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Quality-of-life scales for neurologic diseases
- Updated 01.17.2024
- Released 05.05.2009
- Expires For CME 01.17.2027
Quality-of-life scales for neurologic diseases
Introduction
Overview
Neurologic diseases can have profound impact on all areas of a patient's life, including cognitive, physical, sensory, emotional, and social functioning. Because few of these diseases are curable, treatment tends to focus on limiting disease progression and relieving symptoms. Therefore, health-related quality-of-life (HRQL) outcomes that assess functional ability and well-being are important for evaluating research and clinical practices. HRQL is a useful indicator of overall health. It captures information on the physical, mental, and emotional health status of individuals and on the impact of health status on quality of life. HRQL information is best obtained by asking patients directly, using validated questions with standardized response options. HRQL measurement tools are readily available and can easily be incorporated into practice without adding burden to the clinician. In this article, the authors review the value of collecting HRQL information, the types of HRQL measures that can be used for neurologic diseases, considerations to use when selecting a measure, and recent initiatives that can make HRQL assessment in neurology practical and clinically useful. New HRQL measurement tools, guidance documents, and potential uses of HRQL measures are presented.
Key points
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• HRQL assessment provides patient-centered information that can be useful for research, clinical care, and performance monitoring. | |
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• Selection of HRQL measures should be guided by the purpose of the assessment, the setting in which it will be used, and the characteristics of the measure. | |
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• NIH-funded measurement systems, such as Neuro-QOL, provide standard assessment tools that can enable cross-disease comparisons along with disease-specific results. | |
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• Disease-specific HRQL measures have become especially useful in recent years, and they can provide tailored insight into individual patient populations. |
Rationale for measuring HRQL
Neurologic diseases may affect any aspect of cognitive, physical, sensory, emotional, and social functioning. Whether acute or insidious, stable or progressive, such disorders tend to have a long-lasting impact. Although many neurologic diseases are treatable, few are curable. Medical care tends to focus on limiting disease progression, symptom management, and maximizing quality of life. These objectives are sometimes poorly reflected in traditional clinical measures, such as lab and radiological results. In fact, although these traditional tests remain essential for disease and treatment monitoring, they fail to capture many of the outcomes that are important to patients. They may miss patients' direct experience of how the disease or its treatment has affected them.
Patients frequently care most about HRQL outcomes because those are the ones that reflect their symptom experience and their ability to function meaningfully in their environment. After dealing with the distress associated with diagnosis and treatment demands, patients must appraise their situation and adapt to changes imposed by their condition. Symptoms and functional abilities drive the patient's perception of the disease burden on themselves and their families. It is, therefore, important to assess HRQL to obtain that perspective, which can be surprisingly different from standard clinical severity measures. Some potential advantages of conducting a formal HRQL evaluation include the following:
• Identifying symptoms or concerns that might not be spontaneously reported. |
Routine collection of HRQL information from patients is traditionally uncommon, though the recent publication of guidance documents (International Society for Quality of Life Research 2011; 111) may facilitate increased collection efforts. Barriers to implementation include logistical difficulties, lack of infrastructure, lack of time and support for clinical providers, and provider uncertainty about the clinical value of collecting HRQL information (102). However, studies in general medical practice and specialty areas such as oncology show it can be feasible, efficient, and acceptable to both patients and providers and improves communication (88). A standard, computerized, HRQL questionnaire before a clinic visit with a summary of the results for the patient and/or provider immediately prior to the clinical encounter significantly increases discussion of HRQL issues, particularly those that are less observable (eg, social functioning) or of a diffuse quality (eg, fatigue) and heightens physician awareness of patients' HRQL (117).
Systematic evaluation of the impact of HRQL assessment on treatment and on patient involvement in decision making is in its early stages, with results being inconsistent (114). Nevertheless, HRQL information can potentially help move the scope of interventions beyond clinical impairment and closer to patient-centered care. For instance, attempting to establish good seizure control with minimal adverse medication effects is essential to treating epilepsy. Epilepsy also impacts psychological, social, and school and work functioning. It is associated with academic underachievement in children, un- or under-employment in adults, restricted social activity, stigma, and psychological distress for children and adults (06; 103). Knowledge of these other effects provides clinicians with additional targets for intervention as well as other potential indicators of treatment success (101). Although direct questioning about each of these areas during the actual clinical encounter is possible, collecting this information via HRQL measures that are completed and scored prior to the encounter makes it more organized and feasible.
In the research setting, HRQL outcomes are generally accepted as valid indicators of how well a treatment or intervention works (66). HRQL outcomes can complement and enrich findings based on more traditional measures. For example, in a randomized, double-blind comparison of lamotrigine and valproate for the treatment of seizures, patients taking lamotrigine were significantly more likely to report improvements in their health, levels of energy or fatigue, mood, and social functioning (105). This occurred even though both groups showed equivalent levels of seizure control in this specific study. Of note, a 2016 randomized study of valproate versus lamotrigine first-line monotherapy in newly diagnosed idiopathic generalized tonic-clonic seizures in adulthood showed improved seizure freedom at 12 months but did not comment on HRQL measures (51).
Additional controlled studies have found minor HRQL benefit of lamotrigine over other antiepileptic drugs when used for partial seizures (79) but not general seizures (80). There are insufficient good-quality data on HRQL effects of antiepileptic drugs to draw any firm conclusions (128), but this example does illustrate how such information can be useful during medical decision making. It is also important during the drug and treatment approval process (02).
What is HRQL?
HRQL, a narrower concept than general quality of life (QOL), has been defined as "the extent to which one's usual or expected physical, mental, and social well-being are affected by a medical condition or its treatment" (20). Although multiple definitions of HRQL exist, almost all share the concepts of subjectivity and multidimensionality. Subjectivity acknowledges that only patients, or valid proxies, can provide a complete understanding of the effects of a disease or treatment on their lives. Multidimensionality is a reminder that any or all of life's domains can be affected by health or its absence. Full appreciation of illness and treatment impact may require assessment of several important life domains, even those not directly affected by the specific condition or treatment. These domains can be broad (eg, physical well-being) or further subdivided into increasingly specific constructs (eg, mobility, fine motor function).
Special considerations when assessing HRQL in neurologic illness
Neurologic illness can cause mental and physical limitations that make it challenging to obtain patients' subjective report of their experience. Cognitive and communication difficulties, as well as burden of disease, can restrict a patient's ability to complete HRQL questionnaires. Therefore, measuring characteristics such as length, complexity, and response format can be especially important to consider when choosing an HRQL tool to use with these populations. In addition, some measures may not discriminate well among individuals with poor function, thus, making them of limited use for people with severe neurologic disease. Children's participation may be limited by these same factors in addition to their age or developmental level. Rather than excluding these individuals, the use of proxy respondents should be considered provided that potential biases and limitations of proxies are considered. Both the adult and pediatric literature suggest there is greater agreement between proxy and patient ratings when rating observable functioning (eg, physical HRQL) and less agreement for more subjective dimensions, such as social functioning, pain, and emotional well-being (43; 110; 89). However, a systematic review suggests that rating dimensions that are clinically relevant (eg, physical function for children with muscular dystrophy) may result in greater agreement, even for subjective areas (113).
Types of HRQL measurement approaches
Once the decision is made to assess HRQL, then the next step is to decide which instrument to use. This can be a challenging task. Although many measures of HRQL are available, no gold standard exists. The choice of a measure depends on the setting, the purpose for which it will be used, and the characteristics of the instrument itself. Suggested criteria for instrument review include conceptual model, reliability, validity, responsiveness, interpretability, burden, availability of alternate forms (eg, child, adolescent, proxy), and cultural and language adaptations (01). Content areas covered by the instrument, and how those areas were derived, may be important considerations (124). Additional criteria, such as age-appropriateness, are applicable to selection of pediatric instruments (43; 95).
Most measures of HRQL used to assess neurologic disease are one of two types: generic or targeted. Generic measures include questions that are general enough to be applicable to healthy and clinical populations. Targeted measures may be targeted towards specific diseases (eg, epilepsy), domains (eg, cognition, fatigue), or interventions (eg, radiation therapy). Each type has particular strengths, which are listed below.
Table 1. Characteristics of Generic and Targeted HRQL
| Generic | Targeted |
A frequently advocated third "modular" approach to HRQL measurement combines a generic "core" measure with a targeted instrument or instruments. A modular instrument can be specifically constructed for this purpose or can select among existing separate generic and targeted measures. This approach has the advantages of both while minimizing limitations of each. Potential downsides are the increased time and burden associated with a longer, combined instrument.
Generic HRQL measures. Generic measures have been used to describe the health status or HRQL of people with many common neurologic disorders. For adults, the most common measure is the Medical Outcomes Study Short Form-36 (122). The SF-36 evaluates eight domains: Physical functioning, role limitations due to physical effects of illness, social functioning, pain, energy and vitality, general health perceptions, role limitations due to emotional effects of illness, and mental health. A single additional item assesses change in health status during the past 12 months. A new generic measurement system available since 2006 is the Patient Reported Outcomes Measurement Information System (PROMIS), which offers a flexible range of assessment options for common symptoms and functional problems such as pain, fatigue, depression, anxiety, sleep, physical function, and social function. PROMIS is an NIH Roadmap initiative to create a publicly available and sustainable system for measuring patient reported outcomes across diseases and conditions (25).
After an extensive review of HRQL measures for children, Eiser and Morse (43) recommended three generic tools for use in chronic illness: the Child Health Questionnaire (73), the Pediatric Quality of Life Questionnaire (116), and the Health Utilities Index (47). The Child Health Questionnaire consists of 14 scales with both self-report and parent versions available. However, the length of the self-report version (87 items) limits its practicality. The Pediatric Quality of Life Questionnaire (www.pedsql.org) is a modular instrument available in child, adolescent, and parent forms. The generic core scales consist of 23 items measuring physical, emotional, school, and social functioning. Additional disease- and condition-specific modules are available, with the pediatric brain tumor, cerebral palsy, and neuromuscular disease modules of particular relevance to neurology. The Health Utilities Index (including multiple versions: HUI, HUI1, HUI2, HUI3) provides measures of health status and a preference-based health-related quality-of-life utility score. A 2006 review recommended consideration of newer generic instruments that have been developed according to established methodology (95), such as the KIDSCREEN (96) and DISABKIDS (04), both of which are modular in approach and have been used internationally. Solans and colleagues provide a useful review of the characteristics and psychometric properties of generic and disease-specific pediatric HRQL measures (112).
Targeted measures. Targeted measures may focus on a disease, one or more HRQL domains, or specific interventions. These categories are not mutually exclusive, and a disease-specific instrument may also be domain-specific. Disease-specific measures evaluate aspects of HRQL that are particularly applicable to that condition. For example, targeted instruments to evaluate HRQL in stroke include questions about language and communication problems; memory, thinking and cognitive function; vision; physical functioning (upper extremity function, mobility); and work productivity that generic instruments frequently omit (13; 52). Domain- or symptom-specific measures evaluate one area of HRQL and can range from the broad (eg, social well-being) to the more specific (eg, fatigue).
The last decade has seen tremendous growth in the development and application of disease-specific measures for particular neurologic conditions including, for adults, Alzheimer disease, amyotrophic lateral sclerosis, brain tumors, epilepsy, multiple sclerosis, Parkinson disease, spinal cord injury, and stroke. Instrument development has been slower for children and adolescents, likely because of the many additional theoretical and methodological issues involved. Most pediatric measure development activity has occurred in epilepsy (43a; 43b; 36; 95).
Space does not permit a review or listing of all available HRQL measures. Online databases of patient-reported outcome measures such as PROQOLID and test compilations and reviews such as the Handbook of Neurologic Rating Scales (56) can be additional sources of HRQL measures. An NINDS initiative, the Common Data Elements (CDE) project, can also be helpful in measure selection. The overarching goal of the CDE project is to standardize data collection in neurology clinical research. After reviewing available instruments, CDE working groups made HRQL measurement recommendations for the following diseases: Amyotrophic lateral sclerosis, pediatric and adult epilepsy, headache, Huntington disease, multiple sclerosis, Parkinson disease, stroke, spinal cord injury, and traumatic brain injury. Recommendations for other diseases are likely to follow.
The tables below list common (in some cases only) targeted measures for specific neurologic diseases and for symptoms associated with more than one disease. When found, sources for critical reviews of measures (usually both disease-specific and generic) used with those disease populations have also been included.
Table 2. Pediatric Disease-Specific HRQL Measures for Major Neurologic Diseases
Disease-specific measure | Review articles | |
Attention deficit hyperactivity disorder | ||
• AIM-C: ADHD Impact Module – Child | (78) | |
Autism spectrum disorder | ||
• Pediatric Quality of Life Inventory (PedsQL)–Generoc Core Scales–Parent Report Questionnaire | ||
Brain tumors | ||
• Pediatric Functional Assessment of Cancer Therapy–Childhood Brain Tumor Survivor (PedsFACT-BrS) (71) | (67) | |
Duchenne muscular dystrophy | ||
• Life Satisfaction Index for Adolescents (LSIA) (98) | None identified | |
Cerebral palsy | ||
• Caregiver Priorities and Child Health Index of Life with Disabilities (CPCHILD; proxy) (87) | (19) | |
Epilepsy | ||
• Quality of Life in Pediatric Epilepsy (child and parent/proxy) (03) | (33) | |
Table 3. Adult Disease-Specific HRQL Measures for Major Neurologic Diseases
Disease-specific measure | Review articles | |
Attention deficit disorder | ||
• Adult ADHD Quality-of-Life Scale (AAQoL) (11) | None | |
Amyotrophic lateral sclerosis | ||
• Amyotrophic Lateral Sclerosis Assessment Scales (ALSAQ-40 and 5) (63; 62) | (55; 45) | |
• ALS Specific Quality of Life-Revised (ALSSQOL-R) | (108) | |
Alzheimer disease and dementia | ||
• Cornell Brown Quality of Life in Dementia (97) • Quality of Life in Alzheimer Disease (QOL-AD) (76) • Dementia Quality of Life Instrument (D-QOL) (12) • DEMQOL and DEMQOL-Proxy (109) | (46; 106) | |
• Alzheimer Disease Related Quality of Life (ADRQL) | (10; 92) | |
Brain tumors | ||
• EORTC-30 - Brain Module • Functional Assessment of Cancer Therapy-Brain (FACT- BR) (126) | (50) | |
Epilepsy | ||
• Well-Being Scale (29) • Liverpool Quality of Life Battery (07) • Quality of Life Assessment Schedule (Kendrick and Trimble 1994) • Epilepsy Surgery Inventory (118) • Quality of Life in Epilepsy Instruments (QOLIE; 86-, 31- and 10-items) (39; 31). | (74) | |
Huntington disease | ||
• Huntington Quality of Life Instrument (H-QoL-I) (27) | (58) | |
Migraine | ||
• Migraine Disability Assessment Scale (MIDAS) (75) • Headache Impact Test-6 (08) • Migraine-Specific Quality of Life Questionnaire-Revised (MSQ)(Version 2.1) (81) • Migraine Specific Quality-of-Life (MSQOL) instrument (84) | (09) | |
Multiple sclerosis | ||
• MS Quality of Life Inventory (MSQLI) (100) • MS Quality of Life (MSQOL-54) (119) • Functional Assessment of Multiple Sclerosis (FAMS) (21) | (53; 86; 99) | |
• Preference-Based Multiple Sclerosis Index (PBMSI) | (70) | |
Neuromuscular disease | ||
Individualized Neuromuscular Quality of Life questionnaire (INQoL) (120) | (15) | |
Parkinson disease | ||
• Parkinson’s Disease Questionnaire-39 item version (PDQ-39) (93) • Parkinson’s Disease Quality of Life Questionnaire (PDQL) (35) • Parkinson’s Impact Scale (PIMS) (18) • Parkinson’s Disease Quality of Life Instrument (PDQUALIF) (127) | (37) | |
Spinal cord injury | ||
• Life Situation Questionnaire-Revised (LSQ-R) (68) • Spinal Cord Injuries Quality of Life Questionnaire (SCIQL-23) (77) | (130; 57) | |
Stroke | ||
• Stroke Impact Scale (SIS) (41) • Stroke Specific Quality of Life Scale (SS-QOL) (129); SS-QOL-12 (94) • Newcastle Stroke-specific Quality of Life measure (NEWSQOL) (14) | (13; 52) | |
• Health-Related Quality of Life in Stroke Patients-26 (HRQOLISP-26) | (90) | |
Traumatic brain injury | ||
• Quality of Life after Brain Injury (QOLiBRI) (121) | (16) | |
Table 4. Symptom-Specific HRQL Measures
Symptom-specific measure | Review articles | |
Dizziness and vertigo | ||
• Dizziness Handicap Inventory (DHI) (61) • UCLA Dizziness Questionnaire (UCLA-DQ) (60) • Vertigo Handicap Questionnaire (131) | (42) | |
Fatigue | ||
• Fatigue Severity Scale (FSS) (69) • Fatigue Assessment Instrument (FAI) (107) • Fatigue Impact Scale (FIS) (48) • Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F)(22) | (40) | |
Pain | ||
• Brief Pain Inventory (BPI) (28) • McGill Pain Questionnaire (85) | (82) | |
Sexual function | ||
Derogatis Interview for Sexual Functioning-self-report (DISF-SR) (38) | (30) | |
Sleepiness | ||
• Stanford Sleepiness Scale (SSS) (59) • Epworth Sleepiness Scale (ESS) (64) • Functional Outcomes of Sleep Questionnaire (FOSQ) (125) | (05) | |
The Neuro-QOL measurement system
Despite the existence of many HRQL measures for neurologic disorders, HRQL evaluation in neurology remains challenging. Lack of agreement on the best measure to use and the uncertain psychometric properties of some existing HRQL measures can make the task of measure selection overwhelming. This can lead to confusion and limited understanding of disease impact in any broader context because results of most studies aren't comparable. On recognizing this challenge, the National Institute of Neurological Disorders and Stroke (NINDS) initiated a multiyear, multisite project to develop a bilingual (English and Spanish), clinically relevant, and psychometrically robust HRQL tool responsive to the needs of the neurology community that would ultimately also be appropriate to use in clinical settings. The instruments that were developed through this effort, the Neuro-QOL measurement system, can be found at www.neuroqol.org.
Conceptually and practically linked to the more generic PROMIS effort, Neuro-QOL is comprised of 13 adult and 8 pediatric item banks (sets of questions comprehensively covering a specific symptom or functional area) and 4 scales to assess HRQL in people with neurologic disorders. These instruments assess the areas listed in Table 5. Initial validation studies have been conducted in stroke, multiple sclerosis, amyotrophic lateral sclerosis, Parkinson disease, adult and pediatric epilepsy, and muscular dystrophy. Available in English and Spanish, and currently in the midst of translation into several additional languages, it is suitable for use with people 8 years of age and older. Like PROMIS, Neuro-QOL includes questions about issues that are common across selected neurologic diseases (generic banks) along with targeted banks and scales that evaluate symptoms, concerns, or issues only relevant to persons within specific disease groups. This approach enables cross-disease comparisons (by comparing generic items that are given to individuals across conditions) and evaluation of condition-specific HRQL issues (by reviewing targeted items administered only to individuals with the condition in question).
Table 5. HRQL Areas Assessed by Neuro-QOL
|
Adult |
Pediatric | |
|
Physical function |
• Lower extremity (mobility) |
• Lower extremity (mobility) |
|
Mental function |
• Depression |
• Depression |
|
Social function |
• Ability to participate in social roles and activities |
• Social relations (interactions with peers) |
Details of Neuro-QOL development can be found on the Neuro-QOL website and in several articles (24; 23; 49; 72). In brief, Neuro-QOL followed established procedures for self-report measure development. These included gathering extensive patient, caregiver, and expert input via focus groups, interviews, and surveys; an iterative item generation, review, and selection process; and several phases of field testing. The first waves of field testing (conducted online with general population and clinical panels) were for the purpose of collecting the data needed for item response theory (IRT) analyses. Additional field testing with 694 patients and 197 proxies was conducted at multiple clinical sites across the United States for the purpose of validating brief versions of the instruments for use in the clinical groups initially targeted by the Neuro-QOL effort.
Neuro-QOL’s utilization of modern test development models, such as item response theory (IRT), provides the instruments with certain advantages; these include the ability to be brief yet still precise and valid (26). Using IRT methodology, sets of items can be calibrated along a continuum that covers the full range of the construct to be measured. From this calibrated set, or “item bank,” items can be selected to make up “short forms” (typically consisting of 6 to 8 questions) that fit the goals of the user. For example, a user wishing to assess locomotion in a group of patients who tended to have poor or very poor mobility might select items that cluster near the lower end of the motor bank. Item banks are also the basis for Computerized Adaptive Testing (CAT). This is a specialized type of computer-based testing that allows for frequent assessments, immediate feedback, and precise evaluation of patients at the individual level while placing minimal burden on patients (26; 54). Users can administer short, unique tests to every individual, with reliability and scores equivalent to longer, fixed-length assessments. Item response theory also allows comparisons of patients and questionnaire items across multiple instruments through a mechanism of equating the instruments along a common measurement continuum (McHorney and Cohen 2000). Thus, a given score on a Neuro-QOL measure can be directly linked to what that individual would score on any existing HRQL measure that has been equated to Neuro-QOL.
Relationship of Neuro-QOL to other HRQL measures. Affiliated projects are currently underway to extend the Neuro-QOL system to spinal cord injury, traumatic brain injury, and Huntington disease populations by adding disease specific items or domains as appropriate. Neuro-QOL is not a static system, and it is anticipated that it will be extended to additional diseases in the future. The generic Neuro-QOL measures can be linked to the parallel PROMIS measures. Neuro-QOL has included items from most of these banks into its system. These common items will allow direct comparisons between Neuro-QOL and PROMIS scores and results in both clinical and research settings.
Conclusion
There are many available questionnaires to measure quality of life in neurologic disease. Some are generic and offer an ability to compare across conditions and even to nonclinical populations; others are more specifically tailored to address specific symptoms and treatment effects of targeted neurologic diseases. Both types can add important information to the clinical encounter and to clinical research while being minimally burdensome to patients, clinicians, and researchers. Selection of a measure should be guided by the goals of the assessment and its psychometric and other properties. Guidance documents for the use of HRQL measures in clinical (International Society for Quality of Life Research 2011; 111) and research settings (17) can help in selection. Although the existence of so many measures suggests that clinicians and researchers are likely to find some measure to fit their needs, the lack of a uniformly accepted instrument limits comparability and communication about scores in the research and clinical communities. Neuro-QOL, a NINDS initiative, combines generic and targeted quality-of-life assessment in ways that enable comparability across diseases and disease specificity within the same system. Future adoption and improvements on options such as Neuro-QOL should help improve the way neurologists communicate about outcomes that are best measured by asking patients.
References
- 01
- Anonymous. Assessing health status and quality-of-life instruments: attributes and review criteria. Qual Life Res 2002;11(3):193-205.
- 02
- Anonymous. Guidance for industry patient-reported outcome measures: use in medical product development to support labeling claims. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Center for Biologics Evaluation and Research (CBER), Center for Devices and Radiological Health (CDRH). Accessed 2009.
- 03
- Arunkumar G, Wyllie E, Kotagal P, Ong HT, Gilliam F. Parent- and patient-validated content for pediatric epilepsy quality-of-life assessment. Epilepsia 2000;41(11):1474-84. PMID 11077463
- 04
- Baars RM, Atherton CI, Koopman HM, Bullinger M, Power M; DISABKIDS group. The European DISABKIDS project: development of seven condition-specific modules to measure health related quality of life in children and adolescents. Health Qual Life Outcomes 2005;3:70. PMID 16283947
- 05
- Bailes S, Libman E, Baltzan M, Amsel R, Schondorf R, Fichten CS. Brief and distinct empirical sleepiness and fatigue scales. J Psychosom Res 2006;60(6):605-13. PMID 16731235
- 06
- Baker GA. The psychosocial burden of epilepsy. Epilepsia 2002;43:26-30. PMID 12190975
- 07
- Baker GA, Smith DF, Dewey M, Morrow J, Crawford PM, Chadwick DW. The development of a seizure severity scale as an outcome measure in epilepsy. Epilepsy Res 1991;8(3):245-51. PMID 1743185
- 08
- Bayliss MS, Bjorner JB, Kosinski M, et al. Development of HIT-6, a paper-based short form for measuring headache impact. In: Olesen J, Steiner TJ, Lipton RB. Frontiers in Headache Research, Vol. 11. Reducing the Burden of Headache. New York: Oxford University Press USA, 2003:386-90.
- 09
- Becker WJ. Assessing health-related quality of life in patients with migraine. Can J Neurol Sci 2002;29 Suppl 2:S16-22. PMID 12139081
- 10
- Black BS, Rabins PV, Kasper JD. Alzheimer disease related quality of life (ADRQL): user’s manual. Baltimore, MD: DE Measure, 1999.
- 11
- Brod M, Johnston J, Able S, Swindle R. Validation of the adult attention-deficit/hyperactivity disorder quality-of-life Scale (AAQoL): a disease-specific quality-of-life measure. Qual Life Res 2006;15(1):117-29. PMID 16411036
- 12
- Brod M, Stewart AL, Sands L, Walton P. Conceptualization and measurement of quality of life in dementia: the dementia quality of life instrument (DQoL). Gerontologist 1999;39(1):25-35. PMID 10028768
- 13
- Buck D, Jacoby A, Massey A, Ford G. Evaluation of measures used to assess quality of life after stroke. Stroke 2000;31(8):2004-10. PMID 10926971
- 14
- Buck D, Jacoby A, Massey A, Steen N, Sharma A, Ford GA. Development and validation of NEWSQOL, the Newcastle Stroke-Specific Quality of Life Measure. Cerebrovasc Dis 2004;17(2-3):143-52. PMID 14707414
- 15
- Burns TM, Graham CD, Rose MR, Simmons Z. Quality of life and measures of quality of life in patients with neuromuscular disorders. Muscle Nerve 2012;46(1):9-25. PMID 22644588
- 16
- Bullinger M, Azouvi P, Brooks N, et al. Quality of life in patients with traumatic brain injury-basic issues, assessment and recommendations. Restor Neurol Neurosci 2002;20(3-4):111-24. PMID 12454360
- 17
- Butt Z, Reeve B. Enhancing the Patient’s Voice: Standards in the Design and Selection of Patient-Reported Outcomes Measures (PROMs) for Use in Patient-Centered Outcomes Research Accessed November 30, 2012.
- 18
- Calne S, Schulzer M, Mak E, et al. Validating a quality of life rating scale for idiopathic parkinsonism: Parkinson's Impact Scale (PIMS). Parkinsonism Relat Disord 1996;2(2):55-61. PMID 18591019
- 19
- Carlon S, Shields N, Yong K, Gilmore R, Sakzewski L, Boyd R. A systematic review of the psychometric properties of Quality of Life measures for school aged children with cerebral palsy. BMC Pediatr 2010;10:81. PMID 21059270
- 20
- Cella D. Quality of life: the concept. J Palliat Care 1992;8(3):8-13. PMID 1279152
- 21
- Cella DF, Dineen K, Arnason B, et al. Validation of the functional assessment of multiple sclerosis quality of life instrument. Neurology 1996;47(1):129-39. PMID 8710066
- 22
- Cella D, Lai JS, Chang CH, Peterman A, Slavin M. Fatigue in cancer patients compared with fatigue in the general United States population. Cancer 2002;94(2):528-538. PMID 11900238
- 23
- Cella D, Lai JS, Nowinski CJ, et al. Neuro-QOL: brief measures of health-related quality of life for clinical research in neurology. Neurology 2012;78(23):1860-7. PMID 22573626
- 24
- Cella D, Nowinski C, Peterman A, et al. The neurology quality-of-life measurement initiative. Arch Phys Med Rehabil 2011;92(10 Suppl):S28-36. PMID 21958920
- 25
- Cella D, Yount S, Rothrock N, et al. The Patient-Reported Outcomes Measurement Information System (PROMIS): progress of an NIH Roadmap cooperative group during its first two years. Med Care 2007;45(5 Suppl 1):S3-S11. PMID 17443116
- 26
- Chang CH, Reeve BB. Item Response Theory and its applications to patient-reported outcomes measurement. Eval Health Prof 2005;28(3):264-82. PMID 16123257
- 27
- Clay E, De Nicola A, Dorey J, et al. Validation of the first quality-of-life measurement for patients with Huntington's disease: the Huntington Quality of Life Instrument. Int Clin Psychopharmacol 2012;27(4):208-14. PMID 22508443
- 28
- Cleeland CS. Measurement of pain by subjective report. In: Chapman CR, Loeser JD, editors. Advances in Pain Research and Therapy. Vol. 12. Issues in Pain Measurement. New York: Raven Press, 1989:391-403.
- 29
- Collings JA. Psychosocial well-being and epilepsy: an empirical study. Epilepsia 1990;31(4):418-26. PMID 2369877
- 30
- Corona G, Jannini EA, Maggi M. Inventories for male and female sexual dysfunctions. Int J Impot Res 2006;18(3):236-50. PMID 16267565
- 31
- Cramer JA, Perrine K, Devinsky O, Bryant-Comstock L, Meador K, Hermann B. Development and cross-cultural translations of a 31-item quality of life in epilepsy inventory. Epilepsia 1998;39(1):81-8. PMID 9578017
- 32
- Cramer JA, Westbrook LE, Devinsky O, Perrine K, Glassman MB, Camfield C. Development of the Quality of Life in Epilepsy Inventory for Adolescents: the QOLIE-AD-48. Epilepsia 1999;40(8):1114-21. PMID 10448825
- 33
- Cowan J, Baker GA. A review of subjective impact measures for use with children and adolescents with epilepsy. Qual Life Res 2004;13(8):1435-43. PMID 15503839
- 34
- Davis SE, Hynan LS, Limbers CA, et al. The PedsQL in pediatric patients with Duchenne muscular dystrophy: feasibility, reliability, and validity of the Pediatric Quality of Life Inventory Neuromuscular Module and Generic Core Scales. J Clin Neuromuscul Dis 2010;11(3):97-109. PMID 20215981
- 35
- De Boer AG, Wijker W, Speelman JD, de Haes JC. Quality of life in patients with Parkinson's disease: development of a questionnaire. J Neurol Neurosurg Psychiatry 1996;61(1):70-4. PMID 8676165
- 36
- De Civita M, Regier D, Alamgir AH, Anis AH, Fitzgerald MJ, Marra CA. Evaluating health-related quality-of-life studies in paediatric populations: some conceptual, methodological and developmental considerations and recent applications. Pharmacoeconomics 2005;23(7):659-85. PMID 15987225
- 37
- Den Oudsten BL, Van Heck GL, De Vries J. The suitability of patient-based measures in the field of Parkinson's disease: a systematic review. Mov Disord 2007;22(10):1390-401. PMID 17516489
- 38
- Derogatis LR. The Derogatis interview for Sexual Functioning (DISF, DISF-SR): an introductory report. J Sex Marital Ther 1997;23(4):291-304. PMID 9427208
- 39
- Devinsky O, Vickrey BG, Cramer J, et al. Development of the quality of life in epilepsy inventory. Epilepsia 1995;36(11):1089-104. PMID 7588453
- 40
- Dittner AJ, Wessely SC, Brown RG. The assessment of fatigue: A practical guide for clinicians and researchers. J Psychosom Res 2004;56(2):157-70. PMID 15016573
- 41
- Duncan PW, Wallace D, Lai SM, Johnson D, Embretson S, Laster LJ. The stroke impact scale version 2.0. Evaluation of reliability, validity, and sensitivity to change. Stroke 1999;30(10):2131-40. PMID 10512918
- 42
- Duracinsky M, Mosnier I, Bouccara D, Sterkers O, Chassany O; Working Group of the Societe Francaise d'Oto-Rhino-Laryngologie (ORL). Literature review of questionnaires assessing vertigo and dizziness, and their impact on patients' quality of life. Value Health 2007;10(4):273-84. PMID 17645682
- 43
- Eiser C, Morse R. Quality-of-life measures in chronic diseases of childhood. Health Technol Assess 2001a;5(4):1-157. PMID 11262421
- 44
- Eiser C, Morse R. A review of measures of quality of life for children with chronic illness. Arch Dis Child 2001b;84(3):205-11. PMID 11207164
- 45
- Epton J, Harris R, Jenkinson C. Quality of life in amyotrophic lateral sclerosis/motor neuron disease: a structured review. Amyotroph Lateral Scler 2009;10(1):15-26. PMID 18752087
- 46
- Ettema TP, Droes RM, de Lange J, Mellenbergh GJ, Ribbe MW. A review of quality of life instruments used in dementia. Qual Life Res 2005;14(3):675-86. PMID 16022061
- 47
- Feeny D, Furlong W, Barr RD. Multiattribute approach to the assessment of health-related quality of life: Health Utilities Index. Med Pediatr Oncol 1998;Suppl 1:54-9. PMID 9659947
- 48
- Fisk JD, Ritvo PG, Ross L, Haase DA, Marrie TJ, Schlech WF. Measuring the functional impact of fatigue: initial validation of the fatigue impact scale. Clin Infect Dis 1994;18 Suppl 1:S79-83. PMID 8148458
- 49
- Gershon RC, Lai JS, Bode R, et al. Neuro-QOL: quality of life item banks for adults with neurological disorders: item development and calibrations based upon clinical and general population testing. Qual Life Res 2012;21(3):475-86. PMID 21874314
- 50
- Gilbert M, Armstrong T, Meyers C. Issues in assessing and interpreting quality of life in patients with malignant glioma. Semin Oncol 2000;27(3 Suppl 6):20-6. PMID 10866346
- 51
- Giri VP, Giri OP, Khan FA, Kumar N, Kumar A, Haque A. Valproic acid versus lamotrigine as first-line monotherapy in newly diagnosed idiopathic generalized tonic-clonic seizures in adults - a randomized controlled trial. J Clin Diagn Res 2016;10(7):FC01-4. PMID 27630862
- 52
- Golomb BA, Vickrey BG, Hays RD. A review of health-related quality-of-life measures in stroke. Pharmacoeconomics 2001;9(2):155-85. PMID 11284381
- 53
- Gruenewald DA, Higginson IJ, Vivat B, Edmonds P, Burman RE. Quality of life measures for the palliative care of people severely affected by multiple sclerosis: a systematic review. Mult Scler 2004;10(6):690-704. PMID 15584496
- 54
- Hahn EA, Cella D, Bode RK, Gershon R, Lai JS. Item banks and their potential applications to health status assessment in diverse populations. Med Care 2006;44(11 Suppl 3):S189-97. PMID 17060827
- 55
- Heffernan C, Jenkinson C. Measuring outcomes for neurological disorders: a review of disease-specific health status instruments for three degenerative neurological conditions. Chronic Illn 2005;1(2):131-42. PMID 17136919
- 56
- Herndon, RM, editor. Handbook of Neurologic Rating Scales. Second ed. New York, NY: Demos Medical Publishing, 2006.
- 57
- Hill MR, Noonan VK, Sakakibara BM, Miller WC; SCIRE Research Team. Quality of life instruments and definitions in individuals with spinal cord injury: a systematic review. Spinal Cord 2010;48(6):438-50. PMID 20029393
- 58
- Ho AK, Robbins AO, Walters SJ, Kaptoge S, Sahakian BJ, Barker RA. Health-related quality of life in Huntington's disease: a comparison of two generic instruments, SF-36 and SIP. Mov Disord 2004;19(11):1341-8. PMID 15389986
- 59
- Hoddes E, Zarcone V, Smythe H, Phillips R, Dement WC. Quantification of sleepiness: a new approach. Psychophysiology 1973;10(4):431-6. PMID 4719486
- 60
- Honrubia V, Bell TS, Harris MR, Baloh RW, Fisher LM. Quantitative evaluation of dizziness characteristics and impact on quality of life. Am J Otol 1996;17(4):595-602. PMID 8841705
- 61
- Jacobson GP, Newman CW. The development of the Dizziness Handicap Inventory. Arch Otolaryngol Head Neck Surg 1990;116(4):424-7. PMID 2317323
- 62
- Jenkinson C, Fitzpatrick R. Reduced item set for the amyotrophic lateral sclerosis assessment questionnaire: development and validation of the ALSAQ-5. J Neurol Neurosurg Psychiatry 2001;70(1):70-3. PMID 11118250
- 63
- Jenkinson C, Fitzpatrick R, Brennan C, Bromberg M, Swash M. Development and validation of a short measure of health status for individuals with amyotrophic lateral sclerosis/motor neurone disease: the ALSAQ-40. J Neurol 1999a;246 Suppl 3:III16-21. PMID 10631656
- 64
- Johns MW. A new method for measuring daytime sleepiness: the Epworth Sleepiness Scale. Sleep 1991;14(6):540-5. PMID 1798888
- 65
- Kendrick A, Trimble, M. Repertory Grid in the assessment of quality of life in patients with epilepsy: The Quality of Life Assessment Schedule. In: Trimble MR, Dodson W, editors. Epilepsy and Quality of Life. New York: Raven, 1994:151-64.
- 66
- Kharroubi SA, Elbarazi I. Editorial: health-related quality of life in health care. Front Public Health 2023;11:1123180. PMID 36817896
- 67
- Klassen AF, Strohm SJ, Maurice-Stam H, Grootenhuis MA. Quality of life questionnaires for children with cancer and childhood cancer survivors: a review of the development of available measures. Support Care Cancer 2010;18(9):1207-17. PMID 19834745
- 68
- Krause JS, Anson CA. Adjustment after spinal cord injury: relationship to gender and race. Rehabil Psychol 1997;42(1):31-46.
- 69
- Krupp LB, LaRocca NG, Muir-Nash J, Steinberg AD. The fatigue severity scale. Application to patients with multiple sclerosis and systemic lupus erythematosus. Arch Neurol 1989;46(10):1121-3. PMID 2803071
- 70
- Kuspinar A, Finch L, Pickard S, Mayo NE. Using existing data to identify candidate items for a health state classification system in multiple sclerosis. Qual Life Res 2014;23(5):1445-57. PMID 24338161
- 71
- Lai JS, Cella D, Tomita T, Bode RK, Newmark M, Goldman S. Developing a health-related quality of life instrument for childhood brain tumor survivors. Childs Nerv Syst 2007;23(1):47-57. PMID 16960733
- 72
- Lai JS, Nowinski C, Victorson D, et al. Quality of Life measures in children with neurological conditions: Pediatric Neuro-QOL. Neurorehabil Neural Repair 2012;26(1):36-47. PMID 21788436
- 73
- Landgraf JM, Abetz L, Ware J. Child Health Questionnaire (CHQ): A user's manual. Boston, MA: Integrated Therapeutics Group, Inc, 1996.
- 74
- Leone MA, Beghi E, Righini C, Apolone G, Mosconi P. Epilepsy and quality of life in adults: a review of instruments. Epilepsy Res. 2005;66:23-44. PMID 16154322
- 75
- Lipton RB, Stewart WF, Sawyer J, Edmeads JG. Clinical utility of an instrument assessing migraine disability: the Migraine Disability Assessment (MIDAS) questionnaire. Headache 2001;41(9):854-61. PMID 11703471
- 76
- Logsdon RG, Gibbons E, McCurry SM, Teri L. Assessing quality of life in older adults with cognitive impairment. Psychosom Med 2002;64(3):510-9. PMID 12021425
- 77
- Lundqvist C, Siosteen A, Sullivan L, Blomstrand C, Lind B, Sullivan M. Spinal cord injuries: a shortened measure of function and mood. Spinal Cord 1997;35(1):17-21. PMID 9025214
- 78
- Manos M, Frazier TW, Landgraf JM, Weiss M, Hodgkins P. HRQL and medication satisfaction in children with ADHD treated with the methylphenidate transdermal system. Curr Med Res Opin 2009;25(12):3001-10. PMID 19849639
- 79
- Marson AG, Al-Kharusi AM, Alwaidh M, et al. The SANAD study of effectiveness of carbamazepine, gabapentin, lamotrigine, oxcarbazepine, or topiramate for treatment of partial epilepsy: an unblinded randomised controlled trial. Lancet 2007a;369(9566):1000-15. PMID 17382827
- 80
- Marson AG, Al-Kharusi AM, Alwaidh M, et al. The SANAD study of effectiveness of valproate, lamotrigine, or topiramate for generalised and unclassifiable epilepsy: an unblinded randomised controlled trial. Lancet 2007b;369(9566):1016-26. PMID 17382828
- 81
- Martin BC, Pathak DS, Sharfman MI, et al. Validity and reliability of the migraine-specific quality of life questionnaire (MSQ Version 2.1). Headache 2000;40(3):204-15. PMID 10759923
- 82
- McDowell I. Pain Measurements. In: McDowell, I, editor. Measuring Health: a guide to rating scales and questionnaires. 3rd ed. New York: Oxford University Press, 2006:470-519.
- 83
- McHorney C A, Cohen AS. Equating health status measures with item response theory: illustrations with functional status items. Med Care 2000;38(9 Suppl):1143-59. PMID 10982089
- 84
- McKenna SP, Doward LC, Davey KM. The development and psychometric properties of the MSQOL: A migraine-specific quality-of-life instrument. Clin Drug Investig 1998;15(5):413-23. PMID 18370497
- 85
- Melzack R. The McGill Pain Questionnaire: major properties and scoring methods. Pain 1975;1(3):277-99. PMID 1235985
- 86
- Mitchell AJ, Benito-Leon J, Gonzalez JM, Rivera-Navarro J. Quality of life and its assessment in multiple sclerosis: integrating physical and psychological components of wellbeing. Lancet Neurol 2005;4(9):556-66. PMID 16109362
- 87
- Narayanan UG, Fehlings D, Weir S, Knights S, Kiran S, Campbell K. Initial development and validation of the Caregiver Priorities and Child Health Index of Life with Disabilities (CPCHILD). Dev Med Child Neurol 2006;48(10):804-12. PMID 16978459
- 88
- Nguyen LB, Vu LG, Le TT, et al. Impact of interventions on the quality of life of cancer patients: a systematic review and meta-analysis of longitudinal research. Health Qual Life Outcomes 2023;21(1):112. PMID 37821985
- 89
- Oczkowski C, O'Donnell M. Reliability of proxy respondents for patients with stroke: a systematic review. J Stroke Cerebrovasc Dis 2010;19(5):410-6. PMID 20554222
- 90
- Ojo Owolabi M. HRQOLISP-26: a concise, multiculturally valid, multidimensional, flexible, and reliable stroke-specific measure. ISRN Neurol 2011;2011:295096. PMID 22389812
- 91
- Palmer SN, Meeske KA, Katz ER, et al. The PedsQL brain tumor module: initial reliability and validity. Pediatr Blood Canc 2007;49:287-93.
- 92
- Perales J, Cosco TD, Stephan BC, Haro JM, Brayne C. Health-related quality-of-life instruments for Alzheimer's disease and mixed dementia. Int Psychogeriatr 2013;25(5):691-706. PMID 23347698
- 93
- Peto V, Jenkinson C, Fitzpatrick R, Greenhall R. The development and validation of a short measure of functioning and well being for individuals with Parkinson's disease. Qual Life Res 1995;4(3):241-8. PMID 7613534
- 94
- Post MW, Boosman H, van Zandvoort MM, Passier PE, Rinkel GJ, Visser-Meily JM. Development and validation of a short version of the Stroke Specific Quality of Life Scale. J Neurol Neurosurg Psychiatry 2011;82(3):283-6. PMID 20802211
- 95
- Ravens-Sieberer U, Erhart M, Wille N, Wetzel R, Nickel J, Bullinger M. Generic health-related quality-of-life assessment in children and adolescents: methodological considerations. Pharmacoeconomics 2006;24(12):1199-220. PMID 17129075
- 96
- Ravens-Sieberer U, Gosch A, Rajmil L, et al. The KIDSCREEN-52 quality of life measure for children and adolescents: psychometric results from a cross-cultural survey in 13 European countries. Value Health 2008;11(4):645-58. PMID 18179669
- 97
- Ready RE, Ott BR, Grace J, Fernandez I. The Cornell-Brown Scale for Quality of Life in dementia. Alzheimer Dis and Assoc Disord 2002;16(2):109-15. PMID 12040306
- 98
- Reid DT, Renwick RM. Preliminary validation of a new instrument to measure life satisfaction in adolescents with neuromuscular disorders. Int J Rehabil Res 1994;7(2):184-8. PMID 7960341
- 99
- Riazi A. Patient-reported outcome measures in multiple sclerosis. Int MS J 2006;13(3):92-9. PMID 17101077
- 100
- Ritvo PG, Fischer JS, Miller DM, Andrews H, Paty DW, LaRocca NG. Multiple Sclerosis Quality of Life Inventory: A user's manual. New York, National Multiple Sclerosis Society, 1997.
- 101
- Ronen GM, Streiner DL, Rosenbaum P; Canadian Pediatric Epilepsy Network. Health-related quality of life in children with epilepsy: development and validation of self-report and parent proxy measures. Epilepsia 2003;44(4):598-612. PMID 12681011
- 102
- Rose M, Bezjak A. Logistics of collecting patient-reported outcomes (PROs) in clinical practice: an overview and practical examples. Qual Life Res 2009;18(1):125-36. PMID 19152119
- 103
- Sabaz M, Cairns DR, Bleasel AF, et al. The health-related quality of life of childhood epilepsy syndromes. J Paediatr Child Health 2003;39(9):690-6. PMID 14629502
- 104
- Sabaz M, Cairns DR, Lawson JA, Nheu N, Bleasel AF, Bye AM. Validation of a new quality of life measure for children with epilepsy. Epilepsia 2000;41(6):765-74. PMID 10840411
- 105
- Sackellares CJ, Kwong JW, Vuong A, Hammer AE, Barrett PS. Lamotrigine monotherapy improves health-related quality of life in epilepsy: a double-blind comparison with valproate. Epilepsy Behav 2002;3(4):376-82. PMID 12609336
- 106
- Scholzel-Dorenbos CJ, van der Steen MJ, Engels LK, Olde Rikkert MG. Assessment of quality of life as outcome in dementia and MCI intervention trials: a systematic review. Alzheimer Dis Assoc Disord 2007;21(2):172-8. PMID 17545745
- 107
- Schwartz JE, Jandorf L, Krupp LB. The measurement of fatigue: a new instrument. J Psychosom Res 1993;37(7):753-62. PMID 8229906
- 108
- Simmons Z, Felgoise SH, Rodriguez JL, Walsh SM, Bremer BA, Stephens HE. Validation of a shorter ALS-specific quality of life instrument: the ALSSQOL-R. Neurology 2010;74(suppl 2):A177-8.
- 109
- Smith SC, Lamping DL, Banerjee S, et al. Development of a new measure of health-related quality of life for people with dementia: DEMQOL. Psychol Med 2007;37(5):737-46. PMID 17176501
- 110
- Sneeuw KC, Sprangers MA, Aaronson NK. The role of health care providers and significant others in evaluating the quality of life of patients with chronic disease. J Clin Epidemiol 2002;55(11):1130-43. PMID 12507678
- 111
- Snyder CF, Aaronson NK, Choucair AK, et al. Implementing patient-reported outcomes assessment in clinical practice: a review of the options and considerations. Qual Life Res 2012;21(8):1305-14. PMID 22048932
- 112
- Solans M, Pane S, Estrada MD, et al. Health-related quality of life measurement in children and adolescents: a systematic review of generic and disease-specific instruments. Value Health 2008;11(4):742-64. PMID 18179668
- 113
- Upton P, Lawford J, Eiser C. Parent-child agreement across child health-related quality of life instruments: a review of the literature. Qual Life Res 2008;17(6):895-913. PMID 18521721
- 114
- Valderas JM, Kotzeva A, Espallargues M, et al. The impact of measuring patient-reported outcomes in clinical practice: a systematic review of the literature. Qual Life Res 2008;17(2):179-93. PMID 18175207
- 115
- Varni JW, Burwinkle TM, Katz ER, Meeske K, Dickinson P. The PedsQL in pediatric cancer: reliability and validity of the Pediatric Quality of Life Inventory Generic Core Scales, Multidimensional Fatigue Scale, and Cancer Module. Cancer 2002;94:2090-106. PMID 11932914
- 116
- Varni JW, Seid M, Rode CA. The PedsQL: measurement model for the pediatric quality of life inventory. Med Care 1999;37(2):126-39. PMID 10024117
- 117
- Velikova G, Brown JM, Smith AB, Selby PJ. Computer-based quality of life questionnaires may contribute to doctor-patient interactions in oncology. Br J Cancer 2002;86(1):51-9. PMID 11857011
- 118
- Vickrey BG, Hays RD, Graber J, Rausch R, Engel J Jr, Brook RH. A health-related quality of life instrument for patients evaluated for epilepsy surgery. Med Care 1992;30(4):299-319. PMID 1556879
- 119
- Vickrey BG, Hays RD, Harooni R, Myers LW, Ellison GW. A health-related quality of life measure for multiple sclerosis. Qual Life Res 1995;4(3):187-206. PMID 7613530
- 120
- Vincent KA, Carr AJ, Walburn J, Scott DL, Rose MR. Construction and validation of a quality of life questionnaire for neuromuscular disease (INQoL). Neurology 2007;68(13):1051-7. PMID 17389311
- 121
- von Steinbuechel N, Richter S, Morawetz C, Riemsma R. Assessment of subjective health and health-related quality of life in persons with acquired or degenerative brain injury. Curr Opin Neurol 2005;18(6):681-91. PMID 16280680
- 122
- Ware JE Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care 1992;30(6):473-83. PMID 1593914
- 123
- Waters E, Davis E, Mackinnon A, et al. Psychometric properties of the quality of life questionnaire for children with CP. Dev Med Child Neurol 2007;49(1):49-55. PMID 17209977
- 124
- Waters E, Davis E, Ronen GM, Rosenbaum P, Livingston M, Saigal S. Quality of life instruments for children and adolescents with neurodisabilities: how to choose the appropriate instrument. Dev Med Child Neurol 2009;51(8):660-9. PMID 19627340
- 125
- Weaver TE, Laizner AM, Evans LK, et al. An instrument to measure functional status outcomes for disorders of excessive sleepiness. Sleep 1997;20(10):835-43. PMID 9415942
- 126
- Weitzner MA, Meyers CA, Gelke CK, Byrne KS, Cella DF, Levin VA. The Functional Assessment of Cancer Therapy (FACT) scale. Development of a brain subscale and revalidation of the general version (FACT-G) in patients with primary brain tumors. Cancer 1995;75(5):1151-61. PMID 7850714
- 127
- Welsh M, McDermott MP, Holloway RG, et al. Development and testing of the Parkinson's disease quality of life scale. Mov Disord 2003;18(6):637-45. PMID 12784266
- 128
- Wilby J, Kainth A, Hawkins N, et al. Clinical effectiveness, tolerability and cost-effectiveness of newer drugs for epilepsy in adults: a systematic review and economic evaluation. Health Technol Assess 2005;9(15):1-157. PMID 15842952
- 129
- Williams LS, Weinberger M, Harris LE, Clark DO, Biller J. Development of a stroke-specific quality of life scale. Stroke 1999;30(7):1362-9. PMID 10390308
- 130
- Wood-Dauphinee S, Exner G, Bostanci B, et al. Quality of life in patients with spinal cord injury--basic issues, assessment, and recommendations. Restor Neurol Neurosci 2002;20(3-4):135-49. PMID 12454362
- 131
- Yardley L, Putman J. Quantitative analysis of factors contributing to handicap and distress in vertiginous patients: a questionnaire study. Clin Otolaryngol Allied Sci 1992;17(3):231-6. PMID 1387052
Contributors
All contributors' financial relationships have been reviewed and mitigated to ensure that this and every other article is free from commercial bias.
Authors
-
Aparna M Prabhu MD MRCP
Dr. Prabhu of Thomas Jefferson University and Jefferson Einstein Medical Center has no relevant financial relationships to disclose.
See Profile -
Adam Friedant DO
Dr. Friedant of Jefferson Einstein Medical Center has no relevant financial relationships to disclose.
See Profile
Editor
-
Anthony T Reder MD
Dr. Reder of the University of Chicago received honorariums from Biogen Idec, Genentech, Genzyme, and TG Therapeutics for service on advisory boards and as a consultant as well as stock options from NKMax America for advisory work and an unrestricted lab research grant from BMS.
See Profile
Former Authors
- Cindy J Nowinski MD PhD
- David Cella PhD
Patient Profile
- Age range of presentation
-
- 0 month to 65+ years
- Sex preponderance
-
- male=female
- Heredity
-
- none
- Population groups selectively affected
-
- none selectively affected
- Occupation groups selectively affected
-
- none selectively affected
ICD & OMIM codes
- ICD
-
- Alzheimer disease: G30
- Amyotrophic lateral sclerosis: G12.12
- Attention deficit disorder: F90.9
- Brain tumors: D49.6
- Cerebral palsy: G80.9
- Duchenne muscular dystrophy: G71.01
- Epilepsy: G40.909
- Huntington disease: F02.2
- Migraine: G43.909
- Multiple sclerosis: G35
- Neuromuscular disease: G70.9
- Parkinson disease: G20.B2
- Spinal cord injury: S14.109A
- Stroke: I63.9
- Traumatic brain injury: S06.300A
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