The World Health Organization estimated that major depression is the fourth most significant cause of disability worldwide for people aged 65 and older, where depressed older adults reported decreased independence, poor health, poor quality of life, functional decline, disability, and increased chronic medical problems. Therefore, the objectives of this study were (1) to develop a low-cost point-of-care testing system for psychomotor symptoms of depression and (2) to evaluate the system in community dwelling elderly in India. The preliminary results from the cross-sectional study showed a significant negative linear correlation between balance and depression. Here, monitoring quantitative electroencephalography along with the center of pressure for cued response time during functional reach tasks may provide insights into the psychomotor symptoms of depression where average slope of the Theta-Alpha power ratio versus average slope of baseline-normalized response time may be a candidate biomarker, which remains to be evaluated in our future clinical studies. Once validated, the biomarker can be used for monitoring the outcome of a comprehensive therapy program in conjunction with pharmacological interventions. Furthermore, the frequency of falls can be monitored with a mobile phone-based application where the propensity of falls during the periods of psychomotor symptoms of depression can be investigated further. 1. Introduction The World Health Organization (WHO) estimated that major depression is the fourth significant cause of disability for people aged 65 and above [1], where depression is a major contributor to the healthcare costs associated with the elderly population. Depression is a major health issue for elders, yet late-life depression often goes undiagnosed [2]. One in every four among India’s elderly (age > 60 years) population is depressed, and around one in 10 experiences a fall that results in fracture [3]. In fact, the elderly population is predicted to increase to 12% of the total population by 2025 [3]. Depressed elderly report decreased independence, poor health, poor quality of life, functional decline, disability, and increased chronic medical problems [1]. Moreover, psychomotor symptoms of depression may contribute to falls among elderly and an associated fear of falling [4]. Here, course of depression, diurnal variation, medication status, gender, and age are associated with psychomotor agitation and retardation [5]. The psychomotor symptoms in depression have unique significance where they have high discriminative validity, may
References
[1]
B. G. Pollock and C. F. Reynolds III, “Depression late in life,” The Harvard Mental Health Letter, vol. 17, no. 3, pp. 3–5, 2000.
[2]
B. D. Lebowitz, J. L. Pearson, L. S. Schneider et al., “Diagnosis and treatment of depression in late life: consensus statement update,” Journal of the American Medical Association, vol. 278, no. 14, pp. 1186–1190, 1997.
[3]
Union health ministry's working group on non-communicable disease burden in India, 2013, http://planningcommission.nic.in/aboutus/committee/wrkgrp12/health/WG_3_2non_communicable.pdf.
[4]
G. A. R. Zijlstra, J. C. M. van Haastregt, J. T. M. van Eijk, E. van Rossum, P. A. Stalenhoef, and G. I. J. M. Kempen, “Prevalence and correlates of fear of falling, and associated avoidance of activity in the general population of community-living older people,” Age and Ageing, vol. 36, no. 3, pp. 304–309, 2007.
[5]
C. Sobin and H. A. Sackeim, “Psychomotor symptoms of depression,” American Journal of Psychiatry, vol. 154, no. 1, pp. 4–17, 1997.
[6]
R. Kumar, R. Aikat, and A. Banerjee, “Relationship between balance and depression in elderly,” in Proceedings of the the 2nd IASTED International Conference on Assistive Technologies, 2012.
[7]
K. Berg, “Measuring balance in the elderly: preliminary development of an instrument,” Physiotherapy Canada, vol. 41, no. 6, pp. 304–311, 1989.
[8]
J. A. Yesavage, T. L. Brink, and T. L. Rose, “Development and validation of a geriatric depression screening scale: a preliminary report,” Journal of Psychiatric Research, vol. 17, no. 1, pp. 37–49, 1982.
[9]
A. Dutta, A. Banerjee, and A. Dutta, “Low-cost visual postural feedback with Wii balance board and Microsoft Kinect—a feasibility study,” in Proceedings of the IEEE Point-of-Care Healthcare Technologies (PHT '13), pp. 291–294, 2013.
[10]
M. Ganguli and S. Dube, “Depressive symptoms, cognitive impairment and functional impairment in a rural elderly population in India,” International Journal of Geriatric Psychiatry, vol. 14, no. 10, pp. 807–820, 1999.
[11]
M. Ganguli, G. Ratcliff, V. Chandra et al., “A Hindi version of the MMSE: the development of a cognitive screening instrument for a largely illiterate rural elderly population in India,” International Journal of Geriatric Psychiatry, vol. 10, no. 5, pp. 367–377, 1995.
[12]
The Emotiv EEG Neuroheadset specifications, 2013, http://emotiv.wikia.com/wiki/Emotiv_EPOC.
[13]
F. Koslucher, M. G. Wade, B. Nelson, K. Lim, F. C. Chen, and T. A. Stoffregen, “Nintendo Wii Balance Board is sensitive to effects of visual tasks on standing sway in healthy elderly adults,” Gait Posture, vol. 36, no. 3, pp. 605–608, 2012.
[14]
The WiiBrew website, 2013, http://wiibrew.org/wiki/Wii_Balance_Board.
[15]
A. Delorme and S. Makeig, “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis,” Journal of Neuroscience Methods, vol. 134, no. 1, pp. 9–21, 2004.
[16]
V. A. Grin-Yatsenko, I. Baas, V. A. Ponomarev, and J. D. Kropotov, “EEG power spectra at early stages of depressive disorders,” Journal of Clinical Neurophysiology, vol. 26, no. 6, pp. 401–406, 2009.
[17]
P. W. Duncan, S. Studenski, J. Chandler, and B. Prescott, “Functional reach: predictive validity in a sample of elderly male veterans,” Journals of Gerontology, vol. 47, no. 3, pp. M93–M98, 1992.
[18]
T. C. Hankins and G. F. Wilson, “A comparison of heart rate, eye activity, EEC and subjective measures of pilot mental workload during flight,” Aviation Space and Environmental Medicine, vol. 69, no. 4, pp. 360–367, 1998.
[19]
N. Kose, S. Cuvalci, G. Ekici, A. S. Otman, and M. G. Karakaya, “The risk factors of fall and their correlation with balance, depression, cognitive impairment and mobility skills in elderly nursing home residents,” Saudi Medical Journal, vol. 26, no. 6, pp. 978–981, 2005.
[20]
L. Rochester, V. Hetherington, D. Jones et al., “Attending to the task: interference effects of functional tasks on walking in Parkinson's disease and the roles of cognition, depression, fatigue, and balance,” Archives of Physical Medicine and Rehabilitation, vol. 85, no. 10, pp. 1578–1585, 2004.
[21]
H. Suárez, A. Suárez, and L. Lavinsky, “Postural adaptation in elderly patients with instability and risk of falling after balance training using a virtual-reality system,” International Tinnitus Journal, vol. 12, no. 1, pp. 41–44, 2006.
[22]
R. A. Newton, “Balance screening of an inner city older adult population,” Archives of Physical Medicine and Rehabilitation, vol. 78, no. 6, pp. 587–591, 1997.
[23]
A. J. Sai, J. C. Gallagher, L. M. Smith, and S. Logsdon, “Fall predictors in the community dwelling elderly: a cross sectional and prospective cohort study,” Journal of Musculoskeletal Neuronal Interactions, vol. 10, no. 2, pp. 142–150, 2010.
[24]
The FallAlert Android App, 2013, https://play.google.com/store/apps/details?id=com.gnansys.fallalert.
[25]
M. A. S. Boksem, T. F. Meijman, and M. M. Lorist, “Effects of mental fatigue on attention: an ERP study,” Cognitive Brain Research, vol. 25, no. 1, pp. 107–116, 2005.
[26]
C. Stampi, P. Stone, and A. Michimori, “A new quantitative method for assessing sleepiness: the alpha attenuation test,” Work and Stress, vol. 9, no. 2-3, pp. 368–376, 1995.
[27]
C. Sobin, L. Mayer, and J. Endicott, “The motor agitation and retardation scale: a scale for the assessment of motor abnormalities in depressed patients,” Journal of Neuropsychiatry and Clinical Neurosciences, vol. 10, no. 1, pp. 85–92, 1998.
