This article has Open Peer Review reports available.
Falls and falls efficacy: the role of sustained attention in older adults
- Aisling M O'Halloran†1, 3Email author,
- Nils Pénard†2, 3,
- Alessandra Galli2, 3,
- Chie Wei Fan1, 4,
- Ian H Robertson2, 3 and
- Rose Anne Kenny1, 4, 5
© O'Halloran et al; licensee BioMed Central Ltd. 2011
Received: 25 July 2011
Accepted: 19 December 2011
Published: 19 December 2011
Previous evidence indicates that older people allocate more of their attentional resources toward their gait and that the attention-related changes that occur during aging increase the risk of falls. The aim of this study was to investigate whether performance and variability in sustained attention is associated with falls and falls efficacy in older adults.
458 community-dwelling adults aged ≥ 60 years underwent a comprehensive geriatric assessment. Mean and variability of reaction time (RT), commission errors and omission errors were recorded during a fixed version of the Sustained Attention to Response Task (SART). RT variability was decomposed using the Fast Fourier Transform (FFT) procedure, to help characterise variability associated with the arousal and vigilance aspects of sustained attention.
The number of self-reported falls in the previous twelve months, and falls efficacy (Modified Falls Efficacy Scale) were also recorded.
Significant increases in the mean and variability of reaction time on the SART were significantly associated with both falls (p < 0.01) and reduced falls efficacy (p < 0.05) in older adults. An increase in omission errors was also associated with falls (p < 0.01) and reduced falls efficacy (p < 0.05). Upon controlling for age and gender affects, logistic regression modelling revealed that increasing variability associated with the vigilance (top-down) aspect of sustained attention was a retrospective predictor of falling (p < 0.01, OR = 1.14, 95% CI: 1.03 - 1.26) in the previous year and was weakly correlated with reduced falls efficacy in non-fallers (p = 0.07).
Greater variability in sustained attention is strongly correlated with retrospective falls and to a lesser degree with reduced falls efficacy. This cognitive measure may provide a novel and valuable biomarker for falls in older adults, potentially allowing for early detection and the implementation of preventative intervention strategies.
One-third of people over the age of 65 have at least one fall each year and almost half of these experience more than one fall [1, 2]. Falls are a major cost to healthcare systems worldwide and have significant adverse impacts both physically and psychologically on the older person. Following a fall, older people often voluntarily restrict their activity fearing a reoccurrence. This reduction in exercise leads to further weakness that in turn increases the risk of another fall -- a vicious cycle . Currently, intervention strategies targeted to known risk factors only result in a 30-40% reduction in the reoccurrence of falls after one year [4–6]. This highlights the need to identify additional risk factors which contribute to falls and provide novel interventions to lower falls risk more effectively.
Low falls-related self efficacy (loss of one's confidence to perform activities of daily living without falling) and fear of falling are significant psychological consequences of having fallen . While low falls efficacy and fear of falling were traditionally considered interchangeable concepts, more recent evidence indicates that they are correlated but distinct dimensions [3, 8, 9]. Both low falls efficacy and fear of falling are associated with previous and future falls [10–15], however falls efficacy is a stronger predictor of falls and can be considered a mediator between fear of falling and falls . Interestingly, fear of falling has been reported in older people who have not fallen suggesting that factors other than falls history may influence the manifestation of fear of falling among older people [12, 16].
Several studies have suggested that aspects of cognition, particularly declining executive function, are correlated with and predictive of falls risk in older adults without dementia or overt cognitive impairment [17–19]. Since deficits in executive function increase with age, this may impair the ability of the older person to compensate for age-related changes in gait and balance. This in turn, may compromise the ability of the older person to negotiate and cope with the complexities of their day to day surroundings [20–22]. This is supported by evidence from gait and balance studies, particularly dual tasks, indicating that gait performance and falls are related to executive function, and that falls have been associated with primary ageing of the prefrontal cortex [23, 24]. Attention is a specific component of executive functioning. Low scores on tests of attention have been correlated with postural instability and increasing gait variability, both of which are related to falls [25, 26]. Additional findings revealed that older people allocate more of their attentional resources toward their gait and that the attention-related changes that occur during aging increase the risk of falls [27, 28].
Sustained attention is fundamental executive function in attaining complex goals that require monitoring, over time . Successful sustained attention performance is due to the workings of two interacting subsystems: vigilance and arousal . Vigilant attention is a top-down system that relies on a right lateralized network of cortical areas including the cingulate gyrus, prefrontal cortex and inferior parietal lobule [31, 32]. Arousal is a bottom-up, subcortical system mediated through a subcortical network including the thalamus and noradrenergic brainstem structures, including the locus coeruleus [33, 34]. Sustained attention is modulated by noradrenergic activation. Noradrenaline (NA) is produced by the locus coeruleus, and has a widespread distribution to cortical areas of the brain including the right lateralised fronto-parietal region [35, 36].
In this study, we measured attentional performance and variability in older fallers and non-fallers, without cognitive impairment, using the Sustained Attention to Response Task (SART) . Imaging studies of this task confirmed robust activation within right-lateralised fronto-parietal and subcortical attentional networks [31, 32]. The SART provides ample time-series, reaction time (RT) data for analysis using the Fast Fourier Transform (FFT) procedure which distinguishes discrete components of RT variability. Slow variability, which has a slow temporal characteristic, can be differentiated from fast variability, which has a fast moment-to-moment temporal characteristic. Recent studies by Johnson et al suggest that changes in slow variability may reflect alterations in arousal, while changes in fast variability may reflect fluctuations in the control of vigilant attention [38–40]. We hypothesised that increasing RT variability and higher error rates, signifying poorer sustained attention, would be associated with falls and falls efficacy due to a combination of age-related NA depletion and primary ageing of the frontal cortex. Such age-related changes lead to a decline in top-down functioning and processing speed [41, 42]. Therefore, we also anticipated that more variability in the fast variability (vigilance) aspect of sustained attention would be particularly associated with falls.
A convenience sample of 624 men and women aged ≥ 60 years underwent a comprehensive multidisciplinary geriatric assessment at the Technology Research for Independent Living (TRIL) Clinic, St James's Hospital, Dublin, Ireland, between August 2007 and May 2009. The majority of participants (66.8%) were self-referrals for ''health check'' attracted by the TRIL Centre website http://www.trilcentre.org or articles in the local media. The remainder (33.2%) were referred from medical and health professionals for further assessment of participants with a history of falls. Participants did not have familiarization visits, but prior to attendance they had a telephone conversation with the clinical nurse specialist explaining the content of the scheduled visit.
All 624 participants were ≥ 60 years old, community-dwelling, medically stable, without a history of stroke, or dementia (MMSE score ≥23), able to walk independently (with or without aids), and able to provide written informed consent. Participants were not asked to stop any of their usual medications, fast, or modify lifestyle habits before assessment. Of these, 500 individuals were offered additional cognitive tests including the Sustained Attention to Response Task SART , of which 458 successfully completed this task. The 42 participants who did not complete the task did so due to fatigue or a lack of understanding of the task instructions. The group of 166 individuals who were either not offered or did not complete the SART were significantly older (77.2 versus 71.6 years), with lower mean MMSE scores (25.8 versus 27.8), and had a higher percentage of fallers (65.6% versus 43.3%) compared to those who completed the SART.
Physical, psychosocial and cognitive measurements
Physical measurements included age, gender, weight (kg) and height (cm). The timed up and go test (TUG): measured the length of time (seconds) taken to get up from a sitting position, walk 3 metres, turn around, walk back and resume a sitting position. It is a widely used measure of gait and balance in older people, with longer TUG times (> 11 seconds) associated with a higher risk for falls . The Berg balance scale (BBS): a 14-item scale that measured balance among older people by assessing their performance on functional tasks. Higher scores indicated better balance with minimum and maximum scores of 0 to 56 . The activities of daily living scale (ADL): an 8-item scale that measured the ability to perform daily self-care activities e.g. self-washing, dressing and feeding, as measure of functional status and/or disability. Higher scores indicated better functional activity with minimum and maximum scores of 0 to 24 . The instrumental activities of daily living scale (IADL): a 9-item version of the scale was used which is not a measure of fundamental functioning, but of an individual's ability to live independently in a community. Higher scores signify greater independence with minimum and maximum scores of 0 to 27 . Polypharmacy was determined by the use of ≥ 4 medications daily. The Charlson co-morbidity Index (CCI) was used to classify and score the number of comorbid conditions and is a measure of disease burden. Higher scores indicate greater comorbidity and increased mortality risk with a range in this study sample of 0 - 12 .
Psychosocial measures included the anxiety section of the Hospital Anxiety and Depression Scale (HADS), which is a 7-item scale with higher scores reflective of greater levels of anxiety and with minimum and maximum scores of 0 to 21 . A simplified 8-item version of the Center for Epidemiologic Studies Depression scale (CES-D) was used to measure depression. Higher scores reflected increasing levels of depression with minimum and maximum scores of 0 to 8 .
Measures of cognitive function included the mini-mental state examination (MMSE) as a measure of global cognition with higher scores to a maximum of 30 indicating better levels of cognition . The cognitive failures questionnaire (CFQ) is a 25-item scale that measured self-reported absentmindedness or everyday lapses in perception, memory, and motor function. Higher scores reflect increasing levels of absentmindedness or cognitive lapses, with minimum and maximum scores of 0 to 100 .
Ethical approval was obtained from the St. James's Hospital/Adelaide and Meath Hospital, incorporating the National Children's Hospital Research Ethics Committee (approval reference number 2007/06/13). All participants gave written informed consent before inclusion in the study.
Sustained Attention to Response Task (SART)
Apparatus and procedure
The SART is a computerized task and participants were shown a repeated series of fixed digits from 1 to 9 on the screen. Participants were instructed to "press a key for every number that you see as fast as you can (go answer) but try not to press on the number 3 (no-go answer)". Each digit appeared for 300 milliseconds (ms), with an interval of 800 ms before the next digit appeared. The cycle of digits 1 to 9 was repeated 23 times, giving a total of 207 trials. During a pilot, we noticed that some participants had difficulties performing the SART for the whole length of the task. In order to minimize data attrition, we reduced the number of trials from 225 in the original SART to 207 trials. The task lasted approximately 4 minutes. The recorded variables were: mean reaction time (mean RT) i.e. the time taken for the key presses on digits 1, 2 and 4 to 9 across the entire task; standard deviation (variability) of reaction time (SDRT), the number of commission errors i.e. inappropriate key presses in response to the digit 3, and the number of omission errors i.e. failure to press a key in response to digits 1, 2 and 4 to 9 .
Data pre-processing for FFT analysis
In addition to the traditional analysis of behavioural responses, we also analyzed the reaction time data of the participants using the Fast Fourier Transform (FFT) procedure described previously [38–40]. Briefly, overall variability in reaction time results from the combination of different sources of variance occurring on different time scales. For instance, the variance of a given participant can be made up of a continuous slowing down of the reaction time over the length of the task (slow frequency variability - SFV), and the quick changes occurring on a moment to moment basis (fast frequency variability - FFV). In this paper, we use the FFT procedure to decompose the variance of the reaction times into these two additive components. The FFT procedure can only be applied to continuous, non-zero data, and so pre-processing of the SART reaction time data prior to the FFT analysis was necessary. Therefore, we interpolated zero values (corresponding to omission errors and correct no-go responses on digit 3) using the two reaction times before and after the zero values. Participants that exhibited gaps too large to be considered continuous for the application of the FFT procedure, could not be adequately interpolated using averaging methods. In this context, 74 participants with more than 6 consecutive zero answers were removed from the analysis reducing the number of participants with FFT data to 384. The group of 74 individuals with more than 6 consecutive zero answers were significantly older (75.4 versus 71.0 years, p < 0.001), with lower mean MMSE scores (26.6 versus 28.0, p < 0.001) but not CFQ scores, and had a higher percentage of fallers (56.6% versus 40.7%, p = 0.011) but did not differ significantly by MFES score compared to those with less than 6 consecutive zero answers. The excluded group were physically in poorer health with higher levels of comorbidity (4.9 versus 3.3, p < 0.001), poorer gait from the TUG times (11.0 versus 8.8 s, p < 0.001), and poorer Berg balance scores (49.9 versus 53.1, p < 0.001). They also had lower IADL scores (25.2 versus 26.1, p = 0.001) and reduced levels of visual contrast sensitivity (1.60 versus 1.68, p = 0.001). The two groups did not differ by depression or anxiety score. Those with more than 6 consecutive zero answers also performed significantly more poorly on the traditional SART measures with longer mean RT (466 versus 381 ms, p < 0.001), longer SDRT (227 versus 147 ms, p < 0.001), more commission errors (8.7 versus 5.2, p = 0.001) and more omission errors (42.7 versus 9.3, p < 0.001).
History of falls
Participants who experienced one or more falls in the previous twelve months were classified as a faller (n = 197). Non-fallers had not experienced a fall in the last twelve months (n = 261). Participants were further categorised by faller type: non-faller (n = 261, no falls in previous twelve months); single faller (n = 120, one fall in previous twelve months) and recurrent faller (n = 77, two or more falls in previous twelve months) [23, 52].
Basic characteristics of fallers and non-fallers
(n = 261)
(n = 197)
Gender - Female*
Body Mass Index (BMI)
Timed up and go (TUG)
Berg Balance scale
Activities of Daily Living scale (ADL)
Instrumental Activities of Daily Living scale (IADL)
Mini mental state Examination (MMSE)
Cognitive Failures Questionnaire (CFQ)
Modified Falls Efficacy Scale (MFES)
Center of Epidemiological Study Depression Scale-8 item (CESD8)
Hospital Anxiety and Depression Scale (HADS)
Charlson Comorbidity Index (CCI)
Polypharmacy (≥4 medications daily)*
Statistical analyses were performed using SPSS 16.0 statistical software packages. Differences between non-fallers and fallers were measured using independent samples t-tests for continuous, normally distributed variables, Mann Whitney (U) tests for non-parametric scale variables, and Pearson χ statistics for categorical variables. One-way ANOVA with post hoc Bonferroni pairwise testing was utilised to analyse variance differences in SART measures across the three faller types.
Multivariate logistic regression analyses with non-faller versus faller as the binary dependent variable were performed. Each SART variable was included as an independent variable in separate regression models along with age and gender to allow for the contribution of these factors. For independent variables which remained in the model as significant factors associated with falling, p-values, odds ratios (OR) with 95% confidence intervals (CI) were provided.
MFES scores did not conform to a normal distribution with 70% of participants scoring 9.0 or more on the 0-10 scale. This resulted in skewing of the MFES scores. A statistical measure of skewness (G1) = -2.43, which is larger than twice its standard error (SE) = +/- 0.10, confirmed that the distribution of the MFES scores differed significantly from a normal distribution. For this reason non-parametric tests were employed including Mann Whitney (U) tests to compare MFES scores between non-fallers and fallers or males and females. Similarly, Spearman rho (rs) correlations were computed between MFES score and the SART measures. Partial correlations between MFES score and the SART measures with age and gender partialled out were also performed.
Fallers versus non-fallers
Even when the group which were excluded from the FFT analysis (n = 74) were also removed for the analysis of the traditional SART measures significant differences remained between fallers (n = 157) and non-fallers (n = 227) for mean RT (396 versus 370 ms, p = 0.007), SDRT (161 versus 138, p = 0.009) and omission errors (10.5 versus 8.5, p < 0.014) but not commission errors.
Logistic regression modelling of SART measures between non-fallers and fallers controlling for age and gender
Dependent Variable: non-faller versus faller
Mean Reaction Time
Standard Deviation of Reaction Time
Slow Frequency Variability from FFT
Fast Frequency Variability from FFT
MFES scores were negatively correlated with mean RT, SDRT, FFV and omission errors in the faller group (Correlation range: rs(197) = -0.173 to -0.195, p < 0.05). There were no significant correlations between MFES score and SLV or commission errors among fallers. Meanwhile in non-fallers MFES scores were negatively correlated with mean RT, SDRT, FFV, SLV and omission errors (Correlation range: rs(261) = -0.137 to -0.203, p < 0.05). Among non-fallers MFES score was not significantly correlated with commission errors
Fallers had lower mean MFES scores compared to non-fallers (8.85 versus 9.56, p < 0.001) and females had lower MFES scores compared to males, (p < 0.001). MFES score was also significantly correlated with age (rs(458) = -0.300, p < 0.001). Partial correlations were performed between MFES score and the SART measures in fallers and non-fallers, with age and gender partialled out. Only mean RT (r(261) = -0.159, p = 0.012) and FFV (r(218) = -0.122, p = 0.073) remained weakly correlated with MFES score and only in the non-faller group.
In this study we explored the relationships between sustained attention, falls, and falls efficacy. We found that longer mean reaction times and more variability of reaction time along with higher numbers of omission errors during the sustained attention to response task (SART) was significantly associated with a history of at least one fall in the previous 12 months. Logistic regression modelling of the SART measures controlling for age and gender, revealed that greater variability in reaction time (particularly fast frequency variability or FFV) was strongly associated with falls. In fact a unit increase in this variability measure increased the risk of a previous fall in the past year by 14%. These results suggest that increasing variability in the executive or vigilance aspect of sustained attention is associated with falls in older adults and may represent a novel marker of falls risk. Previous studies have reported significant associations between declining executive function and falls [17–19, 23], or low scores/longer reaction times on tests of attention and increasing gait variability or postural instability, both of which are related to falls However reports looking specifically at attention have tended to focus on divided or selective attention during dual tasking or have selected older adults with cognitive impairment, dementia, stroke and Parkinson's disease (PD)., [25–28, 53]. This paper directly associates reduced levels of sustained attention (and particularly vigilant attention) as measured by the SART) and falls in older community dwelling adults without a history of overt cognitive impairment, dementia, stroke or PD.
We next investigated faller type and found that mean and variability of reaction time, and omission errors, again differed markedly. The strongest differences were observed for recurrent fallers compared to non-fallers, although single fallers also differed significantly from non-fallers. In contrast, the strongest difference in the fast (moment to moment) component of variability were between single fallers and non-fallers, although significant differences between recurrent and non-fallers were also observed. These results are consistent with reports of other cognitive profiles in recurrent and single event fallers [18, 23]. The lengthening of reaction times, accompanied by increasing variability in reaction time and higher omission error rates may indicate that lapsing sustained attention may already have begun to manifest itself in those who have experienced just one fall. These results also suggest that insufficiencies of sustained attention are even more pronounced in recurrent fallers because recurrent falls are more likely to be indicative of neuropathology than a single fall .
Falling was strongly associated with low falls efficacy, exhibited by a significantly lower MFES scores in fallers versus non-fallers. Lower MFES score was correlated with longer mean reaction time and more variability of reaction time in both fallers and non-fallers. Lower MFES score was also significantly correlated with higher numbers of omission errors (but not commission errors) in both fallers and non-fallers. Partial correlations with age and gender partialled out revealed mean reaction time and the fast variability component of sustained attention (FFV) were both weakly correlated with MFES scores but only in the non-faller group. This is difficult to interpret but perhaps here lower falls efficacy may be mirroring fear of falling, which is reported by 20-85% of community-dwelling non-fallers . Fear of falling is correlated with low falls efficacy, and factors other than a previous fall can induce both outcomes in older adults. The interaction between falls and lower falls efficacy, and the influence of gender and age, is in keeping with previous studies, suggesting that sustained attention and falls efficacy may be interacting to influence falls risk [7–9, 55].
It must be noted that longer mean reaction times (among fallers) during the SART may indicate more careful and focused attention to task, rather than slowed processing speed or deficits in sustained attention per se . This view is supported by the fact that non-fallers and fallers made similar numbers of commission errors, a measure of drifting attention in the fixed version of the SART. Thus, in order to achieve similar levels of accuracy during the SART (as determined by the number of commission errors) fallers must slow their reaction times compared to non-fallers. There are two possible explanations for this. Firstly, a general slowing down over the course of the SART has been shown to improve attentional control and hence accuracy on the task . Alternatively, longer mean reaction times may represent a compensatory strategy which may be characteristic of fallers compared to non-fallers. For this reason, differences in variability of reaction time on the SART are generally a more reliable marker of declining sustained attention than mean reaction time.
There is some evidence indicating that greater fast frequency variability (FFV) may reflect deficits in the vigilant attention system. This is supported by research indicating that children with ADHD exhibit increases in the fast frequency component of variability on the SART suggesting a loss of executive functional control, possibly involving the right hemispheric fronto-parietal system, which may then be responsible for deficits in sustained attention [38, 39, 57]. In this context, the data presented here suggest that more variability in the executive aspect of sustained attention may represent a potential marker of declining sustained attention that is associated with falling. This may be further evidence that falls are associated with deficits in top-down (fronto-parietal) systems that are necessary for cognitive control of gait and postural stability. Neuroimaging of older fallers and non-fallers performing this sustained attention test will help to clarify the exact areas of dysfunction.
This study was a convenience sample of medically stable, community-dwelling older adults who were able to walk independently (with or without aids), and may be a potential design limitation. It may be considered more representative of a relatively well sample of older adults rather than representative of the general population over 60 years which would include non-mobile, medically unwell and non-community-dwelling individuals. There were also a high percentage of females in the sample, which can be explained for two reasons. Firstly, this convenience sample was two-thirds self-referred and older females are more likely to engage in volunteered participation than older males. Secondly, one-third of the sample was medically referred with a history of falls and females are more likely to fall and require medical treatment than males. Since falls history in the past twelve months was self reported and thus we must acknowledge that it may be subject to the recall bias inherent to retrospective falls data collection, whereby falls are underreported or forgotten. However, previous studies have shown that 12-month self-reported falls history had high specificity with respect to the gold standard of prospective falls assessment, especially for a cognitively intact sample of older adults without a history of dementia, which this study sample was [23, 52, 58]. Finally, this was a cross-sectional study of older adults and therefore we can not yet confirm causality i.e. that variability of sustained attention is a predictor of future falls. A longitudinal follow up of this cohort is nearing completion and may help to answer this question.
Falls prevention is a major issue in promoting successful ageing and fostering independence among the older population. Current intervention strategies targeted to known risk factors only result in a 30-40% reduction in the reoccurrence of falls [4–6] This highlights the need to identify additional risk factors which contribute to falls and provide novel interventions to lower falls risk more effectively. The recent "Clinical Practice Guidelines: Prevention of Falls in Older Persons"" as set out by the American Geriatric Society (AGS) and British Geriatric Society (BGS) do not incorporate a measure or assessment of sustained attention under "Neurological function" . The SART is a 4 minute test and was successfully completed by 91.6% of older adults who were offered the task. Thus, if the findings presented here for retrospective falls were replicated for prospective falls, it would confirm the potential use of this test in the clinical setting. The SART is a short, inexpensive, easily administered test of sustained attention that could provide an objective, cognitive biomarker of falls risk and reduced falls efficacy. This was also a relatively well sample of older adults indicating that the test may be capable of detecting sub-clinical differences in sustained attention allowing for early detection and intervention. In fact even when the group of poorest performers on the task (i.e. those seventy-four individuals who had six consecutive zero responses and were excluded from the FFT analysis) were removed from the analysis, significant differences in the SART measures between fallers and non-fallers remained. Many aspects of attention are amenable to interventions such as attentional training, therefore the opportunity exists to implement such strategies to improve levels of attention and decrease the risk and prevalence of falls in older adults [60, 61].
In conclusion we provide evidence that greater variability in sustained attention, as measured by the SART, is strongly correlated with both single and recurrent event falls in older community-dwelling adults, without cognitive impairment. In addition, greater variability in sustained attention is also weakly correlated with lower falls efficacy in older adults, particularly in those without a history of falls in the previous year. We suggest that declining sustained attention contributes to an increasing falls risk and that noradrenaline depletion and primary changes in the frontal cortex, are the age-related mechanisms responsible. Sustained attention variability may provide a novel and valuable biomarker for falls in older adults, potentially allowing for early detection and the implementation of preventative intervention strategies.
This research was completed as part of a wider programme of research within the TRIL Centre, (Technology Research for Independent Living). The TRIL Centre is a multi-disciplinary research centre, bringing together researchers from Trinity College Dublin, University College Dublin, National University of Ireland, Galway, Intel Ireland. http://www.trilcentre.org. This work was supported by contributions from Intel Corporation, GE Healthcare and the Irish Government through the Industrial Development Authority (IDA) Ireland. Research ethics committee approval was obtained from the SJH/AMNCH Research Ethics Committee (reference number 2007/06/13).
- Tinetti ME, Speechley M, Ginter SF: Risk factors for falls among elderly persons living in the community. N Engl J Med. 1988, 319: 1701-1707. 10.1056/NEJM198812293192604.View ArticlePubMedGoogle Scholar
- Nevitt MC, Cummings SR, Kidd S, Black D: Risk factors for recurrent nonsyncopal falls. A prospective study. JAMA. 1989, 261: 2663-2668. 10.1001/jama.1989.03420180087036.View ArticlePubMedGoogle Scholar
- Tinetti ME: Clinical practice. Preventing falls in elderly persons. N Engl J Med. 2003, 348: 42-49. 10.1056/NEJMcp020719.View ArticlePubMedGoogle Scholar
- Haines TP, Bennell KL, Osborne RH, Hill KD: Effectiveness of a targeted falls prevention program in a sub-acute hospital setting: a randomised controlled trial. BMJ. 2004, 328: 676-679. 10.1136/bmj.328.7441.676.View ArticlePubMedPubMed CentralGoogle Scholar
- Gates S, Smith LA, Fisher JD, Lamb SE: Systematic review of accuracy of screening instruments for predicting fall risk among independently living older adults. J Rehabil Res Dev. 2008, 45: 1105-1116. 10.1682/JRRD.2008.04.0057.View ArticlePubMedGoogle Scholar
- Gillespie LD, Robertson MC, Gillespie WJ, Lamb SE, Gates S, Cumming RG, Rowe BH: Interventions for preventing falls in older people living in the community. Cochrane Database Syst Rev. 2009, 2: CD007146-PubMedGoogle Scholar
- Tinetti M, Richman D, Powell L: Falls efficacy as a measure of fear of falling. J Gerontol. 1990, 45: 239-243.View ArticleGoogle Scholar
- Moore DS, Ellis R: Measurement of fall-related psychological constructs among independent-living older adults: A review of the research literature. Aging Ment Health. 2008, 12: 684-699. 10.1080/13607860802148855.View ArticlePubMedGoogle Scholar
- Hadjistavropoulos T, Delbaere K, Fitzgerald TD: Reconceptualizing the role of fear of falling and balance confidence in fall risk. J Aging Health. 2011, 23: 3-23. 10.1177/0898264310378039.View ArticlePubMedGoogle Scholar
- Delbaere K, Crombez G, Vanderstraeten G, Willems T, Cambier D: Fear related avoidance of activities, falls and physical frailty. A prospective community-based cohort study. Age Ageing. 2004, 33: 368-373. 10.1093/ageing/afh106.View ArticlePubMedGoogle Scholar
- Zijlstra GA, van Haastregt JC, van Eijk JT, van Rossum E, Stalenhoef PA, Kempen GI: Prevalence and correlates of fear of falling, and associated avoidance of activity in the general population of community-living older people. Age Ageing. 2007, 36: 304-309. 10.1093/ageing/afm021.View ArticlePubMedGoogle Scholar
- Arfken CL, Lach HW, Birge SJ, Miller JP: The prevalence and correlates of fear of falling in elderly persons living in the community. Am J Public Health. 1994, 84: 565-570. 10.2105/AJPH.84.4.565.View ArticlePubMedPubMed CentralGoogle Scholar
- Friedman SM, Munoz B, West SK, Rubin GS, Fried LP: Falls and fear of falling: which comes first? A longitudinal prediction model suggests strategies for primary and secondary prevention. J Am Geriatr Soc. 2002, 50: 1329-1335. 10.1046/j.1532-5415.2002.50352.x.View ArticlePubMedGoogle Scholar
- Fletcher PC, Hirdes JP: Restriction in activity associated with fear of falling among community-based seniors using home care services. Age Ageing. 2004, 33: 273-279. 10.1093/ageing/afh077.View ArticlePubMedGoogle Scholar
- Hadjistavropoulos T, Martin RR, Sharpe D, Lints-Martindale AC, McCreary DR, Asmundson GJG: A longitudinal investigation of fear of falling, fear of pain, and activity avoidance in community dwelling older adults. J Aging Health. 2007, 19: 965-984. 10.1177/0898264307308611.View ArticlePubMedGoogle Scholar
- Howland J, Peterson EW, Levin WC, Fried L, Pordon D, Bak S: Fear of falling among the community-dwelling elderly. J Aging Health. 1993, 5: 229-243. 10.1177/089826439300500205.View ArticlePubMedGoogle Scholar
- Hausdorff JM, Doniger GM, Springer S, Yogev G, Simon ES, Giladi N: A common cognitive profile in elderly fallers and in patients with Parkinson ' s disease: the prominence of impaired executive function and attention. Exp Aging Res. 2006, 32: 411-429. 10.1080/03610730600875817.View ArticlePubMedPubMed CentralGoogle Scholar
- Holtzer R, Friedman R, Lipton RB, Katz M, Xue X, Verghese J: The relationship between specific cognitive functions and falls in aging. Neuropsychology. 2007, 21: 540-548.View ArticlePubMedPubMed CentralGoogle Scholar
- Herman T, Mirelman A, Giladi N, Schweiger A, Hausdorff JM: Executive control deficits as a prodrome to falls in healthy older adults: a prospective study linking thinking, walking, and falling. J Gerontol A Biol Sci Med Sci. 2010, 65: 1086-92.View ArticlePubMedGoogle Scholar
- Yogev-Seligmann G, Hausdorff JM, Giladi N: The role of executive function and attention in gait. Mov Disor. 2008, 23: 329-342. 10.1002/mds.21720.View ArticleGoogle Scholar
- van Iersel MB, Kessels RP, Bloem BR, Verbeek AL, Olde Rikkert MG: Executive functions are associated with gait and balance in community-living elderly people. J Gerontol A Biol Sci Med Sci. 2008, 63: 1344-1349. 10.1093/gerona/63.12.1344.View ArticlePubMedGoogle Scholar
- Hausdorff JM, Schweiger A, Herman T, Yogev-Seligmann G, Giladi N: Dual-task decrements in gait: contributing factors among healthy older adults. J Gerontol A Biol Sci Med Sci. 2008, 63: 1335-1343. 10.1093/gerona/63.12.1335.View ArticlePubMedPubMed CentralGoogle Scholar
- Anstey KJ, Wood J, Kerr G, Caldwell H, Lord SR: Different cognitive profiles for single compared with recurrent fallers without dementia. Neuropsychology. 2009, 23: 500-508.View ArticlePubMedGoogle Scholar
- Blahak C, Baezner H, Pantoni L, Poggesi A, Chabriat H, Erkinjuntti T, Fazekas F, Ferro JM, Langhorne P, O'Brien J, Visser MC, Wahlund LO, Waldemar G, Wallin A, Inzitari D, Hennerici MG, LADIS Study Group: Deep frontal and periventricular age related white matter changes but not basal ganglia and infratentorial hyperintensities are associated with falls: cross sectional results from the LADIS study. J Neurol Neurosurg Psychiatry. 2009, 80: 608-613. 10.1136/jnnp.2008.154633.View ArticlePubMedGoogle Scholar
- Nocera JR, Price C, Fernandez HH, Amano S, Vallabhajosula S, Okun MS, Hwynn N, Hass CJ: Tests of dorsolateral frontal function correlate with objective stability in early to moderate stage Parkinson's disease. Parkinsonism Relat Disord. 2010, 16: 590-594. 10.1016/j.parkreldis.2010.08.008.View ArticlePubMedPubMed CentralGoogle Scholar
- Woollacott M, Shumway-Cook A: Attention and the control of posture and gait: a review of an emerging area of research. Gait Posture. 2002, 16: 1-14.View ArticlePubMedGoogle Scholar
- Lövdén M, Schaefer S, Pohlmeyer AE, Lindenberger U: Walking variability and working-memory load in aging: a dual-process account relating cognitive control to motor control performance. J Gerontol B Psychol Sci Soc Sci. 2008, 63: P121-P128. 10.1093/geronb/63.3.P121.View ArticlePubMedGoogle Scholar
- Harley C, Wilkie RM, Wann JP: Stepping over obstacles: Attention demands and aging. Gait Posture. 2009, 29: 428-432. 10.1016/j.gaitpost.2008.10.063.View ArticlePubMedGoogle Scholar
- Sturm W, Willmes K: On the functional neuroanatomy of intrinsic and phasic alertness. Neuroimage. 2001, 14: S76-S84. 10.1006/nimg.2001.0839.View ArticlePubMedGoogle Scholar
- Paus T, Zatorre RJ, Hofle N, Caramanos Z, Gotman J, Petrides M, Evans AC: Time-related changes in neural systems underlying attention and arousal during the performance of an auditory vigilance task. J Cogn Neurosci. 1997, 9: 392-408. 10.1162/jocn.19188.8.131.522.View ArticlePubMedGoogle Scholar
- Fassbender C, Murphy K, Foxe JJ, Wylie GR, Javitt DC, Robertson IH, Garavan H: A topography of executive functions and their interactions revealed by functional Magnetic Resonance Imaging. Brain Res Cogn Brain Res. 2004, 20: 132-143. 10.1016/j.cogbrainres.2004.02.007.View ArticlePubMedGoogle Scholar
- O'Connor C, Robertson IH, Levine B: The prosthetics of vigilant attention: random cuing cuts processing demands. Neuropsychology. 2011, 25: 535-43.View ArticlePubMedGoogle Scholar
- Coull JT: Neural correlates of attention and arousal: Insights from electrophysiology, functional neuroimaging and psychopharmacology. Prog Neurobiol. 1998, 55: 343-361. 10.1016/S0301-0082(98)00011-2.View ArticlePubMedGoogle Scholar
- Sturm W, de Simone A, Krause BJ, Specht K, Hesselmann V, Radermacher I, Herzog H, Tellmann L, Müller-Gärtner HW, Willmes K: Functional anatomy of intrinsic alertness: Evidence for a fronto-parietal-thalamic-brainstem network in the right hemisphere. Neuropsychologia. 1999, 37: 797-805. 10.1016/S0028-3932(98)00141-9.View ArticlePubMedGoogle Scholar
- Nieuwenhuis S, Aston-Jones G, Cohen JD: Decision making, the P3, and the locus coeruleus-norepinephrine system. Psychol Bull. 2005, 131: 510-532.View ArticlePubMedGoogle Scholar
- Coull JT, Middleton HC, Robbins TW, Sahakian BJ: Clonidine and diazepam have differential effects on tests of attention and learning. Psychopharmacology. 1995, 120: 322-332. 10.1007/BF02311180.View ArticlePubMedGoogle Scholar
- Robertson IH, Manly T, Andrade J, Baddeley BT, Yiend J: 'oops!': performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologia. 1997, 35: 747-758. 10.1016/S0028-3932(97)00015-8.View ArticlePubMedGoogle Scholar
- Johnson KA, Kelly SP, Bellgrove MA, Barry E, Cox M, Gill M, Robertson IH: Response variability in attention deficit hyperactivity disorder: evidence for neuropsychological heterogeneity. Neuropsychologia. 2007, 45: 630-638. 10.1016/j.neuropsychologia.2006.03.034.View ArticlePubMedGoogle Scholar
- Johnson KA, Robertson IH, Kelly SP, Silk TJ, Barry E, Dáibhis A, Watchorn A, Keavey M, Fitzgerald M, Gallagher L, Gill M, Bellgrove MA: Dissociation in performance of children with ADHD and high-functioning autism on a task of sustained attention. Neuropsychologia. 2007, 45: 2234-45. 10.1016/j.neuropsychologia.2007.02.019.View ArticlePubMedPubMed CentralGoogle Scholar
- Castellanos FX, Sonuga-Barke EJ, Scheres A, Di Martino A, Hyde C, Walters JR: Varieties of attention-deficit/hyperactivity disorder-related intraindividual variability. Biol Psychiatry. 2005, 57: 1416-1423. 10.1016/j.biopsych.2004.12.005.View ArticlePubMedPubMed CentralGoogle Scholar
- Gunning-Dixon FM, Raz N: The cognitive correlates of white matter abnormalities in normal aging: A quantitative review. Neuropsychology. 2000, 14: 224-232.View ArticlePubMedGoogle Scholar
- Raz N, Lindenberger U, Rodrigue KM, Kennedy KM, Head D, Williamson A, Dahle C, Gerstorf D, Acker JD: Regional brain changes in aging healthy adults: General trends, individual differences and modifiers. Cereb Cortex. 2005, 15: 1676-1689. 10.1093/cercor/bhi044.View ArticlePubMedGoogle Scholar
- Mathias S, Nayak U, Isaacs B: Balance in elderly patients: the "get-up and go" test. Arch Phys Med Rehabil. 1986, 67: 387-9.PubMedGoogle Scholar
- Berg K, Wood-Dauphinee S, Williams JI, Maki B: Measuring balance in the elderly: Validation of an instrument. Can J Pub Health. 1992, 83: S7-11.Google Scholar
- Katz S: Assessing selfmaintenance: Activities of daily living, mobility and instrumental activities of daily living. J Am Geriatr Soc. 1983, 31: 721-726.View ArticlePubMedGoogle Scholar
- Lawton MP, Brody E: Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist. 1969, 9: 179-186. 10.1093/geront/9.3_Part_1.179.View ArticlePubMedGoogle Scholar
- Charlson ME, Pompei P, Ales KL, MacKenzie CR: A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987, 40: 373-383. 10.1016/0021-9681(87)90171-8.View ArticlePubMedGoogle Scholar
- Zigmund AS, Snaith RP: The Hospital Anxiety and Depression Scale. Acta Psychiatr Scand. 1983, 67: 361-370. 10.1111/j.1600-0447.1983.tb09716.x.View ArticleGoogle Scholar
- Irwin M, Artin KH, Oxman MN: Screening for Depression in the Older Adult Criterion Validity of the 10-Item Center for Epidemiological Studies Depression Scale (CES-D). Arch Intern Med. 1999, 159: 1701-4. 10.1001/archinte.159.15.1701.View ArticlePubMedGoogle Scholar
- Folstein MF, Folstein SE, McHugh PR: "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975, 12: 189-98. 10.1016/0022-3956(75)90026-6.View ArticlePubMedGoogle Scholar
- Broadbent DE, Cooper PF, FitzGerald P, Parkes KR: The Cognitive Failures Questionnaire (CFQ) and its correlates. Br J Clin Psychol. 1982, 21: 1-16. 10.1111/j.2044-8260.1982.tb01421.x.View ArticlePubMedGoogle Scholar
- Ganz DA, Higashi T, Rubenstein LZ: Monitoring falls in cohort studies of community-dwelling older people: Effect of the recall. J Am Geriatr Soc. 2005, 53: 2190-2194. 10.1111/j.1532-5415.2005.00509.x.View ArticlePubMedGoogle Scholar
- Hyndman D, Ashburn A: People with stroke living in the community: Attention deficits, balance, ADL ability and falls. Disabil Rehabil. 2003, 25: 817-822. 10.1080/0963828031000122221.View ArticlePubMedGoogle Scholar
- Scheffer AC, Schuurmans MJ, van Dijk N, van der Hooft T, de Rooij SE: Fear of falling: measurement strategy, prevalence, risk factors and consequences among older persons. Age Ageing. 2008, 37: 19-24.View ArticlePubMedGoogle Scholar
- Filiatrault J, Desrosiers J, Trottier L: An exploratory study of individual and environmental correlates of fear of falling among community-dwelling seniors. J Aging Health. 2009, 21: 881-894. 10.1177/0898264309340694.View ArticlePubMedGoogle Scholar
- Manly , Davison B, Heutink J, Galloway M, Robertson IH: Not enough time or not enough attention?: Speed, error and self-maintained control in the Sustained Attention to Response Test (SART). Clin Neuropsychol Assess. 2000, 3: 167-177.Google Scholar
- Bellgrove MA, Hawi Z, Kirley A, Gill M, Robertson IH: Dissecting the attention deficit hyperactivity disorder (ADHD) phenotype: Sustained attention, response variability and spatial attentional asymmetries in relation to dopamine transporter (DAT1) genotype. Neuropsychologia. 2005, 43: 1847-1982. 10.1016/j.neuropsychologia.2005.03.011.View ArticlePubMedGoogle Scholar
- Cummings SR, Nevitt MC, Kidd S: Forgetting falls. The limited accuracy of recall of falls in the elderly. J Am Geriatr Soc. 1988, 36: 613-6.View ArticlePubMedGoogle Scholar
- Panel on Prevention of Falls in Older Persons, American Geriatrics Society and British Geriatrics Society: Summary of the Updated American Geriatrics Society/British Geriatrics Society clinical practice guideline for prevention of falls in older persons. J Am Geriatr Soc. 2011, 59: 148-1457.View ArticleGoogle Scholar
- O'Connell RG, Bellgrove MA, Dockree PM, Lau A, Fitzgerald M, Robertson IH: Self-Alert Training: volitional modulation of autonomic arousal improves sustained attention. Neuropsychologia. 2008, 46: 1379-1390. 10.1016/j.neuropsychologia.2007.12.018.View ArticlePubMedGoogle Scholar
- MacLean KA, Ferrer E, Aichele SR, Bridwell DA, Zanesco AP, Jacobs TL, King BG, Rosenberg EL, Sahdra BK, Shaver PR, Wallace BA, Mangun GR, Saron CD: Intensive meditation training improves perceptual discrimination and sustained attention. Psychol Sci. 2010, 21: 829-839. 10.1177/0956797610371339.View ArticlePubMedPubMed CentralGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://0-www.biomedcentral.com.brum.beds.ac.uk/1471-2318/11/85/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.