Madison Sunnquist

Madison Sunnquist, B.S., M.A., is a Graduate Research Assistant in Clinical-Community Psychology from DePaul University, Center for Community Research, Chicago, Illinois.

Awards

 * 2014, Junior Investigator Award to encourage young CFS/FM researchers in recognition of their work awarded by IACFSME.

Notable studies

 * 2016, Case definitions integrating empiric and consensus perspectives
 * 2016, Comparing the DePaul Symptom Questionnaire with physician assessments: a preliminary study"'Results: The DSQ identified 60 and the physicians identified 56 as having a CCC diagnosis. The overall agreement between the two ratings on the diagnostic assessment part was moderate (Kappa = 0.45, p < .001). The sensitivity of DSQ was good (98%) while the specificity was 38%. Positive and negative predictive values were 92% and 75%, respectively. Conclusion: DSQ is useful for detecting and screening symptoms consistent with a CCC diagnosis in clinical practice and research. However, it is important for initial screening of self-report symptoms to be followed up by subsequent medical and psychiatric examination in order to identify possible exclusionary medical and psychiatric disorders.'"
 * 2016, Housebound versus nonhousebound patients with myalgic encephalomyelitis and chronic fatigue syndrome"'Abstract - Objectives: The objective of this study was to examine individuals with myalgic encephalomyelitis and chronic fatigue syndrome who are confined to their homes due to severe symptomatology. The existing literature fails to address differences between this group, and less severe, nonhousebound patient populations. Methods: Participants completed the DePaul Symptom Questionnaire, a measure of myalgic encephalomyelitis and chronic fatigue syndrome symptomology, and the SF-36, a measure of health impact on physical/mental functioning. ANOVAs and, where appropriate, MANCOVAS were used to compare housebound and nonhousebound patients with myalgic encephalomyelitis and chronic fatigue syndrome across areas of functioning, symptomatology, and illness onset characteristics. Results: Findings indicated that the housebound group represented one quarter of the sample, and were significantly more impaired with regards to physical functioning, bodily pain, vitality, social functioning, fatigue, post-exertional malaise, sleep, pain, neurocognitive, autonomic, neuroendocrine, and immune functioning compared to individuals who were not housebound. Discussion: Findings indicated that housebound patients have more impairment on functional and symptom outcomes compared to those who were not housebound. Understanding the differences between housebound and not housebound groups holds implications for physicians and researchers as they develop interventions intended for patients who are most severely affected by this chronic illness.'"
 * 2016, Deconstructing [[post-exertional malaise]: An exploratory factor analysis.] "'Abstract: Post-exertional malaise is a cardinal symptom of myalgic encephalomyelitis and chronic fatigue syndrome. There are two differing focuses when defining post-exertional malaise: a generalized, full-body fatigue and a muscle-specific fatigue. This study aimed to discern whether post-exertional malaise is a unified construct or whether it is composed of two smaller constructs, muscle fatigue and generalized fatigue. An exploratory factor analysis was conducted on several symptoms that assess post-exertional malaise. The results suggest that post-exertional malaise is composed of two empirically different experiences, one for generalized fatigue and one for muscle-specific fatigue.'"
 * 2016, Identifying Key Symptoms Differentiating Myalgic Encephalomyelitis and Chronic Fatigue Syndrome from Multiple Sclerosis"'Abstract:It is unclear what key symptoms differentiate Myalgic Encephalomyelitis (ME) and Chronic Fatigue syndrome (CFS) from Multiple Sclerosis (MS). The current study compared self-report symptom data of patients with ME or CFS with those with MS. The self-report data is from the DePaul Symptom Questionnaire, and participants were recruited to take the questionnaire online. Data were analyzed using a machine learning technique called decision trees. Five symptoms best differentiated the groups. The best discriminating symptoms were from the immune domain (i.e., flu-like symptoms and tender lymph nodes), and the trees correctly categorized MS from ME or CFS 81.2% of the time, with those with ME or CFS having more severe symptoms. Our findings support the use of machine learning to further explore the unique nature of these different chronic diseases.'"
 * 2015, Chronic fatigue syndrome versus systemic exertion intolerance disease
 * 2015, Comparing and contrasting consensus versus empirical domains. Abstract
 * 2015, Test–retest reliability of the DePaul Symptom Questionnaire Abstract"'Methods: Test–retest reliability of the measure was examined with a sample of 26 adults self-identifying as having either ME/CFS, ME, and/or CFS and 25 adults who did not self-identify as having these illnesses and were otherwise healthy controls. Results: Overall, the majority of items on the DSQ exhibited good to excellent test–retest reliability, with Pearson's or kappa correlation coefficients that were 0.70 or higher.'"
 * 2014, Examining case definition criteria for chronic fatigue syndrome and myalgic encephalomyelitis. Abstract
 * 2013, Energy conservation/envelope theory interventions. Full Text
 * 2013, Encephalitis, Encephalomyelitis, Encephalopathies: Symptoms, causes and potential complications

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