ABSTRACT
Objective: The Adaptive Behaviour and Community Competency Scale was used to investigate the interrelationship of 22 basic and instrumental activities of daily living (ADL/IADL) in individuals with moderate to severe traumatic brain injury (TBI). The relationship of self-awareness to task performance was also investigated.
Research design: Prospective descriptive study.
Method: The profiles of 100 community dwelling individuals were used to compare the degree to which independence in each ADL/IADL was associated with independence in every other ADL/IADL. The interrelationship of these skills was further explored in a factor analysis, and comparisons made between the degree of self-awareness of those who could and could not complete IADL independently.
Results: We found evidence of a hierarchy of skills: individuals who were independent in IADL were more able to perform ADL, than vice versa.
Factor analysis supported a two-factor solution distinguishing ADL and IADL. Self-awareness was more strongly associated with IADL than with ADL independence.
Conclusions: A subset of individuals with moderate to severe TBI are able to perform a range of IADL. This group appears to have higher levels of self-awareness than those who are limited to performing only ADL skills. Implications for the applications of functional retraining interventions are discussed.
In the USA, functional skills are typically divided into activities of daily living (ADL) and instrumental activities of daily living (IADL). ADL include, but are not limited to, the routine self-care activities of eating, bathing, dressing, toileting, and transferring (Citation1–Citation3). IADL include a range of activities related to independent functioning, such as meal preparation, shopping, community mobility skills (Citation4,Citation5), and other activities that involve managing social interaction or environmental variability. IADL are more varied, and typically performed in a greater range of settings than are ADL (Citation6,Citation7).
Moderate to severe traumatic brain injury (TBI) is a major cause of long-term ADL and IADL disability (Citation8,Citation9). Indeed, direct assessment of ADL and IADL appear to be the best predictor of an individual’s need for support or ability to function independently in the community (Citation6,Citation7). The frequency with which individuals can perform ADL versus IADL would suggest the latter are more difficult to accomplish. For instance, in a 3–5 year follow-up study, Dikmen and colleagues (Citation10) estimated that approximately 10% of persons in their severe TBI sample required long-term assistance or were dependent on others for help with ADL tasks. By contrast, 60% had difficulty or required support with IADL tasks. Powell et al. (Citation11) identified similar rates of ADL and IADL dependence. However, observing the frequency with which deficits occur is insufficient for a full understanding of what makes some tasks more difficult than others. There is evidence that ADL/IADL skills are hierarchically organized in older individuals across diagnostic categories (Citation12), but to our knowledge this finding has not been replicated amongst people following moderate to severe TBI. There is evidence that executive functioning is the best predictor of functional performance in older adults living in the community (Citation13) and predicts progress during acute TBI rehabilitation (Citation14). Lack of insight is generally associated with poorer functional recovery and worse rehabilitation and employment outcomes (Citation15–Citation17). It has been proposed that adequate IADL performance post TBI depends on both the individual’s cognitive status and their self-awareness (Citation18,Citation19). In a prior publication we reported that lack of insight was associated with increased need for case management and care-support for individuals living in the community following TBI (Citation20). To our knowledge, there have been no studies that have examined how insight relates directly to ADL versus IADL performance after TBI (Citation18). An enhanced understanding of the way in which ADL/IADL skills interrelate could provide a rationale for choosing the order in which functional independence skills are addressed in rehabilitation.
The current study was designed to test the following hypotheses:
A hierarchical relationship exists between IADL and ADL skills in individuals following moderate to severe TBI. Independent performance of IADL skills is more strongly associated with independent performance of ADL skills, than the other way around
An exploratory factor analysis of ADL and IADL independence would support a two-factor solution: a procedural/motoric factor, and a planning, problem-solving and decision-making factor
Individuals who are unable to perform IADL would be more impaired in self-awareness than those who are able to perform IADL.
Methods
Procedure
Data were collected as part of a larger study and the current report is a secondary analysis. A detailed description of the data-collection procedures is reported elsewhere (Citation20). There were 141 client records derived from two independent samples of profiles provided by UK brain injury case managers from different case management companies. Case managers were knowledgeable about the clients whose profiles they contributed. In the prior paper, we found that the two samples were comparable in the frequency with which clients could accomplish ADL/IADL tasks, suggesting that the relative frequencies of independence in these skills did not occur by chance. There were no differences in the distribution of the ADL and IADL items between data sets, so the samples were combined to produce a single data set (Citation20). The Mayo system was used to classify injury severity and only those client profiles with a definite moderate to severe TBI classification were included (Citation21), yielding a total of 100 profiles. Samuel Merritt University Institutional Review Board approved the study.
Study population
Client profiles were included if clients were living in the community (i.e., not in residential or other institutional care), over the age of 12 years at the time of injury, and had sustained a TBI (e.g., vehicular, falls, assaults, sporting injuries), hypoxia, anoxia, or anesthetic accident, or a limited range of vascular events (e.g., subarachnoid hemorrhage). Clients were excluded if they had sustained a stroke, or brain injury associated with a cardiac event, as these diagnostic groups were considered to have different patterns of cognitive and functional outcomes from those seen in TBI (Citation22). Individuals were not excluded on the grounds of substance abuse or psychiatric impairment.
Measure
Adaptive Behaviour and Community Competency Scale
The Adaptive Behaviour and Community Competency Scale (ABCCS) was designed specifically to be sensitive to the types of problems experienced by clients with TBI living in the community in the post-acute period (Citation20,Citation23). The ABCCS uses ordinal scales linked to concrete behavioral descriptions or a specification of the need for prompting or physical assistance, in line with the World Health Organization level of impairment and ability. The ABCCS has excellent interrater and test–retest reliability when used with a UK TBI community population (Citation23,Citation24). Each item relating to ADL/IADL was dichotomized into independent or not independent. Independence in an activity was defined as safe, habitual task performance without the need for physical or verbal prompting, assistance or supervision. A definition of each of the 22 ADL/IADL items used in the ABCCS is given in Appendix A. The ABCCS also includes a 4-point Insight scale measuring the client’s global self-awareness, based on the descriptive theory of Crosson et al. (Citation25) ( provides the ABCCS Insight scale).
Table 1. ABCCS “Insight” scale.
Data analysis
SPSS version 19 (Citation26) was used to examine client demographics.
The frequency with which independence was achieved in each of the 22 ABCCS ADL/IADL items was calculated, and the items ordered in terms of most (item 1) to least (item 22) frequently completed independently.
The rates of independence versus dependence and Fisher Exact Probability Test with Bonferroni correction were calculated using a 2 × 2 contingency table as a guide for each bi-directional ADL/IADL skill pair (Citation27)(for further explanation of how data for were calculated see Appendix B).
Table 2. Sample demographic and clinical characteristics.
Table 3. The co-occurrence of functional skill independence in 100 community dwelling individuals with moderate to severe TBI with Bonferroni corrected significance.
An exploratory factor analysis was conducted to determine whether the 22 ADL and IADL items truly represent two different types of activity. Since the data are binary, the exploratory factor analysis was conducted on a tetrachoric correlation matrix for the 22 functional skills using minimum residual observed least squares and varimax rotation. Fox’s (2014) polycor package in R was used to calculate the tetrachoric correlation matrix (Citation28).
Having ordered the 22 ADL and IADL in terms of the frequency with which clients were independent in these skills, the midpoint activity was identified (i.e. Health Maintenance). Items above Health Maintenance in were considered mostly ADL and those below it were considered mostly IADL. Mann–Whitney U test SPSS version 19 (Citation26) was used to analyze whether severity of injury was associated with independence in items above or below this midpoint Health Maintenance (i.e., the ADL versus IADL items). A similar procedure was used to determine if there were differences between the lack of awareness of clients who could perform activities above or below the Health Maintenance item. The z distribution was used to calculate the effect size from the Mann–Whitney U results (Citation29).
Results
Client characteristics
Client demographics, severity of injury and indicators of severity of outcome are provided in . Data were available for 84 individuals regarding their employment status pre- and post-injury (see ). Post-injury only 3 clients were in competitive employment and only 10 clients were in education, with a further 12 clients in voluntary or sheltered work settings. Most clients (74) were living at home alone or with family post-injury with the remainder (26) in their own home with support (see ): all clients were receiving case management.
Independence rates
provides the 462 independent, pairwise comparisons for each ADL or IADL skill with every other ADL or IADL skill, ordered from most-to-least commonly achieved. In examining the table, the reader should first read across the top of the table to locate the predictor ADL or IADL skill of interest (labeled 1–22), below which is given the number of people in the sample who are independent in that predictor skill. Looking at each cell by row, the first number indicates the absolute number of people who are independent in the predictor skill who are also independent in the associated skill (identified by the name at the side of the table row). The second number in each cell is the percentage of people who are independent in the predictor skill (who are also independent in the associated skill). Visual inspection of reveals that the number of people achieving independence in the list of reordered items gradually reduces in each column, with only some minor exceptions (i.e. the hierarchy is not perfect). Those individuals who achieve independence in the lower items in the table (e.g. items 19, 20, 21, 22; items that are typically considered IADL) are far more likely to be independent in items higher in the table (e.g. 1, 2, 3, 4; items that are typically considered ADL) than vice versa. If, for example, the reader examines column 19 Laundry/Housework, 13/100 people are independent in the IADL Laundry/Housework and almost all are also independent in most of the items above the midpoint Health Maintenance in the table (≥84–100%, i.e. in the ADLs items Telephone Use, Continence, Outdoor and Indoor Mobility, Oral Care, Showering, Dressing and Nail Care). If, however, the reader examines column 8, 49/100 people are rated as independent in Dressing, but only 13/100 (26%) are independent in the IADL of Laundry/Housework. Appendix B ( and ) illustrate the creation of the 2 × 2 table.
Table 3. The co-occurrence of functional skill independence in 100 community dwelling individuals with moderate to severe TBI.
Table 4. Exploratory factor analysis loadings for functional skills.
Table 5. Two-by-two contingency table for the functional skills of laundry/housework and street crossing.
Table 6. Two-by-two contingency table for room tidying and dressing.
Exploratory factor analysis
An exploratory factor analysis was conducted to determine whether the 22 ADL and IADL represent two underlying types of skills. First, a tetrachoric correlation matrix was computed (available on request from the first author). Almost all of the tetrachoric correlations for each pair of ADL and IADL skills were significant at the 95 percent confidence level. Using 0.40 as the cut-off value for factor loadings, it was determined that eleven ADL and IADL skills loaded on both factors, seven ADL and IADL skills loaded only on factor 1, and four ADL and IADL skills loaded only on factor 2. In the cases where a functional skill loaded on both factors, the factor with the higher factor loading for each ADL or IADL skill was chosen as the final factor. In , boxes have been drawn around the final ADL and IADL skills included in each factor which we labeled: (1) procedural/motoric, and (2) planning, problem-solving, and decision-making.
A comparison was made between clients who were independent in Health Management and/or independent in one or more of the items below Health Management in (IADL-independent group) and clients who were dependent for Health Management and all tasks below it in (IADL-dependent group). This grouping resulted in an IADL-independent group of 58 clients and an IADL-dependent group of 42 clients. Mann–Whitney analysis of Glasgow Coma Scale (GCS), coma duration or post traumatic amnesia (PTA) indicated that the IADL-independent and IADL-dependent groups did not differ significantly in any of the TBI severity measures. Thus for PTA, the IADL-dependent group (n = 31) had a mean rank of 38.0 while the IADL-independent group (n = 40) had a mean rank of 34.5 with the resultant Mann–Whitney U = 558.0, p = 0.36. For the GCS, the IADL-dependent group (n = 36) had a mean rank of 41.0 and the IADL-independent group (n = 51) had a mean rank of 46.2 with the resultant Mann–Whitney U = 808.5, p = 0.13. For duration of coma, the IADL-dependent group (n = 28) had a mean rank of 38.5, and the IADL-independent group (n = 39) had a mean rank of 30.8 with a resultant Mann–Whitney U that approached, but did not reach, significance = 420.5, p = 0.06.
The Mann–Whitney U test was used to compare the same IADL-dependent and IADL-independent grouping on the 4-point ABCCS Insight scale (see ). The IADL-dependent group had an average score of 1.94 on Insight and the IADL-independent group an average score of 2.21. The IADL-dependent group (n = 42) had a mean rank of 31.0 while the IADL-independent group (n = 58) had a mean rank of 40.0, with the resultant Mann–Whitney U = 772, p = 0.001. Effect size calculation indicates an eta squared of 0.10 indicating that 10% of the variability in self-awareness as measured by the ABCCS variable Insight can be attributed to the IADL-independent and IADL-dependent groups.
Discussion
Hypothesis 1, that a hierarchical relationship exists between IADL and ADL skills in individuals following moderate to severe TBI was supported. Hypothesis 2, that an exploratory factor analysis of the 22 ADL and IADL skills would support a two-factor solution which we labeled procedural/motoric vs planning, problem-solving, and decision-making was partially supported. The two-factor solution found that 7 of the 22 skill items (mostly ADL) loaded predominantly on factor one (procedural/motoric), and four items (predominantly complex IADL) loaded mostly on factor 2 (planning, problem-solving, and decision-making). The other skills loaded on both factors. Hypothesis 3, that clients rated as dependent on IADL items are more impaired in self-awareness than those clients who are able to perform IADL was supported. Severity of injury did not differentiate between those who were or were not independent in IADL.
We interpret the data as suggesting that the key distinction between the ADL and IADL functions is not “complexity” per se, but rather the necessity for planning, in-the-moment problem-solving, and decision-making. Many ADL can be accomplished in a highly routine manner where one step follows another in an invariant sequence. During IADL tasks the client needs to use executive functions to adapt responses to novelty and changing environmental demands. Although IADL tasks can become easier with practice, because of the need for novel problem-solving, they do not become fully automatic (Citation30,Citation31).
Individuals, who lack insight and are poor at recognizing that they might experience difficulty with task performance, are unlikely to use compensatory strategies. The data presented in allow the clinician a method to identify inconsistencies in a client’s pattern of skills, which need to be explained with reference to the client’s particular circumstances or addressed in a more systematic way. Clients with impaired executive functioning may be unable to generate ways to work around their cognitive or physical impairments and may need others to do this for them. For example, one of our early reports (Citation32) described a 23-year-old male with a very severe injury who received 12 months of intensive in-patient rehabilitation. At 25-month post-injury, he could manage his personal finances, but could not bathe or dress due to a combination of physical skills (lower extremity hypertonicity, and contractures) and executive function deficits. The application of a structured bathing and dressing retraining program resulted in independence after only 11 days of treatment (Citation32). Conversely, based on our findings, we hypothesize that, if after sufficient training, a client is unable to manage the problem-solving required for independence in an IADL task, it is unlikely that the client will be able to learn to perform tasks that have greater problem-solving demands (Citation20). What seems to be required is to reduce the on-the-spot decision-making requirements so the task becomes invariant. At the core of decisions about rehabilitation is the recognition that some activities require novel problem-solving and some do not. For ADL, the therapist’s role is to perform a task analysis and construct a procedure the client can carry out successfully (Citation32,Citation33). Learning can then take place via the repetitive enactment of behavior chains using errorless learning procedures (Citation30,Citation32–Citation35). This is not possible for IADL that require a response to novelty and changing task and environmental demands. Independence in such tasks may be more achievable using a problem-solving strategy-based training, but only if the client has the ability to make use of this type of intervention (Citation20).
Clearly these ideas are conjectural and based on a combination of our data and clinical experience. Further work is required to establish their general validity. At present we can simply say that, within this sample of 100 community dwelling individuals with moderate to severe TBI, independence in ADL and IADL skills was hierarchically organized, albeit imperfectly. The conceptual distinction between ADL and IADL skills is supported by the data and corresponds to observations of the everyday performance of individuals with moderate to severe TBI. Level of awareness was associated with IADL independence.
Limitations
The study sample consisted of clients who had sustained a moderate to severe TBI who were receiving case management, and may not be representative of the wider population of individuals with moderate to severe TBI. We were not able to explore the effects of age, gender, or ethnicity of participants, although there is little evidence that these variables are significant predictors of outcome in the post-acute period (Citation36). Despite an overall sample size of 100, for some 2 × 2 comparisons, sample sizes were small. Results are entirely dependent on the specific definition of independence used in the ABCCS definitions of the 22 ADL/IADL items. For example, shopping independence on the ABCCS requires that a client is able to shop for items that typically require planning and problem-solving (e.g., major appliances, furniture: see Appendix A). Additionally, there are many factors that may affect an individual’s engagement in ADL/IADL that are not addressed here. Mood and motivational factors, as well as personal choice, may all affect activity engagement. Our data did not include details of the client’s previous rehabilitation services, and whether, for instance, the current results might have been achieved because of a selective focus on teaching ADL, rather than IADL skills. The current results will need to be validated in a prospective study with a larger sample size.
Acknowledgments
We would like to thank the following organizations and individuals for their contributions to data collection: Anglia Case Management, Ben Holden, Brain Injury Case Management, Bush and Co, Care For Life, Case Management Services, Coochi, Debbie Eaton Case Management Limited, Family Focused Case Management Limited, Head First, Medico Rehab Limited, Independent Case Management, Independent Case Management Services, Jacky Parker and Associates, Maggie Sargent and Associates, N-Able, The Rehabilitation Partnership, RT Disability and West Country Case Management.
Disclosure statement
Jo Clark-Wilson, Stephanie Seymour, Ross Tasker, and Mark Holloway are managing partner, employees, or contractors with Head First, a case management company in Kent, UK. There are no other declarations of interest.
References
- Velikonja D, Oakes J, Brum C, Sachdeva M. Assessing the validity of task analysis as a quantitative tool to measure the efficacy of rehabilitation in brain injury. Brain Inj. 2017;31:68–74. doi:10.1080/02699052.2016.1212090.
- State University of New York. Guide for the uniform data set for medical rehabilitation (including the FIM™ instrument 5.1 ed.). Buffalo, NY: Uniform Data System for Medical Rehabilitation; 1997.
- Mahoney FL, Barthel DW. Functional evaluation: the Barthel Index. Marryland State Med J. 1965;14:61–65.
- Lawton MP, Brody EM. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist. 1969;9:176–86. doi:10.1093/geront/9.3_Part_1.179.
- American Occupational Therapy Association. Occupational therapy practice framework: domain and process. Am J Occup Ther. 2014;68(suppl.1):S1–S48. doi:10.5014/ajot.2014.682006.
- Morrison MT, Edwards DF, Giles GM. Performance-based testing in mild stroke: identification of unmet opportunity for occupational therapy. Am J Occup Ther. 2015;69:1–5.
- Schmitter-Edgecombe M, Parsey C, Cook DJ. Cognitive correlates of functional performance in older adults: comparison of self-report, direct observation, and performance-based measures. J Int Neurosychological Soc. 2011;17:853–64. doi:10.1017/S1355617711000865.
- Livingston DH, Tripp T, Biggs C, Lavery RF. A fate worse than death? Long-term outcome of trauma patients admitted to the surgical intensive care unit. J Trauma. 2009;67:341–48. discussion 348–349. doi:10.1097/TA.0b013e3181a5cc34.
- Bender A. Bauch, S. and Grill, E.: Efficacy of a post-acute interval inpatient neurorehabilitation programme for severe brain injury. Brain Inj. 2014;28:44–50. doi:10.3109/02699052.2013.850177.
- Dikmen SS, Machamer JE, Powell JM, Temkin NR. Outcome 3 to 5 years after moderate to severe traumatic brain injury. Arch Phys Med Rehabil. 2003;84:1449–57.
- Powell JM, Temkin NR, Machamer JE, Dikmen SS. Gaining insight into patients’ perspectives on participation in home management activities after traumatic brain injury. Am J Occup Ther. 2007;61:269–79.
- Spector WD, Katz S, Murphy JB, Fulton JP. The hierarchical relationship between activities of daily living and instrumental activities of daily living. J Chronic Dis. 1987;40:481–89. doi:10.1016/0021-9681(87)90004-X.
- Cahn-Weiner DA, Boyle PA, Malloy PF. Tests of executive function predict instrumental activities of daily living in community-dwelling older individuals. Appl Neuropsychol. 2002;9:187–91. doi:10.1207/S15324826AN0903_8.
- Hanks RA, Rapport LJ, Millis SR, Deshpande SA. Measures of executive functioning as predictors of functional ability and social integration in a rehabilitation sample. Arch Phys Med Rehabil. 1999;80:1030–37.
- Prigatano GP, Altman IM. Impaired awareness of behavioural limitations after traumatic brain injury. Arch Phys Med Rehabil. 1990;71:1058–64.
- O’Callaghan A, McCallister L, Wilson L. Insight vs readiness: factors affecting engagement in therapy from the perspectives of adults with TBI and their significant others. Brain Injury. 2012;26:1599–610. doi:10.3109/02699052.2012.698788.
- Shereer M, Bergloff P, Levin E, High WM, Oden KE, Nick TG. Impaired awareness and employment outcome after traumatic brain injury. J Head Trauma Rehabil. 1998;13:52–61.
- Goverover Y. Categorization, deductive reasoning, and self-awareness: association with everyday competence in persons with acute brain injury. J Clin Exp Neuropsychol. 2004;26:737–49. doi:10.1080/13803390490509321.
- Abreu BC, Seale G, Scheibel RS, Huddleston N, Zhang L, Ottenbacher KJ. Levels of self-awareness after acute brain injury: how patients’ and rehabilitation specialists’ perceptions compare. Arch Phys Med Rehabil. 2001;82:49–56. doi:10.1053/apmr.2001.9167.
- Clark-Wilson J, Giles GM, Seymour S, Tasker R, Baxter DM, Holloway M. Factors influencing community case management and care hours for clients with traumatic brain injury living in the UK. Brain Inj. 2016;30:872–82. doi:10.3109/02699052.2016.1146799.
- Malec JF, Brown AW, Leibson CL, Flaada JT, Mandrekar JN, Diehl NN, Perkins PK. The Mayo classification system for traumatic brain injury severity. J Neurotruama. 2007;24:1417–24. doi:10.1089/neu.2006.0245.
- Malec JF, Kean J, Altman IM, Swick S. Mayo-Portland adaptability inventory: comparing psychometrics in cerebrovascular accident to traumatic brain injury. Arch Phys Med Rehabil. 2012;93:2271–75. doi:10.1016/j.apmr.2012.06.013.
- Giles GM. Assessing adaptive behaviour in the post-acute setting following traumatic brain injury: initial reliability and validity of the Adaptive Behavior and Community Competency Scale (ABCCS). Brain Inj. 2007;21:521–29. doi:10.1080/02699050701311067.
- World Health Organization. International classification of functioning, disability, and health (ICF). Geneva, Switzerland: Author; 2001.
- Crosson B, Barco P, Velozo C, Bolesta MM, Cooper PV, Werts D, Brobeck TC. Awareness and compensation in post acute head injury rehabilitation. J Head Trauma Rehabil. 1989;4:46–54. doi:10.1097/00001199-198909000-00008.
- IBM Corp. IBM SPSS statistics for windows (Version 19.0). Armonk, NY: Author;2010.
- McDonald JH. Fisher’s exact test of independence. In: Handbook of biological statistics. Baltimore, MD: Sparky House Publishing;2014; 77–85.
- R Core Team. R. A language and environment for statistical computing. [Accessed 2015]. https://www.r-project.org/.
- Fritz CO, Morris PE, Richler JJ. Effect size estimates: current use, calculations, and interpretation. J Exp Psychol. 2012;141:2–18. doi:10.1037/a0024338.
- Trevena-Peters J, McKay A, Spitz G, Suda R, Renison B, Ponsford J. Efficacy of activities of daily living retraining during post-traumatic amnesia: a randomised controlled trial. Arch Phys Med Rehabil. 2018;99:329–37. doi:10.1016/j.apmr.2017.08.486.
- Clark-Wilson J, Baxter D, Giles GM. Revisiting the neurofunctional approach: conceptualising the essential components for the rehabilitation of everyday living skills. Brain Inj. 2014;28:646–1656. doi:10.3109/02699052.2014.946449.
- Giles GM, Ridley J, Dill A, Frye S. A consecutive series of brain injured adults treated with a washing and dressing retraining program. Am J Occup Ther. 1997;51:256–66.
- Holmqvist K, Ivarsson A-B, Holmefur M. Occupational therapists practice patterns in relation to clients with cognitive impairment following acquired brain injury. Brain Inj. 2014;28:1365–73. doi:10.3109/02699052.2014.919529.
- Reber PJ. The neural basis of implicit learning and memory: a review of neuropsychological and neuroimaging research. Neuropsychologia. 2013;51:2026–42. doi:10.1016/j.neuropsychologia.2013.06.019.
- Trevena-Peters J, Ponsford J, McKay A. Agitated behavior and activities of daily living retraining during posttraumatic amnesia. J Head Trauma Rehabil. 2017;33:317–325. doi: 10.1097/HTR.0000000000000363.
- Ponsford J. Factors contributing to outcome following traumatic brain injury. NeuroRehabilitation. 2013;32:803–15. doi:10.3233/NRE-130904.
Appendix A.
ABCCS definitions of independence
Appendix B.
Appendix B ( and ) provide examples of how was developed using 2 × 2 tables. The independence rate is the number of clients who are independent in skill A and independent in skill B divided by the total number of clients who are independent in skill A. This provides the number and the percentage of people who are independent in skill B who are also independent in skill A. In , the first 2 × 2 table Laundry/Housework is the predictor skill and Street Crossing is the associated skill and the Fisher’s exact probability test p value with the Bonferroni correction is non-significant. In , the 2 × 2 table Room Tidying is the predictor skill and Dressing is the associated skill and the Fisher’s exact probability test p value with the Bonferroni correction is significant. Visual inspection of the frequency with which clients are independent however suggests that individuals who are independent in doing Laundry/Housework are likely to be independent in Street crossing and individuals who are independent in Room tidying are likely to be independent in Dressing.
Fisher’s exact probability test (two-tailed) was calculated with Bonferroni correction for each 2 × 2 contingency table. In understanding the Fisher’s exact probability test, it should be recognized that the test is influenced by differential rates of both independence and dependence between the predictor and the associated variable. The p values of the tests are also provided in .