Research in Gerontological Nursing

Empirical Research 

Barriers to Promoting Mobility in Hospitalized Older Adults

Gordana Dermody, PhD, RN, CNL; Christine R. Kovach, PhD, RN, FAAN, FGSA

Abstract

Hospitalized older adults who do not receive sufficient mobility are more likely to sustain negative health outcomes, including higher rates of mortality and institutionalization. Accordingly, the purpose of the current secondary data analysis was to examine the nurse-promoted mobility of hospitalized older adults and the association between nurses' barriers and nurse-promoted mobility. In addition, the relationship among patient severity of illness, proxy levels for function, and nurse-promoted mobility was examined. The final study sample included 61 nurses working in medical units caring for a total of 77 older adults. Findings suggest nurse knowledge gaps and attitude barriers could potentially influence the type and frequency of mobility they promote in older patients. A relationship was found between older patients with impaired mobility using assistive devices for mobility at home, and those at high risk for falls and nurses promoting more sedentary activity (e.g., chair sitting, walking in the room). Interestingly, nurses promoted significantly more sedentary mobility for patients with physical therapy orders.

[Res Gerontol Nurs. 2018; 11(1):17–27.]

Abstract

Hospitalized older adults who do not receive sufficient mobility are more likely to sustain negative health outcomes, including higher rates of mortality and institutionalization. Accordingly, the purpose of the current secondary data analysis was to examine the nurse-promoted mobility of hospitalized older adults and the association between nurses' barriers and nurse-promoted mobility. In addition, the relationship among patient severity of illness, proxy levels for function, and nurse-promoted mobility was examined. The final study sample included 61 nurses working in medical units caring for a total of 77 older adults. Findings suggest nurse knowledge gaps and attitude barriers could potentially influence the type and frequency of mobility they promote in older patients. A relationship was found between older patients with impaired mobility using assistive devices for mobility at home, and those at high risk for falls and nurses promoting more sedentary activity (e.g., chair sitting, walking in the room). Interestingly, nurses promoted significantly more sedentary mobility for patients with physical therapy orders.

[Res Gerontol Nurs. 2018; 11(1):17–27.]

Hospitalized older adults are at greater risk for functional decline due to natural age-related musculoskeletal changes that are further complicated by comorbidities, chronic illness, and insufficient mobility (Cruz-Jentoft et al., 2010; Pedersen et al., 2013). Promoting mobility, including ambulation, sitting in a chair, and range of motion, is critical basic nursing care that nurses should be doing routinely (Doenges, Moorhouse, & Murr, 2014). Muscle atrophy and muscle weakness are consequences of immobility (Cruz-Jentoft et al., 2010), leading to hospital readmissions (Fisher, Graham, Krishnan, & Ottenbacher, 2016), hospital-acquired comorbid conditions (Peterson & Braunschweig, 2016), and preventable nursing home admission (Liu et al., 2016). Complications resulting from insufficient mobility while hospitalized can place increased burden on family members and require increased health care system resources (Cadogan & D'Ambruoso, 2012).

The promotion of mobility is important to prevent functional decline and other adverse health outcomes (Brown et al., 2016; Du et al., 2015; Fisher, Graham, Ottenbacher, Deer, & Ostir, 2016). However, nurses may experience barriers to promoting mobility in this population, which may explain why hospitalized older adults are not sufficiently mobilized (Catchpole, 2013; Doherty-King & Bowers, 2011; Moore et al., 2014). The Knowledge, Attitude, and Behavior Framework shows the relationship between interpersonal and external barriers that clinicians may experience, and how these barriers affect their care behavior (Cabana et al., 1999; Woolf, 1993). Three over-arching barriers include knowledge, attitude, and external barriers (Cabana et al., 1999). The central premise is that both interpersonal (knowledge and attitude) and external (patient, interdisciplinary, and environmental) barriers may influence nurse-promoted mobility.

Studies suggest that nurse knowledge and attitudes, as well as other barriers, may be linked to nurse-promoted mobility (Doherty-King & Bowers, 2013; Hoyer, Brotman, Chan, & Needham, 2015; Moore et al., 2014). Nurse knowledge barriers may include not having the training to promote mobility and lacking knowledge of geriatric patients' needs for mobility (Hoyer et al., 2015; Lee & Fan, 2012). Nurses have also reported that external factors, such as patient condition, sedation, being attached to medical devices, and care coordination, were barriers to promoting mobility in patients in the intensive care unit (Leditschke, Green, Irvine, Bissett, & Mitchell, 2012; Lee & Fan, 2012). Finally, nurse attitudes and beliefs about promoting mobility may be associated with insufficient promotion of mobility (Moore et al., 2014). Nurses may perceive having a risk for self-injury, experience stress, and have difficulty managing time to promote mobility (Jolley, Regan-Baggs, Dickson, & Hough, 2014). The current authors' recent study (Dermody & Kovach, 2017) described the perceived barriers nurses encounter to promoting mobility in hospitalized older adults. External barriers were the most frequent, and included inadequate staffing levels, potential for increased workload if mobility was promoted, and risk for self-injury. Other common barriers included time limitations to promote mobility and the perception that patients are resistant to being mobilized by nurses (Dermody & Kovach, 2017).

Although the few studies that have examined barriers to nurse-promoted mobility are promising, incongruence persists between mobility needed and received. To minimize or remove barriers to promoting mobility in hospitalized older adults, and to implement sustainable and scalable solutions in the hospital setting, more studies are needed. It is important to determine the primary barriers nurses have to promoting mobility, as well as how these barriers may be associated with nurse-promoted mobility.

Specifically, the development of practical and theoretical knowledge is critical to addressing this complex phenomenon of incongruence between mobility needed and received. For example, although organizations have increasingly focused on system-based rapid quality and process improvement to improve the care of hospitalized patients (Sollecito & Johnson, 2011), the association between nurses' barriers and promotion of mobility may not have been investigated enough to make mobility interventions sustainable. For increased nurse-promoted mobility to become a reality, a better understanding of how nursing practice behavior is affected by these barriers is critical (Knowles et al., 2015). Further, a conceptual understanding of the association between barriers and nurse-promoted mobility is needed to develop tailored and sustainable mobility interventions. Interventions may be more effective if they are based on a conceptual framework with well-defined concepts (Conn, Rantz, Wipke-Tevis, & Maas, 2001).

The purpose of the current secondary data analysis was to examine the association of nurses' knowledge and attitudes, as well as external barriers, with the promotion of mobility in hospitalized older adults in non-intensive care units. Measures of physical function, severity of illness, body mass index (BMI), presence of activity, and physical therapy (PT) orders were included as descriptive variables. In addition, the relationship among patient impairment of mobility, use of mobility assistive devices at home, being classified as at risk for falls, and nurse-promoted mobility was examined.

Method

Design, Setting, and Sample

A cross-sectional descriptive correlational design with convenience sampling was used. Nurses were recruited from two community-based hospitals in the Pacific Northwest. Internal review board approval was obtained, and a Health Insurance Portability and Accountability Act waiver was obtained for de-identified patient-related data. To participate, nurses had to work at least 20 hours per week in one of these units: stroke, cardiac, pulmonary, nephrology, oncology, or general medical. Night-shift nurses were excluded. Each unit housed between 30 and 40 acute care beds. These units were selected because hospitalized older adults are commonly admitted to these units for chronic or acute illness. Intensive care and orthopedic units were excluded because nurses may have access to greater resources, including safe lifting equipment, staff, and more specific physician orders.

Sample size calculation for linear multiple regression with fixed model R2 deviation from zero was completed a priori with G*Power with an alpha level of 0.05, three predictor variables (i.e., knowledge, attitude, and external barriers), medium effect size (F2 = 0.15), and a statistical power level of 0.8. A total sample of 85 participants was required (Faul, Erdfelder, Buchner, & Lang, 2009; Faul, Erdfelder, Lang, & Buchner, 2007). The rationale for a medium effect size was based on a cross-sectional study by Hoyer et al. (2015), who identified clinically relevant differences in barriers to promoting mobility among health providers with 82 nurses. A total of 101 nurses were recruited for the current study.

Measures and Operationalization of Variables

Independent Variables. Overall Provider Barrier Scale. Nurse knowledge and attitude as well as external barriers were the independent variables and were measured with the modified Overall Provider Barrier Scale. The original Overall Provider Barrier Scale is a validated 26-item, 5-point Likert scale (where 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree) with an internal consistency reliability of 0.87 (Cronbach's alpha). Discriminant validity psychometric characteristics and item consistency were considered adequate, with most correlation coefficients being 0.4 between each item and the subscale (Hoyer et al., 2015). The scale was validated on nurses and contains three subscales that were used to operationalize the variables, including nurse knowledge (four items) about training to promote safe mobility; and questions about nurse attitude (nine items), including perception about patient condition, interdisciplinary communication about promoting mobility, timing of promoting regular mobility, workload, and confidence and outcome expectancy of promoting mobility and perceptions about deferring mobility to other disciplines. External barriers influencing nurse-promoted mobility (12 items) included environmental barriers (e.g., lack of transfer equipment, inadequate staffing levels), contraindications to promoting mobility and patient resistance, and time constraints to promote regular mobility.

Three items were added for the current study:

  • “Promoting mobility in hospitalized older adults is a priority for the organization I work for” (attitude subscale);
  • “I view the promotion of physical activity in hospitalized older adults as a priority” (attitude subscale); and
  • “I know how to assess the lower leg strength of my older adult inpatients” (knowledge subscale).

Nurses were instructed to select responses from the Overall Provider Barrier Scale that most accurately reflected their opinions based on their nursing experience during the past 2 weeks. The modified scale showed adequate reliability (Cronbach's alpha = 0.88). Item total and subscale item correlation for the 29-question scale was considered adequate, with most values ≥0.4, indicating good discrimination (Carmines & Zeller, 1979).

Clinical Barrier Scale. The Clinical Barrier Scale was developed for the current study to capture the frequency of patient-specific barriers that nurses encountered during one shift. Nurses used this scale to record the frequency of 12 clinical barriers to promoting mobility in older patients as encountered during a regular shift (independent variable): location of equipment, availability of equipment, knowledge of how to use equipment, availability of staff, searching for staff, conflicting priorities, workload, patient condition, patient preference, patient family preference, no activity order, and conflicting activity order. A 5-point frequency response option (where 1 = never, 2 = rarely, 3 = sometimes, 4 = often, and 5 = always) was used, and this scale was considered reliable (Cronbach's alpha = 0.9).

Other Measures. Several additional measures were collected as descriptive variables. Proxy measures for patients' physical function, as routinely assessed and documented in their charts by nurses, included: Modified Timed Upand-Go test (0 = no rise, 1 = rise with one assist, 2 = rise with two assists, and 3 = unable to rise), and whether patients had impairment of mobility (yes/no), home use of assistive devices (yes/no), and fall risk (yes/no). These measures are routinely documented by nurses at this hospital as part of the patient assessment during every shift. Physicians' activity order (yes/no) and presence of an order for PT (yes/no) was also captured by chart audit. Demographic data and BMI were obtained for each patient (body weight was converted to kg and height to cm2) (Jensen et al., 2014). The All Patient Refined-Diagnosis Related Groups (APR-DRG) Severity of Illness Scale (Pilotto et al., 2011) was used to obtain measures of illness severity. The scale includes four severity of illness subclasses: minor, moderate, major, and extreme. The APR-DRG is reported to be able to estimate the global impairment of older adults (Pilotto et al., 2011). Patients with increased severity of illness may have greater comorbidities and be more likely to have poor health outcomes (Beveridge et al., 2015).

Dependent Variables. Self-Recorded Mobility Log. Nurses' mobility-promoting behavior was the dependent variable and measured using the self-recorded mobility log, which was developed based on nurses' informal feedback on how best to capture promoted mobility during one shift. Nurses' mobility-promoting behavior was operationalized as the type and frequency of mobility promoted using ordinal scaling, including walking in the hall, walking in the room, repositioning in bed, promotion of active/passive range of motion, and chair sitting. Each instance of promoted mobility was documented in the self-recorded mobility log by asking nurses to select the type of mobility from a drop-down list. Nurses were able to add mobility-promotion instances, which were captured as frequency. If nurses selected “ambulation in hall,” they entered the distance ambulated in feet; they were educated to use markers (10-ft increments) in each unit's hallway to track ambulation distances. Nurses had access to the mobility documentation in the electronic health record, which also minimized recall bias.

Procedures and Data Collection

Informational meetings were conducted on the hospital units in the breakroom, during which nurses learned about the study purpose, were recruited, and provided informed consent. All training and instruction for nurses was conducted by the same researcher (G.D.). Nurses received 30 minutes of training on how to complete the web-based self-recorded mobility log and Overall Provider Barrier Scale. Nurses completed this training in the hospital setting and remained on-the-clock during training. Methodological challenges of conducting research in the hospital setting commonly include problems with enrollment, consenting, and completion of surveys, as well as patient and environmental conditions that may impede participation to generate useful knowledge (Lehman, 2009). Therefore, the researcher met informally with nurses who were interested in participating to discuss the best mechanism to completing the survey and mobility log. Nurses agreed that completion of the survey and mobility log would be best accomplished by using a one-time electronic method (i.e., e-mail with a link). Nurses were sent a link to the Overall Provider Barrier Scale and self-recorded mobility log toward the end of their shift. However, they were not informed of what day they would be receiving the link to complete data collection. To minimize burden and attrition, nurses remained on-the-clock while completing data collection immediately after their shift. For feasibility reasons and to limit confounding and Hawthorne effects, nurses completed the self-recorded mobility log for all adult patients in their care. Each nurse had between one and four patients for the duration of his/her 8-hour day shift. Because the study targeted nurses caring for hospitalized patients 65 and older, data for patients younger than 65 were not included in analyses.

First, nurses completed the Overall Provider Barrier Scale, followed by the self-recorded mobility log and Clinical Barrier Scale. To ensure consistency and protect private health information, Research Electronic Data Capture (REDCap) was used to distribute, manage, and collect survey and log data, and extract patient demographics and other clinically relevant information.

Data Analyses

All data were de-identified, cleaned, and entered into SPSS version 24 for data analysis. Data were summarized as means (standard deviations) and frequencies (percentages), and range of scores for sample characteristics, nurse-promoted mobility (i.e., walk in the hall, walk in the room, chair sitting, bed mobility, range of motion), the Clinical Barrier Scale, and the Overall Provider Barrier Scale. Negative response options from the Overall Provider Barrier Scale were reverse coded for analysis. Likert scale responses were treated as interval data (Allen & Seaman, 2007; Baggaley & Hull, 1983). A Spearman rho correlation coefficient among impaired mobility, use of mobility assistive devices at home, risk for falls, and nurse-promoted mobility was reported.

Analyses of the five mobility measures were stratified by whether a physician's activity order was present. A generalized linear mixed model (GLMM) was used to handle the clustering of patients (with physician activity orders) by nurses. GLMM is a statistical approach to analyze non-normal data when random effects are present (Bolker et al., 2009). GLMM was used to examine the association between three nurse barriers from the Overall Provider Barrier Scale, PT orders, and three outcome mobility measures (i.e., frequency of walking in the hall, walking in the room, and chair sitting). Ten questions in the Overall Provider Barrier Scale had missing values, representing a total of 0.004% of the data. Little's (1988) Missing Completely at Random test was not significant (p = 0.992), and the hypothesis that data were missing completely at random was accepted. The GLMM technique appropriately handles missing data as well as the correlation among patients seen by the same nurse. Knowledge, attitude, and external barriers, along with the presence of PT orders, were specified as fixed effects in the model. Patients without activity orders or with bedrest orders were not included in the final analysis, reducing the number of nurses to 61 and patients to 77. The frequency of bed mobility and range of motion was not examined. All significance testing was conducted using an adjusted alpha level of 0.02 (0.05/three dependent variables examined).

Results

Sample Characteristics

Of 101 nurses, 85 completed the survey. The two main reasons for attrition were being “too busy” and changes in employment status. The 85 remaining nurses cared for 176 patients, of which 98 were 65 and older. Data for patients younger than 65 were not included in the analysis. Patient cases with no activity order or bed-rest orders were removed, leaving a total of 61 nurses and 77 patients (Figure). Nurse characteristics are shown in Table 1 and patient characteristics are shown in Table 2. Nurses' mean age was 40.5 years (SD = 11.6 years). Patients' mean age was 78.4 years (SD = 7.9 years). Among 77 older patients, approximately 30% were overweight (BMI ≥25) and 35% were obese (BMI ≥30). Approximately 64% of patients were classified as having “major” (44%) or “extreme” (19%) severity of illness based on the APR-DRG Severity of Illness Scale.

CONSORT (Consolidated Standards of Reporting Trials) diagram of the flow of participants.

Figure.

CONSORT (Consolidated Standards of Reporting Trials) diagram of the flow of participants.

Nurse Characteristics (N = 61)

Table 1:

Nurse Characteristics (N = 61)

Patient Characteristics (N = 77)

Table 2:

Patient Characteristics (N = 77)

Description of Mobility and Nurses' Perceived Barriers

The most frequently encountered clinical barriers to promoting mobility for patients in nurses' care during one shift included nurse workload (mean = 3.15, SD = 1.4), patient preference (mean = 3.07, SD = 1.18), searching for assistance from staff (mean = 2.92, SD = 1.3), having conflicting priorities (mean = 2.9, SD = 1.3), and patient condition (mean = 2.89, SD = 1.1). Nurse-promoted mobility during one day shift is shown in Table 3. Nurses most frequently assisted patients with chair sitting or walked them to the bed and/or bathroom. Most patients were not ambulated in the hall and, of those who did, they ambulated ≤200 ft per shift. Approximately 80% of patients had physician orders for physical activity without restrictions, and 63% had an order to be seen by a physical therapist while hospitalized.

Nurse-Promoted Mobility During One Day Shifta

Table 3:

Nurse-Promoted Mobility During One Day Shift

Generalized Linear Mixed Model to Compare Nurse Barriers, Physical Therapy Order, and Frequency of Nurse-Promoted Mobility

Table 4 summarizes results from comparing nurse barriers, including knowledge barriers, attitude barriers, external barriers, PT orders, and frequency of nurse-promoted mobility (including chair sitting, walking in the room, and walking in the hall). A significant association was found between nurse knowledge barriers (p < 0.01), attitude barriers (p < 0.05), and walking in the hall. Increased nurse knowledge barriers and nurse attitude barriers were significantly associated with lower frequencies of walking in the hall. The presence of PT orders was significantly associated with greater frequencies of walking in the room (p < 0.01). Nurses who cared for patients with PT orders promoted walking in the room significantly more frequently. However, there was no significant association between PT orders and frequency of walking in the hall. Only 23.4% of patients were ambulated in the hall by nurses. Although not significant, nurse knowledge barriers were associated with chair sitting (p = 0.065) and walking in the room (p = 0.094). Nurses with knowledge and attitude barriers tended to promote more sedentary activity (e.g., walking to and from the bathroom, chair sitting).

GLMMa Results Comparing Knowledge, Attitude, and External Barriers; Presence of PT Order; and Frequency of Mobility

Table 4:

GLMM Results Comparing Knowledge, Attitude, and External Barriers; Presence of PT Order; and Frequency of Mobility

Exploratory Mobility-Related Correlations

There were significant relationships between impaired mobility, use of assistive devices, fall risk, and nurse-promoted mobility. A negative relationship was found between impaired mobility and walking in the room (ρ [75] = −0.229, p < 0.05). Use of assistive devices and frequency of walking in the hall (ρ [75] = −0.252, p < 0.05), and distance ambulated (ρ [75] = −0.276, p < 0.05) were negatively associated. However, assistive devices and chair sitting were positively associated (ρ [75] = 0.237, p > 0.05). Negative relationships were found between fall risk and frequency of walking in the hall (ρ [75] = −0.275, p < 0.05), distance ambulated (ρ [75] = −0.320, p < 0.05), and walking in the room (ρ [75] = −0.360, p < 0.05). Patients with impaired mobility and assistive devices, and who were at risk for falls, tended to be sedentary.

Discussion

A commonly reported finding in the literature is that hospitalized older adults are predominately engaged in low levels of mobility, which results in preventable functional decline (Boltz, Capezuti, Shabbat, & Hall, 2010; Cadogan & D'Ambruoso, 2012; Fisher et al., 2011; Garrison, Mansukhani, & Bohn, 2013; Zisberg & Syn-Hershko, 2016). All 77 patients in the current study had activity orders without restrictions, yet only low levels of mobility were promoted. Nurses' reports of high workload, varied patient preferences, and patient condition may be responsible for low levels of nurse-promoted mobility in hospitalized older adults. For patients with impaired mobility or increased risk for falls, nurses may need to search for assistance from staff to mobilize these individuals. The need and timing for additional assistance to promote mobility could be problematic if staff are not available when the nurse is ready to promote mobility and when the patient is willing to be mobilized. Nurses may have other priorities that could have a higher value to them, which could be why conflicting priorities was considered a barrier to promoting mobility. The current findings are similar to other studies in which nurses reported staffing concerns, heavy workload, and difficulty prioritizing mobility as barriers to promoting mobility (Barber et al., 2015; Doherty-King & Bowers, 2011; Jolley et al., 2014; Lee & Fan, 2012; Moore et al., 2014). If the goal is for nurses to promote mobility in this population, patient preference and condition, in addition to impairment of mobility and fall risk, are important potential barriers that must be considered.

Some of the existing literature on barriers to nurse-promoted mobility has focused on the complexities of the hospital environment rather than older adults' physical condition. The current findings suggest that existing impairment of mobility, using assistive devices for mobility at home, and being at risk for falls may have implications for the type and frequency of nurse-promoted mobility. Older patients with impaired mobility may require nurses to seek the help of other staff to ambulate them in the hall. In the current study, >60% of patients were classified as having major or extreme severity of illness. However, little is known about barriers to engaging in mobility from patients' perspectives. It is conceivable that patients may be resistant to nurse-promoted mobility. However, patient preferences or potential resistance to engage in the promotion of mobility is understudied, and more research is needed to examine the barriers to engaging in mobility during hospitalization that older adults experience, and how these barriers can be addressed. Improving patient engagement in mobilization is important, and nurses must be knowledgeable on how to engage patients and significant others to participate in mobility activities (Burke & Doody, 2012; Moore et al., 2014).

Furthermore, role confusion may be a barrier to nurse-promoted mobility. The literature claims nurses may defer basic nurse-promoted mobility to other disciplines, such as PT (Doherty-King & Bowers, 2013; Moore et al., 2014). Nurses may hold the view that promoting mobility is within the domain of their scope of practice, and should not be deferred to other disciplines (Dermody & Kovach, 2017). However, the current findings show that nurses who cared for patients who had a PT order tended to mobilize them in the room more frequently. This finding may suggest that nurses inadvertently defer ambulation in the hall to PT. There was no significant association between PT orders and frequency of walking in the hall, which may be because ambulation frequency was low overall. In addition, the findings suggest that nurses may have knowledge gaps and attitudes that could potentially influence whether they promote ambulation in the hall and to what extent. Developing a unit-based culture of mobility, and fostering interdisciplinary collaboration, may address some barriers that nurses experience. Based on patient care complexities, nurses may be overwhelmed or ill-prepared to ambulate patients. More research is needed to examine the implications of interdisciplinary collaboration and the role of the member of each discipline on the care processes and workflow that are necessary to promote mobility (Barber et al., 2015; Lee & Fan, 2012; Moore et al., 2014).

Future Research

Care coordination for hospitalized patients has become increasingly complex for nurses (Catchpole, 2013; Ebright, Patterson, Chalko, & Render, 2003). Insufficient mobility during hospitalization has been linked to problems with care coordination (Brown et al., 2009; Doherty-King & Bowers, 2013; Doherty-King, Yoon, Pecanac, Brown, & Mahoney, 2014). Reports of staffing concerns, heavy workload, increased risk for self-injury, lack of time, and difficulty prioritizing mobility speak to the interdisciplinary collaboration that is necessary to promote sufficient mobility in this population. Nurse-led care coordination models at the bedside should be tested as a possible solution to overcome barriers to nurse-promoted mobility (Lamb et al., 2015). In collaboration with the American Nurses Association (ANA) and the American Academy of Nursing, the Care Coordination Task Force has proposed the development of innovative care coordination practice models that could be valuable to improving the promotion of mobility (ANA, 2015). In addition, patient engagement in mobility during hospitalization is an important line of inquiry. Little is known in terms of barriers to engaging in mobility from patients' perspectives (Leditschke et al., 2012) and how to engage them in the promotion of their mobility.

Limitations

Because of the non-experimental study design, there are several limitations: sampling approach, sample size, methods, and measurement. A small convenience sample from one geographic region was used. Because nurse-promoted mobility was stratified by whether physicians' activity order was present (excluding patient cases with bedrest orders), the sample size for nurses was reduced (initially powered for 85 nurses), potentially impacting the findings. The current authors did not control for all potentially confounding variables. However, to handle clustering and PT orders, a GLMM was used to analyze associations between nurse barriers, PT orders, and nurse-promoted mobility. Although a GLMM applied to non-experimental observational research does not permit inferences about causality, the current findings add to the existing literature.

In addition, hospital unit-based culture and practices (e.g., workflow patterns) may vary, which could have introduced biases. Another limitation is the variability between patients' severity of illness, disease processes, and comorbidities, which could potentially influence nurse-promoted mobility. To minimize recall bias, nurses had access to patients' medical records. Maturation or inaccuracies could be additional limitations. Nurses' age, gender, and experience were not included in the a priori sample size calculation.

Although the current findings suggest this to be unlikely, some nurses may have believed that they should promote (or report) more mobility to provide favorable responses in the mobility log. In addition, nurses may have become fatigued from completing the mobility log for multiple patients, which could have led to inaccuracies. The use of Likert scales may have resulted in raters providing neutral responses, which could be problematic in terms of understanding the findings. Further, three items were added to the Overall Provider Barrier Scale, which may have limited comparisons to other studies using this measure. Future studies should conduct a psychometric analysis of the Overall Provider Barrier Scale with a larger sample.

Due to these limitations, the generalizability of the study is limited and findings should be viewed with caution. Although many limitations exist, the findings make valuable contributions to the existing science and high-light existing gaps in barriers that nurses encounter and how these barriers may be associated with nurse-promoted mobility.

Conclusion

Although greater recognition of this problem is apparent in the literature, the problem of insufficient mobility in hospitalized older adults is far from over. Functional decline is preventable, yet nurses primarily engage older adults in low levels of mobility. The current study suggests a variety of barriers may impede the work of nurses to promote walking in the hall. The identification of barriers that nurses may encounter is key to developing, testing, and implementing sustainable solutions to overcome barriers and engage hospitalized older adults in greater levels of mobility and prevent functional decline.

References

  • Allen, E. & Seaman, C.A. (2007). Likert scales and data analyses. Quality Progress, 40, 64–65.
  • American Nurses Association. (2015). Policy agenda for nurse-led care coordination. Retrieved from http://www.nursingworld.org/DocumentVault/Health-Policy/ANAs-Policy-Agenda-for-Nurse-Led-Care-Coordination.pdf
  • Baggaley, A.R. & Hull, A.L. (1983). The effect of nonlinear transformations on a Likert scale. Evaluation & the Health Professions, 6, 483–491. doi:10.1177/016327878300600408 [CrossRef]
  • Barber, E.A., Everard, T., Holland, A.E., Tipping, C., Bradley, S.J. & Hodgson, C.L. (2015). Barriers and facilitators to early mobilisation in intensive care: A qualitative study. Australian Critical Care, 28, 177–182. doi:10.1016/j.aucc.2014.11.001 [CrossRef]
  • Beveridge, C., Knutson, K., Spampinato, L., Flores, A., Meltzer, D.O., Van Cauter, E. & Arora, V.M. (2015). Daytime physical activity and sleep in hospitalized older adults: Association with demographic characteristics and disease severity. Journal of the American Geriatrics Society, 63, 1391–1400. doi:10.1111/jgs.13520 [CrossRef]
  • Bolker, B.M., Brooks, M.E., Clark, C.J., Geange, S.W., Poulsen, J.R., Stevens, M.H.H. & White, J.-S.S. (2009). Generalized linear mixed models: A practical guide for ecology and evolution. Trends in Ecology & Evolution, 24, 127–135. doi:10.1016/j.tree.2008.10.008 [CrossRef]
  • Boltz, M., Capezuti, E., Shabbat, N. & Hall, K. (2010). Going home better not worse: Older adults' views on physical function during hospitalization. International Journal of Nursing Practice, 16, 381–388. doi:10.1111/j.1440-172X.2010.01855.x [CrossRef]
  • Brown, C.J., Foley, K.T., Lowman, J.D. Jr.. , MacLennan, P.A., Razjouyan, J., Najafi, B. & Allman, R.M. (2016). Comparison of posthospitalization function and community mobility in hospital mobility program and usual care patients: A randomized clinical trial. JAMA Internal Medicine, 176, 921–927. doi:10.1001/jamainternmed.2016.1870 [CrossRef]
  • Brown, C.J., Roth, D.L., Allman, R.M., Sawyer, P., Ritchie, C.S. & Roseman, J.M. (2009). Trajectories of life-space mobility after hospitalization. Annals of Internal Medicine, 150, 372–378. doi:10.7326/0003-4819-150-6-200903170-00005 [CrossRef]
  • Burke, K.G. & Doody, O. (2012). Nurses' perceptions of their role in rehabilitation of the older person. Nursing Older People, 24, 33–38. doi:10.7748/nop2012.03.24.2.33.c8956 [CrossRef]
  • Cabana, M.D., Rand, C.S., Powe, N.R., Wu, A.W., Wilson, M.H., Abboud, P.A. & Rubin, H.R. (1999). Why don't physicians follow clinical practice guidelines? A framework for improvement. JAMA, 282, 1458–1465. doi:10.1001/jama.282.15.1458 [CrossRef]
  • Cadogan, M. & D'Ambruoso, S. (2012). Recognizing hospital-acquired disability among older adults. Journal of Gerontological Nursing, 38(12), 12–15. doi:10.3928/00989134-20121106-06 [CrossRef]
  • Carmines, E.C. & Zeller, R.A. (1979). Reliability and validity assessment (Vol. 17). Beverly Hills, CA: Sage. doi:10.4135/9781412985642 [CrossRef]
  • Catchpole, K. (2013). Spreading human factors expertise in health-care: Untangling the knots in people and systems. BMJ Quality & Safety, 22, 793–797. doi:10.1136/bmjqs-2013-002036 [CrossRef]
  • Conn, V.S., Rantz, M.J., Wipke-Tevis, D.D. & Maas, M.L. (2001). Designing effective nursing interventions. Research in Nursing & Health, 24, 433–442. doi:10.1002/nur.1043 [CrossRef]
  • Cruz-Jentoft, A.J., Baeyens, J.P., Bauer, J.M., Boirie, Y., Cederholm, T., Landi, F. & Zamboni, M. (2010). Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age and Ageing, 39, 412–423. doi:10.1093/ageing/afq034 [CrossRef]
  • Dermody, G. & Kovach, C.K. (2017). Nurses' experience with and perception of barriers to promoting mobility in hospitalized older adults: A descriptive study. Journal of Gerontological Nursing, 43(11), 22–29. doi:10.3928/00989134-20170518-01 [CrossRef]
  • Doenges, M.E., Moorhouse, M.F. & Murr, A.C. (2014). Nursing care plans: Guidelines for individualizing client care across the life span (9th ed.). Philadelphia, PA: F.A. Davis.
  • Doherty-King, B. & Bowers, B. (2011). How nurses decide to ambulate hospitalized older adults: Development of a conceptual model. The Gerontologist, 51, 786–797. doi:10.1093/geront/gnr044 [CrossRef]
  • Doherty-King, B. & Bowers, B.J. (2013). Attributing the responsibility for ambulating patients: A qualitative study. International Journal of Nursing Studies, 50, 1240–1246. doi:10.1016/j.ijnurstu.2013.02.007 [CrossRef]
  • Doherty-King, B., Yoon, J.Y., Pecanac, K., Brown, R. & Mahoney, J. (2014). Frequency and duration of nursing care related to older patient mobility. Journal of Nursing Scholarship, 46, 20–27. doi:10.1111/jnu.12047 [CrossRef]
  • Du, W.-J., Tan, J.-P., Yi, F., Zou, Y.-M., Gao, Y., Zhao, Y.M. & Wang, L.-N. (2015). Physical activity as a protective factor against depressive symptoms in older Chinese veterans in the community: Result from a national cross-sectional study. Neuropsychiatric Disease and Treatment, 11, 803–813. doi:10.2147/ndt.s80295 [CrossRef]
  • Ebright, P.R., Patterson, E.S., Chalko, B.A. & Render, M.L. (2003). Understanding the complexity of registered nurse work in acute care settings. Journal of Nursing Administration, 33, 630–638. doi:10.1097/00005110-200312000-00004 [CrossRef]
  • Faul, F., Erdfelder, E., Buchner, A. & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149–1160. doi:10.3758/BRM.41.4.1149 [CrossRef]
  • Faul, F., Erdfelder, E., Lang, A.-G. & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191. doi:10.3758/BF03193146 [CrossRef]
  • Fisher, S.R., Goodwin, J.S., Protas, E.J., Kuo, Y.F., Graham, J.E., Ottenbacher, K.J. & Ostir, 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., Graham, J.E., Krishnan, S. & Ottenbacher, K.J. (2016). Predictors of 30-day readmission following inpatient rehabilitation for patients at high risk for hospital readmission. Physical Therapy, 96, 62–70. doi:10.2522/ptj.20150034 [CrossRef]
  • Fisher, S.R., Graham, J.E., Ottenbacher, K.J., Deer, R. & Ostir, G.V. (2016). Inpatient walking activity to predict readmission in older adults. Archives of Physical Medicine and Rehabilitation, 97(Suppl. 9), S226–S231. doi:10.1016/j.apmr.2015.09.029 [CrossRef]
  • Garrison, G.M., Mansukhani, M.P. & Bohn, B. (2013). Predictors of thirty-day readmission among hospitalized family medicine patients. Journal of the American Board of Family Medicine, 26, 71–77. doi:10.3122/jabfm.2013.01.120107 [CrossRef]
  • Hoyer, E.H., Brotman, D.J., Chan, K.S. & Needham, D.M. (2015). Barriers to early mobility of hospitalized general medicine patients: Survey development and results. American Journal of Physical Medicine & Rehabilitation, 94, 304–312. doi:10.1097/PHM.0000000000000185 [CrossRef]
  • Jensen, M.D., Ryan, D.H., Apovian, C.M., Ard, J.D., Comuzzie, A.G., Donato, K.A. & Yanovski, S.Z. (2014). 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Circulation, 129(Suppl. 2), S102–S138. doi:10.1161/01.cir.0000437739.71477.ee [CrossRef]
  • Jolley, S.E., Regan-Baggs, J., Dickson, R.P. & Hough, C.L. (2014). Medical intensive care unit clinician attitudes and perceived barriers towards early mobilization of critically ill patients: A cross-sectional survey study. BMC Anesthesiology, 14, 84. doi:10.1186/1471-2253-14-84 [CrossRef]
  • Knowles, S., Lam, L.T., McInnes, E., Elliott, D., Hardy, J. & Middleton, S. (2015). Knowledge, attitudes, beliefs and behaviour intentions for three bowel management practices in intensive care: Effects of a targeted protocol implementation for nursing and medical staff. BMC Nursing, 14, 6. doi:10.1186/s12912-015-0056-z [CrossRef]
  • Lamb, G., Newhouse, R., Beverly, C., Toney, D.A., Cropley, S., Weaver, C.A. & Peterson, C. (2015). Policy agenda for nurse-led care coordination. Nursing Outlook, 63, 521–530. doi:10.1016/j.outlook.2015.06.003 [CrossRef]
  • Leditschke, I.A., Green, M., Irvine, J., Bissett, B. & Mitchell, I.A. (2012). What are the barriers to mobilizing intensive care patients?Cardiopulmonary Physical Therapy Journal, 23, 26–29.
  • Lee, C.M. & Fan, E. (2012). ICU-acquired weakness: What is preventing its rehabilitation in critically ill patients?BMC Medicine, 10, 115. doi:10.1186/1741-7015-10-115 [CrossRef]
  • Lehman, C.A. (2009). Practical issues in conducting hospital-based research. Perioperative Nursing Clinics, 4, 269–276. doi:10.1016/j.cpen.2009.05.008 [CrossRef]
  • Little, R.J.A. (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association, 83, 1198–1202. doi:10.1080/01621459.1988.10478722 [CrossRef]
  • Liu, S.K., Montgomery, J., Yan, Y., Mecchella, J.N., Bartels, S.J., Masutani, R. & Batsis, J.A. (2016). Association between hospital admission risk profile score and skilled nursing or acute rehabilitation facility discharges in hospitalized older adults. Journal of the American Geriatrics Society, 64, 2095–2100. doi:10.1111/jgs.14345 [CrossRef]
  • Moore, J.E., Mascarenhas, A., Marquez, C., Almaawiy, U., Chan, W.H., D'Souza, J. & Straus, S.E. (2014). Mapping barriers and intervention activities to behaviour change theory for Mobilization of Vulnerable Elders in Ontario (MOVE ON), a multi-site implementation intervention in acute care hospitals. Implementation Science, 9, 160. doi:10.1186/s13012-014-0160-6 [CrossRef]
  • Pedersen, M.M., Bodilsen, A.C., Petersen, J., Beyer, N., Andersen, O., Lawson-Smith, L. & Bandholm, T. (2013). Twenty-four-hour mobility during acute hospitalization in older medical patients. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 68, 331–337. doi:10.1093/gerona/gls165 [CrossRef]
  • Peterson, S.J. & Braunschweig, C.A. (2016). Prevalence of sarcopenia and associated outcomes in the clinical setting. Nutrition in Clinical Practice, 31, 40–48. doi:10.1177/0884533615622537 [CrossRef]
  • Pilotto, A., Franceschi, M., DiBara, M., Rengo, F., Berabei, R., Lorenzoni, L. & Greco, A. (2011). The ability of the all patient refined (APR) and Centers for Medicare (CMS) diagnosis related groups (DRG) systems to evaluate clinical and functional outcomes of hospitalized elderly patients: A multicenter, prospective study. Journal of Public Health and Epidemiology, 3, 248–253.
  • Sollecito, W.A. & Johnson, J.K. (2011). McLaughlin and Kaluzynsky's continuous quality improvement in health care (4th ed.). Burlington, MA: Jones & Bartlett.
  • Woolf, S.H. (1993). Practice guidelines: A new reality in medicine. III. Impact on patient care. Archives of Internal Medicine, 153, 2646–2655. doi:10.1001/archinte.1993.00410230060008 [CrossRef]
  • Zisberg, A. & Syn-Hershko, A. (2016). Factors related to the mobility of hospitalized older adults: A prospective cohort study. Geriatric Nursing, 37, 96–100. doi:10.1016/j.gerinurse.2015.10.012 [CrossRef]

Nurse Characteristics (N = 61)

Variablen (%)
Age (years) (mean, SD)40.5 (11.6)
Gender
  Female53 (89.9)
  Male8 (13.1)
Ethnicity
  Non-Hispanic46 (75.4)
  Other15 (24.6)
Race
  White54 (88.5)
  Other7 (10.4)
Years of experience
  ≤14 (6.6)
  >1 to 518 (29.5)
  >512 (19.7)
  >1027 (44.3)
Highest degree
  BSN34 (55.7)
  ADN25 (41)
  Other2 (2.6)

Patient Characteristics (N = 77)

Variablen (%)
Age (years) (mean, SD)78.4 (7.9)
Gender
  Male41 (52.9)
  Female36 (47.1)
Ethnicity
  Non-Hispanic63 (81.8)
  Other14 (18.2)
Race
  White57 (74)
  Other20 (26)
BMIa
  Normal21 (27.3)
  Overweight23 (29.9)
  Obese27 (35.1)
  Other6 (7.8)
APR-DRG Severity of Illness Scale score
  Minor3 (3.9)
  Moderate25 (36.4)
  Major34 (44.2)
  Extreme15 (19.5)
Impaired mobility
  Yes65 (84.4)
  No12 (15.6)
Modified TUG
  No rise1 (1.3)
  Rise with one assist31 (40.3)
  Rise with more than one assist30 (39)
  Unable to rise15 (19.5)
Fall risk
  No39 (50.6)
  Yes38 (49.4)
Use of assistive devices
  No42 (54.5)
  Yes35 (45.5)

Nurse-Promoted Mobility During One Day Shifta

Type of MobilityMobility Activity/Shift (%)Mean (SD)
Walking in hall23.40.43 (1.01)
Walking in room44.21.31 (1.74)
Bed mobility28.61.03 (1.67)
Range of motion2.60.06 (0.41)
Sitting in chair46.81.13 (1.43)
Total Distance/Shift (%)Mean (SD)
Distance ambulated (ft)
  079.259.9 (144.6)
  ≤2007.8
  >200 to ≤50011.7
  >5001.3
Physical therapy orderPercentage
  Yes63.3
  No36.4

GLMMa Results Comparing Knowledge, Attitude, and External Barriers; Presence of PT Order; and Frequency of Mobility

ModelCoefficientSEtp Value95% CI
Walking in hall
  Knowledge−0.3070.087−3.544<0.01**[−0.477, 0.136]
  Attitude0.1860.0882.1190.035*[0.013, 0.360]
  External0.0160.0590.2680.789[−0.101, 0.132]
  PT order0.7940.5531.4370.152[−0.295, 1.883]
Walking in room
  Knowledge−0.1190.071−1.6790.094[−0.258, 0.020]
  Attitude0.0740.0681.0940.275[−0.059, 0.207]
  External0.0640.0521.2390.216[−0.038, 0.165]
  PT order1.3910.4992.7880.006**[0.409, 2.374]
Chair sitting
  Knowledge−0.1460.079−1.8500.065[−0.302, 0.009]
  Attitude0.0680.0651.0380.300[−0.061, 0.196]
  External0.0530.0491.0870.278[−0.043, 0.148]
  PT order−0.3220.514−0.6260.532[−1.334, 0.690]
Authors

Dr. Dermody is Lecturer, Nursing, School of Nursing & Midwifery, Edith Cowan University, Joondalup, Western Australia; and Dr. Kovach is Jewish Home and Care Center Research Professor in Aging, College of Nursing, University of Wisconsin–Milwaukee, Milwaukee, Wisconsin.

The authors have disclosed no potential conflicts of interest, financial or otherwise. Dr. Kovach was not involved in the peer review or decision-making process for this manuscript.

The authors thank Denise Smart, DrPH, MPH, RN, for her review of this manuscript; and Dr. Tamara Odom-Maryon for her review and recommendations of the analysis approach.

Address correspondence to Gordana Dermody, PhD, RN, CNL, Lecturer, Nursing, School of Nursing & Midwifery, Edith Cowan University, Building 21, Level 4, Room 21.413, Joondalup, Western Australia 6027, Australia; e-mail: g.dermody@ecu.edu.au.

Received: March 01, 2017
Accepted: September 15, 2017

10.3928/19404921-20171023-01

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