{"paper_id":"061bac1d-c8f2-4caf-b4b6-2fedf8eb0c71","body_text":"Post-discharge light physical activity indicates recovery in acutely hospitalized older adults – the Hospital-ADL study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Post-discharge light physical activity indicates recovery in acutely hospitalized older adults – the Hospital-ADL study Michel Terbraak, Daisy Kolk, Janet L. MacNeil Vroomen, Jos W.R. Twisk, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2166405/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 May, 2023 Read the published version in BMC Geriatrics → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Physical activity (PA) levels might be a simple overall physical marker of recovery in acutely hospitalized older adults; however cut-off values post discharge are lacking. Our objective was to identify cut-off values for post-discharge PA that indicate recovery among acutely hospitalized older adults and stratified for frailty. Methods : We performed a prospective observational cohort study including acutely hospitalized older adults (≥70 years). Frailty was assessed using Fried’s criteria. PA was assessed using Fitbit up to one week post discharge and quantified in steps and minutes light, moderate or higher intensity. The primary outcome was recovery at 3-months post discharge. ROC-curve analyses were used to determine cut-off values, and logistic regression analyses to calculate odds ratios (ORs). Results : The analytic sample included 174 participants with a mean (standard deviation) age of 79.2 (6.7) years of whom 84/174 (48%) were frail. At 3-months, 109/174 participants (63%) had recovered of whom 48 were frail. In all participants, determined cut-off values were 1369 steps/day (OR: 2.5, 95% confidence interval [CI]: 1.3–4.6) and 76 minutes/day of light intensity PA (OR: 3.0, 95% CI: 1.6–5.8). In frail participants, cut-off values were 1043 steps/day (OR: 3.3, 95% CI: 1.3–8.4) and 72 minutes/day of light intensity PA (OR: 4.2, 95% CI: 1.6–10.8). Determined cut-off values were not significantly associated with recovery in non-frail participants. Conclusions : Post-discharge PA cut-offs indicate the odds of recovery in older adults, especially in frail individuals, however are not equipped for use as a diagnostic test in daily practice. This is a first step in providing a direction for setting rehabilitation goals in older adults after hospitalization. accelerometer physical performance rehabilitation post-acute care older patients frailty Figures Figure 1 Figure 2 Background Minimum levels of physical activity (PA) are highly recommended to reduce disease and disability and decrease all-cause morbidity and mortality in older adults [ 1 , 2 ]. PA has been identified as an indicator of health [ 3 ] and as an indicator of post-discharge recovery in acutely hospitalized older adults [ 4 ]. Moreover, PA tracking is a non-invasive measurement that can be easily done during and after hospitalization. PA measurements may provide clinicians a better prognostication of recovery in their patients, which is needed to make clinical decisions and to support functional rehabilitation. However, the association between post discharge PA levels and older adults recovery after hospitalization is unknown, and recommendations for PA levels for this population are lacking. If we can identify a post discharge PA cut-off associated with older adults’ recovery, this would help clinicians to identify patients at risk for insufficient recovery, and this may be a good first step to set rehabilitation goals. Preferably, PA is objectively measured, e.g. with an accelerometer. PA thresholds are often expressed in minutes of PA at specific levels of intensity or in daily step counts. The World Health Organization (WHO) [ 5 ] and PA Guidelines for Americans, 2nd edition [ 6 ] recommend a minimum of 150–300 minutes of moderate PA every week for healthy adults. Together with normal daily activities, this translates to 7000–8000 steps per day [ 7 ]. This recommendation also applies to older adults living with chronic conditions or disability; however, it is unknown whether it applies to older adults who have recently been discharged from hospital [ 8 ]. Older adults typically take few steps after acute hospitalization; a median of 2000 steps per day in the first week after discharge has been reported [ 9 ]. After acute hospitalization, older adults are at high risk for adverse outcomes such as functional decline or hospital readmission [ 10 , 11 ] particularly if they are frail [ 12 ]. Frailty is highly prevalent among acutely hospitalized older adults [ 13 ], and is characterized by reduced physical performance and PA, and a greater vulnerability to adverse outcomes [ 14 ]. Previous research found that older adults who took fewer than 900 steps per day during hospitalization were more likely to experience functional decline at discharge [ 15 ]. The number of steps taken in the first week post discharge has been associated with functional decline and readmission risk [ 9 , 16 ], and may be an important underutilized physical indicator of overall health and risk of readmission in older patients [ 16 – 19 ]. In addition to step counts, insight into PA intensity levels is important, as moderate to vigorous PA increases caloric expenditure and improves muscle mass and endothelial function [ 20 ]. Overall, a cut-off value for the post-discharge amount and intensity of PA that differentiates patients who recover from those who do not is lacking. As post-discharge PA might be a good indicator of overall health, cut-off values may be a first step to help clinicians to identify older adults at risk of insufficient recovery and may direct older adults towards recovery [ 14 ]. The aim of this study was to first identify cut-off values for post-discharge number of steps and intensity levels of PA that differentiate acutely hospitalized older adults who recover from those who do not. Secondly, we aimed to investigate the association of these cut-off values with recovery three months post-discharge. Thirdly, we aimed to perform these analyses also stratified for frail and non-frail patients as we hypothesized that PA is a better indicator for recovery in frail than in non-frail patients. Methods Study participants We included participants from the Hospital-Associated Disability and impact on daily Life (Hospital-ADL) study. This multicenter observational prospective cohort study investigated hospital-associated functional decline among adults aged 70 years and over, who were acutely admitted to Dutch hospitals for ≥ 48 hours between October 2015 and June 2017 [ 21 ]. Participants were recruited from internal medicine, cardiology, and geriatric wards. Further inclusion criteria for the Hospital-ADL study were: 1] approval of the medical doctor; 2] Mini-Mental State Examination score ≥ 15; and 3] sufficient understanding of Dutch. Exclusion criteria were 1] a life expectancy of less than 3 months; or 2] need for help with all six basic activities of daily living (ADLs) (bathing, dressing, eating, toileting, transferring, and maintaining continence) [ 22 ]. For the present study, all participants of the Hospital-ADL study were asked to wear an activity tracker during and after hospital stay and were included after written informed consent was obtained. The study was approved by the Institutional Review board of the Amsterdam University Medical Centers (UMC), Academic Medical Center in The Netherlands (Protocol ID: AMC2015_150).. This study was carried out according to the Dutch Medical Research Involving Human Subjects Act and principles of the Declaration of Helsinki (1964). Local approval was provided by all participating hospitals. Assessments Trained researchers collected measurements according to standardized operating procedures. Baseline variables, including age, education, comorbidities [ 23 ], physical performance [ 24 ], Functional Ambulation Categories (FAC) [ 25 ], and cognition (Mini-Mental State Examination) [ 26 ] were measured at inclusion (< 48 hours after hospital admission). Counting steps and measuring activity intensity Physical activity has been identified as an indicator of health [ 3 ], and as an indicator of recovery [ 4 ]. Preferably, PA is objectively measured, e.g. with an accelerometer. PA thresholds are often expressed in steps or minutes at a specific level of intensity. Therefore, we chose to investigate both steps and minutes of PA at specific levels of intensity. We used the wrist worn Fitbit Flex activity tracker (Fitbit, Inc., San Francisco) to count steps and minutes of PA at different intensities. The Fitbit is user-friendly with a low risk of participant withdrawal, and tracks PA equally accurate as the gold standard Actigraph (r = .96) in healthy adults [ 27 , 28 ] and older adults [ 29 , 30 ], although steps may be underestimated and more variation (up to 30%) is introduced at reduced walking speeds or lower PA levels [ 29 , 30 ]. Participants were instructed to wear the Fitbit continuously on the non-dominant wrist for seven days post discharge, except during charging (1–2 hours per week). The Fitbit synced data frequently to the Fitbit platform. We exported the data from this platform at the end of the study. Steps and PA intensity were quantified every 24 hours, starting at the time of discharge up to seven days post discharge. We omitted incomplete (zero minutes of registered PA in 24 hours) days (e.g., when the participant forgot to wear the activity tracker) and days when data were not collected. Fitbit categorizes PA into light, moderate, or vigorous intensity based on metabolic equivalents (METs) [ 27 ]. One MET is defined as the amount of oxygen consumed at rest and is equal to 3.5 ml of oxygen per kg of body weight × minutes. The Fitbit uses the estimated resting metabolic rate as a base rate to calculate the MET, however the algorithm for this calculation is not provided by Fitbit. PA with 1–3 METs is classified as light intensity (e.g., slow walking), 3–6 METs as moderate intensity (e.g., brisk walking), and > 6 METS as vigorous intensity (e.g., running). To analyze cut-off values for step numbers, we calculated the individuals’ average number of steps taken per day. For analysis of PA intensity, we used the individuals’ average minutes of PA per intensity level per day. Measurement of frailty Within 48 hours after admission, we measured physical frailty using Fried’s five criteria: weight loss, low handgrip strength, low PA, slow walking speed, and fatigue [ 14 ]. Each criterion was scored as 0 (absent) or 1 (present). An individual was considered frail if three or more criteria were present. Weight loss was defined as 6 kg or more within 6 months or 3 kg or more within the past month [ 31 ]. Handgrip strength was measured three times using a dynamometer [ 32 , 33 ]. The highest score from both hands was used. Low handgrip strength was defined as < 18 kg for women and < 30 kg for men [ 33 ]. Low PA was defined as fewer than 30 minutes of self-reported physical exercise (walking, cycling, or swimming) per month in the past 6 months before admission [ 14 , 21 ]. Slow walking speed was defined as walking 4 m in more than 6.42 seconds [ 14 , 24 ]. Fatigue was defined as a score of 4 or more in response to the question “On a scale of 0–10, how would you score your sense of fatigue at this time?” [ 34 ]. Measurement of recovery Recovery was defined as the absence of functional decline, unplanned hospital readmission, and mortality at three months post discharge. Three months after hospital admission has been found to be a critical period for recovery of activities of daily living in older patients [ 11 , 35 ]. Functional decline was assessed based on the participants’ ability to perform basic activities of daily living using the Katz-ADL index score [ 22 ]. Within 48 hours of admission, we asked participants to rate their ability to perform ADLs during the two weeks before hospital admission. We repeated this assessment three months after discharge. We asked participants whether they needed assistance to perform each ADL and calculated a summary score ranging from 0 (independent in all ADLs) to 6 (dependent on help for all ADLs). We considered functional decline as ≥ 1 point higher dependency on help in one or more ADLs compared with two weeks before admission. We defined an unplanned readmission as a non-elective acute admission to a hospital within three months after discharge. Data on readmissions were collected from medical files in the participating hospitals and supplemented with participants’ self-reported readmissions to other hospitals. Data on mortality during the three months after discharge were collected from medical files, family, or the general practitioner. Statistical analyses We described continuous variables as a mean and standard deviation (SD) or median and interquartile range (IQR) if non-normally distributed. Categorical variables are presented as a number (n) and percentage (%). We explored the number of steps and minutes spent at different intensity levels, presented as median and IQR. We used ROC-curve analyses (appendix 1, Fig. 3) to first determine cut-off values for number of steps per day and PA intensity that differentiate between recovered and non-recovered participants. Cut-off values were based on the maximized sum of sensitivity and specificity values according to the Youden index [ 36 , 37 ]. Secondly, we used logistic regression analysis to calculate odds ratios (ORs) with 95% confidence intervals (CI) to determine the association between these cut-off values and recovery. We performed an unadjusted analysis to investigate the association between PA cut-offs and recovery, as we were interested in PA as an overall physical indicator of recovery. Analyses were first performed for all participants and then separately for frail and non-frail participants. We performed a sensitivity analysis by calculating ORs for 10% higher and lower cut-offs. To check for selection bias, we compared all baseline variables between participants included in our analyses versus non-included participants. All statistical analyses were performed in IBM SPSS 26.0 (IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp). Results Within the Hospital-ADL study (n = 401), 346 participants consented to wear the Fitbit. Post discharge, PA measurements were unavailable for 141 participants (Fig. 1 ), mostly due to technical and logistic reasons. Three months post-discharge, 31 of 205 participants with post discharge activity data (15%) were lost to follow up. The analytic sample included 174 participants who had a mean (SD) age of 79.2 (6.7) years, 91/174 (52%) were male, 156/174 (90%) were born in the Netherlands, and 84/174 (48%) were frail (Table 1 ). Three months after discharge, 109 participants (63%) had recovered, of whom 48 were frail. Missing data analysis showed that participants not included in the analysis (n = 227 of n = 401) had a significantly lower body mass index, lower physical performance, longer hospital stay, and more frequent cognitive impairment than included participants did. Table 1 Baseline Characteristics of the Study Population Demographics N = 174 Age , mean (SD), y 79.2 (6.7) Male , No. (%) 91 (52) BMI , * mean (SD) 22.0 (4.1) Born in the Netherlands , No. (%) 156 (90) Education , No. (%) Primary school 37 (21) Elementary technical/domestic science school 38 (22) Secondary vocational education 56 (32) Higher level high school/third-level education 43 (25) Primary admission diagnosis , No. (%) Cardiac 57 (33) Respiratory 29 (17) Other 26 (15) Infection 23 (13) Gastrointestinal 22 (13) Renal 6 (3) Cancer (including hematology) 6 (3) Electrolyte disturbance 5 (3) Length of hospital stay , median (IQR), d 5 (1–9) Living independent after discharge , No. (%) 134 (78) Clinical characteristics Charlson Comorbidity Index , ‡ median (IQR) 2 (0–4) Polypharmacy , † No. (%), (N = 172) 115 (67) Physical performance , § median (IQR), (N = 162) 6 (0–12) Katz-ADL score pre-morbid , ǁ median (IQR) 0 (0–1) FAC , No. (%), (N = 163) Independent 32 (20) Independent on level surfaces 72 (44) Dependent on supervision 35 (22) Dependent on physical assistance I 14 (9) Dependent on physical assistance II 3 (2) Non-functional ambulation 7 (4) Cognitive impairment , { No. (%), (N = 169) 23 (14) Frailty , # No, (%) 84 (48) SD, standard deviation; No, number; y, years; d, days; BMI, body mass index; IQR, interquartile range; SNAQ, short nutritional assessment questionnaire; ADL, activities of daily living; NRS, numeric rating scale; FAC, functional ambulation categories. *Calculated as weight in kg divided by height in m 2 . †Use of 5 or more different medications. ‡Range of 0–31, with a higher score indicating more or severe comorbidity. §Assessed with the short physical performance battery. The score ranges from 0 to 12, with a higher score indicating better physical performance. ǁ Ranging from 0 (independent in all ADLs) to 6 (dependent in all ADLs). { Score of < 24 on the Mini-Mental State Examination. # Score of ≥ 2 on the Fried criteria. Participants wore the activity tracker for a median (IQR) of 6 (5–7) days. Figure 2 shows the daily number of steps, and minutes spent performing PA at light and moderate/vigorous intensity. Participants took a median (IQR) of 1633 (735–4105) steps per day post discharge. Frail participants took a median (IQR) of 886 (421–1682) steps and non-frail participants took 3214 (1501–5767) steps. All participants spent a median (IQR) of 102 (54–171) minutes doing light PA. Frail participants performed a median (IQR) of 79 (53–181) minutes of light PA per day, and non-frail participants performed 206 (120–250) minutes of light PA per day. Among all participants, 28/174 participants (16%) were able to do moderate/vigorous PA. Twelve participants (7%) managed more than 21 minutes of moderate PA per day, which is the daily level of PA recommended by the WHO (Fig. 2 ). We found an optimal cut-off value of 1369 steps with an area under the curve (AUC) of 0.61 (95% CI 0.53–0.70, sensitivity 64%, specificity 58%). In frail participants, the optimal cut-off value was 1043 steps with an AUC of 0.59 (95% CI 0.47–0.72, sensitivity 72%, specificity 56%). In non-frail participants, the optimal cut-off value was 2611 steps with an AUC of 0.59 (95% CI 0.46–0.72, sensitivity 61%, specificity 62%). In all participants, we found an optimal cut-off value of 76 minutes of light PA with an AUC of 0.61 (95% CI 0.52–0.70, sensitivity 70%, specificity 57%). In frail participants, the optimal cut-off was 72 minutes of light PA with an AUC of 0.62 (95% CI 0.50–0.74, sensitivity 58%, specificity 75%). In non-frail older participants, the cut-off was 133 minutes of light PA with an AUC of 0.57 (95% CI 0.44–0.70, sensitivity 57%, specificity 62%). Table 2 presents the ORs for recovery three months after discharge based on the identified optimal cut-off values for number of steps and minutes of light PA. In all participants, we found an OR of 2.5 (95% CI 1.3–4.6) for the 1369-steps cut-off value. In frail participants, we found an OR of 3.3 (95% CI 1.3–8.4) for the 1043-steps cut-off value, and in non-frail participants, we found that the of 2611-steps cut-off value was not significantly associated with recovery (OR: 1.6; 95% CI 0.5–4.8). For minutes of light PA, the cut-off value of 76 minutes in all participants was significantly associated with recovery (OR: 3.0; 95% CI 1.6–5.8) and in frail participants, the cut-off value of 72 minutes was also significantly associated with recovery (OR: 4.2; 95% CI 1.6–10.8). The cut-off value of 133 minutes of light PA in non-frail participants was not significantly associated with recovery (OR: 1.6; 95% CI 0.6–4.9). The sensitivity analyses showed that slightly different cut-off values gave lower ORs which were in the same direction and remained significant among all participants and in the frail group. In the non-frail group, a lower steps cut-off of 2350 steps gave a higher OR and changed to be statistically significant (OR: 2.5; 95% CI 1.0–6.3). Table 2 Odds Ratios for Recovery after Cut-off Values of Light Physical Activity in all Participants, Frail Participants, and Non-frail Participants Odds Ratio 95% Confidence interval All participants (N = 174) Step count ≥ 1369 steps 2.5* 1.3 4.6 Light activity ≥ 76 minutes 3.0* 1.6 5.8 Frail participants (N = 84) Step count ≥ 1043 steps 3.3* 1.3 8.4 Light activity ≥ 72 minutes 4.2* 1.6 10.8 Non-frail participants (N = 90) Step count ≥ 2611 steps 1.6 0.5 4.8 Light activity ≥ 133 minutes 1.6 0.6 4.9 * p < 0.05 Discussion We found that performing more than 1369 steps and 76 minutes of light physical activity per day in the first week after hospital discharge differentiates older adults who were recovered at three months post discharge. We focused on light PA because we found that not all older adults were able to do moderate- or high-intensity PA. In frail older adults, we identified a cut-off value of 1043 steps and 72 minutes of light PA per day. The cut-off values in non-frail older adults were higher and not significantly associated with recovery. Few participants were able to meet the PA levels currently recommended by the WHO [ 1 ]. The WHO recommends 150 minutes of moderate to intense PA per week; to achieve this, an individual would have to take at least 7000–8000 steps per day [ 7 ]. Tudor-Locke et al. have already suggested that recommended PA levels should be lowered for special populations but offered no concrete recommendations. While many studies have investigated step counts in hospitalized older adults [ 4 , 9 , 15 , 38 – 41 ], ours is the first to recommend cut-off values for post-discharge step count and minutes of PA at specific intensities per day. The cut-off value of 1369 steps identified by us is higher than the cut-off value of 900 steps in the hospital from previous research [ 15 ]. This difference can be explained by the fact that older people were more active in the first week after discharge than during admission [ 9 ]. An important addition of our study is that we also investigated cut-offs for intensity of PA. This might be a more representative measure of PA since it includes more activities, like cycling or household tasks. Especially in older adults, step counts may underestimate the intensity of PA, particularly at low walking speeds (< 3 mph) [ 42 ]. Cut-off values for PA can help clinicians predict the chance of recovery in older adults following acute hospitalization. The cut-off values and the use of wrist worn activity trackers can also provide patients insight into their recovery process and may encourage them to become more active and engaged [ 43 ]. Number of steps and intensity of PA are easy to measure using wearable technology without the need for a health professional, which is an important advantage in the care for older adults after being discharged. Moreover, activity trackers, can measure PA over a longer period, which may give more realistic outcomes than point estimates given by for example, a physical performance test [ 19 , 24 , 44 ]. In line with a recent study in the general population, our findings show that light-intensity PA is sufficient for recovery in frail older adults after hospital discharge [ 45 ]. Our results also show that especially in frail older adults, PA levels might be a simple marker of recovery. We found that PA levels were higher in non-frail older adults, but AUC’s were lower, and cut-off values were not significantly associated with recovery, showing limited potential for application of the cut-off in non-frail. This might be explained by the model of Fried et al [ 14 ], suggesting that frailty is associated with more decline in physical health in response to acute illness. Therefore, PA levels might be more reduced in frail older adults and may better reflect their potential to recover. These findings also highlight the importance to identify frailty in acutely hospitalized older adults. Strengths and limitations This study has several potential limitations. We excluded participants with dependency on all basic ADLs. Also, many participants did not wear the activity tracker after discharge which may lead to an underestimation of the observed associations and a reduced generalizability of our findings. The Fitbit Flex needs to be worn to measure PA; therefore, PA may have been underestimated if the participant was not wearing the tracker. The Fitbit Flex uses a standard non-disclosed algorithm, based on a standard length and weight, and on healthy adults, to calculate energy expenditure, which is then used to assess the level of PA. Therefore, the algorithm may misjudge the actual energy expenditure. Both under- and overestimation of energy expenditure by Fitbit has been reported[ 28 , 46 ]. Because of this limited accuracy, our reported cut-offs for minutes at PA intensity levels should by applied with caution, although the low levels of PA seem to be appropriate for our population. An acceptable reliability and high validity for counting steps has been reported for Fitbit [ 28 , 47 ]. However, an underestimation of steps has been reported in older adults [ 30 ]. It is therefore wise to check the reliability of pedometers on an individual basis. We used a composite recovery outcome, which may differ substantially between individuals. This limits conclusions about specific outcomes. However, using a composite outcome increased the power of our study and is congruent with other cut-off studies [ 15 , 16 ]. Another limitation of this study is that the AUCs were moderate in our study. This shows that post discharge PA measurement is not an ideal test for determining who is at risk of non-recovery. However, we also wanted to investigate the association between these cut-off values and recovery. This has been investigated by Agmon et al. during hospitalization[ 15 ], but not yet after hospitalization. Therefore we investigated the performance of the optimal cut-off points by analysing the odds ratios for recovery. Less robust cut-offs were found in the non-frail group, suggesting that the association of PA and recovery is less strong among non-frail older adults. However, among all participants and frail participants, the cut-off values were robust and gave the best odds ratios. Nevertheless, these cut-offs should not be used as strict norms, but as a guidance in clinical practice. Conclusions and Implications Our study is the first to describe the association between post-discharge PA levels and recovery in older patients after hospital discharge. Many acutely hospitalized older adults did not achieve moderate or vigorous PA post discharge, which in the first week after discharge did not seem to be essential for recovery. Considering the low AUC’s, the identified cut-offs are not equipped for use as a diagnostic test in daily practice, however they can provide a direction for setting rehabilitation goals. A clinical trial is necessary to evaluate if PA goals above the cut-off will improve recovery, especially in frail older adults. Declarations Ethics approval and consent to participate The study was approved by the Institutional Review board of the Amsterdam University Medical Centers (UMC), Academic Medical Center in The Netherlands (Protocol ID: AMC2015_150). All participants provided written consent before inclusion. This study was carried out according to the Dutch Medical Research Involving Human Subjects Act and principles of the Declaration of Helsinki (1964). Local approval was provided by all participating hospitals. Consent for publication Not applicable. Availability of supporting data The datasets generated and/or analysed during the current study are not publicly available due to privacy, but are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests Funding This study is funded by the Netherlands Organization for Health Research and Development (NWO-ZonMw), grant number 16156071, and the Dutch Research Council (NWO) under Grant number 023.013.017 . The organizations funding this study had no role in the design or conduct of the study; in the collection, management, analysis, or interpretation of the data; or in the preparation, review, or approval of the manuscript. Acknowledgements In addition to the authors, the Hospital-ADL study group consists of the following members: Lucienne A. Reichardt, PhD, Jesse Aarden, MSc, Rosanne van Seben, PhD, Martin van der Esch, PhD, Raoul H.H. Engelbert, PhD, Jos. A. Bosch, PhD, Ingeborg Kuper, MD, Annemarieke de Jonghe, MD, PhD, Maike Leguit-Elberse, RN, Ad Kamper, MD, PhD, Nynke Posthuma, MD, PhD, Nienke Brendel, MD, and Johan Wold, MD. Further, we thank Suzanne Schilder, MSc, Angelique Heinen, MSc, Robin Kwakman, MSc, Jan Jaap Voigt, MSc, and Hannah van der Pas, BSc, for assistance with data collection. A preliminary part of this study was presented as a poster at the European Society of Cardiology Preventive congress[48]. Authors’ information Affiliations Center of Expertise Urban Vitality, Faculty of Health, Amsterdam University of Applied Sciences, Amsterdam, Netherlands Amsterdam UMC, location University of Amsterdam, Cardiology, Meibergdreef 9, Amsterdam, Netherlands Amsterdam Cardiovascular Sciences, Atherosclerosis & ischemic syndromes, Amsterdam, Netherlands Authors’ contributions Study concept and design: MT, DK, JT, BM, MvdS. Acquisition of data: DK. Analysis and interpretation of data: MT, DK, JMNV, JT, BM, MvdS. Drafting of the manuscript: MT, DK. Critical revision of the manuscript for important intellectual content: MT, DK, JMNV, JT, BM, MvdS. References World Health Organization. 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J Am Geriatr Soc. 2004;52(8):1263–70. doi: 10.1111/j.1532-5415.2004.52354.x . Volpato S, Cavalieri M, Sioulis F, Guerra G, Maraldi C, Zuliani G, et al. Predictive value of the Short Physical Performance Battery following hospitalization in older patients. J Gerontol A Biol Sci Med Sci. 2011;66(1):89–96. doi: 10.1093/gerona/glq167 . Suboc TB, Strath SJ, Dharmashankar K, Coulliard A, Miller N, Wang J, et al. Relative importance of step count, intensity, and duration on physical activity's impact on vascular structure and function in previously sedentary older adults. J Am Heart Assoc. 2014;3(1):e000702. doi: 10.1161/JAHA.113.000702 . Reichardt LA, Aarden JJ, van Seben R, van der Schaaf M, Engelbert RH, Bosch JA, et al. Unravelling the potential mechanisms behind hospitalization-associated disability in older patients; the Hospital-Associated Disability and impact on daily Life (Hospital-ADL) cohort study protocol. BMC Geriatr. 2016;16:59. doi: 10.1186/s12877-016-0232-3 . Katz S, Downs TD, Cash HR, Grotz RC. Progress in development of the index of ADL. Gerontologist. 1970;10(1):20–30. doi: 10.1093/geront/10.1_part_1.20 . 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(5):373–83. doi: 10.1016/0021-9681(87)90171-8 . Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, et al. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol. 1994;49(2):M85–94. doi: 10.1093/geronj/49.2.m85 . Holden MK, Gill KM, Magliozzi MR, Nathan J, Piehl-Baker L. Clinical gait assessment in the neurologically impaired. Reliability and meaningfulness. Phys Ther. 1984;64(1):35–40. doi: 10.1093/ptj/64.1.35 . 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(3):189–98. doi: 10.1016/0022-3956(75)90026-6 . Brewer W, Swanson BT, Ortiz A. Validity of Fitbit's active minutes as compared with a research-grade accelerometer and self-reported measures. BMJ Open Sport Exerc Med. 2017;3(1):e000254. doi: 10.1136/bmjsem-2017-000254 . Evenson KR, Goto MM, Furberg RD. Systematic review of the validity and reliability of consumer-wearable activity trackers. Int J Behav Nutr Phys Act. 2015;12:159. doi: 10.1186/s12966-015-0314-1 . Straiton N, Alharbi M, Bauman A, Neubeck L, Gullick J, Bhindi R, et al. The validity and reliability of consumer-grade activity trackers in older, community-dwelling adults: A systematic review. Maturitas. 2018;112:85–93. doi: 10.1016/j.maturitas.2018.03.016 . Burton E, Hill KD, Lautenschlager NT, Thogersen-Ntoumani C, Lewin G, Boyle E, et al. Reliability and validity of two fitness tracker devices in the laboratory and home environment for older community-dwelling people. BMC Geriatr. 2018;18(1):103. doi: 10.1186/s12877-018-0793-4 . Kruizenga HM, Seidell JC, de Vet HC, Wierdsma NJ, van Bokhorst-de van der Schueren MA. Development and validation of a hospital screening tool for malnutrition: the short nutritional assessment questionnaire (SNAQ). Clin Nutr. 2005;24(1):75–82. doi: 10.1016/j.clnu.2004.07.015 . Trutschnigg B, Kilgour RD, Reinglas J, Rosenthall L, Hornby L, Morais JA, et al. Precision and reliability of strength (Jamar vs. Biodex handgrip) and body composition (dual-energy X-ray absorptiometry vs. bioimpedance analysis) measurements in advanced cancer patients. Appl Physiol Nutr Metab. 2008;33(6):1232–9. doi: 10.1139/H08-122 . Roberts HC, Denison HJ, Martin HJ, Patel HP, Syddall H, Cooper C, et al. A review of the measurement of grip strength in clinical and epidemiological studies: towards a standardised approach. Age Ageing. 2011;40(4):423–9. doi: 10.1093/ageing/afr051 . Hwang SS, Chang VT, Cogswell J, Kasimis BS. Clinical relevance of fatigue levels in cancer patients at a Veterans Administration Medical Center. Cancer. 2002;94(9):2481–9. doi: 10.1002/cncr.10507 . Buurman BM, Hoogerduijn JG, de Haan RJ, Abu-Hanna A, Lagaay AM, Verhaar HJ, et al. Geriatric conditions in acutely hospitalized older patients: prevalence and one-year survival and functional decline. PLoS ONE. 2011;6(11):e26951. doi: 10.1371/journal.pone.0026951 . Liu X. Classification accuracy and cut point selection. Stat Med. 2012;31(23):2676–86. doi: 10.1002/sim.4509 . Hajian-Tilaki K. The choice of methods in determining the optimal cut-off value for quantitative diagnostic test evaluation. Stat Methods Med Res. 2018;27(8):2374–83. doi: 10.1177/0962280216680383 . Arentson-Lantz E, Galvan E, Wacher A, Fry CS, Paddon-Jones D. 2,000 Steps/Day Does Not Fully Protect Skeletal Muscle Health in Older Adults During Bed Rest. J Aging Phys Act. 2019;27(2):191–7. doi: 10.1123/japa.2018-0093 . Cook DJ, Thompson JE, Prinsen SK, Dearani JA, Deschamps C. Functional recovery in the elderly after major surgery: assessment of mobility recovery using wireless technology. Ann Thorac Surg. 2013;96(3):1057–61. doi: 10.1016/j.athoracsur.2013.05.092 . Lee IM, Shiroma EJ, Kamada M, Bassett DR, Matthews CE, Buring JE. Association of Step Volume and Intensity With All-Cause Mortality in Older Women. JAMA Intern Med. 2019. doi: 10.1001/jamainternmed.2019.0899 . Sallis R, Roddy-Sturm Y, Chijioke E, Litman K, Kanter MH, Huang BZ, et al. Stepping toward discharge: Level of ambulation in hospitalized patients. J Hosp Med. 2015;10(6):384–9. doi: 10.1002/jhm.2343 . Bassett DR Jr, Toth LP, LaMunion SR, Crouter SE. Step Counting: A Review of Measurement Considerations and Health-Related Applications. Sports Med. 2017;47(7):1303–15. doi: 10.1007/s40279-016-0663-1 . Pol M, Peek S, van Nes F, van Hartingsveldt M, Buurman B, Krose B. Everyday life after a hip fracture: what community-living older adults perceive as most beneficial for their recovery. Age Ageing. 2019;48(3):440–7. doi: 10.1093/ageing/afz012 . Marzetti E, Calvani R, Tosato M, Cesari M, Di Bari M, Cherubini A, et al. Physical activity and exercise as countermeasures to physical frailty and sarcopenia. Aging Clin Exp Res. 2017;29(1):35–42. doi: 10.1007/s40520-016-0705-4 . Saint-Maurice PF, Troiano RP, Bassett DR Jr, Graubard BI, Carlson SA, Shiroma EJ, et al. Association of Daily Step Count and Step Intensity With Mortality Among US Adults. JAMA. 2020;323(12):1151–60. doi: 10.1001/jama.2020.1382 . Feehan LM, Geldman J, Sayre EC, Park C, Ezzat AM, Yoo JY, et al. Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data. JMIR Mhealth Uhealth. 2018;6(8):e10527. doi: 10.2196/10527 . Alharbi M, Bauman A, Neubeck L, Gallagher R. Validation of Fitbit-Flex as a measure of free-living physical activity in a community-based phase III cardiac rehabilitation population. Eur J Prev Cardiol. 2016;23(14):1476–85. doi: 10.1177/2047487316634883 . Terbraak M, Kolk D, Macneil Vroomen JL, Twisk JWT, Buurman BM, Van Der Schaaf M. Post-discharge light physical activity indicates recovery in acutely hospitalized older adults. Eur J Prev Cardiol May 2022, 29, Issue Supplement_1, zwac056.175, https://doi.org/10.1093/eurjpc/zwac056.175 . Additional Declarations No competing interests reported. Supplementary Files Appendix1ROCcurvesfordeterminingcutoffvalues.docx Cite Share Download PDF Status: Published Journal Publication published 19 May, 2023 Read the published version in BMC Geriatrics → Version 1 posted Editorial decision: Major revision 26 Jan, 2023 Reviews received at journal 24 Jan, 2023 Reviewers agreed at journal 14 Jan, 2023 Reviews received at journal 23 Dec, 2022 Reviewers agreed at journal 07 Dec, 2022 Reviewers invited by journal 15 Nov, 2022 Editor assigned by journal 11 Nov, 2022 Editor invited by journal 04 Nov, 2022 Submission checks completed at journal 04 Nov, 2022 First submitted to journal 14 Oct, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-2166405\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":149474472,\"identity\":\"85809feb-daa1-4eea-8946-ad72c1bedf4b\",\"order_by\":0,\"name\":\"Michel Terbraak\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBCDBCBmZvgAJNnYiVF/AKqFcQZICzMpWph5GMCW4Qe6DewPmD9U3Mszbz/82Njm1zZ5PmYGxg8fc3BrMTvAY8Bw4ExxscyZNOPk3L7bhm3MDMySM7fh1cLAcLAtIXEGQ4Lx4dye24xALWzMvHi1sD9gOPgPqIX/+efDlj237YnQwmDAcLABqEUixziZ4cftRMJaDvMYHDhzLKFYQuJNsWFvw+3kNmbGZvx+Od7+8EFFTUKeBH/6Zokff27bzm9vPvjhIx4toFg4AOcwtoHJBjzqMcAfUhSPglEwCkbBSAEA6x5QkkI0qx8AAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Amsterdam UMC, location University of Amsterdam\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Michel\",\"middleName\":\"\",\"lastName\":\"Terbraak\",\"suffix\":\"\"},{\"id\":149474473,\"identity\":\"ab8234a6-5cf9-4860-a45d-56f12f6d7504\",\"order_by\":1,\"name\":\"Daisy Kolk\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Amsterdam UMC, location University of Amsterdam\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Daisy\",\"middleName\":\"\",\"lastName\":\"Kolk\",\"suffix\":\"\"},{\"id\":149474474,\"identity\":\"b3a77e40-8d66-4ee3-b241-b5b7a4912276\",\"order_by\":2,\"name\":\"Janet L. MacNeil Vroomen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Amsterdam UMC, location University of Amsterdam\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Janet\",\"middleName\":\"L. MacNeil\",\"lastName\":\"Vroomen\",\"suffix\":\"\"},{\"id\":149474475,\"identity\":\"e95a224d-8c0b-4e15-9609-c939c8fafcef\",\"order_by\":3,\"name\":\"Jos W.R. Twisk\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Amsterdam UMC, location Vrije Universiteit Amsterdam, Epidemiology and Biostatistics\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jos\",\"middleName\":\"W.R.\",\"lastName\":\"Twisk\",\"suffix\":\"\"},{\"id\":149474476,\"identity\":\"ab98446a-ee8e-4368-afe6-8f7157392634\",\"order_by\":4,\"name\":\"Bianca M. Buurman\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Amsterdam University of Applied Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Bianca\",\"middleName\":\"M.\",\"lastName\":\"Buurman\",\"suffix\":\"\"},{\"id\":149474479,\"identity\":\"089391a3-ff2a-4206-b8bc-57424396b266\",\"order_by\":5,\"name\":\"Marike Schaaf\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Amsterdam University of Applied Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Marike\",\"middleName\":\"\",\"lastName\":\"Schaaf\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2022-10-14 13:14:42\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-2166405/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-2166405/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1186/s12877-023-04031-9\",\"type\":\"published\",\"date\":\"2023-05-19T20:53:05+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":28874987,\"identity\":\"207d105e-a167-4104-9c4d-305666ebb343\",\"added_by\":\"auto\",\"created_at\":\"2022-11-09 21:11:03\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":56679,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDerivation of the analytic sample.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAbbreviations: MMSE, Mini-Mental State Examination; ADL, activities of daily living\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1Studyflowchart.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2166405/v1/72120363129ae2c66e41ff6a.png\"},{\"id\":28874988,\"identity\":\"e2e0cd87-6741-4d0c-9594-38fa69efd3d5\",\"added_by\":\"auto\",\"created_at\":\"2022-11-09 21:11:03\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":37377,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eMedian levels of physical activity after discharge. \\u003c/strong\\u003eHorizontal lines denote median values; boxes extend from 25\\u003csup\\u003eth\\u003c/sup\\u003e to the 75\\u003csup\\u003eth\\u003c/sup\\u003e percentile; vertical extending lines denote the range of the number of steps and minutes of activity at light and moderate/vigorous intensity\\u003csup\\u003e†\\u003c/sup\\u003e per day performed after discharge by all participants, and stratified by frail and non-frail participants. \\u003csup\\u003e*\\u003c/sup\\u003e150 minutes of moderate intensity physical activity per week divided by seven days ≈ 21 minutes per day.\\u003csup\\u003e †\\u003c/sup\\u003eOnly participants who managed to perform this intensity are shown.\\u003cem\\u003e\\u003cstrong\\u003e\\u003cbr\\u003e\\n\\u003c/strong\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2166405/v1/0c0d4c59e0539994909f8bb0.png\"},{\"id\":44729897,\"identity\":\"4610c1c1-fba0-4003-a4ec-8dc20c4b3da8\",\"added_by\":\"auto\",\"created_at\":\"2023-10-16 21:22:19\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":527700,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2166405/v1/38690c28-f776-452c-bb8c-7dad24363913.pdf\"},{\"id\":28874989,\"identity\":\"7099c708-ac82-4be7-85fc-0315d82fecdd\",\"added_by\":\"auto\",\"created_at\":\"2022-11-09 21:11:03\",\"extension\":\"docx\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":300935,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Appendix1ROCcurvesfordeterminingcutoffvalues.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2166405/v1/699a158ef8eb502f7b28a72a.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Post-discharge light physical activity indicates recovery in acutely hospitalized older adults – the Hospital-ADL study\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eMinimum levels of physical activity (PA) are highly recommended to reduce disease and disability and decrease all-cause morbidity and mortality in older adults [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. PA has been identified as an indicator of health [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e] and as an indicator of post-discharge recovery in acutely hospitalized older adults [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. Moreover, PA tracking is a non-invasive measurement that can be easily done during and after hospitalization. PA measurements may provide clinicians a better prognostication of recovery in their patients, which is needed to make clinical decisions and to support functional rehabilitation. However, the association between post discharge PA levels and older adults recovery after hospitalization is unknown, and recommendations for PA levels for this population are lacking. If we can identify a post discharge PA cut-off associated with older adults\\u0026rsquo; recovery, this would help clinicians to identify patients at risk for insufficient recovery, and this may be a good first step to set rehabilitation goals.\\u003c/p\\u003e \\u003cp\\u003ePreferably, PA is objectively measured, e.g. with an accelerometer. PA thresholds are often expressed in minutes of PA at specific levels of intensity or in daily step counts. The World Health Organization (WHO) [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e] and PA Guidelines for Americans, 2nd edition [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e] recommend a minimum of 150\\u0026ndash;300 minutes of moderate PA every week for healthy adults. Together with normal daily activities, this translates to 7000\\u0026ndash;8000 steps per day [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. This recommendation also applies to older adults living with chronic conditions or disability; however, it is unknown whether it applies to older adults who have recently been discharged from hospital [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eOlder adults typically take few steps after acute hospitalization; a median of 2000 steps per day in the first week after discharge has been reported [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. After acute hospitalization, older adults are at high risk for adverse outcomes such as functional decline or hospital readmission [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e] particularly if they are frail [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. Frailty is highly prevalent among acutely hospitalized older adults [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e], and is characterized by reduced physical performance and PA, and a greater vulnerability to adverse outcomes [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003ePrevious research found that older adults who took fewer than 900 steps per day during hospitalization were more likely to experience functional decline at discharge [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. The number of steps taken in the first week post discharge has been associated with functional decline and readmission risk [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e], and may be an important underutilized physical indicator of overall health and risk of readmission in older patients [\\u003cspan additionalcitationids=\\\"CR17 CR18\\\" citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. In addition to step counts, insight into PA intensity levels is important, as moderate to vigorous PA increases caloric expenditure and improves muscle mass and endothelial function [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eOverall, a cut-off value for the post-discharge amount and intensity of PA that differentiates patients who recover from those who do not is lacking.\\u003c/p\\u003e \\u003cp\\u003eAs post-discharge PA might be a good indicator of overall health, cut-off values may be a first step to help clinicians to identify older adults at risk of insufficient recovery and may direct older adults towards recovery [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. The aim of this study was to first identify cut-off values for post-discharge number of steps and intensity levels of PA that differentiate acutely hospitalized older adults who recover from those who do not. Secondly, we aimed to investigate the association of these cut-off values with recovery three months post-discharge. Thirdly, we aimed to perform these analyses also stratified for frail and non-frail patients as we hypothesized that PA is a better indicator for recovery in frail than in non-frail patients.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy participants\\u003c/h2\\u003e \\u003cp\\u003eWe included participants from the Hospital-Associated Disability and impact on daily Life (Hospital-ADL) study. This multicenter observational prospective cohort study investigated hospital-associated functional decline among adults aged 70 years and over, who were acutely admitted to Dutch hospitals for \\u0026ge;\\u0026thinsp;48 hours between October 2015 and June 2017 [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. Participants were recruited from internal medicine, cardiology, and geriatric wards. Further inclusion criteria for the Hospital-ADL study were: 1] approval of the medical doctor; 2] Mini-Mental State Examination score\\u0026thinsp;\\u0026ge;\\u0026thinsp;15; and 3] sufficient understanding of Dutch. Exclusion criteria were 1] a life expectancy of less than 3 months; or 2] need for help with all six basic activities of daily living (ADLs) (bathing, dressing, eating, toileting, transferring, and maintaining continence) [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. For the present study, all participants of the Hospital-ADL study were asked to wear an activity tracker during and after hospital stay and were included after written informed consent was obtained.\\u003c/p\\u003e \\u003cp\\u003e The study was approved by the Institutional Review board of the Amsterdam University Medical Centers (UMC), Academic Medical Center in The Netherlands (Protocol ID: AMC2015_150)..\\u003c/p\\u003e \\u003cp\\u003e This study was carried out according to the Dutch Medical Research Involving Human Subjects Act and principles of the Declaration of Helsinki (1964). Local approval was provided by all participating hospitals.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAssessments\\u003c/h2\\u003e \\u003cp\\u003eTrained researchers collected measurements according to standardized operating procedures. Baseline variables, including age, education, comorbidities [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e], physical performance [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e], Functional Ambulation Categories (FAC) [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e], and cognition (Mini-Mental State Examination) [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e] were measured at inclusion (\\u0026lt;\\u0026thinsp;48 hours after hospital admission).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eCounting steps and measuring activity intensity\\u003c/h2\\u003e \\u003cp\\u003ePhysical activity has been identified as an indicator of health [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e], and as an indicator of recovery [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. Preferably, PA is objectively measured, e.g. with an accelerometer. PA thresholds are often expressed in steps or minutes at a specific level of intensity. Therefore, we chose to investigate both steps and minutes of PA at specific levels of intensity.\\u003c/p\\u003e \\u003cp\\u003eWe used the wrist worn Fitbit Flex activity tracker (Fitbit, Inc., San Francisco) to count steps and minutes of PA at different intensities. The Fitbit is user-friendly with a low risk of participant withdrawal, and tracks PA equally accurate as the gold standard Actigraph (r\\u0026thinsp;=\\u0026thinsp;.96) in healthy adults [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e] and older adults [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e], although steps may be underestimated and more variation (up to 30%) is introduced at reduced walking speeds or lower PA levels [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. Participants were instructed to wear the Fitbit continuously on the non-dominant wrist for seven days post discharge, except during charging (1\\u0026ndash;2 hours per week). The Fitbit synced data frequently to the Fitbit platform. We exported the data from this platform at the end of the study. Steps and PA intensity were quantified every 24 hours, starting at the time of discharge up to seven days post discharge. We omitted incomplete (zero minutes of registered PA in 24 hours) days (e.g., when the participant forgot to wear the activity tracker) and days when data were not collected.\\u003c/p\\u003e \\u003cp\\u003eFitbit categorizes PA into light, moderate, or vigorous intensity based on metabolic equivalents (METs) [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. One MET is defined as the amount of oxygen consumed at rest and is equal to 3.5 ml of oxygen per kg of body weight \\u0026times; minutes. The Fitbit uses the estimated resting metabolic rate as a base rate to calculate the MET, however the algorithm for this calculation is not provided by Fitbit. PA with 1\\u0026ndash;3 METs is classified as light intensity (e.g., slow walking), 3\\u0026ndash;6 METs as moderate intensity (e.g., brisk walking), and \\u0026gt;\\u0026thinsp;6 METS as vigorous intensity (e.g., running). To analyze cut-off values for step numbers, we calculated the individuals\\u0026rsquo; average number of steps taken per day. For analysis of PA intensity, we used the individuals\\u0026rsquo; average minutes of PA per intensity level per day.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eMeasurement of frailty\\u003c/h2\\u003e \\u003cp\\u003eWithin 48 hours after admission, we measured physical frailty using Fried\\u0026rsquo;s five criteria: weight loss, low handgrip strength, low PA, slow walking speed, and fatigue [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Each criterion was scored as 0 (absent) or 1 (present). An individual was considered frail if three or more criteria were present. Weight loss was defined as 6 kg or more within 6 months or 3 kg or more within the past month [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. Handgrip strength was measured three times using a dynamometer [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. The highest score from both hands was used. Low handgrip strength was defined as \\u0026lt;\\u0026thinsp;18 kg for women and \\u0026lt;\\u0026thinsp;30 kg for men [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. Low PA was defined as fewer than 30 minutes of self-reported physical exercise (walking, cycling, or swimming) per month in the past 6 months before admission [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. Slow walking speed was defined as walking 4 m in more than 6.42 seconds [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Fatigue was defined as a score of 4 or more in response to the question \\u0026ldquo;On a scale of 0\\u0026ndash;10, how would you score your sense of fatigue at this time?\\u0026rdquo; [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eMeasurement of recovery\\u003c/h2\\u003e \\u003cp\\u003eRecovery was defined as the absence of functional decline, unplanned hospital readmission, and mortality at three months post discharge. Three months after hospital admission has been found to be a critical period for recovery of activities of daily living in older patients [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eFunctional decline was assessed based on the participants\\u0026rsquo; ability to perform basic activities of daily living using the Katz-ADL index score [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Within 48 hours of admission, we asked participants to rate their ability to perform ADLs during the two weeks before hospital admission. We repeated this assessment three months after discharge. We asked participants whether they needed assistance to perform each ADL and calculated a summary score ranging from 0 (independent in all ADLs) to 6 (dependent on help for all ADLs). We considered functional decline as \\u0026ge;\\u0026thinsp;1 point higher dependency on help in one or more ADLs compared with two weeks before admission.\\u003c/p\\u003e \\u003cp\\u003eWe defined an unplanned readmission as a non-elective acute admission to a hospital within three months after discharge. Data on readmissions were collected from medical files in the participating hospitals and supplemented with participants\\u0026rsquo; self-reported readmissions to other hospitals. Data on mortality during the three months after discharge were collected from medical files, family, or the general practitioner.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analyses\\u003c/h2\\u003e \\u003cp\\u003eWe described continuous variables as a mean and standard deviation (SD) or median and interquartile range (IQR) if non-normally distributed. Categorical variables are presented as a number (n) and percentage (%). We explored the number of steps and minutes spent at different intensity levels, presented as median and IQR. We used ROC-curve analyses (appendix 1, Fig.\\u0026nbsp;3) to first determine cut-off values for number of steps per day and PA intensity that differentiate between recovered and non-recovered participants. Cut-off values were based on the maximized sum of sensitivity and specificity values according to the Youden index [\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]. Secondly, we used logistic regression analysis to calculate odds ratios (ORs) with 95% confidence intervals (CI) to determine the association between these cut-off values and recovery. We performed an unadjusted analysis to investigate the association between PA cut-offs and recovery, as we were interested in PA as an overall physical indicator of recovery. Analyses were first performed for all participants and then separately for frail and non-frail participants. We performed a sensitivity analysis by calculating ORs for 10% higher and lower cut-offs. To check for selection bias, we compared all baseline variables between participants included in our analyses versus non-included participants. All statistical analyses were performed in IBM SPSS 26.0 (IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eWithin the Hospital-ADL study (n\\u0026thinsp;=\\u0026thinsp;401), 346 participants consented to wear the Fitbit. Post discharge, PA measurements were unavailable for 141 participants (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e), mostly due to technical and logistic reasons. Three months post-discharge, 31 of 205 participants with post discharge activity data (15%) were lost to follow up. The analytic sample included 174 participants who had a mean (SD) age of 79.2 (6.7) years, 91/174 (52%) were male, 156/174 (90%) were born in the Netherlands, and 84/174 (48%) were frail (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Three months after discharge, 109 participants (63%) had recovered, of whom 48 were frail. Missing data analysis showed that participants not included in the analysis (n\\u0026thinsp;=\\u0026thinsp;227 of n\\u0026thinsp;=\\u0026thinsp;401) had a significantly lower body mass index, lower physical performance, longer hospital stay, and more frequent cognitive impairment than included participants did.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBaseline Characteristics of the Study Population\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"2\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDemographics\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eN\\u0026thinsp;=\\u0026thinsp;174\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u003c/b\\u003e, mean (SD), y\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e79.2 (6.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMale\\u003c/b\\u003e, No. (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e91 (52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBMI\\u003c/b\\u003e,\\u003csup\\u003e\\u003cb\\u003e*\\u003c/b\\u003e\\u003c/sup\\u003e mean (SD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e22.0 (4.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBorn in the Netherlands\\u003c/b\\u003e, No. (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e156 (90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEducation\\u003c/b\\u003e, No. (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePrimary school\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e37 (21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eElementary technical/domestic science school\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e38 (22)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSecondary vocational education\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e56 (32)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigher level high school/third-level education\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e43 (25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePrimary admission diagnosis\\u003c/b\\u003e, No. (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCardiac\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e57 (33)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRespiratory\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e29 (17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOther\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e26 (15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eInfection\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e23 (13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGastrointestinal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e22 (13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRenal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6 (3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCancer (including hematology)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6 (3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eElectrolyte disturbance\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5 (3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eLength of hospital stay\\u003c/b\\u003e, median (IQR), d\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5 (1\\u0026ndash;9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eLiving independent after discharge\\u003c/b\\u003e, No. (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e134 (78)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eClinical characteristics\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCharlson Comorbidity Index\\u003c/b\\u003e,\\u003csup\\u003e\\u003cb\\u003e\\u0026Dagger;\\u003c/b\\u003e\\u003c/sup\\u003e median (IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2 (0\\u0026ndash;4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePolypharmacy\\u003c/b\\u003e,\\u003csup\\u003e\\u003cb\\u003e\\u0026dagger;\\u003c/b\\u003e\\u003c/sup\\u003e No. (%), \\u003cem\\u003e(N\\u0026thinsp;=\\u0026thinsp;172)\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e115 (67)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePhysical performance\\u003c/b\\u003e,\\u003csup\\u003e\\u003cb\\u003e\\u0026sect;\\u003c/b\\u003e\\u003c/sup\\u003e median (IQR), \\u003cem\\u003e(N\\u0026thinsp;=\\u0026thinsp;162)\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6 (0\\u0026ndash;12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eKatz-ADL score pre-morbid\\u003c/b\\u003e,\\u003csup\\u003e\\u003cb\\u003eǁ\\u003c/b\\u003e\\u003c/sup\\u003e median (IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0 (0\\u0026ndash;1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFAC\\u003c/b\\u003e, No. (%), \\u003cem\\u003e(N\\u0026thinsp;=\\u0026thinsp;163)\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIndependent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e32 (20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIndependent on level surfaces\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e72 (44)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDependent on supervision\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e35 (22)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDependent on physical assistance I\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e14 (9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDependent on physical assistance II\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3 (2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNon-functional ambulation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7 (4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCognitive impairment\\u003c/b\\u003e,\\u003csup\\u003e{\\u003c/sup\\u003e No. (%), \\u003cem\\u003e(N\\u0026thinsp;=\\u0026thinsp;169)\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e23 (14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFrailty\\u003c/b\\u003e,\\u003csup\\u003e\\u003cb\\u003e#\\u003c/b\\u003e\\u003c/sup\\u003e No, (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e84 (48)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"2\\\"\\u003eSD, standard deviation; No, number; y, years; d, days;\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"2\\\"\\u003eBMI, body mass index; IQR, interquartile range; SNAQ, short nutritional assessment questionnaire; ADL, activities of daily living; NRS, numeric rating scale; FAC, functional ambulation categories.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"2\\\"\\u003e*Calculated as weight in kg divided by height in m\\u003csup\\u003e2\\u003c/sup\\u003e.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"2\\\"\\u003e\\u0026dagger;Use of 5 or more different medications.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"2\\\"\\u003e\\u0026Dagger;Range of 0\\u0026ndash;31, with a higher score indicating more or severe comorbidity.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"2\\\"\\u003e\\u0026sect;Assessed with the short physical performance battery. The score ranges from 0 to 12, with a higher score indicating better physical performance.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"2\\\"\\u003eǁ Ranging from 0 (independent in all ADLs) to 6 (dependent in all ADLs).\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"2\\\"\\u003e{ Score of \\u0026lt;\\u0026thinsp;24 on the Mini-Mental State Examination.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"2\\\"\\u003e# Score of \\u0026ge;\\u0026thinsp;2 on the Fried criteria.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eParticipants wore the activity tracker for a median (IQR) of 6 (5\\u0026ndash;7) days. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e shows the daily number of steps, and minutes spent performing PA at light and moderate/vigorous intensity. Participants took a median (IQR) of 1633 (735\\u0026ndash;4105) steps per day post discharge. Frail participants took a median (IQR) of 886 (421\\u0026ndash;1682) steps and non-frail participants took 3214 (1501\\u0026ndash;5767) steps.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAll participants spent a median (IQR) of 102 (54\\u0026ndash;171) minutes doing light PA. Frail participants performed a median (IQR) of 79 (53\\u0026ndash;181) minutes of light PA per day, and non-frail participants performed 206 (120\\u0026ndash;250) minutes of light PA per day. Among all participants, 28/174 participants (16%) were able to do moderate/vigorous PA. Twelve participants (7%) managed more than 21 minutes of moderate PA per day, which is the daily level of PA recommended by the WHO (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eWe found an optimal cut-off value of 1369 steps with an area under the curve (AUC) of 0.61 (95% CI 0.53\\u0026ndash;0.70, sensitivity 64%, specificity 58%). In frail participants, the optimal cut-off value was 1043 steps with an AUC of 0.59 (95% CI 0.47\\u0026ndash;0.72, sensitivity 72%, specificity 56%). In non-frail participants, the optimal cut-off value was 2611 steps with an AUC of 0.59 (95% CI 0.46\\u0026ndash;0.72, sensitivity 61%, specificity 62%).\\u003c/p\\u003e \\u003cp\\u003eIn all participants, we found an optimal cut-off value of 76 minutes of light PA with an AUC of 0.61 (95% CI 0.52\\u0026ndash;0.70, sensitivity 70%, specificity 57%). In frail participants, the optimal cut-off was 72 minutes of light PA with an AUC of 0.62 (95% CI 0.50\\u0026ndash;0.74, sensitivity 58%, specificity 75%). In non-frail older participants, the cut-off was 133 minutes of light PA with an AUC of 0.57 (95% CI 0.44\\u0026ndash;0.70, sensitivity 57%, specificity 62%).\\u003c/p\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e presents the ORs for recovery three months after discharge based on the identified optimal cut-off values for number of steps and minutes of light PA. In all participants, we found an OR of 2.5 (95% CI 1.3\\u0026ndash;4.6) for the 1369-steps cut-off value. In frail participants, we found an OR of 3.3 (95% CI 1.3\\u0026ndash;8.4) for the 1043-steps cut-off value, and in non-frail participants, we found that the of 2611-steps cut-off value was not significantly associated with recovery (OR: 1.6; 95% CI 0.5\\u0026ndash;4.8). For minutes of light PA, the cut-off value of 76 minutes in all participants was significantly associated with recovery (OR: 3.0; 95% CI 1.6\\u0026ndash;5.8) and in frail participants, the cut-off value of 72 minutes was also significantly associated with recovery (OR: 4.2; 95% CI 1.6\\u0026ndash;10.8). The cut-off value of 133 minutes of light PA in non-frail participants was not significantly associated with recovery (OR: 1.6; 95% CI 0.6\\u0026ndash;4.9). The sensitivity analyses showed that slightly different cut-off values gave lower ORs which were in the same direction and remained significant among all participants and in the frail group. In the non-frail group, a lower steps cut-off of 2350 steps gave a higher OR and changed to be statistically significant (OR: 2.5; 95% CI 1.0\\u0026ndash;6.3).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eOdds Ratios for Recovery after Cut-off Values of Light Physical Activity in all Participants, Frail Participants, and Non-frail Participants\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eOdds Ratio\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e95% Confidence interval\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAll participants\\u003c/b\\u003e (N\\u0026thinsp;=\\u0026thinsp;174)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStep count\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;1369 steps\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.5*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLight activity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;76 minutes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.0*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFrail participants\\u003c/b\\u003e (N\\u0026thinsp;=\\u0026thinsp;84)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStep count\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;1043 steps\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.3*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLight activity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;72 minutes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.2*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e10.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNon-frail participants\\u003c/b\\u003e (N\\u0026thinsp;=\\u0026thinsp;90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStep count\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;2611 steps\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLight activity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;133 minutes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e* p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eWe found that performing more than 1369 steps and 76 minutes of light physical activity per day in the first week after hospital discharge differentiates older adults who were recovered at three months post discharge. We focused on light PA because we found that not all older adults were able to do moderate- or high-intensity PA. In frail older adults, we identified a cut-off value of 1043 steps and 72 minutes of light PA per day. The cut-off values in non-frail older adults were higher and not significantly associated with recovery.\\u003c/p\\u003e \\u003cp\\u003eFew participants were able to meet the PA levels currently recommended by the WHO [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. The WHO recommends 150 minutes of moderate to intense PA per week; to achieve this, an individual would have to take at least 7000\\u0026ndash;8000 steps per day [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. Tudor-Locke et al. have already suggested that recommended PA levels should be lowered for special populations but offered no concrete recommendations. While many studies have investigated step counts in hospitalized older adults [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR39 CR40\\\" citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e], ours is the first to recommend cut-off values for post-discharge step count and minutes of PA at specific intensities per day. The cut-off value of 1369 steps identified by us is higher than the cut-off value of 900 steps in the hospital from previous research [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. This difference can be explained by the fact that older people were more active in the first week after discharge than during admission [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. An important addition of our study is that we also investigated cut-offs for intensity of PA. This might be a more representative measure of PA since it includes more activities, like cycling or household tasks. Especially in older adults, step counts may underestimate the intensity of PA, particularly at low walking speeds (\\u0026lt;\\u0026thinsp;3 mph) [\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eCut-off values for PA can help clinicians predict the chance of recovery in older adults following acute hospitalization. The cut-off values and the use of wrist worn activity trackers can also provide patients insight into their recovery process and may encourage them to become more active and engaged [\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]. Number of steps and intensity of PA are easy to measure using wearable technology without the need for a health professional, which is an important advantage in the care for older adults after being discharged. Moreover, activity trackers, can measure PA over a longer period, which may give more realistic outcomes than point estimates given by for example, a physical performance test [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn line with a recent study in the general population, our findings show that light-intensity PA is sufficient for recovery in frail older adults after hospital discharge [\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]. Our results also show that especially in frail older adults, PA levels might be a simple marker of recovery. We found that PA levels were higher in non-frail older adults, but AUC\\u0026rsquo;s were lower, and cut-off values were not significantly associated with recovery, showing limited potential for application of the cut-off in non-frail. This might be explained by the model of Fried et al [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e], suggesting that frailty is associated with more decline in physical health in response to acute illness. Therefore, PA levels might be more reduced in frail older adults and may better reflect their potential to recover. These findings also highlight the importance to identify frailty in acutely hospitalized older adults.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStrengths and limitations\\u003c/h2\\u003e \\u003cp\\u003eThis study has several potential limitations. We excluded participants with dependency on all basic ADLs. Also, many participants did not wear the activity tracker after discharge which may lead to an underestimation of the observed associations and a reduced generalizability of our findings. The Fitbit Flex needs to be worn to measure PA; therefore, PA may have been underestimated if the participant was not wearing the tracker. The Fitbit Flex uses a standard non-disclosed algorithm, based on a standard length and weight, and on healthy adults, to calculate energy expenditure, which is then used to assess the level of PA. Therefore, the algorithm may misjudge the actual energy expenditure. Both under- and overestimation of energy expenditure by Fitbit has been reported[\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e]. Because of this limited accuracy, our reported cut-offs for minutes at PA intensity levels should by applied with caution, although the low levels of PA seem to be appropriate for our population. An acceptable reliability and high validity for counting steps has been reported for Fitbit [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e]. However, an underestimation of steps has been reported in older adults [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. It is therefore wise to check the reliability of pedometers on an individual basis.\\u003c/p\\u003e \\u003cp\\u003eWe used a composite recovery outcome, which may differ substantially between individuals. This limits conclusions about specific outcomes. However, using a composite outcome increased the power of our study and is congruent with other cut-off studies [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eAnother limitation of this study is that the AUCs were moderate in our study. This shows that post discharge PA measurement is not an ideal test for determining who is at risk of non-recovery. However, we also wanted to investigate the association between these cut-off values and recovery. This has been investigated by Agmon et al. during hospitalization[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e], but not yet after hospitalization. Therefore we investigated the performance of the optimal cut-off points by analysing the odds ratios for recovery. Less robust cut-offs were found in the non-frail group, suggesting that the association of PA and recovery is less strong among non-frail older adults. However, among all participants and frail participants, the cut-off values were robust and gave the best odds ratios. Nevertheless, these cut-offs should not be used as strict norms, but as a guidance in clinical practice.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusions and Implications\",\"content\":\" \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003cp\\u003eOur study is the first to describe the association between post-discharge PA levels and recovery in older patients after hospital discharge. Many acutely hospitalized older adults did not achieve moderate or vigorous PA post discharge, which in the first week after discharge did not seem to be essential for recovery. Considering the low AUC\\u0026rsquo;s, the identified cut-offs are not equipped for use as a diagnostic test in daily practice, however they can provide a direction for setting rehabilitation goals. A clinical trial is necessary to evaluate if PA goals above the cut-off will improve recovery, especially in frail older adults.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study was approved by the Institutional Review board of the Amsterdam University Medical Centers (UMC), Academic Medical Center in The Netherlands (Protocol ID: AMC2015_150). All participants provided written consent before inclusion.\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was carried out according to the Dutch Medical Research Involving Human Subjects Act and principles of the Declaration of Helsinki (1964). Local approval was provided by all participating hospitals.\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of supporting data\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets generated and/or analysed during the current study are not publicly available due to privacy, but are available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study is funded by the Netherlands Organization for Health Research and Development (NWO-ZonMw), grant number 16156071, and\\u0026nbsp;the Dutch Research Council (NWO) under Grant number 023.013.017\\u003cstrong\\u003e.\\u0026nbsp;\\u003c/strong\\u003eThe organizations funding this study had no role in the design or conduct of the study; in the collection, management, analysis, or interpretation of the data; or in the preparation, review, or approval of the manuscript.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn addition to the authors, the Hospital-ADL study group consists of the following members: Lucienne A. Reichardt, PhD, Jesse Aarden, MSc, Rosanne van Seben, PhD, Martin van der Esch, PhD, Raoul H.H. Engelbert, PhD, Jos. A. Bosch, PhD, Ingeborg Kuper, MD, Annemarieke de Jonghe, MD, PhD, Maike Leguit-Elberse, RN, Ad Kamper, MD, PhD, Nynke Posthuma, MD, PhD, Nienke Brendel, MD, and Johan Wold, MD. Further, we thank Suzanne Schilder, MSc, Angelique Heinen, MSc, Robin Kwakman, MSc, Jan Jaap Voigt, MSc, and Hannah van der Pas, BSc, for assistance with data collection. A preliminary part of this study was presented as a poster at the European Society of Cardiology Preventive congress[48].\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026rsquo; information\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAffiliations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCenter of Expertise Urban Vitality, Faculty of Health, Amsterdam University of Applied Sciences, Amsterdam, Netherlands\\u003c/p\\u003e\\n\\u003cp\\u003eAmsterdam UMC, location University of Amsterdam, Cardiology, Meibergdreef 9, Amsterdam, Netherlands\\u003c/p\\u003e\\n\\u003cp\\u003eAmsterdam Cardiovascular Sciences, Atherosclerosis \\u0026amp; ischemic syndromes, Amsterdam, Netherlands\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026rsquo; contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cul\\u003e\\n \\u003cli\\u003eStudy concept and design:\\u0026nbsp;MT, DK, JT, BM, MvdS.\\u0026nbsp;\\u003c/li\\u003e\\n \\u003cli\\u003eAcquisition of data:\\u0026nbsp;DK.\\u003c/li\\u003e\\n \\u003cli\\u003eAnalysis and interpretation of data: MT, DK, JMNV, JT, BM, MvdS.\\u003c/li\\u003e\\n \\u003cli\\u003eDrafting of the manuscript: MT, DK.\\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cp\\u003eCritical revision of the manuscript for important intellectual content: MT, DK, JMNV, JT, BM, MvdS.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eWorld Health Organization. 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JAMA. 2020;323(12):1151\\u0026ndash;60. doi:\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1001/jama.2020.1382\\u003c/span\\u003e\\u003cspan address=\\\"10.1001/jama.2020.1382\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFeehan LM, Geldman J, Sayre EC, Park C, Ezzat AM, Yoo JY, et al. Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data. JMIR Mhealth Uhealth. 2018;6(8):e10527. doi:\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.2196/10527\\u003c/span\\u003e\\u003cspan address=\\\"10.2196/10527\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAlharbi M, Bauman A, Neubeck L, Gallagher R. Validation of Fitbit-Flex as a measure of free-living physical activity in a community-based phase III cardiac rehabilitation population. Eur J Prev Cardiol. 2016;23(14):1476\\u0026ndash;85. doi:\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1177/2047487316634883\\u003c/span\\u003e\\u003cspan address=\\\"10.1177/2047487316634883\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTerbraak M, Kolk D, Macneil Vroomen JL, Twisk JWT, Buurman BM, Van Der Schaaf M. Post-discharge light physical activity indicates recovery in acutely hospitalized older adults. Eur J Prev Cardiol May 2022, 29, Issue Supplement_1, zwac056.175, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/eurjpc/zwac056.175\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/eurjpc/zwac056.175\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-geriatrics\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bgtc\",\"sideBox\":\"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/bgtc/default.aspx\",\"title\":\"BMC Geriatrics\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"accelerometer, physical performance, rehabilitation, post-acute care, older patients, frailty\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-2166405/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-2166405/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground: \\u003c/strong\\u003ePhysical activity (PA) levels might be a simple overall physical marker of recovery in acutely hospitalized older adults; however cut-off values post discharge are lacking. Our objective was to identify cut-off values for post-discharge PA that indicate recovery among acutely hospitalized older adults and stratified for frailty.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods\\u003c/strong\\u003e: We performed a prospective observational cohort study including acutely hospitalized older adults (≥70 years). Frailty was assessed using Fried’s criteria. PA was assessed using Fitbit up to one week post discharge and quantified in steps and minutes light, moderate or higher intensity. The primary outcome was recovery at 3-months post discharge. ROC-curve analyses were used to determine cut-off values, and logistic regression analyses to calculate odds ratios (ORs).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e: The analytic sample included 174 participants with a mean (standard deviation) age of 79.2 (6.7) years of whom 84/174 (48%) were frail. At 3-months, 109/174 participants (63%) had recovered of whom 48 were frail. In all participants, determined cut-off values were 1369 steps/day (OR: 2.5, 95% confidence interval [CI]: 1.3–4.6) and 76 minutes/day of light intensity PA (OR: 3.0, 95% CI: 1.6–5.8). In frail participants, cut-off values were 1043 steps/day (OR: 3.3, 95% CI: 1.3–8.4) and 72 minutes/day of light intensity PA (OR: 4.2, 95% CI: 1.6–10.8). Determined cut-off values were not significantly associated with recovery in non-frail participants.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions\\u003c/strong\\u003e:\\u003c/p\\u003e\\n\\u003cp\\u003ePost-discharge PA cut-offs indicate the odds of recovery in older adults, especially in frail individuals, however are not equipped for use as a diagnostic test in daily practice. 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