Evaluating Individual Change With the Quality of Life in Neurological Disorders (Neuro-QoL) Short Forms

Abstract

Objective To provide a clinically useful means of interpreting change for individual patients on the Neurological Quality of Life (Neuro-QoL) adult short forms (SFs) by applying a Classical Test Theory concept for interpreting individual change. Design Secondary analysis of existing data. Setting Community. Participants Persons with neurological conditions including stroke, epilepsy, amyotrophic lateral sclerosis, multiple sclerosis, and Parkinson’s disease residing in community settings. Interventions Not applicable. Main Outcome Measures Neuro-QOL SFs for Applied Cognition-General Concerns, Applied Cognition-Executive Function, Applied Cognition-Combined, Ability to Participate in Social Roles and Activities, Satisfaction with Social Roles and Activities, Positive Affect and Well-Being, Depression, Stigma, Upper Extremity Function (Fine Motor, Activities of Daily Living), Lower Extremity Function (Mobility), Anxiety, Sleep Disturbance, Fatigue, and Emotional and Behavioral Dyscontrol. Methods We estimated conditional minimal detectable change (cMDC) indices from the pooled standard errors (SEs) adjusted for a 95% confidence interval (CI) using the average of the SEs for any given pair of scores multiplied by the z-score, or [(SEScore1 +SEScore2)/2 · 1.96]. Results The cMDC indices are generally smallest in the mid-range of all scales, ranging from 3.6 to 11.2 T-score points, and higher on the outer quartiles ranging from 3.7 to 21.6 T-score points. The lowest mid-range cMDCs were for Satisfaction with Social Roles and Activities (3.6–4.7 T-score points) and the largest was for Sleep Disturbance (9.4–11.2 T-score points). Conclusions Change indices can help clinicians and investigators identify differences for individual patients or subjects that are large enough to motivate treatment change. cMDCs can reduce misclassification of magnitudes of change that are near the margins of error across the range of the Neuro-QoL SFs.

Prediction of specific depressive symptom clusters in youth with epilepsy: The NDDI‐E‐Y versus Neuro‐QOL SF

“…12 In addition, youth completed the 8-item Depression Scale Short Form of the Pediatric Neuro-QOL (Neuro-QOL SF), which was designed to assess HRoL in children with neurologic conditions, including epilepsy. 19 For each item, there are 5 response choices ranging from 1 "never" to 5 "almost always." 19 For each item, there are 5 response choices ranging from 1 "never" to 5 "almost always."…”

Automated symptom and treatment side effect monitoring for improved quality of life among adults with diabetic peripheral neuropathy in primary care: a pragmatic, cluster, randomized, controlled trial

“…These measures were assessed using the NeuroQOL measurement system instruments that have been previously validated in individuals with neuropathy [17]. These measures were assessed using the NeuroQOL measurement system instruments that have been previously validated in individuals with neuropathy [17].…”

“…We hypothesized that automated monitoring and feedback of information on symptoms and side effects would result in clinically significant improvements in quality of life (EQ-5D = 0.074) and neuropathy symptoms [16,17]. We hypothesized that automated monitoring and feedback of information on symptoms and side effects would result in clinically significant improvements in quality of life (EQ-5D = 0.074) and neuropathy symptoms [16,17].…”

“…In addition to quality of life, we also evaluated changes in neuropathy symptoms, including pain interference (range = 40.7-77.0; 77 indicates maximum interference), sleep disruption (range: 32-84.2; higher score indicates maximum disruption), lower extremity functioning (range: 16.5-58.6; 58.6 indicates best possible functioning) and depressive symptoms (range: 38.2-81.3; 81.3 indicates worst possible symptoms). These measures were assessed using the NeuroQOL measurement system instruments that have been previously validated in individuals with neuropathy [17].…”

“…All person-reported outcomes were collected by trained interviewers during 20-min phone interviews at~2 months (baseline) and again 8 months (follow-up) following treatment start. We hypothesized that automated monitoring and feedback of information on symptoms and side effects would result in clinically significant improvements in quality of life (EQ-5D = 0.074) and neuropathy symptoms [16,17].…”