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Correspondence: Address correspondence to Sharon Wallace Williams, PhD, Department of Allied Health Sciences, Division of Speech and Hearing Sciences; Wing D Medical School, CB #7190, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7190. E-mail: Sharon_Williams{at}unc.edu
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Abstract |
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Key Words: Mobility • Nursing home • Residential care • Assisted living
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Background |
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It is no surprise that the Minimum Data Set mandates quarterly assessments of mobility among nursing home residents. Care for mobility limitation needs to reflect resident characteristics (e.g., dementia, vision, cerebrovascular condition, age, and overall functional status) and the structure and process of care, such as the availability of handrails (structure) and staff training and assessment (process). Care provided by the facility staff is an important component of maintenance and/or restoration of mobility because staff members are the ones who typically identify and assist with mobility problems (Hyatt, 1997).
Based on Donabedian's (1988) structure, process, and outcome model, this article examines components of the care environment and how they relate to mobility for residents with dementia living in nursing homes and residential care/assisted living (RC/AL) settings. Until now, the study of mobility limitation in long-term care has been largely limited to nursing home care. Little information is known about the status and mobility-related care of residents in assisted living settings. Given the growth of this form of care, this study describes the mobility of older adults with dementia in RC/AL and nursing homes and examines structural and process characteristics that relate to mobility limitation.
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Methods |
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Measures
Dependent variable
The Dementia Care study defined mobility based on observation of three characteristics: being on one's feet, changing position, and changing location. Resident-specific data on each of these characteristics were observed during 5-minute intervals between 10 and 11 a.m., 1 and 2 p.m., and 4 and 5 p.m. on one day in each study facility (for a maximum of 36 observations per resident). These times were chosen because they sample the most common activity periods (i.e., when sleep, meals, and morning personal care are least likely to occur). If a scheduled period of observation included a meal, the observation time was adjusted so as to exclude the meal. Up to three residents were observed at a time by each data collector.
Each characteristic was coded to reflect the resident's predominant activity during a 5-minute observation period. Position was noted as on feet, sitting, or lying down. On feet was coded when the resident was standing or walking with or without assistance. Location was coded as bedroom, indoor public area, outdoor public area, or off-site. A change in position was inferred if the resident was observed in two different positions in two sequential 5-minute observations; change in location was similarly defined.
Based on the three characteristics, residents were assigned to one of four levels of mobility limitation. First, if the resident was on his or her feet greater than or equal to 25% of the observations, the resident was coded as having "no mobility limitation." If the resident was on his or her feet less than 25% of observations and did not move (i.e., change position or location between consecutive observations), the resident was coded as having "high mobility limitation." If the resident was on his or her feet less than 25%, but moved between 10% or more of observations, he or she was coded as "low mobility limitation," while those who moved fewer than 10% of observations were coded as having a "moderate mobility limitation."
Independent variables, resident
Cognition was assessed with the Mini-Mental State Examination (MMSE) and the Minimum Data Set Cognition Scale (MDS-COGS); functional status with the Minimum Data Set Activities of Daily Living Scale (MDS-ADL; minus the mobility items), and comorbidity through a list of 11 conditions. Further details about the independent variables can be found in the footnotes to Table 2. Behavioral symptoms were assessed with the Cohen-Mansfield Agitation Inventory; depression using the Cornell Scale for Depression in Dementia; pain with the Philadelphia Geriatric Center Pain Intensity Scale; and low food and fluid intake with the Structured Meal Observation. Family members rated their current involvement on a scale from 1 (very low) to 5 (very high). Supervisors rated resident's vision as adequate, impaired, highly impaired, or severely impaired.
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Supervisors answered several questions regarding assessment and treatment specific to the residents under study: whether the resident had been formally assessed for mobility limitation in the previous year using a written, standardized instrument and/or by a medical doctor or physical therapist; whether or not the resident had been treated for mobility limitation; whether or not the resident used any of a variety of assistive mobility devices (e.g., cane, wheelchair, walker); to what degree they felt treatment was successful; and to what extent limitation in mobility was present (perceived presence). Finally, the environment was assessed by observation and two scales were constructed: the Special Care Unit Environmental Quality Scale (SCUEQS) and the Assisted Living Environmental Quality Scale (ALEQS).
Analysis
We computed simple descriptive statistics—means and standard deviations for continuous measures and percentages for categorical measures—by setting (nursing home vs RC/AL). We completed statistical comparison of these characteristics by setting by fitting linear or logistic (for continuous and binary characteristics, respectively) models, using generalized estimating equations (GEE; Diggle, Heagerty, Liang, & Zeger, 2002), to control for subject clustering within facility through an exchangeable correlation structure; these models have setting as the single explanatory variable. Similarly, we computed descriptive statistics for those observed to have no or low mobility limitation and for those with a moderate to high mobility limitation. We used a four-level ordinal mobility measure as the dependent variable in partial proportional odds logistic regression to estimate odds ratios and 95% confidence intervals for greater mobility limitation. This procedure estimates separate odds ratios for the three cumulative logits for independent variables for which the proportional odds assumption was not met (Stokes, Davis, & Koch, 2000). We estimated adjusted odds ratios, controlling for age, gender, race, cognitive impairment (very severe, severe, moderate, vs mild), number of comorbid conditions, and number of nonmobility related ADL dependencies. We also tested interactions of predictors with setting. Both unadjusted and adjusted models accounted for resident clustering within facility using GEE.
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Results |
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Table 1 describes the mobility status of the sample, and compares RC/AL and nursing home residents by mobility limitation and selected facility components. There was no significant difference in the distribution of residents' mobility limitation across facility type. Overall, about 11% of residents had no mobility limitation, 39% had low limitation, 36% had moderate mobility limitation, and 14% had high mobility limitation.
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Table 2 shows the association between selected resident and facility characteristics and mobility limitation. Residents who had behavioral symptoms had lower odds of being in the high mobility impairment group than those without behavioral symptoms (adjusted OR =.40, 95% CI.17–.91). Residents with low fluid intake were more likely to have a higher level of mobility impairment than residents with adequate fluid intake (adjusted OR = 1.73, 95% CI 1.08–2.79). There was no significant association between other resident characteristics measured in this study and mobility limitation.
In terms of structural facility characteristics, residents of RC/AL facilities with fewer than 16 beds had higher levels of mobility limitation than nursing home residents (adjusted OR = 2.23, 95% CI 1.03–4.82), while those in for-profit facilities have lower odds of having any mobility limitation compared to residents in not-for-profit facilities (adjusted OR = 0.28, 95% CI 0.10–0.82). Process facility characteristics such as professional assessment (adjusted OR = 2.20, 95% CI 1.47–3.30) and perceived presence of mobility limitation (adjusted OR = 4.67, 95% CI 2.43–9.00) were associated with higher levels of mobility limitation.
Similarly, the process facility characteristic of treatment, both professional (adjusted OR = 2.11, 95% CI 1.24–3.61) and informal (adjusted OR = 1.82, 95% CI 1.03–3.22), was associated with higher levels of mobility limitation than that found in residents who did not receive treatment for mobility limitation. Residents using an assistive mobility device also had greater odds of having some mobility impairment (adjusted OR = 3.34, 95% CI 1.28–8.74) than those not using such devices. Finally, the structural variable of a higher environmental quality score was associated with having some degree (high, moderate, or low) of mobility limitation versus having no mobility limitation.
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Discussion |
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These numbers are similar to other findings that document high levels of mobility limitation (75–85%) in long-term care facilities (Horn et al., 2002; Pope & Tarlov, 1991). Despite the fact that nursing homes typically have residents with higher levels of functional limitations than RC/AL facilities (Zimmerman et al., 2003), there was no significant difference in mobility limitation across the two settings among residents with dementia in this study. However, a more detailed analysis of the type of facility found higher levels of mobility limitation in RC/AL facilities with fewer than 16 beds compared to nursing homes. It may be that smaller facilities offer less opportunity for mobility, and/or that there is less access to mobility assistive devices in these less resource-intense settings. Also, facilities with higher environmental scores (e.g., better lighting contrast, handrails) may enable or facilitate management of residents with higher levels of mobility limitation.
Residents who exhibit behavioral symptoms were less likely to have high mobility limitation than those who did not; the association of behavioral symptoms with wandering may account for this relationship. Residents who used assistive mobility devices were more likely to have higher mobility limitation, and this relationship has been noted in other studies (Verbrugge & Sevak, 2002). Low fluid intake (observed during one meal) also remained positively associated with mobility limitation after adjustment for age, gender, race, cognitive impairment, ADLs, and comorbidities. Thus, there is a group of residents who maybe at risk for both mobility-related morbidity (e.g., pressure ulcers) and dehydration. Analysis of this group indicates that only 45% of these residents are very severely cognitively impaired (and may be end-stage dementia); hence, efforts may be indicated to focus care on this vulnerable population.
In terms of process variables, higher levels of assessment and treatment also were associated with having a higher level of mobility limitation. Since assessment and treatment were obtained from supervisor report and mobility was obtained by observation, it is unlikely that the association between assessment and treatment and mobility is due to measurement bias. More likely, this finding reflects that RC/AL and nursing home staff are attentive to residents and intervene when mobility problems occur. However, 38% to 63% of those with moderate to high limitation were not professionally assessed or treated, perhaps indicating the need for more attention for some of these more impaired residents.
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Footnotes |
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1 Department of Allied Health Sciences, Division of Speech and Hearing Sciences; Center on Aging and Diversity, University of North Carolina at Chapel Hill.
2 Cecil G. Sheps Center for Health Services Research and the School of Public Health, University of North Carolina at Chapel Hill.
3 Cecil G. Sheps Center for Health Services Research and the School of Social Work, University of North Carolina at Chapel Hill.
4 Cecil G. Sheps Center for Health Services Research and the Department of Family Medicine, University of North Carolina at Chapel Hill.
5 Department of Biostatistics, School of Public Health, University of North Carolina at Chapel Hill.
6 Regenstrief Institute, Inc., and Indiana University Center for Aging Research, Indianapolis.
7 Alzheimer's Association, National Office, Chicago, IL.
Decision Editor: Richard Schulz, PhD
Received for publication June 28, 2004. Accepted for publication May 13, 2005.
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This article has been cited by other articles:
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