· The entropy of Center of Pressure sway can reveal the risk of falling

· Older adults at high risk of falling present more entropic body oscillations, as reflected by CoP sway

· Young subjects and senior adult at lower risk of falling have a much more entropic sway of 

Human balance is the result of a dynamic equilibrium. Dynamic, in opposition to static, means that we stand in a vertical position, because we keep balancing opposite forces. If we move our chest forward, with the toes we push on the floor, to bring our chest back avoiding falling. So our standing is not static, but we sway around an ideal centre positioned between our feet. This all happen autonomously, without us paying attention to our postural control.

However, our capability to control posture and balance changes along life. This change can be measured to identify critical variations, which may reveal a higher risk of falling in later life. Accurate detection of these changes can be used to better target interventions aiming to mitigate the risk of falling.

“Well, if it is as easy as it sounds, why this was not done before? In my opinion, the answer is that we were looking in the wrong direction. Previous studies investigated changes in our balance control using measures of order (e.g., stats, periodicity), while we have been looking into the non-linear behaviour of body sway, measuring its entropy. Order change with age, but entropy chance catastrophically in older citizen at higher risk of falling”.

The study recently published by Luis Montesinos and Rossana Castaldo from the applied biomedical signal processing and intelligent eHealth Lab directed by Leandro Pecchia, demonstrated that it is possible to reveal the differences in body sway between older adults and older adult at a higher risk of falling, as determined from their clinical history. These findings represent a significant contribution to the development of improved fall-risk assessment tools in geriatric populations.

L. Montesinos: “With this study, we were not aiming to create new methods for signal analysis. Instead, we were interested in learning how existing methods can be used to identify and understand differences in balance control between older adults at lower and higher risk of falling. In the same way a radio needs to be tuned to pick the station of our choice, the methods we applied require some ‘fine-tuning’ in order to pick differences in body sway between different populations. Our study provided us with interesting insights in this respect, which we want to share with researchers and clinicians in our scientific community and beyond.”

The results of the study were recently published in the Journal of NeuroEngineering and Rehabilitation, the leading academic publication in its subject area of rehabilitation medicine according to Scopus. The article can be accessed here: https://rdcu.be/bdihg

• Just one night of disrupted sleep means you are less capable to control posture and balance the day after
• A single bad night's sleep decreases your chance of controlling posture according to researchers at the University of Warwick, who have used state of the art sensors to monitor sleep and balance
• Implications could be that elderly people who have had a bad night's sleep are the most at risk of a fall
• Innovative solutions of how to prevent imminent falls can now by researched

Disturbances during sleep decreases capability to control posture and balance according to researchers from the School of Engineering and Warwick Medical School at the University of Warwick who have an article published today in Scientific Reports (www.nature.com/articles/s41598-018-36053-4)

This is the first study demonstrating the relationship between day-to-day variations in sleep quality and the reduced capability to control posture and balance, and it could pave the way to new interventions to prevent falls in later life, should the results be confirmed by other studies on older adults.

The research shows that fragmented and disrupted sleep leads to acute balance deficit.

The study was conducted by the School of Engineering in collaboration Warwick Medical School at the University of Warwick.

A sample of healthy adults underwent sleep and balance assessment over two consecutive days, in order to determine the links between day-to-day variations in sleep quality and balance.

State-of-the-art wearable sensors available at the School were used for in-home sleep monitoring and lab-based balance testing. These findings are relevant to pave the way to the design of fall prevention programs in populations and settings where normal sleep is frequently disrupted, such as older people and hospital wards.

Dr Leandro Pecchia, team leader of the research from the School of Engineering at the University of Warwick says:

“We all have direct experience of this. When we do not sleep well, we may feel a little dizzy and our capability to control our posture and balance is somehow diminished. When we are fit and in good health, our body is able to adapt and we develop a strategy to keep our balance safe, avoiding falls and incidents. This ability is reduced with ageing or when there are other concomitant conditions that may compromise our ability to adapt.”

Prof Francesco Cappuccio, Head of the Sleep, Health & Society programme at the University of Warwick’s medical school, explains:

“The results obtained in healthy normal volunteers are surprising, given the ability at younger ages to compensate for such acute and short-lived sleep disruptions. We would expect more dramatic effects when these experiments be replicated in older people, whose vulnerability to sleep disruption, postural hypotension and risk of falls is much greater”.

Dr Leandro Pecchia continues:

“These results could contribute to the understanding of in-hospital falls. Hospitalised older patients find themselves in a frail condition, sleeping in an unfamiliar environment, with unusual nocturnal light and noises from other patients and nurses, and perhaps being administered more than one drug. Waking-up to go to the toilet can be more challenging than we can imagine. Having a nurse for each bed is not practical in the modern NHS and not well accepted by many older people. We need to learn how to use available technology to detect early the changes in sleep so that we can design personalised interventions that may avoid falls in the next day. One of the problems in fall prevention is that we know a frail subject will fall, but it is very difficult to predict when. Our study is first step towards finding a solution.”

The paper, Day-to-day variations in sleep quality effect standing balance in healthy adults, is published by Scientific Reports.

UK and international news outlets (e.g. the Daily Mail) have received fairly well our paper, which today is in the top 5% of all articles of a similar age based on the online attention it has received.

Dr Rossana Castaldo, recently awarded her PhD in biomedical engineering, has been awarded the William James Award 2018 by the Institute of Engineering and Technology for her work on fall prediction in hypertensive patients via short-term Heart Rate Variability Analysis recently published on IEEE journal of biomedical and health informatics.

The International Union for Physical and Engineering Sciences (IUPESM) is the umbrella organization for the International Organization for Medical Physics (IOMP) and International Federation of Medical and Biological Engineering (IFMBE). This union is regarded as the world scientific society for biomedical engineering and medical physics and federates more than 100 national societies from all the 6 continents, and the main transnational societies such as IEEE EMBSor EAMBES.

Each 3-years, the IUPESM organizes the World Congress on Medical Physics & Biomedical Engineering, which this year was in Prague (WC2018).

This was the first time that the University of Warwick was so well and successfullyrepresented in this important event!

The first paper proving an association between depressed HRV and risk of falling was published on IEEE Journal of Biomedical and Health Informatics in March 2016.

Enrolling 150 subjects, we trained and tested an automatic classifier basing on Multinomial Naïve Bayes enabled to predict first-time fallers. This achieved sensitivity, specificity and accuracy rates of 72%, 61%, 68% respectively.

This was the first sutdy proving this association.