New applications for independent activities of daily living in measuring disability in multiple sclerosis

Abstract

Background:

Disability outcome measures in multiple sclerosis (MS) focus heavily on ambulation; however, limitations in performing everyday activities encompass another type of disability.

Objectives:

The aim of this study was to examine the ability of instrumental activities of daily living (IADL) scale to discriminate between different levels of disability and to predict disability progression.

Methods:

The North American Research Committee on Multiple Sclerosis (NARCOMS) registry fall 2006 semi-annual survey asked participants to complete the RAND-12, Performance Scales, Patient Determined Disease Steps (PDDS), and IADL questionnaires. We modeled the trajectory of disability change, using the PDDS, over 12 years. Analyses used linear and repeated measures regression methods.

Results:

Of respondents (n = 9931), 9559 (96%) completed the PDDS and IADL scale. Respondents were mostly female (76%), Caucasian (92%), and 52.3 (10.5) years old with moderate disability (median PDDS 4 (early cane)). Mean (SD) IADL total score was 20.5 (3.7). Discriminant ability of the IADL scale was higher than other measures considered at higher levels of disability. Adjusted longitudinal models showed that needing greater assistance on IADLs was independently predictive of trajectories of greater disability change.

Conclusion:

IADL scale had a greater ability to discriminate between higher disability levels than RAND-12 domains. The IADL scale may provide a useful and clinically relevant tool to measure disability in progressive MS populations.

Keywords

Activities of daily living multiple sclerosis disability patient-reported outcomes

Get full access to this article

View all access options for this article.

References

Learmonth

Motl

Sandroff

, et al. Validation of patient determined disease steps (PDDS) scale scores in persons with multiple sclerosis. BMC Neurol 2013; 13: 37.

Marrie

Goldman

. Validity of performance scales for disability. Mult Scler 2007; 13(9): 1176–1182.

Kurtzke

. Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology 1983; 33(11): 1444–1452.

Motl

Dlugonski

Suh

, et al. Accelerometry and its association with objective markers of walking limitations in ambulatory adults with multiple sclerosis. Arch Phys Med Rehabil 2010; 91(12): 1942–1947.

Lawton

Brody

. Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist 1969; 9(3): 179–186.

Minden

Hoaglin

, et al. Quality of life in people with multiple sclerosis: Data from the Sonya Slifka longitudinal multiple sclerosis study. J Health Hum Serv Adm 2007; 30(3): 233–267.

Einarsson

Gottberg

Fredrikson

, et al. Activities of daily living and social activities in people with multiple sclerosis in Stockholm County. Clin Rehabil 2006; 20(6): 543–551.

Månsson

Lexell

. Performance of activities of daily living in multiple sclerosis. Disabil Rehabil 2004; 26: 576–585.

Goldman

Motl

Scagnelli

, et al. Clinically meaningful performance benchmarks in MS: Timed 25-foot walk and the real world. Neurology 2013; 81(21): 1856–1863.

10.

The Consortium of Multiple Sclerosis Centers (CMSC), http://www.mscare.org/?page=about_the_cmsc (accessed 11 June 2019)

11.

Lawton

Moss

Fulcomer

, et al. A research and service oriented multilevel assessment instrument. J Gerontol 1982; 37(1): 91–99.

12.

Lawton

. Scales to measure competence in everyday activities. Psychopharmacol Bull 1988; 24(4): 609–614.

13.

Schwartz

Vollmer

Lee

. Reliability and validity of two self-report measures of impairment and disability for MS. Neurology 1999; 52(1): 63–70.

14.

Marrie

Cutter

Tyry

, et al. Validation of the NARCOMS registry: Fatigue assessment. Mult Scler 2005; 11(5): 583–584.

15.

Marrie

Cutter

Tyry

, et al. Validation of the NARCOMS registry: Pain assessment. Mult Scler 2005; 11(3): 338–342.

16.

Hays

Sherbourne

Mazel

. User’s manual for the medical outcomes study (MOS) core measures of health-related quality of life. Santa Monica, CA: RAND Corporation, 1995.

17.

Learmonth

Hubbard

McAuley

, et al. Psychometric properties of quality of life and health-related quality of life assessments in people with multiple sclerosis. Qual Life Res 2014; 23(7): 2015–2023.

18.

Nortvedt

Riise

Myhr

, et al. Performance of the SF-36, SF-12, and RAND-36 summary scales in a multiple sclerosis population. Med Care 2000; 38(10): 1022–1028.

19.

Horton

Rudick

Hara-Cleaver

, et al. Methods in neuroepidemiology validation of a self-report comorbidity questionnaire for multiple sclerosis. Neuroepidemiology 2010; 35: 83–90.

20.

Marrie

Horwitz

Cutter

, et al. Comorbidity delays diagnosis and increases disability at diagnosis in MS. Neurology 2000; 72(2): 117–124.

21.

Schober

Boer

Schwarte

. Correlation coefficients. Anesth Analg 2018; 126: 1763–1768.

22.

Luo

Wang

, et al. Preference-based SF-6D scores derived from the SF-36 and SF-12 have different discriminative power in a Population Health Survey. Med Care 2012; 50(7): 627–632.

23.

Tremlett

Zhao

Rieckmann

, et al. New perspectives in the natural history of multiple sclerosis. Neurology 2010; 74(24): 2004–2015.

24.

Marrie

Horwitz

Cutter

, et al. Comorbidity, socioeconomic status and multiple sclerosis. Mult Scler 2008; 14(8): 1091–1098.

25.

Gulick

. Symptom and activities of daily living trajectory in multiple sclerosis: A 10-year study. Nurs Res 1998; 47(3): 137–146.

26.

Paltamaa

Sarasoja

Leskinen

, et al. Measures of physical functioning predict self-reported performance in self-care, mobility, and domestic life in ambulatory persons with multiple sclerosis. Arch Phys Med Rehabil 2007; 88(12): 1649–1657.

27.

Kierkegaard

Einarsson

Gottberg

, et al. The relationship between walking, manual dexterity, cognition and activity/participation in persons with multiple sclerosis. Mult Scler 2012; 18(5): 639–646.

28.

McHorney

Tarlov

. Individual-patient monitoring in clinical practice: Are available health status surveys adequate. Qual Life Res 1995; 4(4): 293–307.

29.

Nortvedt

Riise

Myhr

, et al. Quality of life as a predictor for change in disability in MS. Neurology 2000; 55: 51–54.

30.

Harding

Wardle

Carruthers

, et al. Socioeconomic status and disability progression in multiple sclerosis: A multinational study. Neurology 2019; 92(13): e1497–e1506.

31.

Wang

Cutter

Cofield

, et al. Baseline EDSS proportions in MS clinical trials affect the overall outcome and power: A cautionary note. Mult Scler 2017; 23(7): 982–987.

32.

Wallin

Culpepper

Campbell

, et al. The prevalence of MS in the United States: A population-based estimate using health claims data. Neurology 2019; 92: e1029–e1040.

33.

Reuben

Seeman

Keeler

, et al. Refining the categorization of physical functional status: The added value of combining self-reported and performance-based measures. J Gerontol A Biol Sci Med Sci 2004; 59(10): 1056–1061.

34.

Goverover

Kalmar

Gaudino-Goering

, et al. The relation between subjective and objective measures of everyday life activities in persons with multiple sclerosis. Arch Phys Med Rehabil 2005; 86(12): 2303–2308.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.18 MB