Predicting Fall Counts Using Wearable Sensors: A Novel Digital Biomarker for Parkinson’s Disease

People with Parkinson’s disease (PD) experience significant impairments to gait and balance; as a result, the rate of falls in people with Parkinson’s disease is much greater than that of the general population. Falls can have a catastrophic impact on quality of life, often resulting in serious injury and even death. The number (or rate) of falls is often used as a primary outcome in clinical trials on PD. However, falls data can be unreliable, expensive and time-consuming to collect. We sought to validate and test a novel digital biomarker for PD that uses wearable sensor data obtained during the Timed Up and Go (TUG) test to predict the number of falls that will be experienced by a person with PD. Three datasets, containing a total of 1057 (671 female) participants, including 71 previously diagnosed with PD, were included in the analysis. Two statistical approaches were considered in predicting falls counts: the first based on a previously reported falls risk assessment algorithm, and the second based on elastic net and ensemble regression models. A predictive model for falls counts in PD showed a mean R2 value of 0.43, mean error of 0.42 and a mean correlation of 30% when the results were averaged across two independent sets of PD data. The results also suggest a strong association between falls counts and a previously reported inertial sensor-based falls risk estimate. In addition, significant associations were observed between falls counts and a number of individual gait and mobility parameters. Our preliminary research suggests that the falls counts predicted from the inertial sensor data obtained during a simple walking task have the potential to be developed as a novel digital biomarker for PD, and this deserves further validation in the targeted clinical population.

The Dartmouth Falls Prevention Training Program: Primary Care and Community-Based Organization Collaboration

Falls are a leading cause of fatal and non-fatal injuries in older adults. Older adult participation in community-based falls prevention programs can significantly reduce falls risk, however, identifying and referring individuals to appropriate programs can be challenging. Through Administration for Community Living (ACL) funding, we have developed a comprehensive Dartmouth Falls Prevention Training Program for healthcare and community based organizations that includes (1) Falls screening in primary care; (2) “Balance Days”, a community-based education and balance screening event encompassing falls risk stratification and coaching into programs; (3) Instructor Training for Tai Ji Quan: Moving for Better Balance®, a highly effective falls prevention program; and (4) Implementation Training, a research informed workshop shown to enhance community-based program sustainability through participant retention. We will share strategies for sustainable collaborations between primary care and CBOs to reach at-risk individuals and improve lives and decrease costs associated with falls.

Leveraging ACL Funding to Implement an Evidence-Based Falls Prevention Program in Three GWEPs

Primary care practices have a robust capacity to screen older adults for falls risk and refer them to evidence-based falls prevention programs delivered by Community Based Organizations (CBOs). However, due to a difference in the culture and nature of the work done in these two systems of care, there is often a lack of coordination and communication. Dartmouth has worked to bridge this gap for the past five years through our Health Resources and Services Administration (HRSA)-funded Geriatric Workforce Enhancement Program (GWEP). GWEP goals include the promotion of Age-Friendly Health Systems by focusing on the 4 Ms: What Matters Most, Medication, Mentation and Mobility. GWEPs commonly operationalize the Mobility component via falls risk screening and prevention programs. Though CBOs are well suited to deliver falls prevention programs, implementing, disseminating and sustaining community-based falls prevention programs in an environment of cost containment, limited funds for community-based services and workforce issues is challenging. Previous Administration for Community Living (ACL) grant funding enabled us to develop the Dartmouth Falls Prevention Training Center (D-TC) using our expertise in training and community-based implementation of evidence-based interventions. The D-TC offers training and implementation support to primary care and CBOs on screening, referring and capacity-building for falls prevention programs. We will discuss challenges and successes implementing the Dartmouth falls prevention model with two additional GWEP grantees, Baystate and the University of Rhode Island. Benefits of leveraging ACL and HRSA funding to achieve synergistic goals to reduce falls in older adults will be explored.

Implementation and initial evaluation of falls risk reduction resources in a rural Native American Community

Background Although falls are common and can cause serious injury to older adults, many health care facilities do not have falls prevention resources available. Falls prevention resources can reduce injury and mortality rates. Using the Centers for Disease Control and Prevention’s (CDC) Stopping Elderly Accidents, Deaths & Injuries (STEADI) model, a falls risk clinic was implemented in a rural Indian Health Service (IHS) facility. Methods A Fall Risk Questionnaire was created and implemented into the Provider’s Electronic Health Records system interface to streamline provider screening and referral of patients who may be at risk for falls to a group falls risk reduction class. Results Participants exhibited average improvements in the Timed Up and Go (6.8 s) (P = 0.0001), Five-Time Sit-to-Stand (5.1 s) (P = 0.0002), and Functional Reach (3.6 inches) (P = 1.0) tests as compared to their own baseline. Results were analyzed via paired t test. 71% of participants advanced out of an “increased risk for falls” category in at least one outcome measure. Of the participants to complete the clinic, all were successfully contacted and three (18%) reported one or more falls at the 90-day mark, of which one (6%) required a visit to the Emergency Department but did not require hospital admission. Conclusions In regards to reducing falls in the community, per the CDC STEADI model, an integrated approach is best. All clinicians can play a part in reducing elder falls.

Prevalence, Circumstances, and Risk Factors of Falls Among Community Dwelling Members of University of the Third Age

Objective: Study aimed to identify the prevalence of falls and associated contributory factors among older Malaysians.Methods: A cross sectional study among community dwelling older adults aged 50 years and above. Self-administered questionnaires on history of falls in the previous 12 months, physical assessment and computerized and clinical measures of balance were assessed on a single occasion.Results: Forty nine (31.0%) participants fell, with 4.4% reported having multiple falls within the previous 12 months. Slips were the most prevalent cause of falls, accounting for 49% of falls. More than half (54.5%) of falls occurred in the afternoon while participants walked inside the home (32.7%), outside home (30.6%), and 36.7% were in community areas. More than half of respondents were identified as having turning instability. Step Test, turn sway, depression, physical activity level and edge contrast sensitivity were significantly worse for fallers (p < 0.05). Multiple logistic regression analysis showed that turning performance, visual acuity and back pain were significantly associated with falls risk, accounting for 72% of the variance of risk factors for falls among studied population.Conclusion: Falls are common among community dwelling older Malaysians. The findings provide information of falls and falls risk factors among community dwelling older adults in Malaysia. Future intervention studies should target locally identified falls risk factors. This study has highlighted the importance of instability during turning as an important fall risk factor.