# Development and Evaluation of an Online Fall-Risk Questionnaire for Nonfrail Community-Dwelling Elderly Persons: A Pilot Study

Authors: Seraina Obrist; Slavko Rogan; Roger Hilfiker
Journal: Current Gerontology and Geriatrics Research
 (2016)

---

## Abstract

Introduction. Falls are frequent in older adults and may have serious consequences but awareness of fall-risk is often low. A questionnaire might raise awareness of fall-risk; therefore we set out to construct and test such a questionnaire.Methods. Fall-risk factors and their odds ratios were extracted from meta-analyses and a questionnaire was devised to cover these risk factors. A formula to estimate the probability of future falls was set up using the extracted odds ratios. The understandability of the questionnaire and discrimination and calibration of the prediction formula were tested in a cohort study with a six-month follow-up. Community-dwelling persons over 60 years were recruited by an e-mail snowball-sampling method.Results and Discussion. We included 134 persons. Response rates for the monthly fall-related follow-up varied between the months and ranged from low 38% to high 90%. The proportion of present risk factors was low. Twenty-five participants reported falls. Discrimination was moderate (AUC: 0.67, 95% CI 0.54 to 0.81). The understandability, with the exception of five questions, was good. The wording of the questions needs to be improved and measures to increase the monthly response rates are needed before test-retest reliability and final predictive value can be assessed.


---

## Body

## 1. Introduction

Falls are a common cause of accidents and they can have serious consequences ranging from fear of falls to fractures, loss of independency, or even mortality. Approximately 25% of people over 65 years of age and living at home fall each year and about 20% of the falls require medical attention [1]. Mortality after a falls-related hospitalisation is high [2] and the falls specific mortality is still rising, although the mortality due to fractures after falls is declining [3]. Forty percent of the admissions to a long-term stay in a nursing home are due to a fall. Therefore, prevention of falls or their consequences is important. There exist a plethora of known risk factors for falls [4, 5] and the risk factors generally increase with age. However, older people are often not aware of their own fall-risk [6]. They are aware of the increased fall-risk of other elderly persons, but they are often convinced that this does not apply for themselves [7]. Furthermore, some older adults are reluctant to admit that they are at risk for falls because they fear that their families might send them to nursing homes [8, 9]. Health professionals, such as nurses or physiotherapists, might play an important role in raising the awareness of the fall-risk.Screening for falls is usually performed by a health professional. However, in the group of the “young old,” not all are regularly seeing health professionals, or they are seeking care for other health conditions and the potentially increased fall-risk is not recognised or not perceived as an issue and not targeted by them or the health professionals [10]. A self-assessment tool might increase the awareness of the fall-risk and the motivation to discuss the problem with a health professional and to start a preventive programme [11, 12].Current self-administered predictions tools do not cover all dimensions of fall-risk, such as dual tasks, medication, diseases like diabetes, pain, stroke, rheumatic disease, fear of falling, the frequency of toileting, gait problems, balance, muscle weakness, sensibility impairments, or hearing problems [13–17] (see also Table 1 for a comprehensive overview of existing tools).Table 1
Comparison of existing falls prediction tools for community-dwelling older adults.
Name of tool
Publication
Fall outcome
Risk factors used in the final model
Follow-up period (months)
Area under the ROC-curve (if not stated otherwise)
Number of participants
Person with falls
N (%)
Number of falls
AGS/BGS/AAOS algorithm
AGS/BGS/AAOS [18]Tested in [19]
Any fall
Self-reported and performance measures How many falls have you had in the last year? Balance/gait impairment
12
Likelihood ratio+ of 2.71, likelihood ratio− of 0.61
117
52 (44.4%)
Geriatric Postal Screening Survey (GPSS)
Alessi et al., 2003 [20]
Any fall
Self-reported 10 items: falls, balance problems, urinary incontinence, depression, memory loss, functional impairment, health perceptions (two questions), weight loss, polypharmacy, pain
12
Sensitivity 0.94, specificity 0.51
147
“Sturz-Risiko-check” (Falls-risk check)
Anders et al., 2006 [17]
Any Fall
Self-reported Questionnaires covering: vision problems, polymedication, depressive mood, stopped riding bicycle, neurological disease, balance disorder, weight loss, fear of falls, sit-to-stand, heart diseases, slower in activities of daily living, falls experience, injuries as consequences of falls
Cross-sectional study (test of reliability)
117
Bongue et al., 2011 [21]
Any fall
Self-reported and performance measure Gender, living alone, psychoactive drug use, osteoarthritis, previous falls, change in the position of the arms during the one-leg balance
12
0.70 (0.67–0.73)
1795
563 (32%)
Buatois et al., 2010 [22]
Self-reported and performance measure History of falls, living alone, medications, female, five-times-sit-to-stand test
25
619
FRAT-up
Cattelani et al., 2015 [23]
Any fall
Self-reported and performance measure Age, cognition impairment, depression, comorbidity, dizziness and vertigo, fear of falling, female sex, gait problems, hearing impairment, history of falls, history of stroke, instrumental disability, living alone, number of medications, pain, Parkinson, physical activity limitation, physical disability, poor self-perceived health status, rheumatic disease, urinary incontinence, use of antiepileptics, use of antihypertensives, use of sedatives, vision impairment, walking aid use
36
0.64 (0.61–0.67)
977
215 (22%)
Covinsky et al., 2001 [24]
Retrospective; fall or falls in the previous year
Self-reported and performance measure Abnormal mobility, balance difficulties or dizziness, fall in previous year
60
0.71
95 (33%)
197
EFST
Cwikel et al., 1998 [25]
Self-reported and performance measure Near falls, fall in the past year, injurious fall in the past year, walking speed, examiner impression of gait
DFRA
Demura et al., 2011 [26]
Any fall
Self-reported and performance measure 50 items representing the five factors “symptoms of falling,” “physical function,” “disease and physical symptoms,” “environment,” and “behaviour and character”
Retrospective falls history
85.4% correct classified
1122
177 (16%)
FRAS
El Miedany et al., 2011 [15]
Any Fall
Self-reported and performance measure Age, history of any fall, history of more than 1 fall, slowing of walking speed/change in gait, loss of balance, weak hand grip, poor sight
Case-control
0.89 (0.86–0.92)
985
559 (57%)
Gadkaree et al., 2015 [27]
Any fall
Age, gender, race, self-reported balance problem, history of fall
12
0.70 (0.67–0.78)
2539
Hirase et al., 2014 [28]
Self-reported
Have you fallen during the past year? Can you cross the street without resting (during a green traffic signal)? Can you continue to walk for an entire kilometer? Can you put on socks while standing on one leg? Can you wring out a wet towel? Have you admitted yourself to a hospital within the past year? Do you feel dizzy upon standing up? Have you ever had a stroke? Have you ever been diagnosed with diabetes? Do you take sleeping pills, antihypertensive drugs, or minor tranquillizers? Do you often wear sandals or slippers? Can you see the letters in a newspaper, or a person’s face, clearly? Can you hear a person’s voice during a conversation? Do you often stumble or slip in your own house? Do you have a fear of falling or do you hesitate to go out because you have a fear of falling?
(Bold = the seven questions used for the prospective study)
3
0.73 (0.62–0.83) for the seven bold factors; 0.82 (0.70–0.95) for all 15 factors
292
45 (15.6%)
Modified Johns Hopkins fall-risk assessment tool mJH-FRAT
Hnizdo et al., 2013 [29]
Any fall
Self-reported and performance measure Age categories, fall history, elimination problems, high-risk medications, use of patient care equipment, limited mobility, altered cognition
During home health services (2 to 80 days)
0.66 (0.55–0.78)
107
33 (31%)
Physiological profile assessment
Lord et al., 2003 [30]
Self-reported and performance measure Visual acuity-high contrast, yisual acuity-low contrast, edge contrast sensitivity, visual field dependence, proprioception, tactile sensitivity-ankle, vibration sense-knee, ankle dorsiflexion force, knee extension force, knee flexion force, reaction time-hand, reaction time-foot, sway on floor-eyes open, sway on floor-eyes closed, sway on foam rubber mat-eyes open, sway on foam rubber mat-eyes closed
Palumbo et al., 2015 [31]
Self-reported and performance measure Number of previous falls, number of drugs, self-perceived health status, previous falls (yes/no), drugs for dementia (yes/no), CESD depressed mood scale, if you are retired, do you have a veteran pension?, can you walk 300 meters twice without stopping?, gait speed, antihypertensive medication, do you have difficulty walking 400 meters on rough terrain?, antidepressants, walking posture: cautious attitude?, sibling with diabetes?, must you hold onto something (e.g., bannister) while climbing stairs?, quinolone antibacterials, antihypertensives
36
0.64 (0.61–0.67)
976
0.42 falls per person-year
LASA fall-risk profile
Peeters et al., 2010 [32]
At least 2 falls within 6 months
Self-reported and performance measure How often did you fall during the past 12 months, including the last fall?, do you often have dizzy spells?, are you able to use your own method of transport or public transportation?, are you able to go up 15 steps without standing still?, are you able to cut your own toenails?, grip strength of right hand, grip strength of left hand, body weight, do you have a dog or a cat?, how concerned are you that you might fall when … (10 activities listed)?, do you sometimes drink alcohol?, what is the highest level of education that you completed with a certificate?
12
0.65 (0.58–0.72)
408
76 (recurrent fallers, 18.6%)
Pluijm et al., 2006 [33]
Prospective, recurrent falling at 3-year follow-up
Self-reported and performance measure ≥2 falls in the previous year, dizziness, functional limitations, grip strength (men ≤ 56 kg, women ≤ 32 kg), body weight (women ≤ 62 kg; men ≤ 70 kg), fear of falling, dogs or cats in household, education ≥ 11 year, alcohol use (≥18 consumptions per week), alcohol use × education, ≥2 falls in the previous year × fear of falling
36
0.71 (0.67–0.74)
1214
55.3%
2570
Fall-risk assessment and screening tool FRAST
Renfro and Fehrer, 2011 [14]
Self-reported and performance measure 15 items: age, gender, fall history, daily physical activity level, number of prescription medicines, eye care, glasses or contacts, getting dizzy, use of assistive devices to walk, self-perceived risk behaviour, social activity, home-safety checklist, modified falls efficacy scale, mood scale, timed-up-and-go test.
FRQ
Rubenstein et al., 2011 [13]
Self-reported I have fallen in the last 6 months. I am worried about falling. Sometimes, I feel unsteady when I am walking. I steady myself by holding onto furniture when walking at home. I use or have been advised to use a cane or walker to get around safely. I need to push with my hands to stand up from a chair. I have some trouble stepping up onto a curb. I often have to rush to the toilet. I have lost some feeling in my feet. I take medicine that sometimes makes me feel light-headed or more tired than usual. I take medicine to help me sleep or improve my mood. I often feel sad or depressed. Because I do not see well, I have difficulty avoiding hazards in my path, such as tree roots or electrical cords. (This last item was dropped due to low kappa with clinical evaluation.)
FROP-COM
Russell et al., 2008 [34]
13 risk factors covered with 26 questions.Number of falls in the past 12 months, walking safely in the house, observation of balance, incontinence, number of medical conditions, vision deficit, assistance required to perform personal ADLs, number of fall-risk medications, assistance required to perform domestic ADLs, somatosensory deficit, cognitive status, level of physical activity, foot problems, number of medications, food intake, weight loss, nocturia, alcohol intake, inappropriate footwear, injury in past 12 months
12
0.68 (0.63–0.74)
344
164 (47.6%)
FROP-COM screen
Russell et al., 2009 [35]
Any fall
Self-reported and performance measure Number of falls in the past 12 months, observation of balance, assistance required to perform domestic ADLs
12
0.73 (0.67–0.79)
344
164
Stalenhoef et al., 2002 [36]
Prospective, recurrent falls (≥2 falls)
Self-reported and performance measure Women, age (≥80), ≥2 falls in previous year, depression, SCL90 ≥ 22; hand dynamometry (men ≤ 22 kg, women ≤ 12 kg), postural sway abnormal
36
0.79
287
Thai-FRAT
Jittima Thamarpirat et al., 2008 [37]
Any fall
Self-reported and performance measure Six factors: history of falls, impaired body balance, female, specific medication use, impaired visual acuity, Thai style house
24
Positive Likelihood-Ratio: 2.34
115
QuickScreen clinical fall-risk assessment
Tiedemann et al., 2010 [38]
Multiple falls
Self-reported and performance measure Falls in past year, total medications, psychoactive meds, visual acuity test (MAR), touch sensation test, alternate step test, sit-to-stand test, tandem stand test
12
0.72 (0.66–0.79)
362
179 (49%)
Tromp et al., 2001 [39]
Any fall
Self-reported and performance measure Previous falls, urinary incontinence, visual impairment, use of benzodiazepines
12
0.65
1280
33%
Empty cells indicate that we did not found any corresponding information in the publications.Therefore, based on a search for systematic reviews and meta-analysis on risk factors for falls, we set out to (a) collect risk factors that were consistently reported in studies, (b) to extract coefficients from predictive models, (c) to devise a comprehensive set of questions, and (d) to test, in a sample of community-dwelling persons aged sixty years or older, the feasibility, understandability, calibration, and discrimination using the extracted coefficients, including the continuous assessment of falls during a six-month follow-up period. We hypothesize that (a) the monthly response rate is higher than 80%, (b) that the understandability of the questions is good, (c) that the self-predicted fall-risk is not in agreement with the observed fall-risk, (d) that the observed fall-risk is associated with the predicted fall-risk, and (e) that we can discriminate between fallers and nonfallers based on the risk score calculated with the coefficients from the literature and our self-reported questionnaire.


## 2. Materials and Methods

This study included several steps: (1) defining a set of predictors for falls based on published meta-analyses, (2) devising a set of questions for the self-assessment of the risk factors out of seven questionnaires, and (3) prospective cohort study to assess the feasibility and the preliminary predictive values of the online assessment of the fall-risk.


### 2.1. Defining the Set of Predictors

We searched in PubMed for systematic reviews and meta-analyses on risk factors in community-dwelling elderly people; search strategy: (((risk OR odds ORp
r
e
d
i
c
t
∗ OR likelihood OR sensitivity OR specificity OR AUC OR ROC OR calibration OR discrimination))) AND ((((((falls [title]) OR fall [title]) OR faller [title])) AND ((meta-analysis [Publication Type]) OR systematic review [title])) AND ((elderly OR older OR aged OR senior OR seniors))). Inclusion criteria were systematic reviews and meta-analysis on prospective cohort studies including community-dwelling elderly persons. We extracted the risk factors for falls that were statistically significant in the meta-analyses. For each factor we extracted the coefficients (i.e., log of the odds ratio) for the prediction of falls in community-dwelling older adults from the meta-analysis with the most included participants or studies for the given predictor.


### 2.2. Devising the Set of Questions

Based on seven existing questionnaires for the self-assessment of fall-risk [13–17, 44, 45], we devised a set of questions that covered most of the fall-risks found in the previous step (review of reviews). The questions were written in German and translated into French and submitted to seven health professionals and two laypersons with the question about the understandability. Amendments were made if necessary.We included ten questions about personal characteristics and a question about the self-perceived risk of falling, as well as the understandability (comprehension of the questionnaire) and suggestions for different formulations.The questionnaire was implemented in an online survey system (SurveyMonkey [46]).For the monthly follow-up we assessed whether a person fell during the last months and the number of falls. A fall is often defined as “an event which results in a person coming to rest inadvertently on the ground or floor or another lower level.” [47]. For our study, we decided to exclude falls in sports activities such as biking, skiing, or mountaineering. Based on feedback from participants at the first monthly follow-up, we added a question about the activity at which the falls occurred and two questions to assess the level of physical activity as recommended by Gill et al. [48] for the later follow-ups.


### 2.3. Prospective Cohort Study

The main part of this study was a longitudinal cohort study with a six-month follow-up (falls assessment and assessment of physical activity). Study participants were community-dwelling elderly persons aged 60 years or more. They had to be able to walk independently, with or without walking aids. German and French speaking participants were included if they had an e-mail address.Participants were recruited by a snowball-sampling method [49]. This method allows the inclusion of participants that are difficult to achieve. If our hypothesis is true that our target population has a low awareness of their risk to fall, they would, for example, most probably not respond to other sampling methods such as information leaflets or advertisements in journals. Other sampling methods such as phone number lists are nowadays not valid anymore, because a large subset of the population is not listed in directories (phone books). A first set of e-mails with a link to the online survey (SurveyMonkey) was sent to acquaintances with a description of the target population (i.e., describing inclusion criteria); they were then asked to send the e-mail to their acquaintances, and so on. For six months, the monthly fall assessment was sent by e-mail via SurveyMonkey.Sample Size. We used a convenience sample consisting of the 134 participants responding to the e-mails sent out with the snowball method. This sample size allowed the estimation of the incidence of falls and univariable association between risk factors and falls with enough statistical precision.The project was conducted in accordance with the Declaration of Helsinki (1964) and was approved by the relevant ethical committee (CCVEM 014/14). All participants provided informed consent to the participation.


### 2.4. Adaptation of the Questionnaire

Based on the feedbacks on the understandability and the suggestions for alternative formulations, propositions for amendments were prepared. The final amendments will be part of a future project including a larger sample of experts including elderly persons.


### 2.5. Statistical Analysis

Descriptive statistics were presented as mean and standard deviation or as proportions, as appropriate. To express the association between risk factors and falls we calculated odds ratios and risk ratios and corresponding 95% confidence intervals. We used Stata Version 14.0 [50]. We calculated both risk and odds ratios because risk ratios are easier to interpret but the odds ratios allow a better comparison with published prediction tools. If a participant did not return a monthly falls follow-up, we assumed that there was no fall in this month.To test the hypothesis that the participants are not aware of their fall-risk, that is, their self-perceived fall-risk is lower than the actual fall-risk, we calculated the proportion of fallers within each category of the self-perceived risk and calculated a chi-squared test with the null hypothesis that there is no association in the perceived fall-risk with increasing observed fall-risk.


### 2.6. Prediction Formula

Because our sample size was only large enough for univariable analyses and too low for the fitting of a robust multivariable prediction model, we used the coefficients published in the meta-analyses. The prediction formula consisted of a scoring function and a logistic probability function, where the scoring function reads as follows:scoring function = −4.5 + 0.1044∗ (age over 60/5) + 1.351 ∗ fallen last 12 months + 0.495 ∗ low spirit at some days + 0.548 ∗ incontinence + 0.62 ∗ need get up night + 0.215 ∗ rheumatic disease + 0.307 ∗ diziness + 0.779 ∗ neurological disease + 0.239 ∗ diabetes + 0.445 ∗ dichotomous pain + 0.247 ∗ high blood pressure + 0.47 ∗ heart symptoms + 0.875 ∗ fear of falls + 0.94 ∗ walk slower + 0.742 ∗ walking aids + 0.2852 ∗ perceived dual task problem + 0.859 ∗ self perceived balance + 0.457 ∗ any range of motion limitation lower extremity + 0.788 ∗ sensory deficit lower extremity + 0.399 ∗ vision problem + 0.315 ∗ do not hear good + 0.548 ∗ dichotomous home hazards + 0.718 ∗ low BMI + 0.8242 ∗ ADL need help + 0.637 ∗ fracture + 0.54 ∗ polymedication + 1.445 ∗ any medication + 0.24 ∗ postural hypotension + 0.98 ∗ difficult get up chair because of weak legs.And the logistic probability function is as follows:1
/
(
1
+
e
x
p
(
-
1
∗
s
c
o
r
i
n
g
f
u
n
c
t
i
o
n
)
).This formula has to be considered as preliminary because the coefficients of each predictor are not adjusted for all other predictors, which leads to an overestimation of the fall-risk. The coefficients need to be adjusted, for example, by the means of methods proposed by [51]. These methods need larger sample sizes than we had in our study.Based on this preliminary prediction formula, we tested the calibration of the prediction model with a calibration plot (observed versus predicted falls) and a Hosmer-Lemeshow test. The discrimination (i.e., the ability to detect fallers) was tested with a receiver operating characteristic (ROC) curve and the area under the ROC-curve.


## 2.1. Defining the Set of Predictors

We searched in PubMed for systematic reviews and meta-analyses on risk factors in community-dwelling elderly people; search strategy: (((risk OR odds ORp
r
e
d
i
c
t
∗ OR likelihood OR sensitivity OR specificity OR AUC OR ROC OR calibration OR discrimination))) AND ((((((falls [title]) OR fall [title]) OR faller [title])) AND ((meta-analysis [Publication Type]) OR systematic review [title])) AND ((elderly OR older OR aged OR senior OR seniors))). Inclusion criteria were systematic reviews and meta-analysis on prospective cohort studies including community-dwelling elderly persons. We extracted the risk factors for falls that were statistically significant in the meta-analyses. For each factor we extracted the coefficients (i.e., log of the odds ratio) for the prediction of falls in community-dwelling older adults from the meta-analysis with the most included participants or studies for the given predictor.


## 2.2. Devising the Set of Questions

Based on seven existing questionnaires for the self-assessment of fall-risk [13–17, 44, 45], we devised a set of questions that covered most of the fall-risks found in the previous step (review of reviews). The questions were written in German and translated into French and submitted to seven health professionals and two laypersons with the question about the understandability. Amendments were made if necessary.We included ten questions about personal characteristics and a question about the self-perceived risk of falling, as well as the understandability (comprehension of the questionnaire) and suggestions for different formulations.The questionnaire was implemented in an online survey system (SurveyMonkey [46]).For the monthly follow-up we assessed whether a person fell during the last months and the number of falls. A fall is often defined as “an event which results in a person coming to rest inadvertently on the ground or floor or another lower level.” [47]. For our study, we decided to exclude falls in sports activities such as biking, skiing, or mountaineering. Based on feedback from participants at the first monthly follow-up, we added a question about the activity at which the falls occurred and two questions to assess the level of physical activity as recommended by Gill et al. [48] for the later follow-ups.


## 2.3. Prospective Cohort Study

The main part of this study was a longitudinal cohort study with a six-month follow-up (falls assessment and assessment of physical activity). Study participants were community-dwelling elderly persons aged 60 years or more. They had to be able to walk independently, with or without walking aids. German and French speaking participants were included if they had an e-mail address.Participants were recruited by a snowball-sampling method [49]. This method allows the inclusion of participants that are difficult to achieve. If our hypothesis is true that our target population has a low awareness of their risk to fall, they would, for example, most probably not respond to other sampling methods such as information leaflets or advertisements in journals. Other sampling methods such as phone number lists are nowadays not valid anymore, because a large subset of the population is not listed in directories (phone books). A first set of e-mails with a link to the online survey (SurveyMonkey) was sent to acquaintances with a description of the target population (i.e., describing inclusion criteria); they were then asked to send the e-mail to their acquaintances, and so on. For six months, the monthly fall assessment was sent by e-mail via SurveyMonkey.Sample Size. We used a convenience sample consisting of the 134 participants responding to the e-mails sent out with the snowball method. This sample size allowed the estimation of the incidence of falls and univariable association between risk factors and falls with enough statistical precision.The project was conducted in accordance with the Declaration of Helsinki (1964) and was approved by the relevant ethical committee (CCVEM 014/14). All participants provided informed consent to the participation.


## 2.4. Adaptation of the Questionnaire

Based on the feedbacks on the understandability and the suggestions for alternative formulations, propositions for amendments were prepared. The final amendments will be part of a future project including a larger sample of experts including elderly persons.


## 2.5. Statistical Analysis

Descriptive statistics were presented as mean and standard deviation or as proportions, as appropriate. To express the association between risk factors and falls we calculated odds ratios and risk ratios and corresponding 95% confidence intervals. We used Stata Version 14.0 [50]. We calculated both risk and odds ratios because risk ratios are easier to interpret but the odds ratios allow a better comparison with published prediction tools. If a participant did not return a monthly falls follow-up, we assumed that there was no fall in this month.To test the hypothesis that the participants are not aware of their fall-risk, that is, their self-perceived fall-risk is lower than the actual fall-risk, we calculated the proportion of fallers within each category of the self-perceived risk and calculated a chi-squared test with the null hypothesis that there is no association in the perceived fall-risk with increasing observed fall-risk.


## 2.6. Prediction Formula

Because our sample size was only large enough for univariable analyses and too low for the fitting of a robust multivariable prediction model, we used the coefficients published in the meta-analyses. The prediction formula consisted of a scoring function and a logistic probability function, where the scoring function reads as follows:scoring function = −4.5 + 0.1044∗ (age over 60/5) + 1.351 ∗ fallen last 12 months + 0.495 ∗ low spirit at some days + 0.548 ∗ incontinence + 0.62 ∗ need get up night + 0.215 ∗ rheumatic disease + 0.307 ∗ diziness + 0.779 ∗ neurological disease + 0.239 ∗ diabetes + 0.445 ∗ dichotomous pain + 0.247 ∗ high blood pressure + 0.47 ∗ heart symptoms + 0.875 ∗ fear of falls + 0.94 ∗ walk slower + 0.742 ∗ walking aids + 0.2852 ∗ perceived dual task problem + 0.859 ∗ self perceived balance + 0.457 ∗ any range of motion limitation lower extremity + 0.788 ∗ sensory deficit lower extremity + 0.399 ∗ vision problem + 0.315 ∗ do not hear good + 0.548 ∗ dichotomous home hazards + 0.718 ∗ low BMI + 0.8242 ∗ ADL need help + 0.637 ∗ fracture + 0.54 ∗ polymedication + 1.445 ∗ any medication + 0.24 ∗ postural hypotension + 0.98 ∗ difficult get up chair because of weak legs.And the logistic probability function is as follows:1
/
(
1
+
e
x
p
(
-
1
∗
s
c
o
r
i
n
g
f
u
n
c
t
i
o
n
)
).This formula has to be considered as preliminary because the coefficients of each predictor are not adjusted for all other predictors, which leads to an overestimation of the fall-risk. The coefficients need to be adjusted, for example, by the means of methods proposed by [51]. These methods need larger sample sizes than we had in our study.Based on this preliminary prediction formula, we tested the calibration of the prediction model with a calibration plot (observed versus predicted falls) and a Hosmer-Lemeshow test. The discrimination (i.e., the ability to detect fallers) was tested with a receiver operating characteristic (ROC) curve and the area under the ROC-curve.


## 3. Results

The systematic search for systematic reviews and meta-analysis on fall-risk factors yielded 113 abstracts from which 14 systematic reviews were included [4, 5, 40, 42, 43, 52–60]. Because we extracted the coefficients from the meta-analysis with the most participants or studies included, the coefficients were taken from the newest reviews [4, 5, 40, 42, 43]. In addition, we extracted the coefficients from one single study for the variable frequent toileting [41] because we preferred this variable over the variables urinary incontinence or urinary functional sign published in the Block 2013 meta-analysis. Table 2 shows the set of extracted factors as well as its odds ratio, coefficients, and heterogeneity, if available.Table 2
Predictors. Predictors found in review of reviews and odds ratios from an existing self-administered fall-risk questionnaire (multivariable odds ratios) and our questionnaire (univariable odds ratio).
Variable
N studies
N patients
Odds ratio
(95% CI)
Log of the odds ratio
Heterogeneity (I
2)
Meta-analysis
OR El Miedany (FRAS)
OR Obrist
Age (per 5-year increase)
8
1.11
(1.05–1.17)
0.10
p
=
0.007
Deandrea et al., 2010 [5]
1.02 (1.01 to 1.04)
1.69 (1.14 to 2.53)
Living alone
12
5419
1.16
(1.02–1.32)
0.15
I
2 < 25%
Bloch et al., 2010 [40]
Taking drugs (yes/no)
5
508
4.24
(3.06–5.88)
1.45
16%
Bloch et al., 2013 [4]
2.68 (1.02 to 7.02)
Laxatives
7
7 611
2.03
(1.52–2.72)
0.71
0%
Bloch et al., 2013 [4]
Psychotropic drugs
32
43 552
1.74
(1.56–1.95)
0.55
0%
Bloch et al., 2013 [4]
Polymedication
29
25 098
1.71
(1.50–1.96)
0.54
38%
Bloch et al., 2013 [4]
3.04 (1.18 to 7.89)
Benzodiazepines
26
54 919
1.61
(1.35–1.93)
0.48
0%
Bloch et al., 2013 [4]
Antidepressants
46
19 988
1.59
(1.43–1.75)
0.46
21%
Bloch et al., 2013 [4]
Antiepileptics
9
34 021
1.56
(1.28–1.90)
0.45
22%
Bloch et al., 2013 [4]
Antiparkinsonians
13
21 899
1.55
(1.21–1.97)
0.44
42%
Bloch et al., 2013 [4]
Hypnotics
36
4 453
1.53
(1.40–1.68)
0.43
44%
Bloch et al., 2013 [4]
Digoxins
11
8 587
1.48
(1.11–1.99)
0.39
17%
Bloch et al., 2013 [4]
Narcotics
7
22 973
1.43
(1.27–1.61)
0.36
39%
Bloch et al., 2013 [4]
Tranquilizers
12
12 391
1.42
(1.21–1.67)
0.35
47%
Bloch et al., 2013 [4]
Metabolic and endocrine medicines
11
38 846
1.39
(1.20–1.62)
0.33
0%
Bloch et al., 2013 [4]
Antipsychotics
23
29 584
1.37
(1.16–1.61)
0.32
15%
Bloch et al., 2013 [4]
Analgesics
11
9 869
1.33
(1.07–1.65)
0.29
0%
Bloch et al., 2013 [4]
Anti-inflammatory
18
22 209
1.25
(1.11–1.42)
0.22
31%
Bloch et al., 2013 [4]
ACE inhibitor
6
66 696
1.21
(1.15–1.28)
0.19
39%
Bloch et al., 2013 [4]
Vasodilators
17
7 212
1.12
(1.04–1.21)
0.11
71%
Bloch et al., 2013 [4]
Antihypertensives
25
81 908
1.1
(1.05–1.16)
0.10
25%
Bloch et al., 2013 [4]
Cardiovascular medicines
23
88 467
0.78
(0.67–0.90)
-
0.25
80%
Bloch et al., 2013 [4]
Fall history
39
25 808
3.86
(3.42–4.37)
1.35
58%
Bloch et al., 2013 [4]
1.5 (0.8 to 2.7)
2.1 (0.78 to 5.69)
Fear of falling
13
22 809
2.4
(2.07–2.77)
0.88
48%
Bloch et al., 2013 [4]
1.35 (0.37 to 4.99)
Digestive disease
8
10 649
2.2
(1.65–2.93)
0.79
0%
Bloch et al., 2013 [4]
Sensory disorder
9
3 125
2.2
(1.56–3.11)
0.79
38%
Bloch et al., 2013 [4]
1.54 (0.48 to 5.03)
Parkinson’s disease
29
39 477
2.19
(1.68–2.84)
0.78
50%
Bloch et al., 2013 [4]
Neurological disease
17
20 281
2.18
(1.69–2.82)
0.78
0%
Bloch et al., 2013 [4]
2.28 (0 to 11.45)
Disorientation
8
8 648
2.16
(1.68–2.78)
0.77
0%
Bloch et al., 2013 [4]
Low body mass index
12
15 396
2.05
(1.70–2.48)
0.72
0%
Bloch et al., 2013 [4]
4.5 (0.27 to 73.83)
Impaired cognition/dementia
35
59 363
1.96
(1.80–2.14)
0.67
88%
Bloch et al., 2013 [4]
Fracture history
11
1612
1.89
(1.53–2.34)
0.64
51%
Bloch et al., 2013 [4]
Not estimable
Urinary incontinence
34
59 458
1.73
(1.60–1.88)
0.55
49%
Bloch et al., 2013 [4]
2.23 (0 to 17.89)
Self-perceived state problem
18
19 015
1.73
(1.48–2.02)
0.55
56%
Bloch et al., 2013 [4]
Depression
39
67 858
1.64
(1.52–1.76)
0.50
40%
Bloch et al., 2013 [4]
2.38 (0 to 19.27)
Urinary functional sign
4
1 826
1.64
(1.16–2.33)
0.50
19%
Bloch et al., 2013 [4]
Closely vision impairment
7
13 418
1.62
(1.48–1.78)
0.48
0%
Bloch et al., 2013 [4]
Cardiac and vascular problems
14
24 367
1.6
(1.45–1.75)
0.47
0%
Bloch et al., 2013 [4]
1.98 (0.82 to 4.76)
Vision impairment
39
38 671
1.49
(1.39–1.59)
0.40
47%
Bloch et al., 2013 [4]
2.8 (1.1 to 7.6)
3.07 (0.58 to 16.43)
Anemia
5
502
1.47
(1.15–1.88)
0.39
0%
Bloch et al., 2013 [4]
Stroke
49
54 336
1.44
(1.34–1.56)
0.37
20%
Bloch et al., 2013 [4]
Cardiac rhythm disorder
9
3 402
1.42
(1.14–1.75)
0.35
0%
Bloch et al., 2013 [4]
Hearing impairment
17
21 878
1.37
(1.27–1.48)
0.32
11%
Bloch et al., 2013 [4]
1.3 (0.53 to 3.18)
Ophthalmic disease
16
31 443
1.29
(1.18–1.40)
0.26
24%
Bloch et al., 2013 [4]
Altered general health state
9
13 786
1.28
(1.15–1.43)
0.25
34%
Bloch et al., 2013 [4]
High blood pressure
35
45 115
1.28
(1.19–1.37)
0.25
42%
Bloch et al., 2013 [4]
2.09 (0.81 to 5.43)
Diabetes
40
61 028
1.27
(1.19–1.36)
0.24
11%
Bloch et al., 2013 [4]
1.47 (0 to 10.9)
Behavioral disorder
16
35 858
1.27
(1.14–1.42)
0.24
0.25
Bloch et al., 2013 [4]
Postural hypotension
20
11 939
1.27
(1.09–1.47)
0.24
9%
Bloch et al., 2013 [4]
1.18 (0.44 to 3.22)
Arthrosis/osteoarthritis
37
5 284
1.24
(1.20–1.28)
0.22
88%
Bloch et al., 2013 [4]
0.76 (0.3 to 1.94)
Low cognitive score
22
1 754
1.22
(1.12–1.32)
0.20
46%
Bloch et al., 2013 [4]
Cancer
10
26 642
1.22
(1.09–1.35)
0.20
44%
Bloch et al., 2013 [4]
Abnormal walking test
11
14 677
3.34
(2.36–4.72)
1.21
0%
Bloch et al., 2013 [4]
Wandering
3
8 896
2.82
(2.30–3.46)
1.04
0%
Bloch et al., 2013 [4]
Muscular weakness
11
12 705
2.66
(2.12–3.33)
0.98
66%
Bloch et al., 2013 [4]
2.1 (1.1 to 4.4)
4.5 (0.27 to 74.51)
Slow walking speed
8
3 358
2.56
(1.96–3.33)
0.94
0%
Bloch et al., 2013 [4]
5.0 (2.0 to 12.0)
Not estimable
Walking problem
29
20 309
2.48
(2.24–2.75)
0.91
78%
Bloch et al., 2013 [4]
Unable to get up from a chair
10
9 973
2.44
(1.92–3.09)
0.89
78%
Bloch et al., 2013 [4]
Unsteady turning
6
1 274
2.39
(1.50–3.79)
0.87
77%
Bloch et al., 2013 [4]
Self-perceived balance problem
4
3 118
2.36
(1.83–3.03)
0.86
28%
Bloch et al., 2013 [4]
3.5 (1.5 to 7.5)
1.84 (0.66 to 5.17)
Abnormal Tinetti test score
9
3 749
2.35
(1.63–3.40)
0.85
77%
Bloch et al., 2013 [4]
Abnormal balance test
16
7 814
2.26
(1.79–2.85)
0.82
78%
Bloch et al., 2013 [4]
Reduction of step length
9
1565
2.12
(1.46–3.08)
0.75
76%
Bloch et al., 2013 [4]
Balance problem
14
4 318
2.1
(1.72–2.55)
0.74
90%
Bloch et al., 2013 [4]
Lower extremity disability
19
8 691
1.89
(1.65–2.17)
0.64
78%
Bloch et al., 2013 [4]
Lower grip strength
7
5 923
1.78
(1.44–2.21)
0.58
47%
Bloch et al., 2013 [4]
Reduced mobility
11
24 526
1.58
(1.44–1.74)
0.46
0%
Bloch et al., 2013 [4]
1.18 (0.47 to 2.97)
Feet problems
18
15 014
1.5
(1.23–1.82)
0.41
54%
Bloch et al., 2013 [4]
Dizziness
20
22 142
1.36
(1.13–1.63)
0.31
12%
Bloch et al., 2013 [4]
1.71 (0.69 to 4.24)
Limited activity
13
16 198
1.32
(1.01–1.72)
0.28
0%
Bloch et al., 2013 [4]
Physical activity
26
8 981
0.79
(0.71–0.88)
-
0.24
50%
Bloch et al., 2013 [4]
Walking aid
36
54 336
2.1
(1.90–2.32)
0.74
61%
Bloch et al., 2013 [4]
9.39 (1.17 to 0)
Nonadapted shoes
5
1 671
1.97
(1.48–2.62)
0.68
83%
Bloch et al., 2013 [4]
Obstacle on the ground
6
7 522
1.73
(1.55–1.93)
0.55
24%
Bloch et al., 2013 [4]
1.54 (0.48 to 5.03)
Disturbance of one or more ADL
13
17206
2.28
(2.10–2.48)
0.82
58%
Bloch et al., 2010 [40]
Not estimable
Frequent toileting
1
533
1.92
(1.12–3.27)
0.62
—
Sherrington et al., 2010 [41]
1.26 (0.47 to 3.34)
Pain
14
17926
1.56
(1.36–1.79
0.44
52%
Stubbs et al., 2014 [42]
1.61 (0.63 to 4.08)
Dual task
21
1.33
(1.18–1.50)
0.29
89%
Chu et al., 2013 [43]
Not estimable
Empty cells indicate that the values were not reported in the meta-analyses. Italic indicates variables that were not assessed with our questionnaire.


### 3.1. Set of Items Devised for the Self-Administered Fall-Risk Questionnaire

Based on the set of predictors we devised a set of questions. Because there was considerable overlap between the predictors, we selected a subset of 29 predictors with the aim of reducing overlap. Because some constructs were covered with more than one question, our questionnaire consisted of 36 questions, including demographic characteristics. Some of the questions consisted of several response options covering different risk factors.


### 3.2. Characteristics of Included Participants

With the snowball-sampling we could include 134 participants. The response rate during the monthly follow-up varied from 38 to 90% (see Figure1). The mean age of the 134 participants was 69.3 years with a standard deviation of 5.6 years. There were slightly less women than men (45% women and 55% men). The mean body mass index (BMI) was 25.95; 13% had a BMI of 30 or more (i.e., would be classified as obese). The proportion of participants who did fall during the last twelve months was 18%; only a very small proportion had consequences due to these falls. During the 6-month follow-up, 32 participants did fall at least once, we excluded seven falls (three falls on bike, one fall on ski, two falls on icy roads, and one fall during mountaineering on steep paths), resulting in 25 falls (18.7%). For each risk factor, only a small proportion of participants indicated problems which leads to wide confidence intervals in the odds ratios (Table 2) and the risk ratios presented (Table 3).Table 3
Distribution of risk factors. Distribution of risk factors for all participants, for the nonfallers, and for the fallers. All responses are self-reported values and not observed or measured values.
Variables
All
Non fallers during 6 months of follow-up
Fallers during 6 months of follow-up
Risk ratio (95% CI)
N responders
N (%) if not stated otherwise
N responders
N (%) if not stated otherwise
N responders
N (%) if not stated otherwise
Age
134
69.28 (5.59)
68.65 (5.4)
72 (5.66)
Body mass index
134
25.95 (4.04)
25.85 (4)
26.4 (4.25)
Women
134
60 (45%)
109
49 (45%)
25
11 (44%)
0.97 (0.47 to 1.98)
Obese persons (BMI ≥ 30)
134
18 (13%)
109
15 (14%)
25
3 (12%)
0.88 (0.29 to 2.64)
BMI < 18.5
134
2 (1%)
109
1 (1%)
25
1 (4%)
2.75 (0.66 to 11.52)
Fallen during last 12 months
134
2 (1%)
109
1 (1%)
25
1 (4%)
2.75 (0.66 to 11.52)
Emergency room visit after fall in last 12 months
134
24 (18%)
109
17 (16%)
25
7 (28%)
1.78 (0.84 to 3.79)
Fracture after fall during last 12 months
134
2 (1%)
109
1 (1%)
25
1 (4%)
2.75 (0.66 to 11.52)
Commotio after fall during last 12 months
134
1 (1%)
109
0 (0%)
25
1 (4%)
5.54 (3.86 to 7.96)
Fear of falls after fall during last 12 months
134
1 (1%)
109
0 (0%)
25
1 (4%)
5.54 (3.86 to 7.96)
Resting more than half an hour on floor after fall in last 12 months
134
4 (3%)
109
2 (2%)
25
2 (8%)
2.83 (0.99 to 8.06)
Needing help for activities of daily living
134
0 (0%)
109
0 (0%)
25
0 (0%)
0 (0 to 0)
Using walking aids
134
1 (1%)
109
0 (0%)
25
1 (4%)
5.54 (3.86 to 7.96)
Walking slower or making more breaks
134
3 (2%)
109
1 (1%)
25
2 (8%)
3.8 (1.57 to 9.17)
Problems crossing road at traffic light during green phase
134
1 (1%)
109
1 (1%)
25
0 (0%)
0 (0 to 0)
Stumbling at least once per months
134
0 (0%)
109
0 (0%)
25
0 (0%)
0 (0 to 0)
Unsecure if walking in darkness
134
2 (1%)
109
0 (0%)
25
2 (8%)
5.74 (3.96 to 8.32)
Unsecure even if walking in daylight
134
3 (2%)
109
1 (1%)
25
2 (8%)
3.8 (1.57 to 9.17)
Vision problems
134
1 (1%)
109
0 (0%)
25
1 (4%)
5.54 (3.86 to 7.96)
Seeing good without glasses
134
5 (4%)
109
3 (3%)
25
2 (8%)
2.24 (0.72 to 6.98)
Seeing good with glasses
134
25 (19%)
109
24 (22%)
25
1 (4%)
0.18 (0.03 to 1.28)
Does not see good with glasses
134
106 (79%)
109
84 (77%)
25
22 (88%)
1.94 (0.62 to 6.01)
Hearing problem
134
4 (3%)
109
2 (2%)
25
2 (8%)
2.83 (0.99 to 8.06)
Hearing problem but wearing hearing aid
134
42 (31%)
109
33 (30%)
25
9 (36%)
1.23 (0.59 to 2.56)
Taking sedative
134
5 (4%)
109
4 (4%)
25
1 (4%)
1.08 (0.18 to 6.44)
Taking blood pressure decreasing medication
134
2 (1%)
109
0 (0%)
25
2 (8%)
5.74 (3.96 to 8.32)
Taking antiepileptics
134
47 (35%)
109
34 (31%)
25
13 (52%)
2.01 (1 to 4.04)
Taking antidepressants
134
2 (1%)
109
2 (2%)
25
0 (0%)
0 (0 to 0)
Other medicaments
134
3 (2%)
109
1 (1%)
25
2 (8%)
3.8 (1.57 to 9.17)
More than one medicament
134
51 (38%)
109
39 (36%)
25
12 (48%)
1.5 (0.74 to 3.03)
Taking at least one medicament
134
26 (19%)
109
17 (16%)
25
9 (36%)
2.34 (1.17 to 4.68)
Low spirit at more than half of all days
134
3 (2%)
109
1 (1%)
25
2 (8%)
3.8 (1.57 to 9.17)
Low spirit at some days
134
51 (38%)
109
39 (36%)
25
12 (48%)
1.5 (0.74 to 3.03)
No disease
134
26 (19%)
109
17 (16%)
25
9 (36%)
2.34 (1.17 to 4.68)
Parkinson
134
78 (58%)
109
59 (54%)
25
19 (76%)
2.27 (0.97 to 5.33)
Stroke
118
3 (3%)
97
2 (2%)
21
1 (5%)
1.92 (0.37 to 9.97)
Epilepsy
134
37 (28%)
109
27 (25%)
25
10 (40%)
1.75 (0.86 to 3.54)
Diabetes
134
40 (30%)
109
35 (32%)
25
5 (20%)
0.59 (0.24 to 1.46)
Incontinence
134
1 (1%)
109
0 (0%)
25
1 (4%)
5.54 (3.86 to 7.96)
Osteoarthritis
134
4 (3%)
109
3 (3%)
25
1 (4%)
1.35 (0.24 to 7.68)
Rheumatic disease (including osteoarthritis)
134
2 (1%)
109
2 (2%)
25
0 (0%)
0 (0 to 0)
Multiple sclerosis
134
4 (3%)
109
3 (3%)
25
1 (4%)
1.35 (0.24 to 7.68)
Neurologic disease
134
3 (2%)
109
2 (2%)
25
1 (4%)
1.82 (0.35 to 9.39)
High blood pressure
134
39 (29%)
109
32 (29%)
25
7 (28%)
0.95 (0.43 to 2.09)
Fainting
134
44 (33%)
109
37 (34%)
25
7 (28%)
0.8 (0.36 to 1.76)
Leg pain during walking
134
0 (0%)
109
0 (0%)
25
0 (0%)
0 (0 to 0)
Dyspnea
134
6 (5%)
109
4 (4%)
25
2 (8%)
1.86 (0.56 to 6.1)
Heart symptoms
134
28 (21%)
109
20 (18%)
25
8 (32%)
1.78 (0.86 to 3.7)
Dizziness
134
29 (22%)
109
23 (21%)
25
6 (24%)
1.14 (0.5 to 2.6)
Wanted weight loss
118
54 (46%)
97
43 (44%)
21
11 (52%)
1.3 (0.6 to 2.83)
Unwanted weight loss
116
31 (27%)
97
24 (25%)
19
7 (37%)
1.6 (0.69 to 3.69)
Difficulties getting up from a chair
134
42 (31%)
109
31 (28%)
25
11 (44%)
1.72 (0.85 to 3.47)
Self-perceived balance problem
134
36 (27%)
109
27 (25%)
25
9 (36%)
1.53 (0.74 to 3.15)
Back pain
134
36 (27%)
109
27 (25%)
25
9 (36%)
1.53 (0.74 to 3.15)
Hip pain
129
9 (7%)
106
7 (7%)
23
2 (9%)
1.27 (0.35 to 4.58)
Knee pain
134
4 (3%)
109
2 (2%)
25
2 (8%)
2.83 (0.99 to 8.06)
Foot pain
134
3 (2%)
109
1 (1%)
25
2 (8%)
3.8 (1.57 to 9.17)
Pain yes/no
134
22 (16%)
109
16 (15%)
25
6 (24%)
1.61 (0.73 to 3.56)
Limited ROM hip
134
22 (16%)
109
16 (15%)
25
6 (24%)
1.61 (0.73 to 3.56)
Limited ROM knee
134
44 (33%)
109
35 (32%)
25
9 (36%)
1.15 (0.55 to 2.39)
Limited ROM ankle
134
20 (15%)
109
18 (17%)
25
2 (8%)
0.5 (0.13 to 1.94)
Any ROM limitation
134
37 (28%)
109
31 (28%)
25
6 (24%)
0.83 (0.36 to 1.91)
Need to get up during night
134
37 (28%)
109
31 (28%)
25
6 (24%)
0.83 (0.36 to 1.91)
Sensory deficit in lower extremity
134
21 (16%)
109
17 (16%)
25
4 (16%)
1.02 (0.39 to 2.68)
Fear of falls
134
85 (63%)
109
67 (62%)
25
18 (72%)
1.48 (0.67 to 3.3)
Low standing confidence
134
16 (12%)
109
12 (11%)
25
4 (16%)
1.4 (0.55 to 3.57)
Perceived dual task problems
134
24 (18%)
109
20 (18%)
25
4 (16%)
0.87 (0.33 to 2.31)
Cables on the floor at home
134
5 (4%)
109
5 (5%)
25
0 (0%)
0 (0 to 0)
Loose carpets at home
134
39 (29%)
109
31 (28%)
25
8 (32%)
1.15 (0.54 to 2.43)
Objects on the floor
134
97 (72%)
109
78 (72%)
25
19 (76%)
1.21 (0.52 to 2.79)
Objects on the stairs
134
16 (12%)
109
12 (11%)
25
4 (16%)
1.4 (0.55 to 3.57)
Thresholds in doors
134
13 (10%)
109
10 (9%)
25
3 (12%)
1.27 (0.44 to 3.67)
No bath mat
134
22 (16%)
109
16 (15%)
25
6 (24%)
1.61 (0.73 to 3.56)
Any home hazard
134
1 (1%)
109
0 (0%)
25
1 (4%)
5.54 (3.86 to 7.96)
Weak legs
134
24 (18%)
109
19 (17%)
25
5 (20%)
1.15 (0.48 to 2.75)Figure 1
Study diagram.


### 3.3. Self-Perceived Fall-Risk and Actual Falls

For the question about the self-perceived probability to fall within the next six months, 49 participants (37%) reported that they “will not fall” and 7 (14% of the 49) did actually fall; 81 (60%) reported that they “will probably not fall” and 17 (21% of the 81) did fall. Only two persons reported that they will “probably fall” and one of those did fall. Two participants did not respond to the question about the self-perceived fall-risk. There was no association between self-perceived and observed fall-risk (p
=
0.338).


### 3.4. Predictive Values

After calculation of the predicted probability to fall based on the values from our questionnaire and the coefficients published in the meta-analysis (Table2), the prediction model yielded an AUC value for the discrimination of 0.67 (96% CI 0.54 to 0.81) (Figure 2). There was statistically significant miscalibration (p value from the Hosmer-Lemeshow test <0.00001) (Figure 3).Figure 2
Receiver operating characteristic curve.Figure 3
Probability for fall. Calibration plot of observed versus estimated probability for fall.


### 3.5. Understandability of the Questionnaires

Ten participants stated that some questions were unclear and they provided seven specific comments, such as the following: that they were diagnosed with hypertension but had normal blood pressure under medication and did not know what to answer in the questions about present diseases; that some questions were asking about two different pieces of information and that some questions had double negations.


## 3.1. Set of Items Devised for the Self-Administered Fall-Risk Questionnaire

Based on the set of predictors we devised a set of questions. Because there was considerable overlap between the predictors, we selected a subset of 29 predictors with the aim of reducing overlap. Because some constructs were covered with more than one question, our questionnaire consisted of 36 questions, including demographic characteristics. Some of the questions consisted of several response options covering different risk factors.


## 3.2. Characteristics of Included Participants

With the snowball-sampling we could include 134 participants. The response rate during the monthly follow-up varied from 38 to 90% (see Figure1). The mean age of the 134 participants was 69.3 years with a standard deviation of 5.6 years. There were slightly less women than men (45% women and 55% men). The mean body mass index (BMI) was 25.95; 13% had a BMI of 30 or more (i.e., would be classified as obese). The proportion of participants who did fall during the last twelve months was 18%; only a very small proportion had consequences due to these falls. During the 6-month follow-up, 32 participants did fall at least once, we excluded seven falls (three falls on bike, one fall on ski, two falls on icy roads, and one fall during mountaineering on steep paths), resulting in 25 falls (18.7%). For each risk factor, only a small proportion of participants indicated problems which leads to wide confidence intervals in the odds ratios (Table 2) and the risk ratios presented (Table 3).Table 3
Distribution of risk factors. Distribution of risk factors for all participants, for the nonfallers, and for the fallers. All responses are self-reported values and not observed or measured values.
Variables
All
Non fallers during 6 months of follow-up
Fallers during 6 months of follow-up
Risk ratio (95% CI)
N responders
N (%) if not stated otherwise
N responders
N (%) if not stated otherwise
N responders
N (%) if not stated otherwise
Age
134
69.28 (5.59)
68.65 (5.4)
72 (5.66)
Body mass index
134
25.95 (4.04)
25.85 (4)
26.4 (4.25)
Women
134
60 (45%)
109
49 (45%)
25
11 (44%)
0.97 (0.47 to 1.98)
Obese persons (BMI ≥ 30)
134
18 (13%)
109
15 (14%)
25
3 (12%)
0.88 (0.29 to 2.64)
BMI < 18.5
134
2 (1%)
109
1 (1%)
25
1 (4%)
2.75 (0.66 to 11.52)
Fallen during last 12 months
134
2 (1%)
109
1 (1%)
25
1 (4%)
2.75 (0.66 to 11.52)
Emergency room visit after fall in last 12 months
134
24 (18%)
109
17 (16%)
25
7 (28%)
1.78 (0.84 to 3.79)
Fracture after fall during last 12 months
134
2 (1%)
109
1 (1%)
25
1 (4%)
2.75 (0.66 to 11.52)
Commotio after fall during last 12 months
134
1 (1%)
109
0 (0%)
25
1 (4%)
5.54 (3.86 to 7.96)
Fear of falls after fall during last 12 months
134
1 (1%)
109
0 (0%)
25
1 (4%)
5.54 (3.86 to 7.96)
Resting more than half an hour on floor after fall in last 12 months
134
4 (3%)
109
2 (2%)
25
2 (8%)
2.83 (0.99 to 8.06)
Needing help for activities of daily living
134
0 (0%)
109
0 (0%)
25
0 (0%)
0 (0 to 0)
Using walking aids
134
1 (1%)
109
0 (0%)
25
1 (4%)
5.54 (3.86 to 7.96)
Walking slower or making more breaks
134
3 (2%)
109
1 (1%)
25
2 (8%)
3.8 (1.57 to 9.17)
Problems crossing road at traffic light during green phase
134
1 (1%)
109
1 (1%)
25
0 (0%)
0 (0 to 0)
Stumbling at least once per months
134
0 (0%)
109
0 (0%)
25
0 (0%)
0 (0 to 0)
Unsecure if walking in darkness
134
2 (1%)
109
0 (0%)
25
2 (8%)
5.74 (3.96 to 8.32)
Unsecure even if walking in daylight
134
3 (2%)
109
1 (1%)
25
2 (8%)
3.8 (1.57 to 9.17)
Vision problems
134
1 (1%)
109
0 (0%)
25
1 (4%)
5.54 (3.86 to 7.96)
Seeing good without glasses
134
5 (4%)
109
3 (3%)
25
2 (8%)
2.24 (0.72 to 6.98)
Seeing good with glasses
134
25 (19%)
109
24 (22%)
25
1 (4%)
0.18 (0.03 to 1.28)
Does not see good with glasses
134
106 (79%)
109
84 (77%)
25
22 (88%)
1.94 (0.62 to 6.01)
Hearing problem
134
4 (3%)
109
2 (2%)
25
2 (8%)
2.83 (0.99 to 8.06)
Hearing problem but wearing hearing aid
134
42 (31%)
109
33 (30%)
25
9 (36%)
1.23 (0.59 to 2.56)
Taking sedative
134
5 (4%)
109
4 (4%)
25
1 (4%)
1.08 (0.18 to 6.44)
Taking blood pressure decreasing medication
134
2 (1%)
109
0 (0%)
25
2 (8%)
5.74 (3.96 to 8.32)
Taking antiepileptics
134
47 (35%)
109
34 (31%)
25
13 (52%)
2.01 (1 to 4.04)
Taking antidepressants
134
2 (1%)
109
2 (2%)
25
0 (0%)
0 (0 to 0)
Other medicaments
134
3 (2%)
109
1 (1%)
25
2 (8%)
3.8 (1.57 to 9.17)
More than one medicament
134
51 (38%)
109
39 (36%)
25
12 (48%)
1.5 (0.74 to 3.03)
Taking at least one medicament
134
26 (19%)
109
17 (16%)
25
9 (36%)
2.34 (1.17 to 4.68)
Low spirit at more than half of all days
134
3 (2%)
109
1 (1%)
25
2 (8%)
3.8 (1.57 to 9.17)
Low spirit at some days
134
51 (38%)
109
39 (36%)
25
12 (48%)
1.5 (0.74 to 3.03)
No disease
134
26 (19%)
109
17 (16%)
25
9 (36%)
2.34 (1.17 to 4.68)
Parkinson
134
78 (58%)
109
59 (54%)
25
19 (76%)
2.27 (0.97 to 5.33)
Stroke
118
3 (3%)
97
2 (2%)
21
1 (5%)
1.92 (0.37 to 9.97)
Epilepsy
134
37 (28%)
109
27 (25%)
25
10 (40%)
1.75 (0.86 to 3.54)
Diabetes
134
40 (30%)
109
35 (32%)
25
5 (20%)
0.59 (0.24 to 1.46)
Incontinence
134
1 (1%)
109
0 (0%)
25
1 (4%)
5.54 (3.86 to 7.96)
Osteoarthritis
134
4 (3%)
109
3 (3%)
25
1 (4%)
1.35 (0.24 to 7.68)
Rheumatic disease (including osteoarthritis)
134
2 (1%)
109
2 (2%)
25
0 (0%)
0 (0 to 0)
Multiple sclerosis
134
4 (3%)
109
3 (3%)
25
1 (4%)
1.35 (0.24 to 7.68)
Neurologic disease
134
3 (2%)
109
2 (2%)
25
1 (4%)
1.82 (0.35 to 9.39)
High blood pressure
134
39 (29%)
109
32 (29%)
25
7 (28%)
0.95 (0.43 to 2.09)
Fainting
134
44 (33%)
109
37 (34%)
25
7 (28%)
0.8 (0.36 to 1.76)
Leg pain during walking
134
0 (0%)
109
0 (0%)
25
0 (0%)
0 (0 to 0)
Dyspnea
134
6 (5%)
109
4 (4%)
25
2 (8%)
1.86 (0.56 to 6.1)
Heart symptoms
134
28 (21%)
109
20 (18%)
25
8 (32%)
1.78 (0.86 to 3.7)
Dizziness
134
29 (22%)
109
23 (21%)
25
6 (24%)
1.14 (0.5 to 2.6)
Wanted weight loss
118
54 (46%)
97
43 (44%)
21
11 (52%)
1.3 (0.6 to 2.83)
Unwanted weight loss
116
31 (27%)
97
24 (25%)
19
7 (37%)
1.6 (0.69 to 3.69)
Difficulties getting up from a chair
134
42 (31%)
109
31 (28%)
25
11 (44%)
1.72 (0.85 to 3.47)
Self-perceived balance problem
134
36 (27%)
109
27 (25%)
25
9 (36%)
1.53 (0.74 to 3.15)
Back pain
134
36 (27%)
109
27 (25%)
25
9 (36%)
1.53 (0.74 to 3.15)
Hip pain
129
9 (7%)
106
7 (7%)
23
2 (9%)
1.27 (0.35 to 4.58)
Knee pain
134
4 (3%)
109
2 (2%)
25
2 (8%)
2.83 (0.99 to 8.06)
Foot pain
134
3 (2%)
109
1 (1%)
25
2 (8%)
3.8 (1.57 to 9.17)
Pain yes/no
134
22 (16%)
109
16 (15%)
25
6 (24%)
1.61 (0.73 to 3.56)
Limited ROM hip
134
22 (16%)
109
16 (15%)
25
6 (24%)
1.61 (0.73 to 3.56)
Limited ROM knee
134
44 (33%)
109
35 (32%)
25
9 (36%)
1.15 (0.55 to 2.39)
Limited ROM ankle
134
20 (15%)
109
18 (17%)
25
2 (8%)
0.5 (0.13 to 1.94)
Any ROM limitation
134
37 (28%)
109
31 (28%)
25
6 (24%)
0.83 (0.36 to 1.91)
Need to get up during night
134
37 (28%)
109
31 (28%)
25
6 (24%)
0.83 (0.36 to 1.91)
Sensory deficit in lower extremity
134
21 (16%)
109
17 (16%)
25
4 (16%)
1.02 (0.39 to 2.68)
Fear of falls
134
85 (63%)
109
67 (62%)
25
18 (72%)
1.48 (0.67 to 3.3)
Low standing confidence
134
16 (12%)
109
12 (11%)
25
4 (16%)
1.4 (0.55 to 3.57)
Perceived dual task problems
134
24 (18%)
109
20 (18%)
25
4 (16%)
0.87 (0.33 to 2.31)
Cables on the floor at home
134
5 (4%)
109
5 (5%)
25
0 (0%)
0 (0 to 0)
Loose carpets at home
134
39 (29%)
109
31 (28%)
25
8 (32%)
1.15 (0.54 to 2.43)
Objects on the floor
134
97 (72%)
109
78 (72%)
25
19 (76%)
1.21 (0.52 to 2.79)
Objects on the stairs
134
16 (12%)
109
12 (11%)
25
4 (16%)
1.4 (0.55 to 3.57)
Thresholds in doors
134
13 (10%)
109
10 (9%)
25
3 (12%)
1.27 (0.44 to 3.67)
No bath mat
134
22 (16%)
109
16 (15%)
25
6 (24%)
1.61 (0.73 to 3.56)
Any home hazard
134
1 (1%)
109
0 (0%)
25
1 (4%)
5.54 (3.86 to 7.96)
Weak legs
134
24 (18%)
109
19 (17%)
25
5 (20%)
1.15 (0.48 to 2.75)Figure 1
Study diagram.


## 3.3. Self-Perceived Fall-Risk and Actual Falls

For the question about the self-perceived probability to fall within the next six months, 49 participants (37%) reported that they “will not fall” and 7 (14% of the 49) did actually fall; 81 (60%) reported that they “will probably not fall” and 17 (21% of the 81) did fall. Only two persons reported that they will “probably fall” and one of those did fall. Two participants did not respond to the question about the self-perceived fall-risk. There was no association between self-perceived and observed fall-risk (p
=
0.338).


## 3.4. Predictive Values

After calculation of the predicted probability to fall based on the values from our questionnaire and the coefficients published in the meta-analysis (Table2), the prediction model yielded an AUC value for the discrimination of 0.67 (96% CI 0.54 to 0.81) (Figure 2). There was statistically significant miscalibration (p value from the Hosmer-Lemeshow test <0.00001) (Figure 3).Figure 2
Receiver operating characteristic curve.Figure 3
Probability for fall. Calibration plot of observed versus estimated probability for fall.


## 3.5. Understandability of the Questionnaires

Ten participants stated that some questions were unclear and they provided seven specific comments, such as the following: that they were diagnosed with hypertension but had normal blood pressure under medication and did not know what to answer in the questions about present diseases; that some questions were asking about two different pieces of information and that some questions had double negations.


## 4. Discussion

In this longitudinal cohort study with a six-month follow-up of falls, including 134 community-dwelling elderly participants aged 60 years or more, we tested a preliminary version of an online questionnaire to assess the fall-risk. The main findings were that (a) it is feasible to do an online survey of a comprehensive set of fall-risk factors and (b) the understandability of the questions was good with the exceptions of five questions, (c) the response rate of the monthly falls assessment was too low, (d) the discrimination was moderate, and (e) the calibration was insufficient.The strength of our study was the approach to devise a set of questions covering the whole spectre of risk factors for falls based on published meta-analyses. This study is an important first step in the development of a comprehensive self-administered questionnaire. Although we cannot present a final version of the questionnaire, this study provides important information for the future development of fall-risk questionnaires.There are some limitations of our project. The understandability was assessed by semistructured interviews with experts and with an open question in the online questionnaire for the participants. We interviewed only two laypersons before we sent out the questionnaire to the participants. Interviewing of more participants before sending the questionnaire to the participants might have eliminated some problems with the understandability. It is challenging to assess risk factors with self-administered questionnaires. The different visual risk factors especially such as distant contrast sensitivity or depth perception [61] or the dual task problems are difficult to assess. Furthermore, snowball-sampling is a “biased” sampling technique because it is not random and the inclusion of the next participants depends on the previous participants (i.e., participants are not independent). An alternative would have been to search participants by the means of flyers or newspaper or radio advertisements. However, the snowball-sampling has the advantage of being nonexpensive and fast. The nonrandomness is not a large disadvantage in a feasibility study. A further limitation is the low response rate for the monthly fall-risk assessment. We did not systematically send reminders if participants did not respond. Furthermore, we did not present a fall definition to the participant, because we thought that this could confuse more than it would help. Presenting and explaining a fall definition such as the one used by Tinetti et al., “a sudden, unintentional change in position causing an individual to land at a lower level, on an object, the floor, or the ground, other than as a consequence of sudden onset of paralysis, epileptic seizure, or overwhelming external force” [62], might clarify what to report as a fall. Furthermore, the questions about the falls could include examples to illustrate what we understand by a fall. For example, some participants do not consider falling on their knees as a fall, because they were not “lying” on the ground after the fall. It is unclear whether the inclusion of photographs or graphical illustration could improve the reporting. Questions for falls could include examples of specific situations. However, our falls incidence of 19% is compatible with one-year incidences (39% for women, 30% for men), data recently published from Germany [63]; therefore we do not believe that there is an underestimation of the falls. We only assessed falls during six months; a longer follow-up would have increased the number of falls. The frequency of problems reported in the individual fall-risk questions was very low if compared to other studies on self-report fall-risk questionnaires [15, 17]. This could be due to the good health state of our participants but it could also be due to how the questions were formulated (i.e., unclear wording or wording targets only serious problems). Given the very low proportion of present risk factors, the selection of our sample could be problematic. There might be a selection bias towards a higher socioeconomic state, given the high proportion of participants with higher education. Given the low presence of risk factors we would have expected a lower falls incidence rate. Our prediction formula still overestimates the fall-risk. This is most probably due to the high correlation between the included predictors. However, our sample size was sufficiently large for univariable analyses but too low to adjust for this correlation by the means of a multivariable model. Therefore, the prediction formula needs to be adjusted with methods proposed by Steyerberg and colleagues [51] in a larger sample once the questionnaire is in its definitive version and after testing of the reliability.If we compare our results to published studies using questionnaires for the assessment of fall-risk, we have similar values for calibration and discrimination compared to Cattelani et al. [16]. Compared to El Miedany et al. [15] we have lower predictive values; they received an AUC value of 0.89 with only five predictors. However, they included a sample where all had at least one previous fall and where 82% reported to walk slower, 65% reported loss of balance, and 55% had poor sight. Therefore, the two samples are not similar. Our AUC value is low but one has to consider that other tests widely used to predict falls, such as the timed-up-and-go (TUG) test, do not have better predictive values. A recent review on the predictive values for falls of the TUG in community-dwelling elderly people found an AUC value of 0.57 [64].We did not find an association between self-perceived fall-risk and falls. One might expect that the self-perceived risk for falling increases fear of falls, which is known to be associated with future falls. One reason why we did not find an association is that the response options of the question for the self-perceived risk were not optimal and should be improved for future studies.Our study has some implications for further research. The following amendments need to be done before further testing: (1) the question about past falls which should ask about the number of falls in the last year; it is recommended that persons with more than one fall in the past year should be referred to a detailed assessment [65]; (2) rewording of some questions; and (3) explication of what is considered as a fall to exclude falls, for example, due to an overwhelming external force, that is, following the falls definition used by [62]. After a refinement of the questions, test-retest reliability must be tested before the coefficients for a final predictive model should be assessed with a multivariable logistic regression based on results from a larger cohort study with a one-year follow-up in which the analyses should be separated for the prediction of one fall or recurrent falls. Furthermore, a larger sample size would allow evaluating whether some questions might be eliminated without losing discrimination or calibration of the prediction tool.Implications for practice are as follows. Our study showed that in a sample with a relative low risk profile the incidence of falls was 19% during a period of six months and that the participants were mostly not aware of their fall-risk. Health professionals who see patients for other indications, for example, for the treatment of osteoarthritis, back pain, or neurological problems, could use this fall-risk questionnaire as a screening tool or a “flag system” and specifically test the domains where the patients report problems. The health professionals could then refer the patient to a falls-prevention group. The tool could also be used for the preparation of a visit to a medical doctor. The patients could bring the questionnaire to the medical doctor to discuss the results and possible strategies if necessary.


## 5. Conclusion

This study showed that fall-risk awareness is low and that even in a sample of elderly people with a low risk profile in known risk factors the falls incidence is 19% in a six-month period. The present questionnaire needs some adaptation of the wording and reliability testing before a definitive prediction formula can be developed in a large sample and with multivariable analyses. Measures need to be implemented to increase the monthly response rate for the follow-up period.



---

*Source: 1520932-2016-05-10.xml*