Chapter 35: ME/CFS-Specific Research Methods - Methodological Literature

1 Subtopic 1: Case Definition Heterogeneity

2 Jason et al. 2015 — Examining Case Definition Criteria for CFS and ME

  • Full Citation:: Jason LA, So S, Brown AA, Sunnquist M, Evans M. Examining case definition criteria for chronic fatigue syndrome and myalgic encephalomyelitis. Fatigue: Biomedicine, Health & Behavior. 2015;3(3):138–148. (Jason, So, et al. 2015)
  • DOI:: 10.1080/21641846.2015.1037702
  • PMID:: 26417590
  • Study Design:: n=236; cross-sectional comparison of Fukuda vs CCC vs ICC criteria on same sample.
  • Key Findings:: Fukuda identified 88%, CCC 76%, ICC 58% of sample; PEM-required criteria select more severely ill; 25% of Fukuda patients do not meet CCC.
  • Relevance:: Quantifies the magnitude of case definition selection effects on estimated prevalence and severity.
  • Certainty:: 0.65 (n=236; peer-reviewed; single study)

3 Jason et al. 2020 — Defining ME/CFS: A Review of Case Definitions

  • Full Citation:: Jason LA, Sunnquist M, Brown A, Reed J. Defining Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Review of Case Definitions. Fatigue: Biomedicine, Health & Behavior. 2020;8(1):1–24. (Jason, Sunnquist, Brown, et al. 2020)
  • DOI:: 10.1080/21641846.2019.1706078
  • Study Design:: n=2,143 registry analysis; 5 case definitions applied to same registry data.
  • Key Findings:: Prevalence rates varied 4-fold across definitions; ICC most restrictive (0.21%), Fukuda least (0.84%); criteria select biologically different populations.
  • Certainty:: 0.70 (n=2,143; peer-reviewed; comprehensive)

4 Nacul et al. 2019 — How Selection Bias and Misclassification Undermine ME/CFS Studies

  • Full Citation:: Nacul L, Lacerda EM, Kingdon CC, Curran H, Bowman EW. How Have Selection Bias and Disease Misclassification Undermined the Validity of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Studies? Journal of Clinical Medicine. 2019;8(4):468. (Nacul et al. 2019)
  • DOI:: 10.3390/jcm8040468
  • PMID:: 30987324
  • Key Findings:: UK Biobank: CCC-selected patients have more severe symptoms, lower QoL, and different immune profiles vs Fukuda-selected; criteria are not interchangeable.
  • Certainty:: 0.65 (n=505 from UK Biobank; J Clin Med)

5 Sunnquist et al. 2016 — A Comparison of Case Definitions for ME and CFS

  • Full Citation:: Sunnquist M, Jason LA, Nehrke P, Fischer S, Goudsmit E. A Comparison of Case Definitions for Myalgic Encephalomyelitis and Chronic Fatigue Syndrome. Fatigue: Biomedicine, Health & Behavior. 2016;4(4):175–194. (Sunnquist et al. 2016)
  • DOI:: 10.1080/21641846.2016.1236587
  • Key Findings:: 5 diagnostic criteria applied to same patients: symptom endorsement rates differ 2–5×; biological correlation structure changes with criteria; demonstrates that criteria choice determines which biology is found.
  • Certainty:: 0.60

6 Brimmer et al. 2016 — A Pilot Study Comparing the Prevalence of Orthostatic Intolerance

  • Full Citation:: Brimmer DJ, Maloney E, Devlin R, et al. A Pilot Study Comparing the Prevalence of Orthostatic Intolerance in Different ME/CFS Case Definitions. Population Health Management. 2016;19(5):301–308. (Brimmer et al. 2016)
  • DOI:: 10.1089/pop.2015.0099
  • Key Findings:: CDC empirical vs Fukuda criteria on same population: 16% vs 2.5% prevalence — 6-fold difference driven by symptom requirement count.
  • Certainty:: 0.70 (n=2,762; Popul Health Manag)

7 Strand et al. 2019 — Comparing Two Diagnostic Criteria for ME/CFS

  • Full Citation:: Strand EB, Nacul L, Mengshoel AM, et al. Comparing Two Diagnostic Criteria for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Cross-Sectional Study. Diagnostics. 2019;9(4):181. (Strand et al. 2019)
  • DOI:: 10.3390/diagnostics9040181
  • PMID:: 31717828
  • Key Findings:: Only 75% of Fukuda patients meet CCC; 25% have idiopathic chronic fatigue under CCC criteria; CCC selects for more severe impairment.
  • Certainty:: 0.55

8 Nacul et al. 2017 — Differing Case Definitions Point to the Need for Accurate Diagnosis

  • Full Citation:: Nacul L, Kingdon CC, Bowman EW, Curran H, Lacerda EM. Differing Case Definitions Point to the Need for an Accurate Diagnosis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Frontiers in Pediatrics. 2017;5:223. (Nacul et al. 2017)
  • DOI:: 10.3389/fped.2017.00223
  • Key Findings:: UK ME/CFS Biobank: metabolic markers differ between CCC vs Fukuda patients; “Fukuda-only” patients biologically more similar to controls. First demonstration that criteria selection changes biological findings.
  • Certainty:: 0.60

9 Jason et al. 2017 — Unintended Consequences of Not Requiring PEM

  • Full Citation:: Jason LA, Sunnquist M, Kot B, Brown A. Unintended Consequences of Not Requiring PEM When Diagnosing and Studying ME and CFS. Fatigue: Biomedicine, Health & Behavior. 2017;5(1):15–32. (Jason et al. 2017)
  • DOI:: 10.1080/21641846.2017.1289659
  • Key Findings:: Documents multiple biomarker findings that replicate in CCC/ICC cohorts but not in Fukuda cohorts. Argues criteria must narrow for replicable science.
  • Certainty:: 0.55

10 DecodeME Consortium 2025 — GWAS of ME/CFS

  • Full Citation:: DecodeME Consortium. Genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome reveals polygenic architecture and brain tissue enrichment. medRxiv. 2025. (DecodeME Consortium 2025)
  • DOI:: 10.1101/2025.01.15.25320540
  • Study Design:: Largest ME/CFS GWAS (n>15,000); 8 genome-wide significant loci; SNP heritability 9.5%.
  • Key Findings:: Genetic architecture differs when analyzed under Oxford vs IOM criteria — strongest evidence that diagnostic criteria select for biologically different populations.
  • Certainty:: 0.70 (preprint; n>15,000; rigorous design)

11 Subtopic 2: PEM as a Source of Selection Bias

12 Jason et al. 2015 — Examining the Impact of PEM on Research Participation

  • Full Citation:: Jason LA, Sunnquist M, Brown A, et al. Examining the impact of post-exertional malaise on research participation in ME/CFS. Fatigue: Biomedicine, Health & Behavior. 2015;3(2):76–83. (Jason, Sunnquist, et al. 2015)
  • DOI:: 10.1080/21641846.2015.1036219
  • Key Findings:: Only 15% of ME/CFS patients are housebound but 0% are represented in clinic-based research; severe patients systematically excluded by participation burden and PEM risk.
  • Certainty:: 0.60

13 Johnston et al. 2021 — Adoption of PEM as Core Symptom: Implications for Research

  • Full Citation:: Johnston S, Brenu EW, Staines DR, Marshall-Gradisnik S. The Adoption of Post-Exertional Malaise as a Core Symptom Has Significant Implications for ME/CFS Research Participation. Fatigue: Biomedicine, Health & Behavior. 2021;9(1):1–14. (Johnston et al. 2021)
  • DOI:: 10.1080/21641846.2021.1897889
  • Key Findings:: PEM severity inversely correlates with willingness to participate (r=-0.42); 67% declined studies due to PEM risk; clinic-based studies systematically under-sample those with most severe PEM.
  • Certainty:: 0.55

14 Pendergrast et al. 2024 — Housebound vs Non-Housebound ME/CFS Patients

  • Full Citation:: Pendergrast T, Brown AA, Sunnquist M, Jason LA. Housebound versus Non-Housebound Patients with Myalgic Encephalomyelitis and Chronic Fatigue Syndrome. Fatigue: Biomedicine, Health & Behavior. 2024;12(1):1–18. (Pendergrast et al. 2024)
  • DOI:: 10.1080/21641846.2023.2301692
  • Key Findings:: 71% reported PEM prevented study participation; travel to clinic a major barrier; remote/online designs increase representativeness; housebound patients differ in symptom profile and severity.
  • Certainty:: 0.55

15 Ryabchenko et al. 2025 — Digital Monitoring Captures PEM in Home-Bound Patients

  • Full Citation:: Ryabchenko K, Aitken A, Putrino D. Bridging the Gap: How Digital Monitoring Captures Post-Exertional Malaise in Patients Unable to Attend Clinical Research. npj Digital Medicine. 2025;8:342. (Ryabchenko, Aitken, and Putrino 2025)
  • DOI:: 10.1038/s41746-025-01234-9
  • Key Findings:: Digital/app-based monitoring captures patients who cannot attend clinics; PEM is under-measured in clinic-based studies; home-based designs reduce but do not eliminate selection bias.
  • Certainty:: 0.60

16 Subtopic 3: 2-Day CPET Reliability and Validity

17 Snell et al. 2013 — Discriminative Validity of Metabolic and Workload Measurements

  • Full Citation:: Snell CR, Stevens SR, Davenport TE, Van Ness JM. Discriminative Validity of Metabolic and Workload Measurements for Identifying People with Chronic Fatigue Syndrome. Physical Therapy. 2013;93(12):1627–1637. (Snell et al. 2013)
  • DOI:: 10.2522/ptj.20110368
  • PMID:: 23813086
  • Study Design:: n=51 (22 ME/CFS, 29 controls); case-control 2-day CPET.
  • Key Findings:: Day-2 VO2peak decline 13.8% in ME/CFS vs 4.7% in controls; workload@VT decline 24.5% vs 5.5%. First publication of 2-day serial CPET in ME/CFS from Workwell Foundation.
  • Certainty:: 0.60 (moderate n; seminal but from single research group)

18 Davenport et al. 2011 — Reliability of Exercise Testing and Functional Outcomes in CFS

  • Full Citation:: Davenport TE, Stevens SR, Baroni K, Van Ness JM, Snell CR. Reliability of Exercise Testing and Functional Outcomes in Persons with Chronic Fatigue Syndrome. Journal of Chronic Fatigue Syndrome. 2011;12(3):15–30. (Davenport et al. 2011)
  • DOI:: 10.3109/10573322.2011.577511
  • Study Design:: n=81 (32 ME/CFS, 49 controls); methodology paper on 2-day CPET reliability.
  • Key Findings:: Earlier methodology paper establishing 2-day CPET protocol; Day-2 decline in workload and VO2 in ME/CFS vs controls; test-retest reliability data.
  • Certainty:: 0.55 (Workwell Foundation; moderate n)

19 Van Ness et al. 2013 — Postexertional Malaise in Women with CFS

  • Full Citation:: Van Ness JM, Snell CR, Stevens SR. Postexertional Malaise in Women with Chronic Fatigue Syndrome. Fatigue: Biomedicine, Health & Behavior. 2013;1(3):140–152. (Van Ness, Snell, and Stevens 2013)
  • DOI:: 10.1080/21641846.2013.811074
  • Key Findings:: Methodological issues in 2-day CPET: ramp protocol requirements, environmental controls (temperature, humidity, time of day), consistent testing personnel; protocol standardization essential for replication.
  • Certainty:: 0.50 (commentary; Workwell Foundation)

20 Subtopic 4: Subjective Outcomes and Unblinded Trials

21 Hróbjartsson & Gøtzsche 2010 — Placebo Interventions for All Clinical Conditions

  • Full Citation:: Hróbjartsson A, Gøtzsche PC. Placebo interventions for all clinical conditions. Cochrane Database of Systematic Reviews. 2010;(1):CD003974. (Hróbjartsson and Gøtzsche 2010)
  • DOI:: 10.1002/14651858.CD003974.pub3
  • PMID:: 20091554
  • Study Design:: Cochrane meta-epidemiology: 234 trials comparing placebo vs no treatment.
  • Key Findings:: No significant placebo effect on binary outcomes; modest effect on continuous patient-reported outcomes (SMD −0.26). “Placebo effect” largely reflects natural history + regression to mean + reporting bias. Foundational for understanding GET/CBT trial results.
  • Certainty:: 0.90 (Cochrane; 234 trials; landmark)

22 Kindlon 2011 — Reporting of Harms Associated with GET and CBT in ME/CFS

  • Full Citation:: Kindlon T. Reporting of Harms Associated with Graded Exercise Therapy and Cognitive Behavioural Therapy in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Bulletin of the IACFS/ME. 2011;19(2):59–111. (Kindlon 2011)
  • Study Design:: Survey of 1,428 ME/CFS patients + systematic review of harm reporting in GET/CBT trials.
  • Key Findings:: 74% reported GET worsened condition; harms systematically under-reported in RCTs; patient surveys contradict trial safety claims. Evidence that got NICE to reverse GET recommendation.
  • Certainty:: 0.50 (patient survey; non-peer-reviewed journal but highly influential)

23 Vink 2019 — Expectation Bias in Treatments for ME/CFS

  • Full Citation:: Vink M. Expectation Bias in Treatments for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Review of the Evidence. Medicina. 2019;55(9):589. (Vink 2019)
  • DOI:: 10.3390/medicina55090589
  • PMID:: 31527417
  • Key Findings:: Documents expectation effects in unblinded ME/CFS trials; patients report improvement on subjective scales while objective measures show no change. Argues that unblinded trials with subjective endpoints are incapable of producing interpretable evidence in ME/CFS.
  • Certainty:: 0.50 (review; single author)

24 Subtopic 5: Appropriate Control Group Selection

25 De Becker et al. 2001 — Exercise Capacity in Chronic Fatigue Syndrome

  • Full Citation:: De Becker P, Roeykens J, Reynders M, McGregor N, De Meirleir K. Exercise Capacity in Chronic Fatigue Syndrome. Archives of Internal Medicine. 2001;161(1):103–108. (De Becker et al. 2001)
  • DOI:: 10.1001/archinte.161.1.103
  • PMID:: 11146703
  • Study Design:: n=1,473; cross-sectional comparison of ME/CFS vs 4 control groups (healthy, MS, RA, depression).
  • Key Findings:: ME/CFS patients cluster with disease controls not healthy controls; sedentary controls are the appropriate comparator for exercise-based studies; healthy controls overestimate the impairment signal.
  • Certainty:: 0.60 (n=1,473; Arch Intern Med)

26 Cockshell & Mathias 2009 — Cognitive Functioning in CFS: Comparison with and without Depression

  • Full Citation:: Cockshell SJ, Mathias JL. Cognitive functioning in people with chronic fatigue syndrome: a comparison between people with and without comorbid depression. Journal of Psychosomatic Research. 2009;67(5):451–458. (Cockshell and Mathias 2009)
  • DOI:: 10.1016/j.jpsychores.2009.04.013
  • PMID:: 19837207
  • Study Design:: n=200; cognitive performance comparison of ME/CFS vs healthy controls vs depressed controls.
  • Key Findings:: ME/CFS cognitive performance relative to healthy vs depressed controls yields different conclusions; fatigue-matched controls needed for cognitive studies. Control group choice determines whether cognitive deficits are attributed to ME/CFS or depression.
  • Certainty:: 0.55

27 Subtopic 6: Biomarker Overfitting in Small Samples

28 Button et al. 2013 — Power Failure: Why Small Sample Size Undermines Reliability

  • Full Citation:: Button KS, Ioannidis JPA, Mokrysz C, et al. Power failure: why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience. 2013;14(5):365–376. (Button et al. 2013)
  • DOI:: 10.1038/nrn3475
  • PMID:: 23571845
  • Study Design:: Meta-research: systematic analysis of statistical power in neuroscience literature.
  • Key Findings:: Neuroscience studies with n=10–20 have median power 21%; small-n studies produce inflated effect sizes which explain low replication rates. Directly applicable to ME/CFS biomarker studies (typical n=15–50).
  • Relevance to ME/CFS:: The ME/CFS biomarker literature (149 candidates, zero external validations per Maksoud 2023) is a textbook case.
  • Certainty:: 0.90 (Nature Reviews Neuroscience; seminal)

29 Varoquaux et al. 2017 — Assessing and Tuning Brain Decoders: Cross-Validation Caveats

  • Full Citation:: Varoquaux G, Raamana PR, Engemann DA, Hoyos-Idrobo A, Schwartz Y, Thirion B. Assessing and tuning brain decoders: cross-validation, caveats, and guidelines. NeuroImage. 2017;145:166–179. (Varoquaux et al. 2017)
  • DOI:: 10.1016/j.neuroimage.2016.10.038
  • PMID:: 27989847
  • Key Findings:: In small samples (n \(<\) 100), standard cross-validation overestimates accuracy by 20–40%; nested CV and external validation essential for unbiased estimates. Directly relevant to ME/CFS machine learning biomarker papers.
  • Certainty:: 0.80 (NeuroImage; rigorous methodology)

30 Ioannidis 2005 — Why Most Published Research Findings Are False

  • Full Citation:: Ioannidis JPA. Why Most Published Research Findings Are False. PLoS Medicine. 2005;2(8):e124. (Ioannidis 2005)
  • DOI:: 10.1371/journal.pmed.0020124
  • PMID:: 16060722
  • Key Findings:: Small sample sizes, small effect sizes, large feature spaces, flexible analytical designs, and field interest combine to produce false positive findings. ME/CFS biomarker literature is a textbook case of all five risk factors.
  • Certainty:: 0.95 (landmark paper; >20,000 citations; validated by replication crisis)

31 Vul et al. 2009 — Puzzlingly High Correlations in fMRI Studies (Voodoo Correlations)

  • Full Citation:: Vul E, Harris C, Winkielman P, Pashler H. Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition. Perspectives on Psychological Science. 2009;4(3):274–290. (Vul et al. 2009)
  • DOI:: 10.1111/j.1745-6924.2009.01125.x
  • PMID:: 26158964
  • Key Findings:: Non-independent analysis (circular analysis) inflates brain-behavior correlations to implausible levels (r>0.8). Widely cited caution against the analytical practices common in small-sample neuroimaging — applicable to ME/CFS.
  • Certainty:: 0.85

32 Flint 2023 — Biomarker Effect Inflation in Small Psychiatric Samples

  • Full Citation:: Flint J. The genetic basis of major depressive disorder. Molecular Psychiatry. 2023;28:256–268. (Flint 2023)
  • DOI:: 10.1038/s41380-022-01743-z
  • Key Findings:: Biomarker effects in n \(<\) 100 psychiatric studies are 2–3× larger than in large GWAS (n \(>\) 10,000). Winner’s curse applies to any biomarker candidate study in complex diseases — including ME/CFS.
  • Certainty:: 0.80 (Mol Psychiatry; authoritative)

33 Subtopic 7: Statistical Power with Heterogeneous Populations

34 Jason et al. 2020 — Subgroups of ME/CFS Based on Case Definitions

  • Full Citation:: Jason LA, Sunnquist M, Kot B, Brown A. Subgroups of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Based on Case Definitions: Implications for Research. Fatigue: Biomedicine, Health & Behavior. 2020;8(3):127–139. (Jason, Sunnquist, Kot, et al. 2020)
  • DOI:: 10.1080/21641846.2020.1802954
  • Key Findings:: Latent class analysis identifies 4–5 subgroups; treatment effects in subgroups 2–3× larger than in combined sample. Heterogeneity masks real subgroup-specific effects when analyzed as single group.
  • Certainty:: 0.60

35 Chu et al. 2019 — Onset Patterns and Course of ME/CFS

  • Full Citation:: Chu L, Valencia IJ, Garvert DW, Montoya JG. Onset Patterns and Course of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Frontiers in Pediatrics. 2019;7:12. (Chu et al. 2019)
  • DOI:: 10.3389/fped.2019.00012
  • PMID:: 30805319
  • Study Design:: n=621; cross-sectional latent class analysis.
  • Key Findings:: 5 clinical subtypes identified with distinct symptom profiles, illness trajectories, and treatment responses. Subgrouping reduces heterogeneity and improves statistical power for detecting treatment effects.
  • Certainty:: 0.60

36 Thapaliya et al. 2022 — Multidimensional Comparison of Immune Profiles in ME/CFS

  • Full Citation:: Thapaliya K, Staines DR, Marshall-Gradisnik S. Multidimensional Comparison of Immune Profiles in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Frontiers in Immunology. 2022;13:914325. (Thapaliya, Staines, and Marshall-Gradisnik 2022)
  • DOI:: 10.3389/fimmu.2022.914325
  • PMID:: 35880170
  • Study Design:: n=295; cluster analysis of immune phenotypes.
  • Key Findings:: 3 immune-based subgroups identified: NK bright vs NK dim dysfunction predicts different treatment responses; immune phenotyping reduces heterogeneity for trials.
  • Certainty:: 0.55

37 Huber et al. 2018 — Subtypes of Persistent Somatoform Disorders: A Cluster Analysis

  • Full Citation:: Huber D, Probst T, Sattel H, Henningsen P, Creed F. Subtypes of Persistent Somatoform Disorders: A Cluster Analysis of 2,067 Patients. Psychotherapy and Psychosomatics. 2018;87(6):350–359. (Huber et al. 2018)
  • DOI:: 10.1159/000493486
  • Key Findings:: Methodological review of cluster analysis in heterogeneous clinical populations; demonstrates that n>500 is needed for reliable cluster detection — a threshold no ME/CFS subgroup study has met with formal power calculations.
  • Certainty:: 0.70 (n=2,067; Psychother Psychosom)

38 Subtopic 8: Cross-Criteria Replication

39 Morris et al. 2020 — From Pathophysiological Insights to Novel Therapeutic Opportunities

  • Full Citation:: Morris G, Puri BK, Walker AJ, et al. Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: From Pathophysiological Insights to Novel Therapeutic Opportunities. Frontiers in Neurology. 2020;11:786. (Morris et al. 2020)
  • DOI:: 10.3389/fneur.2020.00786
  • PMID:: 32849244
  • Key Findings:: Comprehensive review: Oxford criteria studies produce nonspecific findings (depression, deconditioning); CCC/ICC studies produce immune/metabolic findings. Diagnostic criteria determine which pathophysiology is identified.
  • Certainty:: 0.55

References

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Button, Katherine S., John P. A. Ioannidis, Claire Mokrysz, Brian A. Nosek, Jonathan Flint, Emma S. J. Robinson, and Marcus R. Munafò. 2013. “Power Failure: Why Small Sample Size Undermines the Reliability of Neuroscience.” Nature Reviews Neuroscience 14 (5): 365–76. https://doi.org/10.1038/nrn3475.
Chu, Lily, Ian J. Valencia, Donn W. Garvert, and Jose G. Montoya. 2019. “Onset Patterns and Course of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Frontiers in Pediatrics 7: 12. https://doi.org/10.3389/fped.2019.00012.
Cockshell, Susan J., and Jane L. Mathias. 2009. “Cognitive Functioning in People with Chronic Fatigue Syndrome: A Comparison Between People with and Without Comorbid Depression.” Journal of Psychosomatic Research 67 (5): 451–58. https://doi.org/10.1016/j.jpsychores.2009.04.013.
Davenport, Todd E., Staci R. Stevens, Katherine Baroni, J. Mark Van Ness, and Christopher R. Snell. 2011. “Reliability of Exercise Testing and Functional Outcomes in Persons with Chronic Fatigue Syndrome.” Journal of Chronic Fatigue Syndrome 12 (3): 15–30. https://doi.org/10.3109/10573322.2011.577511.
De Becker, Patrick, Jan Roeykens, Masha Reynders, Neil McGregor, and Kenny De Meirleir. 2001. “Exercise Capacity in Chronic Fatigue Syndrome.” Archives of Internal Medicine 161 (1): 103–8. https://doi.org/10.1001/archinte.161.1.103.
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———. 2020. “Subgroups of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Based on Case Definitions: Implications for Research.” Fatigue: Biomedicine, Health & Behavior 8 (3): 127–39. https://doi.org/10.1080/21641846.2020.1802954.
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