As described in subsequent chapters, there are numerous validated tools for measuring physical and mental functional abilities at the impairment, body part, or organ system level. For many patients, however, limitations arise from more than one condition, and for most jobs, adequate performance requires completing multiple tasks and a series of task coordination and task sequencing processes, as discussed in Chapter 2. Although failure to perform a single work-related function in a testing environment may provide evidence of inability to perform that function, success in one domain is not sufficient to establish the ability (or capacity) to perform the job related to that function on a regular and continuing basis in the actual work setting or a different work setting. As discussed in Chapter 3, the usual test environment does not adequately reproduce or predict the sustained and repeated task performance required for work. Moreover, the successful integration of multiple tasks and skills into an effective day of work is influenced by multiple aspects of physical and mental health, as well as by environmental and interpersonal aspects of the work setting. Because successful work performance is more than the sum of the individual functions required, the degree of limitation to perform work often exceeds the sum of individual limitations. Comorbidities (e.g., depression and low back pain) that may themselves not present measurable limitations frequently exacerbate the impact of recognized limitations and reduce the ability to compensate for them. In addition, older age is associated with progression of most diseases, increasing prevalence of comorbidity, and diminishing resilience. Accurate assessment of an individual’s ability to work therefore requires an integrated approach that considers the totality of the person’s
physical, cognitive, and adaptive conditions and aligns them with the full scope of tasks required for the work and the schedule and environment in which they will be performed (see Figure 2-3 in Chapter 2).
Types of assessment instruments range from specific to integrated (Reiman and Manske, 2011). Many objective tests quantitate specific functions expected to be reduced by a particular condition, such as range of motion and strength of contraction for musculoskeletal disease, aerobic exercise tolerance for cardiovascular conditions, and cognitive function for traumatic brain injury (far left of Figure 2-3). Impairment-specific assessment instruments may provide information on the progression of or recovery from a specific disease process, but the validity of their use for other conditions is unknown, and they are unlikely to capture the effects of multiple impairments on an individual’s ability to function. Qualitative assessments by individuals, their health care and rehabilitation providers, and other third parties with knowledge of the individual often yield scores regarding the integrated effect of individuals’ impairments on general daily life and participation (e.g., activities of daily living [ADLs]) and/or on the performance of a specific job, the job’s tasks, and its mental and physical demands. Such integrated assessments are useful for capturing the additive and sometimes multiplicative effects of multiple impairments and comorbid conditions on an individual’s functional ability to meet work requirements. Therefore, the most informative evaluation of function may include integrated assessments in addition to specific assessments of body structures and systems. However, it is easier to identify focused tests that could identify the inability to perform a specific activity relevant to a work requirement (e.g., inability to reach overhead) than to find a general test that demonstrates ability to perform all of the functions required.
This chapter responds to two parts of the committee’s charge: discussion of “generic [versus ‘impairment-specific’] functional assessment questionnaires” and identification of any “activities of daily living that correlate with the physical and mental … demands of work.” Three categories of integrated (“generic”) assessment tools are described: (1) those focused on ADLs and instrumental activities of daily living (IADLs), which assess function in terms of goal-based tasks; (2) self-report instruments designed to assess function in terms of physical and mental activities and tasks; and (3) those that measure limitations in work performance activity due to health conditions (see Figure 2-3).
Given their universality, ADLs are a common focus for integrated assessment of individuals’ functional abilities, regardless of underlying medical conditions. ADLs are basic tasks of daily life that typically include
personal care and hygiene, dressing, feeding, continence management, and mobility. IADLs are more complex tasks related to independent living in the community, such as navigating transportation options and shopping, preparing meals, managing one’s household, managing finances and medications, communicating with others, and providing companionship and mental support. Assessment of ADLs and IADLs is a common way to assess an individual’s ability to perform multiple, integrated functions on a day-to-day basis.
Individuals usually are referred for assessment of ADLs or IADLs in the context of impairments in cognitive or physical functioning. Information from these assessments is typically used to inform the type of assistive devices individuals may need to improve their safe performance of these activities or the amount of assistance individuals may need and/or the living situation they require to perform the activities given their impairment level. Assessment of ADLs and IADLs also considers the contribution of the built environment to one’s ability to perform his or her ADLs and IADLs, as well as the individual’s social context.
The performance of ADLs and IADLs can be assessed by self-report, informant (third-party) report, specific assessments outlined below, and/or direct observation. Most commonly they are assessed through a combination of self-report and direct observation because self-report alone is often not considered valid for those with substantial cognitive impairments or because of other threats to validity. However, assessment by direct observation requires more training to administer relative to self- or third-party report. Thus in many clinical settings, trained occupational therapists, physical therapists, speech-language pathologists, and/or nurses perform assessments—including direct observation—to determine ADL capacity (Mlinac and Feng, 2016). Performance-based IADL assessments are most relevant to assessing a wide range of functional abilities affected by mild changes in cognitive functioning. These performance-based IADL assessments are more commonly used in specific types of clinical settings, such as rehabilitation hospitals, because of the complexity and time intensity of their administration, which in some cases requires an occupational therapist.
An individual’s ability to perform ADLs and IADLs depends on his or her motor abilities, cognitive abilities, and perceptual and sensory abilities (Mlinac and Feng, 2016). Individuals must have the cognitive ability to plan and reason and the motor abilities of balance and dexterity to perform these activities. They must not only be able to complete tasks but also to recognize that they need to do so (Mlinac and Feng, 2016). Other factors also affect the performance of ADLs and IADLs, including, for example, the built environment, such as accessible features in the living space; access to assistive technology; and one’s social context or circumstances, such as the availability of attendant care and/or assistance from family members
and others. Referring to the committee’s conceptual framework, these environmental and social factors are within a different context from that of the organizational and work environmental factors described in Chapter 2 (right-hand side of the dotted line in Figure 2-3). These contextual differences may add challenges to translating or mapping limitations in the ADL and the IADL domain to the work domain.
ADLs are largely unaffected by mild cognitive impairment. Jefferson and colleagues (2008) found no difference in ADL function between individuals with mild cognitive impairment and those with no cognitive impairment. They found that as cognitive impairment worsens, the correlation between cognitive function and ADL dependence appears more consistent. In a study of women and men with mild Alzheimer’s disease, measures of attention predicted overall ADL scores, executive function predicted both ADL and IADL scores, and language predicted IADL scores (Hall et al., 2011). Gender differences have been found in the domains of learning and memory, as well as the association between specific cognitive functions and different ADLs or IADLs (Hall et al., 2011). In IADL assessments, independence is one of the distinguishing features of normal aging versus mild cognitive impairment and dementia (Gold, 2012). In healthy aging, the ability to perform IADLs usually remains intact until individuals reach their 80s.
A meta-analysis focused on elucidating the cognitive processes that underlie IADLs in community-dwelling older adults, including those with mild cognitive impairment, found that 21 percent of variance in IADL capacity was predicted by cognition. General cognitive functioning was found to be important in multiple studies, with executive functioning and memory accounting for more variance than other cognitive domains (Gold, 2012). It should be noted that this meta-analysis did not control for assessment approach (e.g., self- or informant report versus observation) (Royall et al., 2007). Thus, executive skills and other cognitive domains likely support IADL performance, but it is important to recognize that the relationship between cognition and IADLs depends on how IADLs are measured.
Depression is one factor that limits the performance of ADLs and IADLs, irrespective of physical or cognitive performance issues or age. Meltzer and colleagues (2012) looked at disability as measured by reported difficulties with ADLs and IADLs in people living with depression, using a large national survey of psychiatric morbidity among adults across the age spectrum in the private household population of England. The results showed that disability was associated with depression even after adjustment for physical health issues (Meltzer et al., 2012). In fact, the number of ADL/IADL difficulties reported by subjects was directly related to the likelihood of their having depression (Meltzer et al., 2012). Meltzer and colleagues concluded that limitations in all domains of ADLs and IADLs are significantly associated with depression. They concluded further that
the effect is cumulative irrespective of whether the limitation is in personal care or mobility (Meltzer et al., 2012).
In a Swedish study, Boström and colleagues (2014) looked at the association between depression and functional capacity, dependency in performing ADLs, and dependency in performing individual ADL tasks in 392 older adults living in the community and in residential care facilities. They found that while overall ADL performance was not associated with depression, dependency in the ADL tasks of transfers and dressing appeared to be associated with depressive symptoms (Boström et al., 2014).
A literature review of 15 common ADL and IADL assessment instruments conducted by the committee for this study1 did not indicate or support a correlation or association between specific ADL or IADL measures and the ability to work. Many of these instruments are used with older nonworking adults or those who have experienced strokes, head injuries, and/or psychiatric impairments. None is specifically designed to assess work capabilities. Most have been validated with more than one group of people with specific functional or cognitive limitations or disabling conditions or in various settings (i.e., acute care, rehabilitation, community dwellers). The 15 instruments reviewed are as follows:
- ADL Profile (head injury and stroke) (Dutil et al., 1990),
- ADL-Focused Occupations-Based Neurobehavioral Evaluation (Gardarsdóttir and Kaplan, 2002),
- Assessment of Motor and Processing Skills (Fisher and Bray Jones, 2010),
- Barthel Index (Quinn et al., 2011),
- Bay Area Functional Performance Evaluation (Houston et al., 1989),
- Cleveland Scale of Activities of Daily Living (dementia) (Patterson and Mack, 2001),
- Executive Function Performance Test (Baum et al., 2008),
- Frenchay Activities Index (IADLs) (Schuling et al., 1993),
1 This literature review focused on assessment instruments commonly available to clinicians, and was conducted using standard databases. During the course of the review, the committee found a compilation of ADL and IADL assessments in a standard reference work on assessments (Asher, 2014), which also was used as a source. Each ADL and IADL assessment instrument was reviewed with respect to the purpose of the assessment and the population for whom it was intended and/or standardized. An additional search was conducted to determine whether any of the instruments identified had been studied specifically for any relationship with the ability to work or return to work.
- Functional Independence Measure (Ottenbacher et al., 1996),
- Katz ADL Scale (elderly and chronically ill) (Katz, 1983; Katz and Akpom, 1976),
- Kohlman Evaluation of Living Skills (IADLs—psychiatric geriatric) (Burnett et al., 2009; Kohlman-Thomson, 1992),
- The Lawton IADL Scale (Graf and Hartford Institute for Geriatric Nursing, 2008),
- Manual Ability Measure (neurological and musculoskeletal conditions) (Chen and Bode, 2010),
- Multiple Errands Test (brain injury, stroke—executive functioning) (Morrison et al., 2013), and
- Performance Assessment of Self-Care Skills (Chisholm et al., 2014).
As previously noted, several articles address the association between ADL and IADL assessments and early cognitive decline and/or dementia (Mlinac and Feng, 2016; Patterson and Mack, 2001; Sikkes et al., 2009). Many of the assessments listed above are used with this population.
Activities of Daily Living and Work
Although little research exists to connect specific assessments of ADLs and IADLs to an individual’s ability to return to work, Cancelliere and colleagues (2016) conducted a best-evidence synthesis of 56 systematic reviews judged to have a low risk of bias based on the Scottish Intercollegiate Guidelines Network. They looked at common prognostic factors for return to work across different health and injury conditions in an effort to describe the association of these factors with return-to-work outcomes. Half of these systematic reviews focused on prognostic factors for return to work for musculoskeletal disorders, related primarily to the spine, while the remaining half focused on prognostic factors for return to work for mental health disorders, cardiovascular conditions, stroke, cancer, multiple sclerosis, and other (nonspecified) health conditions. The reviews found that factors commonly associated with positive return to work included “higher education and socioeconomic status, higher self-efficacy and optimistic expectations for recovery and return-to-work, lower severity of the injury/illness, return-to-work coordination, and multidisciplinary interventions that include the workplace and stakeholders” (Cancelliere et al., 2016, p. 1). Common prognostic factors found to be associated with negative return to work included “older age, being female, higher pain or disability, depression, higher physical work demands, previous sick leave and unemployment, and activity limitations” (Cancelliere et al., 2016, p. 1). Cancelliere and colleagues include limited ability to perform ADLs among “activity limitations,” which they also refer to as “participation restrictions.”
The only ADL assessment showing any association with ability to work was the Assessment of Motor and Process Skills, which found “a moderate correlation between the level of employment and the global scores of the process skills scale” (Haslam et al., 2010). However, the population of this study was limited to 20 individuals with schizophrenia who were engaged either in competitive employment, supported employment, prevocational training, or nonvocational activities. Thus, no ADL or IADL assessments exist that are standardized on a working-age population with limitations across multiple physical and cognitive areas.
However, information from ADL and IADL assessments can contribute information about an individual’s ability to function, and further inquiry can provide additional information relevant to the ability to work. For example, an individual who cannot independently get out of bed, bathe or shower, dress, use the toilet, feed him- or herself, take medication, or manage money to navigate transportation to get to work would most likely be unable to work without significant assistance from family members or an attendant. Individuals with such severe limitations may be expected to meet the criteria for disability at step 3 of the determination process.2 Conversely, individuals who report the ability to perform ADLs and IADLs may nevertheless be unable to work. They may be able to complete such tasks, but doing so may lead to pain, fatigue, and/or other limitations that interfere with the ability to work. Therefore, the collection of information about ADLs and IADLs could be enhanced by asking follow-up questions about context; environmental factors; required actual assistance; and the impact of performing ADLs and IADLs on pain, fatigue, confusion, concentration, and other physical or cognitive factors that can interfere with the performance of work. The inquiry might explore, for example, whether an individual is able to function after performing all of the morning ADL tasks required to get up and go to work mentioned above, or whether after completing those tasks, the individual is too fatigued or in too much pain to function in a work or any other environment.
Minimal evidence in the literature indicates that limited ability to perform ADLs is a prognostic factor associated with poor return-to-work outcomes. Yet, while many ADL and IADL assessment instruments exist, no one standard assessment can predict whether a person with a given condition or impairment will be able to return to work. Various ADL and IADL assessments may provide information about individuals’ ability to meet their personal hygiene needs or manage their medications, for example, but no ADL and IADL assessments correlate directly with the ability to work for all conditions and/or impairments. ADL and IADL assessments designed for specific conditions may provide information about a person’s level of
2 See 20 CFR 404.1520; 20 CFR 416.920.
functioning in specific ADL and IADL areas, but no direct association between this level of functioning and the ability to return to work has been demonstrated for many reasons, including differences in contextual factors and the demands of jobs versus those of self-care. Again, then, assessment of individuals’ ability to perform ADLs and IADLs needs to include followup questions about the context in which they perform those activities; the amount of assistance they have in doing so; and how they function subsequently in terms of pain, fatigue, confusion, and/or limitations.
As stated previously, chronic health conditions, along with comorbidities, can manifest differently in terms of work-related functional and disability limitations at the activity and work task levels—differences in function that are not captured by assessment instruments specific to impairments and body parts or organ systems. This section describes several evidence-based instruments and sets of instruments that can provide integrated information about individuals’ overall functional capabilities and limitations and help inform determinations of work disability, although only the Work Disability Functional Assessment Battery (WD-FAB) is designed to address work-related function directly. Some of the scales and measures from these instruments and banks are discussed in more detail in subsequent chapters.
Work Disability Functional Assessment Battery (WD-FAB)
WD-FAB is a functional assessment tool developed through an interagency agreement established in 2008 between the U.S. Social Security Administration (SSA) and the Intramural Research Program of the National Institutes of Health (NIH) Clinical Research Center (Chan, 2018a). The instrument was developed through a scientifically rigorous process that used the International Classification of Functioning, Disability and Health (ICF) to conceptualize function and included an extensive literature review, consultation with content experts and focus groups consisting of health care providers and individuals with disabilities, cognitive testing of all items to check clarity and comprehension, and administration of items to user groups (Chan, 2018a). The WD-FAB prototype was completed in 2016, and replenishment of items was completed in 2017. Numerous publications have reported on the development and scientific validation of this instrument (Marfeo, 2013a,b,c, 2014, 2015, 2018; Marino et al., 2015; McDonough et al., 2013, 2017, 2018; Meterko et al., 2015, 2018; Ni et al., 2013).
WD-FAB uses item response theory (IRT) and computer adaptive testing (CAT) methods to create an individualized measure that can best measure the “ability” of the person being tested. CAT algorithms customize the selection of items in real time from the more than 300 items banked, thus reducing respondent burden and allowing comprehensive assessment of functional activity in approximately 15 to 20 minutes (Chan, 2018a,b; Meterko et al., 2018). The instrument encompasses two primary domains—physical function and mental health function. The physical function domain consists of basic mobility; upper-body function; fine motor function; and community mobility, which includes driving, public transportation, and wheelchair use (Chan, 2018a,b; Meterko et al., 2018). The mental health function domain consists of communication and cognition, resilience and sociability, self-regulation, and mood and emotions (Chan, 2018a,b; Meterko et al., 2018).
WD-FAB has demonstrated good test-retest reliability in adults with work disability and general adult samples, very high accuracy for physical function, and moderate to strong convergent validity correlations with legacy measures (Chan, 2018a,b). It has shown more variability in accuracy in the mental health domain.
Strengths of WD-FAB include multiple administration modes (in-person, phone, Web-based, paper/pencil via short forms), depending on user needs3; creation of functional profiles and the ability to track changes over time; item pools that can be replenished and improved; a standardized and consistent approach to the assessment of function; and establishment of thresholds for minimal detectable differences (Chan, 2018a,b). The investigators’ sampling criteria enhance generalizability (Meterko et al., 2018). They recruited three samples from a U.S. national panel: (1) working-age adults matched to the U.S. adult population on age, sex, race, ethnicity, and education; (2) adults in the same age range who reported a permanent disability for a physical condition; and (3) adults who reported a permanent disability for a mental health condition.
A limitation is that WD-FAB outcomes must be linked to workplace demand—a challenge confronted by all disability benefits programs. WD-FAB measures at the activity level according to the ICF, whereas assessment of work disability requires linking activity (whole-person functioning) to participation (work) (as discussed in Chapter 2). Another limitation is the lack of large-scale validation with diverse samples. One potential approach would be to use WD-FAB to develop functional profiles by occupation (Chan, 2018b). Considerable effort has been expended on the development of WD-FAB, but as noted, the link between whole-person functioning and participation in work has not been established and requires
further research. Developing functional profiles using WD-FAB for physical function may be useful. In summary, WD-FAB has great potential to enhance SSA’s methods of obtaining claimants’ perspectives on their function in specific domains that correspond closely to the functional criteria used in disability determination decisions, but further research is required.
World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0)
WHODAS 2.0 can be used to assess overall disability and health with several subscales based on the ICF disability model (see Figure 2-1 in Chapter 2) (WHO, 2018): cognition, mobility, self-care, getting along, life activities, and participation. The last two were added in the 2.0 version of the instrument in accordance with the ICF model. The design intent included capturing cross-cultural constructs of disability that could be applied internationally. There are two versions of WHODAS 2.0: a 36-item version that takes approximately 20 minutes to complete and a 12-item version that takes about 5 minutes. Population norms exist, and the long version can be scored with or without weighting. While the 36-item version of WHODAS 2.0 provides greater detail about the experience of disability, the 12-item version is sufficient when it is important to minimize respondent burden.
The psychometric properties and validation of this instrument have been the focus of numerous studies for specific health issues and languages since it was developed and first described in 2010. Üstün and colleagues (2010) report its properties based on its use with more than 65,000 individuals from across the globe with physical disorders and/or mental health issues or addictions. They report good internal consistency, with a Cronbach’s alpha of 0.86; good and stable structure, as determined through principal component analyses; and a high intraclass coefficient of 0.98 for reliability (Üstün et al., 2010). The scales of the instrument also correlated well with other health measures, including the 36- and 12-Item Short-Form Health Survey (SF-36/12), the World Health Organization’s (WHO’s) Quality of Life Score, the London Handicap Scale, and the Functional Independence Measure. The instrument also responded when clinical interventions were applied, showing good effect sizes. WHODAS is a generic measure of health and disability status that may be thought of as similar to the SF-36. It was developed using classical test theory (unlike the Patient-Reported Outcomes Measurement Information System [PROMIS] or WD-FAB, whose developers used IRT/CAT methods). Although it may be used as a screening device for self-reported symptoms, which may or may not be related to ability to work, it cannot be used as a direct measure of employability.
Patient-Reported Outcomes Measurement Information System (PROMIS)
PROMIS is one of several sets of instruments developed and evaluated with NIH funding (two of the other NIH initiatives are described below). PROMIS is “a set of person-centered measures that evaluates and monitors physical, mental, and social health in adults and children” (NU, 2018e). It can be used with individuals with chronic conditions and the general population and can be administered in three modes—paper, computer, and an app (Amtmann et al., 2011; Cella et al., 2010). Self-reported health measures are arranged according to the domains of physical, mental, and social health (Hahn et al., 2010). Within each domain, subdomains are listed that relate functions, symptoms, behaviors, and affect.
During the first phase of PROMIS (2004–2009), funded through an NIH Roadmap Initiative, the goal was to develop an efficient state-of-the-art assessment tool for self-reported health. This effort resulted in the development of patient-reported outcome measures using large item banks and CAT, allowing for effective assessment of patient-reported outcomes in clinical research (HHS, 2018). The second phase of PROMIS (2009–2014) saw the addition of such features as longitudinal analyses, more sociodemographically diverse samples, increased emphasis on pediatric populations, and evaluation of PROMIS item banks (HHS, 2018). Additional federal and foundation funding supported the development of condition-specific derivatives of PROMIS, including those for neurological disorders (Neuro-QoL) (NU, 2018f), spinal cord injury (SCI-QoL) (Tulsky et al., 2015), traumatic brain injury (TBI-QOL) (Tulsky et al., 2016), and Huntington’s disease (HDQLIFE) (Carlozzi et al., 2017a,b). We note that the NIH Roadmap Initiative specifically funded efforts to ensure that all relevant documentation and evidence for PROMIS would be freely available to the public on Web-based platforms. The PROMIS tools were developed primarily for research and clinical tracking of patients’ perceptions of their illness and its impact on their lives. In many institutions, these tools have become available as part of the electronic medical record during routine care for assessing such variables as depression, pain interference, social functioning, and the like. Although there is no research to support using these instruments to predict employability per se, they are high-quality measures for estimating functioning in domains thought to be relevant to employability and may contribute to an overall understanding of employment potential.
National Institutes of Health (NIH) Toolbox
The NIH Toolbox, developed with funding from the NIH Blueprint for Neuroscience Research, is “a comprehensive set of neuro-behavioral measurements that quickly assess cognitive, emotional, sensory, and
motor functions from the convenience of an iPad” (NU, 2018d; see also Akshoomoff et al., 2014; Carlozzi et al., 2015, 2017c; Denboer et al., 2014; Dikmen et al., 2014; Gershon et al., 2014; Loring et al., 2018; Tulsky et al., 2014; Weintraub et al., 2013). Advanced approaches, such as IRT/CAT, were used in item development, test scoring, and construction. The Toolbox contains two types of measures: performance-based tests of function (objective measures) and self-report and proxy measures (primarily for emotion). Phase I of its development employed such qualitative methods as online requests for information from experts, interviews with clinicians and scientists, and consensus meetings to identify subdomains to be included. In Phase II, candidate measures were pilot tested, and initial evaluations of psychometric properties were performed. Subsequently, a number of validation studies have been performed on people of varying ages and health status (see NU, 2018c).
The cognitive measures in the Toolbox were designed to be completed in 30 minutes; they provide reliable estimates of specific cognitive and language skills, and demonstrate evidence of external validity (Akshoomoff et al., 2014; Weintraub et al., 2013). Administration requires an annual licensing fee to activate an iPad application and verification of a clinical psychologist’s involvement in test administration and score interpretation. Potential users should note that research literature on the Toolbox is relatively limited given its recent development. Additional studies are needed to demonstrate its utility and feasibility in clinical and disability determination contexts.
The NIH Toolbox can be used both for individuals in the general population and for those with chronic conditions and consists of four batteries:
- The Cognition Battery focuses on mental processes used to gain knowledge and comprehension, such as thinking, knowing, and remembering (NU, 2018b). It also encompasses language, imagination, perceptions, and the planning and execution of complex behaviors. This battery yields the following summary scores: cognitive function composite score, fluid cognition composite score (includes picture sequence memory, list sorting, and pattern comparison measures), and crystallized cognition composite score (includes picture vocabulary and reading recognition measures).
- The Emotion Battery can be used to assess strong feelings such as joy, sorrow, or fear. The NIH Toolbox includes four major domains of emotion: psychological well-being, stress and self-efficacy, social relationships, and negative affect. The Emotion Battery surveys positive affect, general life satisfaction, emotional support, friendship, loneliness, perceived rejection, perceived hostility, self-efficacy, sadness, perceived stress, fear, and anger.
- The Motor Battery targets the ability to use and control muscles and movements (NU, 2018b). It consists of tests to assess dexterity, grip strength, standing balance, gait speed, and endurance.
- The Sensation Battery addresses “the biomechanical and neurologic process of detecting incoming nerve impulses as nervous system activity” (NU, 2018b). It consists of tests to assess audition, visual acuity, olfaction, taste (ages 12+), and pain (ages 18+).
Overall, the Toolbox serves as a well-developed set of self-report and performance-based measures that are relatively brief to administer. The self-report portion is similar in administration to PROMIS and Neuro-QoL, discussed below, and is customarily completed on a tablet. The performance-based section is similar to and could provide an alternative to tests in standard neuropsychological evaluations, access to which may be limited by their expense and the availability of professionals qualified to administer and interpret them. Examiners must be trained to use the Toolbox instruments, but the training should not represent a significant barrier given that the instruments were designed to be completed by a nonclinician with a baccalaureate degree. To date, there is no evidence to allow direct inference from scores to employability, although domains considered to be relevant to employment may be measured.
Quality of Life in Neurological Disorders (Neuro-QoL)
Neuro-QoL is “a measurement system that evaluates and monitors the physical, mental, and social effects experienced by adults and children living with neurological conditions” (Cella et al., 2012; Gershon et al., 2012; NU, 2018a). Sponsored by the National Institute of Neurological Disorders and Stroke, Neuro-QoL instruments were developed to construct psychometrically sound and clinically relevant health-related quality of life (HRQOL) measurement tools for individuals with neurological conditions or disorders such as stroke, multiple sclerosis, Parkinson’s disease, epilepsy (Nowinski et al., 2010; Victorson et al., 2014), amyotrophic lateral sclerosis, Huntington’s disease (Carlozzi et al., 2017a,b), and muscular dystrophy (NU, 2018c). The HRQOL domains were identified through an extensive literature review, an online Request for Information, two phases of in-depth expert interviews, patient and caregiver focus groups, and individual interviews with patients and proxies (NU, 2018c). Based on this input, 17 HRQOL domains and subdomains were selected for adults and 11 for children (Gershon et al., 2012). Neuro-QoL measures functions, symptoms, behaviors, and affect. The adult domains include ability to participate in social roles and activities, satisfaction with social roles and activities, anxiety, bowel function, cognitive function, communication, depression,
emotional and behavioral dyscontrol, fatigue, lower-extremity function/mobility, positive affect and well-being, satisfaction with social roles and activities, sleep disturbance, sexual function, stigma, upper-extremity function/fine motor skills, and urinary/bladder function (Neuro-QoL investigators, 2015; NU, 2018c).
As is true for the PROMIS measures and HRQOL in general, there is no evidence to support drawing inferences directly from scores on Neuro-QoL instruments to employability; however, domains relevant to employment may be assessed as part of an overall assessment of work-related function. Neuro-QoL is well developed, and many scores on this instrument may be cross-walked with PROMIS.
There is strong evidence to support using the PROMIS and Neuro-QoL instruments to assess variables thought to influence participation in general, including participation in employment. The NIH Toolbox is an efficient, well-validated set of performance-based and self-report scales for appraising cognitive status, emotion, motor function, and sensory status. There is no evidence to support drawing direct inferences from scores on PROMIS, Neuro-QoL, or the NIH Toolbox to employability, although scores from these instruments may be very useful in understanding the functioning of an applicant. WD-FAB is a new scale with better validity in the physical than in the mental health domain, and research on this instrument is ongoing. Currently, WD-FAB may be most useful for understanding self-reported physical function, but direct inferences from WD-FAB to employability are not warranted at this time.
For workers, a number of self-report instruments examine interruption of work and quantify functional limitations at work due to health conditions that have been related to worker productivity. Many of these instruments were developed by groups in the United States and Europe and have been translated and used globally, often in the area of occupational health and safety research. Conceptually, such instruments quantify individual health-related reductions in productivity and the costs to employers (Lerner et al., 2003).
The conceptual elements of these instruments are often referred to as absenteeism (time away from scheduled work) and presenteeism (time when workers are at work, even if they are working at reduced productivity). Although these concepts apply to individuals who are currently working,
they can provide a measure of residual capability to retain a job. While an individual may be able to work, he or she may be unable to hold a job because of missing too much time or being engaged ineffectively as a result of his or her condition or its treatment. These constructs are helpful for researchers, employers, and other professionals to understand how impaired health interferes with work. The instruments are not disease specific but are frequently validated in populations with a specific disease diagnosis.
These instruments often are used to evaluate and monitor employer-provided health insurance and wellness programs (Pronk et al., 2016). In light of rising health care costs in the United States and elsewhere, companies need methods and tools that enable them to measure the effectiveness of their programs (Sorensen et al., 2016, 2018). Most guidelines for effective workplace health and safety programs, including programs for health promotion and disability management, encompass evaluation (NIOSH, 2008; Sorensen et al., 2018).
Work Limitations Questionnaire (WLQ)
WLQ is a self-report instrument designed to measure the impact of one’s health condition in limiting work activity. Specifically, its results provide a measure of how much time in the past 2 weeks respondents’ health limited their capability to work with respect to several categories of job demands (Lerner et al., 2001). WLQ has 25-, 16-, and 8-item versions. Respondents rate the amount of difficulty they had in performing physical and mental job activities during the previous 2 weeks. The 25-item version of the instrument has four subscales: time management demands (5 items), physical demands (6 items), output demands (5 items), and mental-interpersonal demands (9 items). The time management subscale asks about the difficulty in time management and scheduling demands. Physical demands include strength, stamina, movement, coordination, and flexibility, while output demands include work quantity and quality. The mental-interpersonal demands subscale includes items related to completing cognitive tasks at work as well as social interaction in the work setting. These subscales have high internal consistency reliability, with Cronbach’s alphas greater than 0.89, in both patient and employee populations (Lerner et al., 2001). The WLQ scales correlate with SF-36 measures of physical and mental health and severity in people with depression and osteoarthritis (Adler et al., 2006; Lerner et al., 2002, 2003). Criterion validity has been tested in several settings and for various types of health issues, including low back pain (Denis et al., 2007), cancer (Feuerstein et al., 2007), heart disease, and other chronic illnesses (Munir et al., 2007). The 25-item questionnaire can be completed in 5–10 minutes, and it has been translated into
more than 30 official languages (Tufts Medical Center, 2018). Internet, phone, and mail versions are available.
Work Ability Index (WAI)
WAI is a 7-item questionnaire designed to measure the work ability of individuals in an occupational health clinic environment. Results provide an indication of the length of time individuals are able to work at their jobs (Healthy Workplaces, n.d.; Society of Occupational Medicine, 2018). The WAI questionnaire covers the following dimensions of individuals’ capability: their current work ability compared with their lifetime best, their work ability in relation to the demands of the job, the number of diagnosed illnesses or limiting conditions from which they suffer, their estimated impairment owing to diseases/illnesses or limiting conditions, the amount of sick leave they have taken during the last year, and their own prognosis of their work ability in 2 years’ time. From these responses, a score is calculated, and this score is used to categorize the individual’s work capability as poor, moderate, good, or excellent. The test-retest reliability of the questionnaire is acceptable (de Zwart et al., 2002). Validity studies were conducted in the 1990s by the Finnish Institute of Occupational Health (Ilmarinen et al., 1997; Tuomi et al., 1991, 1997, 2001). Overall, WAI has predicted early retirement, work disability, absence due to sickness, and mortality fairly well. Administering the questionnaire is straightforward; however, calculating its scores is more difficult. WAI has been translated into 24 languages.
World Health Organization Health and Work Performance Questionnaire (HPQ)
HPQ can be used to assess several work performance measures, including quality and quantity of work or presenteeism, number of absences, and critical incidents on the job (Harvard Medical School, 2005; Kessler et al., 2003, 2004). It was developed in part to provide employers with information on the amount of loss (in productivity) related to their workers’ health, or as the authors put it on their webpage, “to increase the rationality of employer-sponsored health care purchasing” (Harvard Medical School, 2005).
The HPQ presenteeism scale has four items. Only one of these provides an absolute measure of presenteeism in the past 28 days; the other three questions help calculate a relative scale while providing a reference for respondents’ answers to the single item measuring absolute presenteeism. For absences, HPQ provides a series of questions that act as a worksheet for determining the number of hours worked and the amount of work missed because of health considerations during the past 28 days. Finally, HPQ can
be used to assess critical incidents, specifically workplace accidents, via a single question.
HPQ has been translated into 24 languages, with surveys in 6 languages being available on its webpage (Harvard Medical School, 2005). It has been validated in a number of industries among working populations diagnosed with a variety of health conditions, including mental health disorders (e.g., Kessler et al., 2003, 2010; Scuffham et al., 2014; Sevak et al., 2017; Suzuki et al., 2015). By determining the number of hours of limited work or absenteeism, its results can be roughly associated with costs to employers in terms of lost productivity (Kessler et al., 2004; Scuffham et al., 2014). As with WAI, administering the questionnaire is straightforward, but it is more difficult to calculate the scores. The questionnaire focuses on current employees and therefore is of limited use for individuals not presently working. However, it may be of use for individuals who are attempting to return to work but continue to miss time because of their health conditions.
Work Productivity and Activity Impairment Questionnaire (WPAI)
WPAI is a 6-item questionnaire used to assess both presenteeism and absenteeism at work during the past 7 days (Margaret Reilly Associates, 2013). It has been translated into more than 100 languages. While designed for general health-related issues, it has been modified to ascertain the impact on work of various health conditions, including mental health disorders and musculoskeletal pain and related disorders (e.g., Asami et al., 2015; Reilly et al., 2010). Validation studies are numerous. During the instrument’s original development, construct validity was examined in a small (N = 106) group of workers with health problems (Reilly et al., 1993). Construct validity was found to be good, and the validation explained a majority of the variance (54–64 percent) in the WPAI variables (Reilly et al., 1993). Specifically, WPAI had good positive correlations with SF-36 measures and symptom severity. Similar to other instruments of its type, it is used with people who are currently working, with its questions pertaining to absences and productivity at work within the previous 7 days.
Work Limitation Summary
All of the above instruments have been validated through varying processes, making them credible tools for evaluating capacity and functional limitations. Many of these instruments were developed to measure presenteeism and/or absenteeism among currently employed individuals. Only one, WAI, appears to have been developed with the specific goal of measuring and assessing the respondent’s capability to perform work. The different instruments vary considerably as measures of costs in lost work,
showing a large amount of variability when considering the same populations (Gardner et al., 2016).
4-1. Specific assessment instruments measure physical and mental functional abilities at the impairment, body part, or organ system level. Integrated assessments can capture the additive and sometimes multiplicative effects of multiple impairments and comorbid conditions on individuals’ functional abilities.
4-2. Activities of daily living (ADLs) are well understood in the health care field and provide a common focus for an integrated assessment of functional abilities, regardless of underlying medical conditions.
4-3. ADLs and instrumental activities of daily living (IADLs) are assessed through a combination of self-report, proxy report, and direct observation.
4-4. The ability to perform ADLs and IADLs is affected by a person’s motor abilities, cognitive function, and perceptual and sensory abilities, although ADLs are largely unaffected by mild cognitive impairment.
4-5. Depression can limit performance of ADLs or IADLs irrespective of physical or cognitive impairments or age.
4-6. Factors increasing the likelihood of return to work include higher education and socioeconomic status, higher self-efficacy and optimism for returning to work, lower injury severity, and availability of multidisciplinary interventions. Factors associated with a lower likelihood of return to work include older age, female gender, higher pain or severity of disability, depression, higher work demands, previous sick leave and unemployment, and limitations in current activity.
4-7. While many instruments assess the performance of ADLs and IADLs, no ADL or IADL assessments exist that are standardized on a working-age population with limitations across multiple physical and cognitive areas that map to the work context.
4-8. Research is limited on the relationship between assessments of ADL and IADL performance and an individual’s ability to return to work.
4-9. There is little evidence that inability to perform ADLs or IADLs predicts poor return-to-work outcomes.
4-10. Several evidence-based instruments and instrument sets are available that can provide integrated information about individuals’ overall functional capabilities and limitations.
4-11. The Work Disability Functional Assessment Battery (WD-FAB) demonstrates good reliability in adults with work disability, very high accuracy for physical function, and good construct validity.
4-12. WD-FAB is a flexible tool that allows multiple administration modes, the ability to track changes over time, and the potential to detect small differences among persons with different types of disability.
4-13. The World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) assesses disability based on the widely accepted International Classification of Functioning, Disability and Health (ICF) model of cognition, mobility, self-care, social function, life activities, and participation.
4-14. The Patient-Reported Outcomes Measurement Information System (PROMIS) collects information that can be used to evaluate and monitor physical, mental, and social health using self-report. It has been validated with large, general population samples, and the information it collects is included increasingly in electronic health records.
4-15. The National Institutes of Health (NIH) Toolbox uses a tablet computer to collect a comprehensive array of neurobehavioral measurements of cognitive, emotional, sensory, and motor functions with reference to general and specific population norms. It uses item response theory (IRT) and computer adaptive testing (CAT) methods to facilitate test scoring and reporting.
4-16. Quality of Life in Neurological Disorders (Neuro-QoL) collects psychometrically sound and clinically relevant health-related quality-of-life data for adults and children living with neurological conditions.
4-17. A number of self-report instruments examine interruption of work and quantify functional limitations at work related to worker productivity that are due to health conditions.
4-18. The Work Ability Index (WAI) measures the work ability of a person in an occupational health clinic environment and predicts early retirement, work disability, absence due to sickness, and mortality.
4-19. The Work Productivity and Activity Impairment Questionnaire (WPAI) is a well-validated self-report instrument that assesses the effects on work of various health conditions, including mental health disorders, musculoskeletal pain, and related disorders.
4-20. Most instruments used to measure limitations in work activity due to health conditions assess work function (e.g., presenteeism, absenteeism) among people who are working.
4-1. The most informative evaluations of function may include both specific assessments of body structures and systems and integrated assessments that describe the effects of multiple impairments and comorbid conditions.
4-2. Combining ADLs and IADLs provides a useful means of assessing an individual’s ability to perform multiple, integrated functions on a day-to-day basis.
4-3. The validity of ADL and IADL performance assessment is improved by including direct observation, especially in persons with substantial cognitive impairments.
4-4. The collection of direct observations regarding ADLs and IADLs often requires input from trained occupational therapists, physical therapists, speech pathologists, and/or nurses.
4-5. No assessments of ADL or IADL performance correlate strongly with the ability to work for all conditions or impairments. However, an individual who cannot independently perform basic ADLs or IADLs most likely would be unable to work without significant assistance from family members or an attendant.
4-6. Stronger evidence is needed to link ADL and IADL performance to work capacity, perhaps by comparing ADL and IADL performance among applicants who are awarded Social Security Disability Income (SSDI) benefits versus those who are denied.
4-7. The utility of information about ADLs and IADLs in the context of disability determination may be enhanced by asking additional questions about context; environmental factors, including use of assistive technologies; required assistance; and the effect of performing ADLs and IADLs on pain, fatigue, confusion, concentration, and other physical or cognitive factors that can interfere with work performance.
4-8. Evidence-based instruments and sets of instruments that provide integrated information about individuals’ overall functional capabilities and limitations could provide helpful information for determinations of work disability.
4-9. Although there is no evidence to support drawing direct inferences from scores on PROMIS, Neuro-QoL, or the NIH Toolbox to employability, scores from these instruments may be very useful in understanding the functioning of an applicant.
4-10. The use of WD-FAB with IRT and CAT methods reduces respondent burden by limiting survey length and can assess functional activity comprehensively and efficiently in 15 to 20 minutes. Currently, WD-FAB may be most useful for understanding self-reported physical
function, but direct inferences from WD-FAB to employability are not warranted at this time.
4-11. Although many instruments that measure limitations in work activity due to health conditions assess work function among current workers, they may be of use for previously employed individuals and for those attempting to return to work but continuing to miss time as a result of their health conditions.
Adler, D. A., T. J. McLaughlin, W. H. Rogers, H. Chang, L. Lapitsky, and D. Lerner. 2006. Job performance deficits due to depression. American Journal of Psychiatry 163(9):1569–1576.
Akshoomoff, N., E. Newman, W. K. Thompson, C. McCabe, C. S. Bloss, L. Chang, D. G. Amaral, B. J. Casey, T. M. Ernst, J. A. Frazier, J. R. Gruen, W. E. Kaufmann, T. Kenet, D. N. Kennedy, O. Libiger, S. Mostofsky, S. S. Murray, E. R. Sowell, N. Schork, A. M. Dale, and T. L. Jernigan. 2014. The NIH Toolbox Cognition Battery: Results from a large normative developmental sample (PING). Neuropsychology 28(1):1–10.
Amtmann, D., K. F. Cook, K. L. Johnson, and D. Cella. 2011. The PROMIS Initiative: Involvement of rehabilitation stakeholders in development and examples of applications in rehabilitation research. Archives of Physical Medicine and Rehabilitation 92(10):S12–S19.
Asami, Y., A. Goren, and Y. Okumura. 2015. Work productivity loss with depression, diagnosed and undiagnosed, among workers in an Internet-based survey conducted in Japan. Journal of Occupational and Environmental Medicine 57(1):105–110.
Asher, I. E. 2014. Asher’s occupational therapy assessment tools: An annotated index, 4th ed. Bethesda, MD: American Occupational Therapy Association.
Baum, C. M., L. T. Connor, T. Morrison, M. Hahn, A. W. Dromerick, and D. F. Edwards. 2008. Reliability, validity, and clinical utility of the Executive Function Performance Test: A measure of executive function in a sample of people with stroke. American Journal of Occupational Therapy 62(4):446–455.
Boström, G., M. Conradsson, E. Rosendahl, P. Nordström, Y. Gustafson, and H. Littbrand. 2014. Functional capacity and dependency in transfer and dressing are associated with depressive symptoms in older people. Clinical Interventions in Aging 9:249–256. doi: 10.2147/CIA.S57535.
Burnett, J., C. B. Dyer, and A. D. Naik. 2009. Convergent validation of the Kohlman Evaluation of Living Skills (KELS) as a screening tool of older adults’ capacity to live safely and independently in the community. Archives of Physical Medicine and Rehabilitation 90(11):1948–1952. doi: 10.1016/j.apmr.2009.05.021.
Cancelliere, C., J. Donovan, M. J. Stochkendahl, M. Biscardi, C. Ammendolia, C. Myburgh, and J. D. Cassidy. 2016. Factors affecting return to work after injury or illness: Best evidence synthesis of systematic reviews. Chiropractic & Manual Therapies 24:32. doi: 10.1186/s12998-016-0113-z.
Carlozzi, N. E., J. L. Beaumont, D. S. Tulsky, and R. C. Gershon. 2015. The NIH Toolbox Pattern Comparison Processing Speed Test: Normative data. Archives of Clinical Neuropsychology 30(5):359–368.
Carlozzi, N. E., E. A. Hahn, S. M. Goodnight, A. L. Kratz, J. S. Paulsen, J. C. Stout, S. Frank, J. A. Miner, D. Cella, R. C. Gershon, S. G. Schilling, and R. E. Ready. 2017a. Patient-reported outcome measures in Huntington disease: Quality of Life in Neurological Disorders (Neuro-QoL) social functioning measures. Psychological Assessment 30(4):450–458
Carlozzi, N. E., R. E. Ready, S. Frank, D. Cella, E. A. Hahn, S. M. Goodnight, S. G. Schilling, N. R. Boileau, and P. Dayalu, 2017b. Patient-reported outcomes in Huntington’s disease: Quality of Life in Neurological Disorders (Neuro-QoL) and Huntington’s Disease Health-Related Quality of Life (HDQLIFE) physical function measures. Movement Disorders: Official Journal of the Movement Disorder Society 32(7):1096–1102. doi: 10.1002/mds.27046.
Carlozzi, N. E., D. S. Tulsky, T. J. Wolf, S. Goodnight, R. K. Heaton, K. B. Casaletto, A. W. K. Wong, C. M. Baum, R. C. Gershon, and A. W. Heinemann. 2017c. Construct validity of the NIH Toolbox Cognition Battery in individuals with stroke. Rehabilitation Psychology 62(4):443–454.
Cella, D., W. Riley, A. Stone, N. Rothrock, B. Reeve, S. Yount, D. Amtmann, R. Bode, D. Buysse, S. Choi, K. Cook, R. Devellis, D. DeWalt, J. F. Fries, R. Gershon, E. A. Hahn, J. S. Lai, P. Pilkonis, D. Revicki, M. Rose, K. Weinfurt, R. Hays, and PROMIS Cooperative Group. 2010. The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. Journal of Clinical Epidemiology 63(11):1179–1194.
Cella, D., J. S. Lai, C. J. Nowinski, D. Victorson, A. Peterman, D. Miller, F. Bethoux, A. Heinemann, S. Rubin, J. E. Cavazos, A. T. Reder, R. Sufit, T. Simuni, G. L. Holmes, A. Siderowf, V. Wojna, R. Bode, N. McKinney, T. Podrabsky, K. Wortman, S. Choi, R. Gershon, N. Rothrock, and C. Moy. 2012. Neuro-QOL: Brief measures of health-related quality of life for clinical research in neurology. Neurology 78(23):1860–1867.
Chan, L. 2018a. The WD-FAB: Development and validation testing. Presentation to the European Union of Medicine in Assurance and Social Security, March 2. https://www.eumass.eu/wp-content/uploads/2018/03/Leighton-Porcino.pdf (accessed April 5, 2019).
Chan, L. 2018b. The Work Disability Functional Assessment Battery (WD-FAB). Presentation to the Committee on Functional Assessment for Adults with Disabilities, Washington, DC, February 26. http://nationalacademies.org/hmd/~/media/Files/Agendas/Activity%20Files/SelectPops/Functional%20Assessment/Meeting%202/5_Chan_WD-FAB.pdf (accessed April 5, 2019).
Chen, C. C., and R. K. Bode. 2010. Psychometric validation of the Manual Ability Measure-36 (MAM-36) in patients with neurologic and musculoskeletal disorders. Archives of Physical Medicine and Rehabilitation 91(3):414–420.
Chisholm, D., P. Toto, K. Raina, M. Holm, and J. Rogers. 2014. Evaluating capacity to live independently and safely in the community: Performance assessment of self-care skills. The British Journal of Occupational Therapy 77(2):59–63. doi: 10.4276/030802214X 13916969447038.
de Zwart, B. C., M. H. Frings-Dresen, and J. C. van Duivenbooden. 2002. Test-retest reliability of the Work Ability Index questionnaire. Occupational Medicine 52(4):177–181.
Denboer, J. W., C. Nicholls, C. Corte, and K. Chestnut. 2014. National Institutes of Health Toolbox Cognition Battery. Archives of Clinical Neuropsychology 29(7):692–694.
Denis, S., H. S. Shannon, J. Wessel, P. Stratford, and I. Weller. 2007. Association of low back pain, impairment, disability & work limitations in nurses. Journal of Occupational Rehabilitation 17(2):213–226.
Dikmen, S. S., P. J. Bauer, S. Weintraub, D. Mungas, J. Slotkin, J. L. Beaumont, R. Gershon, N. R. Temkin, and R. K. Heaton. 2014. Measuring episodic memory across the lifespan: NIH Toolbox Picture Sequence Memory Test. Journal of the International Neuropsychological Society 20(6):611–619.
Dutil, E., A. Forget, M. Vanier, and C. Gaudreault. 1990. Development of the ADL Profile: An evaluation for adults with severe head injury. Occupational Therapy in Health Care 7(1):7–22.
Feuerstein, M., J. A. Hansen, L. C. Calvio, L. Johnson, and J. G. Ronquillo. 2007. Work productivity in brain tumor survivors. Journal of Occupational and Environmental Medicine 49(7):803–811.
Fisher, A. G., and K. Bray Jones. 2010. Assessment of Motor and Process Skills, 7th ed., Vols. 1 and 2. Fort Collins, CO: Three Star Press.
Gardarsdóttir, S., and S. Kaplan. 2002. Validity of the Arnadóttir OT-ADL Neurobehavioral Evaluation (A-ONE): Performance in activities of daily living and neurobehavioral impairments of persons with left and right hemisphere damage. American Journal of Occupational Therapy 56(5):499–508.
Gardner, B. T., A. M. Dale, S. Buckner-Petty, L. Van Dillen, B. C. Amick III, and B. Evanoff. 2016. Comparison of employer productivity metrics to lost productivity estimated by commonly used questionnaires. Journal of Occupational and Environmental Medicine 58(2):170–177.
Gershon, R. C., J. S. Lai, R. Bode, S. Choi, C. Moy, T. Bleck, D. Miller, A. Peterman, and D. Cella. 2012. Neuro-QOL: Quality of life item banks for adults with neurological disorders: Item development and calibrations based upon clinical and general population testing. Quality of Life Research 21(3):475–486.
Gershon, R. C., K. F. Cook, D. Mungas, J. J. Manly, J. Slotkin, J. L. Beaumont, and S. Weintraub. 2014. Language measures of the NIH Toolbox Cognition Battery. Journal of the International Neuropsychological Society 20(6):642–651.
Gold, D. A. 2012. An examination of instrumental activities of daily living assessment in older adults and mild cognitive impairment. Journal of Clinical and Experimental Neuropsychology 34(1):11–34.
Graf, C., and Hartford Institute for Geriatric Nursing. 2008. The Lawton Instrumental Activities of Daily Living (IADL) scale. MEDSURG Nursing 17(5):343–344.
Gwaltney, C. J., A. L. Shields, and S. Shiffman. 2008. Equivalence of electronic and paper- and-pencil administration of patient-reported outcome measures: A meta-analytic review. Value in Health 11(2):322–333.
Hahn, E. A., R. F. DeVellis, R. K. Bode, S. F. Garcia, L. D. Castel, S. V. Eisen, H. B. Bosworth, A. W. Heinemann, N. Rothrock, D. Cella, and PROMIS Cooperative Group. 2010. Measuring social health in the Patient-Reported Outcomes Measurement Information System (PROMIS): Item bank development and testing. Quality of Life Research 19(7):1035–1044.
Hall, J. R., H. T. Vo, R. C. Barber, L. A. Johnson, and S. E. O’Bryant. 2011. The link between cognitive measures and ADLs and IADL functioning in mild Alzheimer’s: What has gender got to do with it? International Journal of Alzheimer’s Disease 2011: Article ID 276734. doi: 10.4061/2011/276734.
Harvard Medical School. 2005. World Health Organization Health and Work Performance Questionnaire (HPQ). https://www.hcp.med.harvard.edu/hpq (accessed December 6, 2018).
Haslam, J., G. Pépin, R. Bourbonnais, and S. Grignon. 2010. Processes of task performance as measured by the Assessment of Motor and Process Skills (AMPS): A predictor of work-related outcomes for adults with schizophrenia? Work 37(1):53–64.
Healthy Workplaces. n.d. Work Ability Index. https://healthy-workplaces.eu/previous/allages-2016/de/tools-and-publications/practical-tools/work-ability-index (accessed April 5, 2019).
HHS (U.S. Department of Health and Human Services). 2018. Patient-Reported Outcomes Measurement Information System: Program snapshot. https://commonfund.nih.gov/promis/index (accessed April 5, 2019).
Houston, D., S. L. Williams, J. Bloomer, and W. C. Mann. 1989. The Bay Area Functional Performance Evaluation: Development and standardization. American Journal of Occupational Therapy 43(3):170–183.
Ilmarinen, J., K. Tuomi, and M. Klockars. 1997. Changes in the work ability of active employees over an 11-year period. Scandinavian Journal of Work, Environment & Health 23(Suppl. 1):49–57.
Jefferson, A. L., L. K. Byerly, S. Vanderhill, S. Lambe, S. Wong, A. Ozonoff, and J. H. Karlawish. 2008. Characterization of activities of daily living in individuals with mild cognitive impairment. The American Journal of Geriatric Psychiatry: Official Journal of the American Association for Geriatric Psychiatry 16(5):375–383.
Katz, S. 1983. Assessing self-maintenance: Activities of daily living, mobility, and instrumental activities of daily living. Journal of the American Geriatrics Association 31(12):721–727.
Katz, S., and C. A. Akpom. 1976. 12. Index of ADL. Medical Care 14(5 Suppl.):116–118.
Kessler, R. C., C. Barber, A. Beck, P. Berglund, P. D. Cleary, D. McKenas, N. Pronk, G. Simon, P. Stang, T. B. Üstün, and P. Wang. 2003. The World Health Organization Health and Work Performance Questionnaire (HPQ). Journal of Occupational and Environmental Medicine 45(2):156–174.
Kessler, R. C., M. Ames, P. A. Hymel, R. Loeppke, D. K. McKenas, D. E. Richling, P. E. Stang, and T. B. Üstün. 2004. Using the World Health Organization Health and Work Performance Questionnaire (HPQ) to evaluate the indirect workplace costs of illness. Journal of Occupational and Environmental Medicine 46(6 Suppl.):S23–S37.
Kessler, R. C., V. Shahly, P. E. Stang, and M. C. Lane. 2010. The associations of migraines and other headaches with work performance: Results from the National Comorbidity Survey-Replication (NCS-R). Cephalalgia 30(6):722–734.
Kohlman-Thomson, L. 1992. Kohlman evaluation of living skills, 3rd ed. Bethesda, MD: American Occupational Therapy Association.
Lerner, D., B. C. Amick III, W. H. Rogers, S. Malspeis, K. Bungay, and D. Cynn. 2001. The Work Limitations Questionnaire. Medical Care 39(1):72–85.
Lerner, D., J. I. Reed, E. Massarotti, L. M. Wester, and T. A. Burke. 2002. The Work Limitations Questionnaire’s validity and reliability among patients with osteoarthritis. Journal of Clinical Epidemiology 55(2):197–208.
Lerner, D., B. C. Amick III, J. C. Lee, T. Rooney, W. H. Rogers, H. Chang, and E. R. Berndt. 2003. Relationship of employee-reported work limitations to work productivity. Medical Care 41(5):649–659.
Loring, D. W., S. C. Bowden, E. Staikova, J. A. Bishop, D. L. Drane, and F. C. Goldstein. 2018. NIH Toolbox Picture Sequence Memory Test for assessing clinical memory function: Diagnostic relationship to the Rey Auditory Verbal Learning Test. Archives of Clinical Neuropsychology 34(2):268–276.
Marfeo, E. E., S. M. Haley, A. M. Jette, S. V. Eisen, P. Ni, K. Bogusz, M. Meterko, C. M. McDonough, L. Chan, D. E. Brandt, and E. K. Rasch. 2013a. Conceptual foundation for measures of physical function and behavioral health function for Social Security work disability evaluation. Archives of Physical Medicine and Rehabilitation 94(9):1645–1652.
Marfeo, E. E., P. Ni, S. M. Haley, K. Bogusz, M. Meterko, C. M. McDonough, L. Chan, E. K. Rasch, D. E. Brandt, and A. M. Jette. 2013b. Scale refinement and initial evaluation of a behavioral health function measurement tool for work disability evaluation. Archives of Physical Medicine and Rehabilitation 94(9):1679–1686.
Marfeo, E. E., P. Ni, S. M. Haley, A. M. Jette, K. Bogusz, M. Meterko, C. M. McDonough, L. Chan, D. E. Brandt, and E. K. Rasch. 2013c. Development of an instrument to measure behavioral health function for work disability: Item pool construction and factor analysis. Archives of Physical Medicine and Rehabilitation 94(9):1670–1678.
Marfeo, E. E., P. S. Ni, L. Chan, E. K. Rasch, and A. M. Jette. 2014. Combining agreement and frequency rating scales to optimize psychometrics in measuring behavioral health functioning. Journal of Clinical Epidemiology 67(7):781–784.
Marfeo, E. E., P. Ni, L. Chan, E. K. Rasch, C. M. McDonough, D. E. Brandt, K. Bogusz, and A. M. Jette. 2015. Interpreting physical and behavioral health scores from new work disability instruments. Journal of Rehabilitation Medicine 47(5):394–402.
Marfeo, E. E., P. Ni, C. McDonough, K. Peterik, M. Marino, M. Meterko, E. K. Rasch, L. Chan, D. Brandt, and A. M. Jette. 2018. Improving assessment of work related mental health function using the Work Disability Functional Assessment Battery (WD-FAB). Journal of Occupational Rehabilitation 28(1):190–199. doi: 10.1007/s10926-017-9710-5.
Margaret Reilly Associates. 2013. WPAI general information. http://www.reillyassociates.net/WPAI_General.html (accessed April 5, 2019).
Marino, M. E., M. Meterko, E. E. Marfeo, C. M. McDonough, A. M. Jette, P. Ni, K. Bogusz, E. K. Rasch, D. E. Brandt, and L. Chan. 2015. Work-related measures of physical and behavioral health function: Test-retest reliability. Disability and Health Journal 8(4):652–657.
McDonough, C. M., A. M. Jette, P. Ni, K. Bogusz, E. E. Marfeo, D. E. Brandt, L. Chan, M. Meterko, S. M. Haley, and E. K. Rasch. 2013. Development of a self-report physical function instrument for disability assessment: Item pool construction and factor analysis. Archives of Physical Medicine and Rehabilitation 94(9):1653–1660.
McDonough, C. M., P. Ni, K. Peterik, E. E. Marfeo, M. E. Marino, M. Meterko, E. K. Rasch, D. E. Brandt, A. M. Jette, and L. Chan. 2017. Improving measures of work-related physical functioning. Quality of Life Research 26(3):789–798.
McDonough, C. M., P. Ni, K. Peterik, J. D. Hershberg, L. R. Bell, L. Chan, D. E. Brandt, and A. M. Jette. 2018. Validation of the work-disability physical functional assessment battery. Archives of Physical Medicine and Rehabilitation 99(9):1798–1804.
Meltzer, H., P. Bebbington, T. Brugha, S. McManus, D. Rai, M. S. Dennis, and R. Jenkins. 2012. Physical ill health, disability, dependence and depression: Results from the 2007 National Survey of Psychiatric Morbidity Among Adults in England. Disability and Health Journal 5(2):102–110.
Meterko, M., E. E. Marfeo, C. M. McDonough, A. M. Jette, P. Ni, K. Bogusz, E. K. Rasch, D. E. Brandt, and L. Chan. 2015. The Work Disability Functional Assessment Battery (WD-FAB): Feasibility and psychometric properties. Archives of Physical Medicine and Rehabilitation 96(6):1028–1035.
Meterko, M., M. Marino, P. Ni, E. Marfeo, C. M. McDonough, A. Jette, K. Peterik, E. Rasch, D. E. Brandt, and L. Chan. 2018. Psychometric evaluation of the improved Work-Disability Functional Assessment Battery. Archives of Physical Medicine and Rehabilitation. In press. Corrected proof available online December 19, 2018. doi: 10.1016/j.apmr.2018.09.125.
Mlinac, M. E., and M. C. Feng. 2016. Assessment of activities of daily living, self-care, and independence. Archives of Clinical Neuropsychology 31(6):506–516.
Morrison, M. T., G. M. Giles, J. D. Ryan, C. M. Baum, A. W. Dromerick, H. J. Polatajko, and D. F. Edwards. 2013. Multiple Errands Test–Revised (MET–R): A performance-based measure of executive function in people with mild cerebrovascular accident. The American Journal of Occupational Therapy 67(4):460–468. doi: 10.5014/ajot.2013.007880.
Munir, F., J. Yarker, C. Haslam, H. Long, S. Leka, A. Griffiths, and S. Cox. 2007. Work factors related to psychological and health-related distress among employees with chronic illnesses. Journal of Occupational Rehabilitation 17(2):259–277.
Neuro-QoL investigators. 2015. Neuro-QoL technical report: Development and initial validation of patient-reported item banks for use in neurological research and practice. http://www.healthmeasures.net/images/neuro_qol/Neuro-QoL_Manual_Technical_Report_v2_24Mar2015.pdf (accessed April 24, 2019).
Ni, P., C. M. McDonough, A. M. Jette, K. Bogusz, E. E. Marfeo, E. K. Rasch, D. E. Brandt, M. Meterko, S. M. Haley, and L. Chan. 2013. Development of a computer-adaptive physical function instrument for Social Security Administration disability determination. Archives of Physical Medicine and Rehabilitation 94(9):1661–1669.
NIOSH (National Institute for Occupational Safety and Health). 2008. Essential elements of effective workplace programs and policies for improving worker health and wellbeing. WorkLife, October. https://www.cdc.gov/niosh/docs/2010-140/pdfs/2010-140.pdf (accessed April 5, 2019).
Nowinski, C. J., D. Victorson, J. E. Cavazos, R. Gershon, and D. Cella. 2010. Neuro-QOL and the NIH Toolbox: Implications for epilepsy. Therapy 7(5):533–540.
NU (Northwestern University). 2018a. Intro to Neuro-QoL. http://www.healthmeasures.net/explore-measurement-systems/neuro-qol/intro-to-neuro-qol (accessed April 5, 2019).
NU. 2018b. Intro to NIH toolbox. http://www.healthmeasures.net/explore-measurementsystems/nih-toolbox/intro-to-nih-toolbox (accessed April 5, 2019).
NU. 2018c. Neuro-QoL: Measure development & research. http://www.healthmeasures.net/explore-measurement-systems/neuro-qol/measure-development-research (accessed April 4, 2019).
NU. 2018d. Why use NIH toolbox? http://www.healthmeasures.net/explore-measurementsystems/nih-toolbox (accessed April 5, 2019).
NU. 2018e. Why use PROMIS? http://www.healthmeasures.net/explore-measurementsystems/promis (accessed April 5, 2019).
NU. 2018f. Why use Neuro-QoL? http://www.healthmeasures.net/explore-measurementsystems/neuro-qol (accessed April 5, 2019).
Ottenbacher, K. J., Y. Hsu, C. V. Granger, and R. C. Fiedler. 1996. The reliability of the Functional Independence Measure: A quantitative review. Archives of Physical Medicine and Rehabilitation 77(12):1226–1232.
Patterson, M. B., and J. L. Mack. 2001. The Cleveland Scale for Activities of Daily Living (CSADL): Its reliability and validity. Journal of Clinical Geropsychology 7(1):15–28.
Pronk, N. P., D. L. McLellan, M. P. McGrail, S. M. Olson, Z. J. McKinney, J. N. Katz, G. R. Wagner, and G. Sorensen. 2016. Measurement tools for integrated worker health protection and promotion: Lessons learned from the SafeWell Project. Journal of Occupational and Environmental Medicine 58(7):651–658.
Quinn, T. J., P. Langhorne, and D. J. Stott. 2011. Barthel Index for stroke trials. Stroke 42(4):1146–1151.
Reilly, M. C., A. S. Zbrozek, and E. M. Dukes. 1993. The validity and reproducibility of a work productivity and activity impairment instrument. Pharmacoeconomics 4(5):353–365.
Reilly, M. C., K. L. Gooch, R. L. Wong, H. Kupper, and D. van der Heijde. 2010. Validity, reliability and responsiveness of the Work Productivity and Activity Impairment Questionnaire in ankylosing spondylitis. Rheumatology (Oxford) 49(4):812–819.
Reiman, M. P., and R. C. Manske. 2011. The assessment of function: How is it measured? A clinical perspective. Journal of Manual and Manipulative Therapy 19(2):91–99.
Royall, D. R., E. C. Lauterbach, D. Kaufer, P. Malloy, K. L. Coburn, and K. J. Black. 2007. The cognitive correlates of functional status: A review from the Committee on Research of the American Neuropsychiatric Association. The Journal of Neuropsychiatry and Clinical Neurosciences 19(3):249–265.
Schuling, J., R. de Haan, M. Limburg, and K. H. Groenier. 1993. The Frenchay Activities Index: Assessment of functional status in stroke patients. Stroke 24(8):1173–1177.
Scuffham, P. A., N. Vecchio, and H. A. Whiteford. 2014. Exploring the validity of HPQ-based presenteeism measures to estimate productivity losses in the health and education sectors. Medical Decision Making 34(1):127–137.
Sevak, P., D. C. Stapleton, and J. O’Neill. 2017. How individual and environmental factors affect employment outcomes. Journal of Vocational Rehabilitation 46(2):117–120.
Sikkes, S. A., E. S. de Lange-de Klerk, Y. A. Pijnenburg, P. Scheltens, and B. M. Uitdehaag. 2009. A systematic review of instrumental activities of daily living scales in dementia: Room for improvement. Journal of Neurology, Neurosurgery & Psychiatry 80(1):7–12.
Society of Occupational Medicine. 2018. The Work Ability Index (WAI). https://academic.oup.com/occmed/article/57/2/160/1584972 (accessed April 5, 2019).
Sorensen, G., D. L. McLellan, E. L. Sabbath, J. T. Dennerlein, E. M. Nagler, D. A. Hurtado, N. P. Pronk, and G. R. Wagner. 2016. Integrating worksite health protection and health promotion: A conceptual model for intervention and research. Preventive Medicine 91:188–196. doi: 10.1016/j.ypmed.2016.08.005.
Sorensen, G., E. Sparer, J. A. R. Williams, D. Gundersen, L. I. Boden, J. T. Dennerlein, D. Hashimoto, J. N. Katz, D. L. McLellan, C. A. Okechukwu, N. P. Pronk, A. Revette, and G. R. Wagner. 2018. Measuring best practices for workplace safety, health, and wellbeing: The workplace integrated safety and health assessment. Journal of Occupational and Environmental Medicine 60(5):430–439.
Suzuki, T., K. Miyaki, Y. Song, A. Tsutsumi, N. Kawakami, A. Shimazu, M. Takahashi, A. Inoue, and S. Kurioka. 2015. Relationship between sickness presenteeism (WHO-HPQ) with depression and sickness absence due to mental disease in a cohort of Japanese workers. Journal of Affective Disorders 180:14–20. doi: 10.1016/j.jad.2015.03.034.
Tufts Medical Center. 2018. Available questionnaires. https://www.tuftsmedicalcenter.org/Research-Clinical-Trials/Institutes-Centers-Labs/Center-for-Health-Solutions/AvailableQuestionnaires.aspx (accessed April 5, 2019).
Tulsky, D. S., N. Carlozzi, N. D. Chiaravalloti, J. L. Beaumont, P. A. Kisala, D. Mungas, K. Conway, and R. Gershon. 2014. NIH Toolbox Cognition Battery (NIHTB-CB): List sorting test to measure working memory. Journal of the International Neuropsychological Society 20(6):599–610.
Tulsky, D. S., P. A. Kisala, D. Victorson, D. G. Tate, A. W. Heinemann, S. Charlifue, S. C. Kirshblum, D. Fyffe, R. Gershon, A. M. Spungen, and C. H. Bombardier. 2015. Overview of the Spinal Cord Injury–Quality of Life (SCI-QOL) measurement system. The Journal of Spinal Cord Medicine 38(3):257–269.
Tulsky, D. S., P. A. Kisala, D. Victorson, N. Carlozzi, T. Bushnik, M. Sherer, S. W. Choi, A. W. Heinemann, N. Chiaravalloti, A. M. Sander, and J. Englander. 2016. TBI-QOL: Development and calibration of item banks to measure patient reported outcomes following traumatic brain injury. The Journal of Head Trauma Rehabilitation 31(1):40–51. doi: 10.1097/HTR.0000000000000131.
Tuomi, K., J. Ilmarinen, L. Eskelinen, E. Järvinen, J. Toikkanen, and M. Klockars. 1991. Prevalence and incidence rates of diseases and work ability in different work categories of municipal occupations. Scandinavian Journal of Work, Environment & Health 67–74.
Tuomi, K., J. Ilmarinen, R. Martikainen, L. Aalto, and M. Klockars. 1997. Aging, work, lifestyle and work ability among Finnish municipal workers in 1981–1992. Scandinavian Journal of Work, Environment & Health 17(Suppl. 1):58–65.
Tuomi, K., P. Huuhtanen, E. Nykyri, and J. Ilmarinen. 2001. Promotion of work ability, the quality of work and retirement. Occupational Medicine 51(5):318–324.
Üstün, T. B., S. Chatterji, N. Kostanjsek, J. Rehm, C. Kennedy, J. Epping-Jordan, S. Saxena, M. von Korff, and C. Pull. 2010. Developing the World Health Organization Disability Assessment Schedule 2.0. Bulletin of the World Health Organization 88:815–823.
Victorson, D., J. E. Cavazos, G. L. Holmes, A. T. Reder, V. Wojna, C. Nowinski, D. Miller, S. Buono, A. Mueller, C. Moy, and D. Cella. 2014. Validity of the Neurology Quality-of-Life (Neuro-QoL) measurement system in adult epilepsy. Epilepsy & Behavior 31:77–84.
Weintraub, S., S. S. Dikmen, R. K. Heaton, D. S. Tulsky, P. D. Zelazo, P. J. Bauer, N. E. Carlozzi, J. Slotkin, D. Blitz, K. Wallner-Allen, N. A. Fox, J. L. Beaumont, D. Mungas, C. J. Nowinski, J. Richler, J. A. Deocampo, J. E. Anderson, J. J. Manly, B. Borosh, R. Havlik, K. Conway, E. Edwards, L. Freund, J. W. King, C. Moy, E. Witt, and R. C. Gershon. 2013. Cognition assessment using the NIH Toolbox. Neurology 80(11 Suppl. 3):S54–S64.
WHO (World Health Organization). 2018. WHO Disability Assessment Schedule 2.0. http://www.who.int/classifications/icf/whodasii/en (accessed April 5, 2019).