Abstract
Objectives:
The fall risk appraisal (FRA) matrix provides multidimensional approaches to detecting falls in late adulthood. This study investigated a modified FRA (mFRA) matrix based on self-assessed fall risk (SFR) and fear of falling (FOF). We also compared the mental health and characteristics of the four mFRA groups: Rational (low SFR and low FOF), Irrational (low SFR and high FOF), Incongruent (high SFR and low FOF), and Congruent (high SFR and high FOF).
Methods:
This study used data from 181 older adults (MeanAge = 74.58, SDAge = 7.13) in Central Florida. SFR was measured using the CDC Stopping Elderly Accidents, Deaths, and Injuries fall checklist. FOF was measured using the Short Fall Efficacy Scale-International. Descriptive and group comparison analyses were performed using SPSS.
Results:
The participants were categorized: Rational (n = 80, 44.2%), Irrational (n = 12, 6.6%), Incongruent (n = 26, 14.4%), and Congruent (n = 63, 34.8%). Rational showed better mental health, fewer falls, and higher health literacy. In contrast, Congruent reported worse mental health, more falls, and lower health literacy.
Conclusions:
Findings provided empirical evidence to design fall prevention and interventions that consider older adults’ experiential and psychological fall risks.
Clinical Implications:
Fall risks of older adults may be measured and comprehended as a multidimensional concept.
Keywords: Fall risk, fall risk appraisal, fear of falling, mental health
Introduction
Falls in late adulthood pose a major health concern, often leading to a decline in the health and well-being of older adults. To prevent older adults from falling, researchers have investigated environmental, physiological, and psychological fall risk factors that contribute to fall incidents in late adulthood (Weijer et al., 2018). Furthermore, given the linked association between various aspects of health and well-being and the vulnerable status of older adults, it is important to consider the synergetic impact of multiple fall risks causing fall incidents. Therefore, tools that measure an individual’s fall risk using multiple assessments, such as the Fall Risk Appraisal (FRA) matrix (Ng et al., 2023; Thiamwong, 2020), have been developed.
The FRA matrix is a hybrid fall risk model based on two types of fall risk measurements, such as perceived fall risk and physiological fall risk (Thiamwong, 2020). According to the cutoff values of each measure, four FRA groups were generated: Rational (low perceived fall risk – low physiological fall risk); Irrational (high perceived fall risk – low physiological fall risk); Incongruent (low perceived fall risk – high physiological fall risk); Congruent (high perceived fall risk – high psychological fall risk). Therefore, the FRA matrix enables comparisons between older adults who show discrepancies between these two measurements (maladaptive FRA, including Irrational and Incongruent) and those who have consistent levels of both fall risk measurements (adaptive FRA, including Rational and Congruent) (Thiamwong et al., 2021, 2023; Thiamwong, Sole, et al., 2020); Congruent may be suggested as the negative state of adaptive FRA (Ng et al., 2023). In this context, the FRA matrix is expected to provide screening and identify the current status of older adults’ fall risks.
The FRA matrix has been validated as a fall risk measurement to classify older adults into the four groups based on their balance and fear of falling (FOF) levels. Thiamwong, Sole, et al. (2020) identified the FRA group of community-dwelling older adults in Central Florida. Of 102 older adults, 40.2% were categorized as having maladaptive FRA (Irrational, 15.7%; Incongruent, 24.5%) and 59.8% were categorized as having adaptive FRA (Rational, 45.1 %; Congruent, 14.7%). Notably, a significantly higher proportion of Congruent (66.7%) reported a history of falls, followed by those in the Irrational (28.0%), Rational (21.7%), and Incongruent (18.8%). Moreover, Thiamwong et al. (2023) identified FRA groups of 123 community-dwelling older adults: 46.3% were classified as Rational, followed by Incongruent (19.5%), Irrational (17.9%), and Congruent (16.3%). Lastly, the FRA matrix was utilized as an indicator to assess the effectiveness of fall-preventive intervention programs (Thiamwong, Huang, et al., 2020). Particularly, by investigating the percentages of participants whose FRA changed from maladaptive to adaptative FRA (i.e., positive shifting), the study highlighted the FRA matrix as a valuable tool for assessing the effectiveness of fall preventive interventions.
As the FRA matrix has been validated, several studies have attempted to modify and expand upon it. The initial FRA model was composed of two primary components: psychological fall risk (i.e., FOF) and physiological fall risk (i.e., static balance) (Thiamwong, 2020). Regarding a modified FRA model, Thiamwong et al. (2021) utilized the scores of full-tandem stand test and FOF for the FRA matrix. Of 433 older adults in Thailand, 57.3% were classified as Irrational, 20.8% as Rational (20.8%), 19.6% as Congruent, and 2.3% as Incongruent. Furthermore, Ng et al. (2023) applied the FRA matrix to a sample of 2487 older adults aged 65 years and older using secondary data. In this study, the matrix incorporated perceived fall risk (i.e., FOF) and physiological limitations (e.g., disabilities in daily living, difficulty in walking/climbing stairs). They identified 42.0% as Congruent, 25.1 % as Incongruent, 23.5% as Rational, and 9.4% as Irrational. These studies provide the potential for hybrid fall risk measurements employing other dimensions of fall risk measures to detect the status of older adults’ fall risks, such as experiential fall risk and psychological fall risk (i.e., FOF). Experiential fall risk may reflect the personal fall risk that older adults encounter in their daily living (Rubenstein et al., 2011). By adding the experiential fall risk as one fall risk measurement to modify FRM, individuals could assess their personal fall risks by themselves and be aware of their fall risk status. This study proposes a modified FRA matrix that emphasizes experiential fall risk (i.e., self-assessed fall risk (SFR)) and psychological fall risk.
FRA group differences in health behavior and outcomes in late adulthood have been investigated. For example, Thiamwong et al. (2023) documented that Rational was significantly more likely to engage in moderate-vigorous PA (MVP A) than Irrational and Congruent, whereas no significant group differences were observed in light PA (LPA) and sedentary behaviors (SB). Although discrepancies between two fall risks of older adults may also be associated with their mental health, the limited studies have highlighted the association between FRA groups and mental health, more specifically, depression (Delbaere et al., 2010; Ng et al., 2023). Delbaere et al. (2010) tested FRA group differences in depression. Among the low physiological fall risk groups, high perceived fall risk was likely to experience higher depressive symptoms than low perceived fall risk. In contrast, among the high physiological fall risk groups, high perceived fall risk was likely to report more depressive symptoms than low perceived fall risk. Ng et al. (2023) showed that, except for high perceived and high physiological fall risks, older adults experiencing discrepancies in two types of fall risk measures (i.e., low perceived fall risks but high physiological fall risks and high perceived fall risks but low physiological fall risks) were more likely to experience depression.
This study aimed to investigate the modified FRA (mFRA) matrix based on SFR and FOF. Additionally, we examined the characteristics of the four FRA groups and tested group comparisons in several mental health outcomes including depression, anxiety, and self-perception of aging.
Methods
Participants
We analyzed data from 181 community-dwelling older adults who were engaged in an intervention program in Central Florida. The study protocol for the program has been published (Thiamwong et al., 2023). The College of Nursing at the University of Central Florida (UCF) launched the program after approval by the UCF Institutional Review Board (IRB) (STUDY00003206). The program has been preregistered on ClinicalTrials.gov (NCT05778604). Recruitment was accomplished at low-income senior apartments and neighborhood/community centers in partnership with the Mayor’s Committee on Livability and Healthy Aging (MCLHA). Participants were included in this study if they were older adults aged 60 years or older, exhibited a total score of 22 or greater on the Rowland Universal Dementia Assessment Scale: Multicultural Cognitive Assessment Scale (RUDAS; Storey et al., 2004), and could stand on the scale without any assistance. Exclusion criteria included reporting shortness of breath, receiving treatment from rehabilitation facilities, planning to move within 1 year, and having been hospitalized four times or more in the past year. Participants provided written informed consent before starting the program.
Measurements
Self-assessed fall risk
Self-assessed fall risk (SFR) was measured using the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) fall checklist (Rubenstein et al., 2011), provided by the Centers for Disease Control and Prevention (CDC). The measures have 12 items presenting fall-related experiences that are commonly encountered by older adults in their daily lives. Participants responded to two items using yes (2) or no (0) and to the remaining 10 items using yes (1) or no (0). The sum score of responses to all 12 items was used. Higher scores indicate a greater SFR, with a score of four or higher suggesting that the participant is at risk of falling. The Cronbach’s alpha of SFR in this study was .81.
Fear of falling
Fear of falling (FOF) was measured using the Short Fall Efficacy Scale-International (Short FES-I; Hauer et al., 2011). The measure includes seven items assessing the level of concern about falls while doing daily activities. Participants rated each item using a four-point Likert scale (1 = not at all concerned to 4 = very concerned). The sum score of the measure was used (Range = 7–28). Higher scores indicate higher FOF, with scores higher than 10 considered as high FOF. The Cronbach’s alpha of FOF in this study was .88.
Mental health
Depression. Depression was measured using the Patient Health Questionnaire-9 (PHQ-9; Kroenke et al., 2001). The PHQ-9 includes nine items asking about the severity of depressive symptoms that bother an individual’s their lives over the last 2 weeks. Each item was rated on a four-point Likert scale (0 = Not at all to 3 = Nearly every day). The sum score of responses to each item was used, with higher scores indicating more severe depressive symptoms. The Cronbach’s alpha of PHQ-9 in this study was .84. Anxiety. Anxiety was measured using the Geriatric Anxiety Inventory-Short form (GAI-SF; Byrne & Pachana, 2011). The measure consists of five items asking about the severity of the anxiety level within the past week. Each item was rated as either yes (1) or no (0). The sum score of response to each item was used, with higher scores indicating greater anxiety level. The Cronbach’s alpha of GAI-SF in this study was .82. Self-Perception of Aging. Self-perception of aging (SPA) was assessed using the Brief Ageing Perceptions Questionnaire (B-APQ; Sexton et al., 2014). The B-APQ has 17 items representing five domains: timeline-chronic (TC; 3 items, α = .63), consequence-positive (CP; 3 items, α = .80), control-positive (CtrlP; 3 items, α = .87), emotional representations (ER; 3 items, α = .69), consequence and control-negative (CCN; 5 items, α = .76). Participants responded to each item using a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). After reversing the score for the CCN item, each domain score was calculated by averaging the items within that domain. CP, CtrlP, and reversed CCN are considered positive SP As, and TC and ER are considered negative SP As. Higher scores of positive SP As indicate more positive perception of aging, whereas the higher scores of negative SP As indicate more negative perception of aging.
Social determinants of health
Demographics. This study analyzed participants’ age, gender (female and male), race/ethnicity (White, non-Hispanic, Black, non-Hispanic, Asian, non-Hispanic, and Hispanic), education level (less than high school, high school diploma, and college and above), perceived financial status (1 = Much less than adequate to 5 = much more than adequate), cohabitation status (alone, partner/spouse, family/friends, and others), and self-rated health. Fall History. Participants reported whether they had experienced falls in the past 12 months. Health Literacy. Health literacy (HL) was assessed using one item asking about the level of confidence of filling out medical forms by themselves. Responses were recoded a five-point Likert scale: 1 = Extremely to 5 = Not at all. The HL scores were reversed-coded, so higher scores reflected greater HL.
Statistical strategies
Descriptive and frequency analyses were performed to investigate the characteristics of the total population and each FRA group. Crosstab analyses, chi-square test, and Analysis of Variance (ANOVA) or the Kruskal–W allis test were conducted to test group comparisons. The effect sizes of each group comparison analysis were also tested: Epsilon-squared (ε2) values for Kruskal-Wallis, Cramer’s V for crosstab, and Eta-squared (η2) values for ANOV A. Analyses in this study were performed using SPSS 29.0.
Results
Based on the scores of SFR and FOF, the study sample were categorized as the mFRA matrix: Rational (n = 80, 44.2%); Irrational (n = 12, 6.6%); Incongruent (n = 26, 14.4%); and Congruent (n = 63, 34.8%). Figure 1 presents the mFRA matrix in this study. Across the four mFRA groups, Rational (M = 1.13, SD = 1.06, Mean rank = 43.88) and Irrational (M = 2.00, SD = 1.04, Mean rank = 64.00) significantly perceived less fall risks than Incongruent (M = 6.19, SD = 1.98, Mean rank = 121.15) and Congruent (M = 8.41, SD = 2.60, Mean rank = 143.54), H(3) = 141.459, p < .001, ε2 = .79. We also found mFRA category differences in FOF, H(3) = 142.313, p < .001, ε2 = .79: Rational (M = 7 .38, SD = 0.68, Mean rank = 49 .11) and Incongruent (M = 7.85, SD= 0.78, Mean rank = 67.00) had a lower FOF than Irrational (M = 11.83, SD = 1.75, Mean rank = 126.79) and Congruent (M = 15.44, SD = 4.33, Mean rank = 147.28) (see Table 1).
Figure 1.
mFRA Matrix based on Self-Assessed Fall Risk and Fear of Falling (FOF) (N = 181).
Table 1.
Social determinants of health statistics (n = 181).
| Total Sample (N = 181) M(SD)/n(%) |
1. Rational (n = 80, 44.2%) M(SD)/n(%) |
2. Irrational (n = 12, 6.6%) M(SD)/n(%) |
3. Incongruent (n = 26, 14.4%) M(SD)/n(%) |
4. Congruent (n = 63, 34.8%) M(SD)/n(%) |
p | Effect size | |
|---|---|---|---|---|---|---|---|
| Age | 74.58 (7.13) | 73.34 (6.26) | 74.33 (7.96) | 74.96 (7.53) | 76.05 (7.70) | .158 | .03 |
| Range = 61.08–91.98 | |||||||
| Gender | .522 | .11 | |||||
| Male | 19 (10.5%) | 11 (13.8%) | 1 (8.3%) | 1 (3.8%) | 6 (9.5%) | ||
| Female | 162 (89.5%) | 69 (86.3%) | 11 (91.7%) | 25 (96.2%) | 57 (90.5%) | ||
| Race/Ethnicity | .335 | .14 | |||||
| White, non-Hispanic | 17 (9.4%) | 7 (8.8%) | 1 (8.3%) | 1 (8.3%) | 8 (12.7%) | ||
| Black, non-Hispanic | 81 (44.8%) | 36 (45.0%) | 3 (25.0%) | 12 (46.2%) | 30 (47.6%) | ||
| Asian, non-Hispanic | 12 (6.6%) | 3 (3.8%) | 1 (8.3%) | 1 (3.8%) | 7 (11.1%) | ||
| Hispanic | 70 (38.7%) | 34 (42.5%) | 7 (58.3%) | 12 (46.2%) | 17 (27.0%) | ||
| Education level | .546 | .12 | |||||
| Less than high school | 25 (13.8%) | 8 (10.0%) | 2 (16.7%) | 5 (19.2%) | 10 (15.9%) | ||
| High school diploma | 86 (47.5%) | 36 (45.0%) | 5 (41.7%) | 15 (57.7%) | 30 (47.6%) | ||
| College or above | 70 (38.7%) | 36 (45.0%) | 5 (41.7%) | 6 (23.1%) | 23 (36.5%) | ||
| Perceived financial status | .493 | .16 | |||||
| Much less than adequate | 17 (9.4%) | 7 (8.8%) | 1 (8.3%) | 3 (11.5%) | 6 (9.5%) | ||
| Less than adequate | 34 (18.8%) | 13 (16.3%) | 5 (41.7%) | 6 (23.1%) | 10 (15.9%) | ||
| Just enough | 100 (55.2%) | 45 (56.3%) | 3 (25.0%) | 15 (57.7%) | 37 (58.7%) | ||
| More than adequate | 25 (13.8%) | 11 (13.8%) | 3 (25.0%) | 2 (7.7%) | 9 (14.3%) | ||
| Much more than adequate | 4 (2.2%) | 4 (5.0%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | ||
| Self-rated health | 3.07 (0.84) | 3.43 (0.71) [4] | 3.17 (0.94) | 3.04 (0.66) | 2.62 (0.85) [1] | <.001 | .17 |
| Cohabitation status | |||||||
| Alone | 123 (68.0%) | 57 (71.3%) | 8 (66.7%) | 17 (65.4%) | 41 (65.1%) | .326 | .14 |
| Partner/Spouse | 25 (13.8%) | 14 (17.5%) | 2 (16.7%) | 3 (11.5%) | 6 (9.5%) | ||
| Family/Friends | 30 (16.6%) | 9 (11.3%) | 2 (16.7%) | 6 (23.1%) | 13 (20.6%) | ||
| Others | 3 (1.7%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | 3 (4.8%) | ||
| Falls in the past year* | 59 (32.6%) | 8 (10.0%) | 0 (0.0%) | 17 (65.4%) | 31 (54.0%) | <.001 | .52 |
| Health literacy | 3.84 (1.34) | 4.27 (0.93) [4] | 3.92 (1.68) | 3.73 (1.25) | 3.33 (1.58) [1] | .004 | .07 |
Note. P-values are from non-parametric group comparisons, Analysis of Variance (ANOVA), or crosstab analyses between the four FRA groups. Effect sizes are from Epsilon-squared (ε2) for Kruskal–Wallis test, Cramer’s V for crosstab analyses, Eta-squared (η2) for Analysis of Variance (ANOVA). Statistically different groups in the post hoc Bonferroni comparison at p < .05 are listed in brackets under mean/SD values.
There was a significant group difference, but post hoc did not show significant associations among each two-pair.
Mental health and self-perception of aging
Rational reported fewer depressive symptoms (M = 10.83, SD = 3.22, Mean rank = 63.49) than Incongruent (M = 12.92, SD = 3.22, Mean rank = 102.54) and Congruent (M = 15.46, SD = 5.21, Mean rank = 122.63), H(3) = 47.69, p < .001, ε2 = .26. Likewise, Relational showed lower anxiety (M = 0.76, SD = 1.28, Mean rank = 72.91) than Incongruent (M = 1.42, SD = 1.55, Mean rank = 94.38) and Congruent (M = 2.24, SD = 1.92, Mean rank = 94.38), H(3) = 26.78, p < .001, ε2 = .15. As for the SP A domains, Rational was more likely to accept the negative consequences and control of aging (M = 16.21, SD = 3.68) than Congruent (M = 13.82, SD = 4.00), F(3, 163) = 4.09, p < .01, η2 = .09. Moreover, Irrational was more likely to show greater timeline-chronic (M = 9.67, SD = 3.63, Mean rank = 99.04), than Congruent (M = 9.43, SD = 3.93, Mean rank = 97.14), Incongruent (M = 8.88, SD = 2.76, Mean rank = 85.98), and Rational (M = 8.30, SD = 2.49, Mean rank = 74.77), H(3) = 7.803, p = .050, ε2 = .05. However, there were no significant differences in timeline-chronic between each two-pair. However, there were no significant category differences in the other four SP A domains (see Table 2).
Table 2.
Research variables characteristics (n = 181).
| Total Sample (N = 181) M(SD)/Median |
1. Rational (n = 80, 44.2%) M(SD)/Median |
2. Irrational (n = 12, 6.6%) M(SD)/Median |
3. Incongruent (n = 26, 14.4%) M(SD)/Median |
4. Congruent (n = 63, 34.8%) M(SD)/Median |
p | Effect size | |
|---|---|---|---|---|---|---|---|
| Self-assessed fall risk | 4.45 (3.83)/3.00 | 1.13 (1.06)/1.00 [3,4] | 2.00 (1.04)/2.00 [3,4] | 6.19 (1.98)/6.00 [1,2] | 8.41 (2.60)/8.00 [1,2] | <.001 | .79 |
| Fear of falling | 10.55 (4.58) | 7.38 (0.68) [2,4] | 11.83 (1.75) [1,3] | 7.85 (0.78) [2,4] | 15.44 (4.33) [1,3] | <.001 | .79 |
| Mental health | |||||||
| Depression | 12.87 (6.43) | 10.83 (3.22) [3,4] | 12.83 (5.52) | 12.92 (3.22) [1] | 15.46 (5.21) [1] | <.001 | .26 |
| Anxiety | 1.39 (1.71) | 0.76 (1.28) [4] | 1.08 (1.68) | 1.42 (1.55) | 2.24 (1.92) [1] | <.001 | .15 |
| Self-perception of aging | |||||||
| Timeline chronic | 8.85 (2.80) | 8.30 (2.49) | 9.67 (3.63) | 8.88 (2.76) | 9.43 (2.93) | .050 | .05 |
| Consequence positive | 11.72 (2.33) | 11.57 (2.64) | 12.33 (1.16) | 12.64 (1.98) | 11.39 (2.10) | .092 | .04 |
| Emotional representation | 7.45 (2.75) | 7.03 (2.43) | 7.50 (3.78) | 7.04 (2.28) | 8.21 (3.00) | .096 | .04 |
| Consequence-control negative | 15.12 (4.17) | 16.21 (3.68) [4] | 14.00 (5.72) | 15.20 (4.42) | 13.82 (4.00) [1] | .002 | .09 |
| Control positive | 12.05 (2.41) | 11.90 (2.58) | 13.00 (1.54) | 11.80 (3.16) | 12.18 (1.87) | .598 | .01 |
Note. P-values are from non-parametric group comparisons, Analysis of Variance (ANOVA), or crosstab analyses between the four FRA groups. Effect sizes are from Epsilon-squared (ε2) for Kruskal–Wallis test, Cramer’s V for crosstab analyses, Eta-squared (η2) for Analysis of Variance (ANOVA). Statistically different groups in the post hoc Bonferroni comparison at p < .05 are listed in brackets under mean/SD values.
There was a significant group difference but post hoc did not show significant associations among each two-pair.
sedentary behaviors.
light physical activity.
Moderate-vigorous physical activity.
Social determinants of health
Table 1 presents the social determinants of health characteristics of the total study sample and each FRA group. Among the four mFRA groups, Congruent was the oldest (M = 76.05, SD = 7.70, range = 61.39–91.98), followed by Incongruent (M = 74.96, SD = 7.53. range = 63.53–89.47), Irrational (M = 74.33, SD = 7.96, range = 61.99–86.50), and Rational (M = 73.34, SD = 6.26, range = 61.08–88.98). Rational reported a significantly better health (M = 3.43, SD =0.71, Mean rank = 111.04) than Congruent (M = 2.62, SD = 0.85, Mean rank = 65.81), H(3) = 30.462, p < .001, ε2 = .17. As for the fall experiences, 65.4% of (Incongruent n = 17) and 54.0% of (Congruent n = 31) have experienced falls in the last year, x2(3) = 47.93, p < .001, Cramer’s V = .52. Lastly, Rational showed significantly greater health literacy (M = 4.27, SD = 0.93, Mean rank= 101.55) than Congruent (M = 3.33, SD= 1.58, Mean rank= 72.96), H(3) = 13.114, p < .01, ε2 = .07. There was no significant difference in other social determinants of health, including age, among the mFRA groups.
Discussion
This study aimed to categorize low-income community-dwelling older adults into four mFRA groups, based on their scores of SFR and FOF. Moreover, the differences across the mFRA groups were investigated in relation to SFR, FOF, mental health, and social determinants of health were investigated.
First, the largest FRA group was Rational (44.2%), followed by Congruent (34.8%), Incongruent (14.4%), and Irrational (6.6%). The majority of the sample in this study were aware of both FOF and SFR at a similar level (i.e., adaptive FRA including Rational and Congruent), whereas 21.0% showed a discrepancy in both fall risk measures (i.e., maladaptive FRA including Irrational and Incongruent). The discrepancies between SFR and FOF may be caused by individual experiences and resources to cope with those two types of fall risks. The concept of linked lives posits that older adults whose spouses, family members, or friends experienced falls may be traumatized; even though they perceived low fall risks in their daily living, their concerns or fears toward falling may be high (Elder, 1998). Also, the high level of older adults’ health literacy may alert them to be aware of fall risks in their daily living settings, encourage them to search for appropriate information regarding how to prevent falls and to modify their health behaviors such as physical activity. The proportions of each FRA group in this study were not consistent with findings from previous research. Thiamwong, Sole, et al. (2020) and Thiamwong et al. (2023) documented Rational as the largest group; however, in both studies, the second largest category was Incongruent, followed by Irrational and Congruent. Also, Ng et al. (2023) identified Congruent as the largest FRA group, followed by Incongruent, Rational, and Irrational. A study in Thailand (Thiamwong et al., 2023) reported Irrational as the largest group, followed by Incongruent, Rational, and Irrational. These inconsistencies in the proportions of FRA groups might have occurred since this study applied the mFRA model focusing more on the association between psychological fall risk (i.e., FOF) and experiential fall risk (i.e., SFR). Therefore, compared to previous studies focusing on discrepancies between subjective and objective fall risks, self-biased report could be reflected in this study. Furthermore, social determinants of health based on cultural background, socioeconomic status, and neighborhood environment may be associated with how individuals perceive fall risks.
Next, we tested the FRA group differences in SFR, FOF, mental health and social determinants of health. Both Rational and Irrational reported lower SFR than Incongruent and Congruent. Rational and Incongruent reported lower FOF than Irrational and Congruent. Rational was likely to have fewer depressive symptoms, lower anxiety levels, and a more positive perception of negative consequences of and control over aging. Rational was significantly more likely to consider their health as good and have better health literacy than Congruent. Incongruent reported experiencing more falls in the past year than those in the other categories.
In FRA literature, the association between FRA groups and demographics has not been consistent. Thiamwong, Sole, et al. (2020) found no FRA group differences in demographics, whereas the other studies documented significant differences in demographics between FRA groups (Ng et al., 2023; Thiamwong et al., 2021, 2023). In contrast to the findings of the current study, previous research has reported that Congruent was more likely to experience falls than other groups (Ng et al., 2023; Thiamwong et al., 2021; Thiamwong, Sole, et al., 2020). In addition, Congruent and Incongruent were likely to report repeated falls more than Rational and Irrational (Ng et al., 2023).
Limitation
Irrational had a relatively small sample size, and no one has experienced falls in the past year. Therefore, the association between the mFRA matrix and fall incidents needs to be interpreted with caution. Second, this study analyzed data of community-dwelling older adults residing in Central Florida. To improve the generalizability of the mFRA model focusing more on psychological fall risks, a large sample size may be required in future research.
Implications
This study is the first suggesting a modified FRA model emphasizing psychological fall risk (i.e., FOF) – experiential fall risk (i.e., SFR) association and testing the model using data collected from community-dwelling older adults. By shedding light on the multidimensional assessment of fall risk, this study suggests that the mFRA may serve as a useful tool for detecting the fall risk status of older adults given psychological and SFR, which may be associated with fall incidents. Particularly, when a senior center provides a fall-related interventions, the mFRA may be utilized as a screening to measure the current fall risks of older adults and, also, be a tool to track the changes in their fall risks before, after, and following-up the intervention. Lastly, this study provides empirical evidence to inform the design of more targeted fall preventive intervention programs for older adults. In particular, considering one of mFRA measurements estimating psychological fall risk, a preventive intervention may focus on improving older adults’ resilience and self-efficacy to coping with their FOF by proving both PA and education. The purpose of such interventions may be to support older adults categorized as Irrational, Incongruent, and Congruent in transitioning toward Rational.
Clinical Implications.
The modified FRA matrix, composed of experiential and psychological fall risks, contributes to detecting a multidimensional concept of the fall risk status of older adults.
Given the higher fall incident experience in Incongruent than other groups, prevention/intervention may focus on reducing the discrepancy between experiential and psychological fall risks of older adults.
Funding
This work was supported by the National Institute on Minority Health and Health Disparities under Grant [R01MD018025] and the Office of the Director, Chief Officer for Scientific Workforce Diversity, Office the National Institutes of Health under supplemental Grant number [3R01MD018025-02S1].
Biographies
Dahee Kim, PhD, is a postdoctoral scholar in the College of Nursing at the University of Central Florida. Her research interests focus on diversity and intersectionality in aging and adult development, emotional well-being, resilience and coping strategies, psychosocial factors of falls, community-based prevention and intervention programs, and life course perspective to enhance the coping strategies of older adults, experiencing multiple stressors within contexts, improving their resilience and emotional well-being.
Ladda Thiamwong, PhD, RN, FAAN, FNAP, is a Full Professor, Florida Blue Endowed Professor for Healthy Communities. An aging expert with 20+ years of experience in gerontological nursing education, Thiamwong has spent most of her career and research on healthy aging, fall prevention in older adults and aging education. Her research has been published and presented both nationally and internationally.
Yingru Li, PhD, is an Associate Professor in the Department of Sociology at the University of Central Florida. Her research focuses on using Geographic Information Systems (GIS), statistical and survey methods to understand socio-environmental disparities and their impact on health outcomes (e.g. fall risk, obesity) and health behaviors. She also examines health disparities from social, spatial, and environmental dimensions.
Jethro Raphael M. Suarez, MS, is a Biomedical Engineering PhD student in the Department of Mechanical and Aerospace Engineering at the University of Central Florida’s College of Engineering and Computer Science. His research focuses on balance, physical activity, human kinetics, biomechanics, and the development of rehabilitative assistive devices designed to enhance the well-being of older adults, with the ongoing goal ofleveraging technology to address real-world challenges.
Rui Xie, PhD, is an Associate Professor of Statistics and Data Science at the University of Central Florida, a biostatistician for the College of Nursing, and a member of the Disability, Aging and Technology (DAT) Cluster. His research focuses on developing innovative statistical and machine learning methods to tackle complex, data-intensive challenges in health and biomedical sciences. He has extensive expertise in designing scalable algorithms for online learning, streaming data sampling, spatiotemporal data modeling, and statistical reinforcement learning. His work aims to bridge methodological advances with impactful applications, enabling more accurate, efficient, and timely insights from large and dynamic datasets.
Yan Wang, PhD is an Associate Professor of Epidemiology at the University of Florida. Her research interests focus on leveraging advanced technologies and methods (e.g., wearable sensors, ecological momentary assessment/EMA) to improve the understanding of health behaviors (e.g., alcohol/cannabis use) and their impacts on health outcomes and quality of life among various populations (e.g., older adults with chronic pain, cancer patients, persons with HIV).
Victoria Loerzel, PhD, RN, FAAN, is the Beat M. and Jill L. Kahli Endowed Professor in Oncology Nursing and an oncology nurse with more than two decades of experience. Her recent studies have worked towards reducing negative outcomes of cancer treatment in adults through innovative technology-based interventions that empower adults to engage in preventative and self-management self-care strategies at home. Her current R0l (R01NR20003) is funded by the National Institute of Nursing Research and the goal of the project is to examine the effectiveness of serious gaming and reducing negative outcomes from chemotherapy-induced nausea and vomiting. Vicki has been with the CON since 2007 and a long history of leadership within the college and UCF. She served as the president of faculty association from 2013 through 2018 and has been chairperson for numerous committees within the college. She has also served as the Honors Undergraduate Thesis liaison/coordinator since 2010.
Footnotes
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The data that support the findings of this study are available on request from the corresponding author, DK. The data are not publicly available due to their containing information that could compromise the privacy of research participants.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author, DK. The data are not publicly available due to their containing information that could compromise the privacy of research participants.