Background
There is a relatively high level of worry concerning falls in older adults in the US. Bjerk
et al. (2019) report a substantial increase in fall prevalence, primarily among older adults in the US. Given that falls lead to different types of consequences, including dangers such as brain injury or physical injuries such as hip fractures that might be especially difficult to treat, their propensity to the other injuries and hazards is heightened. Chronic conditions among older adults, including cardiovascular pathologies, diabetes, or thyroid disorders, which have also been on the rise, have a direct impact on how the elderly stay balanced, raising their odds of falling (Chen et al., 2023).
Overview of the challenge of falls
Most fall intervention measures are between physiotherapy and exercise programs. According to Bernocchi et al. (2019), stability requires a complex interaction of muscles, sensory information, and basal ganglia to maintain a stable gait and reduce falls. Nevertheless, the aging process alters these systems so that older adults are likely to fall (Hopewell et al., 2019). The most significant aspect of this weakness is that it mainly has diminished limb strength in limbs. The second reason is that older people are more susceptible to falls due to biological and health-related factors (Bjerk et al., 2019). It is challenging to assess which of the two methods is e more effective as a fall intervention or a source of solace. Some current measures for minimizing falls include physiotherapy, physical exercise, or a combination.
Theoretical framework
The ecological theoretical model examines multi-level factors that influence the outcome of an individual, ranging from their environment to social and personal factors (Chen et al., 2023). Furthermore, environmental factors have been identified as influential in fall outcomes realized by patients in healthcare facilities, and they encompass the type of exercise intervention that the elderly are subject to. Physiotherapy encompasses focused training on gait, balance, and posture; physical exercise focused on flexibility, strength, and balance; and a combination of the two (Quinn et al., 2020). The expected relationships examined for potent associations and effectiveness in minimizing falls include the association between the fall intervention measure and the risk and severity of the falls realized. The theoretical framework, therefore, provides a foundation for assessing and determining the risk and effectiveness of the various fall intervention exercises.
Rationale
The dominant challenge manifests in identifying the most effective approaches to mitigate these falls among adults due to their increased susceptibility compared to other age groups. Even though exercise intervention is considered appropriate for fall prevention, the dominant gap manifests in specifying the most effective form of exercise. Thus, an advanced Research Project (ARP) is designed to address the dominant gap in the literature that needs to identify the form of exercise ideal for fall prevention. These gaps are focused on the problem of falls among older adults, which is still topical and critical, giving rise to questions concerning the effectiveness of preventive interventions against falling. It is one of the most vital gaps since it relates to the necessity for extensive evaluation of fall prevention interventions, namely focusing on physical exercises. Although several studies have examined the efficacy of exercises in fall prevention, there needs to be more review that comprehensively synthesizes and evaluates this collective information.
Significance
This research helps provide helpful information on the efficacy of physical exercises in preventing falls. Thus, it may be used to develop policies and practices in an evidence-based manner, which will substantially reduce the burden on healthcare institutions and lower the cost associated with healthcare. Further, as the article fills a gap in the current literature with comprehensive results, it will add to the collective knowledge about preventing falls among older adults, facilitating informed and evidence-based research and healthcare practices in the future. Therefore, this descriptive study uses secondary data to examine the effective exercise methods for preventing falls among older adults. The descriptive study design is ideal for examining a complex phenomenon such as fall prevention among adults based on exercise.
Research Question
RQ1: How effective are different intervention programs (physiotherapy, physical exercise, and combined exercise program) in reducing the incidence of inpatient falls and other fall-linked injuries within the acute care settings for older adults aged 65 and above?
Method
Research Design
This study will utilize a descriptive study methodology that uses secondary based on available documents to determine the effectiveness of the different intervention measures in helping reduce fall rates and severity of falls among older adults. It will be used to determine the most effective intervention measures (physiotherapy, physical exercise, and a combination of physical exercise and physiotherapy) in helping minimize falls. An assessment of the demographic characteristics with which each intervention measure is most effective would be considered and the different settings where the interventions would be applied. The principal investigator will examine the fall risks for each category before and after the intervention measures have been introduced. Secondly, it will assess the characteristics of the participants assigned to teach the intervention measures. It will then examine the severity of fall accidents for each intervention category during the study period and after. The participants’ adherence to each intervention measure will also serve as a baseline for determining its effectiveness, as a low level of adherence would undeniably result in worse outcomes.
Study Participants or Source of Data
This study will leverage available data from medical records in the Agency for Health Care Administration (AHCA) database. AHCA is a healthcare agency in Florida that oversees the regulation of healthcare facilities and also contains databases with information regarding patient records and healthcare facilities (Facility Operations 2023). The AHCA database contains records from several healthcare facilities denoting the fall prevalence, fall risks, and the severity of different falls based on the age and intervention measures as well as the demographic characteristics of the other individuals.
Ethical Considerations
Before accessing the relevant data, several concerns will have to be addressed. Permission to obtain the relevant data will be obtained from the management and ethics personnel of the AHCA using the IRB letter as a justification for the requests, which will have to be obtained first. Data quality will be ensured that the patients should have been in either category or classified under no interventions. The potential risks for participants’ data include a risk to their confidentiality and private information. The records used in the study contain personal information and details about the participants, which have a risk of exposure, which could lead to loss of privacy and breach of confidentiality. The study will utilize a waiver of informed consent from IRB before continuation since the study has no interaction with human subjects and poses minimal risks to the patients whose data will be used in the investigation. The private information regarding participants will have all the direct names, addresses, [and personal numbers changed to prevent direct identification. Coded identifiers such as A001 would be used instead. Data will then be compiled into simpler, larger forms to avoid this identification. Encrypted data will be stored securely in password-locked folders, with only persons with the password able to access them. A system for monitoring attempts to access the data will be assessed. Data will be stored for a maximum of 7 days after the research [presentation, after which it will be deleted using reliable deleterious software.
Study Measures
Intervention Measures
The specific AHCA databases will be determined, and the organization under assessment will be determined. Permission obtained will be used to gain access through the numerous data restrictions. The AHCA facility has a portal and an avenue through which all the different types of data regarding patients and staff can be obtained. The dataset would encompass patients undergoing either of the three interventions. Detailing their name and sociodemographic status, including race, gender, and age. Secondly, the data will describe the type of intervention the patients underwent: physical exercise, physiotherapy, or combining both. Thirdly, the data will highlight previous instances of falls and the duration of their intervention. Different records will be obtained for the patients, and the data from the other records will be combined under the three categories of physical exercise, physiotherapy, or combined measures. This data will be summarized, including the number of falls undergoing all the different interventions alone.
Outcome measures
The other variable of interest encompasses the actual avenue of measurement because it details the outcomes that will be crucial for the study. Each patient, therefore, will be examined to determine the reoccurrence of the falls for each patient and their mobility rates after the interventions were introduced. This data will be obtained from the patient’s records review, which is a record that details different elements and facets regarding a patient’s data and information (Upadhyay & Hu, 2022). Data will also be obtained from each patient’s electronic health records. These records can also be obtained from the facility records review, which contains data on patient mobility and fall incidents (Tsai et al., 2020). By the end of the collection exercise, the patient’s data will have their name and sociodemographic details, fall intervention, and the interim outcomes of fall reoccurrence or levels of mobility. A code will be used to de-identify the patients and preserve their privacy and confidentiality. After introducing the interventions, patient mobility rates will be classified using the Berg Balance Scale (Berg Balance Test (scale): Scoring & results interpretation 2024). The Berg Scale will help standardize the protocol used in the assessment to ensure consistency.
Data Collection
Once the data has been obtained from the different healthcare facilities in the AHCA database, an assessment of the characteristics of the other intervention groups, including their age, race, and baseline levels of risk for falls, will be conducted. An evaluation of the relations between the outcome and the intervening types will then be determined. A descriptive summary of the participants’ demographics for each intervention category will be provided. Secondly, the frequency analysis regarding fall rates and rate of fall severity for each intervention category will then be determined. Data will be obtained from the Hospital’s Electronic health records from different facilities whose data can be found in the AHCA database, with SPSS used to summarize and simplify the data. Other measures to be determined include the adherence rate of participants to each intervention.
Data Analysis
The findings of the data will be reported in different ways. The characteristics of participants from each category will be noted in terms of their average age, underlying medical conditions, and sociodemographic background. The second measure will encompass the intervention outcomes. Finally, a comparative assessment will be conducted, determining whether there are differences in the levels of effectiveness for the different interventions employed. The primary measure will encompass the fall reoccurrence for elder adults taken through each measure within 1 year after the intervention was introduced. The secondary measure will contain the mobility rate of individuals in each intervention group within 1 year of the intervention’s use. Obtained data will initially be displayed in graphs, indicating the mobility and fall reoccurrence rates for the three intervention groups. A hypothetical analysis of the fall reoccurrence rate will be conducted using a Chi-square test to determine whether the differences in the fall reoccurrence are significant/ To assess differences in mobility rates, paired t-tests will be used for each data to help determine whether there were substantial differences. ANOVA will also be used to determine whether the mobility rate differences are significant. The adherence rate of participants to each intervention will also be assessed as a potential factor affecting the success of the interventions. The findings of the comparative analysis in terms of demographic characteristics, fall risk, fall severity, and the adherence rate of participants to the interventions will then be unveiled.
References
Berg Balance Test (scale): Scoring & results interpretation. Cleveland Clinic. (2024). https://my.clevelandclinic.org/health/diagnostics/22090-berg-balance-scale
Bernocchi, P., Giordano, A., Pintavalle, G., Galli, T., Ballini Spoglia, E., Baratti, D., & Scalvini, S. (2019). Feasibility and Clinical Efficacy of a Multidisciplinary Home-Telehealth Program to Prevent Falls in Older Adults: A Randomized Controlled Trial. Journal of the American Medical Directors Association, 20(3), 340–346. https://doi.org/10.1016/j.jamda.2018.09.003
Bjerk, M., Brovold, T., Skelton, D. A., Liu-Ambrose, T., & Bergland, A. (2019). Effects of a falls prevention program on health-related quality of life in older home care randomized controlled trial. Age and Ageing, 48(2), 213–219. https://doi.org/10.1093/ageing/afy192
Chen, W., Li, M., Li, H., Lin, Y., & Feng, Z. (2023). Tai Chi for fall prevention and balance improvement in older adults: a systematic review and meta-analysis of randomized controlled trials. Frontiers in Public Health, 11, 1236050. https://doi.org/10.3389/fpubh.2023.1236050
Facility operations. FHCA. (2023). https://www.fhca.org/facility_operations/ahca_covid_emergency_order_faqs
Hopewell, S., Copsey, B., Nicolson, P., Adedire, B., Boniface, G., & Lamb, S. (2019). Multifactorial interventions for preventing falls in older people living in the community: a systematic review and meta-analysis of 41 trials and almost 20,000 participants. British Journal of Sports Medicine, 54(22), bjsports-2019-100732. https://doi.org/10.1136/bjsports-2019-100732 https://doi.org/10.1377/hlthaff.2020.01470
Quinn, L., Kegelmeyer, D., Kloos, A., Rao, A. K., Busse, M., & Fritz, N. E. (2020). Clinical recommendations to guide Physical Therapy Practice for Huntington’s disease. Neurology, 94(5), 217–228. https://doi.org/10.1212/wnl.0000000000008887
Tsai, C. H., Eghdam, A., Davoody, N., Wright, G., Flowerday, S., & Koch, S. (2020). Effects of electronic health record implementation and barriers to adoption and use: A scoping review and qualitative content analysis. Life, 10(12), 327. https://doi.org/10.3390/life10120327
Upadhyay, S., & Hu, H. (2022). A qualitative analysis of the impact of electronic health records (EHR) on Healthcare Quality and safety: Clinicians’ lived experiences. Health Services Insights, p. 15, 117863292110707. https://doi.org/10.1177/11786329211070722
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