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Author Contributions: Hocker, Khan: study concept and design, acquisition of subjects and data, analysis and interpretation of data, preparation of manuscript. Singh, Simpson, Hook, Vollbrecht, Malsch, Malone: analysis and interpretation of data, preparation of manuscript. Sponsor’s Role: None.
REFERENCES 1. Wright AA, Zhang B, Ray A et al. Associations between end-of-life discussions, patient mental health, medical care near death, and caregiver bereavement adjustment. JAMA 2008;300:1665–1673. 2. Detering KM, Hancock AD, Reade MC et al. The impact of advance care planning on end of life care in elderly patients: Randomised controlled trial. BMJ 2010;340:c1345.
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3. Yourman LC, Lee SJ, Schonberg MA et al. Prognostic indices for older adults: A systematic review. JAMA 2012;307:182–192. 4. Weissman DE, Meier DE. Identifying patients in need of a palliative care assessment in the hospital setting: A consensus report from the center to advance palliative care. J Palliat Med 2011;14:17–23. 5. Malone ML, Vollbrecht M, Stephenson J et al. Acute Care for Elders (ACE) Tracker and e-Geriatrician: Methods to disseminate ACE concepts to hospitals with no geriatricians on staff. J Am Geriatr Soc 2010;58:161– 167. 6. Skilbeck J, Mott L, Page H et al. Palliative care in chronic obstructive airway disease: A needs assessment. Palliat Med 1998;12:4245–4254. 7. Conventry PA, Grande GE, Richards DA et al. Prediction of appropriate timing of palliative care for older adults with non-malignant life-threatening disease: A systematic review. Age Ageing 2005;34:218–227. 8. Ciaran BT, Brasel KJ. Developing guidelines that identify patients who would benefit from palliative care services in the surgical intensive care unit. Crit Care Med 2009;37:946–950.
APPENDIX 1 ACUTE CARE FOR ELDERS (ACE) TRACKER: A REAL-TIME CLINICAL DECISION SUPPORT TOOL THAT AUTOMATICALLY GENERATES A REPORT FROM THE ELECTRONIC HEALTH RECORD
“Goals of care” is an automated alert for serious medical illnesses reflecting a poor prognosis. ASSOCIATION BETWEEN RED BLOOD CELL INDICES AND QUANTITATIVE GAIT VARIABLES IN OLDER ADULTS To the Editor: Gait is a robust marker of frailty1 and is associated with multiple adverse health outcomes.2 Hematological investigations are routinely ordered in clinical practice, and some red blood cell (RBC) indices have been linked to frailty and gait.3 Establishing the contribution of RBC indices to gait in older adults will provide insights into new biomarkers and potential interventions.
METHODS Study Population This cross-sectional investigation was conducted within the Central Control of Mobility in Aging Study (CCMA). The
goal of CCMA is to identify brain mechanisms for mobility in aging. Inclusion criteria included aged 70 and older and community dwelling. The Albert Einstein College of Medicine institutional review board approved the study protocol.
Biomarkers Blood collection was introduced in CCMA in 2013 as part of a new substudy. Of the 310 CCMA participants evaluated between July 2013 and September 2014, 230 (74%) underwent blood tests; blood was collected and analyzed using standard protocols. RBC indices, white blood cell (WBC) count (k/lL) and interleukin (IL)-6 levels (pg/mL) were examined for this study4,5 Reasons for missing data included refusal (n = 42) and logistical constraints (n = 38). Based on reported and predicted associations with mobility,3 the following RBC indices were examined: RBC
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Table 1. Association Between Red Blood Cell (RBC) Indices (Independent Variable) and Three Gait Domains (Dependent Variable) Assessed Using Multivariable Linear Regression Adjusted for Age, Sex, and Education Pace Factor
b (95% Confidence Interval) P-Value
RBC count, cells/lL Hemoglobin, g/dL Hematocrit, % Mean corpuscular volume, fL Mean corpuscular hemoglobin, pg Mean corpuscular hemoglobin concentration, g/dL Red cell distribution width, %
0.005 0.025 0006 0.005 0.02 0.023
( 0.248 – ( 0.064 – ( 0.025 – (0.018 to ( 0.041 – ( 0.082 –
0.248) .97 0.114) .57 0.036) .71 0.028) .65 0.081) .51 0.128) .67
0.09 ( 0.193 – 0.014) .09
count (cells/lL), hemoglobin (g/dL), hematocrit (%), mean corpuscular volume (fl), mean corpuscular hemoglobin (pg), mean corpuscular hemoglobin concentration (g/dL) and red cell distribution width (RDW, %). Because chronic diseases may affect gait,6 a summary Global Health Status index (GHS; range 0–10) was derived based on presence of diabetes mellitus, chronic heart failure, arthritis, hypertension, depression, stroke, Parkinson’s disease, chronic obstructive pulmonary disease, angina pectoris, and myocardial infarction.2
Gait Participants walked at their normal pace on a computerized walkway (180 9 35.5 9 0.25 inches) in a quiet, welllit hallway. The computer software recorded gait variables as the mean of two trials.2,7 Eight quantitative variables were summarized into three statistically independent gait factors (domains) using factor analysis, as reported previously.2,7 These three factors (standard deviation units, higher scores better) accounted for 88% of variance in gait performance. The pace factor loaded highest on gait velocity and stride length; the rhythm factor on cadence, swing time, and double support time; and the variability factor on swing time and stride length variability. Independent predictive validity of these gait domains has been shown for dementia (rhythm, variability), vascular dementia (pace), and falls (rhythm, variability) in older adults.2,7
Analysis Multiple linear regression analyses were used to determine associations between RBC indices and gait factors, controlling for age, sex, and education. All analyses were performed on SPSS version 21 (SPSS, Inc., Chicago, IL).
RESULTS The mean age of the participants was 78.3 6.6, 55.2% were female, and mean education was 14.7 3.2 years. Higher rhythm factor was associated with higher RBC count (P = .01), hematocrit (P = .02), and RDW (P = .04) (Table 1). RBC indices were not associated with the pace and variability domains. Inflammation may confound the association between rhythm and RBC indices.4 After controlling for inflamma-
0.315 0.087 0.039 0.009 0.043 0.052
(0.062 – 0.569) .01 ( 0.007 – 0.180) .07 (0.008 – 0.071) .02 ( 0.034 – 0.015) .44 ( 0.107 – 0.021) .19 ( 0.163 – 0.059) .36
0.112 (0.003 – 0.221) .04
0.055 0.025 0.005 0.004 0.006 0.03
( ( ( ( ( (
0.320 0.122 0.038 0.021 0.072 0.145
– – – – – –
0.209) .68 0.072) .61 0.028) .78 0.029) .75 0.06) .85 0.084) .60
0.047 ( 0.160 – 0.066) .41
tion using WBC count and IL-6 levels,4 the association between gait rhythm and RBC count (P < .001) and between gait rhythm and hematocrit (P < .001) persisted but not between gait rhythm and RDW (P = .11). The association between rhythm and RBC count (P < .001), hemoglobin (P = .001), and hematocrit (P < .001) remained after adjustment for chronic diseases.
DISCUSSION The current study found an association between RBC indices (RBC count, hematocrit, RDW) and gait rhythm but not the pace and variability domains in older adults. Higher RBC indices were associated with better gait rhythm scores. These findings suggest a specificity of hematological mechanisms for causing perturbations in discrete aspects of gait and not a global effect of RBC indices on overall gait performance in aging. Abnormal RBC indices are markers of disease conditions such as pernicious anemia, stroke, and vasculitis, which could affect gait through the central and peripheral nervous system, muscles, and vasculature. Low hemoglobin (a constituent of RBC count and hematocrit) is associated with risk of frailty.8 Low RBC count and hematocrit may also act through cerebral hypoperfusion and tissue hypoxia to create gait rhythm deficits. The effect of RDW on rhythm was attenuated after adjustment for inflammatory markers, which is linked to risk of frailty.2 The cross-sectional design is a limitation. Longitudinal studies are needed to establish causality. RBC indices are widely available in clinical settings. If the current findings are cross-validated in future studies, early identification and management of this modifiable risk factor could help ameliorate distal gait-related cognitive and mobility outcomes. Somechukwu Onuoha, MD Department of Neurology, Albert Einstein College of Medicine, Bronx, New York Joe Verghese, MBBS Department of Neurology, Albert Einstein College of Medicine, Bronx, New York Department of Medicine, Albert Einstein College of Medicine, Bronx, New York
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ACKNOWLEDGMENTS The study was supported by National Institute on Aging Grants R01AG03692101A1 (PI: R. Holtzer), 3R01AG03 6921–02S1 (PI: R. Holtzer), and RO1AGO44007–01A1 (PI: J. Verghese). Conflict of Interest: Dr. Verghese has received research support from National Institutes of Health (NIH) Grants R01AG039330–01, PO1 AG03949, R01AG036921–01A1, RO1 AGO44829–01A1, and RO1AGO44007–01A1) Dr. Verghese has reviewed for the NIH. Dr. Verghese is a member of the editorial board of the Journal of the American Geriatrics Society. Author Contributions: Onuoha, Verghese: study concept, data acquisition, data analysis, data interpretation, drafting manuscript. Sponsor’s Role: The funding sources had no role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript.
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METHODS A convenience sample of older adults attending a university-linked day hospital and caregivers were recruited over an 8-week period to complete a questionnaire detailing the frequency and breadth of their use of technology (response rate 95%). Participants were aged 60 and older and community dwelling. The local ethics committee granted ethical approval. The questionnaire was developed for the purposes of this research and consisted of questions relating to participants’ age, sex, level of educational attainment, experience with a range of technology (a predefined list of electrical devices ranging from mobile telephones to tablet computers), self-rated skill with technology, and frequency of overall technology use (every day, most days, every week, rarely). All data were analyzed using SPSS version 20.0 (SPSS, Inc., Chicago, IL).
RESULTS REFERENCES 1. Fried P, Tangen C, Walston J et al. Frailty in older adults: Evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001;56A:M146–M156. 2. Verghese J, Wang C, Lipton RB et al. Quantitative gait dysfunction and risk of cognitive decline and dementia. J Neurol Neurosurg Psychiatry 2007;78:929–935. 3. Silva JC, Moraes ZV, Silva C et al. Understanding red blood cell parameters in the context of the frailty phenotype: Interpretations of the FIBRA (Frailty in Brazilian Seniors) study. Arch Gerontol Geriatr 2014;59:636– 641. 4. Verghese J, Holtzer R, Oh-Park M et al. Inflammatory markers and gait speed decline. J Gerontol A Biol Sci Med Sci 2011;66A:1083–1089. 5. Sarma PR. Red cell indices. In: Walker HK, Hall WD, Hurst JW, eds. Clinical Methods: The History, Physical and Laboratory Examinations, 3rd Ed. Boston: Butterworths 1990, pp 720–723. 6. Alexander NB, Goldberg A. Gait disorders: Search for multiple causes. Clev Clin J Med 2005;72:586–600. 7. Verghese J, Holtzer R, Lipton RB et al. Quantitative gait markers and incident fall risk in older adults. J Gerontol A Biol Sci Med Sci 2009;64A:896– 901. 8. Chaves PH, Semba RD, Leng SX et al. Impact of anemia and cardiovascular disease on frailty status of community-dwelling older women: The Women’s Health and Aging Studies I and II. J Gerontol A Biol Sci Med Sci 2005;60A:729–735.
TECHNOLOGY USE AND FREQUENCY AND SELF-RATED SKILLS: A SURVEY OF COMMUNITYDWELLING OLDER ADULTS To the Editor: Increasing use of technology, particularly smartphones, computer tablets, and computer-based games, is apparent in the general population, but the ability of older adults to engage with these newer technologies requires more investigation. Studies suggest that older adults are not technology averse but that their pattern and frequency of usage varies from that of younger adults.1 As the importance of information and communication technology (ICT) in the care and monitoring of older adults grows, their experience with and attitudes toward devices become more relevant. The purpose of this study was to explore technology use of community-dwelling older adults.
Two hundred fifty-five participants (median age 78, interquartile range 11) were included; 98 (38%) were male. Educational level varied among participants; 248 (97%) had a primary level education, 115 (45%) a secondary level, and 69 (27%) a third level. Two hundred fifteen (84%) participants reported using technology regularly; 185 (73%) a mobile telephone, 184 (72%) a digital television, 79 (31%) any form of computer, 11 (4%) a tablet computer, and seven (3%) a games console. Despite the high levels of technology use, 195 (76%) participants rated their technology skills as poor or average, and 18 (7%) rated their skills as very good or excellent. There was no significant association between age and frequency of technology use (correlation coefficient = 0.09, P = .15), although those who reported that they used mobile phones, digital video disk (DVD) players, or any form of computer were significantly younger (Table 1). There was no effect of educational level on use of technology (F(3, 250) = 1.42, P = .24) or self-rated technology skills (F(3, 250) = 0.5, P = .69), although self-rated technology skill was independently associated with use of a tablet computer (F(1,252) = 8.31, P < .01).
DISCUSSION This study explored the prevalence of use of technology by community-dwelling older adults, their experience using it,
Table 1. Use of Technology-Based Devices by Older People (n = 255): Percentage and Age Age, Median (Interquartile Range)
185 (73) 184 (72) 175 (69)
76 (9) 77 (12) 76.5 (11)