Falls Prevention Horizon Scanning Bulletin Volume 7 Issue 5

17/05/2017
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Home and community based occupational therapy improves functioning in frail older people: a systematic review

17/05/2017

Source: Journal of the American geriatrics Society

Follow this link for the abstract

Date of publication: 3rd April 2017

 Publication type: Journal article

In a nutshell: The objective of this study is to assess the effectiveness of occupational therapy to improve performance in daily living activities in community-dwelling physically frail older people.

 Length of publication: 7 pages

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.

 


Trust to ‘pimp’ walking frames to try and reduce falls

17/05/2017

Source: Nursing Times

Follow this link for the abstract 

Date of publication: 7th April 2017

 Publication type: Journal article

In a nutshell: One page

 Length of publication: The aim of the new programme is to create a culture where older patients take more ownership of their frames, encouraging them to use them rather than leave them by the bedside, said the trust.

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.

 


How to reduce falls: essential reading

17/05/2017

Source: Nursing Times

Follow this link for the abstract

Date of publication: 4th April 2017

 Publication type: Editorial 

In a nutshell: One in two women and one in five men over the age of 50 experience fractures, mostly as a result of low bone density.

 Length of publication: One page

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.

 


Slow processing speed predicts falls in older adults with a falls history: 1-year prospective cohort study

17/05/2017

Source: Journal of the American Geriatrics Society 

Follow this link for the abstract

Date of publication: 8 April 2017

 Publication type: Journal article

In a nutshell: A previous fall is a strong predictor of future falls. Recent epidemiologic data suggest that deficits in processing speed predict future injurious falls. Our primary objective was to determine a parsimonious predictive model of future falls among older adults who experienced ≥1 fall in the past 12 months based on the following categories: counts of (1) total, (2) indoor, (3) outdoor or (4) non-injurious falls; (5) one mild or severe injury fall (yes vs no); (6) an injurious instead of a non-injurious fall; and (7) an outdoor instead of an indoor fall.

 Length of publication: 8 pages

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.

 


Using Tai Chi to reduce fall risk factors among older adults: an evaluation of a community-based implementation

17/05/2017

Source: Journal of Applied Gerontology

Follow this link for the abstract 

Date of publication: 11th April 2017

 Publication type: Journal article

In a nutshell: This study aimed to evaluate a community-based implementation of an evidence-based fall prevention program, in which 131 individuals participated in Tai Chi: Moving for Better Balance. Self-report and functional performance assessments included demographics, health and fall history, the Activities-Specific Balance Scale, the Timed Up and Go test, and the Functional Reach test. Pre–post scores were compared with the Wilcoxon signed rank test. The mostly female participants were 73 years old, on average. At baseline, 18% reported being afraid or very afraid of falling, and 18% had fallen in the past 6 months. At follow-up, there was significant improvement in Timed Up and Go (p < .001), Functional Reach (p < .01), and Activities-Specific Balance Scale scores (p < .01). These results demonstrate that a 12-week evidence-based Tai Chi program can be feasibly implemented by novice instructors, is well-received by older adults, and can effectively reduce fall risk when implemented in community settings.

 Length of publication: 17 pages

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.

 


A hierarchical model for recognizing alarming states in a batteryless sensor alarm intervention for preventing falls in older people

17/05/2017

Source: Pervasive and Mobile Computing

Follow this link for the abstract 

Date of publication: 10th April 2017

 Publication type: Journal article

In a nutshell: Falls are common among older people, especially in hospitals and nursing homes. The combination of pervasive sensing and statistical learning methods is creating new possibilities for automatic monitoring of activities of hospitalized older people to provide targeted and timely supervision by clinical staff to reduce falls. In this paper we introduce a hierarchical conditional random fields model to predict alarming states (being out of the bed or chair) from a passive wearable embodiment of a sensor worn over garment to provide an intervention mechanism to reduce falls. Our approach predicts alarm states in real time and avoids the use of empirically determined heuristics methods alone or in combination with machine learning based models, or multiple cascaded classifiers for generating alarms from activity prediction streams. Instead, the proposed hierarchical approach predicts alarms based on learned relationships between alarms, sensor information and predicted low-level activities. We evaluate the performance of the approach with 14 healthy older people and 26 hospitalized older patients and demonstrate similar or better performance than machine learning based approaches combined with heuristics based methods.

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.

 


Accelerometry-based assessment and detection of early signs of balance deficits

17/05/2017

Source: Computers in Biology and Medicine

Follow this link for the abstract

Date of publication: 1 June 2017 vol. 85, pps. 25–32

 Publication type: Journal article

In a nutshell: Falls are the cause for more than half of the injury-related hospitalizations among older people. Accurate assessment of individuals’ fall risk could enable targeted interventions to reduce the risk. This paper presents a novel method for using wearable accelerometers to detect early signs of deficits in balance from gait. Gait acceleration data were analyzed from 35 healthy female participants (73.86±5.40 years). The data were collected with waist-mounted accelerometer and the participants performed three supervised balance tests: Berg Balance Scale (BBS), Timed-Up-and-Go (TUG) and 4 m walk. The follow-up tests with the same protocol were performed after one year. Altogether 43 features were extracted from the accelerometer signals. Sequential forward floating selection and ten-fold cross-validation were applied to determine models for 1) estimating the outcomes of BBS, TUG and 4 m walk tests and 2) predicting decline in balance during one-year follow-up indicated as decline in BBS total score and one leg stance. Normalized root-mean-square errors (RMSE) of the assessment scale result estimates were 0.28 for BBS score, 0.18 for TUG time, and 0.22 for 4 m walk test. Area under curve (AUC) was 0.78 for predicting decline in BBS total score and 0.82 for one leg stance, respectively. The results suggest that the gait features can be used to estimate the result of a clinical balance assessment scale and predict decline in balance. A simple walk test with wearable monitoring could be applicable as an initial screening tool to identify people with early signs of balance deficits.

 Length of publication: 7 pages

Some important notes: Please contact your local NHS Library for the full text of the article. Follow this link to find your local NHS Library.