Using Gait Data to Inform Prescription Practice Among Nursing Home Residents to Reduce Medication-Induced Gait Disturbances
试验速览
- 阶段
- 不适用
- 状态
- 终止
- 发起方
- 入组人数
- 17
- 试验地点
- 1
- 主要终点
- Participant safety
研究概览
简要总结
This pilot study will explore the use of the BioIntellisense BioButton, a remote wearable multi-parameter monitor, to identify gait disturbances that occur as a side effect of polypharmacy.
详细描述
By 2030, an anticipated seven adults over 65 years old are projected to die every hour from a fall in the United States. This highlights the growing percentage of the elderly in our population and the impact of falls on them. Nationally, over 25% of older adults report falling each year and falls are the leading cause of fatal and non-fatal injuries. In Pennsylvania, 30% of older adults report falling each year, an underreported value that can be as high as 60%. The cost of care for falls is over $50 billion annually in the United States according to the Centers for Disease Control (CDC). Nursing home residents are especially at risk; among nursing home residents, the risk of falling is 2x greater than community residents.
Nursing home residents who take multiple medications especially antidepressants, anxiolytics, and blood pressure drugs have an increased risk for falling. Polypharmacy especially the use of five or more medications is significantly associated with a 21% increase of falls. Unfortunately, gait data is not routinely collected or available to geriatric clinicians for making medication decisions. Empowering clinicians with gait data can be a powerful piece of the puzzle; this information may help them decide whether the benefit of starting a new anti-hypertensive or mood medication is worth the risk.
Clinicians informed with gait data can make better medication decisions for their elderly patients; they will be able to consider gait disturbance and fall risk in their clinical judgement. As a result, gait data from continuous wearable technology can adjust medication practices, and reduce medication-induced falls. Moreover, the concept for gait-informed prescription practice complements the 4Ms (what matters, medication, mentation, and mobility) employed by age-friendly health systems. Continuous gait data can inform the implementation of the 4Ms by (1) engaging patients and their families about their care priorities related medications and their impact on gait, (2) adjusting medications that affect mobility, and (3) addressing depression treatment with behavioral modifications instead of medications. Results from this project can inform future studies that will move the needle towards implementing care practices consistent with the 4Ms.
研究设计
- 研究类型
- Interventional
- 分配方式
- Non Randomized
- 干预模型
- Parallel
- 主要目的
- Treatment
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Age >18yrs
- •Presence of gait documentation in EMR
排除标准
- •Age <18yrs
- •Non-English-speaking patients
- •Patients who cannot provide consent due to cognitive status
- •Bedbound, unable to stand
研究组 & 干预措施
Patient Group
Patients in this arm will wear the device, BioButton, continuous for 30 days as it collects physiologic and gait data, while receiving otherwise routine, standard of care.
干预措施: BioButton Device (Device)
Nurse Group
Nurses caring for patients wearing the device, BioButton, will assist in placing and removing the device. Otherwise, they will provide routine, standard of care to the enrolled patients to determine the overall feasibility of the device for clinical care providers.
干预措施: Providing Clinical Care with BioButton Device (Device)
结局指标
主要结局
Participant safety
时间窗: 30-days
This outcome measure will be reported via the % of patients who reported experiencing adverse events related to wearing the device (skin irritations, significant discomfort, etc.)
Protocol compliance
时间窗: 30-days
This outcome measure will be reported via the % of patients who were able to wear the device for the full 30-days.
Acceptability
时间窗: 30-days
This outcome measure will be reported via % of nurses who report that 'Yes' to the question: Would you recommend the patch to other nurses? on their acceptability survey.
次要结局
未报告次要终点
研究者
Charles Lin
Assistant Professor
University of Pittsburgh
