The Efficacy of Fall Hazards Identification on Fall Outcomes: A Systematic Review With Meta-analysis

[Fall prevention in old people through occupational therapy home assessment, consultation and modification: a process outline]

Falls occur frequently in old people and are associated with relevant short-term and long-term trauma sequelae. Particularly in the light of demographic change, preventive measures such as falls prevention are increasingly gaining in importance. Occupational therapy home assessments enable the evaluation of external and behavioral risk factors for falls in the context of environmental prevention. Environmental prevention is an effective measure in preventing morbidity and mortality related to falls in old people and the increase in healthcare costs associated with falls. Occupational therapy home assessments effectively reduce fall risk and fall rate among older people at risk of falling and can be implemented in the context of existing regulatory frameworks. The aim of this overview is to illustrate the optimal process of home assessment and adaptation for patients at risk of falling by general practitioners, orthopedic surgeons and occupational therapists.

Ageing in place - exploring the safety of the environment of Slovenian older adults

Objective

The importance of subjective and objective living environment was investigated.

Methods

A new questionnaire was developed, validated and distributed to community-dwelling adults in various Slovenian counties. Data were analysed using descriptive statistics and t-tests in SPSS Statistics 24. Older adults (N = 358), 33% men and 67% women, mean age 74.3 years, participated in the cross-sectional study.

Results

Statistically significant differences (at 5% significance level) in subjective opinions of older adults regarding views on appropriate design of the living environment between the group with and without risk factors exist only for the statement that a thermostat to regulate room temperature should be placed in a visible and accessible location (p = 0.014).

Conclusion

The living environment has some risk factors, but they are not statistically significant between groups. The results may prompt occupational therapists to develop innovative strategies to facilitate occupational participation of users, increase participation in preventive services and obtain policy support.

Comparing Machine Learning Methods to Improve Fall Risk Detection in Elderly with Osteoporosis from Balance Data

Falls are a multifactorial cause of injuries for older people. Subjects with osteoporosis are particularly vulnerable to falls. We study the performance of different computational methods to identify people with osteoporosis who experience a fall by analysing balance parameters. Balance parameters, from eyes open and closed posturographic studies, and prospective registration of falls were obtained from a sample of 126 community-dwelling older women with osteoporosis (age 74.3 ± 6.3) using World Health Organization Questionnaire for the study of falls during a follow-up of 2.5 years. We analyzed model performance to determine falls of every developed model and to validate the relevance of the selected parameter sets. The principal findings of this research were (1) models built using oversampling methods with either IBk (KNN) or Random Forest classifier can be considered good options for a predictive clinical test and (2) feature selection for minority class (FSMC) method selected previously unnoticed balance parameters, which implies that intelligent computing methods can extract useful information with attributes which otherwise are disregarded by experts. Finally, the results obtained suggest that Random Forest classifier using the oversampling method to balance the data independent of the set of variables used got the best overall performance in measures of sensitivity (>0.71), specificity (>0.18), positive predictive value (PPV >0.74), and negative predictive value (NPV >0.66) independent of the set of variables used. Although the IBk classifier was built with oversampling data considering information from both eyes opened and closed, using all variables got the best performance (sensitivity >0.81, specificity >0.19, PPV = 0.97, and NPV = 0.66).