3 Novel Technologies for Geriatric Psychiatry

1. An Integrated Medical and Psychiatric Self-Management Intervention Has Been Adapted to a Smartphone Application: Ten middle-aged and older participants (mean age, 55.3 years) with serious mental illness and other chronic health conditions went through 10 sessions with the app over approximately 3 months. The app covered topics such as stress vulnerability and illness, medication adherence and strategies, and substance and medication abuse. The patients reported a high level of usability and satisfaction with the smartphone app.[1]

Clinical Implications for Study 1: Physicians can remotely monitor use of the app and intervene when problems are detected, which facilitates telemedicine for less accessible populations. “The use of mobile health interventions by adults with serious mental illness is a promising approach that has been shown to be highly feasible and acceptable,” said Karen L. Fortuna, PhD, of the Dartmouth Centers for Health and Aging and the Geisel School of Medicine at Dartmouth in Hanover, NH. “These technologies are associated with many advantages compared with traditional psychosocial interventions, including the potential for individually tailored, just-in-time delivery along with wide dissemination and high population impact.”

2. An Experimental Optical Imaging System Can Detect Retinal Beta-Amyloid, a Hallmark of Alzheimer Disease: A clinical trial of 16 patients demonstrated the feasibility of identifying beta-amyloid in the eye using autofluorescence imaging. There was a 4.7-fold increase in retinal plaque burden in patients with Alzheimer disease compared with controls.[2]

Clinical Implications for Study 2: These findings may lead to a practical approach for large-scale identification of the at-risk population and monitoring of Alzheimer disease. “This is the first study demonstrating the potential to image and quantify retinal findings related to beta-amyloid plaques noninvasively in living patients using a retinal scan with high resolution. This clinical trial is reinforced by an in-depth exploration of the accumulation of beta-amyloid in the retina of Alzheimer’s patients versus matched controls, and a comparison analysis between retina and brain pathologies. Findings from this study strongly suggest that retinal imaging can serve as a surrogate biomarker to investigate and monitor Alzheimer’s disease,” said senior author Maya Koronyo-Hamaoui, PhD, at the Maxine Dunitz Neurosurgical Institute at Cedars-Sinai Medical Center in Los Angeles, CA.

3. A New Machine Learning Program Appears to Outperform Other Methods for Diagnosing Alzheimer Disease: The computer program integrates a range of Alzheimer disease indicators, including mild cognitive impairment. The algorithm was tested using data from 149 patients collected via the Alzheimer’s Disease Neuroimaging Initiative.

The algorithm integrates measurements from MRI scans, features of the hippocampus, glucose metabolism rates in the brain, proteomics, genomics, mild cognitive impairment, and other parameters. In 2 successive stages, the algorithm selects the most pertinent information to predict who has Alzheimer disease.[3]

Clinical Implications for Study 3: Early diagnosis and treatment of Alzheimer disease may allow patients to remain independent longer. “Many papers compare the healthy to those with the disease, but there's a continuum. We deliberately included mild cognitive impairment, which can be a precursor to Alzheimer’s disease, but not always,” said Anant Madabhushi, PhD, of Case Western Reserve University in Cleveland, OH.

1. Whiteman KL, Lohman MC, Gill LE, et al. Adapting a psychosocial intervention for smartphone delivery to middle-aged and older adults with serious mental illness. Am J Geriatr Psychiatry. 2017;25:819-828. doi: 10.1016/j.jagp.2016.12.007.

2. Koronyo Y, Biggs D, Barron E, et al. Retinal amyloid pathology and proof-of-concept imaging trial in Alzheimer’s disease. JCI Insight. 2017;2(16). pii: 93621. doi: 10.1172/jci.insight.93621.

3. Singanamalli A, Wang H, Madabhushi A, Alzheimer’s Disease Neuroimaging Initiative. Cascaded multi-view canonical correlation (CaMCCo) for early diagnosis of Alzheimer’s disease via fusion of clinical, imaging and omic features. Sci Rep. 2017;7:8137. doi: 10.1038/s41598-017-03925-0.