Research in Gerontological Nursing

Research Brief 

Ambulation Patterns Post-Discharge in Older Adults Identified as Fall Risk: A Descriptive Pilot Study

Barbara J. King, PhD, APRN-BC; Roger Brown, PhD; Linsey Steege, PhD; Haocen Wang, MS, RN; Fang-lin Kuo, PhD, MS; Cynthia Brown, MD, MSPH

Abstract

Older adults identified as fall risk during a hospital stay may be at high risk for decreased ambulation during hospitalization and after discharge. Little is known about ambulation frequency in older adults identified as fall risk during a hospital stay or their trajectory of ambulation patterns after an acute hospitalization. Individual ambulation frequency, patterns, functional performance, and fear of falls for 14 older adults were studied. Accelerometers were worn by patients during their hospital stay and for 4 weeks post-discharge. Novel analytics using piecewise regression were used to analyze data. Patterns of ambulation were heterogeneous, and 64% of patients demonstrated no change in the first 2 weeks post-discharge. Increase in ambulation frequency was positively correlated with higher Katz Activities of Daily Living Index scores, gait speed, and lower fear of falls score. Ambulation patterns in older adults identified as fall risk show promise in capturing ambulation recovery and functional ability post-discharge.

[Res Gerontol Nurs. 2019; 12(3):113–119.]

Abstract

Older adults identified as fall risk during a hospital stay may be at high risk for decreased ambulation during hospitalization and after discharge. Little is known about ambulation frequency in older adults identified as fall risk during a hospital stay or their trajectory of ambulation patterns after an acute hospitalization. Individual ambulation frequency, patterns, functional performance, and fear of falls for 14 older adults were studied. Accelerometers were worn by patients during their hospital stay and for 4 weeks post-discharge. Novel analytics using piecewise regression were used to analyze data. Patterns of ambulation were heterogeneous, and 64% of patients demonstrated no change in the first 2 weeks post-discharge. Increase in ambulation frequency was positively correlated with higher Katz Activities of Daily Living Index scores, gait speed, and lower fear of falls score. Ambulation patterns in older adults identified as fall risk show promise in capturing ambulation recovery and functional ability post-discharge.

[Res Gerontol Nurs. 2019; 12(3):113–119.]

Falls in hospitalized older adults (i.e., age ≥65 years) are common events and often associated with negative outcomes, including longer lengths of stay; discharge to institutional care settings; anxiety among patients, family members, and hospital staff; and injuries (Oliver, 2004; Spoelstra, Given, You, & Given, 2012; Stubbs, Denkinger, Brefka, & Dallmeier, 2015). Rates of falls vary widely, with reports ranging from 1.3 to 25 falls per 1,000 patient days (Oliver, Healey, & Haines, 2010; Schiller, Kramarow, & Dey, 2007). Other studies have reported that 10% of older adults will fall during hospitalization (McCarter-Bayer, Bayer, & Hall, 2005) and that falls have steadily increased over the past 3 decades (Wanless, 2006).

Many older adults, those who have and have not experienced falls, develop a fear of falls. Fear of falls can produce significant psychological consequences such as increased anxiety, depression, and loss of confidence, resulting in self-imposed activity restriction, which further increases risk for falls (Boyd & Stevens, 2009). Boltz, Resnick, Capezuti, and Shuluk (2014) found a correlation between fear of falls and loss of physical function in hospitalized older adults. Patients who had a fear of falls often engaged in activity restriction. In addition, nursing staff experience guilt and anxiety when a patient falls and may limit ambulation to prevent falls (Boltz, Capezuti, & Shabbat, 2010; King, Pecanac, Krupp, Liebzeit, & Mahoney, 2018). The combined effect of patient fear of falls and nurses' fears that patients will fall may increase fall rates.

In general, older adults infrequently ambulate during a hospital stay (Brown, Redden, Flood, & Allman, 2009; Fisher et al., 2011). Lack of ambulation in older adult patients has been associated with loss of muscle mass and strength, producing increased risk for falls during and after a hospital stay (Mahoney, 1998). However, research performed on older adult patient ambulation has not investigated how often individuals who are identified as at risk for falls ambulate.

What is known is that older adults commonly experience significant declines in ability to perform activities of daily living (ADL), walk independently, and community mobility after discharge (Gill, Allore, Gahbauer, & Murphy, 2010; Loyd et al., 2018; Zisberg et al., 2011). Further, 50% of older adults who have functional decline at discharge are still disabled 1 year after hospitalization (Brown, Friedkin, & Inouye, 2004). Because of concerns for falls during hospitalization, older adults identified as at risk for falls may be further restricted in ambulation during their stay and therefore experience high rates of functional decline and falls post-discharge. No studies have examined ambulation performance and recovery post-discharge in older adults identified as fall risk during hospitalization.

The purpose of the current descriptive longitudinal pilot study was to identify trajectory patterns of ambulation performance post-discharge and to evaluate the relationship between the number of steps and functional ability in older adults identified as fall risk post-discharge. This study was not guided by a theoretical or conceptual model.

Method

Study Population

Twenty-four older adult patients admitted to an academic teaching hospital in the Midwest were enrolled in the study. Eligibility criteria were age ≥65 years, identified as fall risk by nursing staff, and able to walk with or without an assistive walking device. Exclusion criteria were bilateral lower extremity amputation and severe lower extremity edema. In addition, patients with an activated power of attorney were excluded because written consent was required, and patients with a terminal illness were excluded as their ambulation patterns may be more affected by progressing disease. Data were collected from January 2016 to July 2016. Seven patients dropped out, three during their hospital stay and four within the first week of discharge. Two patients were removed due to increased acuity and re-admission to other hospitals outside of the study area. One patient died post-discharge, unrelated to the study. The final sample included 14 patients with complete data for in-hospital and 4 weeks post-discharge. The study was approved the University of Wisconsin–Madison Institutional Review Board.

Study Variables

One member of the research team (B.J.K.) collected the study variables, Katz ADL Index score, gait speed, and Falls Self-Efficacy Scale International (FES-I) score during weekly in-person data collection visits that occurred for the 4-week post-discharge period. In-person post-discharge data were collected in the patient's community-dwelling residence. Demographic and clinical data were collected by review of the electronic medical record (EMR) immediately after the patient was discharged. One study team member (B.J.K.) performed all EMR data abstraction. Another research member (F.K.) collected gait speed during the hospital stay immediately after the patient signed written consent to participate in the study.

Assessment of Ambulation Frequency. A research-grade accelerometer was used to measure number of steps taken. The StepWatch® Activity Monitor (SAM) is a waterproof dual-axis accelerometer that weighs 1.3 oz with reported 98% accuracy in a variety of clinical populations, including older adults (Storti et al., 2008). Stride counts (steps) were recorded in 1-minute intervals to a 24-hour synchronized clock. Data were uploaded from the SAM to a laptop computer at the end of the study period (4 weeks post-discharge). The SAM was placed around the patient's ankle by a member of the research team (F.K.) within 36 hours of admission to the hospital and worn during the hospital stay. Another member of the research team (B.J.K.) removed the SAM at 4 weeks post-discharge. The placement of the SAM was checked (by B.J.K.) during each in-person weekly data collection time period.

Ambulation Performance. Ambulation performance was measured by conducting a 4-meter walk test (gait speed). Gait speed is highly reproducible and sensitive to change (Ostir, Volpato, Fried, Chaves, & Guralnik, 2002); therefore, it can detect a possible change in ambulation performance over time. A straight, clearly marked course was used. Patients were instructed to walk at their usual pace from a still standing position behind the starting line. Timing started at the first foot movement and ended when a foot completely crossed the finish line. Canes and walkers were allowed if patients normally used these devices for ambulating. Gait speeds in hospitalized older adults are generally below speeds used for community benchmarks (Graham, Fisher, Marie-Berges, Kuo, & Ostir, 2010). Mean walking speeds between 0.23 m/s and 0.50 m/s have been reported for older adults in hospital settings (Purser et al., 2005). Further, gait speed ≤0.60 m/s is diagnostic for mobility disability in older adults (Cummings, Studenski, & Ferrucci, 2014).

Functional Measures. Ability to perform basic ADLs was measured using the self-report Katz ADL Index. The Katz ADL Index measures five ADLs on three levels (i.e., independent, requiring assistance of another, and unable to do). The scale has demonstrated reliability and predictive validity and is sensitive to change over time (Katz, Downs, Cash, & Grotz, 1970). The Katz ADL Index score ranges from 0 to 6, with higher scores indicating functional independence.

Fear of Falls. Fear of falls was measured using the self-report FES-I. The FES-I has established reliability and validity for hospitalized older adults and is capable of measuring change over time (Delbaere et al., 2010). FES-I scores range from 16 to 64, with lower scores (16 to 19) indicating low concern for falls.

Demographic and clinical characteristics were collected during hospitalization by reviewing patients' medical records and included age, gender, body mass index, admitting diagnosis, Charlson Comorbidity Index, fall risk score (Hendrich II), hospital length of stay, residence prior to hospitalization, discharge destination, and use of walking device on admission and discharge. Self-reports of falls post-discharge were collected weekly during in-person visits by asking patients if they experienced a near fall (i.e., slip or trip without falling to the ground) (Steinberg, Cart-wright, Peel, & Williams, 2000) or fall since the last visit.

Data Analysis

Descriptive statistics along with Pearson correlations were used to describe the sample and the relationships between measures. To assess patient post-hospitalization trajectories of ambulation and possible change in ambulation, single-patient segmented linear regression modeling was used. Segmented linear regression analysis applied linear regression to data (i.e., steps per day) that do not have a linear relationship. It introduces a breakpoint (i.e., change in ambulation), whereupon separate linear regressions are made for linear segments. Thus, the non-linear relationship is approximated by linear segments. A critical element is the location of the breakpoint. Oosterbaan, Sharma, and Singh (1990) presented a method for calculating confidence intervals of the breakpoints so that the breakpoint with the smallest interval (i.e., the optimum breakpoint) can be selected. This breakpoint represents a change in the trajectory of post-hospitalization ambulation. Using segmented linear trajectory analysis allowed patterns of change post-discharge to be identified. Slope 1 identifies the initial trajectory of ambulation, whereas slope 2 demonstrates a change in the trajectory. SegReg was used to detect slopes and breakpoints in the segmented linear trajectory analysis and estimate 95% confidence intervals.

Results

Table 1 shows patient demographic and clinical characteristics. Mean age of the sample (N = 14) was 80 years (SD = 8.12 years). The majority of participants were female, admitted from home, and used a walking device on admission. The Charlson Comorbidity Index ranged from 0 to 9 (mean = 2.79, SD = 2.58). Fall risk score, calculated using the Hendrich II, was retrieved from patients' medical records as recorded by nursing staff. The mean fall risk score was 5.02 (SD = 1.61). No patients reported a fall or near fall during the 4-week post-discharge period.

Sample Demographic and Clinical Characteristics

Table 1:

Sample Demographic and Clinical Characteristics

Table 2 illustrates individual trajectory ambulation patterns of patients post-discharge. The range of mean daily number of steps taken during the hospital stay was 60 to 2,418 steps, with an overall mean of 1,126.29 steps (SD = 773.63 steps), whereas the range of mean daily steps taken post-discharge was 3,788 to 9,416 steps with an overall mean of 3,412.71 steps (SD = 2,473.15 steps), indicating participants took fewer steps than they were capable of taking during the hospital stay. In general, it took approximately 15 days for most patients to demonstrate a change in ambulation trajectory post-discharge. Sixty-four percent of patients had a slope of 0, indicating little or no change (i.e., increase or decrease in number of steps) occurring in the first 2 weeks post-discharge. Individual trajectory patterns of ambulation were heterogeneous and demonstrated the following trajectory patterns: a flat pattern (no breakpoint), an initial flat trajectory followed by a linear increase, an initial increase followed by a flat trajectory, an initial flat pattern with an abrupt change in trajectory, or two distinct patterns of ambulation frequency.

Individual Trajectory Ambulation Patterns Post-Discharge

Table 2:

Individual Trajectory Ambulation Patterns Post-Discharge

Table 3 identifies positive correlations between increased number of steps taken post-discharge with higher Katz ADL Index scores, gait speed, and lower FES-I scores (negative correlation), meaning that participants demonstrated better functional performance and decreased concern for falls post-discharge with high frequency in number of steps taken.

Pearson Correlation Coefficients Between Number of Weekly Post-Discharge Steps and Measures of Activities of Daily Living (ADL), Fear of Falling, and Gait Speed

Table 3:

Pearson Correlation Coefficients Between Number of Weekly Post-Discharge Steps and Measures of Activities of Daily Living (ADL), Fear of Falling, and Gait Speed

Discussion

Ambulation requires activation and coordination of multiple biological systems, such as somatosensory, visual, vestibular, autonomic, musculoskeletal, and cognitive (Takakusaki, 2017). During an acute illness, the impact of physiological stress can produce a decrease in mobility. As illness resolves, mobility should increase (Fisher et al., 2013). The current study findings of no or little change in participant ambulation trajectory in the first 2 weeks post-discharge may be an indication of lingering physiological instability (i.e., persistent burden of illness) (Fisher et al., 2013) and a high vulnerability period for further functional decline and falls. Frequency of ambulation and whether older adults are able to increase ambulation in the first 2 weeks after a hospital stay may be an important physical biomarker for functional decline, falls, and readmission (Fisher et al., 2013).

Unlike other studies on ambulation in older adults either during hospitalization or post-discharge, the current study focused primarily on patients identified as fall risk. It is well-documented that older adults have limited ambulation during a hospital stay, leading to significant negative outcomes, including functional decline, falls, and new nursing home placement (Brown et al., 2004). Acute care nurses are hesitant to ambulate fall-risk patients (King et al., 2018); therefore, this population may be at even higher risk for functional decline and falls post-discharge. Further research is needed to determine if older adult patients identified as fall risk are at even greater risk for low levels of ambulation during a hospital stay and what impact fall risk identification has on functional recovery post-discharge.

Despite the small sample size, the number of steps per week was found to be positively correlated with ADLs, fear of falls, and gait speed. Only one other study has measured frequency of ambulation in older adults post-discharge. Fisher et al. (2013) measured mean daily steps older adult patients took 5 days post-discharge. Their findings identified mean daily steps as the strongest predictor for 30-day readmission (Fisher et al., 2013). However, Fisher et al. (2013) did not identify if participants were labeled as fall risk during their hospital stay. A strength of the current study is the collection of data for 4 weeks post-discharge, which allowed for a more comprehensive picture of when functional performance changes occurred. Additional studies that span a longer recovery period could help guide the development of predictive models that better support ambulation performance recovery in older adult patient populations.

To the current authors' knowledge, this is the first study to explore ambulation patterns post-discharge in hospitalized older adults identified as fall risk. Moreover, a novel analytic technique using piecewise regression (Toms & Lesperance, 2003) was used to determine trajectory patterns of change and to identify a breakpoint when a change in pattern occurred. Using this method, multiple ambulation patterns and the number of days post-discharge when a breakpoint occurred could be identified. With a larger sample, patient factors associated with various patterns may be identified. Identifying patient-specific ambulation patterns would aid in designing patient-centered interventions to prevent further functional decline and fall risk post-discharge. Furthermore, it was found that most patients did not demonstrate a change in ambulation pattern until 15 days post-discharge.

Currently, Medicare recipients pay $0 for the first 20 days of skilled care (including physical and occupational therapy) (Medicare.gov, n.d.). If rehabilitation services are needed beyond 20 days, a copayment is required (Medicare. gov, n.d.). In the current study, by the time patients started to demonstrate a change in ambulation pattern they would have already used a large portion of their 20-day Medicare benefits. The ability to identify breakpoints where patients may receive the most benefit, both physically and financially, from rehabilitative services could have policy implications at a national level. Additional research is needed with larger samples to determine breakpoints and alignment with functional performance and Medicare reimbursement.

Limitations

The current study has several limitations. First, the sample size is small, which limits the ability to generalize findings to a population of older adults identified as fall risk during a hospital stay. However, participants were successfully recruited and retained over a 4-week period of time. Although the sample size may have limited statistical power, at least two significant findings between number of steps and ADL performance and gait speed were found.

Despite these limitations, this study demonstrates feasibility of this type of data collection and analysis with older adults identified as fall risk and contributes new knowledge regarding risk for poor ambulation performance and functional recovery for this population post-discharge.

Conclusion

Negative outcomes for hospitalized older adults due to limited ambulation have been well-documented. However, little research has been performed on ambulation frequency and outcomes post-discharge in older adult patients identified as fall risk. Understanding patterns of ambulation and when breakpoints occur post-discharge will be important for all older adults but may be particularly important for those identified as fall risk during a hospital stay. Further research is needed with larger samples to determine ambulation recovery and impact on functional status.

References

  • Boltz, M., Capezuti, E. & Shabbat, N. (2011). Nursing staff perceptions of physical function in hospitalized older adults. Applied Nursing Research, 24, 215–222. doi:10.1016/j.apnr.2010.01.001 [CrossRef]
  • Boltz, M., Resnick, B., Capezuti, E. & Shuluk, J. (2014). Activity restriction vs. self-direction: Hospitalised older adults' response to fear of falling. International Journal of Older People Nursing, 9, 44–53. doi:10.1111/opn.12015 [CrossRef]
  • Boyd, R. & Stevens, J.A. (2009). Falls and fear of falling: Burden, beliefs and behaviours. Age and Ageing, 38, 423–428. doi:10.1093/ageing/afp053 [CrossRef]
  • Brown, C.J., Friedkin, R.J. & Inouye, S.K. (2004). Prevalence and outcomes of low mobility in hospitalized older patients. Journal of the American Geriatrics Society, 52, 1263–1270. doi:10.1111/j.1532-5415.2004.52354.x [CrossRef]
  • Brown, C.J., Redden, D.T., Flood, K.L. & Allman, R.M. (2009). The underrecognized epidemic of low mobility during hospitalization of older adults. Journal of the American Geriatrics Society, 57, 1660–1665. doi:10.1111/j.1532-5415.2009.02393.x [CrossRef]
  • Cummings, S.R., Studenski, S. & Ferrucci, L. (2014). A diagnosis of dismobility—Giving mobility clinical visibility: A Mobility Working Group recommendation. JAMA, 311, 2061–2062. doi:10.1001/jama.2014.3033 [CrossRef]
  • Delbaere, K., Close, J.C., Mikolaizak, A.S., Sachdev, P.S., Brodaty, H. & Lord, S.R. (2010). The Falls Efficacy Scale International (FES-I). A comprehensive longitudinal validation study. Age and Ageing, 39, 210–216. doi:10.1093/ageing/afp225 [CrossRef]
  • Fisher, S.R., Goodwin, J.S., Protas, E.J., Kuo, Y.F., Graham, J.E., Ottenbacher, K.J. & Ositr, G.V. (2011). Ambulatory activity of older adults hospitalized with acute medical illness. Journal of the American Geriatrics Society, 59, 91–95. doi:10.1111/j.1532-5415.2010.03202.x [CrossRef]
  • Fisher, S.R., Kuo, Y.F., Sharma, G., Raji, M.A., Kumar, A., Goodwin, J.S. & Ottenbacher, K.J. (2013). Mobility after hospital discharge as a marker for 30-day readmission. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 68, 805–810. doi:10.1093/gerona/gls252 [CrossRef]
  • Gill, T.M., Allore, H.G., Gahbauer, E.A. & Murphy, T.E. (2010). Change in disability after hospitalization or restricted activity in older persons. JAMA, 304, 1919–1928. doi:10.1001/jama.2010.1568 [CrossRef]
  • Graham, J.E., Fisher, S.R., Berges, I.M., Kuo, Y.F. & Ostir, G.V. (2010). Walking speed threshold for classifying walking independence in hospitalized older adults. Physical Therapy, 90, 1591–1597. doi:10.2522/ptj.20100018 [CrossRef]
  • Katz, S., Downs, T.D., Cash, H.R. & Grotz, R.C. (1970). Progress in development of the index of ADL. The Gerontologist, 10, 20–30. doi:10.1093/geront/10.1_Part_1.20 [CrossRef]
  • King, B., Pecanac, K., Krupp, A., Liebzeit, D. & Mahoney, J. (2018). Impact of fall prevention on nurses and care of fall risk patients. The Gerontologist, 58, 331–340. doi:10.1093/geront/gnw156 [CrossRef]
  • Loyd, C., Beasley, T.M., Miltner, R.S., Clark, D., King, B. & Brown, C.J. (2018). Trajectories of community mobility recovery after hospitalization in older adults. Journal of the American Geriatrics Society, 66, 1399–1403. doi:10.1111/jgs.15397 [CrossRef]
  • Mahoney, J.E. (1998). Immobility and falls. Clinics in Geriatric Medicine, 14, 699–726. doi:10.1016/S0749-0690(18)30087-9 [CrossRef]
  • McCarter-Bayer, A., Bayer, F. & Hall, K. (2005). Preventing falls in acute care: An innovative approach. Journal of Gerontological Nursing, 31(3), 25–33. doi:10.3928/0098-9134-20050301-07 [CrossRef]
  • Medicare.gov. (n.d.). Skilled nursing facility (SNF) care. Retrieved from https://www.medicare.gov/coverage/skilled-nursing-facility-snf-care
  • Oliver, D. (2004). Prevention of falls in hospital inpatients. Agendas for research and practice. Age and Ageing, 33, 328–330. doi:10.1093/ageing/afh145 [CrossRef]
  • Oliver, D., Healey, F. & Haines, T.P. (2010). Preventing falls and fall-related injuries in hospitals. Clinics in Geriatric Medicine, 26, 645–692. doi:10.1016/j.cger.2010.06.005 [CrossRef]
  • Oosterbaan, R.J., Sharma, D.P., Singh, K.N. & Rao, K.V.G.K. (1990). Crop production and soil salinity: Evaluation of field data from India by segmented linear regression with breakpoint. Proceedings of the Symposium on Land Drainage for Salinity Control in Arid and Semi-Arid Regions, 3, 373–383. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.630.6690&rep=rep1&type=pdf
  • Ostir, G.V., Volpato, S., Fried, L.P., Chaves, P. & Guralnik, J.M. (2002). Reliability and sensitivity to change assessed for a summary measure of lower body function: Results from the Women's Health and Aging Study. Journal of Clinical Epidemiology, 55, 916–921. doi:10.1016/S0895-4356(02)00436-5 [CrossRef]
  • Purser, J.L., Weinberger, M., Cohen, H.J., Pieper, C.F., Morey, M.C., Li, T. & Lapuerta, P. (2005). Walking speed predicts health status and hospital costs for frail elderly male veterans. Journal of Rehabilitation Research and Development, 42, 535–546. doi:10.1682/JRRD.2004.07.0087 [CrossRef]
  • Schiller, J.S., Kramarow, E.A. & Dey, A.N. (2007). Fall injury episodes among noninstitutionalized older adults: United States, 2001–2003. Advance Data, 392, 1–16.
  • Spoelstra, S.L., Given, B., You, M. & Given, C.W. (2012). The contribution falls have to increasing risk of nursing home placement in community-dwelling older adults. Clinical Nursing Research, 21, 24–42. doi:10.1177/1054773811431491 [CrossRef]
  • Steinberg, M., Cartwright, C., Peel, N. & Williams, G. (2000). A sustainable programme to prevent falls and near falls in community dwelling older people: Results of a randomized trial. Journal of Epidemiology and Community Health, 54, 227–232. doi:10.1136/jech.54.3.227 [CrossRef]
  • Storti, K.L., Pettee, K.K., Brach, J.S., Talkowski, J.B., Richardson, C.R. & Kriska, A.M. (2008). Gait speed and step-count monitor accuracy in community-dwelling older adults. Medicine and Science in Sports and Exercise, 40, 59–64. doi:10.1249/mss.0b013e318158b504 [CrossRef]
  • Stubbs, B., Denkinger, M.D., Brefka, S. & Dallmeier, D. (2015). What works to prevent falls in older adults dwelling in long term care facilities and hospitals? An umbrella review of meta-analysis of randomized controlled trials. Maturitas, 81, 335–342. doi:10.1016/j.maturitas.2015.03.026 [CrossRef]
  • Takakusaki, K. (2017). Functional neuroanatomy for posture and gait control. Journal of Movement Disorders, 10, 1–17. doi:10.14802/jmd.16062 [CrossRef]
  • Toms, J.D. & Lesperance, M.L. (2003). Piecewise regression: A tool for identifying ecological thresholds. Ecology, 84, 2034–2041. doi:10.1890/02-0472 [CrossRef]
  • Wanless, D. (2006). Securing good care for older people: Taking a long term view. London, UK: King's Fund.
  • Zisberg, A., Shadmi, E., Sinoff, G., Gur-Yaish, N., Srulovici, E. & Admi, H. (2011). Low mobility during hospitalization and functional decline in older adults. Journal of the American Geriatrics Society, 59, 266–273. doi:10.1111/j.1532-5415.2010.03276.x [CrossRef]

Sample Demographic and Clinical Characteristics

VariableMean (SD) (Range)
Age (years)80 (8.12) (67 to 92)
Length of stay (days)4.5 (3.28) (2 to 12)
Charlson Comorbidity Index2.79 (2.58) (0 to 9)
4-meter gait speed (m/s)0.53 (0.19) (0.25 to 0.84)
n (%)
Gender
  Female10 (71.4)
  Male4 (28.6)
Medical diagnosis on admission
  Cardiovascular3 (21.4)
  Gastrointestinal3 (21.4)
  Back pain3 (21.4)
  Miscellaneous symptoms4 (28.6)
  Respiratory1 (7.1)
Admitted from
  Own home13 (92.9)
  Assisted living1 (7.1)
Discharged to
  Own home7 (50)
  Skilled nursing care facility6 (42.9)
  Another acute care hospital1 (7.1)
Use of walking device on admission
  Yes10 (71.5)
    Walker6 (60)
    Cane4 (40)
  No4 (28.6)
Use of walking device on discharge
  Yes11 (78.6)
    Walker10 (90.9)
    Cane1 (9.1)
  No3 (21.4)
Self-report of fall or near fall post-discharge
  No14 (100)
  Yes0

Pearson Correlation Coefficients Between Number of Weekly Post-Discharge Steps and Measures of Activities of Daily Living (ADL), Fear of Falling, and Gait Speed

Steps Post-DischargePearson Correlation Coefficient [95% Confidence Interval]
ADLaFear of FallingbGait Speed
Week 10.566* [0.050, 0.843]−0.369 [−0.752, 0.200]0.374 [−0.194, 0.755]
Week 20.546 [−0.006, 0.843]−0.333 [−0.747, 0.266]0.531 [−0.027, 0.837]
Week 30.348 [−0.280, 0.768]−0.464 [−0.860, 0.149]0.392 [−0.234, 0.788]
Week 40.426 [−0.331, 0.849]−0.627 [−0.911, 0.063]0.700* [0.068, 931]
Authors

Dr. King is Associate Professor, Dr. R. Brown is Professor, Dr. Steege is Associate Professor, and Ms. Wang is PhD Candidate, School of Nursing, University of Wisconsin–Madison, Madison, Wisconsin; Dr. Kuo is Postdoctoral Research Fellow, National Health Research Institutes, Taiwan; and Dr. C. Brown is Professor and Director, Division of Gerontology, Geriatrics, and Palliative Care, University of Alabama at Birmingham, Birmingham, Alabama.

The authors have disclosed no potential conflicts of interest, financial or otherwise.

Address correspondence to Barbara J. King, PhD, APRN-BC, Associate Professor, School of Nursing, University of Wisconsin- Madison, 701 Highland Avenue, Room 4157, Madison, WI 53705; e-mail: Bjking2@wisc.edu.

Received: September 24, 2018
Accepted: December 03, 2018
Posted Online: February 28, 2019

10.3928/19404921-20190131-01

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