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Upright time during hospitalization for older inpatients: A prospective cohort study
Experimental Gerontology 126 (2019) 110681
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Experimental Gerontology journal homepage: www.elsevier.com/locate/expgero
Upright time during hospitalization for older inpatients: A prospective cohort study
T
Olga Theou , D. Scott Kehler, Judith Godin, Kayla Mallery, Mark A. MacLean, Kenneth Rockwood ⁎
Department of Medicine, Dalhousie University, Halifax, NS, Canada
ARTICLE INFO
ABSTRACT
Section Editor: Christiaan Leeuwenburgh
Background: The purpose of this study was to examine: a) how long and how frequently older hospitalized patients spend upright; b) whether duration and frequency of upright time change by time of the day, the day of the week, and during hospitalization; and c) whether these relationships differ based on the mobility level of patients at admission. Methods: This prospective cohort study included 111 patients (82.2 ± 8 years old, 52% female) from the Emergency Department and a Geriatric Assessment Unit who were at least 60 years old and had an anticipated length of stay of at least three days. The main outcomes were accelerometer-measured total upright time and number of bouts of upright time during awake hours. Results: Patients were upright 15.9 times/day (interquartile range (IQR): 8.4–27.4) for a total of 54.2 min/day (IQR: 17.8–88.9) during awake hours. Time of day and day of week had little impact on the outcomes. Patients who walked independently at admission had 151.5 min (95% CI: 87.7–215.3) of upright time on hospital day 1 and experienced a decline of 4.5 min/day (−7.2 to −1.8). Those who needed personal mobility assistance or were bedridden had 29.5 min (−38.5–97.4) and 25 min (−48.3–100.3) of upright time on day 1, and demonstrated an increase of 3.6 (1.3–5.9) and 2.4 (0.05–4.5) min/day, respectively. Conclusion: Hospitalized older adults spend only 6% of their awake hours upright while in hospital. Patients who can walk independently are more active but experience a decline in their upright time during hospitalization.
Keywords: Hospital Upright time Accelerometers Aging
1. Introduction Older adults, who make up approximately half of hospitalized patients, have greater hospital readmission rates and a longer length of stay compared with their younger peers (Victor et al., 2000). During hospitalization, older adults are at a non-trivial risk for decline in their functional abilities, putting their future independence at risk (Covinsky et al., 2003; Hoogerduijn et al., 2012). This decline appears to be related to high levels of bed rest and reduced physical activity levels during hospitalization (Brown et al., 2009; Baldwin et al., 2017). The dangers of prolonged bed rest have long been recognized (Asher, 1947). Even short duration bed rest causes decline in the physiological and cognitive function of younger adults, and these effects are amplified with age (Kortebein et al., 2007; Lipnicki and Gunga, 2009). For example, 10 days of bed rest decreases leg strength by 13% and aerobic capacity by 12% in healthy older adults (Kortebein et al., 2008). Indeed, a decline in daily steps during hospitalization has been associated with a more than four times greater risk of death 2 years after discharge (Ostir et al., 2013). These effects can manifest early:
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among older patients whose mobility declined following admission, the relative risk of death was 17 times greater than it was in those whose mobility was stable for the first 48 h (Hubbard et al., 2011). Despite these well-recognized relationships, the levels of objectively measured upright time in hospitalized older adults require more thorough investigation. Such an understanding is vital to providing guidance on the best evidence practice to limit bed rest. In consequence, the purpose of this study was to examine: a) how long and how frequently older hospitalized patients spend upright; b) whether duration and frequency of upright time change by time of the day, the day of the week, and during hospitalization, and; c) whether these relationships differ based on the mobility level of patients at admission. 2. Methods 2.1. Study design and participants For this prospective cohort study, we approached and recruited eligible patients within 48 h of their admission to a tertiary care
Corresponding author at: Rm 1313, Veterans' Memorial Building, 5955 Veterans' Memorial Lane, Halifax, Nova Scotia B3H2E1, Canada. E-mail address: [email protected] (O. Theou).
https://doi.org/10.1016/j.exger.2019.110681 Received 26 March 2019; Received in revised form 28 June 2019; Accepted 2 August 2019 Available online 02 August 2019 0531-5565/ © 2019 Elsevier Inc. All rights reserved.
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hospital in Halifax, Canada. Patients were consecutively recruited either directly from the Emergency Department (ED), following consultation to Internal Medicine, or from a Geriatric Assessment Unit (inpatient unit specializing in geriatric care) between October 2013 and June 2015. Patient inclusion criteria were: females and males 60 years or older; an anticipated hospital length of stay of at least three days (estimated by a physician), and; the patient or care partner able to communicate in English. Patients were excluded if consent could not be provided by either the patient or their care provider.
We used multilevel growth modeling to test whether trajectories of upright time and bouts during a participants' hospital length of stay were patterned by between-person differences in mobility assessed within 48 h of hospital admission. Hospital length of stay was a timevarying variable from baseline to a maximum of 2 weeks or until hospital discharge. This analysis produces less biased results compared to other techniques because participants can be included even if they are missing time points due to different durations of length of stay. We developed unconditional growth models (model 1) to determine the variation in upright time and upright bouts that could be attributed to inter-individual differences (Intraclass Correlation Coefficients). The second model (model 2) determined the average pattern of change in upright time and upright bouts across hospital days without the influence of mobility and other covariates. Last (model 3), we estimated trajectories of upright time and upright bouts by baseline HABAM categories (bedridden, person assist, and independent) and adjusted for age, sex, and CTAS scores (urgent versus non-urgent). A quadratic term for hospital day was not statistically significant (p = 0.14) and was removed from the model. Restricted likelihood estimation method and the Kenward Roger between-within degree of freedom method were used in all growth models. An alpha of 0.05 determined statistical significance. Sensitivity analyses were conducted to determine whether a) extending the awake hours beyond the routine scheduling of the acute care units, from 7 am–10 pm to 5 am–12 am, impacted the findings and b) pre-hospital mobility level would have a different impact on the results than admission mobility level.
2.2. Measurements Participant mobility was assessed within the first 48 h of hospital admission using the Hierarchical Assessment of Balance and Mobility (HABAM) (Hubbard et al., 2011). Scores within the mobility component of HABAM range from 0 to 28. Patients were categorized as bedridden (a score of 0 [needs help to be repositioned in bed] to 4 [positions self in bed]), person assistance required (a score of 7 [lying or sitting independently] to 12 [1 person standby with or without a walking aid]), and independent (a score of 14 [can walk with aid < 8 m] to 28 [patient has unlimited, vigorous mobility]). Pre-admission mobility level was assessed by asking patients or caregivers about the ability of the patient to walk 100 m prior to hospitalization. The level of priority to treat a patient in the ED based on the severity of the patient condition was measured with the Canadian Triage and Acuity Scale (CTAS); scores range from 1 to 5 (Beveridge, 1998). Participants were categorized as Urgent (Level 1 [resuscitation required and need immediate healthcare provision] to 3 [Resuscitation, Emergency, Urgent]) or Non-urgent (Level 4 to 5 [less urgent or non-urgent] or not admitted through the ED [admitted directly to the Geriatric Assessment Unit]). The main outcomes of interest were total upright time and number of bouts of upright time during awake hours (7 am–10 pm). Awake hours were based on the routine scheduling of day and nighttime hours within the acute care units. Both outcomes were measured every 15 s, 24 h per day, with an ActivPAL3 VT™ (PAL Technologies Ltd) accelerometer for up to two weeks during a participants' hospitalization, or until hospital discharge for those discharged in < 2 weeks. The ActivPAL was fitted by a trained research assistant on the mid-thigh of the participants' dominant leg. ActivPALs have an inclinometer that detects the joint angle of the thigh to capture lying, sitting, standing, and stepping activities. Upright time was defined as standing or stepping. Stepping time could not be accurately captured because the ActivPAL devices were not sensitive to detect the slow gait speeds of the participants (Taraldsen et al., 2011). We only included ActivPAL data from patients with at least one day of data.
2.4. Ethics approval This study was approved by the regional health authority ethics committee. Informed consent was obtained from participants prior to study enrolment. 3. Results Of the 286 patients who were considered eligible, 130 participants consented to participate in the study. The study sample for analysis was 111 after removal of participants with missing ActivPAL data, no mobility assessment within the first 48 h, or if they withdrew from the study (Supplementary Fig. S1). The bedridden group at admission was slightly older than the other two HABAM groups and had a longer hospital length of stay. The independent group had the shortest length of stay compared to the other two groups (Table 1). 3.1. Upright time and frequency across all days
2.3. Statistical analysis
On average, participants were upright 15.9 times per day (median; interquartile range 8.4–27.4) for a total of 54.2 min per day (17.8–88.9) during awake hours. The group defined as independent by HABAM at admission had significantly higher total upright time and number of bouts during daytime (7 am–10 pm) and nighttime (10 pm–7 am) hours compared to the bedridden and person assist groups (p < 0.01 for all comparisons) (Fig. 1).
Analyses were performed using SAS 9.4 (SAS Institute, Cary, North Carolina). Descriptive statistics are presented as mean (standard deviation) or median (interquartile range) for normally and non-normally distributed data, respectively. The Mann-Whitney test and Chi square test were used to compare baseline descriptive characteristics and the total upright time and upright bouts during awake (7 am–10 pm) and nighttime hours (10 pm–7 am) between HABAM groups for continuous and categorical variables, respectively. The Wilcoxon Signed-Rank test was used for comparisons of continuous outcomes within groups. We examined how upright time and bouts were accumulated by the time of day within the first 48 h of hospitalization, including morning (7 am–12 pm), afternoon (12 pm–5 pm), and the evening (5 pm–10 pm). We also examined whether upright time and bouts were different during weekend versus weekday days for participants with both weekday and weekend day data during awake hours. For participants who spent two weekends in the hospital, we only used the first weekend data for this analysis.
3.2. Upright time and frequency based on time of the day Time of the day had little impact on the amount of upright time and bouts within the first 48 h, especially for the HABAM bedridden and person assist groups (Fig. 2). Participants were upright 6.5 (0.0–25.6) minutes in the morning (7 am–12 pm), 7.3 (0.8–23.0) minutes in the afternoon (12 pm–5 pm) and 6.0 (0.0–25.6) minutes in the evening (5 pm–10 pm); no statistically significant differences were found between these time periods across all participants. Upright bouts were lower in the evening (3.0 [0.0–6.0]) compared to the morning (4.0 [0.0–10.0]) and afternoon (4.0 [1.0–7.0]) across all participants 2
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Table 1 Baseline characteristics of included participants. Variable
Total sample (n = 111)
HABAM bedridden (n = 32)
HABAM person assist (n = 44)
HABAM independent (n = 35)
Age (years); mean (SD) Sex (% female) Reason for admission (%) Respiratory Falls Functional decline Delirium Cardiovascular Urinary Other CTAS scores Level 1: resuscitation Level 2: emergency Level 3: urgency Level 4: less urgency Level 5: non urgent Did not admit through ED Hospital length of stay (days); median (IQR) Prolonged hospital length of stay ≥ 14 days (%) Mortality rates (%)
82.2 (8.0) 58 (52.3%)
84.8 (8.5) 19 (59.4%)
81.6 (6.5)⁎ 22 (50.0%)
80.0 (8.9)⁎ 17 (48.6%)
22 (19.8%) 16 (14.4%) 15 (13.5%) 12 (10.8%) 12 (10.8%) 8 (7.2%) 26 (23.4%)
4 5 7 5 5 2 4
9 (20.4%) 6 (13.6%) 6 (13.6%) 3 (6.8%) 4 (9.1%) 3 (6.8%) 13 (29.5%)
9 5 2 4 3 3 9
2 (1.8%) 31 (27.9%) 57 (51.3%) 2 (1.8%) 0 (0.0%) 19 (17.1%) 12 (8–23) 42 (37.8%) 11 (9.9%)
0 (0.0%) 8 (25.0%) 22 (69.0%) 1 (3.1%) 0 (0.0%) 1 (3.1%) 16.5 (9.5–32.5) 19 (59.3%) 5 (14.7%)
1 (2.3%) 17 (38.6%) 21 (47.7%) 1 (2.3%) 0 (0.0%) 4 (9.1%) 12.0 (8.0–26.0) 15 (34.1%) 5 (11.3%)
1 (2.9%) 6 (17.1%) 14 (40.0%) 0 (0.0%) 0 (0.0%) 14 (40.0%) 9.0 (4.0–17.0)⁎,† 9 (25.7%)† 1 (2.9%)
(12.5%) (15.6%) (21.9%) (15.6%) (15.6%) (6.3%) (12.5%)
(25.7%) (14.3%) (5.7%) (11.4%) (8.6%) (8.6%) (25.7%)
Mobility (HABAM) assessed within first 48 h. SD, standard deviation; ED, emergency department; HABAM, Hierarchical Assessment of Balance and Mobility; IQR, interquartile range. ⁎ Significant differences with HABAM bedridden group (p < 0.05). † Significant differences with HABAM person assist group (p < 0.05).
significant (p = 0.0688). Person-assist participants averaged 25.9 upright bouts per day within the first 48 h of admission (p < 0.0001) and this increased by 0.8 bouts per day (p = 0.003) on average. Bedridden participants averaged 21.9 upright bouts within the first 48 h of admission (p = 0.0001) and experienced an average increase of 0.5 bouts each day (p = 0.05) (Fig. 3).
(p < 0.05). The independent group had most upright bouts between 9 and 11 am (Fig. 2). 3.3. Upright time and frequency based on day of the week There were no differences between weekday and weekend upright time (47.1 [19.2–82.8] vs. 42.8 [18.2–98.2], respectively), for those with both weekday and weekend day data during awake hours (n = 60). When we stratified analysis by HABAM group, we also found that upright time of the bedridden (n = 18, 29.3 [16.8–63.6] vs. 30.8 [5.2–58.1]), person assist (n = 22, 27.5 [16.4–47.0] vs. 25.3 [8.9–54.2]), or the independent group (n = 20, 98.5 [56.0–172.8] vs. 120.4 [69.7–218.4]) groups were not statistically different between weekday and weekend days. Similarly, there were no within-group differences for the number of upright bouts accumulated between weekday versus weekend days for the total sample (14.0 [8.0–26.0] vs. 15.5 [7.5–27.0]), the bedridden, (10.0 [5.0–170] vs. 9.5 [5.5–17.0]), person assist (14.5 [6.0–26.0] vs. 14.0 [6.5–23.0]) or independent (27.5 [18.0–41.0] vs. 29.0 [21.5–39.0]) groups, respectively.
3.5. Sensitivity analyses Extending the awake hours to 5 am–12 am had a minimal impact on the results (data not shown). When we compared admission to preadmission mobility levels we found that among the groups assessed at baseline by the HABAM as bedridden, person assist, and independent groups, 78.7%, 78.1%, and 45.2% reported that they needed assistance or could not walk 100 m prior to hospitalization, respectively. Multilevel growth modeling analysis showed that the pre-admission mobility level did not have an impact on the change in upright time and upright bouts during hospitalization (data not shown). 4. Discussion
3.4. Upright time and frequency based on hospital day
This study of 111 older inpatients showed that hospitalized patients spent less than 1 h per day upright. Even though health care staffing is less during evening and weekends, time of day and day of the week had little impact on the upright time of the patients; mobility level at admission did impact upright time. Participants walking independently at admission had the highest upright time – even this, however, was limited (< 2 h per day). During hospitalization, patients who were bedridden or needed person assistance at admissions increased their upright time whereas patients who could walk independently decreased their upright time from 16% at day 1 to 10% at day 14. We found that on average patients were upright for 54 min/day during their waking hours, which is less than the community dwelling older population and long-term care residents who are upright for 4–5 h/day (Lord et al., 2011) and 2–3 h/day,(Chan et al., 2016; Reid et al., 2013) respectively. Similar to our study, Ostir et al. showed that 244 patients admitted to a US Acute Care for Elders (ACE) hospital unit were active for 80 min in the first 24 h of hospitalization and most
A significant interaction between time and HABAM group indicated that trajectories of upright time varied depending on mobility status at admission (Table 2). Within 48 h of admission, independent participants averaged of 151.5 min per day upright (p < 0.0001) and had an average decline of 4.5 min per day (p < 0.0013). Person-assist participants averaged 29.5 min upright per day within the first 48 h of admission (p = 0.3918) and this increased by 3.6 min per day (p = 0.0016) on average. Bedridden participants averaged 25.0 min a day upright within the first 48 h of admission (p = 0.4894) and experienced an average increase of 2.4 (p = 0.05) minutes per day (Fig. 3). Similarly, a significant interaction between time and HABAM group indicated that trajectories of bouts of upright time varied depending on mobility status at admission (Supplementary Table S1). Independent participants averaged 43.4 (p < 0.0001) upright bouts per day upright within 48 h of admission but change over time was not statistically 3
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Fig. 1. Total upright time characteristics across all hospital days. Panel A = upright time. Panel B = upright bouts. Data are presented as median (interquartile range). Mobility (HABAM) assessed within first 48 h. *Significant differences with HABAM bedridden group (p < 0.05). †Significant differences with HABAM person assist group (p < 0.05).
patients increased their activity during hospitalization (Ostir et al., 2013). A recent systematic review (Baldwin et al., 2017) included 42 studies that used accelerometers to examine active and sedentary behaviour in inpatients. Inpatients spent 93%–99% of their hospital stay sedentary. In most studies patients completed < 1000 steps/day despite their up to 50 postural transitions/day. Multiple factors including severity of illness, pre-existing health status, and hospital structure and processes of care impact upright time during hospitalization. To control for some of these factors we adjusted our analysis for urgency of hospital admission (CTAS) and stratified participants by admission and pre-admission mobility status. A previous
study showed that changes in muscle strength and mass were not associated with having delirium, falls, malnutrition, and functional disability at admission (Van Ancum et al., 2017a). It is possible that changes in upright time may have a similar relationship with geriatric syndromes and other factors at admission. This requires further investigation. Changes in upright time during hospitalization could represent how patients recover while in hospital. Participants who were dependent for mobility at admission had the highest illness severity, and as they recovered in hospital, their upright time increased. Approximately, 21% of the people who were classified as bedridden at admission could walk
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Fig. 2. Upright time (Panel A) and upright bouts (Panel B) by the time of day within the first 24 h of hospitalization. HABAM bedridden = triangles with dotted lines. HABAM dependent = squares with solid lines. HABAM independent = circles with dashed line.
100 m without assistance prior to hospitalization. Many of these people would have recovered from the state of being bedridden during the hospitalization, which could explain the increase in upright time that we observed in this group. However, those who were independent had the lowest illness severity and had the highest baseline upright time but experienced a slow decline in upright time. This latter effect is novel and unexpected. Here, as in other work, change in mobility in the first 48 h forecast the hospital course, with those whose mobility improved early doing much better than those whose mobility declined in the first 48 h (Hatheway et al., 2017). Even so, a meta-analysis on change in muscle strength and muscle mass in older patients during hospitalization showed that the findings depended on the type of admission with muscle strength and mass significantly decreasing in electively admitted older patients but not in acutely admitted older patients. In our
study, we only included acutely admitted patients, but many of our patients from our independent group were admitted directly to the inpatient geriatric unit – not through the ED – and may have similar characteristics with electively admitted older patients (Van Ancum et al., 2017b). These conflicting trends of early improvement with better outcomes and those doing well on admission slowly moving less may reflect some homogenization of mobility expectations. These observations serve as hypotheses for future study. In this regard, our data highlight the need to use objective measures of upright time as vital signs during hospitalization. In fact, a recent study showed that impaired mobility on presentation predicted mortality for acutely ill patients independently and better than traditional vital signs (i.e. low blood pressure, pulse, elevated respiratory rate, hypothermia, low oxygen saturation, old age, and coma) (Brabrand
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Table 2 Estimates from multilevel model examining changes in total upright time.
Fixed effects Intercept Time (change per day) HABAM person assistance HABAM bedridden Time × HABAM person assistance Time × HABAM bedridden Random effects Residual Intercept Hospital day Model fit AIC BIC Deviance
Model 1: unconditional growth
Model 2: fixed and random effects of hospital day
Model 3: fixed effects of HABAMa
75.0 (57.9–92.1)⁎
70.1 (51.3–89.0)⁎ 1.0 (−0.5–2.4)
151.5 (87.7–215.3)⁎ −4.5 (−7.2 to −1.8)⁎ −122.0 (−163.5 to −80.6)⁎ −126.5 (−170.5 to −80.5)⁎ 8.1 (4.6–11.7)⁎ 6.9 (3.2–10.2)⁎
2984.5 (2716.1–3295.0)⁎ 7717.5 (5919.1–10,486.0)⁎
2690.7 (2436.1–2987.7)⁎ 8301.5 (6303.8–11,431.0)⁎ 18.7 (10.9–39.9)⁎
2705.2 (2407.5–2945.4)⁎ 6028.4 (4426.2–8307.5)⁎ 14.7 (8.0–34.6)⁎
10,470.4 10,475.8 10,466.4
10,444.6 10,452.7 10,438.6
10,163.9 10,172.0 10,157.9
Data are presented as parameter estimates (95% CI). Intraclass correlation coefficient for unconditional growth model (Model 1): 0.72. AIC, Akaike information criterion; BIC, Bayesian information criterion. The ICC of 0.72 indicates that 72% of the variation in total upright time scores occurred between persons, while 28% of the variation in upright time was due to within-person changes over time. Model 2 describes the average upright time within 48 h of hospital admission (intercept) and the average rate of change (time) by hospital day among the entire study sample, without including HABAM scores or covariates. Model 3 describes the average upright time within 48 h of hospital admission and the rate of change for the reference HABAM category (independent). ⁎ p < 0.05. a Adjusted for age, sex, and Canadian Triage Acuity Scale score (urgent vs. non-urgent).
the units, but sensitivity analysis showed that extending the awake hours had no impact on the findings. In conclusion, this study showed that older inpatients spend upright only 6% of their awake hours while in hospital. Patients who can walk independently at admission are more active but experience a decline in their upright time during hospitalization.
et al., 2018). Another study showed that number of steps during hospitalization was a stronger predictor of 30-day hospital readmission than functional ability (Fisher et al., 2016). Even so, accelerometers have limited step-count accuracy in older and frail inpatients (McCullagh et al., 2017). Whether increasing upright time is sufficient to improve health outcomes is unclear, but some evidence supports this. For example, older patients who self-reported that they walked at least once per day outside their room had 1.5 days shorter length of stay compared with those who stayed in their room (Shadmi and Zisberg, 2011). On the other hand, the pressures on length of stay can mean that even small signs of independence prompt activation of discharge plans. Still, more postural transitions/mobilizations are associated with 4.7-fold decrease in ADL impairment, 6.0-fold decrease in institutionalization, and 3.3fold decrease post-discharge mortality (Zisberg et al., 2011; Brown et al., 2004). The MOVE-ON initiative recommends that mobility should be assessed within 24 h of hospital admission and that patients should mobilize at least 3 times a day using progressive and scaled mobilizations to reduce sedentary time (Liu et al., 2018). Future studies should examine whether increasing upright time during a patients' stay in acute care will improve health outcomes during and post-hospitalization. Our study has a number of limitations. We only assessed upright time up to 2 weeks due to the limited battery life of the device. Even so, most accelerometer studies last 1 week or less and the device we used (ActivPAL) is one of the most accurate measures of assessing upright time in hospital studies (Lim et al., 2018). Combining sources of information on ambulation, such as reports from health care professionals, is also important when trying to understand the physical activity levels of patients. We relied on self-reported mobility to measure mobility levels of participants at pre-admission, which may be less accurate than objectively measured mobility status. Timing and level of physical therapy sessions were not recorded and CTAS was used as an indicator of illness severity. The ED of the participating hospital uses this triage score to determine the clinical priority of patients and to identify patients who need immediate attention. Even so, using these triage scores as measures of illness severity has limitations and other scores such as the Modified and National Early Warning scores may be more useful. We estimated awake hours based on routine scheduling of
Funding This work was supported in part by a Nova Scotia Health Authority Research Fundgrant, Dalhousie University Internal Medicine Research Fellowship, and Canadian Institutes of Health Research Banting Fellowship. Author contributions OT contributed to conception and design, acquisition of data, analysis and interpretation of data. SK and JG contributed to analysis and interpretation of data. KM, MAM contributed to acquisition of data. KR contributed to conception and design. OT and SK drafted the article. JG, KM, MAM, and KR revised the manuscript critically for important intellectual content. All authors approved the final submitted version of the manuscript. Declaration of competing interest Kenneth Rockwood is President and Chief Science Officer of DGI Clinical, which in the last five years has contracts with pharma and device manufacturers (Baxter, Baxalta, Shire, Hollister, Nutricia, Roche, Otsuka) on individualized outcome measurement. In 2017 he attended an advisory board meeting with Lundbeck. Otherwise any personal fees are for invited guest lectures and academic symposia, received directly from event organizers, chiefly for presentations on frailty. He is Associate Director of the Canadian Consortium on Neurodegeneration in Aging, which is funded by the Canadian Institutes of Health Research, and with additional funding from the Alzheimer Society of Canada and several other charities, as well as, in its first phase (2013–2018), from Pfizer Canada and Sanofi Canada. The rest of the authors have no conflicts of interest. 6
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Fig. 3. Change in total upright time (Panel A) and bouts of upright time (Panel B) by HABAM category. Bedridden = triangles with dotted line. Person assist = squares with solid line. Independent = circles with dashed line. The lines on the graph represent the predicted values (expected change) obtained from the multilevel model for each HABAM category. The 95% CIs for each coefficient can be found in Table 2.
Acknowledgments
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We would like to thank all study participants and the health care staff at the participating units without whom the study would not be possible. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.exger.2019.110681. References Asher, R.A., 1947. The dangers of going to bed. Br. Med. J. 2 (4536), 967. Baldwin, C., van Kessel, G., Phillips, A., Johnston, K., 2017. Accelerometry shows
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O. Theou, et al. walking activity to predict readmission in older adults. Arch. Phys. Med. Rehabil. 97 (9 Suppl), S226–S231 Sep. Hatheway, O.L., Mitnitski, A., Rockwood, K., 2017. Frailty affects the initial treatment response and time to recovery of mobility in acutely ill older adults admitted to hospital. Age Ageing 46 (6), 920–925. Hoogerduijn, J.G., Buurman, B.M., Korevaar, J.C., Grobbee, D.E., de Rooij, S.E., Schuurmans, M.J., 2012. The prediction of functional decline in older hospitalised patients. Age Ageing 41 (3), 381–387. Hubbard, R.E., Eeles, E.M., Rockwood, M.R., et al., 2011. Assessing balance and mobility to track illness and recovery in older inpatients. J. Gen. Intern. Med. 26 (12), 1471–1478. Kortebein, P., Ferrando, A., Lombeida, J., Wolfe, R., Evans, W.J., 2007. Effect of 10 days of bed rest on skeletal muscle in healthy older adults. JAMA 297 (16), 1772–1774. Kortebein, P., Symons, T.B., Ferrando, A., et al., 2008. Functional impact of 10 days of bed rest in healthy older adults. J. Gerontol. A Biol. Sci. Med. Sci. 63 (10), 1076–1081. Lim, S.E.R., Ibrahim, K., Sayer, A.A., Roberts, H.C., 2018. Assessment of physical activity of hospitalised older adults: a systematic review. J. Nutr. Health Aging 22 (3), 377–386. Lipnicki, D.M., Gunga, H.C., 2009. Physical inactivity and cognitive functioning: results from bed rest studies. Eur. J. Appl. Physiol. 105 (1), 27–35. Liu, B., Moore, J.E., Almaawiy, U., et al., 2018. Outcomes of Mobilisation of Vulnerable Elders in Ontario (MOVE ON): a multisite interrupted time series evaluation of an implementation intervention to increase patient mobilisation. Age Ageing 47 (1), 112–119. Lord, S., Chastin, S.F., McInnes, L., Little, L., Briggs, P., Rochester, L., 2011. Exploring patterns of daily physical and sedentary behaviour in community-dwelling older
adults. Age Ageing 40 (2), 205–210. McCullagh, R., Dillon, C., O’Connell, A.M., Horgan, N.F., Timmons, S., 2017. Step-count accuracy of 3 motion sensors for older and frail medical inpatients. Arch. Phys. Med. Rehabil. 98 (2), 295–302 Feb. Ostir, G.V., Berges, I.M., Kuo, Y.F., Goodwin, J.S., Fisher, S.R., Guralnik, J.M., 2013. Mobility activity and its value as a prognostic indicator of survival in hospitalized older adults. J. Am. Geriatr. Soc. 61 (4), 551–557. Reid, N., Eakin, E., Henwood, T., et al., 2013. Objectively measured activity patterns among adults in residential aged care. Int. J. Environ. Res. Public Health 10 (12), 6783–6798. Shadmi, E., Zisberg, A., 2011. In-hospital mobility and length of stay. Arch. Intern. Med. 171 (14), 1298. Taraldsen, K., Askim, T., Sletvold, O., et al., 2011. Evaluation of a body-worn sensor system to measure physical activity in older people with impaired function. Phys. Ther. 91 (2), 277–285. Van Ancum, J.M., Scheerman, K., Pierik, V.D., et al., 2017a. Muscle strength and muscle mass in older patients during hospitalization: the EMPOWER study. Gerontology 63 (6), 507–514. Van Ancum, J.M., Scheerman, K., Jonkman, N.H., et al., 2017b. Change in muscle strength and muscle mass in older hospitalized patients: a systematic review and meta-analysis. Exp. Gerontol. 92, 34–41 Jun. Victor, C.R., Healy, J., Thomas, A., Seargeant, J., 2000. Older patients and delayed discharge from hospital. Health Soc. Care Commun. 8 (6), 443–452. Zisberg, A., Shadmi, E., Sinoff, G., Gur-Yaish, N., Srulovici, E., Admi, H., 2011. Low mobility during hospitalization and functional decline in older adults. J. Am. Geriatr. Soc. 59 (2), 266–273.
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Experimental Gerontology journal homepage: www.elsevier.com/locate/expgero
Upright time during hospitalization for older inpatients: A prospective cohort study
T
Olga Theou , D. Scott Kehler, Judith Godin, Kayla Mallery, Mark A. MacLean, Kenneth Rockwood ⁎
Department of Medicine, Dalhousie University, Halifax, NS, Canada
ARTICLE INFO
ABSTRACT
Section Editor: Christiaan Leeuwenburgh
Background: The purpose of this study was to examine: a) how long and how frequently older hospitalized patients spend upright; b) whether duration and frequency of upright time change by time of the day, the day of the week, and during hospitalization; and c) whether these relationships differ based on the mobility level of patients at admission. Methods: This prospective cohort study included 111 patients (82.2 ± 8 years old, 52% female) from the Emergency Department and a Geriatric Assessment Unit who were at least 60 years old and had an anticipated length of stay of at least three days. The main outcomes were accelerometer-measured total upright time and number of bouts of upright time during awake hours. Results: Patients were upright 15.9 times/day (interquartile range (IQR): 8.4–27.4) for a total of 54.2 min/day (IQR: 17.8–88.9) during awake hours. Time of day and day of week had little impact on the outcomes. Patients who walked independently at admission had 151.5 min (95% CI: 87.7–215.3) of upright time on hospital day 1 and experienced a decline of 4.5 min/day (−7.2 to −1.8). Those who needed personal mobility assistance or were bedridden had 29.5 min (−38.5–97.4) and 25 min (−48.3–100.3) of upright time on day 1, and demonstrated an increase of 3.6 (1.3–5.9) and 2.4 (0.05–4.5) min/day, respectively. Conclusion: Hospitalized older adults spend only 6% of their awake hours upright while in hospital. Patients who can walk independently are more active but experience a decline in their upright time during hospitalization.
Keywords: Hospital Upright time Accelerometers Aging
1. Introduction Older adults, who make up approximately half of hospitalized patients, have greater hospital readmission rates and a longer length of stay compared with their younger peers (Victor et al., 2000). During hospitalization, older adults are at a non-trivial risk for decline in their functional abilities, putting their future independence at risk (Covinsky et al., 2003; Hoogerduijn et al., 2012). This decline appears to be related to high levels of bed rest and reduced physical activity levels during hospitalization (Brown et al., 2009; Baldwin et al., 2017). The dangers of prolonged bed rest have long been recognized (Asher, 1947). Even short duration bed rest causes decline in the physiological and cognitive function of younger adults, and these effects are amplified with age (Kortebein et al., 2007; Lipnicki and Gunga, 2009). For example, 10 days of bed rest decreases leg strength by 13% and aerobic capacity by 12% in healthy older adults (Kortebein et al., 2008). Indeed, a decline in daily steps during hospitalization has been associated with a more than four times greater risk of death 2 years after discharge (Ostir et al., 2013). These effects can manifest early:
⁎
among older patients whose mobility declined following admission, the relative risk of death was 17 times greater than it was in those whose mobility was stable for the first 48 h (Hubbard et al., 2011). Despite these well-recognized relationships, the levels of objectively measured upright time in hospitalized older adults require more thorough investigation. Such an understanding is vital to providing guidance on the best evidence practice to limit bed rest. In consequence, the purpose of this study was to examine: a) how long and how frequently older hospitalized patients spend upright; b) whether duration and frequency of upright time change by time of the day, the day of the week, and during hospitalization, and; c) whether these relationships differ based on the mobility level of patients at admission. 2. Methods 2.1. Study design and participants For this prospective cohort study, we approached and recruited eligible patients within 48 h of their admission to a tertiary care
Corresponding author at: Rm 1313, Veterans' Memorial Building, 5955 Veterans' Memorial Lane, Halifax, Nova Scotia B3H2E1, Canada. E-mail address: [email protected] (O. Theou).
https://doi.org/10.1016/j.exger.2019.110681 Received 26 March 2019; Received in revised form 28 June 2019; Accepted 2 August 2019 Available online 02 August 2019 0531-5565/ © 2019 Elsevier Inc. All rights reserved.
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hospital in Halifax, Canada. Patients were consecutively recruited either directly from the Emergency Department (ED), following consultation to Internal Medicine, or from a Geriatric Assessment Unit (inpatient unit specializing in geriatric care) between October 2013 and June 2015. Patient inclusion criteria were: females and males 60 years or older; an anticipated hospital length of stay of at least three days (estimated by a physician), and; the patient or care partner able to communicate in English. Patients were excluded if consent could not be provided by either the patient or their care provider.
We used multilevel growth modeling to test whether trajectories of upright time and bouts during a participants' hospital length of stay were patterned by between-person differences in mobility assessed within 48 h of hospital admission. Hospital length of stay was a timevarying variable from baseline to a maximum of 2 weeks or until hospital discharge. This analysis produces less biased results compared to other techniques because participants can be included even if they are missing time points due to different durations of length of stay. We developed unconditional growth models (model 1) to determine the variation in upright time and upright bouts that could be attributed to inter-individual differences (Intraclass Correlation Coefficients). The second model (model 2) determined the average pattern of change in upright time and upright bouts across hospital days without the influence of mobility and other covariates. Last (model 3), we estimated trajectories of upright time and upright bouts by baseline HABAM categories (bedridden, person assist, and independent) and adjusted for age, sex, and CTAS scores (urgent versus non-urgent). A quadratic term for hospital day was not statistically significant (p = 0.14) and was removed from the model. Restricted likelihood estimation method and the Kenward Roger between-within degree of freedom method were used in all growth models. An alpha of 0.05 determined statistical significance. Sensitivity analyses were conducted to determine whether a) extending the awake hours beyond the routine scheduling of the acute care units, from 7 am–10 pm to 5 am–12 am, impacted the findings and b) pre-hospital mobility level would have a different impact on the results than admission mobility level.
2.2. Measurements Participant mobility was assessed within the first 48 h of hospital admission using the Hierarchical Assessment of Balance and Mobility (HABAM) (Hubbard et al., 2011). Scores within the mobility component of HABAM range from 0 to 28. Patients were categorized as bedridden (a score of 0 [needs help to be repositioned in bed] to 4 [positions self in bed]), person assistance required (a score of 7 [lying or sitting independently] to 12 [1 person standby with or without a walking aid]), and independent (a score of 14 [can walk with aid < 8 m] to 28 [patient has unlimited, vigorous mobility]). Pre-admission mobility level was assessed by asking patients or caregivers about the ability of the patient to walk 100 m prior to hospitalization. The level of priority to treat a patient in the ED based on the severity of the patient condition was measured with the Canadian Triage and Acuity Scale (CTAS); scores range from 1 to 5 (Beveridge, 1998). Participants were categorized as Urgent (Level 1 [resuscitation required and need immediate healthcare provision] to 3 [Resuscitation, Emergency, Urgent]) or Non-urgent (Level 4 to 5 [less urgent or non-urgent] or not admitted through the ED [admitted directly to the Geriatric Assessment Unit]). The main outcomes of interest were total upright time and number of bouts of upright time during awake hours (7 am–10 pm). Awake hours were based on the routine scheduling of day and nighttime hours within the acute care units. Both outcomes were measured every 15 s, 24 h per day, with an ActivPAL3 VT™ (PAL Technologies Ltd) accelerometer for up to two weeks during a participants' hospitalization, or until hospital discharge for those discharged in < 2 weeks. The ActivPAL was fitted by a trained research assistant on the mid-thigh of the participants' dominant leg. ActivPALs have an inclinometer that detects the joint angle of the thigh to capture lying, sitting, standing, and stepping activities. Upright time was defined as standing or stepping. Stepping time could not be accurately captured because the ActivPAL devices were not sensitive to detect the slow gait speeds of the participants (Taraldsen et al., 2011). We only included ActivPAL data from patients with at least one day of data.
2.4. Ethics approval This study was approved by the regional health authority ethics committee. Informed consent was obtained from participants prior to study enrolment. 3. Results Of the 286 patients who were considered eligible, 130 participants consented to participate in the study. The study sample for analysis was 111 after removal of participants with missing ActivPAL data, no mobility assessment within the first 48 h, or if they withdrew from the study (Supplementary Fig. S1). The bedridden group at admission was slightly older than the other two HABAM groups and had a longer hospital length of stay. The independent group had the shortest length of stay compared to the other two groups (Table 1). 3.1. Upright time and frequency across all days
2.3. Statistical analysis
On average, participants were upright 15.9 times per day (median; interquartile range 8.4–27.4) for a total of 54.2 min per day (17.8–88.9) during awake hours. The group defined as independent by HABAM at admission had significantly higher total upright time and number of bouts during daytime (7 am–10 pm) and nighttime (10 pm–7 am) hours compared to the bedridden and person assist groups (p < 0.01 for all comparisons) (Fig. 1).
Analyses were performed using SAS 9.4 (SAS Institute, Cary, North Carolina). Descriptive statistics are presented as mean (standard deviation) or median (interquartile range) for normally and non-normally distributed data, respectively. The Mann-Whitney test and Chi square test were used to compare baseline descriptive characteristics and the total upright time and upright bouts during awake (7 am–10 pm) and nighttime hours (10 pm–7 am) between HABAM groups for continuous and categorical variables, respectively. The Wilcoxon Signed-Rank test was used for comparisons of continuous outcomes within groups. We examined how upright time and bouts were accumulated by the time of day within the first 48 h of hospitalization, including morning (7 am–12 pm), afternoon (12 pm–5 pm), and the evening (5 pm–10 pm). We also examined whether upright time and bouts were different during weekend versus weekday days for participants with both weekday and weekend day data during awake hours. For participants who spent two weekends in the hospital, we only used the first weekend data for this analysis.
3.2. Upright time and frequency based on time of the day Time of the day had little impact on the amount of upright time and bouts within the first 48 h, especially for the HABAM bedridden and person assist groups (Fig. 2). Participants were upright 6.5 (0.0–25.6) minutes in the morning (7 am–12 pm), 7.3 (0.8–23.0) minutes in the afternoon (12 pm–5 pm) and 6.0 (0.0–25.6) minutes in the evening (5 pm–10 pm); no statistically significant differences were found between these time periods across all participants. Upright bouts were lower in the evening (3.0 [0.0–6.0]) compared to the morning (4.0 [0.0–10.0]) and afternoon (4.0 [1.0–7.0]) across all participants 2
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Table 1 Baseline characteristics of included participants. Variable
Total sample (n = 111)
HABAM bedridden (n = 32)
HABAM person assist (n = 44)
HABAM independent (n = 35)
Age (years); mean (SD) Sex (% female) Reason for admission (%) Respiratory Falls Functional decline Delirium Cardiovascular Urinary Other CTAS scores Level 1: resuscitation Level 2: emergency Level 3: urgency Level 4: less urgency Level 5: non urgent Did not admit through ED Hospital length of stay (days); median (IQR) Prolonged hospital length of stay ≥ 14 days (%) Mortality rates (%)
82.2 (8.0) 58 (52.3%)
84.8 (8.5) 19 (59.4%)
81.6 (6.5)⁎ 22 (50.0%)
80.0 (8.9)⁎ 17 (48.6%)
22 (19.8%) 16 (14.4%) 15 (13.5%) 12 (10.8%) 12 (10.8%) 8 (7.2%) 26 (23.4%)
4 5 7 5 5 2 4
9 (20.4%) 6 (13.6%) 6 (13.6%) 3 (6.8%) 4 (9.1%) 3 (6.8%) 13 (29.5%)
9 5 2 4 3 3 9
2 (1.8%) 31 (27.9%) 57 (51.3%) 2 (1.8%) 0 (0.0%) 19 (17.1%) 12 (8–23) 42 (37.8%) 11 (9.9%)
0 (0.0%) 8 (25.0%) 22 (69.0%) 1 (3.1%) 0 (0.0%) 1 (3.1%) 16.5 (9.5–32.5) 19 (59.3%) 5 (14.7%)
1 (2.3%) 17 (38.6%) 21 (47.7%) 1 (2.3%) 0 (0.0%) 4 (9.1%) 12.0 (8.0–26.0) 15 (34.1%) 5 (11.3%)
1 (2.9%) 6 (17.1%) 14 (40.0%) 0 (0.0%) 0 (0.0%) 14 (40.0%) 9.0 (4.0–17.0)⁎,† 9 (25.7%)† 1 (2.9%)
(12.5%) (15.6%) (21.9%) (15.6%) (15.6%) (6.3%) (12.5%)
(25.7%) (14.3%) (5.7%) (11.4%) (8.6%) (8.6%) (25.7%)
Mobility (HABAM) assessed within first 48 h. SD, standard deviation; ED, emergency department; HABAM, Hierarchical Assessment of Balance and Mobility; IQR, interquartile range. ⁎ Significant differences with HABAM bedridden group (p < 0.05). † Significant differences with HABAM person assist group (p < 0.05).
significant (p = 0.0688). Person-assist participants averaged 25.9 upright bouts per day within the first 48 h of admission (p < 0.0001) and this increased by 0.8 bouts per day (p = 0.003) on average. Bedridden participants averaged 21.9 upright bouts within the first 48 h of admission (p = 0.0001) and experienced an average increase of 0.5 bouts each day (p = 0.05) (Fig. 3).
(p < 0.05). The independent group had most upright bouts between 9 and 11 am (Fig. 2). 3.3. Upright time and frequency based on day of the week There were no differences between weekday and weekend upright time (47.1 [19.2–82.8] vs. 42.8 [18.2–98.2], respectively), for those with both weekday and weekend day data during awake hours (n = 60). When we stratified analysis by HABAM group, we also found that upright time of the bedridden (n = 18, 29.3 [16.8–63.6] vs. 30.8 [5.2–58.1]), person assist (n = 22, 27.5 [16.4–47.0] vs. 25.3 [8.9–54.2]), or the independent group (n = 20, 98.5 [56.0–172.8] vs. 120.4 [69.7–218.4]) groups were not statistically different between weekday and weekend days. Similarly, there were no within-group differences for the number of upright bouts accumulated between weekday versus weekend days for the total sample (14.0 [8.0–26.0] vs. 15.5 [7.5–27.0]), the bedridden, (10.0 [5.0–170] vs. 9.5 [5.5–17.0]), person assist (14.5 [6.0–26.0] vs. 14.0 [6.5–23.0]) or independent (27.5 [18.0–41.0] vs. 29.0 [21.5–39.0]) groups, respectively.
3.5. Sensitivity analyses Extending the awake hours to 5 am–12 am had a minimal impact on the results (data not shown). When we compared admission to preadmission mobility levels we found that among the groups assessed at baseline by the HABAM as bedridden, person assist, and independent groups, 78.7%, 78.1%, and 45.2% reported that they needed assistance or could not walk 100 m prior to hospitalization, respectively. Multilevel growth modeling analysis showed that the pre-admission mobility level did not have an impact on the change in upright time and upright bouts during hospitalization (data not shown). 4. Discussion
3.4. Upright time and frequency based on hospital day
This study of 111 older inpatients showed that hospitalized patients spent less than 1 h per day upright. Even though health care staffing is less during evening and weekends, time of day and day of the week had little impact on the upright time of the patients; mobility level at admission did impact upright time. Participants walking independently at admission had the highest upright time – even this, however, was limited (< 2 h per day). During hospitalization, patients who were bedridden or needed person assistance at admissions increased their upright time whereas patients who could walk independently decreased their upright time from 16% at day 1 to 10% at day 14. We found that on average patients were upright for 54 min/day during their waking hours, which is less than the community dwelling older population and long-term care residents who are upright for 4–5 h/day (Lord et al., 2011) and 2–3 h/day,(Chan et al., 2016; Reid et al., 2013) respectively. Similar to our study, Ostir et al. showed that 244 patients admitted to a US Acute Care for Elders (ACE) hospital unit were active for 80 min in the first 24 h of hospitalization and most
A significant interaction between time and HABAM group indicated that trajectories of upright time varied depending on mobility status at admission (Table 2). Within 48 h of admission, independent participants averaged of 151.5 min per day upright (p < 0.0001) and had an average decline of 4.5 min per day (p < 0.0013). Person-assist participants averaged 29.5 min upright per day within the first 48 h of admission (p = 0.3918) and this increased by 3.6 min per day (p = 0.0016) on average. Bedridden participants averaged 25.0 min a day upright within the first 48 h of admission (p = 0.4894) and experienced an average increase of 2.4 (p = 0.05) minutes per day (Fig. 3). Similarly, a significant interaction between time and HABAM group indicated that trajectories of bouts of upright time varied depending on mobility status at admission (Supplementary Table S1). Independent participants averaged 43.4 (p < 0.0001) upright bouts per day upright within 48 h of admission but change over time was not statistically 3
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Fig. 1. Total upright time characteristics across all hospital days. Panel A = upright time. Panel B = upright bouts. Data are presented as median (interquartile range). Mobility (HABAM) assessed within first 48 h. *Significant differences with HABAM bedridden group (p < 0.05). †Significant differences with HABAM person assist group (p < 0.05).
patients increased their activity during hospitalization (Ostir et al., 2013). A recent systematic review (Baldwin et al., 2017) included 42 studies that used accelerometers to examine active and sedentary behaviour in inpatients. Inpatients spent 93%–99% of their hospital stay sedentary. In most studies patients completed < 1000 steps/day despite their up to 50 postural transitions/day. Multiple factors including severity of illness, pre-existing health status, and hospital structure and processes of care impact upright time during hospitalization. To control for some of these factors we adjusted our analysis for urgency of hospital admission (CTAS) and stratified participants by admission and pre-admission mobility status. A previous
study showed that changes in muscle strength and mass were not associated with having delirium, falls, malnutrition, and functional disability at admission (Van Ancum et al., 2017a). It is possible that changes in upright time may have a similar relationship with geriatric syndromes and other factors at admission. This requires further investigation. Changes in upright time during hospitalization could represent how patients recover while in hospital. Participants who were dependent for mobility at admission had the highest illness severity, and as they recovered in hospital, their upright time increased. Approximately, 21% of the people who were classified as bedridden at admission could walk
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Fig. 2. Upright time (Panel A) and upright bouts (Panel B) by the time of day within the first 24 h of hospitalization. HABAM bedridden = triangles with dotted lines. HABAM dependent = squares with solid lines. HABAM independent = circles with dashed line.
100 m without assistance prior to hospitalization. Many of these people would have recovered from the state of being bedridden during the hospitalization, which could explain the increase in upright time that we observed in this group. However, those who were independent had the lowest illness severity and had the highest baseline upright time but experienced a slow decline in upright time. This latter effect is novel and unexpected. Here, as in other work, change in mobility in the first 48 h forecast the hospital course, with those whose mobility improved early doing much better than those whose mobility declined in the first 48 h (Hatheway et al., 2017). Even so, a meta-analysis on change in muscle strength and muscle mass in older patients during hospitalization showed that the findings depended on the type of admission with muscle strength and mass significantly decreasing in electively admitted older patients but not in acutely admitted older patients. In our
study, we only included acutely admitted patients, but many of our patients from our independent group were admitted directly to the inpatient geriatric unit – not through the ED – and may have similar characteristics with electively admitted older patients (Van Ancum et al., 2017b). These conflicting trends of early improvement with better outcomes and those doing well on admission slowly moving less may reflect some homogenization of mobility expectations. These observations serve as hypotheses for future study. In this regard, our data highlight the need to use objective measures of upright time as vital signs during hospitalization. In fact, a recent study showed that impaired mobility on presentation predicted mortality for acutely ill patients independently and better than traditional vital signs (i.e. low blood pressure, pulse, elevated respiratory rate, hypothermia, low oxygen saturation, old age, and coma) (Brabrand
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Table 2 Estimates from multilevel model examining changes in total upright time.
Fixed effects Intercept Time (change per day) HABAM person assistance HABAM bedridden Time × HABAM person assistance Time × HABAM bedridden Random effects Residual Intercept Hospital day Model fit AIC BIC Deviance
Model 1: unconditional growth
Model 2: fixed and random effects of hospital day
Model 3: fixed effects of HABAMa
75.0 (57.9–92.1)⁎
70.1 (51.3–89.0)⁎ 1.0 (−0.5–2.4)
151.5 (87.7–215.3)⁎ −4.5 (−7.2 to −1.8)⁎ −122.0 (−163.5 to −80.6)⁎ −126.5 (−170.5 to −80.5)⁎ 8.1 (4.6–11.7)⁎ 6.9 (3.2–10.2)⁎
2984.5 (2716.1–3295.0)⁎ 7717.5 (5919.1–10,486.0)⁎
2690.7 (2436.1–2987.7)⁎ 8301.5 (6303.8–11,431.0)⁎ 18.7 (10.9–39.9)⁎
2705.2 (2407.5–2945.4)⁎ 6028.4 (4426.2–8307.5)⁎ 14.7 (8.0–34.6)⁎
10,470.4 10,475.8 10,466.4
10,444.6 10,452.7 10,438.6
10,163.9 10,172.0 10,157.9
Data are presented as parameter estimates (95% CI). Intraclass correlation coefficient for unconditional growth model (Model 1): 0.72. AIC, Akaike information criterion; BIC, Bayesian information criterion. The ICC of 0.72 indicates that 72% of the variation in total upright time scores occurred between persons, while 28% of the variation in upright time was due to within-person changes over time. Model 2 describes the average upright time within 48 h of hospital admission (intercept) and the average rate of change (time) by hospital day among the entire study sample, without including HABAM scores or covariates. Model 3 describes the average upright time within 48 h of hospital admission and the rate of change for the reference HABAM category (independent). ⁎ p < 0.05. a Adjusted for age, sex, and Canadian Triage Acuity Scale score (urgent vs. non-urgent).
the units, but sensitivity analysis showed that extending the awake hours had no impact on the findings. In conclusion, this study showed that older inpatients spend upright only 6% of their awake hours while in hospital. Patients who can walk independently at admission are more active but experience a decline in their upright time during hospitalization.
et al., 2018). Another study showed that number of steps during hospitalization was a stronger predictor of 30-day hospital readmission than functional ability (Fisher et al., 2016). Even so, accelerometers have limited step-count accuracy in older and frail inpatients (McCullagh et al., 2017). Whether increasing upright time is sufficient to improve health outcomes is unclear, but some evidence supports this. For example, older patients who self-reported that they walked at least once per day outside their room had 1.5 days shorter length of stay compared with those who stayed in their room (Shadmi and Zisberg, 2011). On the other hand, the pressures on length of stay can mean that even small signs of independence prompt activation of discharge plans. Still, more postural transitions/mobilizations are associated with 4.7-fold decrease in ADL impairment, 6.0-fold decrease in institutionalization, and 3.3fold decrease post-discharge mortality (Zisberg et al., 2011; Brown et al., 2004). The MOVE-ON initiative recommends that mobility should be assessed within 24 h of hospital admission and that patients should mobilize at least 3 times a day using progressive and scaled mobilizations to reduce sedentary time (Liu et al., 2018). Future studies should examine whether increasing upright time during a patients' stay in acute care will improve health outcomes during and post-hospitalization. Our study has a number of limitations. We only assessed upright time up to 2 weeks due to the limited battery life of the device. Even so, most accelerometer studies last 1 week or less and the device we used (ActivPAL) is one of the most accurate measures of assessing upright time in hospital studies (Lim et al., 2018). Combining sources of information on ambulation, such as reports from health care professionals, is also important when trying to understand the physical activity levels of patients. We relied on self-reported mobility to measure mobility levels of participants at pre-admission, which may be less accurate than objectively measured mobility status. Timing and level of physical therapy sessions were not recorded and CTAS was used as an indicator of illness severity. The ED of the participating hospital uses this triage score to determine the clinical priority of patients and to identify patients who need immediate attention. Even so, using these triage scores as measures of illness severity has limitations and other scores such as the Modified and National Early Warning scores may be more useful. We estimated awake hours based on routine scheduling of
Funding This work was supported in part by a Nova Scotia Health Authority Research Fundgrant, Dalhousie University Internal Medicine Research Fellowship, and Canadian Institutes of Health Research Banting Fellowship. Author contributions OT contributed to conception and design, acquisition of data, analysis and interpretation of data. SK and JG contributed to analysis and interpretation of data. KM, MAM contributed to acquisition of data. KR contributed to conception and design. OT and SK drafted the article. JG, KM, MAM, and KR revised the manuscript critically for important intellectual content. All authors approved the final submitted version of the manuscript. Declaration of competing interest Kenneth Rockwood is President and Chief Science Officer of DGI Clinical, which in the last five years has contracts with pharma and device manufacturers (Baxter, Baxalta, Shire, Hollister, Nutricia, Roche, Otsuka) on individualized outcome measurement. In 2017 he attended an advisory board meeting with Lundbeck. Otherwise any personal fees are for invited guest lectures and academic symposia, received directly from event organizers, chiefly for presentations on frailty. He is Associate Director of the Canadian Consortium on Neurodegeneration in Aging, which is funded by the Canadian Institutes of Health Research, and with additional funding from the Alzheimer Society of Canada and several other charities, as well as, in its first phase (2013–2018), from Pfizer Canada and Sanofi Canada. The rest of the authors have no conflicts of interest. 6
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Fig. 3. Change in total upright time (Panel A) and bouts of upright time (Panel B) by HABAM category. Bedridden = triangles with dotted line. Person assist = squares with solid line. Independent = circles with dashed line. The lines on the graph represent the predicted values (expected change) obtained from the multilevel model for each HABAM category. The 95% CIs for each coefficient can be found in Table 2.
Acknowledgments
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