Quality of life (QOL) is defined as a subjective concept with multiple dimensions comprising physical, social, and environmental conditions. It is a broad, multidimensional concept that includes both positive and negative aspects of life and constitutes a major issue in the older person.1
In older adults, the psychological impact and effects related to the loss of functional capacity are directly related to perceived QOL.2 The predictors of better QOL are increased physical activity, lower prevalence of overweight, lower depression rates, and less reported alcohol abuse.3 On the contrary, the predictors of worsened QOL are female gender, comorbidity, deficient nutritional condition, polypharmacy,4 loss of mobility,5 depression and dependency,6 poor economic conditions, and social isolation and loneliness.7 Different authors have described a relationship between perceived QOL and fractures in the older adults,8 bone and joint disease,9 and also after a fall,10 recurrent falls11 and fear of falling.12 Furthermore, QOL in the older adults is more dependent on the number of falls than comorbidity.13 , 14
One-third of all autonomous and independent people older than 65 years experience some form of fall over the course of a year.15 Limitation of the capacity to perform basic and instrumental activities for daily living and walking difficulties are predictors of isolated and recurrent falls in community-dwelling older adults.16 Falls constitute a common health problem, in view of their medical, psychological, socio-family, and economic consequences.17
Quality of life is increasingly relevant for studying the health of the population and for analyzing the efficacy and effectiveness of health care interventions.18 A number of studies have pointed to the importance of measuring QOL when validating the effectiveness of such interventions,19 so one predictor of good QOL is physical activity.20 , 21
The most widely used instrument for assessing perceived QOL in older adults is the EuroQoL-5 Dimensions (EQ-5D) scale, which consists of 2 parts: a descriptive part used to calculate the EQ-5D index and a visual analog scale (VAS).22 These observations all suggest the potential significance of falls and risk factors as an indicator of QOL in older adults. However, while some studies have related QOL to the existence of falls in community-dwelling older adults,18 there is a lack of evidence as to how much different factors and the number of falls are related to QOL.
There are different risk factors related to falls such as gender, number of falls, comorbidity, nutritional status, drugs, loss of dependency, and levels of physical activity that could influence QOL in a positive or negative way in community-dwelling older adults. Therefore, this study aimed to investigate the determinants of perceived QOL among independent community-dwelling older adults and to quantify the influence of number of falls and number of risk factors on QOL.
Study Design and Participants
A cross-sectional study was carried out in older adults of both genders in the region of La Ribera (Valencia, Spain). The 2012 census data state that this region has a population of about 222,500 inhabitants, of which 15.8% are 70 years of age and older. The study sample was established on a randomized basis among the municipalities in the region, with overrepresentation of individuals of 70 years of age or older. The municipalities included 10 towns (Alberic, Algemesí, Albalat de la Ribera, Alginet, Alzira, Benifaio, Carcaixent, Carlet, Guadassuar, and Sueca). Recruitment covered the period from December 2012 to May 2013 and was carried out in primary care centers and in social centers for the older adults.
The inclusion criteria were: an age of 70 years and older, and capability of walking independently (with the possibility of ambulatory assistive devices but excluding the help of another person) and routine attendance in primary care centers in La Ribera (Valencia, Spain). Individuals were excluded if they had disease processes, indicating a life expectancy of less than 6 months; total hearing or vision loss; serious psychiatric disorders or moderate or severe cognitive impairment; or if they declined to participate or provide written consent. A publicity strategy was designed to facilitate recruitment, consisting of notices and panels placed in senior centers; meetings with the managing bodies of senior centers and associations; and telephone calls to care providers in primary care centers. Repeat telephone calls to the families were made to avoid losses attributable to immobility problems or lack of autonomy for attending a health education session on fall risk-free environments.23
Sample Size Description
The sample size was calculated on the basis of the population census for 2012 of the aforementioned region. A sample of 482 participants was required to estimate a 35% incidence of older adults with falls for a percentage reduction of 8% with an α and β error of 5% and a statistical power of 95%; to control attrition, there was an oversampling of 20% that determined a final minimum sample of 572 participants.
Comprehensive geriatric assessments were performed in primary care centers by 4 nurses with at least 10 years of experience. They had previously participated in 2 physical activity–related fall prevention programs.
The functional assessment tools used were the Barthel Index Basic Activity of Daily Living,24 Lawton Instrumental Activity of Daily Living Scale,25 and Tinetti Balance and Gait Scale,26 Spanish validated versions.27 , 28 The Barthel Index consists of 10 items that measure a person's daily functioning, particularly the activities of daily living. The items include feeding, transfers (from bed to chair and back and to and from the toilet), grooming and toileting, walking on a level surface, going up and down the stairs, getting dressed, and bowels and bladder continence. The total score ranges from 0 (dependent) to 100 (independent) as a continuous variable.24
The Lawton IADL Scale consists of 8 domains for women (using the telephone, shopping for groceries, food preparation, housekeeping, laundering, self-medicating, transportation, and managing finances). As a continuous variable, the total score ranges from 0 (dependent) to 8 (independent). As cooking, housekeeping, and laundering are not always applicable to men, the total score in men ranges from 0 (dependent) to 5 (independent).25
The Tinetti index consists of 2 parts: the first seeks to assess balance and it has 9 items, totaling 16 points; the second corresponds to gait assessment and it has 8 items, totaling 12 points. In total, the index has 17 items, amounting up to 28 points as a continuous variable. A total score lower than 19 points indicates a 5-fold increased risk of falls, so the lower the total score, the higher the risk of falls.
The following variables were collected since they are described by the literature as possible determinants of QOL: sociodemographic (age, gender, and cohabitation), body mass index (BMI), hand grip strength, sarcopenia—40 kg Kern-map—spring dynamometer,29 and clinical (comorbidity as 3 or more diseases and assessed from medical chart review, number of previous fractures, number of falls defined as unintentional falls, with the exclusion of falls secondary to stroke or epileptic seizures30 in the 12 months prior to inclusion in the study, number of risk factors, number of daily drugs prescribed, and the use of drugs of the main pharmaceutical groups directly related to falls). Drugs acting upon the central nervous system (CNS) were classified as benzodiazepines, antidepressants, anticholinergic agents, and antivertigo drugs.
Health-related QOL was measured using the EQ-5D Index and EQ-5D VAS, according to the parameters of the Spanish population.31 The EQ-5D-3L (levels) version has been translated into more than 140 languages,22 and its descriptive system is composed of 5 dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Each dimension in turn has 3 levels of impairment: no problems (level 1), some/moderate problems (level 2), and extreme problems (level 3).
The combination of the values of all dimensions generates 5-digit numbers, having 243 possible health status combinations, which can be used as profiles. For example, an individual who indicates having no trouble walking (1), no problems with personal care (1), some problems to perform his or her daily activities (2), moderate pain or discomfort (2), and no anxiety or depression (1), is awarded a score of 11221 in the health status. To award a value to any health status, we begin with 1 if there are no health problems in any of the dimensions; therefore, we will award 11111. If the status is different than 11111, the value of the constant (Table 1) is subtracted. Subsequently, if there are level 2 problems, we will subtract 1 for each dimension. The same procedure is followed when there are level 3 problems, although we will previously multiply the value of the affected dimension by 2. Finally, the coefficient corresponding to the parameter N3—a parameter representing the importance given to level 3 problems in any dimension—is subtracted once when there is, at least, 1 dimension with level 3 problems (Table 1). For example, according to the parameters of the Spanish population,31 for the score 11231, we will begin with 1 constant and will subtract the constant (0.1502). As we have problems with the third dimension in level 2, we will subtract the value of that dimension (0.0551) once and we will also subtract the value of the third dimension twice (2 × 0.0596) and, finally, the coefficient belonging to the parameter N3 (0.2219).
The descriptive response from the EQ-5D can be converted into an index score that is useful for clinical and economic evaluations.32 The score ranges from 0 to 1 and it is an ordinal scale treated as a continuous variable, where 0 = most negative score and 1 = most positive score. The index is calculated from the score corresponding to each of the dimensions. The VAS score, in turn, is obtained by asking the patients to rate their health on a 20-cm vertical scale. The scale ranges from 0 to 100, where 0 = worst imaginable health and 100 = best imaginable health.
The study was approved by the Research Ethics Committee of La Ribera University Hospital. The data obtained were processed in abidance with Spanish Personal Data Protection Law 15/1999, of 13 December, and Law 14/2002, of 14 November, regulating patient autonomy, rights, and obligations referred to clinical information and documentation. The study likewise complied with the ethical principles guiding research, as specified in the Declaration of Helsinki.
Each participant signed the corresponding informed consent before inclusion in the study and statistical processing of the data obtained.
The variables were reported as proportions and/or the mean and standard deviation. Parametric tests (Student t test and Fisher-Snedecor F test) were used for the comparison of means, while nonparametric tests (χ2 test and linear trend test) were used for the comparison of proportions. Mean scores and standard deviations are given as descriptive statistics. Our study varied the definition of recurrent falling by varying the time unit between falls and by varying the number of falls per time unit. We examined the number of falls in the previous 12 months and the definition (at least 2 falls within 6 months) showed the lower association between the predictors for falls in a sample with high functionality. Previous studies33 by our research group, and by others, support a greater predictive value of fall risk profiles using the outcome variable “at least 2 falls within 6 months.” Therefore, this definition was adapted for this study, so falls were stratified into 3 categories: nonfallers, isolated fallers (1 or 2 falls), and recurrent fallers (>2 falls).16
As for the data analysis, normality in the repeated measures was tested with Shapiro-Wilk Test. The comparison of the proportions in functional condition and falls categories was performed using χ2 test. The comparison of means, falls category, risk factors, and EQ-5D Index and VAS in each falls category was performed using Student t test and variance analysis (1-way analysis of variance) for those that demonstrated normal distribution and Mann-Whitney U and Kruskall-Wallis tests for those that did not demonstrate normal distribution. Homogeneity of the variances was tested with Levene Test. Paired comparisons were performed using Bonferroni, when the homogeneity requirement was met, and Games-Howell test when it was not met.
To adjust the P value for multiple comparisons, Bonferroni corrections were calculated with a result of P value of less than .05.
The Pearson correlation coefficient was performed to correlate quantitative variables and EQ-5D Index and VAS scores and the concordance of EQ-5D Index and VAS scores.
The degree of association between the QOL index and the different risk factors was studied, and 2 linear regression models were constructed to assess the importance of the risk factors related to QOL—the latter being defined by the EQ-5D Index and VAS scores.
We first considered the complete model with all the variables found in the bivariate analysis to be significantly associated to the QOL index, while in a second step, we eliminated from the model all those variables failing to produce an important change (defined as the absence of an adjusted effect of >10%) or which did not result in an improved standard error of the estimate on adjusting the model without such variables. Consensus was sought among the investigators in those cases in which 2 or more subsets of variables with the same degree of fit were obtained.
Based on these criteria, the variables included in the model were age, gender, the use of drugs affecting the CNS, multiple drug prescription, comorbidity, BMI, hand grip strength, previous fractures, physical activity, and number of fall risk factors. The dependent variable was firstly EQ-5D Index and secondly EQ-5D VAS. The Tinetti Scale functional score refers to dimension 1 (mobility) in EQ-5D. The Barthel Scale functional score refers to dimension 2 (self-care) in EQ-5D. The Lawton Scale functional score refers to dimension 3 (usual activities) in EQ-5D. Therefore, none of these ordinal scales was included in the regression model.
The study data were entered in MS Excel spreadsheets, followed by statistical analysis using the SPSS (IBM Corp. Released 2010. IBM SPSS Statistics for Windows, Version 19.0. Armonk, New York: IBM Corp).
The study population consisted of 572 individuals with a mean age (standard deviation) of 76.1 (3.9) years, and with a predominance of females (63.3%). A total of 35.6% of the older adults had suffered falls in the last 12 months. The prevalence of falls was 38.1%. Recurrent fallers had higher mean of falls than isolated fallers (Mean differences [MD]: 2.9; 95% confidence interval [CI]: 2.3-3.6; P < .01).
As it can be seen in Table 2, most of the older adults were functionally independent. Our community-dwelling older adults had high functionality in the Barthel, Lawton, and Tinetti Scales, whose values were similar to the age- and gender-based Spanish population norm for community older people.34
A statistically significant association was observed between lower Barthel, Lawton, and Tinetti functional scores and falls occurring in the previous year. Recurrent fallers yielded lower scores in Barthel Index than nonfallers (−5.9; 95% CI: −10.6 to −1.3; P < .01). Lawton Scale showed lower scores for recurrent fallers and isolated fallers (MD: −0.78; 95% CI: −1.41 to −0.17; P < .01) and nonfallers (MD: −0.78; 95% CI: −1.4 to −0.16; P < .01). Moreover, there were differences in Tinetti Scale scores between recurrent fallers and nonfallers (MD: −2.4; 95% CI: −3.9 to −0.87; MD: P < .01) and also between isolated fallers and nonfallers (MD: 0.99; 95% CI: −1.7 to −0.27; P < .01). There were also differences in the balance and gait subscales. In contrast, an increased BMI was observed in the recurrent fallers, although there was no statistical significance.
In addition to the functional variables, Table 2 reports the data referred to risk factors for falls. In this regard, the study population presented an average of 3.1 (1.9) risk factors. There were differences between all groups. Recurrent fallers had a higher number of risk factors than isolated fallers (MD: 2; 95% CI: 0.81-3.3; P < .01) and nonfallers (MD: 1.3; 95% CI: 0.01-2.5; P = .04), and isolated fallers had also a higher number of risk factors than nonfallers (MD: 0.77; 95% CI: 0.39-1.2; P < .01) (Table 2). The mean number (SD) of daily drugs prescribed was 4.7 (3), and recurrent fallers showed greater daily drug consumption (MD: 5.9 [3.1]) than nonfallers (MD: 1.5; 95% CI: 0.31-2.6; P < .01). The most prevalent risk factors were sensory deficits, specifically visual (89.2%) and hearing problems (37.1%), comorbidity (57.7%), and previous fractures (24.8%). Differences were observed only in terms of previous fractures, with a prevalence of 10.1% in older adults without falls, 41.1% in isolated fallers, and 68.1% in recurrent fallers (P < .01).
The most prevalent comorbidities were arterial hypertension (65.9%), hyperlipidemia (43.4%), osteoporosis (33.5%), diabetes (28.5%), and anxiety-depressive syndrome (22.7%). The least prevalent disease conditions were chronic obstructive pulmonary disease (6.8%), followed in lesser proportion by renal failure, liver disease, and epilepsy. There were no statistically significant differences among the studied groups.
The mean of grip strength of the dominant hand was 20.3 (8.8) kg, with significant differences between the presence and recurrence of falls (MD: −5.3; 95% CI: −5.9 to −2.2; P > .01) and between isolated falls (MD: −4.1; 95% CI: −7.9 to −2.5; P > .01) and nonfallers. A linear decrease in hand grip strength was observed in the presence of falls—the older adults without falls showed the greatest mean strength value (21.9 [8.9] kg), while the recurrent fallers showed the lowest strength value (16.6 [7.1 kg]) (Table 2).
The mean EQ-5D Index score (0.79 [0.22]) was correlated to the mean EQ-5D VAS score (75.1 [20.6]) (r = 0.37, P < .001).
The nonfallers presented an EQ-5D Index score of 0.83 (0.2), which was higher than the score recorded among the isolated fallers (MD: 0.22; 95% CI: 0.12-0.31; P < .01) and in the recurrent fallers (MD: 0.15; 95% CI: 0.05-0.25; P < .01). In addition, there were differences between nonfallers and isolated fallers (MD: 0.07; 95% CI: 0.02-0.11; P < .01). Differences were also observed in the EQ-5D VAS scores but only in nonfallers and recurrent fallers (MD: 10.8; 95% CI: 3.2-18.4; P < .01) (Table 3).
The mean EQ-5D Index score among females was significantly lower than that in the case of the males (MD: −0.11; 95% CI: −0.14 to −0.07; P < .01). This gender difference was also observed in the nonfallers (MD: −0.08; 95% CI: −0.12 to −0.04; P < .01) and in the isolated fallers (MD: −0.11; 95% CI: −0.17 to −0.03; P < .01). No such differences were seen in the recurrent fallers.
The EQ-5D Index score showed no significant differences in relation to patient age, except among the recurrent fallers. The group of older adults between 70 and 74 years of age yielded lower scores (MD: −0.27; 95% CI: −0.51 to −0.04; P = .01) than the 80 to 84 years age group (Table 4). No statistically significant association was found between cohabitation and the presence or absence of falls (Table 3).
The distribution of means in the EQ-5D VAS score showed gender differences among the recurrent fallers. In contrast to the EQ-5D Index scores, we observed no differences in the EQ-5D VAS scores according to age groups and cohabitation by fall categories (Table 3).
QOL Predictive Model in Community-Dwelling Older Adults
Most baseline characteristics and risk factors demonstrated small to moderate correlation to QOL (Table 4). Lower scores of functionality measured by Barthel and Tinetti Index, loss of strength measured by Hand grip, obesity (high BMI), number of falls, and higher number of risk factor for falls are correlated to lower scores of QOL. There are also differences for female gender, presence of comorbidity, previous fractures, and drugs affecting CSN in EQ-5D Index and VAS with lower scores. Lower levels of physical activity are correlated to lower scores of QOL
Because of the existence of factors that can interfere with health-related QOL, which are not contemplated by the dimensions of the EQ-5D Scale, we developed a predictive model by EQ-5D Index scores, based on the criteria described in the “Methods” section.
The male gender (β = .09; 95% CI: 0.05-0.13) was found to be predictive of better QOL, together with physical activity (β =.04; 95% CI: 0.02-0.06), while the use of drugs affecting the CNS (β = −.08; 95% CI: −0.12 to −0.03), overweight (β = −.06; 95% CI: 0.10 to −0.02), comorbidity (β = −.09; 95% CI: −0.13 to −0.05), the presence of fall risk factors (β = −.02; 95% CI: −0.03 to 0.01), and the number of previous falls (β = −.03; 95% CI: −0.06 to 0.01) had a negative impact upon the EQ-5D Index score. This model yielded R 2 = 0.29 (F = 18.8, P < .01) (Table 5). The regression equation was as follows:
Patient age, sarcopenia, hearing problems, and cohabitation in the home were not found to be related to perceived QOL.
A second predictive model was obtained by EQ-5D VAS as a variable dependent, but it had a lower R 2 than EQ-5D Index. The male gender (β = 6.08; 95% CI: 1.81-10.35) was found to be predictive of better QOL, together with physical activity (β = 3.32; 95% CI: 0.86-5.79), while the number of previous falls (β = −3.78; 95% CI: −6.88 to 0.68), overweight (β = −4.06; 95% CI: −7.96 to −0.15), and the number of daily drugs (β = −.97; 95% CI: −1.65 to −0.28) had a negative impact upon the EQ-5D VAS. This model yielded R 2 = 0.12 (F = 9.73, P < .01) (Table 6). The regression equation was as follows:
Our community-dwelling older adults had high function and cognitive levels, resulting in greater independence and autonomy than in another study conducted in a similar Spanish population.34 Intrinsic factors (including particularly hearing and vision problems and previous falls and fractures) were more prevalent than extrinsic risk factors, in coincidence with the findings of other studies in community-dwelling older adults.35 , 36 While older adults had high functional conditions, recurrent fallers had poorer functional conditions than isolated fallers and even poorer than nonfallers, as several studies indicate.11 , 37
The recorded EQ-5D Index score (0.79 [0.22]) is consistent with the EQ-5D VAS score (75.1 [20.6]), thus affording intrinsic validity. These figures are slightly lower than the Index scores obtained in the Spanish population (0.85).38 , 39
Many interventional studies for the prevention of falls in community-dwelling older adults have measured QOL as a parameter for rating the intervention. However, no analysis has been made of the factors that directly influence perceived QOL. If the results are analyzed without paying attention to the range of physical activity, gender, or prescribed drugs, they could be biased and this could influence the failure or success of physical therapist interventions.18 , 40
In our study, we found older adults who experience falls to have poorer QOL and function, and this decrease was, moreover, linearly correlated to the fall category (nonfallers, isolated fallers, and recurrent fallers). Functional condition is related to perceived QOL.35 , 38–43 In addition, females showed poorer perceived QOL than males, in concurrence with the findings of other authors.44 , 45 Overweight and comorbidity were found to have a negative impact upon QOL, in contrast to the consulted literature.46 , 47
In contrast to other studies48 , 49 that describe older age as having a negative impact upon perceived QOL, we recorded no statistically significant differences in this regard, except among the recurrent fallers, and the youngest age group was found to yield the lowest QOL scores. Likewise, no differences were observed in relation to cohabitation in the home.
Regarding the predictive model obtained with Index scores, male gender and weekly physical activity were found to be predictors of good QOL. According to the literature, physical activity improves QOL20 and female gender was historically dedicated to the job, family, and home care, often leading to the perception of worse QOL compared with that of the male gender.50 In contrast, the presence of fall risk factors, multiple drug prescription, the use of drugs affecting the CNS, and history of previous falls worsen perceived QOL, in coincidence with the observations of a recent study.51 The presence of overweight and comorbidity also worsened perceived QOL in community-dwelling older adults.38 , 52 In contrast to the observations of Polku et al,53 the factors related to sensory defects were not considered to be predictive of diminished QOL.
Physical activity is a good treatment to improve QOL.54 Some studies have shown the benefits of physical activity not only for QOL but also for cognition, emotional and social networking in older adults, and also for frailty syndrome.55 Moreover, the rehabilitation for asthma, cognitive impairment, peripheral arterial disease, or cancer in older adults includes physical activity that improves QOL levels.56 , 57 Higher levels of physical activity could reduce overweight and the need for prescription drugs and could improve functionality, balance, and gait, reducing the number of risk factors for falls.
The model obtained by the EQ-5D VAS had less predictive power than the EQ5D Index Score model due to the fact that the variables of comorbidity, number of risk factors, and drugs that affect the CNS are not considered. This means that less importance would be given to the impact the missing variables have on the sample.
If perceived QOL is used as an indicator of the success of intervention programs, it must be taken into account that certain factors following the adoption of measures for the prevention of falls may mask the results (failure or success) of the intervention. Older adults who are overweight, have obesity, or with polypharmacy or drugs affecting the CNS, have lower scores in QOL, so the intervention could be less effective than in other older adults. Therefore, the analyses of the results should be adapted to the determinants of QOL.
The limitations of our study are the lack of data on the doses of the prescribed drugs and the timing of their administration. There may be measurement bias in those variables that influence the risk factors for falls, and, finally, interrater reliability was not established among the nurse raters.
Falls and fall risk factors are determinants of QOL. These determinants should be taken into account when caring for older adults to promote those with a positive influence and minimize those with a negative impact on QOL. Older adults with isolated falls and recurrent falls yield lower EQ-5D Index scores. The number of daily drugs prescribed, comorbidity, the use of drugs affecting the CNS, the number of fall risk factors, and the number of falls have a negative impact upon QOL, while the male gender and physical activity exert a positive impact. The factors related to QOL must be considered in the analysis of fall prevention programs. Most determinants of QOL are modifiable. Physical activity as one of these determinants is also a treatment for falls and fall risk factors, so more physical activity should be implemented.
The authors thank all the members of the Departamento de Salud de la Ribera and Esther Navarro Illana for helping to make this study possible.
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