Stumbling Blocks and Safety Nets: Addressing Falls among Nursing Home Residents
Aged Care | Research | Sonja Phutachad Neef
Examining the impact of modifiable risk factors on falls is essential for facilities designed to support our elderly. This study examined how environmental and life-space factors impact falls in elderlies living in residential care facilities.
“A society that does not value its older people denies its roots and endangers its future. Let us strive to enhance their capacity to support themselves for as long as possible and, when they cannot do so anymore, to care for them.”
- Nelson Mandela, 1998 [1]
Introduction
By 2028, an estimated one million elderly persons will live in Aotearoa, New Zealand [2]. Research on elderly populations has increased in recent years, particularly on the risk of falling. Falls are a major cause of injury, rapid deterioration of health, and death. Thus, examining environmental influences on falls and fall risk are essential so that facilities such as nursing homes can be designed to protect and support our elderly people more effectively. Older persons in aged-residential care are three times more likely to experience falls than their community-dwelling peers [3]. Past studies have concluded that the causes of falls are multifactorial [4] and that fall risk is strongly related to the interaction between a person and their environment [5].
My summer research project topic looked at Environmental factors in the movement space of aged-care residents and their impact on falls and fallrelated injuries in participants from the Staying UpRight study. This project was an environmental sub-study of the larger Staying UpRight parent study. Staying UpRight is a randomised controlled study looking at the efficacy of a long-term exercise program to prevent falls of elderly living in longterm aged-residential care. Our sub-study research question focused on whether environmental indices are associated with falls and fall-injury risk. We also looked at whether changes in life space mobility during the COVID period were associated with falls. Life space refers to the area a resident moves in and from over a given time [6]. Several studies have looked at falls and fall risk in community-dwelling elderly and those in hospital care settings. However, to our knowledge, this is the first study looking at both fall-risk and life space mobility in long-term aged-residential care. We hypothesise that residents exposed to more hazards will have higher fall rates, as found by Jiang and colleagues (2021) [7].
Methods
The sub-study sample included 126 participants (main study = 303) from 12 facilities (main study = 25) in the Auckland | Tāmaki Makaurau region. Several research assistants carried out data collection. Due to the impact of the COVID-19 pandemic and subsequent lockdowns, particularly affecting residential care facilities, data collection spanned almost two years (see timeline in Figure 1[8]).
Data collection was conducted in two parts. In Part A, research assistants (RAs) interviewed the caregivers on how often the resident went to areas such as the toilet and the specific route the resident takes to get to these areas. For the Nursing Home Life Space Diameter (NHLSD), caregivers were asked about the residents’ movement within their room, outside the room (but within the unit), outside the unit (within the facility), and outside the facility. The index score was calculated using an equation from Tinetti and Ginter (1990) [6]. Due to the COVID-related delays, RAs also asked caregivers to think back to how the resident’s movement was one to two years prior to the interview when the main study began. The level of assistance required for walking, showering, and using the toilet was also collected.
Part B of data collection involved the RAs collecting environmental data. Environmental hazards were counted and scored in each area, adapted from a questionnaire by Jiang and colleagues (2021) [7]. Environmental hazards were scored on a scale of zero to two (zero = no hazards present). They included hazards such as unsecured furniture, slippery/uneven floors, and unmarked light switches (examples in Figure 2). Other environmental factors included bed and toilet height, and the distance from the resident’s bedroom to other areas in the facility (e.g., bedroom to dining). Elevations in these routes were also calculated and converted into a yes/no variable. Falls data and data on each participant’s physical, cognitive, and health indicators were taken from the Staying UpRight main study.
Distance and hazard scores were calculated and adjusted for weekly exposure to create distance and hazard indices. For example, a resident who needed to walk 21 metres to get to the dining room from their bedroom and who went to the dining room twice a day was assumed to have a weekly dining distance index of around 590 metres. After completing the calculations, falls and confounding variable data were merged from the Staying UpRight main study. Subsequently, preliminary data analysis was carried out, including analysis of the descriptive statistics for each variable and a correlation matrix of all variables in SPSS. The primary regression analysis used negative binomial regression and Cox proportional-hazard models.
Figure 1. Study Timeline During the COVID-19 Pandemic and Lockdowns
Figure 2. Typical fall hazards in an elderly home. Photo credit: Author’s own
The average age of our participants was 83.5 years (± 7.8). 63.5% of participants were female, and the rest were male (36.5%). Tables 1 and 2 outline the mean scores for distance and hazard indices, including the maximum and minimum possible scores for hazards (weekly and raw scores).
Within our sample, 68.8% of residents experienced falls, with an average fall rate being 3.6 (/person-years ± 11.9) (Figure 3).
Table 1: Descriptive Findings.
Table 2: Descriptive Findings - Weekly Hazard Scores.
Figure 3. Falls data for the sub-study participants from Staying UpRight
The project’s results have been interesting and at the same time, quite unexpected. Findings from the bivariate analysis indicate that hazard scores and distance indices were significantly associated with falls, albeit negatively (See Tables 3, 4, and 5). This contradicts our hypothesis that more hazards would be linked to higher fall rates, as residents who were exposed to more hazards were associated with fewer falls. However, the correlation coefficients are relatively weak, between -0.15 to -0.40.
Results from the initial negative binomial regression and Cox proportionalhazards regression model suggest that the only variable that significantly predicted falls (p-value < .001) and time to first fall (p-value = .002) in our sample was walking assistance. This may suggest that walking assistance may accurately indicate frailty, which would be associated with elderly persons being more prone to falls.
This project is still ongoing. Therefore, data cleaning and calculation processes will be repeated and re-checked before re-running the main regression in the coming months.
Table 3: Falls data for the sub-study participants from Staying UpRight.
Table 4 & 5: Significant correlations between fall rate, number of falls with hazards scores, and other key variables
This study examined whether environmental and life-space indices may predict falls and fall risk of the elderly living in aged-residential care in Tāmaki Makaurau | Auckland. While the findings suggest a weak negative association may be present between distance travelled, exposure to hazards, and fall rates, this may result from facilities improving the environment of residents who have had more falls or moving more frail individuals to a room closer to common areas. Thus, in an effort to prevent more falls, their surrounding environment was made less hazardous. Therefore, participants who were not prone to falling may not have required any intervention. Perhaps looking at the cause of the falls could shine a light on whether a hazard caused the fall and where facility staff may have modified a hazard to improve the room’s safety for that resident.
Jiang and colleagues (2021) assumed that participants from one facility were exposed to the same number of hazards, and these hazards were not adjusted for exposure [7]. Therefore, a strength of our study is that each participant has a raw hazard score based on the rooms they visit and an adjusted hazard score based on their weekly hazard. Thus, we can examine whether the frequency of movement to a specific room may be indicative of fall risk.
Limitations of the study include a relatively small sample size, which was impacted by the COVID-19 pandemic. In Auckland NZ, this had the greatest impact through late 2020 to early 2021, and again in late 2021. As the study only looked at movement, distance, and hazards at the time of the interview and one to two years prior, changes in mobility and environment as a result of falls and subsequent mitigation and adjustment of hazards could not be examined. Future research should examine whether the weak negative associations between fall rates with distance and environmental indices may be due to rapid intervention by aged-residential facilities. Thus, longitudinal studies should analyse whether such interventions effectively prevent falls and fall risk in this vulnerable population.
Discussion
In the next five years, around one in five people in Aotearoa will be aged over 65 [2]. As many move from the community into purpose-built facilities, improving facility design to minimise fall and fall injury risk is imperative. Our study findings allude to the complex interactions between personal and environmental factors that drive fall-risk in a nursing home context. These findings also have implications for elderly people living in other settings, including community-dwelling and those living in acute care homes. The multifactorial nature of falls and how both environmental and person-related factors interact must be considered when implementing fall-prevention measures and conducting future research.
The quote at the start of this article encapsulates the overall aim of this study and related research, which is to improve facilities and practices that would provide the elderly with opportunities to remain independent for as long as possible. Once they are unable to live independently, they should receive exceptional care and support tailored to their individual needs, which would improve the overall outcomes for all elderly people in our society.
Conclusion
Lessons that I have learned from this summer research project were:
• Do not underestimate the time and importance of data cleaning, as starting with a clean data-sheet is nearly impossible. Trying to get it as clean as possible is vital before continuing with data analysis, as this would reduce the need to go back and fix errors post analyses.
• Get as much knowledge and professional insights from your supervisors by asking lots of questions, engaging in debates, and being open to exploring the project deeper than you expected. Your supervisors are there to help and support you both in enhancing your understanding and thinking critically about your research process and results.
• Do not be afraid to apply for projects outside your faculty and degree, as you may find new interests and develop new skills that will be vital in future jobs and academic endeavours.
• Take every opportunity to practice presenting your findings to your supervisors, family, faculty staff, and fellow students, as they may suggest interesting ideas that help explain specific findings.
• Constructive feedback is crucial to improve how you conduct research, data cleaning, data analyses, and data presentation for your future studies and subsequent jobs.
Key Lessons
I was motivated to apply for this summer research project due to my personal experiences with my grandfather, who had Parkinson’s Disease, and my grandmother, who was his primary caretaker. I want to thank my supervisor, Dr Catherine Bacon, for all her continued support and guidance over the past few months. I would also like to acknowledge my supervisory team, Dr Lynne Taylor and Professor Ngaire Kerse, and the Staying UpRight main study statisticians, Simon Moyes, and Alana Cavadino, for all their help and feedback, and statistical guidance. I want to thank the HOPE Foundation for funding my summer research scholarship and giving me the opportunity to learn from an incredible group of researchers in the field of ageing, in which I hope to continue my studies. I want to acknowledge all the work done by the research assistants who completed data collection despite several COVID-19 lockdowns and restrictions in aged-residential care homes.
Acknowledgements
[1] N. Mandela. “Item 728 - Message from President Nelson Mandela on announcing 1999 the International Year of Older Persons.” The Nelson Mandela Foundation Archive.
[2] https://archive.nelsonmandela.org/index.php/za-com-mr-s-728 (accessed Feb. 5, 2023)
[3] Stats NZ. “One million people aged 65+ by 2028.” Stats.gov.nz. https:// www.stats.govt.nz/news/one-million-people-aged-65-by-2028/ (accessed Feb. 5, 2023)
[4] M. Q. Vu, N. Weintraub, & L. Z. Rubenstein “Falls in the Nursing Home: Are They Preventable?” Journal of the American Medical Directors Association, vol. 7, no. 3, pp. S53–S58, 2006, https://doi.org/10.1016/j. jamda.2005.12.016
[5] D. Lytras, E. Sykaras, P. Iakovidis, K. Kasimis, I. Myrogiannis, & A. Kottaras, “Recording of Falls in Elderly Fallers in Northern Greece and Evaluation of Aging Health-Related Factors and Environmental Safety Associated with Falls: A Cross-Sectional Study.” Occupational Therapy International, vol. 2022, 9292673, 2022. https://doi. org/10.1155/2022/9292673
[6] S. Iwarsson, V. Horstmann, G. Carlsson, F. Oswald, & H.-W. Wahl. “Person-environment fit predicts falls in older adults better than the consideration of environmental hazards only.” Clinical Rehabilitation, vol. 23, no. 6, pp. 558-567, 2009. https://doi. org/10.1177/0269215508101740
[7] M. E. Tinetti & S. F. Ginter. “The nursing home life-space diameter. A measure of extent and frequency of mobility among nursing home residents.” Journal of the American Geriatrics Society, vol. 38 no. 12, pp.1311-1315, 1990. https://doi.org/10.1111/j.1532-5415.1990. tb03453.x
[8] Y. Jiang, Q. Xia, P. Zhou, S. Jiang, V. K. Diwan, & B. Xu. “Environmental hazards increase the fall risk among residents of long-term care facilities: a prospective study in Shanghai, China.” Age and Ageing, vol. 50 no. 3, pp. 875-881, 2021. https://doi.org/10.1093/ ageing/afaa218
[9] Covid-19.govt.nz. “History of the COVID-19 Alert System.” Covid19.govt.nz. 2022. https://covid19.govt.nz/about-our-covid19-response/history-of-the-covid-19-alertsystem/ (accessed Feb. 6, 2023)
Credits: Figures and Tables taken from presentation slides. Presentation template created by Slidesgo (including icons by Flaticon, infographics and images by Freepik
Sonja Phutachad Neef - MSc, Psychology
Sonja will graduate in May with a BSc in Psychology and will be starting her Research Master’s majoring in Psychology. She is passionate about improving the emotional, psychological, physical, health, and cultural outcomes of the elderly. Additionally, she is interested in the cross-cultural differences in aging.