Patients
Since July 2005, all patients seen at the Seligman Center for Advanced Therapeutics at the NYU Hospital for Joint Diseases complete an MDHAQ at all visits, while waiting to see the rheumatologist, in the infrastructure of routine care. MDHAQ scores, laboratory tests, and medications are recorded in a database. A physician global estimate of status (DOCGL) also is collected in routine care. A primary diagnosis was assigned by the treating physician according to ICD-9 codes. Patients with available data on exercise status at baseline and one year later through April 2011 were included in the study. The Institutional Review Board (IRB) of New York University School of Medicine approved a retrospective chart review of the data; therefore, no specific consent was needed for this study.
Patient self-report MDHAQ
The MDHAQ is a two-page patient self-report questionnaire [13–15] developed for quantitative assessment in routine clinical care [16], which includes physical function in 10 activities, scored 0–3 (0 = without any difficulty, 1 = with some difficulty, 2 = with much difficulty, and 3 = unable to do) for a total physical function (FN) score of 0–30. The raw FN 0–30 score for the 10 activities is converted to 0–10 using a template on the MDHAQ, and added to two 0–10 visual analog scale (VAS) scores for pain and patient global estimate of status (PATGL), to provide a composite 0–30 RAPID3 score [12].
The MDHAQ also includes an RA disease activity index (RADAI) self-report joint count [17], which queries patients to score pain in 16 specific joint groups, 8 each on the right and left sides: fingers, wrists, elbows, shoulders, hips, knees, ankles, and toes. Scoring options are: 0 = none, 1 = mild, 2 = moderate, or 3 = severe pain; the total score range is 0–48 [17]. Queries about the back and neck in an identical format are added on the MDHAQ, but not included in scoring, to be comparable to RADAI scores elsewhere.
The MDHAQ also includes scores for anxiety, depression, and sleep quality, a 60-item symptom checklist, review of 12 recent medical history events, morning stiffness, fatigue VAS, change in status, demographic data, and a query about exercise [12, 13].
Self-report exercise status
The MDHAQ exercise query is: “How often do you exercise aerobically (sweating, increased heart rate, shortness of breath) for at least one-half hour?” Five possible response options are: “3 or more times a week”, “1–2 times per week”, “1–2 times per month”, “do not exercise regularly”, “cannot exercise due to disability/handicap”. In this study, no patient selected the option “1–2 times per month” at baseline or a year later. Responses of weekly exercise ≥3 times and 1–2 times were pooled as “regular exercise” (EXER-Yes). Responses of “do not exercise regularly” or “cannot exercise due to disability/handicap” were pooled as “no exercise” (EXER-No) [7]. Patients were classified into four groups according to exercise status at baseline and one year later: exercise at both baseline and one year later (EXER-Yes, EXER-Yes), no exercise at baseline but exercise one year later (EXER-No, EXER-Yes), exercise at baseline but not one year later (EXER-Yes, EXER-No), and no exercise at both baseline or one year later (EXER-No, EXER-No).
Statistical analysis
The MDHAQ is incorporated into the medical record for clinical care. The data also are entered into a database, which includes demographic, MDHAQ, medication and laboratory data. Means and standard deviations (SD) were calculated for normally distributed data, and medians and interquartile ranges (IQR) for non-normally distributed data. Baseline differences between patients in the four groups according to exercise status at baseline and one year later were compared using the chi square and Kruskall-Wallis tests. Mean change from baseline to one-year follow up was compared by analysis of variance (ANOVA) and presented as percentage of change. A negative change indicates improvement and a positive change indicates worsening. Bivariate regression analyses were performed to identify baseline variables associated with exercise or no exercise; variables that were significant in bivariate analyses were included as covariates in multivariate logistic regression analyses. All analyses were performed using STATA 12.0® for Mac (StataCorp LP, College Station, TX, USA).