Short Physical Performance Battery as a Crosswalk Between Frailty Phenotype and Deficit Accumulation Frailty Index (original) (raw)

Journal Article

Hee-Won Jung, MD, PhD ,

Division of Geriatrics, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine

,

Seoul

,

Republic of Korea

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Ji Yeon Baek, MD ,

Division of Geriatrics, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine

,

Seoul

,

Republic of Korea

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Il-Young Jang, MD ,

Division of Geriatrics, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine

,

Seoul

,

Republic of Korea

Address correspondence to: Il-Young Jang, MD, Division of Geriatrics, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea. E-mail: [email protected]

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Jack M Guralnik, MD, PhD ,

Division of Gerontology, Department of Epidemiology and Public Health, University of Maryland School of Medicine

,

Baltimore

,

USA

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Kenneth Rockwood, MD, PhD ,

Kenneth Rockwood, MD, PhD

Divisions of Geriatric Medicine & Neurology, Dalhousie University and Nova Scotia Health

,

Halifax

,

Canada

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Eunju Lee, MD, PhD ,

Division of Geriatrics, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine

,

Seoul

,

Republic of Korea

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Dae Hyun Kim, MD, ScD

Marcus Institute for Aging Research, Hebrew SeniorLife

,

Boston, Massachusetts

,

USA

Division of Gerontology, Department of Medicine, Beth Israel Deaconess Medical Center

,

Boston, Massachusetts

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USA

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Received:

26 November 2020

Editorial decision:

15 March 2021

Corrected and typeset:

30 April 2021

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Hee-Won Jung, Ji Yeon Baek, Il-Young Jang, Jack M Guralnik, Kenneth Rockwood, Eunju Lee, Dae Hyun Kim, Short Physical Performance Battery as a Crosswalk Between Frailty Phenotype and Deficit Accumulation Frailty Index, The Journals of Gerontology: Series A, Volume 76, Issue 12, December 2021, Pages 2249–2255, https://doi.org/10.1093/gerona/glab087
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Abstract

Background

Growing evidence supports the clinical importance of evaluating frailty in older adults, with its strong outcome relevance. We aimed to assess whether the Short Physical Performance Battery (SPPB) correlates with frailty status according to phenotype and deficit accumulation models and can be used as a link between these models.

Methods

We analyzed records of 1064 individuals from the Aging Study of Pyeongchang Rural Area, a population-based, prospective cohort from South Korea. Frailty was determined using the Cardiovascular Health Study (CHS) phenotype (phenotype model), 26- and 34-item frailty indices (deficit accumulation model). Associations of SPPB score and frailty with a composite outcome of mortality or long-term institutionalization were assessed. Crosswalks for SPPB, the CHS frailty phenotype, and the frailty index were created.

Results

The mean age of the study population was 76.0 years, and 583 (54.8%) were women. According to the CHS phenotype, 26- and 34-item frailty indices, 242 (22.7%), 161 (15.1%), and 280 (26.3%) participants, respectively, had frailty. Sensitivities/specificities for classifying CHS phenotype, 26- and 34-item frailty indices were 0.93/0.55, 0.71/0.84, and 0.80/0.83 by SPPB cut points of ≤9, ≤6, and ≤7, respectively. C-index of SPPB score (0.78) showed a predictive ability for the composite outcome that was comparable to that of CHS frailty phenotype (0.79), 26- (0.78), and 34-item frailty indices (0.79).

Conclusions

We could create a crosswalk linking frailty phenotype and frailty index from correlations between SPPB and frailty models. This result may facilitate clinical adoption of the frailty concept in a broader spectrum of older adults.

There is a growing need to assess physical performance and frailty in clinical practice and research (1,2). The prognostic importance of frailty—defined as a state of increased vulnerability to stressors caused by decreased physiological reserves—is well-documented (1). Frailty assessment is useful to select appropriate candidates for invasive treatments (3,4). Frailty can be an outcome to evaluate the effect of an intervention (5).

Frailty has largely been defined based on 2 concepts—the phenotype model of frailty, which focuses on core clinical presentations of physical frailty, and the deficit accumulation frailty index, which focuses on the total burden of health deficits or abnormalities from self-report or observer-assessed data (eg, comprehensive geriatric assessment) (6–8). Even though varying instruments have been validated for these models of frailty, unmet needs exist for objective and clinically feasible measures on frailty (9). Also, frailty assessment might be affected by cultural differences among populations because these 2 frailty models commonly involve self-reported data on mood, physical, or functional status (10).

The Short Physical Performance Battery (SPPB), a test of lower extremity function that combines standing balance, walking speed, and chair stand, has been used to predict adverse clinical outcomes (11,12). SPPB scores have served as a selection criterion and key efficacy parameter in clinical trials on sarcopenia or physical frailty (9,13–15). Furthermore, the total SPPB score and its components (eg, walking speed and chair stand) are used to define sarcopenia (16). SPPB has been also suggested as a preferred measure in clinical trials to characterize baseline frailty by European Medicines Agency, given its prognostic value, validation status, and clinical feasibility, suggesting that assessing both frailty index and frailty phenotype criteria can be difficult in real-world settings (17). With a recent development of multisensor technology to allow a standardized semiautomatic assessment, it is expected that SPPB will be adopted further in both research and clinical practice (18) as a useful screening tool for frailty. A recent study suggests that SPPB can be useful to identify frailty both by the phenotype and deficit accumulation models in geriatric outpatients, even though SPPB may measure a biological construct distinct from other models of frailty, especially by deficit accumulation model that focuses more on burdens of morbidities and disabilities (19). Therefore, SPPB may have the potential to serve as a crosswalk, connecting varying frailty concepts, especially in cross-cultural settings.

Albeit with these potential advantages of SPPB in detecting frailty, we recognized that there is still a paucity of literature supporting its role as a measure for frailty in community-dwelling older adults. The objective of this study was to cross-compare between SPPB, SPPB and frailty defined by phenotype, and deficit accumulation model. Specifically, we examined the discriminatory ability of SPPB score for each frailty definition and created a crosswalk between SPPB and frailty measures. We also compared SPPB and frailty measures for their associations with death or long-term care institutionalization.

Method

Study Population

We analyzed the records of community-dwelling older Korean adults who participated in the Aging Study of Pyeongchang Rural Area (ASPRA), a population-based prospective cohort study on frailty, sarcopenia, and geriatric syndromes. Details of the baseline survey study have been described previously (20). In brief, the ASPRA, established in 2014, enrolled people aged 65 years or older living in Pyeongchang County, Gangwon Province, Korea, located approximately 180 km east of Seoul. Individuals who were registered in the National Healthcare Service, were living at home at the time of the baseline survey, were ambulatory with or without an assistive device, and provided informed consent were included. Individuals living in nursing homes/chronic care hospitals or receiving nursing-home-level care at home due to disabilities at the time of baseline assessment were excluded. The protocol of this study was approved by the Institutional Review Board of Asan Medical Center. Written informed consent was obtained from all participants and/or their legal proxy.

For the analysis in the present study, we used the records of the 1064 participants recruited from July 2015 to June 2018, underwent baseline examination, who had complete data on frailty status, and SPPB scores. We excluded 382 participants who had assessments from October 2014 to June 2015 with no SPPB data, because SPPB was introduced in the ASPRA in July 2015.

Assessment of SPPB

Measurements for SPPB were performed by dedicated nurses, based on previously published methods (12). For standing balance, the participants’ ability to stand up for 10 seconds with their feet in 3 positions—side-by-side, semi-tandem, and tandem—while using their arms and trunk to remain balanced was recorded. Walking speed was measured in the usual gait speed at which a 4-m section was covered after a 1-m acceleration section, which was not included in the measurement. For the chair stand test, participants were instructed to rise completely from a chair 5 times as swiftly as possible. The time taken from when “ready, start” was called out to when the participant completed the fifth stand was recorded. By combining scores of 3 tests, the SPPB score ranged from 0 (worst) to 12 (best), and we gave 0 points to those who cannot perform any of these 3 tests. According to previous literature (11,21), we initially categorized individuals into 2 groups: those with a total SPPB score of 9 or less and those with 10 or higher. For outcome analyses, we grouped the total SPPB score into 0–5, 6–9, and 10–12, considering the crosswalk created in the present study between SPPB and frailty (Figure 1).

Figure 1.

(A) Box plot showing distributions of the frailty index according to Short Physical Performance Battery (SPPB) scores. Numbers on the top of each box plot denote the mean frailty index by SPPB scores. (B) Bar plot showing proportions of robust, prefrail, and frail individuals categorized using the Cardiovascular Health Study frailty phenotype according to SPPB scores. In box plot, upper, mid, and lower lines of the box denote 75th, 50th, and 25th percentiles, respectively. Upper and lower margins of whiskers denote ±1.5 interquartile range from 50th percentile. Data outside ±1.5 interquartile range from 50th percentile are shown as an outlier.

Assessment of Frailty Status

We assessed frailty status using the Cardiovascular Health Study (CHS) frailty phenotype and a 34-item frailty index (Supplementary Table 1) constructed using the deficit accumulation approach (6,22–24). We also used a 26-item frailty index after removing 8 items related to mobility and physical performance (impaired mobility, musculoskeletal problems, bradykinesia of the limbs, poor muscle tone in limbs, poor limb coordination, poor trunk coordination, poor standing posture, and irregular gait pattern) in the original 34-item frailty index, as a sensitivity analysis to assess whether SPPB is even predictive for frailty index without mobility items (7). The criteria we used for defining frailty were 3 or more positive items on the CHS frailty phenotype and a frailty index of 0.25 or higher. For outcome analysis, we defined prefrail status as one or more positive items on the CHS frailty phenotype and mildly frailty status as a frailty index of 0.15 or higher. Cutoff values for frailty index were established in a priori manner adopting definitions and observations of previous studies (7,19,25).

Assessment of Other Variables

A comprehensive geriatric assessment including detailed interviews and physical examinations was conducted annually by nurses trained in the ASPRA. Basic demographic and anthropometric, social information including education level were acquired. We measured 7 activities of daily living (ADLs) using previously validated Korean ADL: bathing, continence, dressing, eating, toileting, transferring, and washing the face and hands (26). We considered a disability in ADL as the presence of dependency in at least one of these items. Additionally, we examined the ability to perform 10 instrumental activities of daily living (IADLs) using previously validated Korean IADL: food preparation, household chores, going out a short distance, grooming, handling finances, doing laundry, managing medications, shopping, transportation, and using a phone (26). A dependency in at least one of these items was deemed a disability in IADL (20). Cognitive dysfunction was determined with the Korean version of the Mini-Mental State Examination score of less than 24 (27). After obtaining case histories for 11 physician-diagnosed illnesses—angina, arthritis, asthma, cancer, chronic lung disease, congestive heart failure, diabetes, heart attack, hypertension, kidney disease, and stroke—we defined multimorbidity as the presence of 2 or more of these diseases. Furthermore, polypharmacy was defined as the use of 5 or more prescription medications, from medication history acquired by an interview. Fall history in the previous 12 months was recorded.

Measurement of Adverse Clinical Outcome

As an outcome, we used a composite endpoint of death and long-term institutionalization due to functional impairment, acquired by telephone interview on participants or their family members performed every 3 months. For this study, outcome data until December 2018 were used. Drop-out reasons during the observation period included move out to other area (n = 52), decline for the follow-up (n = 89), and unable to contact (n = 6).

Statistical Analyses

For comparison of demographic and functional characteristics by groups defined by SPPB scores, we used the independent t test (continuous variables) and the χ 2 test (categorical variables). Distribution of frailty status according to CHS frailty phenotype and the frailty index corresponding to SPPB total scores and subcomponent scores was examined. Associations of SPPB total and component scores with education level, frailty scores, and other geriatric conditions were assessed by each gender, using multivariate linear regression to adjust for age groups (65–69, 70–74, 75–79, 80–84, and 85+). Receiver operating characteristics (ROC) analysis was used to find a cut-point of SPPB total score for identifying frailty according to each frailty model. Cross-validated means and bootstrap bias-corrected 95% confidence intervals (CIs) of C-statistics were calculated using K-fold method. In addition, we developed a crosswalk among the CHS frailty phenotype, frailty index, and SPPB total scores using an equipercentile equating method (28). For outcome analysis, we used Cox proportional hazard models to assess the associations between SPPB total score, component score, and the composite outcome. The discriminatory ability was assessed using Harrell’s C-index (29), and compared using linear comparison, C-indices were internally cross-validated using Jackknife estimations. We considered two-sided p values less than .05 as statistically significant. All statistical analyses were performed using Stata 15.0 (StataCorp, College Station, TX)

Results

Characteristics of the Study Population

Characteristics of the population with SPPB who had baseline examination since 2015 and without SPPB who had examined in 2014 were compared in Supplementary Table 2. Generally, the population without SPPB were older, had a lower multimorbidity burden, and were frailer. The mean age of the 1064 individuals with SPPB included in the final analysis was 76.0 years (standard deviation [_SD_] 6.8) and 583 (54.8%) were women. Based on the CHS frailty phenotype and the 34-item frailty index, 242 (22.7%) and 280 (26.3%) individuals, respectively, had frailty. Individuals with an SPPB score of 9 or less were older and more likely to be women (Table 1). They had a lower educational level, a greater burden of multimorbidity, frailty, disability, cognition, polypharmacy, and falls.

Table 1.

Baseline Characteristics in Participants With Short Physical Performance Battery (SPPB) Scores At Least 10 (intact) and 9 or Less (impaired)

Note: ADL = activity of daily living; CHS = Cardiovascular Health Study; IADL = instrumental activity of daily living; SD = standard deviation.

Table 1.

Baseline Characteristics in Participants With Short Physical Performance Battery (SPPB) Scores At Least 10 (intact) and 9 or Less (impaired)

Note: ADL = activity of daily living; CHS = Cardiovascular Health Study; IADL = instrumental activity of daily living; SD = standard deviation.

Distributions of total SPPB and component-specific scores showed left skewedness (Supplementary Figure 1). SPPB total scores and all 3 component scores were significantly associated with years of education, number of chronic diseases, CHS frailty phenotype scores, the frailty index, number of impaired ADL/IADL, number of medications, and fall history for men (Supplementary Table 3) and women (Supplementary Table 4) after adjusting for age.

Physical Performance as a Measure of Frailty Status

Frailty status by CHS frailty phenotype and frailty index showed a negative correlation with SPPB total score (Figure 2) and component scores (Supplementary Figure 2). Through linear regression analysis, regression coefficients (standardized beta, B) for the CHS frailty phenotype score, 34-item frailty index, and 26-item frailty index without mobility items were −0.67 (p < .001) −0.79 (p < .001), and −0.62 (p < .001), respectively, when SPPB total scores were used as an independent variable. In this analysis, determination coefficients of SPPB (_R_2) for CHS frailty phenotype score, 34-item frailty index, and 26-item frailty index were 0.45, 0.63, and 0.39, respectively.

Figure 2.

A crosswalk between corresponding scores of the 34-item frailty index, the Short Physical Performance Battery (SPPB), and the Cardiovascular Health Study (CHS) frailty phenotype, using equipercentile equating methods.

A crosswalk between corresponding scores of 34-item frailty index, SPPB, and the CHS frailty phenotype was established using equipercentile equating methods (Figure 1). By ROC analyses, an SPPB score cut-point of 9 or less maximized the sensitivity + specificity for classifying frailty according to the frailty phenotype, 7 or less for the 34-item frailty index, and 6 or less for the 26-item frailty index. Internally cross-validated C-indices of SPPB on frailty phenotype, 34-item frailty index, and 26-item index were 0.83 (95% CI 0.79–0.85), 0.90 (0.87–0.91), and 0.87 (0.82–0.88), respectively. The sensitivities and specificities for detecting vulnerability and frailty by CHS frailty phenotype, 34- and 26-item frailty indices at each total SPPB cutoff score are given in Table 2.

Table 2.

Sensitivity and Specificity of Various Short Physical Performance Battery (SPPB) Scores in Predicting Mildly Frail Status and Frail According to the Cardiovascular Health Study (CHS) Frailty Phenotype, 34- and 26-Item Frailty Indices

Notes: Sen = sensitivity; Spe = specificity.

Table 2.

Sensitivity and Specificity of Various Short Physical Performance Battery (SPPB) Scores in Predicting Mildly Frail Status and Frail According to the Cardiovascular Health Study (CHS) Frailty Phenotype, 34- and 26-Item Frailty Indices

Notes: Sen = sensitivity; Spe = specificity.

When SPPB total score was grouped into 3 categories (1–5, 6–9, 10–12) 145, 448, and 471 individuals were included in these categories, respectively. Corresponding mean CHS frailty phenotype scores were 2.81 (SD 0.94), 1.97 (SD 1.01), and 0.69 (SD 0.85), respectively. Also, corresponding mean frailty indices were 0.28 (SD 0.13), 0.15 (SD 0.09), and 0.10 (SD 0.07) for 26-item index and 0.39 (SD 0.12), 0.21 (SD 0.10), and 0.10 (0.07) for 34-item index, respectively.

Incidence of Composite Outcome With SPPB Score and Frailty Status

In a mean follow-up period of 28.7 months (SD 18.5), 55 died and 65 were institutionalized to long-term care facilities. Lower SPPB total scores were associated with an increased rate of the composite outcome of mortality or institutionalization, even after adjusting age, gender, and multimorbidity at baseline (Table 3). Similarly, the increasing CHS frailty phenotype 26- and 34-item frailty indices showed greater rates of the composite outcome. Internally cross-validated Harrell’s C-statistics by Jackknife estimations were 0.78 (95% CI 0.73–0.83), 0.79 (95% CI 0.74–0.83), 0.78 (95% CI 0.72–0.83), and 0.79 (95% CI 0.74–0.84) for SPPB total score, CHS frailty phenotype, 26- and 34-item frailty indices. By comparing C-statistics, SPPB score showed no significant difference on outcome prediction ability when compared to CHS frailty phenotype (p = .625), 26- (p = .876), and 34-item frailty indices (p = .337).

Table 3.

Comparison of SPPB Total Score, CHS Frailty Scale, and Frailty Index on Predicting the Composite Outcome of Death or Long-Term Institutionalization

Notes: CI = confidence interval; CHS = Cardiovascular Health Study; HR = hazard ratio; SPPB = Short Physical Performance Battery; py = person-years. Model 1: unadjusted; Model 2: age and gender adjusted; and Model 3: age, gender, and multimorbidity adjusted.

Table 3.

Comparison of SPPB Total Score, CHS Frailty Scale, and Frailty Index on Predicting the Composite Outcome of Death or Long-Term Institutionalization

Notes: CI = confidence interval; CHS = Cardiovascular Health Study; HR = hazard ratio; SPPB = Short Physical Performance Battery; py = person-years. Model 1: unadjusted; Model 2: age and gender adjusted; and Model 3: age, gender, and multimorbidity adjusted.

Discussion

We found that the SPPB correlates with 2 widely used models of frailty. The SPPB score has a comparable ability to frailty phenotype and frailty index for predicting mortality and long-term institutionalization. From these observations, we created a crosswalk linking frailty phenotype and frailty index.

Because components of the SPPB and CHS frailty phenotype overlap, associations between SPPB parameters and CHS frailty phenotypes are expected. However, interestingly, individual SPPB components and total SPPB scores were also correlated with the frailty index, which consists of variable health deficits such as comorbidities, cognitive function, and disabilities. These correlations attenuated somewhat yet remained statistically significant even after removing mobility-related items from the frailty index. These notable correlations between physical performance and 2 models of frailty are in accordance with our previous study showing correlations between Sarcopenia Phenotype Scores—a sarcopenia index developed using the frailty index, composed of 3 parameters, muscle mass, gait speed, and grip strength—and these 2 frailty models (30). Based on this previous evidence and the population-based observation supporting frailty index as a measure of biological age (31), our study suggests that impaired physical performance may be a core phenotype of human biological aging.

While frailty has been considered as an advanced phenotype in human aging, controversies exist on the biological construct and standard measures of frailty (1). For instance, some tools focus more on comorbidities and laboratory test abnormalities, while others focus on disabilities that may be affected by cultural differences (10). Moreover, the ceiling effect and dynamic ranges sometimes limit the transportability of a frailty measure developed from one population to another. Furthermore, different frailty measures may vary in terms of their responsiveness to change from an intervention. Meanwhile, SPPB is an objective measure of lower extremity function (mobility, strength, and balance) that can be assessed in less than 10 minutes. It is less affected by cultural backgrounds (compared with frailty index) and floor/ceiling effects (compared with frailty phenotype). Moreover, mobility and strength are core components of frailty phenotype and also related to several items in the frailty index, Therefore, we thought that it could be a useful anchor that correlates with both frailty models. In addition, our results on the frailty index, SPPB scores, and CHS frailty phenotype may help researchers compare or merge different data sets involving various frailty measures.

In our study population, we found that SPPB scores of ≤6, ≤7, and ≤9 could classify frailty by 26- and 34-item frailty indices and CHS frailty phenotype, respectively. Although there are some differences, these findings are in accordance with a previous study (21) performed using SPPB total scores of 4–9 as a criterion for selecting participants amenable to interventions aimed on sarcopenia. It is also in agreement with a recent European consensus guideline on sarcopenia, suggesting that SPPB scores of 8 or less indicate a state of impaired physical performance (16). With a meaningful clinical difference of 0.8 points (32), our findings suggest that the SPPB can be used as both a selection criterion and an outcome measure for intervention schemes aimed at preventing the progression of frailty.

There are several limitations to our study. Because the ASPRA was based on community-dwelling older people from rural areas with socioeconomic characteristics distinct from those of urban populations, the generalizability of our findings might be limited. Characteristics of the population with and without SPPB were differed significantly, while the covered area of ASPRA was gradually expanded from 2 small towns in 2014 to finally 8 small towns of Pyeongchang-gun (County) in 2018, also limiting the generalizability of the study findings. While our study supports the intriguing idea that SPPB may serve as a transportable measure of frailty, potential cultural influences on the physical performance itself are possible, which warrants cross-cultural studies (23). Moreover, the responsiveness of the frailty index, CHS frailty phenotype, and SPPB score after intervention could not be assessed in the current cross-sectional study, while meaningful natural changes of frailty index and CHS frailty phenotype in the same population have been published before (33). Because we did not have any data on laboratory measures, we were unable to investigate biomarkers associated with frailty and aging. Future research featuring an interventional study design with biomarker studies may shed light on further associations between physical parameters and aging biomarkers (34).

In conclusion, SPPB as a measure of lower extremity function, correlated with both frailty phenotype and frailty index, could predict adverse outcomes similarly to these 2 frailty measures. Supported by these results, we created a crosswalk using SPPB as an anchor linking to frailty phenotype and frailty index to allow comparison and pooled analysis of studies using different frailty measures. We hope this result will facilitate clinical adoption of the frailty concept in a broader spectrum of the older population.

Funding

This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI18C2383), and a grant (2020IF0001) from the Asan Institute for Life Science, Asan Medical Center, Seoul, Republic of Korea. The study was also supported by R01AG062713 from the National Institute on Aging (NIA). The funding sources had no role in the data design, collection, analysis, or interpretation or the decision to submit the manuscript for publication. K.R.’s work on frailty is supported by the Canadian Frailty Network (grants CAT2018-27 and TG2015-24) and the Canadian Institutes for Health Research (project grant PJT-156114), Research Nova Scotia, Nova Scotia Health, and the QEII Health Research Foundation, with personal research support from the Dalhousie Medical Research Foundation as the Kathryn Allen Weldon Professor of Alzheimer Research.

Conflict of Interest

H.-W.J. cofounded Dyphi Inc., a startup company developing sensor technologies on human movement and robotics. D.H.K. provides paid consultative services to Alosa Health, a nonprofit educational organization with no relationship to any drug or device manufacturers. Through the Dalhousie University Industry Liaison Office, K.R. has asserted copyright of the Clinical Frailty Scale which is free for use for research and educational purposes and not-for-profit health care. Users agree not to modify or commercialize it. He is also the founder of and shareholder in DGI Clinical, which in the last 3 years has had contracts for individualized outcome measurement with Genentech-Roche, Hollister, LuMind, Novartis, Shire, and Takeda.

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