The Influences of Age, Obesity and Adverse Drug Effects on Balance and Mobility Testing Scores in Ambulatory Older Adults

Abstract

Background and Purpose

The adverse effects of drugs may influence results on tests of mobility and balance, but the drug specific impact is not identified when using these tests. We propose that a quantitative drug index (QDI) will assist in assessing fall risk based on these tests, when combined with other fall risk variables.

Methods

Fifty-seven community-dwelling older adults who could walk independently on a treadmill and with “Mini-mental state examination” (MMSE) scores equal to or greater than 24 participated. Mobility and balance outcome measures included the Balance Evaluation Systems Test (BESTest), Berg Balance Scale (BBS), Timed-Up-and-Go (TUG) and cognitive dual task TUG (TUGc). Fall history, current drug list and Activity Specific Balance Confidence scale (ABC) scores were also collected, and body mass index (BMI) was calculated. The QDI was derived from the drug list for each individual, and based on fall-related drug adverse effects. Multiple linear regression analyses were conducted using age, BMI and QDI as predictor variables for determining mobility and balance test scores, and ABC scale scores. Subsequently, participants were divided into low (QDI=0) impact drug group (LIDG) and high (QDI>0) impact drug group (HIDG) for Mann-Whitney 2 group comparisons.

Results

Age, BMI and QDI were all significant (p<0.001) independent variables in multiple regression analyses for mobility and balance test scores, but not for the ABC scale. Separately, the 2 group comparisons for the BESTest, BBS, TUG and TUGc demonstrated that HIDG scored significantly (p<0.05) worse on these tests compared to the LIDG. Drug counts were also significantly higher for the HIDG compared to the LIDG. In contrast, age, BMI, MMSE and reported falls in the last twelve months were not significantly different between groups.

Discussion

Age, BMI and QDI independently contributed to scores on mobility and balance tests commonly used to determine fall risk. Both QDI and BMI also significantly predicted both balance ability and balance confidence. Cumulative drug adverse effects and body morphology are both modifiable through therapeutic intervention, allowing healthcare professionals an avenue to reduce fall risk.

Conclusion

Age, BMI and QDI all contributed independently to the mobility and balance test scores examined, and may provide healthcare professionals a screening tool to determine whether additional mobility and balance testing is required. Additionally, the QDI is a more precise marker of adverse effects of drugs compared to drug counts, as the latter does not quantitate the influence of drugs on physiologic function.

INTRODUCTION

Identification of fall risk requires examination of the factors that increase the likelihood of falling. Factors such as mobility and balance, which can be described by several clinical assessment tools such as the Berg Balance Scale (BBS), the Balance Evaluation Systems Test (BESTest), and the Timed Up and Go (TUG), have been used to characterize fall risk in older adults.^1–4 Other factors associated with fall risk, such as age and obesity (as defined by body mass index (BMI)), may also be measured.^5–8 Increased age is associated with lower scores on the BBS and longer times for the TUG.⁴ Similarly, higher BMI is associated with lower BBS scores, suggesting an increased fall risk for those individuals.⁹ Adverse drug effects are a unique and separate potential contributor to fall risk, yet no comprehensive metric exists to account for their influence.

Many pharmacodynamic mechanisms contribute to drug-related fall risk. Orthostatic hypotension, for example, is an adverse effect of many drugs used in the treatment of cardiovascular diseases and has been associated with falls.^10–13 Drugs used in the treatment of central nervous system (CNS) disorders have also been shown to increase fall risk through psychomotor delay and reduced cognitive function.^10–14 Additionally, drugs that have CNS anticholinergic effects also cause confusion, sedation and delirium, resulting in increased fall risk.^10,12,13 Other pharmacodynamic mechanisms which may contribute to increased fall risk include electrolyte alterations, generalized asthenia and visual dysfunction.^12,13

Drug-related fall risk in older adults is a concern in all healthcare settings and at home. The incidence of mortality after an injurious fall rises sharply after the age 65.¹⁵ In the United States a review of emergency hospitalization resulting from adverse drug effects in adults 65 and older found that falls were most commonly associated with cardiovascular and CNS drug classes.¹⁶ Combined, these 2 classes resulted in a total of 2404 emergency department cases annually, with subsequent hospital admissions of 40% and 36%, respectively. Surprisingly, the majority of these hospitalizations were not associated with inappropriate prescribing. Thus, even drugs that are appropriately prescribed contribute to elevated fall risk for older adults. The risk of falling increases as the number of drugs an individual takes increases, but only when one or more of the drugs is independently associated with falls.¹⁷ This challenges the concept that accurate information related to fall risk can be determined simply by counting the number of drugs taken by an individual (i.e. drug counts).

There are several broad categories of tools designed to capture drug-associated fall risk in older adults. The first category consists of prescription guidelines which compare the clinical benefit versus risk and include: Beers Criteria, Screening Tool of Alert Doctors to the Right Treatment (START) and Screening Tool of Older Person’s potentially inappropriate Prescriptions (STOPP).^11,18 These tools allow prescribers to make informed decisions based on known adverse effects. The second category consists of indexes which quantify the adverse effects of drugs and include: the Drug Burden Index (DBI), Anticholinergic Risk Scale (ARS), and the Anticholinergic Cognitive Burden (ACB).^19–21 These quantitative assessments focus primarily on adverse anticholinergic effects, although the DBI also includes adverse sedative effects. While both sedation and anticholinergic effects are associated with falls, there are additional previously described pharmacologic mechanisms that may also precipitate falls and are not captured by these indices.^10–14 Thus, these quantitative assessments may miss drugs that contribute to fall risk.

For the purposes of this investigation, the variables considered that contribute to fall risk were age, BMI, adverse drug effects, fall history and drug counts. This investigation examined whether these variables were predictors of mobility and balance testing scores. Second, the investigation examined whether drug adverse effects were independently associated with mobility and balance test scores. A potential clinical benefit of this approach would be to incorporate these variables into electronic medical records. Thus, individuals who have not fallen yet but who may be at risk of falling could receive more comprehensive balance and mobility testing by physical therapists.

METHODS

Study Population

This study was approved by the institutional review boards at the University of Maryland, Temple University, and East Tennessee State University. Informed consent was obtained from all individual participants included in this study, and individual rights of all participants were protected. Participants were recruited for a balance training clinical trial (ClinicalTrials.gov #366151-1) via advertisements in a newspaper for older adults, and via flyers placed at local retirement communities from 2012–2015. Results reported here are a secondary analysis of these data and do not reflect results from the randomized clinical trial. Each participant completed a series of forms providing medications and whether or not they experienced symptoms of pain or dizziness or had a history of falling. A total of 61 participants were evaluated at the initial time point for this part of the investigation. All participants were, by self-report, free from uncontrolled or unstable cardiovascular disease. All participants reported problems with their balance. Eligibility criteria included passing the “Mini-mental state examination” (MMSE) with a score of 24 or higher,²² and the ability to walk independently on a treadmill at a self-selected speed for 2 minutes. These inclusion criteria were designed for the larger clinical trial, but were also applied to this secondary analysis. Four participants were excluded (2 were unable to walk independently on the treadmill at a self-selected pace, 1 with a MMSE score of less than 24, and the drug list for 1 participant was not recorded. Data from fifty-seven subjects participating in the original clinical trial were analyzed for this investigation.

Setting and Outcome Measures

All participants underwent the following series of tests. The MMSE was conducted to determine eligibility. Balance ability and mobility were characterized with the following standard clinical assessments: BESTest,¹ BBS,² TUG and cognitive dual task Timed Up and Go (TUGc).³ Each of the balance and mobility outcome measures were included as part of the larger clinical trial, and are included here to demonstrate consistency across multiple different measures of balance. In addition to the battery of balance and walking tests, all participants reported the number of falls they experienced during the previous 12 months. Participant characteristics of age, weight, height, and gender were recorded, and body mass index (BMI) was calculated. Finally, the Activities-Specific Balance Confidence (ABC) scale was completed to characterize an individual’s confidence in their ability to perform daily activities without losing their balance. The BMI was not calculated for 1 participant as the weight was not taken and 1 participant refused to complete the TUGc.

The mobility and balance tests were selected to capture a range of standing and walking balance ability. The BESTest is a 27 item physical performance test, each item is scored on a 4 point ordinal scale; and the final score is reported as a percentage with a maximum of 100.¹ The BESTest has excellent test reliability for individuals with a wide variety of balance difficulties and scores less than 69 discriminate fallers from non-fallers among individuals with Parkinson’s disease.^1,23 The BBS is a 14 item scale (each item scored on a 5 point ordinal scale) designed to identify balance deficits in older adults, with a maximum score of 56 and excellent test-retest reliability.² Scores on the BBS scale below 46 are associated with an increased fall risk.²⁴ The TUG and TUGc are single and dual task versions of a screening test designed to identify fall risk with high test-retest reliability.^3,4 Individuals that require more than 13.5 seconds to complete the TUG or 14.5 seconds to complete the TUGc are considered to have increased risk for falling.³ The ABC is a self-report questionnaire that measures an individual’s perceived balance ability and has been related to functional balance in older adults.²⁵ The ABC has both high test-retest reliability and association with several other balance and mobility tests,^25,26 and scores below 67% are associated with an increased fall risk.²⁴

Development and Scoring of the Quantitative Drug Index

Drug lists for participants were obtained by recording the drugs that were brought in at the initial evaluation. Alternatively, some participants provided a written list of the drugs they were currently taking. All of the following were recorded on the drug list spreadsheet: prescribed and over-the-counter drugs, vitamins and supplements. From each participant’s drug list, the adverse effects for each drug that could impact fall risk were identified based on pharmacodynamic effects. Drug adverse effects were compiled from entries in LexiComp (Indianapolis, IN), and then the impact of each adverse effect on balance or falls was determined by the authors. A list of the adverse effects for this investigation are included in Table 1. Both prescription and over the counter drugs were evaluated for fall risk. Herbal and vitamin supplements were also evaluated if information was found in LexiComp. The QDI was calculated as follows: 1) A drug was first identified as having adverse effect(s) related to fall risk; 2) All of the fall-associated adverse effects for that drug were summed to provide a score for that drug; 3) Then the total score for each participant was calculated by summing the individual scores for each of the drugs the participant was currently taking. For example, if drug A is associated with sedation and hypotension and drug B is associated with diplopia, ataxia and myalgia then drug A would have a score of 2 and drug B would have a score of 3. For an individual taking both drugs A and B, then the individual’s QDI would be 5. A drug with no adverse effects related to falls would have a score of 0. Drug dosages were not included as part of the QDI calculation. Similarly, the potential for drug-drug interactions to alter, decrease or increase, concentrations of other drugs was not incorporated into the scale. Drug counts (Drug Counts) were calculated by counting the number of drugs an individual was taking, regardless of whether the drug was considered to have an adverse effect related to falls.

Table 1.

List of all adverse effects for the QDI in this investigation that were scored as associated with fall. All adverse effects were weighted equally.

accidental injury	dizziness	orthostatic hypotension
arthralgia	drowsiness	sedation
ataxia	falling	skeletal weakness
blurred vision	fatigue	somnolence
catatonic states	hypoglycemia	syncope
cognitive dysfunction	hypotension	tremor
confusion	incoordination	unsteady gait
coordination impaired	lethargy	vertigo
delirium	lightheadedness	visual disturbances
diplopia	muscle weakness	weakness
disoriented	myalgia

Data collection and analysis

Data analyses were completed using IBM SPSS Statistics 22 (IBM Inc., Armonk, NY).^27,28 A multiple linear regression analysis was conducted with the BESTest total score as the dependent variable and the age, BMI, fall history, QDI and drug counts as independent predictor variables. A stepwise forward regression method was used in the model with the entry and exit criteria for the independent variables in the model set at 0.05 and 0.10, respectively.²⁷ Age, BMI and QDI were selected by the step forward regression model, and were subsequently used in a forced entry model to repeat the analysis with test scores for the ABC scale, BBS, TUG, and TUGc as the dependent variables. For each regression analysis, actual residuals were plotted against predicted residuals to determine normality of distribution about the regression function, using a scatter plot and frequency histogram. Durbin-Watson test scores for autocorrelation of residuals were reported for all regression analyses as were the variance inflation factors (VIF) for age, BMI and QDI. Multiple regression functions were assessed two-tailed post hoc for power when sample size, number of predictor variables (k=3), coefficient of determination (R²) and error probability (α=0.05) were known, using the software program G*Power (Version 3.1.9.1.2, 2014).^29,30 Subsequently, scores for the ABC scale, mobility and balance tests, MMSE, drug counts, age, and BMI were divided into 2 groups based on the QDI scores. The low impact drug group (LIDG) was defined as a QDI score equal to 0, and the high impact drug group (HIDG) was defined as a QDI score greater than 0. Several variables were not normally distributed (Appendix Item 1); therefore, nonparametric Mann-Whitney U-tests for independent samples were performed to determine group differences between the LIDG and HIDG groups. Significance was set at p≤ 0.05 for all analyses.

RESULTS

The average age of the participants was 79 (range 66–92) and 72% (41) were female. A multiple regression analysis was conducted with age, BMI, drug counts, fall history, and QDI as independent predictor variables and BESTest total score as the outcome variable. The final model included age, BMI and QDI as the predictors for the BESTest total score (Table 2). Subsequently, these same 3 independent variables were examined to determine their predictive potential when ABC scale, BBS, TUG and TUGc were the dependent outcome variables. In all analyses, the regression models for predicting these mobility and balance test scores were significant (p<0.001) (Table 2). Individually within the multiple regression models, each of the 3 variables significantly (p<0.05) contributed to the prediction of the outcome scores for each mobility and balance test. Age, BMI and QDI were all negatively associated with the BESTest and BBS. An increase in any of the predictor variables would result in lower scores for these tests, suggesting higher fall risk. Age, BMI and QDI were all positively associated with TUG and TUGc scores. TUG and TUGc times increased as age, BMI, and QDI scores increased, again suggesting elevated fall risk. The relative independent contribution of age, BMI and QDI toward predictive potential for the mobility and balance test scores is shown by the standardized beta values in each multiple regression analysis (Table 2). In general, age demonstrated the greatest predictive contribution for the mobility and balance test scores. Body mass index had a greater effect than the QDI for BESTest, BBS and TUGc. In contrast, the QDI had a greater effect than BMI for the TUG.

Table 2.

Multiple Regression Analyses with Age, Body Mass Index (BMI) and Quantitative Drug Index (QDI) as Predictors of Gait and Balance Test Scores, and Activities-specific Balance Confidence (ABC) Self-assessment Scores.

Test	R2	Durbin-Watson	Model ANOVA (p)	Variables	Beta (SE)	Standardized Beta	p	Collinearity VIF
BESTest (N=56)	0.31	0.71	<0.001	Constant	130.9 (13.9)		<0.001
				Age	−0.54 (0.16)	−0.40	0.001	1.05
				BMI	−0.53 (0.17)	−0.36	0.003	1.05
				QDI Score	−0.43 (0.21)	−0.24	0.04	1.01
BBS (N=56)	0.37	0.75	<0.001	Constant	78.7 (5.7)		<0.001
				Age	−0.29 (0.07)	−0.50	<0.001	1.05
				BMI	−0.19 (0.07)	−0.30	0.01	1.05
				QDI Score	−0.20 (0.09)	−0.26	0.02	1.01
TUG (N=56)	0.34	1.57	<0.001	Constant	−10.7 (5.2)		0.04
				Age	0.23 (0.06)	0.44	<0.001	1.05
				BMI	0.14 (0.06)	0.25	0.04	1.05
				QDI Score	0.22 (0.08)	0.32	0.007	1.01
TUGc (N=55)	0.34	1.69	<0.001	Constant	−25.8 (9.1)		0.007
				Age	0.41 (0.10)	0.50	<0.001	1.04
				BMI	0.29 (0.11)	0.30	0.01	1.05
				QDI Score	0.33 (0.14)	0.27	0.02	1.01
ABC Scale (N=56)	0.29	1.58	<0.001	Constant	142.9 (26.4)		<0.001
				Age	−0.44 (0.30)	−0.17	0.15	1.05
				BMI	−1.08 (0.32)	−0.40	0.002	1.05
				QDI Score	−1.06 (0.39)	−0.32	0.009	1.01

BBS Berg balance scale, BESTest Balance Evaluation Systems Test total score, R² Coefficient of Determination, SE standard error, TUG & TUGc timed up and go without and with cognitive dual task, VIF variance inflation factor.

The multiple regression function for the ABC scale resulted in several differences compared to the mobility and balance tests examined above. The overall multiple regression model with ABC scale as the dependent variable was significant when age, BMI and QDI were the independent predictor variables (Table 2). However, only BMI and QDI individually were significant within the model and BMI was the strongest predictor of balance confidence. BMI and QDI were negatively associated with balance confidence, suggesting an increase in either of these variables would lead to a decrease the ABC scale score and increase fall risk.

The coefficients of determination from these multiple regression analyses for the 4 mobility and balance tests varied between 0.31 for the BESTest to 0.37 for the BBS. The ABC scale coefficient of determination was 0.29. Post hoc power analysis on the coefficients of determination demonstrated power for the balance and mobility tests were BESTest (0.99), BBS (0.99), TUG (0.99) and TUGc (0.99). Power analysis for the ABC scale was 0.99. All of these values represent a large effect size for the power analysis. The Durbin-Watson H statistic for BESTest and BBS demonstrated significantly more positive autocorrelation of the residuals with scores less than 1, compared to the ABC scale, TUG and TUGc values which were between 1.57 and 1.69. The VIF for age, BMI and QDI in all regression analyses were approximately 1, suggesting minimal multicollinearity of these predictors. Residuals from the regression analyses were normally distributed (Appendix Item 2). Forty-four percent of the participants reported no falls within the previous 12 months, and a total of 74% reported one or less falls. This frequency would make fall history a poor outcome variable in a linear regression analysis (Appendix Item 3).

The participants in the HIDG demonstrated significantly lower scores on the BESTest (p = 0.02) and BBS (p = 0.02), compared to participants in the LIDG (Table 3). Individuals in the HIDG were also significantly slower on the TUG (p = 0.01) and TUGc (p = 0.04) compared to individuals in the LIDG. As expected, the HIDG had significantly higher drug counts compared to the drug counts of the LIDG (p < 0.001). Additionally, the HIDG scored marginally, but not statistically worse (p=0.11) on the ABC scale compared to LIDG. There were no significant differences between the HIDG and LIDG for reported fall history, age, BMI or MMSE scores.

Table 3.

Nonparametric Comparison of Participant Characteristics, Mobility and Balance Tests Scores, Drug Counts, and “Mini-mental state examination” (MMSE) after grouping into Low Impact and High Impact Drug Groups based on the Quantitative Drug Index (QDI).

Dependent Variable	QDI Group	N	Median	Inter-quartile range	95% Confidence Interval	P Value
ABC Scale	Low	16	82.3	12.8	74.8–88.2	.11
	High	41	75.0	25.7	68.1–78.9
Age	Low	16	74.0	8.0	72.1–79.7	.26
	High	41	77.0	11.0	75.9–79.9
BESTest	Low	16	78.7	9.1	73.7–81.2	.02
	High	41	72.5	12.5	68.7–74.4
BBS	Low	16	51.5	5.0	50.4–53.8	.02
	High	41	50.0	3.0	48.4–50.8
Body mass index	Low	16	25.5	6.0	23.4–28.0	.32
	High	40	26.6	11.0	26.1–30.3
Drug Counts	Low	16	.5	2.0	.3–1.3	<.001
	High	41	5.0	6.0	5.3–8.4
MMSE	Low	16	28.0	1.8	27.7–28.9	.56
	High	41	29.0	2.0	27.7–28.8
12 month Fall History	Low	16	1.0	1.8	.4–1.3	.67
	High	41	1.0	2.5	.7–2.2
TUG	Low	16	9.7	2.6	8.4–11.7	.01
	High	41	11.4	2.5	10.8–12.9
TUGc	Low	16	10.6	5.8	9.9–16.8	.04
	High	40	15.0	7.0	13.9–17.4

ABC activities-specific balance confidence, BBS Berg balance scale, BESTest Balance Evaluation Systems Test, TUG and TUGc timed up and go without and with cognitive dual task.

DISCUSSION

This investigation demonstrates that outcome scores of 4 commonly used mobility and balance tests may all be partially predicted by age, BMI and adverse drug effects (QDI). Both age and BMI are readily available (or easily calculated) from electronic records. The information for calculating the QDI is also electronically available, and thus may be incorporated into electronic medical records. The purpose of this study was to determine whether these 3 independent variables could be used to assist in preliminary screening of fall risk, a determination that would then require additional mobility and balance testing. Each of the mobility or balance tests reported in this study are used to screen or more deeply probe balance ability and fall risk. There should be no surprise that as scores become worse on one test other tests may also be similarly impacted. That age, BMI and QDI were found to be significant predictors for all 4 mobility and balance tests are due to the strong associations between these mobility and balance tests.^9,23,26 These observations are even more surprising considering the low percentage of reported recurrent fallers, defined as 2 or more falls in the past year, among the participants in this sample. Only twenty-six percent of the fifty-seven subjects in this investigation self-reported more than one fall in the previous year. Post hoc power analysis also demonstrated large effects for these coefficients of variation and sample sizes.

Additionally, division of the participants into low impact and high impact drug groups, based on the QDI scores, resulted in significant differences in the mobility and balance scores between these 2 groups. Even more interesting was that the median scores on the mobility and balance tests for the HIDG, were within the normal ranges for these 4 tests.^4,23 These results suggest that adverse drug effects may influence mobility and balance ability, even when the scores for these tests are within ranges that otherwise were not associated with elevated fall risk.

Both greater age and higher BMI contribute to increased fall risk. Age is associated with lower mobility and balance outcome scores. Both the BBS and the TUG demonstrated age-related declines in test scores for participants between the ages of 61 to 89.⁴ Age was also used as a covariate adjustment in predicting differences in fall risk for TUG under traditional, cognitive and manual conditions.³ The current results also support the negative influence of increasing age on BBS, TUG and TUGc scores, and are the first to identify a similar negative influence on overall balance as measured by the BESTest. In contrast to the negative age-related influence on mobility and balance scores, the influence of BMI on balance and mobility is less well defined. This lack of association is surprising as several factors that are positively associated with the severity of BMI have also been linked to fall risk, and may act as mediators between the two.⁶ These BMI-related variables mediating the elevated fall risk include chronic moderate to extreme pain, sedentary lifestyle, cardiac disease, diabetes mellitus, sedative and hypnotic drugs, and anxiety or depression and the drugs to treat these latter two disorders. Body mass index was previously reported to have a negative association with scores on the BBS, but not the TUG.⁹ The influence of BMI on BESTest scores has not been previously reported. The current investigation identified a negative influence of BMI on scores for all 4 mobility and balance tests such that higher BMI was associated with worse performance. The differing results for the influence of BMI on TUG outcomes, between the current and previous investigation, will require further evaluation.

This study represents the initial presentation of the QDI. There may be some overlap in identified drug-related adverse effects between the current QDI and previously published drug indices. This is not surprising, as a sub-set of the adverse effects identified in the development of the QDI are also represented in other drug indices. However, there are several important differences between the QDI used in the current investigation and the ACB, ARS, and DBI. The ACB identified a list of drugs with documented anticholinergic effects. These drugs were then scored on a scale of 1 to 3, with 3 being the most severe, based on their reported clinical or laboratory documentation.²⁰ Similarly the ARS examined 500 of the most prescribed drugs for anticholinergic activity, and scored these drugs on a 0 to 3 scale, with 3 being the most severe. Again, the scoring for the drugs was based on their observed laboratory effects or clinical anticholinergic activities.²¹ A comparison of these 2 indices demonstrates that both the ACB and ARS capture decreased cognitive and subjective activities of daily living scores.³¹ The DBI examines the potential fall risk for drugs that have either anticholinergic activity or sedating potential, with higher scores on the DBI related to lower functional capabilities.¹⁹ Thus, the DBI expands the list of drugs that may contribute to falls risk compared to the ACB and ARS, but is still limited to only 2 pharmacologic mechanisms of action. In contrast, the QDI examines all potential adverse drug effects that may increase the risk of fall, regardless of the drug’s principal mechanism(s) of action. As such, the QDI encompasses a larger number of drugs in more classes than previous indices. Another advantage the QDI has over the DBI, ARS and ACB is that there is no upper limit on the number of adverse effects related to fall risk for any drug on the QDI. The scores for each individual’s drugs are summed, and this sum represents the QDI score for that individual at that time. Capturing a larger number of the pharmacologic adverse effects which contribute to falls, and creating an index score with no upper limit would theoretically increase the sensitivity of the QDI compared to the previously discussed quantitative indices.

A separate but related issue to determining which drug-associated adverse effects may be influencing fall risk, is how to weight these fall risk associated adverse effects. To date all previously published investigations which used drug-associated adverse effects to determine fall risk have weighted these adverse effects equally.^{19–21,31,32} In the present investigation, the QDI continued the convention of equal weighting for drug-associated adverse effects. Part of the limitation to weighting adverse effects may be the mechanism(s) of the weighting. That is, should the weighting be based on the severity of the adverse effect? Such a weighting would require an agreement among researchers in the field as to which adverse effects are associated with falls and a rank ordering of the severity for these adverse effects. Alternatively, the weighting for adverse effects could be based on the frequency of the occurrence for these in the general population medicated with these drugs. There may be additional alternative weighting schemes for these adverse effects not recognized in this discussion as well. Overall, weighting of adverse effects may or may not increase the sensitivity of any drug-associated fall-risk score. Future studies are needed to investigate whether unequal weighting of the drug adverse effects improves the sensitivity of the drug-associated fall-risk score.

Additionally, both the drug dosage and the potential for drug-drug interactions may influence the drug-associated fall risk. To date there is no consistency about incorporating dosage into the calculations of published drug-associated fall risk scoring. Both the DBI and the ARS include dosage in their calculations for drug-associated fall risk.^19,32 In contrast, neither the QDI in the present investigation nor the ACB scale incorporated drug dosage into the drug-associated fall risk scoring.²⁰ The fact that drug dosage was not incorporated into either the QDI, in the present investigation, or the ACB scale, in the previous investigation, suggests that addition of the drug dosage may increase the accuracy of calculating drug-associated fall risk but may not be essential. The presence or absence of drug dosage in calculating fall risk is extremely important as not all records will list drug dosages. Having a drug-associated fall risk scale, such as the QDI, which reflects fall risk without inclusion of dosage may lend itself to wider clinical use. The potential for drugs to alter the pharmacokinetics of other drugs, increasing or decreasing their concentrations in the body, is a recognized clinical problem influencing both clinical efficacy and adverse effects. To date, neither the QDI nor any of the previously published investigations have included drug-drug interactions in their calculation for drug-associated fall risk. The absence of drug-drug interactions in these calculations may be related to the complexity of developing a scale that includes these interactions, yet such interactions should be explored in future research.

Several previous investigations have examined the adverse effects of drugs on functional activity. Gnjidic and colleagues examined adverse drug effects on 5 functional tests in older community-dwelling Australian men using the DBI.³³ The investigation utilized time to complete 5 chair stands, gait speed over a six meter distance, narrow gait speed, balance, grip force and a subjective activities of daily living scale. High DBI scores were associated with slower gait speeds in normal and narrow walking conditions, balance difficulties, decreased grip force and lower scores in the activities of daily living scale. A second investigation also found that participants with higher DBI scores took longer to complete the TUG and 5 chair stands, had decreased grip force, slower gait speed and lower activity of daily living scores.³⁴ In the present investigation, the participant cohort was divided into LIDG and HIDG based on QDI scores. The results of the present investigation are consistent with the previous investigation, in that the HIDG scored worse on all 4 mobility and balance tests. Furthermore, this statistical difference occurred even when the median scores for the HIDG were within the low fall risk range of scores for these mobility and balance tests.^3,23,24 These results suggest that drug effects on fall risk may begin when global fall risk as identified by those same tests may be sub-clinical. A separate investigation used the Fall-Risk-Increasing Drugs (FRIDs) list to examine fall risk in older participants.³⁵ The FRIDs is a list of prescription drugs without quantitation and similar to Beers List. The FRIDs lists a wider category of drugs associated with fall risk and includes anticholinergics, those with sedating properties, and drugs which decrease blood pressure. The spectrum of drugs assumed to be associated with falls in the FRIDs list investigation more closely parallels the drugs of the QDI in the present investigation. The TUG times were recorded at baseline and approximately 6 months following removal or reduced dosing of drugs from the FRIDs list. After modifying the dosage of these drugs there was a significant reduction in TUG times (better performance) compared to the participants’ baseline attempts. One limitation of the investigation with the FRIDs list was a lack of quantitation or rating of these fall risk associated drugs. Thus current and previous investigations demonstrate a direct relationship between drug profiles and mobility and balance test scores, ultimately influencing fall risk. In this study, we classified a larger group of drugs listed as being associated with fall risk, and compared the influence of the drug-derived QDI on multiple mobility and balance test scores within a single cohort of participants. This provides support that drugs with specific adverse effects have an independent influence on overall balance and fall risk.

Balance confidence as measured by the ABC scale was not associated with age, but was found to be associated with BMI and QDI. These results are surprising given that the ABC scale scores have strong associations with the other mobility and balance test scores examined.^1,24,26 Future research is needed to determine the mechanisms through which balance confidence is influenced by the adverse effects of drugs. Potential mechanisms may include, but not be limited to, altered alertness or decreased mental cognition. The marginal difference for the ABC scale between the LDIG and HDIG may be biased since 74% of this cohort did not report falls in the past year. Despite enrolling only individuals with a history of falling or a self-identified balance problem, many individuals did not score at elevated fall risk on the BESTest, BBS, TUG or TUGc. This likely reflects a high functioning cohort concerned about their balance, but who largely are still very confident in their abilities. Future research on populations with greater balance impairments are needed to clarify the discordant results between balance confidence and balance ability reported here.

Drug counts would be expected to demonstrate similar discrimination to the QDI in the multiple regression functions; however, this similarity may be accounted by the fact that the adverse effects of drugs are what account for the fall risk, not just the number of drugs.¹⁷ This demonstrates that the QDI is at least as powerful in the prediction of drug-related fall risk as drug counts, the current gold standard. Future investigations need to specifically address the interactions between drug dosage, drug-drug interactions, and weighting of adverse effects.

An alternative interpretation of the drug-associated fall risk scales/indexes is that they represent the severity of the physical disabilities in individuals with balance difficulties. The treatment of dysfunction often results in the prescription of drugs which are both clinically efficacious and minimize adverse effects compared to other drug alternatives. When these first line drugs are no longer efficacious, less optimal drugs are often prescribed. These latter drugs are equally efficacious, or sometimes have greater efficacy, but almost always have a higher severity or frequency of adverse effects (or entirely different and more severe adverse effects). Thus, the QDI may simply be a surrogate for measuring the severity of the physical dysfunction for these individuals. To date there has been no discussion in the literature of this alternative explanation for these scale/indexes, and the present investigation was not structured to answer this question. Future research is needed to address which if any physical dysfunctions are associated with these fall risk scales/indexes.

The major limitation of the present investigation is that the underlying mechanisms by which age, BMI and adverse drug effects influence balance, mobility and potential fall risk were not delineated. Such an analysis would require a larger sample of participants. Additionally, there may be independent predictor variables beyond the 3 identified in the multiple regression modeling for this investigation. Only 31 to 37 percent of the variation in the 4 mobility and balance test scores, and 29 percent for the ABC scale, were accounted for in these models. Additional independent variables may increase the predictive potential for the multiple regression analyses. However, age, BMI and QDI were chosen as they would all be potentially available in electronic medical records and could provide a preliminary screen for fall risk.

CONCLUSION

The present investigation demonstrated that age, BMI and QDI independently predicted the outcome scores for BESTest, BBS, TUG and TUGc. Whereas age is not a modifiable fall risk, both body morphology and drug adverse effects are, allowing healthcare professionals an avenue to reduce fall risk. Additionally, these variables are either readily available in electronic medical records or could be easily calculated. Thus these variables may provide an initial screen which could identify individuals who are at fall risk, but not yet fallen, and trigger a referral for more comprehensive balance evaluation.