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1.  Smartphone-Based Solutions for Fall Detection and Prevention: Challenges and Open Issues 
Sensors (Basel, Switzerland)  2014;14(4):7181-7208.
This paper presents a state-of-the-art survey of smartphone (SP)-based solutions for fall detection and prevention. Falls are considered as major health hazards for both the elderly and people with neurodegenerative diseases. To mitigate the adverse consequences of falling, a great deal of research has been conducted, mainly focused on two different approaches, namely, fall detection and fall prevention. Required hardware for both fall detection and prevention are also available in SPs. Consequently, researchers' interest in finding SP-based solutions has increased dramatically over recent years. To the best of our knowledge, there has been no published review on SP-based fall detection and prevention. Thus in this paper, we present the taxonomy for SP-based fall detection and prevention solutions and systematic comparisons of existing studies. We have also identified three challenges and three open issues for future research, after reviewing the existing articles. Our time series analysis demonstrates a trend towards the integration of external sensing units with SPs for improvement in usability of the systems.
PMCID: PMC4029687  PMID: 24759116
fall detection; fall prevention; smartphone; ubiquitous computing; pervasive computing; elderly
2.  Allele-specific polymerase chain reaction for the detection of Alzheimer’s disease-related single nucleotide polymorphisms 
BMC Medical Genetics  2013;14:27.
The incidence of Alzheimer’s disease, particularly in developing countries, is expected to increase exponentially as the population ages. Continuing research in this area is essential in order to better understand this disease and develop strategies for treatment and prevention. Genome-wide association studies have identified several loci as genetic risk factors of AD aside from apolipoprotein E such as bridging integrator (BIN1), clusterin (CLU), ATP-binding cassette sub-family A member 7 (ABCA7), complement receptor 1 (CR1) and phosphatidylinositol binding clathrin assembly protein (PICALM). However genetic research in developing countries is often limited by lack of funding and expertise. This study therefore developed and validated a simple, cost effective polymerase chain reaction based technique to determine these single nucleotide polymorphisms.
An allele-specific PCR method was developed to detect single nucleotide polymorphisms of BIN1 rs744373, CLU rs11136000, ABCA7 rs3764650, CR1 rs3818361 and PICALM rs3851179 in human DNA samples. Allele-specific primers were designed by using appropriate software to permit the PCR amplification only if the nucleotide at the 3’-end of the primer complemented the base at the wild-type or variant-type DNA sample. The primers were then searched for uniqueness using the Basic Local Alignment Search Tool search engine.
The assay was tested on a hundred samples and accurately detected the homozygous wild-type, homozygous variant-type and heterozygous of each SNP. Validation was by direct DNA sequencing.
This method will enable researchers to carry out genetic polymorphism studies for genetic risk factors associated with late-onset Alzheimer’s disease (BIN1, CLU, ABCA7, CR1 and PICALM) without the use of expensive instrumentation and reagents.
PMCID: PMC3635888  PMID: 23419238
Alzheimer’s disease; Single nucleotide polymorphism; Apolipoprotein E; Bridging integrator; Clusterin; ATP-binding cassette sub-family A member 7; Complement receptor 1; Phosphatidylinositol binding clathrin assembly protein; Allele-specific polymerase chain reaction
3.  A reliable measure of frailty for a community dwelling older population 
Frailty remains an elusive concept despite many efforts to define and measure it. The difficulty in translating the clinical profile of frail elderly people into a quantifiable assessment tool is due to the complex and heterogeneous nature of their health problems. Viewing frailty as a 'latent vulnerability' in older people this study aims to derive a model based measurement of frailty and examines its internal reliability in community dwelling elderly.
The British Women's Heart and Health Study (BWHHS) cohort of 4286 women aged 60-79 years from 23 towns in Britain provided 35 frailty indicators expressed as binary categorical variables. These indicators were corrected for measurement error and assigned relative weights in its association with frailty. Exploratory factor analysis (EFA) reduced the data to a smaller number of factors and was subjected to confirmatory factor analysis (CFA)which restricted the model by fitting the EFA-driven structure to observed data. Cox regression analysis compared the hazard ratios for adverse outcomes of the newly developed British frailty index (FI) with a widely known FI. This process was replicated in the MRC Assessment study of older people, a larger cohort drawn from 106 general practices in Britain.
Seven factors explained the association between frailty indicators: physical ability, cardiac symptoms/disease, respiratory symptoms/disease, physiological measures, psychological problems, co-morbidities and visual impairment. Based on existing concepts and statistical indices of fit, frailty was best described using a General Specific Model. The British FI would serve as a better population metric than the FI as it enables people with varying degrees of frailty to be better distinguished over a wider range of scores. The British FI was a better independent predictor of all-cause mortality, hospitalization and institutionalization than the FI in both cohorts.
Frailty is a multidimensional concept represented by a wide range of latent (not directly observed) attributes. This new measure provides more precise information than is currently recognized, of which cluster of frailty indicators are important in older people. This study could potentially improve quality of life among older people through targeted efforts in early prevention and treatment of frailty.
PMCID: PMC2988728  PMID: 21029450

Results 1-3 (3)