Fatigue in cancer patients: Development and validation of a short form of the Multidimensional Fatigue Inventory (MFI-10)

Baussard, Louise; Carayol, Marion; Porro, Bertrand; Baguet, Fanny; Cousson‐Gélie, Florence

doi:10.1016/j.ejon.2018.07.005

Cited by 16 publications

(19 citation statements)

References 35 publications

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“…Although the authors of the original test did not recommend calculating a total score over all 20 items, it is possible to use such a sum score [29]. The shortened MFI-10, according to Baussard et al [25], consists of the 10 positively oriented items (items 2, 5, 9, 10, 13, 14, 16, 17, 18, and 19) which indicate the presence of fatigue and omits those negatively oriented items which indicate the absence of fatigue. Though this MFI-10 can also be decomposed into three subscales [25], we use the MFI-10 as a 10-item scale in the descriptive analyses.…”

Section: Mfi-20mentioning

confidence: 99%

“…We tested the onedimensional and the originally designed five-dimensional model of the MFI-20. In addition, we tested the short form MFI-10, also in terms of a one-dimensional model and a threedimensional model according to Baussard et al [25]. Several fit indices were examined to evaluate the overall fit of each model: The χ 2 goodness-of-fit statistic, the comparative fit index (CFI), the Tucker-Lewis index (TLI), the standardized root mean square residual (SRMR), and the root mean square error of approximation (RMSEA) according to Hu and Bentler [34].…”

Section: Statistical Analysesmentioning

confidence: 99%

“…Using items of different orientation can reduce the reliability of the scales [23,24]. Therefore, Baussard et al [25] developed a shortened version of the questionnaire, the MFI-10. It consists only of those 10 items which positively indicate fatigue and excludes the items with the opposite direction.…”

Section: Introductionmentioning

confidence: 99%

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Fatigue in cancer patients: comparison with the general population and prognostic factors

Hinz

Weis²,

Brähler

et al. 2020

Support Care Cancer

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Purpose The aims of this examination were to compare cancer patients' fatigue burden with that of the general population, to identify clinical factors that are associated with fatigue, and to test psychometric properties of the fatigue questionnaire MFI-20 including the short-form MFI-10. Methods A sample of 1818 German cancer patients was tested with the MFI-20. Results The study confirmed that the cancer patients demonstrate a high level of burden from fatigue. The effect size for the comparison between the cancer patients and a sample of the general population (n = 1993) was d = 0.58 based on MFI-20 total scores. In the cancer patients' sample, females reported slightly higher levels of fatigue than males did (p < 0.05). There was no significant effect of age on fatigue. Advanced tumor stage, the presence of metastases, and a "poorer" Eastern Cooperative Oncology Group (ECOG) performance status were significantly associated with fatigue. The results of the confirmatory factor analyses (CFAs) only partly confirmed the factorial structure of the MFI-20. Conclusion Despite the insufficient CFA indices, we believe that the scale structure of the MFI-20 should not be changed and that calculating a total fatigue score is justifiable. For those seeking a shorter questionnaire, the MFI-10, which only contains those 10 items which positively indicate fatigue, is a good alternative.

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Section: Mfi-20mentioning

confidence: 99%

Section: Statistical Analysesmentioning

confidence: 99%

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Fatigue in cancer patients: comparison with the general population and prognostic factors

Hinz

Weis²,

Brähler

et al. 2020

Support Care Cancer

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“…A recent systematic literature review and meta-analysis found conflicting evidence on whether there is an association between fatigue and BC survivors' RTW (9). The lack of consistency may be due to the failure to measure cancer-related fatigue (CRF) in a multidimensional way, despite the fact that CRF has been defined in terms of at least three dimensions: physical, cognitive and emotional (22,24). Moreover, all the studies except that of Hedayati et al (8), which found no link between CRF and RTW 8 months after BC diagnosis, assessed the association between CRF and RTW at the end of treatment, but not during treatment (9,13,(25)(26)(27)(28).…”

Section: Introductionmentioning

confidence: 99%

Quality of life, fatigue and changes therein as predictors of return to work during breast cancer treatment

Porro

Michel

Zinzindohoué³

et al. 2019

Scandinavian Caring Sciences

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Purpose To our knowledge, only one study has assessed Quality of Life (QOL) as a predictor of return to work (RTW) during breast cancer treatment and one has evaluated multidimensional cancer‐related fatigue (CRF) as a determinant of RTW. However, no study has assessed the impact of changes in these variables on RTW. The objective of this study was to evaluate QOL, multidimensional CRF and changes in these variables as determinants of RTW during breast cancer treatment. Methods We performed a longitudinal study of 68 patients with a mean age of 46.97 years (SD = 6.92), who were employed at the time of diagnosis. Women were assessed at the beginning of adjuvant treatments (T0) and followed up with by telephone at three (T1) and 6 months later (T2), using questionnaires (QLQ‐C30; MFI‐20). Results Global QOL, OR = 1.12 [1.01–1.25], sleep disturbance, OR = 1.04 [1.002–1.08], fatigue (QLQ‐C30), OR = 0.93 [0.88–0.99], nausea‐vomiting, OR = 0.84 [0.73–0.97], reduced motivation, OR = 1.49 [1.05–2.11] and general fatigue, OR = 0.79 [0.63–0.99] at T0 were associated with RTW at T1. At T2 global QOL, OR = 1.09 [1.01–1.17], cognitive functioning, OR = 1.10 [1.03–1.17], general fatigue, OR = 1.82 [1.04–3.17] and mental fatigue, OR = 0.29 [0.11–0.81] were associated with RTW. Furthermore, changes in mental fatigue were associated with RTW at T2, OR = 0.02 [0.001–0.29]. Conclusions Quality of life, fatigue and their changes in them, especially cognitive functioning and mental fatigue, can play an important role in predicting the RTW of women with breast cancer. This confirms the importance of multidisciplinary care for cancer and the emergence of a theoretical psychological model of RTW.

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“…Additionally, it is defined as the inability of the muscles to maintain the required level of strength during exercises. It can also result in the deterioration of health in the long term, including work-related musculoskeletal disorders [ 28 ], chronic fatigue syndrome [ 29 ] and compromised immune function [ 30 ]. Therefore, fatigue is a common concern among clinicians and individuals who participate in physical activities based on training or rehabilitation [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States

Pinto-Bernal

Cifuentes

Perdomo

et al. 2021

Sensors

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Physical exercise contributes to the success of rehabilitation programs and rehabilitation processes assisted through social robots. However, the amount and intensity of exercise needed to obtain positive results are unknown. Several considerations must be kept in mind for its implementation in rehabilitation, as monitoring of patients’ intensity, which is essential to avoid extreme fatigue conditions, may cause physical and physiological complications. The use of machine learning models has been implemented in fatigue management, but is limited in practice due to the lack of understanding of how an individual’s performance deteriorates with fatigue; this can vary based on physical exercise, environment, and the individual’s characteristics. As a first step, this paper lays the foundation for a data analytic approach to managing fatigue in walking tasks. The proposed framework establishes the criteria for a feature and machine learning algorithm selection for fatigue management, classifying four fatigue diagnoses states. Based on the proposed framework and the classifier implemented, the random forest model presented the best performance with an average accuracy of ≥98% and F-score of ≥93%. This model was comprised of ≤16 features. In addition, the prediction performance was analyzed by limiting the sensors used from four IMUs to two or even one IMU with an overall performance of ≥88%.

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Fatigue in cancer patients: Development and validation of a short form of the Multidimensional Fatigue Inventory (MFI-10)

Cited by 16 publications

References 35 publications

Fatigue in cancer patients: comparison with the general population and prognostic factors

Fatigue in cancer patients: comparison with the general population and prognostic factors

Quality of life, fatigue and changes therein as predictors of return to work during breast cancer treatment

A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States

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