ank">

Table 1. Demographic and clinical data (n = 141).

aPCI, percutaneous coronary intervention; bCABG, coronary artery bypass grafting.

Multi-Dimensional Fatigue Inventory (MFI-20): This inventory was originally designed to measure fatigue in persons treated for cancer and provides information on the degree and multi-dimensionality of fatigue experiences. The scale consists of 20 items with a 5-point rating format, where a higher score indicates a higher level of fatigue [13] . In the present study, to measure convergent validity, we used a global score transformed from raw scale scores into interval scale sores, confirmed in a psychometric evaluation of MFI-20 in a sample of persons treated for MI [26] . Cronbach’s alpha = 0.88.

Cardiac Self-Efficacy (CSE): This scale was designed to measure self-efficacy in persons treated for coronary heart disease and provides information on confidence about maintaining function and controlling symptoms. The scale consists of 13 items with a 5-point rating format, where a higher score indicates a higher level of self-efficacy [16] . In the present study, to measure discriminant validity, we used a global score confirmed in a psychometric evaluation of CSE in a sample of persons treated for acute coronary syndrome [30] . Cronbach’s alpha = 0.90.

Montgomery Åsberg Depression Rating Scale (MADRS): This scale was designed to measure depressive symptoms. The sale consists of nine items with a 6-point rating scale and with a possible range of 0 - 54, where a higher global score indicates depression symptoms [29] . In the present study, we used the global score to evaluate the overlap between fatigue and symptoms of depression. Cronbach’s alpha = 0.84.

2.4. Analysis

Confirmatory factor analysis (CFA) was performed to examine the extent to which the factor structure replicated the hypothesized hierarchical models, for each of the three scales. In this effort, the Mplus version 5 [31] , within the STREAMS environment [32] , was used. The total amount of internal missing data was relatively small, 21 scores distributed across 13 items. To include as much information as possible, the missing data modeling technique implemented in the Mplus program was used [33] . This technique gives unbiased estimates under moderate assumptions, which in the current study are fully satisfied [34] [35] .

Model fit was estimated by: 1) χ2 goodness-of-fit, χ2/df, which should be beyond 2 - 3, 2) the Root Mean Square Error of Approximation (RMSEA), where a value should be below 0.05 for a good fit and below 0.08 for a model to be acceptable, 3) the Standardized Root Mean Square Residual (SRMR), which should be below 0.08 for a good fit and, 4) the Comparative Fit Index (CFI), where a value of 0.90 or higher indicates acceptable model fit [36] .

Mean summed scores for each scale were assessed, and then convergent and discriminant validity were considered based on calculations of Pearson’s correlations [28] .

3. Results

In an evaluation of the factor structure, CFA was performed, in line with the a priori, hypothesized three-factor model design, using the 22 items as indicators of the factor structures: Physical fatigue eight items, Cognitive fatigue seven items, and Emotional fatigue seven items. Initially, in a test of the three-factor model, items found to have low factor loadings were visually examined, questioned and―after careful consideration―deleted. Accordingly, the number of items was reduced from 22 to 15. Further, the fit indices were improved, and the hypothesized factor structure was successfully confirmed. The standardized factor loading showed that all items were positively and significantly associated with one of the three factors to which they belonged, resulting in five items in each of three subscales (Figure 1). Each of the 15 items was an indicator of one of the three factors, showing that the final confirmatory model was built up with one latent variable for each of the three subscales; physical, cognitive and emotional fatigue, and with covariance between the three latent variables, resulting in a good fit; χ2 (87, N = 141) = 126.376, CFI (0.972), SRMR (0.049) and RMSEA (=0.057). The covariance values between the three factors were quite high; 0.78 for physical fatigue and cognitive fatigue, 0.72 for cognitive fatigue and emotional fatigue, and 0.73 for physical and emotional fatigue.

Figure 1. The three-factor model with covariances among the three factors.

Because the intercorrelations between the factors indicated an underlying commonality representing the same construct, a latent global fatigue factor―a higher-order model representing a global fatigue factor―was tested. This model was just as suitable as the three-factor model; χ2 (87, N = 141) =126.376, CFI (0.972), SRMR (0.049) and RMSEA (=0.057). This test of a hypothesized higher-order factor should be carefully interpreted, because such statistical models should preferably rely on at least four first-order factors. Nevertheless, the factor loadings on the global factor were high: physical fatigue = 0.89, cognitive fatigue = 0.88 and emotional fatigue = 0.82 (Figure 2). Thus, when the factor loadings are generally high on both levels, it is reasonable to use a total summary score of the scale, in this case reflecting a post-MI fatigue global dimension.

The results from analyses of convergent and discriminant validity showed that the PMFS was positively correlated with the MFI-20 scale and negatively correlated with the CSE scale, conforming the second hypothesis that the PMIF was associated with external variables in expected directions. The strongest associations were found between PMIF-physical fatigue and MFI-20 and PMIF-emotional fatigue and CSE scale (Table 2). The correlation between depression (MADRs) and fatigue (PMFS) was r = 0.50, indicating that there are differences between these two symptoms, but also an overlap because, according to DSM-V [14] , one of the symptoms of depression is fatigue.

Figure 2. The second-order model.

Table 2. Descriptive data and correlations.

**p < 0.01.

4. Discussion

The main purpose of the present study was to develop and psychometrically evaluate a scale for assessment of fatigue in persons treated for MI. The process of studying this phenomenon and creating the PMIF scale progressed from the verbal expressions of fatigue symptoms made by patients treated for MI, as well as statements from healthcare professionals, to research questions and hypotheses developed during the years in our research group. The present paper shortly describes these steps, including ideas and knowledge about post-MI fatigue, ending in development of scale items tested here. In the present evaluation of this new scale, the number of items was reduced from 32 to 15, resulting in a stable multi-factor solution. Our hypothesis that the PMIF represents three dimensions―physical, cognitive and emotional fatigue―has been confirmed. This solution was achieved by summing the five items in each dimension. Moreover, the hierarchical structure evaluation supported the legitimacy of summing the 15 items into a total score to obtain a post-MI global fatigue dimension. In tests of convergent and discriminant validity, the correlation analyses showed expected results, confirming the second hypothesis.

Fatigue can cause disability, and patients consider it a distressing symptom [3] [27] . Ordinary coronary care rehabilitation programs include advice about diet, smoking, physical activity and emotions. Thus, for all those whose daily activities are limited by fatigue, standard rehabilitation care cannot help them in this regard. Consequently, to alleviate the negative consequences of fatigue, a person-centered care plan [37] , emphasizing subjective symptom experiences [38] , should be a useful tool. Early identification of fatigue, together with health behavior support, may prevent progression toward a more severe state of fatigue or vital exhaustion [22] [39] .

Many fatigue scales have been developed over the years to measure fatigue both in the general population and in populations of persons with different chronic diseases [11] [21] . Still, there is a lack of disease-specific scales designed to measure fatigue after MI. In our earlier research, we have used and evaluated the MFI-20, originally developed to measure fatigue in different populations, but not patients with coronary heart disease. These assessments had acceptable results, but one of the five dimensions (reduced motivation) did not function as expected [26] . Therefore, we wanted to start again by constructing a fatigue scale based on the experiences and statements of persons treated for MI. The present PMIF is a first attempt to develop a fatigue assessment for persons treated for acute coronary heart disease. Fatigue is often neglected in treatment, because its etiology is unknown and because the symptom appears to be unrelated to disease severity and process [21] .

Mental distress has been found to be overrepresented among patients with coronary heart disease [40] . Symptoms of fatigue and depression after MI tend to overlap, as one of the cardinal symptoms of depression is fatigue or loss of energy [14] . Depressed people need medical treatment to prevent a poor prognosis [41] . Notably, some patients recovering from myocardial infarction report fatigue without concomitant symptoms of depression [15] . When a person experiences fatigue after MI, without a coexisting diagnosis of depression, self-care strategies to improve health should be identified [22] . Health behaviors, e.g., physical activity, may have influences on prognosis and morbidity and should be considered in relation to recuperation from fatigue, as research suggests that post-MI fatigue is likely to lead to decreased physical activity [5] .

In the present study, we have included aspects such as internal, convergent and discriminate validity. One limitation is that we did not evaluate sensitivity to change over time and possible cutoff scores. Fatigue is a subjective experience and the items in the PMIF cover the phenomenology of fatigue experiences, often experienced by persons in the post-MI rehabilitation phase. Summing all items to obtain a global score gives an indication of who is experiencing fatigue. Further, future research and development of the scale will be needed to ensure identification through cutoff scores. In addition, our earlier research indicates that fatigue increases during a period up to four months after MI [4] . Thus, if we are to learn more about the sensitivity of the PMIF and when fatigue relief support will have its optimal effects, further research is needed on how fatigue changes over time.

Moreover, by summing the item scores for the three dimensions―physical, cognitive and emotional fatigue―a multi-dimensional fatigue profile for each person can be defined, providing a basis for offering person-centered support. Further, when fatigue interferes negatively with desired activity, it could be referred to as fatigability [20] . Understanding a person’s fatigability requires an assessment of fatigue itself, but also identification of daily life ability and activity. Consequently, a fatigue relief intervention could be developed that affects both symptoms and function [42] . For this reason, in further development of the PMIF, the concept of fatigability should be considered.

5. Conclusion

Fatigue is often neglected in treatment, because its etiology is unknown and because the symptom appears to be unrelated to disease severity and process. Early identification of fatigue, together with health behavior support, might be useful in promoting fatigue relief. In this work, the PMIF scale―a brief and easily completed scale―may be useful for identifying persons experiencing post-MI fatigue.

Cite this paper
Brink, E. , Fredriksson-Larsson, U. , Alsén, P. , Lång, M. and Cliffordson, C. (2018) Development of a Tool for Assessment of Post-Myocardial Infarction Fatigue. Open Journal of Nursing, 8, 811-822. doi: 10.4236/ojn.2018.811061.
References
[1]   Brink, E., Karlson, B.W. and Hallberg, L.R.-M. (2002) Health Experiences of First-Time Myocardial Infarction: Factors Influencing Women’s and Men’s Health-Related Quality of Life after Five Months. Psychology, Health and Medicine, 7, 5-16.
https://doi.org/10.1080/13548500120101522

[2]   Smith, O.R.F., Kupper, N., Denollet, J. and de Jonge, P. (2011) Vital Exhaustion and Cardiovascular Prognosis in Myocardial Infarction and Heart Failure: Predictive Power of Different Trajectories. Psychological Medicine, 41, 731-738.
https://doi.org/10.1017/S0033291710001133

[3]   Alsén, P., Brink, E. and Persson, L.-O. (2008) Living with Incomprehensible Fatigue after Recent Myocardial Infarction. Journal of Advanced Nursing, 64, 459-468.
https://doi.org/10.1111/j.1365-2648.2008.04776.x

[4]   Fredriksson-Larsson, U., Alsén, P., Karlson B.W. and Brink, E. (2015) Fatigue Two Months after MI and Its Relationships with Other Concurrent Symptoms, Sleep Quality and Coping Strategies. Journal of Clinical Nursing, 24, 2192-2200.
https://doi.org/10.1111/jocn.12876

[5]   Crane, P.B., Abel, W.M. and McCoy, T.P. (2015) Fatigue and Physical Activity after Myocardial Infarction. Biological Research for Nursing, 7, 276-284.
https://doi.org/10.1177/1099800414541961

[6]   Piepoli, M.F., Hoes, A.W., Agewall, S., Albus, C., Brotons, C., Catapano, A.L., et al. (2016) European Guidelines on Cardiovascular Disease Prevention in Clinical Practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice. European Heart Journal, 37, 2315-2381.
https://doi.org/10.1093/eurheartj/ehw106

[7]   Nordlund, F., et al. (2018) Factors Associated with Emotional Distress in Patients with Myocardial Infarction: Results from the SWEDEHEART Registry. European Journal of Preventive Cardiology, 25, 910-920.
https://doi.org/10.1177/2047487318770510

[8]   Ream, E. and Richardson, A. (1996) Fatigue: A Concept Analysis. International Journal of Nursing Studies, 33, 519-529.
https://doi.org/10.1016/0020-7489(96)00004-1

[9]   Jason, L.A., Evans, M., Brown, M. and Porter, N. (2010) What Is Fatigue? Pathological and Nonpathological Fatigue. Physical Medicine and Rehabilitation, 2, 327-331.
https://doi.org/10.1016/j.pmrj.2010.03.028

[10]   Rosentahl, T.C., Majeroni, B.A., Pretorius, R. and Khalid, M. (2008) Fatigue: An Overview. American Family Physician, 78, 1173-1179.

[11]   Whitehead, L. (2009) The Measurement of Fatigue in Chronic Illness: A Systematic Review of Unidimensional and Multidimensional Fatigue Measures. Journal of Pain and Symptom Management, 37, 107-128.
https://doi.org/10.1016/j.jpainsymman.2007.08.019

[12]   Krupp, L.B., LaRocca, N.G., Muir-Nash, J. and Steinberg, A.D. (1989) The Fatigue Severity Scale. Application to Patients with Multiple Sclerosis and Systemic Lupus Erythematosus. Archives of Neurology, 46, 1121-1123.
https://doi.org/10.1001/archneur.1989.00520460115022

[13]   Smets, E., Garssen, B., Cull, A. and De Haes, J. (1996) Application of the Multidimensional Fatigue Inventory (MFI-20) in Cancer Patients Receiving Radiotherapy. British Journal of Cancer, 73, 241-245.
https://doi.org/10.1038/bjc.1996.42

[14]   American Psychiatric Association (APA) (2013) Diagnostic and Statistical Manual of Mental Disorders. 5th Edition, DSM-5, American Psychiatric Association, Washington DC.

[15]   Alsén, P., Brink, E., Brändström, Y., Karlson, B.W. and Persson, L.-O. (2010) Fatigue after Myocardial Infarction: Relationships with Indices of Emotional Distress, and Sociodemographic and Clinical Variables. International Journal of Nursing Practice, 16, 326-334.
https://doi.org/10.1111/j.1440-172X.2010.01848.x

[16]   Sullivan, M.D., LaCroix, A.Z., Russo, J. and Katon, W.J. (1998) Self-Efficacy and Self-Reported Functional Status in Coronary Heart Disease: A Six-Month Prospective Study. Psychosomatic Medicine, 60, 473-478.
https://doi.org/10.1097/00006842-199807000-00014

[17]   Darko, D.F., McCutchan, J.A., Kripke, D.F., Gillin, J.C. and Golshan, S. (2006) Fatigue, Sleep Disturbance, Disability, and Indices of Progression of HIV Infection. The American Journal of Psychiatry, 149, 514-520.
https://doi.org/10.1176/ajp.149.4.514

[18]   Sharpe, M. and Wilks, D. (2002) ABC of Psychological Medicine: Fatigue. British Medical Journal, 325, 480-483.
https://doi.org/10.1136/bmj.325.7362.480

[19]   Kralik, D., Teleford, K., Price, K. and Koch, T. (2005) Women’s Experiences of Fatigue in Chronic Illness. Journal of Advanced Nursing, 52, 372-380.
https://doi.org/10.1111/j.1365-2648.2005.03602.x

[20]   Kluger, B.M., Krupp, L.B., Roger, M. and Enoka, R.M. (2013) Fatigue and Fatigability in Neurologic Illnesses. Proposal for a Unified Taxonomy. Neurology, 80, 409-416.
https://doi.org/10.1212/WNL.0b013e31827f07be

[21]   Dittner, A.J., Wessely, S.C. and Brown, R.G. (2004) The Assessment of Fatigue a Practical Guide for Clinicians and Researchers. Journal of Psychosomatic Research, 56, 157-170.
https://doi.org/10.1016/S0022-3999(03)00371-4

[22]   Appels, A. (2004) Exhaustion and Coronary Heart Disease. Patient Education Counseling, 55, 223-229.
https://doi.org/10.1016/j.pec.2003.09.008

[23]   Piper, B.F., Dibble, S.L., Dodd, M.J., Weiss, M.C., Slaughter, R.E. and Paul, S.M. (1998) The Revised Piper Fatigue Scale: Psychometric Evaluation in Women with Breast Cancer. Oncoly Nursing Forum, 25, 677-684.

[24]   Crane, P.B., Efird, J.T. and Abel, W.M. (2016) Fatigue in Older Adults Postmyocardial Infarction. Frontiers in Public Health, 4, 55.
https://doi.org/10.3389/fpubh.2016.00055

[25]   Liao, S. and Ferrell, B.A. (2000) Fatigue in an Older Population. Journal of the American Geriatrics Society, 48, 426-430.
https://doi.org/10.1111/j.1532-5415.2000.tb04702.x

[26]   Fredriksson-Larsson, U., Brink, E., Alsén, P., Falk, K. and Lundgren-Nilsson, Å. (2015) Psychometric Analysis of the Multidimensional Fatigue Inventory in a Sample of Persons Treated for Myocardial Infarction. Journal of Nursing Measurement, 23, 154-167.
https://doi.org/10.1891/1061-3749.23.1.154

[27]   Fredriksson-Larsson, U., Alsen, P. and Brink, E. (2013) I’ve Lost the Person I Used to Be—Experiences of the Consequences of Fatigue Following Myocardial Infarction. International Journal of Qualitative Studies on Health and Well-Being, 8, Article ID: 20836.

[28]   Fayers, P.M. and Machin, D. (2007) Quality of Life: The Assessment, Analysis and Interpretation of Patient-Reported Outcomes. 2nd Edition, Wiley, Chichester, UK.
https://doi.org/10.1002/9780470024522

[29]   Montgomery, S.A. and Asberg, M. (1979) A New Depression Scale Designed to be Sensitive to Change. The British Journal of Psychiatry, 134, 382-389.
https://doi.org/10.1192/bjp.134.4.382

[30]   Fors, A., Ulin, K., Cliffordson, C., Ekman, I. and Brink, E. (2015) The Cardiac Self-Efficacy Scale, a Useful Tool with Potential to Evaluate Person-Centred Care. European Journal of Cardiovascular Nursing, 14, 536-543.

[31]   Muthén, L.K. and Muthén, B.O. (1998-2012). Mplus User’s Guide. 7th Edition, Muthén & Muthén, Los Angeles, CA.

[32]   Gustafsson, J-E. and Stahl, P.A. (2005) STREAMS User’s Guide. Version 3.0 for Windows. MultivariateWare, Mölndal, Sweden.

[33]   Muthén, B., Kaplan, D. and Hollis, M. (1987) On Structural Equation Modeling with Data That Are Not Missing Completely at Random. Psychometrika, 52, 431-462.
https://doi.org/10.1007/BF02294365

[34]   Allison, P.D. (2003) Missing Data Techniques for Structural Equation Modeling. Journal of Abnormal Psychology, 112, 545-557.
https://doi.org/10.1037/0021-843X.112.4.545

[35]   Schafer, J.L. and Graham, J.W. (2002) Missing Data: Our View of the State of the Art. Psychological Methods, 7, 147-177.
https://doi.org/10.1037/1082-989X.7.2.147

[36]   Brown, T.A. (2006) Confirmatory Factor Analysis for Applied Research. Guilford Press, New York.

[37]   Ekman, I., Swedberg, K., Taft, C., Lindseth, A., Norberg, A., Brink, E., et al. (2011) Person-Centered Care—Ready for Prime Time. European Journal of Cardiovascular Nursing, 10, 248-251.

[38]   Brink, E. and Skott, C. (2013) Caring about Symptoms in Person-Centred Care. Open Journal of Nursing, 3, 563-567.
https://doi.org/10.4236/ojn.2013.38077

[39]   van Geffen, M.E., Ter Hoeve, N., Sunamura, M., Stam, H.J., van Domburg, R.T. and van den Berg-Emons, R.J. (2015) Fatigue During and after Cardiac Rehabilitation. Journal of Rehabilitation Medicine, 47, 569-574.

[40]   Lichtman, J.H., Froelicher, E.S., Blumenthal, J.A., Carney, R.M., Doering, L.V., Frasure-Smith, N., et al. (2014) Depression as a Risk Factor for Poor Prognosis among Patients with Acute Coronary Syndrome: Systematic Review and Recommendations a Scientific Statement from the American Heart Association. Circulation, 129, 1350-1369.
https://doi.org/10.1161/CIR.0000000000000019

[41]   Sher, Y., Lolak, S. and Maldonado, J.R. (2010) The Impact of Depression in Heart Disease. Current Psychiatry Reports, 12, 255-264.
https://doi.org/10.1007/s11920-010-0116-8

[42]   Eldadah, B.A. (2010) Fatigue and Fatigability in Older Adults. PM&R—The Journal of Injury, Function and Rehabilitation, 2, 406-413.
https://doi.org/10.1016/j.pmrj.2010.03.022

 
 
Top