Stepping-forward affordance perception test cut-offs: Red-flags to identify community-dwelling older adults at high risk of falling and of recurrent falling

Catarina Pereira; Jorge Bravo; Guida Veiga; José Marmeleira; Felismina Mendes; Gabriela Almeida

doi:10.1371/journal.pone.0239837

Abstract

The stepping-forward affordance perception test (SF-APT) fills an important gap within the screening of falls risk factors by considering the perception of affordances. The test showed to be a valid instrument for community-dwelling older adults falls risk assessment. The present study aimed to distinguish and test the key outcomes of the SF-APT usable for falls risk assessment in community-dwelling older adults to determine the respective cut-offs. This cross-sectional study enrolled 347 participants (73.1 ± 6.2 years; non-fallers: 57.9%; fallers: 42.1%; recurrent-fallers: 17.9%). Falls occurrence and SF-APT outcomes were assessed. Analyses were performed using multivariate binary logistic regression analysis and receiver operating characteristic (ROC). The area under the ROC curve was computed (AUC) for each built model explaining falling or recurrent falling. Results distinguished the Estimated stepping-forward, and Absolute-error in interaction with Error-tendency as the SF-APT key outcomes for falls risk assessment [AUC_falling: 0.665 (CI 95%: 0.608–0.723); AUC_{falling recurrently}: 0.728 (CI 95%: 0.655–0.797)]. Computed cut-offs’ analysis showed that (i) a boundary stepping-forward estimation >58 cm plus an underestimation bias >5 cm (>42^nd percentile) avoid older adults to be recurrent-fallers, and (ii) a boundary stepping-forward estimation >62 cm plus an underestimation bias >6 cm (>54^th percentile) avoid older adults to be fallers. In conclusion, results suggest that SF-APT is a valuable tool for falls risk assessment in community-dwelling older adults. Interventions targeting the prevention of falls should consider the above key outcomes and the respective cut-offs as alert red-flags.

Citation: Pereira C, Bravo J, Veiga G, Marmeleira J, Mendes F, Almeida G (2020) Stepping-forward affordance perception test cut-offs: Red-flags to identify community-dwelling older adults at high risk of falling and of recurrent falling. PLoS ONE 15(10): e0239837. https://doi.org/10.1371/journal.pone.0239837

Editor: Jean L. McCrory, West Virginia University, UNITED STATES

Received: January 21, 2020; Accepted: September 14, 2020; Published: October 8, 2020

Copyright: © 2020 Pereira et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data are available at DOI: 10.17605/OSF.IO/GM8BK.

Funding: This work was supported by European Fund for regional development through Horizon 2020 –Portugal 2020 - Programa Operacional Regional do Alentejo (ALT20-03- 0145- FEDER-000007) with respect to the ESACA Portuguese study - Ageing Safety in Alentejo. Understanding for action. The sponsors had no role in the design, analysis, or preparation of this manuscript. There is no conflict of interest.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Researchers have been looking for tools to identify older adults at high risk for falls [1–3], mainly because falls risk assessment represents a crucial strategy to prevent falls occurrence [4]. Recently, Palumbo et al. [5] computed an algorithm explaining falls occurrence, which included previous falls, gait, and balance. Nonetheless, the ability of previously mentioned falls risk assessment tools has shown a low-moderate ability to correctly discriminate those older adults who fall, suggesting a gap in the assessed outcome used for falls risk screening. An ecological framework was developed by Gibson as regards the concept of affordance concerning the relationship between perception and action [6,7]. According to affordance theory, opportunities for actions emerge under a particular set of conditions and body characteristics [8]. A safe and successful action performance would depend on personal self-perceived action boundaries, and a perceptual misestimation in locomotor skills in older adults may lead to an accidental fall [9]. Luyat et al. [10], have already reported that ageing-associated misperception of affordances could be a risk factor for falling in older adults. Thus, we hypothesized that the affordance´s perception could be a key element for being considered on falls risk assessment.

A recent study developed and examined the validity and the reliability of the Stepping-Forward Affordance Perception Test (SF-APT), which has shown to be a valid instrument for community-dwelling older adult’s falls risk assessment [11]. This test measures the perceived stepping-forward boundary as well as the real stepping-forward boundary, such that the judgment bias (real performance–estimation) provides information about actual performance and the perceived action capability. The SF-APT is a valid instrument for community-dwelling older adults falls risk assessment [11], which fills an important gap within the screening of falls risk factors [2,3,12] by considering the perception of affordances [11]. Besides, this test is quick and easy to administer and almost inexpensive. Several authors have provided cut-offs values of risk for falls concerning their measuring tools, such as for the Berg Balance Scale [13], the Fullerton Advanced Balance Scale [14], or the Performance Oriented Mobility Assessment [15]. Such cut-off values work as red-flags of alert, which may be compared to the patient tests performance, allowing health professionals, family members, and patients themselves to become aware of their falls risk status [16,17]. However, the role of SF-APT outcomes on falls occurrence is still unclear; particularly, the cut-off values which best discriminate non-fallers from fallers and recurrent-fallers are unknown. Hence, the establishment of SF-APT key outcomes for falls risk assessment and the determination of the respective cut-off values of risk will allow researchers and clinicians to quantify the remoteness between individual SF-APT scores and the recommended reference values in each key outcome. Therefore, it will be possible to interpret the SF-APT results and to identify the necessary changes (which and how much) to avoid the threat of falls. This approach may be useful to the design of more effective falls’ prevention programs.

Thus, the present study aims to distinguish the SF-APT outcomes, which should be used for falls risk assessment in community-dwelling older adults, and determine the respective cut-offs usable to identify people at high risk of falling and of falling recurrently.

Material and methods

Participants

Three hundred and sixty-seven Portuguese community-dwelling older adults were originally enrolled in the study. The participants were recruited by invitation and through printed flyers distributed in community settings (including sports/recreation centers). The inclusion criteria were age ≥65 years old, living independently in the community, absence of falls occurrence due to hazardous occurrences and unusual tasks, and absence of cognitive impairment according to the Portuguese version of the Mini-Mental State Examination [18,19]. Twenty volunteers were excluded: 11 showed cognitive impairment, and 9 had fallen due to hazardous occurrences and unusual tasks, such as jumping over a wall with a bicycle, going rollerblading, or, unusually, acting as a longshoreman to change the furniture in the house. Thus, 347 older adults (aged 73.1 ± 6.2 years; with 5.2 ± 3.7 years of school; non-fallers: 57.9% vs. fallers: 42.1%; recurrent-fallers: 17.9%) were included in the study. Of them, 76.7% were female (height: 152.6 ± 6.4 cm, BMI: 29.0 ± 4.0 m/kg², and body fat mass percentage 41.0 ± 6.4%) and 23.3% were male (height: 166.6 ± 6.8 cm, BMI: 28.2 ± 3.7 m/kg², and body fat mass percentage: 26.4 ± 6.2%). Ethical approval was obtained from the Ethics committee for research in the areas of human health and well-being–Universidade de Évora (reference number 16–012), and informed written consent was provided by the participants.

Data collection

Participants were assessed individually for all measures by a multidisciplinary team, including human kinetics, rehabilitation, nursing, and gerontology fields, who attended a course on the measuring protocols. The evaluators were blind to the study’s objectives. Intra-rater reliability (intraclass correlation coefficient (ICC) ranging from 0.83 to 0.97) and in inter-rater reliability (ICC ranging from 0.99 to 1.00) showed a good test-retest reliability.

Perception and stepping-forward boundaries.

The SF-APT was used to assess the perception and stepping-forward boundaries, and its protocol is described in detail elsewhere [11]. In order to avoid learning bias, a scoring attempt was preceded by a trial attempt, without any feedback between attempts. The following main outcomes are measured: Estimated stepping-forward (cm) and Real stepping-forward (cm). Posterior computation was performed to produce the following outcomes: Algebraic-error (Real performance–Estimation), Absolute-error (|Algebraic-error|), Absolute-percent-error (|1 –Estimation/Real performance| x 100) and Error-tendency which concerns the error direction (overestimation: Real<Estimated vs. underestimation: Real>Estimated) [20]. These computed variables measure the error between the real action boundary and the precepted action boundary, which is the judgment bias magnitude and respective direction.

Falls.

The occurrence of falls in the previous 12 months and the circumstances surrounding each fall (such as where did the fall occur, what action did it take, what was the reason for the fall, which were the fall-related injuries) were carefully assessed by an interviewer who filled out a questionnaire. A non-faller was defined as a person who had not fallen in the previous 12 months, a faller as a person who had fallen at least once in this period, and a recurrent faller as a person who had fallen more than once in the same period [16,21].

Complementary measures.

Sociodemographic characteristics were assessed by a questionnaire filled out by the interviewer. Height and weight were evaluated by using a stadiometer (Secca 770, Hamburg, Germany) and an electronic scale (Secca Bella 840, Hamburg, Germany), to compute body mass index (m/kg²). Body fat and lean mass were measured [22] by bioimpedance (Omron BF 511, USA).

Data analysis

Descriptive statistics, based on mean and 95% confidence interval (CI), relative frequencies, and percentiles, were used to characterize the SF-APT outcomes participants’ data and illustrate fall occurrence. An exploratory analysis of the relationship between Estimated and Real stepping-forward outcomes was performed using Pearson’s correlation test.

In a previous study [11], multivariate binary logistic regression analysis and receiver operating characteristic (ROC) analysis were used to identify the key variables (outcomes) from the SF-APT, which should be included in the falls risk assessment. In the present study, these same techniques were used to test alternative models to the ones found in the previous research of Almeida et al. [11]—and respective key variables—as well as to quantify the role of each selected key variable on falls occurrence. For that, an overestimation error-tendency was coded as 0, and an underestimation error-tendency was coded as 1. Therefore, the internal validation of both most parsimonious and fit models (fallers vs. non-fallers and recurrent-fallers vs. non-fallers) was tested by using a resampling or cross-validation procedure [23]. For cross-validation, participants were clustered into ten equal groups by sampling randomly without replacement. The area under the ROC curve (AUC) was calculated using the probabilities that were generated by cross-validation [24]. Finally, multivariate binary logistic regression analysis and ROC analysis were used again to establish the cut-offs of risk of falling and of recurrent-falling. The cut-off point for falling probability was established by maximizing both sensitivity and specificity of the built and validated model. Therefore, the outlined cut-off point for falling probability was used to compute the percentile and correspondent absolute value on each SF-APT key variable, which best discriminates fallers from non-fallers. For this, the equation generated by regression modeling was successively solved using the respective key variables absolute values from the 1^st to the 99^th percentile. The percentile and correspondent absolute values that equaled the mentioned above falling probability cut-off point were identified as the cut-off values for each key variable (point estimation); namely, the cut-off values usable to discriminate fallers from non-fallers were identified. The same procedure was used to establish the cut-offs regarding falling recurrently. A similar methodology has been previously carried out [24].

Statistical analyses were performed using the SPSS package version 24 (SPSS Inc, Chicago, IL, USA). Statistical significance was set as p < 0.05.

Results

Participants’ results on the SF-APT variables according to falls occurrence are displayed in Table 1. Descriptive analysis shows that in general, either the Estimated stepping-forward or the Real stepping-forward were smaller for fallers than for non-fallers and even smaller for recurrent-fallers. This analysis concerning error results evidenced that, in general, the bias between Estimated and Real stepping-forward was smaller in fallers than in non-fallers, and that this bias was even smaller in recurrent-fallers. Moreover, it was observed that the percentage of non-fallers showing an underestimation Error-tendency bias was higher than the percentage of fallers and recurrent-fallers showing this same Error-tendency. Hence, the underestimation bias was more prevalent for all participants (either non-fallers, fallers, or recurrent-fallers). However, the overestimation bias was more frequent in recurrent-fallers than in fallers, and more frequent in fallers than in non-fallers. Also, it should be noted that the analysis of the percentile values denotes a great variation of the SF-APT outcomes data. Finally, correlation analysis evidenced a positive relationship between Estimated and Real stepping-forward (r = 0.847, p < 0.001).

Download:

Table 1. Description of participants SF-APT outcomes data.

https://doi.org/10.1371/journal.pone.0239837.t001

Table 2 presents the results of the two most parsimonious and fit models, built by multivariate binary regression, explaining falling and falling recurrently regarding the SF-APT outcomes. This Table illustrates that the multivariate binary regression analysis selected the variables Estimated stepping-forward and Absolute-error in interaction with Error-tendency as the key variables from the SF-APT, which should be included in falls risk assessment. These variables selection was computed both for assessing the risk of falling and for assessing the risk of falling recurrently, p < 0.05. Age and gender were not selected as significant factors explaining fall occurrence in multivariate modeling. Hence, in both models described above it was observed that, the larger the Estimated stepping-forward was, the lower the likelihood of falling (~3.6% for each additional cm) and of falling recurrently (~4.9% for each additional cm); and that, the larger the Absolute-error was, if the Error-tendency was of underestimation, the lower the likelihood of falling (5.9% for each additional cm) and of falling recurrently (8.6% for each additional cm). The Hosmer-Lemeshow goodness-of-fit test was not significant either regarding the falling model (p = 0.591) or the falling recurrently model (p = 0.241). Regarding the falling model, a sensitivity of 67.1% and a specificity of 58.7% were observed for the computed optimal cut-off point of 0.412 (41.2%) for the probability of falling. This model revealed an AUC of 0.665 (CI 95%: 0.608–0.723), and the predicted probabilities that were generated by the cross-validation procedure showed an AUC of 0.587 (CI 95%: 0.527–0.647). In what concerns to the falling recurrently model, a sensitivity of 66.1% and a specificity of 71.6%, were observed for the computed optimal cut-off point of 0.261 (26.1%) for the probability of falling recurrently. This model revealed an AUC of 0.728 (CI 95%: 0.655–0.797), and the predicted probabilities that were generated by the cross-validation procedure showed an AUC of 0.605 (CI 95%: 0.525–0.685).

Download:

Table 2. The two most parsimonious and fit build binary logistic regression models and respective key SF-APT variables explaining falling and falling recurrently (falling vs. non-falling model: N = 347; falling recurrently vs. non-falling model: N = 263).

https://doi.org/10.1371/journal.pone.0239837.t002

It is important to note that, in the modeling procedure, two similar multivariate binary regression models were built (with the same variables included in the models explaining falling and falling recurrently, as shown in Table 2). In both models, the reference category for the variable Error-tendency was “underestimation bias”. In this analysis, it was observed that, when an overestimation Error-tendency bias occurred, for each additional cm on the Absolute-error, there was an increase of 8.9% of the likelihood of falling [OR: 1.089 (95% CI: 1.001–1.185)] and there was an increase of 10.8% of the likelihood of falling recurrently [OR: 1.108 (95% CI: 1.043–1.177)].

Fig 1 illustrates the change of the probability (point estimation) of falling and of falling recurrently as a function of Estimated stepping-forward and Absolute-error in interaction with Error-tendency (underestimation). A decrease in the likelihood of falling and in the probability of recurrently failing was observed with the increase in the Estimated stepping-forward, and the increase in the underestimated Absolute-error. Regarding the cut-off point (0.412), fallers were identified as those who estimated a boundary stepping-forward smaller than 62 cm and who showed an underestimated Absolute-error shorter than 6 cm. These cut-offs values correspond to the 54^th percentile. Regarding the cut-off point (0.261), recurrent-fallers were identified as those who estimated a boundary stepping-forward smaller than 58 cm and who showed an underestimated Absolute-error shorter than 5 cm. These cut-offs values correspond to the 42^nd percentile.

Download:

Fig 1. Probability falling and of falling recurrently associated with the key SF-APT outcomes (estimated stepping-forward, and the Absolute-error in interaction with Error-tendency) and respective cut-offs discriminating fallers from non-fallers or recurrent-fallers from non-fallers.

https://doi.org/10.1371/journal.pone.0239837.g001

Discussion

The results from the present study suggest that affordance´s perception works as a key component of falls risk assessment. Above all, this study results validated and confirmed (through a cross-validation procedure) that the best models to explain either falling recurrently or falling include the Estimated stepping-forward and the Absolute-error in interaction with Error-tendency as key SF-APT outcomes). Both models showed that there was a quantitative and a qualitative relationship between the participants’ results on these variables and the risk of falls; that is, the larger the Estimated stepping-forward was, the lower the likelihood of falling recurrently and of falling; and, in cases of Error-tendency underestimation bias, the larger the Absolute-error was, the lower the likelihood of falling recurrently and of falling. Moreover, data analysis identified cut-off values as red-flags of alert for the risk of falling recurrently and falling, which discriminate non-fallers from recurrent-fallers and non-fallers from fallers, corresponding to the 42^nd and 54^th percentiles, respectively. Our results suggest that to avoid being a recurrent-faller, older adults should estimate a boundary stepping-forward higher than 58 cm and underestimate an Absolute-error larger than 5 cm (i.e., above the 42^nd percentile). Besides, to avoid being a faller, older adults should estimate a boundary stepping-forward higher than 62 cm and underestimate an Absolute-error larger than 6 cm (i.e., above the 54^th percentile). These values were valid for both genders, independently of age.

Regarding the SF-APT outcomes, the results of the present study are consistent with the studies focused on community-dwelling older adults’ risk of falls, which found that poor performance in physical abilities is associated with an increased risk of falls [14,25,26], particularly if involving gait or stepping ability [27]. The present study showed that for non-fallers, the Real stepping-forward was larger than for recurrent-fallers and fallers. Nonetheless, it was the Estimated stepping-forward variable, which best explained the risk of falls, either of falling recurrently or of falling. This variable also showed higher results for non-fallers compared to recurrent-fallers and fallers and evidenced to be strongly related to the Real stepping-forward for general participants. This relationship is in line with Konczak and colleagues’ [28] observations that also found a link between older adults’ perception and their action capabilities, although regarding stair climbing. Thus, the present study results indicate that, despite the Real minus Estimated bias, most participants have a good perception of their ability to step-forward, suggesting that an appropriated approach explaining the occurrence of falls requires the combination of the physical ability to perform the task (Real stepping-forward) and the respective perception judgment (Estimated stepping-forward). This hypothesis is in accordance with Fajen and colleagues [29], who argued that secure behaviors in daily routine require the ability to perceive which actions are possible within the limits of capabilities.

Although some results from the present study are in line with Fajen and colleagues’ [29] suggestion that the judgment accuracy of the Estimated and Real stepping-forward could avoid falls occurrence, the present study showed that a large underestimation of the real capabilities—Absolute-error—is inversely associated with falls occurrence. In contrast, the alternative built model showed that the higher the overestimated Absolute-error, the greater the risk of falls. These results evidenced that the underestimation bias works as a safety buffer to avoid falls, and that over excessive belief on the stepping-forward task performance (evidenced by an overestimated bias) works as a risk for falls occurrence. Thus, concerning the ecological approach of falls and affordances [6,30], falls may result from a failed action consequent from an overestimation mismatch between what older adults believe they can do and what in reality, they are capable. Such mismatch can put older adults at risk by leading them to perform actions that they are no longer physically capable of doing.

The use of the equations generated by multivariate binary logistic regression modeling evidenced that a larger underestimated Absolute-error may partially compensate a smaller Estimated stepping-forward as regards to the risk (probability) of falling recurrently and of falling and vice versa (see Fig 1). Nonetheless, ROC analysis allowed the establishment of cut-off values on these variables, which are red-flags for these negative occurrences and should be used as a reference to compare with the values achieved by each subject on the SF-APT. For instance, an older adult who estimates to perform a step-forward of 50 cm and who performs the task with an underestimated Absolute-error of 2 cm (results corresponding to the 25^th percentile, see Table 1) is at a very high risk of falling recurrently and of falling since he/she is categorized either as a recurrent-faller as well as a faller (see Fig 1). However, the comparison of his/her results with the alert red-flag gives the information that if the older adult increases his/her Estimated stepping-forward by ≥8 cm [red-flag for falling recurrently (58 cm)—gets Estimated stepping-forward (50 cm)] = 8 cm], and increases his/her underestimated Absolute-error on ≥ 3 cm [red-flag for falling recurrently (5 cm)—gets Estimated stepping-forward (2 cm) = 3 cm], this person would no longer be a recurrent-faller. Moreover, if the older adult increases his/her Estimated stepping-forward on ≥12 cm [red-flag for falling (62 cm)—gets Estimated stepping-forward (50 cm) = 12 cm] and increase his/her underestimated Absolute-error on ≥4 cm [red-flag for falling (6 cm)—gets Estimated stepping-forward (2 cm) = 4 cm], this person would no longer be a faller.

The present study was able to identify new modifiable key outcomes as risk factors for falls. Such finding is particularly important considering recent studies [31] alerting that, to be effective, falls prevention interventions must target modifiable risk factors. Although the development of older adults’ self-confidence may be important for the maintenance of physical functioning and, therefore, contribute to falls prevention [32], the present study suggests that excessive belief in performance ability is a serious risk to falls occurrence. Thus, in what concerns to falls prevention, it would be crucial to increase individuals’ stepping-forward capacity and individuals’ perception of action boundaries for a securer stepping-forward estimation, that is, ensuring that the Real-Estimation bias (Error-tendency) would be of underestimation.

The present study findings suggest that SF-APT should be included in falls risk screening once a year, as recommended by the American Geriatric Society and the Centers for Disease Control and Prevention [33] because it has shown to assess a key risk factor for falls and most likely, susceptible to change. The established risk cut-off will allow quantifying how far the assessed older adult is of the safe test result and, therefore, the definition of effective rehabilitation programs designed to improve the impaired high fall risk factor. Nonetheless, future researches shall study the role of affordance perception outcomes on falling risk considering other risk factors, such as the previous falls, gait, and balance reported by Palumbo et al. [5], to include the SF-APT in the algorithms recommended for falls risk screening.

Limitations of this study include the use of a retrospective recall for assessing falls occurred in the previous 12 months. Nonetheless, this data was considered acceptable given that a healthy cognitive function was required for all participants, and that the information was collected by detailed interviews focusing on the circumstances surrounding each fall. Still, it is possible that there was an underestimation of the accessed falls. Although gender was not computed as significantly explaining fall occurrence in multivariate modeling, the relatively small number of men compared to women may be a limitation of the study since some authors point to gender as a moderator of fall occurrence [34]. Another limitation is the relatively small number of fallers, and the small number of recurrent-fallers, limiting the present study’s statistical power. Therefore, more extensive research could be necessary to confirm and generalize the findings of this study to other work sites. Nonetheless, the predicted probabilities generated by multivariate binary regression modeling (concerning falling and falling recurrently), and the predicted probabilities produced by the cross-validation procedures (concerning falling and falling recurrently) were both significant. Finally, as the relationship between SF-APT variables and reported falls was explored cross-sectionally, some precautions should be taken in the interpretation of directionality and cause-effect relationships. Hence, future follow-up researches using the SF-APT and prospective falls assessment should be carried out with older populations. Besides these limitations, the SF-APT is quick and easy to apply, is well tolerated by older adults, requires ordinary material and can be used in several settings by professionals of different fields, as a geriatric screening for determining the risk of falling.

Conclusion

The SF-APT Estimated stepping-forward and the Absolute-error in interaction with Error-tendency were confirmed as the key outcomes which should be used to assess the risk of falls of community-dwelling older adults. A qualitative and quantitative relationship was found between these outcomes and the risk of falls. Moreover, it was found that estimating a boundary stepping-forward larger than 58 cm plus showing an underestimation bias larger than 5 cm (values above 42^nd percentile) avoids older adults to be recurrent-fallers while estimating a boundary stepping-forward larger than 62 cm plus showing an underestimation bias larger than 6 cm (values above 54^th percentile) avoids older adults to be fallers. Giving the relevance of these results, interventions targeting the prevention of falls should consider the above key outcomes and the respective cut-offs as alert red-flags. Therefore, SF-APT would be a valuable tool for falls risk assessment in community-dwelling older adults.

Supporting information

S1 Fig. Probability falling and of falling recurrently associated with the key SF-APT outcomes (estimated stepping-forward, and the Absolute-error in interaction with Error-tendency) and respective cut-offs discriminating fallers from non-fallers or recurrent-fallers from non-fallers.

https://doi.org/10.1371/journal.pone.0239837.s001

(DOCX)

S1 Table. Description of participants SF-APT outcomes data.

https://doi.org/10.1371/journal.pone.0239837.s002

(DOCX)

S2 Table. The two most parsimonious and fit build binary logistic regression models and respective key SF-APT variables explaining falling and falling recurrently (falling vs. non-falling model: N = 347; falling recurrently vs. non-falling model: N = 263).

https://doi.org/10.1371/journal.pone.0239837.s003

(DOCX)

Acknowledgments

The authors would like to thank all the volunteers, who participated in this study.

References

1. Shimada H, Suzukawa M, Tiedemann A, Kobayashi K, Yoshida H, Suzuki T. Which neuromuscular or cognitive test is the optimal screening tool to predict falls in frail community-dwelling older people? Gerontology. 2009;55(5):532–8. pmid:19776609.
- View Article
- PubMed/NCBI
- Google Scholar
2. Palumbo P, Palmerini L, Bandinelli S, Chiari L. Fall risk assessment tools for elderly living in the community: can we do better? PLoS one. 2015;10(12):e0146247. pmid:26716861
- View Article
- PubMed/NCBI
- Google Scholar
3. Lusardi MM, Fritz S, Middleton A, Allison L, Wingood M, Phillips E, et al. Determining risk of falls in community dwelling older adults: a systematic review and meta-analysis using posttest probability. J Geriatr Phys Ther. 2017;40(1):1. pmid:27537070
- View Article
- PubMed/NCBI
- Google Scholar
4. Coughlin D, Nordman-Oliveira SE, Schlaak M, Ford JH 2nd. Falls Prevention Process in Assisted Living Communities. J Appl Gerontol. 2019;38(6):805–24. pmid:29258374.
- View Article
- PubMed/NCBI
- Google Scholar
5. Palumbo P, Becker C, Bandinelli S, Chiari L. Simulating the effects of a clinical guidelines screening algorithm for fall risk in community dwelling older adults. Aging Clin Exp Res. 2019;31(8):1069–76. pmid:30341644.
- View Article
- PubMed/NCBI
- Google Scholar
6. Gibson JJ. The theory of affordances, In” Perceiving, Acting and Knowing”, Eds. RE Shaw and J. Bransford. Erlbaum; 1977.
7. Gibson J. The theory of affordances. The Ecological Approach to Visual Perception (pp. 127–143). Boston: Houghton Miffin; 1979.
8. Fajen BR, Riley MA, Turvey MT. Information, affordances, and the control of action in sport. Int J Sport Phychol. 2009;40(1):79.
- View Article
- Google Scholar
9. Butler AA, Lord SR, Taylor JL, Fitzpatrick RC. Ability versus hazard: risk-taking and falls in older people. J Gerontol A Biol Sci Med Sci. 2015;70(5):628–34. Epub 2014/11/13. pmid:25387729.
- View Article
- PubMed/NCBI
- Google Scholar
10. Luyat M, Domino D, Noël M. Can overestimating one's own capacities of action lead to fall? A study on the perception of affordance in the elderly. Psychol Neuropsychiatr Vieil. 2008;6(4):287–97. pmid:19087910
- View Article
- PubMed/NCBI
- Google Scholar
11. Almeida G, Bravo J, Folgado H, Rosado H, Mendes F, Pereira C. Reliability and construct validity of the stepping-forward affordance perception test for fall risk assessment in community-dwelling older adults. PLoS One. 2019;14(11):e0225118. Epub 2019/11/21. pmid:31747394.
- View Article
- PubMed/NCBI
- Google Scholar
12. Lord SR, Menz HB, Sherrington C. Home environment risk factors for falls in older people and the efficacy of home modifications. Age Ageing. 2006;35 Suppl 2:ii55–ii9. pmid:16926207.
- View Article
- PubMed/NCBI
- Google Scholar
13. Lima CA, Ricci NA, Nogueira EC, Perracini MR. The Berg Balance Scale as a clinical screening tool to predict fall risk in older adults: a systematic review. Physiotherapy. 2018;104(4):383–94. pmid:29945726.
- View Article
- PubMed/NCBI
- Google Scholar
14. Hernandez D, Rose DJ. Predicting which older adults will or will not fall using the Fullerton Advanced Balance scale. Arch Phys Med Rehabil. 2008;89(12):2309–15. pmid:18976981.
- View Article
- PubMed/NCBI
- Google Scholar
15. Tinetti ME. Performance-oriented assessment of mobility problems in elderly patients. J Am Geriatr Soc. 1986;34(2):119–26. pmid:3944402.
- View Article
- PubMed/NCBI
- Google Scholar
16. Pereira C, Baptista F, Infante P. Role of physical activity in the occurrence of falls and fall-related injuries in community-dwelling adults over 50 years old. Disabil Rehabil. 2014;36(2):117–24. pmid:23594055.
- View Article
- PubMed/NCBI
- Google Scholar
17. Hou WH, Kang CM, Ho MH, Kuo JM, Chen HL, Chang WY. Evaluation of an inpatient fall risk screening tool to identify the most critical fall risk factors in inpatients. J Clin Nurs. 2017;26(5–6):698–706. pmid:27533486.
- View Article
- PubMed/NCBI
- Google Scholar
18. Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–98. Epub 1975/11/01. pmid:1202204.
- View Article
- PubMed/NCBI
- Google Scholar
19. Guerreiro M, Silva AP, Botelho M, Leitão O, Castro-Caldas A, Garcia C. Adaptação à população portuguesa da tradução do Mini Mental State Examination (MMSE). Sinapse. 1994;3(9–10).
- View Article
- Google Scholar
20. Almeida G, Luz C, Martins R, Cordovil R. Do children accurately estimate their performance of fundamental movement skills? JMLD. 2017;5(2):193–206.
- View Article
- Google Scholar
21. Gill DP, Zou GY, Jones GR, Speechley M. Comparison of regression models for the analysis of fall risk factors in older veterans. Ann Epidemiol. 2009;19(8):523–30. pmid:19394862.
- View Article
- PubMed/NCBI
- Google Scholar
22. Zaluska W, Malecka T, Mozul S, Ksiazek A. Whole body versus segmental bioimpedance measurements (BIS) of electrical resistance (Re) and extracellular volume (ECV) for assessment of dry weight in end-stage renal patients treated by hemodialysis. Przegl Lek. 2004;61(2):70–3. pmid:15230144.
- View Article
- PubMed/NCBI
- Google Scholar
23. Efron B, Tibshirani R. An introduction to the bootstrap. Chapman and Hall: Boca Raton; 1993.
24. Pereira C, Fernandes J, Raimundo A, Biehl-Printes C, Marmeleira J, Tomas-Carus P. Increased Physical Activity and Fitness above the 50(th) Percentile Avoid the Threat of Older Adults Becoming Institutionalized: A Cross-sectional Pilot Study. Rejuvenation Res. 2016;19(1):13–20. pmid:26083134.
- View Article
- PubMed/NCBI
- Google Scholar
25. Yang NP, Hsu NW, Lin CH, Chen HC, Tsao HM, Lo SS, et al. Relationship between muscle strength and fall episodes among the elderly: the Yilan study, Taiwan. BMC Geriatr. 2018;18(1):90. pmid:29653515.
- View Article
- PubMed/NCBI
- Google Scholar
26. Pereira CL, Baptista F, Infante P. Men older than 50 yrs are more likely to fall than women under similar conditions of health, body composition, and balance. Am J Phys Med Rehabil. 2013;92(12):1095–103. pmid:23811613.
- View Article
- PubMed/NCBI
- Google Scholar
27. Pieruccini-Faria F, Montero-Odasso M. Obstacle Negotiation, Gait Variability and Risk of Falling. Results from "Gait and Brain Study". J Gerontol A Biol Sci Med Sci. 2018. pmid:30380013.
- View Article
- PubMed/NCBI
- Google Scholar
28. Konczak J, Meeuwsen HJ, Cress ME. Changing affordances in stair climbing: the perception of maximum climbability in young and older adults. J Exp Psychol Hum Percept Perform. 1992;18(3):691–7. pmid:1500869.
- View Article
- PubMed/NCBI
- Google Scholar
29. Fajen BR, Diaz G, Cramer C. Reconsidering the role of movement in perceiving action-scaled affordances. Hum Mov Sci. 2011;30(3):504–33. pmid:21333367
- View Article
- PubMed/NCBI
- Google Scholar
30. Noel M, Bernard A, Luyat M. [The overestimation of performance: a specific bias of aging?]. Geriatr Psychol Neuropsychiatr Vieil. 2011;9(3):287–94. pmid:21896432.
- View Article
- PubMed/NCBI
- Google Scholar
31. Bhasin S, Gill TM, Reuben DB, Latham NK, Gurwitz JH, Dykes P, et al. Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE): A Cluster-Randomized Pragmatic Trial of a Multifactorial Fall Injury Prevention Strategy: Design and Methods. J Gerontol A Biol Sci Med Sci. 2018;73(8):1053–61. pmid:29045582.
- View Article
- PubMed/NCBI
- Google Scholar
32. Gazibara T, Kurtagic I, Kisic-Tepavcevic D, Nurkovic S, Kovacevic N, Gazibara T, et al. Falls, risk factors and fear of falling among persons older than 65 years of age. Psychogeriatrics. 2017;17(4):215–23. pmid:28130862.
- View Article
- PubMed/NCBI
- Google Scholar
33. Stevens JA, Phelan EA. Development of STEADI: a fall prevention resource for health care providers. Health Promot Pract. 2013;14(5):706–14. pmid:23159993.
- View Article
- PubMed/NCBI
- Google Scholar
34. Etman A, Wijlhuizen GJ, van Heuvelen MJ, Chorus A, Hopman-Rock M. Falls incidence underestimates the risk of fall-related injuries in older age groups: a comparison with the FARE (Falls risk by Exposure). Age Ageing. 2012;41(2):190–5. Epub 2012/02/22. pmid:22345295.
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Shimada H, Suzukawa M, Tiedemann A, Kobayashi K, Yoshida H, Suzuki T. Which neuromuscular or cognitive test is the optimal screening tool to predict falls in frail community-dwelling older people? Gerontology. 2009;55(5):532–8. pmid:19776609.
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Palumbo P, Palmerini L, Bandinelli S, Chiari L. Fall risk assessment tools for elderly living in the community: can we do better? PLoS one. 2015;10(12):e0146247. pmid:26716861
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Lusardi MM, Fritz S, Middleton A, Allison L, Wingood M, Phillips E, et al. Determining risk of falls in community dwelling older adults: a systematic review and meta-analysis using posttest probability. J Geriatr Phys Ther. 2017;40(1):1. pmid:27537070
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Coughlin D, Nordman-Oliveira SE, Schlaak M, Ford JH 2nd. Falls Prevention Process in Assisted Living Communities. J Appl Gerontol. 2019;38(6):805–24. pmid:29258374.
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Palumbo P, Becker C, Bandinelli S, Chiari L. Simulating the effects of a clinical guidelines screening algorithm for fall risk in community dwelling older adults. Aging Clin Exp Res. 2019;31(8):1069–76. pmid:30341644.
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. Gibson JJ. The theory of affordances, In” Perceiving, Acting and Knowing”, Eds. RE Shaw and J. Bransford. Erlbaum; 1977.

[ref7] 7. Gibson J. The theory of affordances. The Ecological Approach to Visual Perception (pp. 127–143). Boston: Houghton Miffin; 1979.

[ref8] 8. Fajen BR, Riley MA, Turvey MT. Information, affordances, and the control of action in sport. Int J Sport Phychol. 2009;40(1):79.
View Article
Google Scholar

[24] View Article

[25] Google Scholar

[ref9] 9. Butler AA, Lord SR, Taylor JL, Fitzpatrick RC. Ability versus hazard: risk-taking and falls in older people. J Gerontol A Biol Sci Med Sci. 2015;70(5):628–34. Epub 2014/11/13. pmid:25387729.
View Article
PubMed/NCBI
Google Scholar

[27] View Article

[28] PubMed/NCBI

[29] Google Scholar

[ref10] 10. Luyat M, Domino D, Noël M. Can overestimating one's own capacities of action lead to fall? A study on the perception of affordance in the elderly. Psychol Neuropsychiatr Vieil. 2008;6(4):287–97. pmid:19087910
View Article
PubMed/NCBI
Google Scholar

[31] View Article

[32] PubMed/NCBI

[33] Google Scholar

[ref11] 11. Almeida G, Bravo J, Folgado H, Rosado H, Mendes F, Pereira C. Reliability and construct validity of the stepping-forward affordance perception test for fall risk assessment in community-dwelling older adults. PLoS One. 2019;14(11):e0225118. Epub 2019/11/21. pmid:31747394.
View Article
PubMed/NCBI
Google Scholar

[35] View Article

[36] PubMed/NCBI

[37] Google Scholar

[ref12] 12. Lord SR, Menz HB, Sherrington C. Home environment risk factors for falls in older people and the efficacy of home modifications. Age Ageing. 2006;35 Suppl 2:ii55–ii9. pmid:16926207.
View Article
PubMed/NCBI
Google Scholar

[39] View Article

[40] PubMed/NCBI

[41] Google Scholar

[ref13] 13. Lima CA, Ricci NA, Nogueira EC, Perracini MR. The Berg Balance Scale as a clinical screening tool to predict fall risk in older adults: a systematic review. Physiotherapy. 2018;104(4):383–94. pmid:29945726.
View Article
PubMed/NCBI
Google Scholar

[43] View Article

[44] PubMed/NCBI

[45] Google Scholar

[ref14] 14. Hernandez D, Rose DJ. Predicting which older adults will or will not fall using the Fullerton Advanced Balance scale. Arch Phys Med Rehabil. 2008;89(12):2309–15. pmid:18976981.
View Article
PubMed/NCBI
Google Scholar

[47] View Article

[48] PubMed/NCBI

[49] Google Scholar

[ref15] 15. Tinetti ME. Performance-oriented assessment of mobility problems in elderly patients. J Am Geriatr Soc. 1986;34(2):119–26. pmid:3944402.
View Article
PubMed/NCBI
Google Scholar

[51] View Article

[52] PubMed/NCBI

[53] Google Scholar

[ref16] 16. Pereira C, Baptista F, Infante P. Role of physical activity in the occurrence of falls and fall-related injuries in community-dwelling adults over 50 years old. Disabil Rehabil. 2014;36(2):117–24. pmid:23594055.
View Article
PubMed/NCBI
Google Scholar

[55] View Article

[56] PubMed/NCBI

[57] Google Scholar

[ref17] 17. Hou WH, Kang CM, Ho MH, Kuo JM, Chen HL, Chang WY. Evaluation of an inpatient fall risk screening tool to identify the most critical fall risk factors in inpatients. J Clin Nurs. 2017;26(5–6):698–706. pmid:27533486.
View Article
PubMed/NCBI
Google Scholar

[59] View Article

[60] PubMed/NCBI

[61] Google Scholar

[ref18] 18. Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–98. Epub 1975/11/01. pmid:1202204.
View Article
PubMed/NCBI
Google Scholar

[63] View Article

[64] PubMed/NCBI

[65] Google Scholar

[ref19] 19. Guerreiro M, Silva AP, Botelho M, Leitão O, Castro-Caldas A, Garcia C. Adaptação à população portuguesa da tradução do Mini Mental State Examination (MMSE). Sinapse. 1994;3(9–10).
View Article
Google Scholar

[67] View Article

[68] Google Scholar

[ref20] 20. Almeida G, Luz C, Martins R, Cordovil R. Do children accurately estimate their performance of fundamental movement skills? JMLD. 2017;5(2):193–206.
View Article
Google Scholar

[70] View Article

[71] Google Scholar

[ref21] 21. Gill DP, Zou GY, Jones GR, Speechley M. Comparison of regression models for the analysis of fall risk factors in older veterans. Ann Epidemiol. 2009;19(8):523–30. pmid:19394862.
View Article
PubMed/NCBI
Google Scholar

[73] View Article

[74] PubMed/NCBI

[75] Google Scholar

[ref22] 22. Zaluska W, Malecka T, Mozul S, Ksiazek A. Whole body versus segmental bioimpedance measurements (BIS) of electrical resistance (Re) and extracellular volume (ECV) for assessment of dry weight in end-stage renal patients treated by hemodialysis. Przegl Lek. 2004;61(2):70–3. pmid:15230144.
View Article
PubMed/NCBI
Google Scholar

[77] View Article

[78] PubMed/NCBI

[79] Google Scholar

[ref23] 23. Efron B, Tibshirani R. An introduction to the bootstrap. Chapman and Hall: Boca Raton; 1993.

[ref24] 24. Pereira C, Fernandes J, Raimundo A, Biehl-Printes C, Marmeleira J, Tomas-Carus P. Increased Physical Activity and Fitness above the 50(th) Percentile Avoid the Threat of Older Adults Becoming Institutionalized: A Cross-sectional Pilot Study. Rejuvenation Res. 2016;19(1):13–20. pmid:26083134.
View Article
PubMed/NCBI
Google Scholar

[82] View Article

[83] PubMed/NCBI

[84] Google Scholar

[ref25] 25. Yang NP, Hsu NW, Lin CH, Chen HC, Tsao HM, Lo SS, et al. Relationship between muscle strength and fall episodes among the elderly: the Yilan study, Taiwan. BMC Geriatr. 2018;18(1):90. pmid:29653515.
View Article
PubMed/NCBI
Google Scholar

[86] View Article

[87] PubMed/NCBI

[88] Google Scholar

[ref26] 26. Pereira CL, Baptista F, Infante P. Men older than 50 yrs are more likely to fall than women under similar conditions of health, body composition, and balance. Am J Phys Med Rehabil. 2013;92(12):1095–103. pmid:23811613.
View Article
PubMed/NCBI
Google Scholar

[90] View Article

[91] PubMed/NCBI

[92] Google Scholar

[ref27] 27. Pieruccini-Faria F, Montero-Odasso M. Obstacle Negotiation, Gait Variability and Risk of Falling. Results from "Gait and Brain Study". J Gerontol A Biol Sci Med Sci. 2018. pmid:30380013.
View Article
PubMed/NCBI
Google Scholar

[94] View Article

[95] PubMed/NCBI

[96] Google Scholar

[ref28] 28. Konczak J, Meeuwsen HJ, Cress ME. Changing affordances in stair climbing: the perception of maximum climbability in young and older adults. J Exp Psychol Hum Percept Perform. 1992;18(3):691–7. pmid:1500869.
View Article
PubMed/NCBI
Google Scholar

[98] View Article

[99] PubMed/NCBI

[100] Google Scholar

[ref29] 29. Fajen BR, Diaz G, Cramer C. Reconsidering the role of movement in perceiving action-scaled affordances. Hum Mov Sci. 2011;30(3):504–33. pmid:21333367
View Article
PubMed/NCBI
Google Scholar

[102] View Article

[103] PubMed/NCBI

[104] Google Scholar

[ref30] 30. Noel M, Bernard A, Luyat M. [The overestimation of performance: a specific bias of aging?]. Geriatr Psychol Neuropsychiatr Vieil. 2011;9(3):287–94. pmid:21896432.
View Article
PubMed/NCBI
Google Scholar

[106] View Article

[107] PubMed/NCBI

[108] Google Scholar

[ref31] 31. Bhasin S, Gill TM, Reuben DB, Latham NK, Gurwitz JH, Dykes P, et al. Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE): A Cluster-Randomized Pragmatic Trial of a Multifactorial Fall Injury Prevention Strategy: Design and Methods. J Gerontol A Biol Sci Med Sci. 2018;73(8):1053–61. pmid:29045582.
View Article
PubMed/NCBI
Google Scholar

[110] View Article

[111] PubMed/NCBI

[112] Google Scholar

[ref32] 32. Gazibara T, Kurtagic I, Kisic-Tepavcevic D, Nurkovic S, Kovacevic N, Gazibara T, et al. Falls, risk factors and fear of falling among persons older than 65 years of age. Psychogeriatrics. 2017;17(4):215–23. pmid:28130862.
View Article
PubMed/NCBI
Google Scholar

[114] View Article

[115] PubMed/NCBI

[116] Google Scholar

[ref33] 33. Stevens JA, Phelan EA. Development of STEADI: a fall prevention resource for health care providers. Health Promot Pract. 2013;14(5):706–14. pmid:23159993.
View Article
PubMed/NCBI
Google Scholar

[118] View Article

[119] PubMed/NCBI

[120] Google Scholar

[ref34] 34. Etman A, Wijlhuizen GJ, van Heuvelen MJ, Chorus A, Hopman-Rock M. Falls incidence underestimates the risk of fall-related injuries in older age groups: a comparison with the FARE (Falls risk by Exposure). Age Ageing. 2012;41(2):190–5. Epub 2012/02/22. pmid:22345295.
View Article
PubMed/NCBI
Google Scholar

[122] View Article

[123] PubMed/NCBI

[124] Google Scholar

Figures

Abstract

Introduction

Material and methods

Participants

Data collection

Perception and stepping-forward boundaries.

Falls.

Complementary measures.

Data analysis

Results

Discussion

Conclusion

Supporting information

S1 Fig. Probability falling and of falling recurrently associated with the key SF-APT outcomes (estimated stepping-forward, and the Absolute-error in interaction with Error-tendency) and respective cut-offs discriminating fallers from non-fallers or recurrent-fallers from non-fallers.

S1 Table. Description of participants SF-APT outcomes data.

S2 Table. The two most parsimonious and fit build binary logistic regression models and respective key SF-APT variables explaining falling and falling recurrently (falling vs. non-falling model: N = 347; falling recurrently vs. non-falling model: N = 263).

Acknowledgments

References