Psychometric properties of a short version of Lee Fatigue Scale used as a generic PROM in persons with stroke or osteoarthritis: assessment using a Rasch analysis approach

Validation of a short version of the Lee fatigue scale in adults living in Norway: a cross-sectional population survey

BACKGROUND

Due to the nature of fatigue, a brief reliable measure of fatigue severity is needed. Thus, the aim of our study was to evaluate a short version of the Lee Fatigue Scale (LFS) in the Norwegian general population.

METHODS

This cross-sectional survey consists of a representative sample from the Norwegian population drawn by The National Population Register in Norway. The study is part of a larger study (NORPOP) aimed at collecting normative data from several questionnaires focused on health in adults living in Norway. Registered citizens between 18 and 94 years of age were randomly selected stratified by age, sex and geographic region. Of the 4971 respondents eligible for the study, 1792 (36%) responded to the survey. In addition to age and sex, we collected responses on a 5-item version of the LFS measuring current fatige severity. The psychometric properties focusing on internal structure and precision of the LFS items were analyzed by a Rasch rating scale model.

RESULTS

Complete LFS scores for analyses were available for 1767 adults. Women had higher LFS-scores than men, and adults < 55 years old had higher scores than older respondents. Our analysis of the LFS showed that the average category on each item advanced monotonically. Two of the five items demonstrated misfit, while the three other items demonstrated goodness-of-fit to the model and uni-dimensionality. Items #1 and #4 (tired and fatigue respectively) showed differential item functioning (DIF) by sex, but no items showed DIFs in relation to age. The separation index of the LFS 3-item scale showed that the sample could be separated into three different groups according to the respondents' fatigue levels. The LFS-3 raw scores correlated strongly with the Rasch measure from the three items. The core dimensions in these individual items were very similarly expressed in the Norwegian language version and this may be a threat to the cultural-related or language validity of a short version of the LFS using these particular items.

CONCLUSIONS

The study provides validation of a short LFS 3-item version for estimating fatigue in the general population.

Key considerations to reduce or address respondent burden in patient-reported outcome (PRO) data collection

Patient-reported outcomes (PROs) are used in clinical trials to provide evidence of the benefits and risks of interventions from a patient perspective and to inform regulatory decisions and health policy. The collection of PROs in routine practice can facilitate monitoring of patient symptoms; identification of unmet needs; prioritisation and/or tailoring of treatment to the needs of individual patients and inform value-based healthcare initiatives. However, respondent burden needs to be carefully considered and addressed to avoid high rates of missing data and poor reporting of PRO results, which may lead to poor quality data for regulatory decision making and/or clinical care.

Prediction of evening fatigue severity in outpatients receiving chemotherapy: less may be more

Background

Fatigue is the most common and debilitating symptom experienced by oncology patients undergoing chemotherapy. Little is known about patient characteristics that predict changes in fatigue severity over time.

Purpose

To predict the severity of evening fatigue in the week following the administration of chemotherapy using machine learning approaches.

Methods

Outpatients with breast, gastrointestinal, gynecological, or lung cancer (N=1217) completed questionnaires one week prior to and one week following administration of chemotherapy. Evening fatigue was measured with the Lee Fatigue Scale (LFS). Separate prediction models for evening fatigue severity were created using clinical, symptom, and psychosocial adjustment characteristics and either evening fatigue scores or individual fatigue item scores. Prediction models were created using two regression and three machine learning approaches.

Results

Random forest (RF) models provided the best fit across all models. For the RF model using individual LFS item scores, two of the 13 individual LFS items (i.e., "worn out", "exhausted") were the strongest predictors.

Conclusion

This study is the first to use machine learning techniques to predict evening fatigue severity in the week following chemotherapy from fatigue scores obtained in the week prior to chemotherapy. Our findings suggest that the language used to assess clinical fatigue in oncology patients is important and that two simple questions may be used to predict evening fatigue severity.