In a prospective population-based study, the degree of mobility impairment during hospitalisation is associated with higher degrees of frailty

doi:10.21203/rs.3.rs-6580479/v1

Background

Hospitals pose a high risk for frailty to develop or accelerate. Still, few community-based cohort studies follow patients before, during, and after hospitalisation. We investigated the degree of immobility during hospitalisation and its impact on subsequent frailty.

Methods

In a prospective population-based cohort of individuals aged ≥70 from a London UK borough, we performed comprehensive community assessments at baseline and after two years. At each hospitalisation, we measured daily mobility and other clinical variables. Acute immobility burden, a summative level of poor mobility for all hospitalisations, was calculated for each participant and operationalized as low/high based on the population median. A frailty index was calculated for all participants during baseline and follow-up assessments. We estimated the effect of these exposures on follow-up frailty index scores using linear regression.

Results

We included 1177 participants. Those admitted (N=114) were assessed over 1999 bed-days. The degree of baseline frailty had the largest association with subsequent frailty. However, a high immobility burden during hospitalisation was consistently related to additional increases in frailty (low burden: b=0.02 per unit increase in FI (95%CI: -0.002-0.04), high burden: b=0.07, (95%CI: 0.041-0.10)). Immobility burden remained associated with subsequent frailty even when limiting the analysis to: those who were independently mobile; the first seven days of hospitalisation; and accounting for illness severity. High immobility burden was prognostic of subsequent death.

Conclusions

The degree of immobility during hospitalisation, a potentially modifiable risk factor, may determine whether hospitalisation contributes to increasing frailty.

hospital

frailty

mobility

Frailty is crucial to understanding our ageing population because it identifies individuals at risk for a broad range of adverse outcomes.¹ Frailty transition occurring though acute illness is not often quantified, and very few studies have prospectively incorporated community and hospitalisation information.

In community-dwelling samples, frailty typically develops over years and is associated with several factors (e.g., age, sex, comorbidities, cognitive function).^2,3 In this setting, hospitalisation appears strongly associated with worsening frailty. In hospital cohort studies, frailty is prevalent in several populations.^4–6 Individuals who previously were fit have been found to have a notable burden of frailty following hospitalisation. At its inception, the DELPHC study was unique in being a population-based cohort that followed people through each day of hospitalization and ascertained relevant health information on individuals living with various degrees of fitness/frailty. Because hospitalisation is a potent risk factor for frailty, we considered what types of hospitalisations are associated with developing or accelerating frailty. Higher acuity hospitalisations presents a potential risk from both a theoretical and observational perspective. For instance, in patients admitted to critical care units, frailty is an essential marker for adverse outcomes, and the development of frailty is common in previously robust patients.⁷

Although older patients often present acutely with reduced mobility or delirium, rather than dyspnoea, fever or pain, these core presentations do not generally feature in our reification of acute illness.^8,9 The degree to which inpatients are impaired when they move, whether independently or through assessment, is associated with adverse outcomes.^10–12 Older inpatients with deteriorating mobility on the first days of admission for acute illness have an expected mortality of 70% at one-month.¹³ Further, the degree of immobility can indicate delirium in patients with dementia ¹⁴ and is a marker of delirium severity.¹⁵ Increased hospital mobilization is linked to shorter lengths of stay and functional independence.^16,17

Although mobility is a distinct and measurable entity, cross-sectionally, mobility is a significant component of frailty when patients are clinically stable. During acute illness, however, mobility, as a measure of function, appears to depend on both baseline frailty status and the nature and severity of acute illness.^18,19 Here, we investigate over two years whether cumulative immobility (immobility burden) during hospitalisation is associated with subsequent frailty in order to distinguish between ‘at-risk’ and ‘safe’ hospital admissions on a community population level. We hypothesized that the burden of hospitalised immobility would demarcate at-risk admissions for subsequent increased frailty.

Study design and participants

The Delirium and Population Health Informatics Cohort (DELPHIC) study^20,21 is a prospective population-based sample initiated in March 2017 in the borough of Camden (London, UK). Eligible participants were Camden residents aged ≥70 years. Participants were excluded if they had severe hearing impairment or aphasia, were in the terminal phase of illness (expected life expectancy of <6 months), or could not speak English sufficiently well to undertake a cognitive assessment. Participants were primarily enrolled via general practitioner lists (80%) or, to include a greater range of cognitive impairment and frailty, from memory clinics (10%) and recent hospital discharges (10%). DELPHIC’s primary outcome was detecting a meaningful change in cognitive testing at a two-year follow-up. The protocol received approval from an NHS Research Ethics Committee (16/LO/1217) and the Health Research Authority (IRAS 164446).

Baseline health assessments were performed in the community by telephone or home visit, with identical follow-up two years later. Participants admitted to hospital were automatically flagged to be seen daily (excluding public holidays and weekends) by a trained clinical researcher. Several health variables were assessed daily in hospital, including mobility, cognition, and physiological measures. Individuals, or their nominated proxies, gave consent or agreement to participate. Death notification was from the NHS Spine, a statutory register for all deaths in England, and were cross-referenced with local hospital electronic records systems (last update 21^st June 2021).

Measures

Frailty was measured at baseline and follow-up using a frailty index (FI)^21,22 (Supplementary Table 1). This previously published FI, created using a standard process to ensure validity²³, was modified to exclude mobility deficits. We did this to avoid collinearity with immobility burden, a relevant exposure in this study. The same items in both the baseline and follow-up frailty indices were used.

Mobility was assessed using the Hierarchical Assessment of Balance and Mobility (HABAM). The HABAM measures the highest daily attained performance in balance (21 points), transfers (18 points), and mobility (28 points), operating as an integrated measure of mobility. The HABAM was ascertained prospectively daily during hospitalisation. The mobility measured is functional, not intensity-based. Immobility was defined using the inverse of the HABAM, where higher scores indicate poorer mobility.^24,25

We used the National Early Warning Score (NEWS, version 1) which is a composite scoring system that uses physiological measurements to assess and monitor acute illness severity in hospitalised patients.²⁶ NEWS, a standard assessment tool mandated to be used at least daily by the NHS for all acute inpatient units, adds clinically abnormal indices (heart rate, blood pressure, respiratory rate, oxygen saturation, supplemental oxygen requirements, alertness), giving a score from 0 to 20. The index of multiple deprivation (2019) is an ecological measure of overall deprivation using 37 separate indicators across seven domains (income, employment, health, crime, education, barriers to services, and living environment). It is used throughout England and represents population sizes between 1200 and 3000 people.²⁷

Statistical analysis

Outcome measure

Frailty: A frailty index was used to quantify baseline (exposure) and follow-up (outcome). Assuming an alpha of 0.05, a sample size of at least 82 hospitalisations would be required to detect the effect of immobility on mortality with a power of 80%.²⁸

Exposures

Immobility burden: We quantified the total immobility burden during hospitalisation, measuring duration and severity, by summing daily immobility scores across inpatient assessments. This was additive across multiple admissions to include all immobility in the hospital during the two years of study enrollment. No mobility measurements were included following any transfer to subacute care units. No metrics were available on days individuals were waiting before transfer to subacute care.

Notionally, a low or high immobility burden could differentiate the type of hospital admission. As the HABAM had no established method to measure cumulative mobility burden during hospitalisation, we implemented the metric as follows: participants who were not hospitalised had no hospital immobility burden. Otherwise, for each participant, we calculated immobility burden by summing their daily HABAM scores across all hospital admissions. Hospitalised immobility burden was then dichotomised as high or low, based on the median score.

Missing data

In keeping with previous analyses, missing hospitalisation data during weekends and public holidays were assumed to be missing at random. Mobility data were forward and backfilled for weekends (Friday carried over to Saturday and data on Sunday from Monday) and public holidays for up to four days.²¹

Models

Frailty: We used linear regression to estimate the frailty index after two years in the study, adjusted by age, sex and baseline frailty). The main exposure was hospitalised immobility burden level (none/low/high) during the course of the 2-year study. Sensitivity analysis included excluding any participants with elective admissions, using immobility burden (continuous), mobility burden limited by the first 7 days of hospitalisation, correcting for baseline mobility status (mobility component of the Barthel Index), index of multiple deprivation and National Early Warning Score and only including those who were independently mobile at baseline assessment.

Additional analysis: Differences in baseline and follow-up mobility are reported to check for meaningful changes in mobility throughout the study that might be explained by immobility while not hospitalised.

We used R (4.0.2), Python (3.7.6) and Stata (17.0) for all analyses.

A total of 1177 participants were included in this analysis. Ninety-three participants of the original cohort study died before their 2-year follow-up; others were lost to attrition (n=199) (Figure 1). Higher immobility burden had higher study mortalty (53.6%, 19.4% and 2.7% for high, low and no hospital immobility burden respectively). Those not included in the final analysis were older, frailer at baseline, and had more limitations in their activities of daily living and mobility dependence at enrollment. Most people remained in the community without incident hospitalisation during the study period (n=1063). The 114 hospitalised participants experienced 167 hospitalisations. Admitted participants tended to be older and frailer (Table 1). During the study period, there were 1,999 person-days of inpatient assessment.

In those admitted to hospital, the immobility score had a median of 50 (IQR 40 to 56) out of 67 possible points. This would be clinically equivalent to a participant being able to sit up in bed independently, maintain this statically and require one person to assist with transfers out of bed. The median hospitalised immobility burden during the 2-year study duration was 145. This would be the equivalent of a participant being a full lift and unable to position themselves in bed for just over two days of hospitalisation in the intervening two years of the study, but less in-hospital immobility if spread over more days, acutely ill in hospital. The proportion of the population who were independently mobile during the baseline and two-year follow-up assessment was 99% and 96%, 99% and 93%, and 92% and 79% in the nonhospitalised, admissions with low burden of hospital immobility, and admissions with high burden of hospital immobility groups, respectively.

For the level of frailty at follow-up, high immobility burden was associated with worse FI scores (low burden: b=0.018, 95%CI: -0.002-0.038 and high burden: b=0.069, 95%CI: 0.041-0.097) (Table 2). Interpreted, a high immobility burden would contribute an additional 0.07 to the frailty index score or 2.24 deficts to the deficit burden. In our model, hospitalisations with high immobility burdens have the same effect size regarding frailty progression as does an additional 20 years of ageing. This was consistent across sensitivity analyses – when accounting for baseline mobility, removing individuals who were not independently mobile at baseline, removing individuals who had any elective admissions, only for immobility measured in the first 7 days of hospital admission, and the index of multiple deprivations (Supplemental Table 2). Likewise, mean NEWS, being admitted (the combined individuals with low and high immobility) was also associated with subsequent frailty. After adjusting for immobility and NEWS, only a high level of immobility during hospitalisation appeared to be related to subsequent frailty. The two-year increase in FI for the non-hospitalised sample was 0.02, whereas the average increase in those with high immobility was 0.07 (Figure 2).

In this population-based prospective cohort, over two years of observation, higher immobility during hospitalisation was associated with higher degrees of frailty. This effect was not apparent in individuals with minimal hospitalised immobility. A low level of immobility was not associated with a subsequently increased frailty burden, potentially suggesting that regardless of pre-hospitalisation frailty, not all hospital admissions in older adults increase frailty.

Despite this seemingly straightforward result, very few clinical acute care inpatient environments track or trigger medical concerns when mobility remains low. Also, contrary to some teaching, not all hospital admissions appear harmful to frail older patients.²⁹ True to our understanding of frailty, frailty is associated with adverse outcomes; however, when experiencing an adverse outcome (hospitalisation), mobility may appear to track outcomes. This is also consistent with the observation that, in patients with delirium, change in mobility tracks the overall course of recovery.¹⁵ We note too that poor hospital mobility is potentially modifiable.^30–32

Our analyses are subject to certain limitations. Immobility during all hospital admissions were included in the analysis of which approximately 12 appeared to be elective surgical admissions as opposed to acute medical/surgical illness. Our study focuses on change within a hospital episode as the primary setting for capturing acute illness. This potential immortality bias may have had little impact, considering the minimal change in those independently mobilizing in the non-hospitalised group during the 2-year follow-up. This representative community study is a single population group and may not be externally generalizable. Our chosen operationalized exposure has been validated (HABAM), but no current studies have operationalized hospitalised immobility burden as an integrative measure using the HABAM. Further validation will be required. We also recognize that immobility over time is less clinically translatable than a single-point measurement. Despite these, these data are the first to discriminate, in a population-based study, hospitalisation risks by immobility to subsequent frailty.

Unsurprisingly, baseline frailty was the most significant risk factor for frailty in two years. Usually, deficit accumulation is somewhere in the 3-4% per year from previous studies, depending on the population.³³ Here, we identified that only high levels of in-hospital immobility resulted in an increased frailty burden. We propose three potential explanations provided our results are valid. First is that some hospitalisations are not harmful for frailty development and this is particularly marked not necessary by illness severity, but rather low levels of immobility during hospitalisation. A second possible explanation is that there is a potential dose response to immobility during hospitalisation, and simply, we have too few numbers of hospitalised patients to determine whether or not low levels of immobility lead to future frailty within two years. Another intriguing idea, which is relatively well understood in clinical experience but with relatively low data evidence, is that immobility is a marker for illness severity, much more specific to frail older individuals than traditional physiological markers of illness severity.

Potential implications of these results could be important for decision-making and care planning at the time of presentation to hospital, before presentation to hospital and during hospital stay, bearing in mind that, yes, hospitalisations can be harmful for frail older individuals with respect to mortality and frailty. Still, not all hospitalisations appear to be harmful in this way from the community-dwelling sample. Additionally, many frailer, older individuals are focused on avoiding age-related health deficits and accumulation, associated disability, as it might be more of a concern than death. As such, this would support the idea that older individuals with relatively good mobility during a hospitalisation are likely to survive and not compound frailty.

Funding

The Delirium and Population Health Informatics Cohort study is supported by the Wellcome Trust through a fellowship award to DD (WT107467). The Medical Research Council Unit for Lifelong Health and Ageing at University College London received core funding through the Medical Research Council (MC_UU_00019/1). SDS received fellowship funding from the Dalhousie Medical Research Foundation.

Kim DH, Rockwood K. Frailty in Older Adults. New England Journal of Medicine. 2024;391(6):538-548. Doi:10.1056/NEJMra2301292
Kim DH. Measuring Frailty in Health Care Databases for Clinical Care and Research. Ann Geriatr Med Res. 2020;24(2):62-74. Doi:10.4235/agmr.20.0002
Zhang W, Zhou L, Zhou Y, et al. The correlation between frailty trajectories and adverse outcomes in older patients: A systematic review. Arch Gerontol Geriatr. 2025;128. Doi:10.1016/j.archger.2024.105622
Hogan DB, Maxwell CJ, Afilalo J, et al. A scoping review of frailty and acute care in middle-aged and older individuals with recommendations for future research. Canadian Geriatrics Journal. 2017;20(1):22-37. Doi:10.5770/cgj.20.240
Capin JJ, Wilson MP, Hare K, et al. Prospective telehealth analysis of functional performance, frailty, quality of life, and mental health after COVID-19 hospitalisation. BMC Geriatr. 2022;22(1):251. Doi:10.1186/S12877-022-02854-6
Courtwright AM, Zaleski D, Tevald M, et al. Discharge frailty following lung transplantation. Clin Transplant. 2019;33(10):e13694. Doi:10.1111/CTR.13694
Brummel NE, Girard TD, Pandharipande PP, et al. Prevalence and course of frailty in survivors of critical illness. Crit Care Med. Published online 2020:1419-1426. Doi:10.1097/CCM.0000000000004444
Zazzara MB, Penfold RS, Roberts AL, et al. Probable delirium is a presenting symptom of COVID-19 in frail, older adults: a cohort study of 322 hospitalised and 535 community-based older adults. Age Ageing. 2020;(September 2020):40-48. Doi:10.1093/ageing/afaa223
Jarrett PG, Rockwood K, Carver D, Stolee P, Cosway S. Illness presentation in elderly patients. Arch Intern Med. 1995;155(10):1060-1064. Doi:http://dx.doi.org/10.1001/archinte.155.10.1060
Pedersen MM, Bodilsen AC, Petersen J, et al. Twenty-four-hour mobility during acute hospitalisation in older medical patients. Journals of Gerontology – Series A Biological Sciences and Medical Sciences. 2013;68(3):331-337. Doi:10.1093/erona/gls165
Villumsen M, Jorgensen MG, Andreasen J, Rathleff MS, Mølgaard CM. Very low levels of physical activity in older patients during hospitalisation at an acute geriatric ward: A prospective cohort study. J Aging Phys Act. 2015;23(4):542-549. Doi:10.1123/japa.2014-0115
Covinsky KE, Pierluissi E, Johnston CB. Hospitalisation-associated disability “She was probably able to ambulate, but i’m not sure.” JAMA. 2011;306(16):1782-1793. Doi:10.1001/jama.2011.1556
Hubbard RE, Eeles EMP, Rockwood MRH, et al. Assessing balance and mobility to track illness and recovery in older inpatients. J Gen Intern Med. 2011;26(12):1471-1478. Doi:10.1007/s11606-011-1821-7
Gual N, Richardson SJ, Davis DHJ, et al. Impairments in balance and mobility identify delirium in patients with comorbid dementia. Int Psychogeriatr. 2019;31(5):749-753. Doi:10.1017/S1041610218001345
Richardson S, Murray J, Davis D, et al. Delirium and Delirium Severity Predict the Trajectory of the Hierarchical Assessment of Balance and Mobility in Hospitalised Older People: Findings from the DECIDE Study. Journals of Gerontology – Series A Biological Sciences and Medical Sciences. 2022;77(3):531-535. Doi:10.1093/erona/glab081
Fisher SR, Goodwin JS, Protas EJ, et al. Ambulatory activity of older adults hospitalised with acute medical illness...[corrected] [published errata appear in J AM GERIATR SOC 2011 59(4):777]. J Am Geriatr Soc. 2011;59(1):91-95. Doi:10.1111/j.1532-5415.2010.03202.x.Ambulatory
Hartley P, Adamson J, Cunningham C, Embleton G, Romero‐Ortuno R. Clinical frailty and functional trajectories in hospitalised older adults: A retrospective observational study. Geriatr Gerontol Int. 2017;17(7):1063-1068. Doi:10.1111/GGI.12827
Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. The Lancet. 2013;381(9868):752-762. Doi:10.1016/S0140-6736(12)62167-9
Lyons A, Romero-Ortuno R, Hartley P. Functional mobility trajectories of hospitalised older adults admitted to acute geriatric wards: A retrospective observational study in an English university hospital. Geriatr Gerontol Int. Published online 2019. Doi:10.1111/ggi.13623
Davis D, Richardson S, Hornby J, et al. The delirium and population health informatics cohort study protocol: Ascertaining the determinants and outcomes from delirium in a whole population. BMC Geriatr. 2018;18(1):45. Doi:10.1186/s12877-018-0742-2
Tsui A, Searle SD, Bowden H, et al. The effect of baseline cognition and delirium on long-term cognitive impairment and mortality: a prospective population-based study. Lancet Healthy Longev. 2022;3(4):e232-e241. Doi:10.1016/S2666-7568(22)00013-7
Chitalu P, Tsui A, Searle SD, Davis D. Life-space, frailty, and health-related quality of life. BMC Geriatr. 2022;22(1):1-8. Doi:10.1186/s12877-022-03355-2
Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating a frailty index. BMC Geriatr. 2008;8:24. Doi:10.1186/1471-2318-8-24
MacKnight C, Rockwood K. A hierarchical assessment of balance and mobility. Age Ageing. 1995;24(2):126-130. Doi:10.1093/ageing/24.2.126
MacKnight C, Rockwood K. Rasch analysis of the hierarchical assessment of balance and mobility (HABAM). J Clin Epidemiol. 2000;53(12):1242-1247. Doi:10.1016/S0895-4356(00)00255-9
RCP London. Royal College of Physicians. “National Early Warning Score (NEWS).”; 2012.
Goodyer E, Mah JC, Rangan A, et al. The relative impact of socioeconomic position and frailty varies by population setting. Aging Medicine. 2022;5(1):10-16. Doi:10.1002/agm2.12200
Sleiman I, Rozzini R, Barbisoni P, et al. Functional trajectories during hospitalisation: A prognostic sign for elderly patients. Journals of Gerontology – Series A Biological Sciences and Medical Sciences. 2009;64(6):659-663. Doi:10.1093/gerona/glp015
Gill TM, Gahbauer EA, Han L, Allore HG. The relationship between intervening hospitalisations and transitions between frailty states. J Gerontol A Biol Sci Med Sci. 2011;66(11):1238-1243. doi:10.1093/gerona/glr142
Hartley P, Keating JL, Jeffs KJ, Raymond MJ, Smith TO. Exercise for acutely hospitalised older medical patients. Cochrane Database of Systematic Reviews. 2022;2022(11). doi:10.1002/14651858.CD005955.pub3
Pérez-Zepeda MU, Martínez-Velilla N, Kehler DS, Izquierdo M, Rockwood K, Theou O. The impact of an exercise intervention on frailty levels in hospitalised older adults: secondary analysis of a randomised controlled trial. Age Ageing. 2022;51(2). doi:10.1093/ageing/afac028
Lozano-Vicario L, Zambom-Ferraresi F, Zambom-Ferraresi F, et al. Effects of Exercise Intervention for the Management of Delirium in Hospitalised Older Adults: A Randomized Clinical Trial. J Am Med Dir Assoc. 2024;25(8). doi:10.1016/j.jamda.2024.02.018
Mitnitski A, Rockwood K. The rate of aging: the rate of deficit accumulation does not change over the adult life span. Biogerontology. 2016;17(1):199-204. doi:10.1007/s10522-015-9583-y

Table 1. Baseline characteristics of the study population.

.	Overall	Not Hospitalised	Low Hospital Immobility	High Hospital Immobility
n	1177	1063	75	39
Age, mean (SD)	78.1 (5.7)	77.9 (5.6)	78.7 (5.6)	82.8 (6.3)
Female, n (%)	682 (57.9)	617 (58.0)	42 (56.0)	23 (59.0)
Barthel index, median [Q1,Q3]	17.0 [17.0,17.0]	17.0 [17.0,17.0]	17.0 [17.0,17.0]	17.0 [15.5,17.0]
Index of Multiple Deprevation, n (%) First decile Second decile Third decile Fourth decile
	249 (21.4)	232 (22.1)	11 (14.7)	6 (15.4)
	252 (21.6)	228 (21.7)	18 (24.0)	6 (15.4)
	241 (20.7)	223 (21.2)	10 (13.3)	8 (20.5)
	203 (17.4)	176 (16.8)	17 (22.7)	10 (25.6)
Fifth decile	219 (18.8)	191 (18.2)	19 (25.3)	9 (23.1)
Frailty Index, median [Q1,Q3]	0.12 [0.06,0.21]	0.12 [0.06,0.19]	0.19 [0.12,0.28]	0.28 [0.19,0.41]
Baseline Mobility (Barthel), median [Q1,Q3]	3.0 [3.0,3.0]	3.0 [3.0,3.0]	3.0 [3.0,3.0]	3.0 [3.0,3.0]
Mean NEWS Score, median [Q1,Q3]	N/A	N/A	0.5 [0.0,1.5]	0.9 [0.5,1.5]

Table 2. Linear regression for follow-up frailty. Baseline frailty index for the purpose of the analysis was multiplied by 10. Participants admitted were dichotomized into low and high at the median hospitalised immobility burden score of 145. Age was normalized from the minimum enrollment age (70 years old). NEWS is the National Early Warning Score version 1 mean for all inpatient days.

Model 1
	Coefficient	95% CI		P Value
Frailty Index (per 10% increase)	0.072	0.067	0.077	<0.001
Age (per year)	0.003	0.002	0.004	<0.001
Female (reference to males)	-0.009	-0.019	0.001	0.064
No Hospitalised Immobility	[ref]	-	-	-
Low Hospitalised Immobility	0.018	-0.002	0.038	0.083
High Hospitalised immobility	0.069	0.041	0.097	<0.001

Model 2
	Coefficient	95% CI		P Value
Frailty Index (per 10% increase)	0.072	0.067	0.077	<0.001
Age (per year)	0.003	0.002	0.004	<0.001
Female (reference to males)	-0.010	-0.019	0	0.053
No Hospitalised Immobility	[ref]	-	-	-
Low Hospitalised Immobility	0.007	-0.018	0.031	0.584
High Hospitalised immobility	0.057	0.026	0.089	<0.001
NEWS score	0.011	-0.003	0.026	0.131

Competing interest reported. In the past three years KR has received honoraria for invited lectures, rounds and academic symposia on frailty from: Wake Forest Baptist health Center for Health care Innovation, Chinese Geriatrics Society (virtual), China & Sichuan Provincial People’s Hospital, Chengdu (virtual), University of Nebraska-Omaha, Spanish Society of Geriatrics, the Australia New Zealand Society of Geriatric Medicine, the Atria Institute of New York, University of British Columbia, International Conference on Far-UVC Science and Technology, Columbia University, New York, Canadian Geriatrics Society, McMaster University, the Fraser Health Authority, the Canadian Translational Geroscience Network, University of Connecticut at Hartford, International Conference of Geriatric Emergency and Critical Care Medicine, Taipei, Taiwan, and the British Geriatrics Society. KR currently chairs a data safety monitoring board for EIP Pharma Inc. on a study funded jointly by them an the National Institute on Aging. In the past three years has served as a member of the NIA-funded ADMET-2 advisory board (Johns Hopkins), and the Wake Forest University Medical School Centre advisory board. KR is co-founder of Ardea Outcomes (DGI Clinical until 2021), which in the past 3 years has had contracts with pharma and device manufacturers (Danone, Hollister, INmune, Novartis, Takeda) as well as the LuMind IDSC Down Syndrome Foundation.

SupplementaryFigureTables.docx

In a prospective population-based study, the degree of mobility impairment during hospitalisation is associated with higher degrees of frailty

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