Fall Prediction Model Research Articles

Validation of an accelerometer-based fall prediction model.

Falls are a common and serious problem faced by older populations. There is a growing interest in estimating the risk of falling for older people using body-worn sensors and simple movement tasks, allowing appropriate fall prevention programs to be administered in a timely manner to the high risk population. This study investigated the capability and validity of using a waist-mounted triaxial accelerometer (TA) and a directed routine (DR) that includes three movement tasks to discriminate between fallers and non-fallers and between multiple fallers and non-multiple fallers. Data were collected from 98 subjects who were stratified into two separate groups, one for model training and the other for model validation. Logistic regression models were constructed using the TA features from the entire DR and from each single DR task, and were validated using unseen data. The best models were obtained using features from the alternate step test to classify between fallers and non-fallers with κ = 0.34-0.41, sensitivity = 68%-71% and specificity = 63%-73%. However, the overall validation performances were poor. The study emphasizes the importance of independent validation in fall prediction studies.

Fall Prediction Model for Community-dwelling Elders based on Gender

This study was done to explore factors relating to number of falls among community-dwelling elders, based on gender. Participants were 403 older community dwellers (male=206, female=197) aged 60 or above. In this study, 8 variables were identified as predictive factors that can result in an elderly person falling and as such, supports previous studies. The 8 variables were categorized as, exogenous variables; perceived health status, somatization, depression, physical performance, and cognitive state, and endogenous variables; fear of falling, ADL & IADL and frequency of falls. For men, ability to perform ADL & IADL (β(32)=1.84, p<.001) accounted for 16% of the variance in the number of falls. For women, fear of falling (β(31)=0.14, p<.05) and ability to perform ADL & IADL (β(32)=1.01, p<.001) significantly contributed to the number of falls, accounting for 15% of the variance in the number of falls. The findings from this study confirm the gender-based fall prediction model as comprehensive in relation to community-dwelling elders. The fall prediction model can effectively contribute to future studies in developing fall prediction and intervention programs.

Evaluation of the Fall Prevention Program in an Acute Care Setting

To evaluate the effectiveness of a Falls Prevention Program (FPP) in reducing patient fall rate and to examine predictors of falls. A nonrandom, stratified convenience sample of 292 subjects was selected from medical-surgical/critical care patients at a large community hospital system. The sample included 101 patients who fell prior to the FPP, 98 patients who fell after FPP, and 93 patients who did not fall after FPP. A retrospective, preimplementation/long-term, postimplementation, comparative, descriptive design was used to address differences in patients who fell before and after FPP. Prediction factors associated with incidence of falls were assessed using a sample that included patients who fell and patients who did not fall in 1995. Data were collected about the patients and the fall incidents via a retrospective chart and incident report review. No decrease in patient fall rate was found between patients who fell before and after implementation of the FPP. Patients tended to fall attempting to get out of the bed, suffering no injury. Model testing of the linear results of patients who fell and patients who did not fall after the implementation of the FPP demonstrated that fall prediction factors included age 60 or over, impaired memory, muscle weakness, and need of ambulatory assistance (Log linear chi-square: 6.048, df 4, p = 0.811 (p > .05). Identification of patients who exhibit characteristics of the fall prediction model may be useful in reducing falls in medical-surgical/critical care patients. Further testing of the four-factor model with subsequent inclusion of focused interventions may impact the incidence of falls.

Predictors of Falls Among Elderly People

A study was performed to identify and rank risk factors for falling among populations of institutionalized (fallers, N = 79, nonfallers, N = 70) and noninstitutionalized (fallers, N = 34, nonfallers, N = 34) elderly persons. Fallers were matched by age, sex, and living location to nonfaller control subjects. A nurse practitioner performed a comprehensive physical assessment in all subjects using a standardized protocol and physician consultation. Fallers in both populations were significantly more physically and functionally impaired than control subjects. Logistic regression identified hip weakness, poor balance, and number of prescribed medications as factors most strongly associated with falling among institutionalized subjects. A fall prediction model was developed from these findings yielding 76% overall predictive accuracy (89% sensitivity, 60% specificity). Using the model, the predicted 1-year risk of falling ranged from 12% for persons with none of the three risk factors to 100% for persons with all three risk factors. Findings among noninstitutionalized subjects were similar. These data support the concept of performing focused fall risk assessments to identify elderly patients at high risk for falling.