Characterization, Treatment, and Long-term Follow-up of Fatigued Patients in Primary Care

Background:

Fatigue can be defined as extreme and persistent tiredness, weakness, or exhaustion that that could be mental physical, or both. Fatigue is associated with increased healthcare consumption, work disability, and excess mortality, and has been studied extensively under different labels since the 1800's (e.g., neurasthenia, burnout, chronic fatigue syndrome). Studies in primary care populations indicate that about 20-30% of patients report fatigue, with up to 10% of patients presenting with fatigue as their main complaint. Although often considered a disorder-specific characteristic, the etiology and pathogenesis of fatigue are largely unknown and are generally believed to be multifactorial. No biological markers or other objectively measurable factors (such as cognitive impairment) have been found thus far that consistently and sufficiently explain the onset and perpetuation of disorder-specific fatigue. The high prevalence and non-specific nature of fatigue presents a challenge to general practitioners who generally have limited time and resources for assessment and intervention.

Importantly, a potential break-through to how we can understand fatigue has been achieved in recent years, with studies showing that variance in fatigue severity is better explained by transdiagnostic factors (i.e., factors not attributable to a specific medical condition, such as demographic and psychosocial variables and aspects of daily functioning) than by disorder-specific pathophysiology. Further, similar cognitive and behavioral perpetuating mechanisms of fatigue (such as fear avoidance, symptom catastrophizing, self-efficacy, and resting-behavior) have been found across disorders. These findings suggest that a transdiagnostic approach to understanding and treating persistent fatigue might be beneficial for patients and healthcare practitioners, with potentially important implications for treatment.

Treatment of fatigue:

As with other aspects of fatigue, intervention research on fatigue has primarily been conducted in disorder-specific pipelines using disorder-specific protocols. CBT is the most studied treatment, with promising effects for patients with, for example, chronic fatigue syndrome, post-infectious fatigue, and various long-term medical conditions where fatigue is often central (both face-to-face and when delivered via the internet; ICBT). Results from previous RCTs conducted by the investigators have indicated that CBT can be an effective treatment to reduce symptom burden in patients diagnosed with stress-induced exhaustion disorder. Importantly, disorder-specific CBT-protocols for fatigue are largely similar across clinical groups, and the same cognitive and behavioral responses to fatigue have been found to moderate and mediate fatigue severity after CBT across a range of fatigued patient groups.

Even though CBT may hold promise to reduce fatigue severity in different clinical groups, many fatigued patients still do not receive treatment, and not all patients who receive CBT are sufficiently helped. Further research is needed to understand symptom presentation and development as well as treatment moderators, predictors, and mediators of change. Also, there is a significant knowledge gap regarding how fatigue can be identified and treated in an early phase in the primary care context. Given the similarities in effective treatment protocols across fatigued samples, together with potentially common change mechanisms, investigating the effectiveness of a transdiagnostic treatment protocol is a promising avenue with enormous potential utility to increase clinical effects, accessibility, and large-scale implementation. To date, no transdiagnostic treatment specifically targeting fatigue across patient groups has been evaluated.

The current study:

Based on previous disorder-specific treatment protocols aimed to reduce fatigue severity in various fatigued populations, the investigators have developed a transdiagnostic CBT treatment (tCBT) adapted for primary care patients who suffer from persistent fatigue independent of primary diagnosis. The treatment is delivered in a blended format (face-to-face therapy combined with internet-delivered texts and exercises) and is administrated over 4 - 6 months. The feasibility of tCBT has recently been evaluated in a non-randomized feasibility trial (Clinical trials ID: NCT06341751).

Study design:

The planned study is a randomized clinical superiority trial that will recruit fatigued patients listed at primary healthcare clinics in Region Stockholm. Study participants will be randomized to tCBT or to CAU by a person not related to the study. Due to the nature of the study, blinding to treatment condition will not be possible.

Data collection includes clinician-rated data, self-rated symptom measures, and register-based data using interlinked microdata from regional and national registers. Cognitive functioning will be assessed using a digital cognitive test-battery that will be administered at baseline and at the 12-month follow up. Additionally, the study will explore changes in physiological variables (in a subset of participants) from baseline to the 12-month follow-up using continuous data collected from biometric rings.

Research questions:

RQ1. Is tCBT associated with a greater post-treatment reduction in fatigue severity (primary outcome) and secondary outcome domains (self-rated symptoms, cognitive functioning, net days on sick leave) compared to CAU?

RQ2. What characterizes the fatigued sample at baseline in terms of sociodemographic, clinical, and biometric variables? Can clinically relevant subgroups of patients be identified that share similar characteristics?

RQ3. Which factors moderate and mediate the effect of tCBT vs. CAU and which factors predict symptom development? We hypothesize (1) that there will be transdiagnostic subgroups of patients that differ in treatment response based on baseline characteristics as identified in RQ2, that (2) these characteristics can be used to model treatment outcomes using supervised machine learning, and further that (3) changes in cognitive and behavioral responses to fatigue (e.g., fear avoidance, catastrophizing about symptoms, all-or-nothing behavior) as well as sleep- and physical activity patterns will mediate the effect of tCBT vs. CAU.

RQ4. Are there differences between participants who received tCBT vs. CAU at the 12- and 60-month follow-up and do individual characteristics moderate these differences?

RQ5. Is tCBT vs. CAU a cost-effective treatment at the 12 month follow-up? Indirect (e.g., work absenteeism, sick leave) and direct (e.g., healthcare consumption) costs will be evaluated using data from national registers.

RQ6. Which healthcare interventions are provided for participants who are randomized to CAU, and do sociodemographic, geographic, and clinical factors predict the type and extent of treatment delivered?

Recruitment procedure:

This study will recruit participants directly from primary care clinics in Region Stockholm. Hence, no advertisement in newspapers or in social media will be conducted to target potential study participants. All information about the study, aimed at both study participants and primary care staff, will be made available on a study webpage.

Patients can be referred to the study by their primary care physician if they (a) report at least three months of persistent, functionally disabling fatigue as a central symptom and (b) the general practitioner (GP) has assessed that the fatigue is not a direct effect of an active disease process motivating another treatment (e.g., hypo-/hyperthyroidism, anemia, cancer, dementia, sleep apnea, post-traumatic stress disorder) or a side-effect of medication. See below ("Eligibility") for inklusion/exclusion criteria.

Estimated sample size and power:

When the trial was initially planned, a preliminary power-analysis was conducted based on research investigating the effect of transdiagnostic CBT vs. active control conditions and our assessment of the smallest clinically meaningful difference. With 90% power to detect a standardized effect size of d = 0.25 on the primary outcome fatigue severity, given an alpha of .05, an intraclass correlation between measurements of 0.7, and an expected attrition of 20%, we needed to include 250 participants in each arm (a total sample size of N = 500).

A later Monte Carlo simulation was conducted based on more relevant previous literature and data from the feasibility trial of the current study (see file:///C:/Users/50QZ/Downloads/power_analysis_report.html). This analysis recommended a sample size of 95 participants per group (190 total), ensuring a 90% power to detect an effect size of Cohen's d=0.6 on the primary outcome.

Statistical methods:

Analyses will be based on an intention-to-treat approach. Change in the primary outcome measure will be analyzed using mixed effects linear regression. Change from baseline to treatment completion (6 months post baseline) will be the primary endpoint. Fixed predictors in these analyses will be time, group and their interaction effect while taking individual variation in baseline symptom levels and change over time into account (i.e., random intercept and/or slope).