 2nd edition. Cephalalgia 2004; 24 (Suppl. 01) 1-160.
24. Aygun D, Altintop L, Doganay Z, Guven H, Baydin A. Electrocardiographic changes during migraine attacks. Headache 2003; 43: 861-866.