Falls in a community sample of Portuguese elderly
, E. Pinto
, P. Chaves
Department of Physiotherapy, CESPU,
Institute of Public Health, University of Porto,
The purpose of this study was to investigate the risk factors
of falling in a community sample of Portuguese elderly.
Sixty-three elderly subjects (68.3% male; mean age 77.4
years ±8.48), agreed to participate in this cross-sectional study. Self-
reported data regarding falls in the previous year and possible risk
factors were collected by questionnaire. The mobility and the fear of
falling were evaluated using two validated and standardized tools:
Timed-up and Go test (TUG) and Fall Efficacy Scale (FES). We assessed
the risk of falls through odds ratios (OR), with 95% confidence intervals
(95% CIs), obtained using Logistic regression.
The studied sample had a high incidence of reported falls in
the previous year (60.3%; median of 1.0 fall, AIQ: 2.0). The median
score of TUG was 14.5 seconds (AIQ: 9.2) and the median score of FES
was 50.0 points (AIQ: 41.0). Compared to non-fallers, fallers were more
likely to be women (OR = 0.289; 95% CI: 0.096
0.873). No association
with age (OR = 1.026; 95% CI: 0.965
1.090), hours per day in sedentary
lifestyles (OR = 1.181; 95% CI: 0.674
2.069), and mobility (OR = 1.049;
95% CI: 0.980
1.124), was found. Fallers had less confidence during
activities of daily living and greater fear of falling, even after
adjustment for sex (adjOR = 0.977; 95% CI: 0.956
It is important to recognise the risk factors that identify a
faller. Fear of falling seems to have a significant contribution to risk of
falls, which may be useful in trying to reduce falls in the elderly.
PERSSILAA platform: algorithms and tools for decision support
, F. Garate
, E. Hernando
, E. Gomez
Engineering and Telemedicine Centre (GBT), ETSI Telecomunicacion,
Universidad Politecnica de Madrid (UPM), Madrid,
Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN),
This research work is part of the PERSSILAA project ,
a unique project that aims to develop and validate a new service
model for older people, to screen for and prevent frailty in older
adults. This multimodal service model, focusing on nutrition, physical
and cognitive functioning, is supported by an interoperable ICT
service infrastructure, utilizing intelligent decision support systems
and gamification for enhancing both efficacy and adherence to the
The intelligent core of the PERSSILAA platform consists of
computational methods aimed at performing knowledge discovery,
pattern recognition, classification, automatic detection of changes in
behaviour across the three domains (cognitive, physical and nutrition)
and inference of personal context.
This layer is based on classification methods that allow us to
cluster users, based on both demographic and screening variables.
Then, we are able to compare historic performance results stored in
the database. This way, we try to anticipate the evolution of the user
and react to detected changes in expected behaviours, by implement-
ing automatic recommendations aiming at preventing functional
decline based on the services provided in PERSSILAA.
For the moment, 222 users have been used for a first
validation study, resulting in 6 different clusters, to demonstrate the
technical feasibility of the algorithms and tools implemented. In the
coming months a clinical validation will be performed, with the main
challenge of achieving automatic deviations detection that can be
considered a risk factor, in order to automatically react accordingly and
prevent functional decline in users.
 PERSSILAA project, available online:http:/ /www.perssilaa.eu/
Trends in the selective exclusion of older participants from clinic
M. Thake, A. Lowry.
Sheffield Teaching Hospitals
The upward trend in life expectancy means the ageing
population accounts for an increasing proportion of medical inves-
tigations and treatments compared to their younger counterparts.
This is due to the age-related accumulation of chronic conditions,
increased susceptibility to acute diseases and prophylactic prescribing
based on higher absolute risk of disease. This ageing population is
entitled to evidence based treatments, tailored to their needs and
physiology. Research developments have repeatedly demonstrated the
disparate responses of this older cohort to standard medical
treatments , implying that clinical trial data from younger
participants cannot not be merely extrapolated to incorporate this
unique population. Concern has been raised that this older population
is selectively excluded from clinical trials [2
10], creating research
populations that are non-representative of the target geriatric
All randomised control trials (RCTs) in Lancet, BMJ, JAMA
and NEJM from 1998 to 2015 were analysed to see if they had upper
age limits and assess whether these limits were justified in the
26.4% of RCTs (1168/4341) had unexplained upper age limits.
Over the 18-year period analysed therewas a moderate but statistically
significant improvement in the proportion of RCTs excluding older
participants (Pearson Correlation
0.609, P valve 0.007).
Despite being the highest consumers of healthcare, older
patients remain under-represented in clinical trials. Research must
adapt to provide insight into the differential effects of medical
treatments on those at the upper end of the age spectrum by ensuring
that trial participants are representative of those receiving the
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studies submitted to research ethics committee for approval:
Poster presentations / European Geriatric Medicine 7S1 (2016) S29