Development of calisthenics for fall prevention in workers and its verification

BACKGROUND Data visualization experts have identified core principles to follow when creating visual displays of data that facilitate comprehension. Such principles can be applied to creating effective reports for clinicians that display compliance with quality improvement protocols. A basic tenet of implementation science is continuous monitoring and feedback. Applying best practices for data visualization to reports for clinicians can catalyze implementation and sustainment of new protocols. OBJECTIVE To apply best practices for data visualization to create reports that clinicians find clear and useful. METHODS First, we conducted a systematic literature review to identify best practices for data visualization. We applied these findings and feedback collected via a questionnaire to improve the Fall TIPS Monthly Report (FTMR), which shows compliance with an evidence-based fall prevention program, Fall TIPS (Tailoring Interventions for Patient Safety). This questionnaire was based on the requirements for effective data display suggested by expert Stephen Few. We then evaluated usability of the FTMR using a 15-item Health Information Technology Usability Evaluation Scale (Health-ITUES). Items were rated on a 5-point Likert scale from strongly disagree (1) to strongly agree (5). RESULTS The results of the systematic literature review emphasized that the ideal data display maximizes the information communicated while minimizing the cognitive efforts involved with data interpretation. Factors to consider include selecting the correct type of display (e.g. line vs bar graph) and creating simplistic reports. The qualitative and quantitative evaluations of the original and final FTMR revealed improved perceptions of the visual display of the reports and their usability. Themes that emerged from the staff interviews emphasized the value of simplified reports, meaningful data, and usefulness to clinicians. The mean (SD) rating on the Health-ITUES scale when evaluating the original FTMR was 3.86 (0.19) and increased to 4.29 (0.11) when evaluating the revised FTMR (Mann Whitney U Test, z=-12.25, P<0.001). CONCLUSIONS Best practices identified through a systematic review can be applied to create effective reports for clinician use. The lessons learned from evaluating FTMR perceptions and measuring usability can be applied to creating effective reports for clinician use in the context of other implementation science projects.

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Factors Influencing Fall Prevention Activities in Nursing Student

The Journal of Humanities and Social sciences 21 ◽

10.22143/hss21.11.3.102 ◽

2020 ◽

Vol 11 (3) ◽

pp. 1423-1436

Author(s):

Ilseon Hyeon

Keyword(s):

Fall Prevention ◽

Nursing Student ◽

Factors Influencing ◽

Prevention Activities

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Building a Case for Pediatric Fall Prevention

Journal of Trauma Nursing ◽

10.1097/jtn.0000000000000429 ◽

2019 ◽

Vol 26 (2) ◽

pp. E2

Keyword(s):

Fall Prevention

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Minimal shoes improve stability and mobility in persons with a history of falls

Scientific Reports ◽

10.1038/s41598-020-78862-6 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Tomasz Cudejko ◽

James Gardiner ◽

Asangaedem Akpan ◽

Kristiaan D’Août

Keyword(s):

Fall Prevention ◽

Postural Stability ◽

Center Of Pressure ◽

Stability Margin ◽

Timed Up And Go ◽

Balance Test ◽

Margin Of Stability ◽

Eyes Open ◽

History Of ◽

Falls In Older Adults

AbstractPostural and walking instabilities contribute to falls in older adults. Given that shoes affect human locomotor stability and that visual, cognitive and somatosensory systems deteriorate during aging, we aimed to: (1) compare the effects of footwear type on stability and mobility in persons with a history of falls, and (2) determine whether the effect of footwear type on stability is altered by the absence of visual input or by an additional cognitive load. Thirty participants performed standing and walking trials in three footwear conditions, i.e. conventional shoes, minimal shoes, and barefoot. The outcomes were: (1) postural stability (movement of the center of pressure during eyes open/closed), (2) walking stability (Margin of Stability during normal/dual-task walking), (3) mobility (the Timed Up and Go test and the Star Excursion Balance test), and (4) perceptions of the shoes (Monitor Orthopaedic Shoes questionnaire). Participants were more stable during standing and walking in minimal shoes than in conventional shoes, independent of visual or walking condition. Minimal shoes were more beneficial for mobility than conventional shoes and barefoot. This study supports the need for longitudinal studies investigating whether minimal footwear is more beneficial for fall prevention in older people than conventional footwear.

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Identifying Fall Risk Predictors by Monitoring Daily Activities at Home Using a Depth Sensor Coupled to Machine Learning Algorithms

Sensors ◽

10.3390/s21061957 ◽

2021 ◽

Vol 21 (6) ◽

pp. 1957

Author(s):

Amandine Dubois ◽

Titus Bihl ◽

Jean-Pierre Bresciani

Keyword(s):

Machine Learning ◽

Fall Prevention ◽

Fall Risk ◽

Learning Algorithms ◽

Step Length ◽

Machine Learning Algorithms ◽

Daily Activities ◽

Sitting Time ◽

Behavioral Parameters ◽

At Home

Because of population ageing, fall prevention represents a human, economic, and social issue. Currently, fall-risk is assessed infrequently, and usually only after the first fall occurrence. Home monitoring could improve fall prevention. Our aim was to monitor daily activities at home in order to identify the behavioral parameters that best discriminate high fall risk from low fall risk individuals. Microsoft Kinect sensors were placed in the room of 30 patients temporarily residing in a rehabilitation center. The sensors captured the patients’ movements while they were going about their daily activities. Different behavioral parameters, such as speed to sit down, gait speed or total sitting time were extracted and analyzed combining statistical and machine learning algorithms. Our algorithms classified the patients according to their estimated fall risk. The automatic fall risk assessment performed by the algorithms was then benchmarked against fall risk assessments performed by clinicians using the Tinetti test and the Timed Up and Go test. Step length, sit-stand transition and total sitting time were the most discriminant parameters to classify patients according to their fall risk. Coupling step length to the speed required to stand up or the total sitting time gave rise to an error-less classification of the patients, i.e., to the same classification as that of the clinicians. A monitoring system extracting step length and sit-stand transitions at home could complement the clinicians’ assessment toolkit and improve fall prevention.

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