Bishop, Mark D. PT, PhD1,2; Romero, Sergio PhD3,4; Patterson, Tara S. PhD5; Singletary, Floris F. MS6; Light, Kathye PT, PhD1
A patient seeking intervention for an impairment or functional limitation brings with him or her intrinsic expectations about that intervention and the entire rehabilitation process. Recent reviews,1–3 in addition to many experimental trials, have indicated that these patient expectations may have a profound influence on the outcome of the intervention for a wide variety of conditions. Patient expectations are associated with clinical and experimental outcomes of all types across a wide variety of health conditions. This is especially true of predictive outcomes based on what the patient expects to happen as a result of treatment.4 By way of example, consider a patient arriving at a bariatric clinic. The maximum weight lost by this patient is best predicted by asking the patient, “What are your chances of succeeding in this program?”5 This type of association between expectation and outcome may seem somewhat intuitive at first glance.
However, consider potentially less obvious associations: time to return to general activities after cardiac bypass surgery6 or distance walked after hip fracture,7 for example, are all associated with patients' expectations. In addition, some individuals with Parkinson disease undergoing deep brain stimulation experienced increased clinical benefits when they were told that the brain stimulator was on (when it was actually off) and performed worse when they were told the stimulator was off (but was actually on).8 Expectations about outcome are hypothesized to be generated through direct personal experiences, the suggestion of others or by observing other individuals interacting with the health care environment.2 A conceptual model of how these factors develop in a health care setting was presented by Janzen and colleagues.1
The health care professional who is performing the intervention also has criteria for what constitutes a successful therapeutic interaction. Many times success is based on criteria derived from the findings of a clinical examination including performance-based tests.
In this study, our primary aim was to examine the influences of patient expectations and clinic-based performance measures on improvements perceived by older adults seeking intervention for gait and balance disorders. We hypothesized that success of the intervention, from the patient perspective, would be weighted heavily toward patients' expectations (specifically the intrinsic criteria for success) and less reliant on measured change on clinic-based tests and measures.
Twenty-seven older adults were recruited from the north-central Florida area. Potential participants responded to advertisements or requested information from research personnel at regional health fairs. To be eligible to participate in this experiment, participants had to be between 60 and 90 years of age and had to report having fallen 2 or more times in the previous 12 months. A fall was defined as “an event which results in a person coming to rest unintentionally on the ground or lower level, not as a result of a major intrinsic event (such as a stroke) or overwhelming hazard.”9 In addition, participants needed to be fluent in English, have the ability to walk 20 ft with or without an assistive device, and an overall score of 24 or higher on the Mini-Mental State Examination (MMSE). There was no restriction of study participation on the basis of race or gender, but participants were excluded if they were receiving ongoing medical care for stroke or fracture, or had any unstable cardiac conditions including uncontrolled blood pressure. All volunteers read and signed an informed consent document approved by the university institutional review board.
There were 4 evaluation sessions. The first was the initial evaluation at which baseline data were recorded. There were 2 progress evaluations at 4 and 8 weeks. A discharge evaluation was performed at the completion of the 12-week training period. The evaluators included a physical therapist specializing in geriatric physical therapy, and this evaluator remained blinded to the results of behavioral testing over the course of all the evaluations. Before beginning the study, evaluators completed 8 hours of training including the performance of standardized testing and the decision-making process used to assign the home exercise program.
Tests and Measures
Mini-Mental State Examination is a simple examination that includes 11 questions focusing on the cognitive aspects of mental functioning that has been shown to effectively distinguish individuals without significant cognitive impairment from those with dementia.10 The lower the score, the lower the level of cognitive functioning.11 Inclusion criteria required a score of 24 or greater on the MMSE.
Other measures taken at the initial examination for screening purposes but not used in data analysis for this study were Orthosthatic Blood Pressure Testing,12 Vestibular Occular Reflex testing (if items on the Berg Balance Test and Dynamic Gait Index [DGI] are positive for a possible vestibular component to the balance problem) and sensory testing of proprioception/joint position sense, light-touch location discrimination, and vision and hearing screening. No participant was excluded from this study on the basis of the findings of these tests.
Lower extremity strength was assessed isometrically, using handheld dynamometry. Isometric muscle performance was measured from the hip abductors, knee extensors, and ankle dorsiflexors and plantar flexors. These muscle groups were targeted on the basis of the importance of hip abductor strength during high-level lower extremity function including normal walking, turning,13 and stopping.14,15 Hip abductors were measured with the participant in the supine position, with the dynamometer immediately proximal to the lateral malleolus and the hip in the neutral position. During testing of ankle plantar flexors, the participant was supine, with a towel roll under posterior calf and the dynamometer placed against the plantar aspect of the first and second metatarsal heads. To test quadriceps, the participant sat at the edge of the plinth table with the knee in 90° of flexion. The dynamometer was placed midline between the malleoli on the anterior surface of the distal leg. Ankle dorsiflexor was also tested in sitting with the participant sitting so that the knee was maintained at 90° of knee flexion and the feet were flat on the floor. The dynamometer was placed on the dorsal surface of the foot. Three repetitions of each test were performed, and the average of the peak isometric test was recorded. Reliability of handheld dynamometry has been previous reported as greater than 0.9 at the hip16 and for knee extension.17 Results were used to create the home exercise program.
Fall-related efficacy was measured using the modified Falls Efficacy Scale (m-FES). The m-FES is a 14-item scale. Each item is scored on a 1 to 10 scale, where a score of 1 indicates no confidence and 10 indicates complete confidence that the subject can perform the task without any fear of falling.18 In the original study, the m-FES demonstrated high internal consistency (Cronbach α = 0.95) and test-retest reliability (intraclass correlation coefficients [ICC] = 0.93).18 We measured fall-related efficacy because this construct is associated with self-reported performance of activities of daily living,19,20 such as bathing, grooming, dressing, eating, transfers, and using a toilet, and perceived quality of life, especially physical functioning over 1 year.20
Pain-related fear of movement
The Tampa Scale of Kinesiophobia (TSK11) was used to assess the fear of movement or injury. This measure has been used in multiple studies in subjects with low back pain21–23 and groups of patients with cardiac disease24 and fibromyalgia.25 Reliability has been reported as excellent.26 We assessed this construct in participants because we hypothesized that older adults with a history of falls might limit movement because they had fear of pain. Our previous work has indicated that approximately 22% of older adults with gait and balance disorders report pain that interferes with daily activity.27
Geriatric Depression Scale
The Geriatric Depression Scale (GDS15) is a test used to rate depression in elderly patients. The GDS has been shown to have a high internal consistency (0.94) and good test-retest reliability (0.85).28,29 Depression has been demonstrated to influence change in clinical measures gait and balance in older adults with a history of falls.27
Pain visual analog scale
The pain visual analog scale is a series of three 10-cm lines. Each line is used to rate “current,” “best,” and “worst” pain intensity in the past week. Each line is anchored at one end with “none” or “not bad at all” and at the other with “worst imaginable.”30 Pain has been demonstrated to influence change in clinical measures gait and balance in older adults with a history of falls.27
Berg Balance Scale
The Berg Balance Scale (BBS) was developed to assess balance in elderly patients. The BBS consists of 14 common tasks: sitting to standing, standing unsupported, sitting unsupported, standing to sitting, transfers, standing with eyes closed, standing with feet together, reaching forward with outstretched arm, retrieving an object from floor, turning to look behind, turning 360°, placing alternate foot on stool, standing with 1 foot in front, and standing on 1 foot. Each item is scored on a 5-point Likert scale, with the total score ranging from 0 to 56, and higher scores indicating better performance.31 The correlations between the BBS and the balance subscale of the Tinetti Performance-Oriented Mobility Assessment and the Barthel Index Mobility subscale were 0.91 and 0.67, respectively.32
High correlations also exist between BBS scores and other motor and functional measurements: Fugl-Meyer Test Motor and Balance subscales (r = 0.62−0.94) and Timed Up & Go Test scores (r = −0.76),33,34 The minimal detectable change (MDC) for the BBS is 7.3 when a participant scored less than 45, and 6.3 for participants scoring more than 45.35 These scores were associated with an intrarater reliability ICC of 0.93 when testing community-dwelling adults with gait and balance disorders.35 The BBS was a primary outcome measure of balance in our study.
Dynamic Gait Index
This is an 8-item index created by rating the subject's performance on the following tasks: walking on a level surface, changing speed while walking, turning the head to the side, and up and down while walking, sudden turns, obstacle, and stair negotiation.36 Scores range from 0 to 24, with higher scores indicating better mobility. The DGI can be administered in 10 minutes and requires minimal equipment. Validity of the scale has been supported by moderate correlation with the BBS: Spearman rank order correlation (r) ranging from 0.71 (Whitney et al37) to 0.79 (Bishop et al38). Sensitivity and specificity to identify individuals with a history of falls have been established at 59% and 64%, respectively.39 The intrarater ICC for the DGI when testing older community-dwelling adults was 0.95, and the MDC value reported for the DGI was 2.9.35
To determine the participant expectations for the intervention we used the Patient-Centered Outcome Questionnaire modified to include descriptive domains from the International Classification of Function resulting in a new questionnaire, the Patients' Perspective Outcomes Questionnaire (PPOQ). The PPOQ uses a 101-point numeric rating scale, anchored at one end with “0 (none/not at all)” and at the other with “100 (completely/most),” and the PPOQ includes consideration of 4 sections. The instructions for completing each section are indicated as follows:
1. On a scale of 0 (none/not affected) to 100 (worst imaginable/most affected), indicate your usual level (during the past week) of impairment.
2. On a scale of 0 (none/not affected) to 100 (worst imaginable/most affected), indicate the level each of these areas would have to be to consider treatment successful. The difference between current impairment and the success criteria was considered the “desired” change.
3. On a scale of 0 (none/not affected) to 100 (worst imaginable/most affected), indicate the levels you expect after treatment. The difference between current impairment and expected impairment was considered the “expected” change.
4. On a scale of 0 (not at all important) to 100 (most important), indicate how important it is for you to see improvement in your....
Participants were asked to consider these questions across 9 domains: mobility, self-care, interactions with people, community and social life, energy and drive, mental function, emotional distress, sensory function, and pain. Full descriptions of each domain are listed in Table 3. The “current impairment” section of the PPOQ was also completed at the final evaluation. The difference between current impairment at the initial evaluation and the current impairment at the final evaluation was considered the “actual change” that occurred over the 12 weeks of intervention.
In addition, we also used the global rating of change (GRC) scale to determine whether the intervention was successful from the participant's perspective. This served as our overall measure of how the patient viewed the outcome of his or her participation in the intervention. The GRC is a 15-point scale anchored at one end with “very much worse” and at the other with “very much improved.” The GRC scale is commonly used in clinical research.40 The scale is recommended for improving the applicability of information from clinical trials to clinical practice.41
We used home-based exercise programs as the intervention in this study. Several randomized trials support the use of light-intensity exercise programs to reduce the number of falls in older adults.42–44 Other evidence, from the meta-analysis of the 7 FICSIT trials,45 indicates that general exercise and balance-specific activities can both reduce the incidence of falls. In addition, the American College of Sports Medicine has issued a position indicating that there is sufficient supportive evidence to recommend that “a broad-based exercise program that includes balance training, resistive exercise, walking, and weight transfer should be included as part of a multifaceted intervention to reduce the risk of falling.”46 Our previous work has indicated that performing these multifaceted programs in a home exercise format has resulted in improvements in measures of balance and dynamic walking ability.27,38
In the current study, the evaluating therapist instructed participants in a home exercise program and provided written instructions with illustrations of the home exercises. Home exercises varied from participant to participant but were developed on the basis of the findings from the examination. For example, difficulty with stabilization (assessed using items from the BBS), or weakness in the hip abductor, quadriceps, plantar-flexor, or dorsiflexor muscles, was addressed using specific strengthening exercises. Parameters for the strengthening exercises were determined by the evaluating therapist and based on the tolerance of the participant.
Feedback-based balance control was progressed by having participants practice standing on hard and soft surfaces with eyes open and closed. Participants with limitations to walking (unable to walk farther than 152.4 m [500 ft] in 2 minutes) were placed on both an endurance training program for walking and an interval training program. During interval training, participants were trained in walking agility, including fast and slow walking, walking backward, walking and turning in various directions, stopping and starting frequently, and walking while carrying objects. The programs also were progressed by requiring walking on a variety of uneven surfaces, inclines, and stairs.
The exercises taught to the participants varied from participant to participant on the basis of the findings of the clinical examinations. Each exercise program was progressed such that the amount of time required to perform the exercises was approximately 30 minutes for all participants. This exercise prescription strategy has been used in our clinical practice in the Gait and Balance Disorders Clinic as well our in previous studies of older adults with balance disorders.27,38
Participants were required to demonstrate understanding of the program to the therapist, and any additional instructions regarding exercise performance were provided in writing to the subject. When a participant returned for the progress evaluations, he or she demonstrated the current program to the therapist. Accuracy of performance was recorded by the evaluating therapist.
Any new exercises assigned were provided in writing with illustrations. Weekly telephone calls were made by a research assistant. The telephone calls used a structured interview format during which exercise performance was reviewed and motivation and encouragement was provided to the subject.
Summary statistics were calculated for all demographic variables. These statistics are presented in Table 1.
Pre- and postintervention scores on continuous measures were compared using Wilcoxon rank sum tests. Distribution free statistics were chosen, given that we expected nonnormal distribution among our data because of the small sample being analyzed. Changes in the BBS and the DGI were dichotomized on the basis of whether change scores exceeded MDC for the BBS and the DGI as described by Romero et al.35 These authors indicated that MDC values for the BBS were 7.3 when a participant scored less than 45 and 6.3 for participants scoring more than 45. The MDC value reported for the DGI was 2.9.
Several calculations were performed on data collected using the PPOQ prior to statistical analysis. The data used in these calculations included participant ratings of current impairment and expected impairment, collected at baseline, and the rating of current impairment collected at the final evaluation. Desired change was calculated by subtracting the desired impairment levels from baseline current impairment. Expected change over the intervention was calculated by subtracting the expected impairment levels from baseline current impairment. The actual change that occurred over the intervention period was determined by subtracting the final rating of current impairment from the baseline. These data are summarized in Table 2. Actual change was compared with desired change and scores for each participant dichotomized into “met” or “did not meet” expectations for change. This process was repeated for expected change.
Finally, participant responses on the GRC were coded as “successful” if the participant reported that he or she was “a great deal better” or a “very great deal better.” This approach has been used in studies in the rehabilitation literature to define “successful” intervention from the patient's perspective.47–51
Baseline psychological characteristics were compared between participants who had a successful intervention and those who did not using Wilcoxon rank sum testing. Bivariate correlations were also performed between these psychological variables and participant ratings of impairment at baseline.
Simple χ2 tests were calculated to examine associations between the GRC and dichotomized outcomes for the BBS and the DGI, as well as whether the participant met or did not meet expectations. Of particular interest to us were expectations about change in mobility; however, each category of expectation was examined. Exact logarithmic regression was used to calculate unadjusted odds ratios for rating intervention as successful.
All analyses were completed using SPSS 18.0 (IBM, New York City, New York).
Of the 27 original volunteers for this study, results for 5 participants were not included in data analysis. Two participants had MMSE scores below 20, and 3 participants withdrew from the study after the initial evaluation. This left 22 participants between 63 and 90 years of age, 12 of whom were women. One participant did not attend the final evaluation, and we used last-point forward to provide complete data. Table 1 includes summaries of demographic information.
When the group was considered as a whole, significant changes were noted for both BBS (P < .001) and DGI (P = .006). See Table 2. The average change from pre- to postintervention was 4.6 points on the BBS and 1.4 for the DGI. However, when individuals were considered, 8 participants made measurable change (greater than MDC) on the BBS and 6 for the DGI. Only 3 participants made measurable change on both measures. No significant changes were made to gait velocity; however, median pain intensity was significantly lower at the final evaluation (P = .050) and pain unpleasantness also decreased by the final evaluation (P = .047). No changes were noted in psychological variables, although the group had improved falls efficacy (P = .09).
A summary of the PPOQ data is presented in Table 3. In general, this group of older adults considered it most important to change in the domains of mobility, pain, and mental function and least important to change in self-care. However, a wide range of responses existed, with all categories showing the entire range of responses (ratings of not important to completely important). Less than 30% of participants desired change in sensory function, self-care, and interactions with people or emotional distress. Therefore, our subsequent analyses examined those categories in which more than 50% of participants desired some change—mobility, pain, mental function, energy and drive, and community and social life.
In general, desired change in impairment was slightly greater than expected change. Only 40% of participants exceeded or met their desired change in impairment for mobility. Thirty-seven percent of participants met desired reduction in pain, whereas 68% met desired reductions in impairment to their mental function and 60% met desired reductions in impairment to community and social life. Less than 35% of participants met their desired reductions impairment to energy and drive.
At the final evaluation, 41% of participants met their expectations for change in mobility and 45% met their expectations for change in pain. More than 60% of participants met expectations for mental function, energy and drive, and community life (68%, 60%, and 64%, respectively).
Association Between Clinic- and Participant-Based Measures
No significant associations were noted between exceeding MDC on the BBS or DGI and any of the domains on the PPOQ (all Ps = .263) that are shown in Table 4. Bivariate associations were noted between baseline measures of psychological factors and many of the participant ratings of impairment. This was particularly true of the m-FES, which was negatively associated with 6 of the 9 domains (lower falls efficacy associated with higher ratings of impairment). Fear of movement was positively associated with self-care, interactions with people, community and social life, and energy and drive (higher fear associated with higher ratings of impairment).
Associations With Perceived Outcome
When asked to consider whether the intervention program had been successful, 12 participants (55%) indicated that they were a great deal or a very great deal better. There were no statistical differences in any of the baseline psychological variables or changes in these variables comparing the “successful” group to the remainder of participants.
Significant association was identified between success and exceeding MDC on the BBS (χ2 = 5.84, P = .016) but not the DGI (χ2 = 0.50, P = 0.99). This association resulted in an odds ratio of 13.3, suggesting that a participant who exceeded the MDC on the BBS was 13 times more likely to report that the intervention was successful than a participant who did not reach the MDC on the BBS.
When considering participants' desired change in impairment, only meeting desired changes in mental function was associated with considering the intervention a success (χ2 = 4.55, P = .033). The odds ratio of 9.5, however, was not significant (P = .099). Similarly, expected reduction in impairments to mental function was the only statistically significant association with rating success (χ2 = 4.20, P = 0.041); however, the odds ratio for meeting expectations for change in mental function was not significant. Table 5 includes summary details of the unadjusted regression models from which the odds ratios were determined.
In this study, we examined changes in outcomes measured by the clinical professional and changes in the perceived outcomes of participants. We had hypothesized that if a participant's intrinsic criteria for success (desired change in impairment) were met, the participant would consider himself or herself to be improved. This was not the case in this group of older adults whom we followed. In fact, the only statistical association to outcome that we identified was with change on the BBS. That is, participants who made measurable change (exceeded the MDC) on the BBS had higher odds of rating themselves to be improved at the end of the intervention.
Of the 7 different domains examined using the PPOQ, we had expected that desired changes in mobility impairment would be of particular influence on outcome. Participants did rate mobility as the most important domain in which they wanted to change. However, less than half of the participants met their desired change or their expected change during the intervention. In addition, there were no associations between improvement in the BBS and the desired or expected change, nor was there any association with change in the DGI. Similarly, there were no associations between clinic-based measures of mobility and participant-rated impairments in other domains.
The only domain in which there was an association between rating the intervention a success and meeting expectations was mental function. The definition that we used for mental function was “issues related to memory, attention, concentration, and decision making.” The particular domain was not considered as part of our original aims, as we did not hypothesize that participants would have any expectations regarding changes in this domain if they were volunteering for a study of exercise interventions.
In general, participant ratings of impairment were better associated with psychological measures such as the FES, and the Tampa Scale of Kinesiophobia, a measure of fear of movement. The only domains not associated with a measure of fear, depression, or efficacy were sensory function and pain. Our previous work with older adults with balance disorders has consistently demonstrated links among psychological measures and clinic-based measures.27 Here, we have extended that work and indicate that participant ratings of impairment seem to be particularly linked to psychological factors.
A challenge that we faced is that in the current study there were several domains in which more than half of the participants did not expect any changes to occur. The way in which we interpreted change for the analyses in this article was such that if a participant desired no change in a domain and then made no change in that domain, that person would have met his or her expectation for treatment. The one reason that we may not have seen stronger links between participant expectations and outcome in this study is that we recruited volunteer community-dwelling older adults. In patients, particularly those referred for services, there may have been differences in the both the level of impairment and the desired and expected changes for those impairments. We are unable to comment at this time about how these factors may be different for a group of patients.
Also, our statistical approach—to use the MDC to determine whether there is measurable improvement—was hampered by several participants experiencing ceiling effects in the BBS. Before initiating this experiment, meaningful change in the BBC was suggested to be 5 points (determined in adults with stroke). However, the work done by our group immediately before and in the early stages of the experiment indicated that the MDC was slightly larger for this specific group of older adults. In addition, we may have been underpowered to identify real effects given our sample of 22 individuals. This sample size was predicated on identifying change in the BBS and the DGI, both of which did show statistical improvement. This suggests that we chose the incorrect variables on which to power this study. Future work in this area should power studies based on effect sizes for the expectation-MDC interaction that are likely to be substantially smaller than those for a measure such as the BBS.
Other factors may also have influenced our findings and are likely targets for future investigations. We did not determine how the expectations we measured developed prior to intervention or how they evolved during the interventions. Expectations are affected by personal experience.2 We did not collect prior interactions that the participant may have had with the health care system to identify whether the participant had generally positive or negative expectations about the intervention, participation in an experiment, or the research therapists performing the evaluations, nor did we identify when these expectations changed. Was it immediately after the first evaluation? Was it during the final debriefing? These issues could be important specifically because our measure of success was to ask the participant, after the final evaluation, how much improvement they felt that they had made. Alternative strategies could include collecting this information at each evaluation or at intervals between the evaluations.
Another similar avenue of investigation is compliance to the intervention and the relationship of compliance to expectation and outcome. For example, one might hypothesize that a person who expects the intervention to be beneficial is more likely to be compliant to recommendations for treatment. We did not measure compliance to exercise in this study. Participants were called and encouraged to perform their exercises, but no measures were made per se. Compiling information regarding the compliance to the home exercise program should be an important aspect of future investigations into this area.
Finally, the home programs were developed by the individual physical therapists on the basis of the findings of participant performance during the evaluations. However, the physical therapists used the guidelines reported in the Methods section of this article and each therapist had the clinical flexibility during the study to modify and adapt the exercise program to the needs of the participants during the program. Consequently, exact replication of the specific home exercise programs prescribed during this study will be difficult for other therapists hoping to implement these findings.
We had hypothesized that the perspective of the person receiving treatment would be most important in determining whether the treatment was a success. In this study, we measured outcomes from a clinical performance perspective, using statistical cutoffs for measurable change. We also asked participants what they wanted and what they expected from treatment. The results indicated that if a participant exceeded the MDC for the BBS, they had better odds of reporting success from the treatment than someone who did not.
The authors thank Nicole Preito-Lewis, MSPT, Dawn Saracino, PT, and Dianna Saunders, PT, MS.
1. Janzen JA, Silvius J, Jacobs S, Slaughter S, Dalziel W, Drummond N. What is a health expectation? Developing a pragmatic conceptual model from psychological theory. Health Expect. 2006;9(1):37–48.
2. Stewart-Williams S. The placebo puzzle: putting together the pieces. Health Psychol. 2004;23:198–206.
3. Bialosky JE, Bishop MD, Cleland JA. Individual expectation: an overlooked, but pertinent, factor in the treatment of individuals experiencing musculoskeletal pain. Phys Ther. 2010;90(9):1345–1355.
4. Mondloch MV, Cole DC, Frank JW. Does how you do depend on how you think you'll do? A systematic review of the evidence for a relation between patients' recovery expectations and health outcomes. Can Med Assoc J. 2001;165(2):174–179.
5. Karlsson J, Hallgren P, Kral J, Lindroos AK, Sjostrom L, Sullivan M. Predictors and effects of long-term dieting on mental well-being and weight loss in obese women. Appetite. 1994;23(1):15–26.
6. Ruiz BA, Dibble SL, Gilliss CL, Gortner SR. Predictors of general activity 8 weeks after cardiac surgery. Appl Nurs Res. 1992;5:59–65.
7. Borkan JM, Quirk M. Expectations and outcomes after hip fracture among the elderly. Int J Aging Hum Dev. 1992;34:339–350.
8. Mercado R, Constantoyannis C, Mandat T, et al. Expectation and the placebo effect in Parkinson's disease patients with subthalamic nucleus deep brain stimulation. Mov Disord. 2006;21(9):1457–1461.
9. Tinetti ME, Doucette J, Claus E, Marottoli R. Risk factors for serious injury during falls by older persons in the community. J Am Geriatr Soc. 1995;43(11):1214–1221.
10. Cullen B, O'Neill B, Evans JJ, Coen RF, Lawlor BA. A review of screening tests for cognitive impairment. J Neurol Neurosurg Psychiatry 2007;78(8):790–799.
11. Folstein MF, Folstein SE, McHugh PR. “Mini-Mental State.” A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–198.
12. Carlson J. Assessment of orthostatic blood pressure: measurement technique and clinical applications. South Med J. 1999;92(2):167–173.
13. Hase K, Stein RB. Turning strategies during human walking. J Neurophysiol. 1999;81:2914–2922.
14. Bishop MD, Brunt D, Kukulka C, Tillman MD, Pathare N. Braking impulse and muscle activation during unplanned gait termination in human subjects with parkinsonism. Neurosci Lett. 2003;348(2):89–92.
15. Hase K, Stein RB. Analysis of rapid stopping during human walking. J Neurophysiol. 1998;80:255–261.
16. Youdas JW, Mraz ST, Norstad BJ, Schinke JJ, Hollman JH. Determining meaningful changes in hip abductor muscle strength obtained by handheld dynamometry. Physiother Theory Pract. 2008;24(3):215–220.
17. Rantanen T, Guralnik JM, Izmirlian G, et al. Association of muscle strength with maximum walking speed in disabled older women. Am J Phys Med Rehabil. 1998;77(4):299–305.
18. Hill KD, Schwarz JA, Kalogeropoulos AJ, Gibson SJ. Fear of falling revisited. Arch Phys Med Rehabil. 1996;77(10):1025–1029.
19. Cumming RG, Salkeld G, Thomas M, Szonyi G. Prospective study of the impact of fear of falling on activities of daily living, SF-36 scores, and nursing home admission. J Gerontol A Biol Sci Med Sci 2000;55:M299–M305.
20. Myers AM, Powell LE, Maki BE, Holliday PJ, Brawley LR, Sherk W. Psychological indicators of balance confidence: relationship to actual and perceived abilities. J Gerontol A Biol Sci Med Sci. 1996;51(1):M37–M43.
21. Vlaeyen JW, de Jong J, Geilen M, Heuts PH, van Breukelen G. The treatment of fear of movement/(re)injury in chronic low back pain: further evidence on the effectiveness of exposure in vivo. Clin J Pain. 2002;18(4):251–261.
22. Vlaeyen JW, de Jong J, Geilen M, Heuts PH, van Breukelen G. Graded exposure in vivo in the treatment of pain-related fear: a replicated single-case experimental design in four patients with chronic low back pain. Behav Res Ther. 2001;39(2):151–166.
23. Verbunt JA, Seelen HA, Vlaeyen JW, van der Heijden GJ, Knottnerus JA. Fear of injury and physical deconditioning in patients with chronic low back pain. Arch Phys Med Rehabil. 2003;84(8):1227–1232.
24. van Ittersum M, de Greef M, van Gelder I, Coster J, Brugemann J, van der Schans C. Fear of exercise and health-related quality of life in patients with an implantable cardioverter defibrillator. Int J Rehabil Res. 2003;26(2):117–122.
25. Crombez G, Vlaeyen JW, Heuts PH, Lysens R. Pain-related fear is more disabling than pain itself: evidence on the role of pain-related fear in chronic back pain disability. Pain. 1999;80(1–2):329–339.
26. Kori SH, Miller RP, Todd DD. Kinesiophobia: a new view of chronic pain behavior. Pain Manag. 1990;1:35–43.
27. Bishop MD, Meuleman J, Robinson M, Light KE. Influence of pain and depression on fear of falling, mobility, and balance in older male veterans. J Rehabil Res Dev. 2007;44(5):675–684.
28. Lesher EL, Berryhill JS. Validation of the Geriatric Depression Scale–Short Form among inpatients. J Clin Psychol. 1994;50(2):256–260.
29. Sheik J, Yesavage JA. Geriatric Depression Scale (GDS) recent evidence and development of a shorter version. Clin Gerontol. 1986;5(1):165–173.
30. Price DD, Bush FM, Long S, Harkins SW. A comparison of pain measurement characteristics of mechanical analogue and simple numerical rating scales of pain. Pain. 1994;56:217–226.
31. Berg K, Wood-Dauphinee S, Williams J, Gayton D. Measuring balance in the elderly: preliminary development of an instrument. Physiother Can. 1989;41(6):304–311.
32. Berg KO, Maki BE, Williams JI, Holliday PJ, Wood-Dauphinee SL. Clinical and laboratory measures of postural balance in an elderly population. Arch Phys Med Rehabil. 1992;73(11):1073–1080.
33. Wolf SL, Catlin PA, Gage K, Gurucharri K, Robertson R, Stephen K. Establishing the reliability and validity of measurements of walking time using the Emory Functional Ambulation Profile. Phys Ther. 1999;79(12):1122–1133.
34. Liston RAL, Brouwer BJ. Reliability and validity of measures obtained from stroke patients using the balance master. Arch Phys Med Rehabil. 1996;77(5):425–430.
35. Romero S, Bishop MD, Velozo CA, Light K. Minimum detectable change of the Berg Balance Scale and Dynamic Gait Index in older persons at risk for falling. J Geri Phys Ther. 2011;34(3):131–137.
36. Whitney SL, Hudak MT, Marchetti GF. The Dynamic Gait Index relates to self-reported fall history in individuals with vestibular dysfunction. J Vestib Res. 2000;10(2):99–105.
37. Whitney S, Wrisley D, Furman J. Concurrent validity of the Berg Balance Scale and the Dynamic Gait Index in people with vestibular dysfunction. Physiother Res Int. 2003;8(4):178–186.
38. Bishop MD, Patterson TS, Romero S, Light KE. Improved fall-related efficacy in older adults related to changes in dynamic gait ability. Phys Ther. 2010;90(11):1598–1606.
39. Shumway-Cook A, Baldwin M, Polissar NL, Gruber W. Predicting the probability for falls in community-dwelling older adults. Phys Ther. 1997;77(8):812–819.
40. Kamper SJ, Maher CG, Mackay G. Global rating of change scales: a review of strengths and weaknesses and considerations for design. J Man Manip Ther. 2009;17(3):163–170.
41. Fischer D, Stewart AL, Bloch DA, Lorig K, Laurent D, Holman H. Capturing the patient's view of change as a clinical outcome measure. JAMA. 1999;282(12):1157–1162.
42. MacRae PG, Asplund LA, Schnelle JF, Ouslander JG, Abrahamse A, Morris C. A walking program for nursing home residents: effects on walk endurance, physical activity, mobility, and quality of life. J Am Geriatr Soc. 1996;44(2):175–180.
43. Wolf B, Feys H, De W, van der Meer J, Noom M, Aufdemkampe G. Effect of a physical therapeutic intervention for balance problems in the elderly: a single-blind, randomized, controlled multicentre trial. Clin Rehabil. 2001;15(6):624–636.
44. Tinetti ME, Baker DI, McAvay G, et al. A multifactorial intervention to reduce the risk of falling among elderly people living in the community. N Engl J Med. 1994;331:821–827.
45. Province MA, Hadley EC, Hornbrook MC, et al. The effects of exercise on falls in elderly patients: a preplanned meta-analysis of the FICSIT Trials-Frailty and Injuries: Cooperative Studies of Intervention Techniques. JAMA. 1995;273:1341–1347.
46. Mazzeo RS, Cavanagh P, Evans WJ, et al. American College of Sports Medicine Position Stand. Exercise and physical activity for older adults. Med Sci Sports Exerc. 1998;30(6):992–1008.
47. Childs JD, Fritz JM, Flynn TW, et al. A clinical prediction rule to identify patients with low back pain most likely to benefit from spinal manipulation: a validation study. Ann Intern Med. 2004;141(12):920–928.
48. Cleland JA, Childs JD, Fritz JM, Whitman JM, Eberhart SL. Development of a clinical prediction rule for guiding treatment of a subgroup of patients with neck pain: use of thoracic spine manipulation, exercise, and patient education. Phys Ther. 2007;87(1):9–23.
49. Cleland JA, Childs JD, McRae M, Palmer JA, Stowell T. Immediate effects of thoracic manipulation in patients with neck pain: a randomized clinical trial. Man Ther. 2005;10(2):127–135.
50. Cleland JA, Fritz JM, Childs JD, Kulig K. Comparison of the effectiveness of three manual physical therapy techniques in a subgroup of patients with low back pain who satisfy a clinical prediction rule: study protocol of a randomized clinical trial [NCT00257998]. BMC Musculoskelet Disord. 2006;7:11.
51. Flynn T, Fritz J, Whitman J, et al. A clinical prediction rule for classifying patients with low back pain who demonstrate short-term improvement with spinal manipulation. Spine. 2002;27(24):2835–2843.
aging; balance; expectations; mobility; rehabilitation
Copyright © 2012 the Section on Geriatrics of the American Physical Therapy Association