Abstract
A randomized controlled trial (RCT) is the gold standard for evaluating the efficacy and effectiveness of an intervention. However, primary trial analyses and conclusions generally consider the entire study sample, often disregarding differences between subgroups and participants who did or did not benefit from the intervention. Understanding which patients may benefit from treatment can be helpful for clinicians when selecting patients who are more likely to be responsive to an intervention.
Energy conservation management (ECM) is a treatment commonly used by occupational therapy practitioners for people with multiple sclerosis (MS). ECM involves coaching people to identify and develop modifications to their activities to reduce the impact of fatigue on daily life. This goal is achieved by a systematic analysis of daily work, home, and leisure activities, with the ultimate goal of greater activity and better participation (Multiple Sclerosis Council for Clinical Practice Guidelines, 1998). We recently conducted an RCT that examined the effectiveness of ECM in people with severe MS-related fatigue. In this RCT, we included an information-only control condition (MS nurse consultations; Blikman et al., 2017). We found no notable difference between ECM and MS nurse consultations in terms of effects on fatigue and participation—that is, we found no added value of ECM compared with MS nurse consultations. However, within-group analyses showed a considerable decrease in fatigue in the ECM group (at 16, 26, and 52 wk). This finding suggests that a considerable proportion of participants in this group responded positively to ECM. Similar results were found in the control group.
Before recommending the ECM intervention to people with MS-related fatigue, clinicians should distinguish which factors contribute to the effect of treatment. From previous research (Finlayson et al., 2012; Holberg & Finlayson, 2007), it is known that demographic, disease-related, cognitive, and behavioral factors moderate the effects of fatigue interventions. For example, the effects of ECM on fatigue as measured with the Fatigue Impact Scale (Fisk et al., 1994) were moderated by age and gender but not by physical impairment or employment status. The greatest benefit was experienced by younger participants and women (Finlayson et al., 2012).
A more qualitative study (Holberg & Finlayson, 2007) elucidated factors that influenced the use of energy conservation strategies, with the results indicating that themes such as experience with the disease (i.e., progression, level of disability, and fatigue experience), sense of self, and environmental factors (i.e., physical surroundings and social support) influence the use of energy conservation strategies. In particular, the progressiveness of the disease, the effect of fatigue on everyday life, and strong social support enhanced the use of many strategies, whereas variability of the disease, struggles with sense of self, and rigid environments with limited social support all hindered strategy use (Holberg & Finlayson, 2007).
The aim of the current study was to investigate whether demographic, disease-related, and personal determinants influenced the effect of the ECM intervention on fatigue in people with MS. We also included a comparison with the determinants in the control group to define the distinctive determinants for ECM.
Method
Participants
This study was a secondary analysis of data from the RCT that investigated the effect of ECM compared with a control group (MS nurse consultations; Blikman et al., 2017). The RCT was part of a multicenter research program: Treating Fatigue in Multiple Sclerosis with Aerobic Training, Cognitive Behavioral Therapy, and Energy Conservation Management (TREFAMS–ACE; Beckerman et al., 2013). The protocol for this study was approved by the Medical Ethics Committee of the VU University Medical Center (Amsterdam, the Netherlands). Details of the TREFAMS–ACE study have been described in Beckerman et al.’s (2013) article.
The following inclusion criteria were used for this research program: definite diagnosis of MS; severe fatigue as indicated by a score of ≥35 on the fatigue domain of the Checklist Individual Strength (CIS20r; Vercoulen et al., 1996); ambulatory status (i.e., Expanded Disability Status Scale [Kurtzke, 1983] score ≤ 6.0); no diagnosis of depression (i.e., Hospital Anxiety and Depression Scale [Zigmond & Snaith, 1983] score < 11); no initiation of or change in pharmacological or nonpharmacological treatment of fatigue; no evident signs of an MS exacerbation or a corticosteroid treatment during the previous 3 mo; no infections, anemia, or thyroid dysfunction; and ages 18–70 yr. The participants were randomly assigned to the ECM group or to the control group (Blikman et al., 2017). All participants provided written informed consent.
Study Groups
Energy Conservation Management Group.
The individual ECM intervention protocol was based on the group program developed by Packer et al. (1995). For the purposes of this study, the original content of the ECM group program was adapted to fit 12 one-on-one 45-min sessions by an occupational therapist over the 4-mo intervention period. Measurements took place at baseline, at 8 wk (T8), and at 16 wk (T16) after the start of treatment, thus during and directly after treatment. For the long-term follow-up, measurements were performed at 26 wk (T26) and at 52 wk (T52).
Information-Only Control Group.
The protocol for the control group consisted of three 45-min consultations with a nurse specialized in MS care, given over a period of 4 mo. The nurses were trained to refrain from providing treatment or treatment advice. Instead, standardized information about MS-related fatigue was provided. The control group intervention was intended as a control for the effects of attention and information about fatigue. For an extensive overview of the content of the ECM and control group interventions, we refer readers to the study design article (Beckerman et al., 2013) and the RCT article (Blikman et al., 2017).
Outcomes and Determinants
The primary outcomes of the original RCT study were fatigue, measured by the fatigue domain of the CIS20r (Vercoulen et al., 1996), and participation, measured by the Impact on Participation and Autonomy Questionnaire (Kersten et al., 2007). For the purposes of this study, we focused on the change in fatigue as measured with the CIS20r.
Responders and nonresponders were categorized by their CIS20r fatigue change score between baseline and T16. The measurement at T16 was chosen because it directly followed the end of the intervention. A responder was a participant with a clinically relevant fatigue change score during the intervention (CIS20r fatigue baseline minus CIS20r fatigue at T16), defined as a change of ≥8 points on the fatigue domain of the CIS20r (Beckerman et al., 2013).
Baseline scores (i.e., before intervention) were used for the determinants. On the basis of previous literature (Finlayson et al., 2012; Holberg & Finlayson, 2007) and clinical experience, and taking into account the sample size, we selected demographic, disease-related, and personal factors.
Demographic and Disease-Related Factors.
Demographic factors were age (years) and gender (male or female). Disease-related factors were years with definite MS and earlier experience with ECM intervention (yes or no).
Personal Factors.
Fatigue severity was measured with the CIS20r, which addresses four dimensions of fatigue: subjective experience of fatigue, reduction in motivation, reduction in physical activity, and reduction in concentration. We only used the subjective experience of fatigue (eight items), which focuses on the experienced severity of fatigue in the past 2 wk. The CIS20r has been validated for people with MS (Rietberg et al., 2010), and higher scores reflect greater fatigue.
Perception of fatigue was measured with an adaptation of the Brief Illness Perception Questionnaire (BIPQ; Knoop et al., 2012). The adaptation involves replacing the word illness with fatigue to allow measurement of beliefs about MS-related fatigue instead of beliefs about the illness MS. Two items were excluded because they were not relevant to fatigue perceptions (Knoop et al., 2012). The remaining seven questions of the modified BIPQ were rated on a scale ranging from 0 (not at all) to 10 (extremely); these questions were used to measure a patient’s cognitive and emotional representation of his or her fatigue, including consequences, timeline (how long the fatigue lasts), personal control, treatment control, illness coherence, concern, and emotional responses. The BIPQ has been shown to be a valid and reliable measure of MS (Dennison et al., 2010; Wilski & Tasiemski, 2016). Higher scores represent a more negative perception of fatigue, and lower scores represent a more positive view.
Self-efficacy was assessed with the Dutch General Self-Efficacy Scale (Schwarzer & Jerusalem, 1995). This scale measures the patient’s belief in his or her ability to perform difficult tasks in various domains of functioning in terms of appropriate action. The questionnaire has been reported to be valid (Luszczynska et al., 2005). Higher scores reflect higher perceived self-efficacy.
Illness cognitions about MS were measured with the Illness Cognitions Questionnaire (ICQ; Evers et al., 2001), which has been considered valid and reliable. This questionnaire has three subscales: Helplessness (focusing on the negative consequences of the disease and generalizing them to functioning in daily life), Acceptance (acknowledging being chronically ill and perceiving the ability to manage the negative consequences of the disease), and Disease Benefits (experiencing positive, long-term consequences of the disease). The subscales focus on the experience that people attribute to their disease. Higher scores represent more helplessness, acceptance, and disease benefits.
Social support was measured with Social Support List (Bridges et al., 2002; Van Sonderen, 1993) interactions and discrepancies sum scores. The Social Support List has been a reliable and valid instrument in Dutch. For interactions, that is, the extent to which people are supported, a high score means receiving much support. For discrepancies, that is, the extent to which the received support fulfills the specific needs of the participant, a high score indicates that the participant perceives a serious shortage in social support.
Possible mood disorders with respect to anxiety were measured as a part of the Hospital Anxiety and Depression Scale (Zigmond & Snaith, 1983), for which internal consistency and test–retest reliability have been considered good. Higher scores indicate more anxiety.
Coping styles (task oriented, emotion oriented, and avoidance) were measured with the 21-item Coping Inventory for Stressful Situations (Cohan et al., 2006), which has been shown to be valid and reliable in Dutch patients with MS (Fournier et al., 1999). Higher scores reflect more use of a coping style.
Statistical Analyses
Using univariable and multivariable logistic regression analyses, we investigated whether the baseline determinants in the ECM group were associated with the probability of being a responder. Initially, each determinant was investigated in separate univariable models. For the multivariable analyses, a backward selection procedure was used, starting with the variables with a p < .20 in the univariable analysis. Because of the relatively low sample size (p < .10), we used the multivariable model to keep variables in the model. The results are expressed as odds ratios (ORs) with 90% confidence intervals (CIs). We performed the same analyses for the information-only control group data.
We additionally investigated whether the influence of ECM group determinants differed from that of the control group determinants. Therefore, for the significant ECM determinants, we determined whether the point estimate, that is, the OR of the determinant in the univariable model of the control group, fell within the 90% CI range of the multivariable OR of the ECM group. If the ORs of the control group fell outside this range, then the determinant was distinctive for ECM. IBM SPSS Statistics (Version 22; IBM Corp., Armonk, NY) was used for statistical analysis.
Results
Eligible participants in the RCT study were recruited from November 2011 to March 2014. In total, 86 participants were included in the RCT. Seventeen participants were excluded from analysis: 10 dropped out after baseline measurement, 3 dropped out after the second measurement (T8), 2 missed the third measurement (T16), and 2 did not comply with treatment. Of the 69 remaining participants, 34 were assigned to the ECM group, and 35 were assigned to the control group, of whom 14 were classified as responders in both the ECM and the control groups. Table 1 shows the baseline personal and clinical characteristics of the ECM and control groups.
Participant Characteristics
Note. Unless otherwise indicated, values indicate means (standard deviations) for the baseline scores of the variables except for the fatigue change score. CISS–21 = 21-item Coping Inventory for Stressful Situations; CIS20r = Checklist Individual Strength; ECM = energy conservation management; EDSS = Expanded Disability Status Scale; GSES = General Self-Efficacy Scale; HADS = Hospital Anxiety and Depression Scale; ICQ = Illness Cognitions Questionnaire; IQR = interquartile range; M = mean; mBIPQ = modified Brief Illness Perception Questionnaire; MS = multiple sclerosis; PP = primary progressive; RR = relapsing remitting; SD = standard deviation; SP = secondary progressive; SSL = Social Support List.
The responders in the two study groups differed.
Multivariable logistic regression analysis of the ECM group revealed that fatigue severity, perception of fatigue, ICQ disease benefits, and social support discrepancies were related to the probability of being a responder to ECM (Table 2). These results indicate that people with MS-related fatigue are more likely to show a positive response when, at baseline, they are more severely fatigued, have a less negative perception of fatigue, experience fewer disease benefits, and perceive a higher discrepancy in social support. The ORs for perception of fatigue (1.01) and disease benefits (1.25) from the univariable analysis of the control group fell outside the 90% CI of these outcomes in the multivariable analysis of the ECM group (see Table 2), which indicates that only the influence of these outcomes differed significantly between the ECM and control groups.
Determinants Explaining the Probability of a Fatigue Response to the ECM or Control Groups
Note. Outcome variables refer to responders or nonresponders. A p = .20 is used as the α for the univariable analysis, and p = .10 is used as the α level for the multivariable analysis. CI = confidence interval; CISS–21 = 21-item Coping Inventory for Stressful Situations; CIS20r = Checklist Individual Strength; ECM = energy conservation management; GSES = General Self-Efficacy Scale; HADS = Hospital Anxiety and Depression Scale; ICQ = Illness Cognitions Questionnaire; mBIPQ = modified Brief Illness Perception Questionnaire; MS = multiple sclerosis; NA = not applicable; OR = odds ratio; SSL = Social Support List.
Indicates variables with a p < .20 in the univariable analysis that were selected for multivariable analysis.
Discussion
Investigation of the efficacy of ECM in people with severe MS-related fatigue in an RCT setting found no notable differences in effects on fatigue between the intervention (ECM) and the information-only control (nurse consultations; Blikman et al., 2017) groups. However, in the course of that study, we noticed that some participants showed a meaningful response to ECM that corresponded to a clinically relevant decrease in fatigue. This finding indicates that certain people respond to ECM, whereas others show little or no response. Occupational therapy practitioners in rehabilitation practice should know who will or will not benefit from ECM. Therefore, the aim of this study was to identify at baseline the demographic, disease-related, and personal determinants that define a clinically relevant response to the ECM intervention.
The four baseline determinants of a positive response to ECM in people with MS-related fatigue were being more severely fatigued, having a less negative perception of fatigue, experiencing fewer disease benefits, and perceiving a discrepancy in social support. When we compared the ECM group with the control group, two determinants distinguished the ECM group: perception of fatigue as measured with the modified BIPQ and disease benefits as measured with the ICQ.
Our results show that people who were more fatigued at baseline improved more than those who were less fatigued. Possible explanations for this finding are that people who are more severely fatigued have a larger potential range of improvement, might be more open to fatigue management advice, and more urgently feel the necessity of implementing energy management strategies to relieve fatigue.
Similar results were found for a perceived social support discrepancy and disease benefits: People with poorer baseline scores, that is, those who perceived less social support and fewer disease benefits, responded better to ECM. Topics concerning social support and disease benefits are part of the strategies that are advised and practiced within ECM treatment. For example, disease benefit components are reflected in strategies on life priorities and personal goals, positive personality changes, and strengthened personal relationships. Similarly, ECM strategies concerning social support are reflected in delegating activities, communicating a need for help, and planning. Therefore, the effect of ECM on fatigue might be mediated by changes in perceptions of social support and disease benefits during ECM treatment.
Consequently, poorer scores on disease benefits and a social support discrepancy at baseline might have increased the implementation of energy management strategies during ECM and had larger effects on the discrepancy in social support and disease benefits, which subsequently reduced the levels of fatigue. Our results on social support discrepancy contrast with those of Holberg and Finlayson (2007). In their qualitative study, Holberg and Finlayson examined the determinants (“themes”) influencing the implementation and continued use of energy conservation strategies. They found that higher levels of social support were related to enhanced use of ECM strategies. However, we should note that a one-to-one comparison of the studies is difficult because these authors focused not on fatigue but on the implementation of energy management strategies, and they made no use of questionnaires such as the Social Support List.
In our study, the results for the perception of fatigue determinant differed from other determinants in that better scores from a clinical perspective (i.e., a less negative perception of fatigue) resulted in a greater probability of being an ECM responder. This finding corresponds with results from numerous cognitive–behavioral therapy (CBT) studies. For example, Knoop et al. (2012) studied variables mediating the effect of CBT on MS fatigue, and they concluded that a change in the negative perception of fatigue plays a crucial role in the reduction of fatigue in MS after CBT. More positive views of fatigue (i.e., perceiving it as more controllable, as something one can understand, as time limited, and as having less serious concrete and emotional consequences) were closely related to a reduction in the severity of fatigue. The CBT trial of the TREFAMS program also showed that improved fatigue perception mediated the effect of CBT on MS-related fatigue (van den Akker et al., 2018).
Not all determinants were important. For example, no effects were found for the component of the Social Support List that focuses on social interactions. We also found no effects of determinants that were related to the “sense of self” theme of Holberg and Finlayson (2007), such as illness cognitions, self-efficacy, and coping. Besides the qualitative study by Holberg and Finlayson, the only other study to focus on the determinants of the ECM effects on fatigue was reported by Finlayson et al. (2012). These investigators found a moderating effect of age and gender, that is, younger participants and women experienced greater benefits. By contrast, in our study, these factors were not significant, which might be due to a lower mean age and a smaller age range, thus reducing the power to find a moderating effect. A power issue might also be the explanation for gender not being a determinant in our study: Only 1 male participant was included in the responder group.
One of the main limitations of our study was the sample size. Power calculations in the original RCT (Beckerman et al., 2013) were based on detection of the overall effect of ECM and not on subgroup analysis (Brookes et al., 2004). Moreover, a clinically important interaction effect is difficult to determine. Therefore, the focus was on differential effects rather than statistical significance (Brookes et al., 2001). Because of the specific (individualized) format of our ECM intervention and the specific (ambulant) participants included, another limitation is the generalizability of our results. Finally, because of sample size and to minimize the chance of unintended findings, we focused on only a selection of all possible determinants. Although the determinants in this study were carefully selected on the basis of previous literature and clinical experience, there is no consensus on which determinants are the most important. For all these reasons, the results of this study should be interpreted cautiously, and apparent effects should be further explored. Future researchers should study the effects in a larger sample and in various intervention formats (group, individual, e-learning); they should also include more specific measures on MS, ECM, and fatigue.
Implications for Occupational Therapy Practice
The results of this study have the following implications for occupational therapy practice:
Four determinants could potentially identify responders to ECM: fatigue severity, fatigue perception, disease benefits, and social discrepancies.
The results suggest that being more severely fatigued, having a less negative perception of fatigue, perceiving fewer disease benefits, and perceiving a higher discrepancy in social support increases the probability of being a responder to ECM.
Conclusion
The individual ECM intervention protocol given by occupational therapists was based on the group program developed by Packer et al. (1995). For the purposes of our study, the original content of the ECM group program was adapted to fit 12 one-on-one 45-min sessions by an occupational therapist over a 4-mo intervention period. Details on our ECM treatment have been published in the RCT (Blikman et al., 2017). In the RCT, positive within-group effects were found. However, it was not clear which patients derived the greatest benefit from ECM treatment. To determine which people with MS-related fatigue benefited the most from the ECM treatment, we defined who were classified as responders directly after treatment. At that time, they had implemented the energy conservation strategies. We found no long-term effects in the RCT; therefore, to identify responders, more research is needed. If patients respond to treatment, it will become a challenge to maintain effects; however, this aspect was outside the scope of our research. Instead, we investigated whether demographic, disease-related, and personal baseline determinants predicted a positive response to ECM directly after treatment.
Footnotes
Acknowledgments
This study was performed on behalf of the Treating Fatigue in Multiple Sclerosis with Aerobic Training, Cognitive Behavioral Therapy, and Energy Conservation Management (TREFAMS–ACE) Study Group and was financially supported by Fonds NutsOhra Grant 89000005. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors certify that no party having a direct interest in the results of the research supporting this article has or will confer a benefit on them or on any organization with which they are associated. This article is registered with the ISRCTN registry (ISRCTN82353628; 
). The TREFAMS–ACE Study Group includes the following members: V. de Groot (program coordination), H. Beckerman (program coordination), A. Malekzadeh, L. E. van den Akker, M. Looijmans (to September 2013), S. A. Sanches (to February 2012), J. Dekker, E. H. Collette, B. W. van Oosten, C. E. Teunissen, M. A. Blankenstein, I. C. J. M. Eijssen, and M. Rietberg (VU University Medical Center, Amsterdam, the Netherlands); M. Heine, O. Verschuren, G. Kwakkel, J. M. A. Visser-Meily, I. G. L. van de Port (to February 2012), and E. Lindeman (to September 2012; Center of Excellence for Rehabilitation Medicine, University Medical Centre Utrecht, and Rehabilitation Centre, De Hoogstraat, Utrecht, the Netherlands); L. J. M. Blikman, J. van Meeteren, J. B. J. Bussmann, H. J. Stam, and R. Q. Hintzen (Erasmus MC University Medical Center, Rotterdam, the Netherlands); H. G. A. Hacking, E. L. Hoogervorst, and S. T. F. M. Frequin (St. Antonius Hospital, Nieuwegein, the Netherlands); H. Knoop, B. A. de Jong (to January 2014), and G. Bleijenberg (to April 2012; University Medical Center, St. Radboud, Nijmegen, the Netherlands); F. A. J. de Laat (Libra Rehabilitation Medicine and Audiology, Tilburg, the Netherlands); M. C. Verhulsdonck (Rehabilitation Center, Sint Maartenskliniek, Nijmegen, the Netherlands); E. T. H. L. van Munster (Jeroen Bosch Hospital, Den Bosch, the Netherlands); and C. J. Oosterwijk and G. J. Aarts (to March 2013; Dutch Patient Organization, Multiple Sclerosis Vereniging Nederland, The Hague, the Netherlands).