Migraine is a common and disabling disorder. Fibromyalgia has been shown to be commonly comorbid in patients with migraine and can intensify disability. The aim of this study was to determine if patients with co-morbid fibromyalgia and migraine report more depressive symptoms, have more headache related disability, or report higher intensity of headache as compared to patients with migraine only. Cases of comorbid fibromyalgia and migraine were identified using a prospectively maintained headache database at Mayo Clinic Rochester. One-hundred and fifty seven cases and 471 controls were identified using this database and the Mayo Clinic electronic medical record. Depressive symptoms as assessed by PHQ-9, intensity of headache, and migraine related disability as assessed by MIDAS were primary measures used to compare migraine patients with comorbid fibromyalgia versus those without. Patients with comorbid fibromyalgia reported significantly higher PHQ-9 scores (OR 1.08, p < .0001) and headache intensity scores (OR 1.149, p = .007). There was no significant difference in migraine related disability (OR 1.002, p = .075). Patients with fibromyalgia were more likely to score in a higher category of depression severity (OR 1.467, p < .0001) and more likely to score in a higher category of migraine related disability (OR 1.23, p = .004). Patients with comorbid fibromyalgia and migraine report more depressive symptoms, higher headache intensity, and are more likely to have severe headache related disability as compared to controls without fibromyalgia. Clinicians who care for patients with migraine may consider screening for comorbid fibromyalgia particularly in patients with moderate to severe depressive symptoms, high headache intensity and/or high headache related disability. This is the first matched study to look at these characterisitcs, and it replicates previous findings from unmatched studies.

The purpose of the present study was to investigate body mass index, multi-morbidity, and COVID-19 Risk Score as predictors of severe COVID-19 outcomes. Patients from this study are from a well-characterized patient cohort collected at Mayo Clinic between January 1, 2020 and May 23, 2020; with confirmed COVID-19 diagnosis defined as a positive result on reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assays from nasopharyngeal swab specimens. Demographic and clinical data were extracted from the electronic medical record. The data included: date of birth, gender, ethnicity, race, marital status, medications (active COVID-19 agents), weight and height (from which the Body Mass Index (BMI) was calculated, history of smoking, and comorbid conditions to calculate the Charlson Comorbidity Index (CCI) and the U.S Department of Health and Human Services (DHHS) multi-morbidity score. An additional COVID-19 Risk Score was also included. Outcomes included hospital admission, ICU admission, and death. Cox proportional hazards models were used to determine the impact on mortality or hospital admission. Age, sex, and race (white/Latino, white/non-Latino, other, did not disclose) were adjusted for in the model. Patients with higher COVID-19 Risk Scores had a significantly higher likelihood of being at least admitted to the hospital (HR = 1.80; 95% CI = 1.30, 2.50; P < .001), or experiencing death or inpatient admission (includes ICU admissions) (HR = 1.20; 95% CI = 1.02, 1.42; P = .028). Age was the only statistically significant demographic predictor, but obesity was not a significant predictor of any of the outcomes. Age and COVID-19 Risk Scores were significant predictors of severe COVID-19 outcomes. Further work should examine the properties of the COVID-19 Risk Factors Scale.

To describe current tobacco use among patients with newly confirmed fibromyalgia and evaluate the association between tobacco use status and severity of reported pain and other fibromyalgia symptoms. Participants in this study were adult patients (N=1068) with fibromyalgia who met American College of Rheumatology 2010/2011 clinical criteria for fibromyalgia at the time of initial presentation to a Midwest fibromyalgia clinic (June 1, 2018, through May 31, 2019). Multiple linear regression analyses were performed to assess the association of tobacco use status with the Widespread Pain Index (WPI) and Symptom Severity Scale (SSS) scores. Covariates included in these analyses included age, sex, body mass index, depression, opioid medication use, and use of fibromyalgia-specific pharmacotherapy. The patients were largely women (87.0%; n=929), white (87.9%; n=939), and with an average ± SD age of 46.6±13.9 years. The WPI and SSS scores were significantly greater in current tobacco users compared with never tobacco users (WPI effect estimate [EE] = 1.03; 95% CI, 0.30 to 1.76; type III P=.020; SSS EE = 0.47; 95% CI, 0.11 to 0.84; type III P=.036). The WPI score was negatively associated with age (EE = −0.02 per year; 95% CI, −0.03 to −0.001 per year; P=.037) and no use of opioid medication (EE = −1.08; 95% CI, −1.59 to −0.57; P<.001) while positively associated with higher body mass index (EE = 0.03 per 1 kg/m2; 95% CI, 0.001 to 0.06 per kg/m2; P=.04) and higher Patient Health Questionnaire-9 score (EE = 0.12; 95% CI, 0.08 to 0.16; P<.001). The results of our study suggest that tobacco use is associated with greater pain and other symptom severity in patients with fibromyalgia. These findings have important clinical and research implications for patients with fibromyalgia who use tobacco and who may benefit from early identification and timely implementation of tobacco cessation treatment to decrease pain and improve overall quality of life.

Using self-report questionnaires of key fibromyalgia symptom domains (pain, fatigue, sleep disturbance, function, stiffness, dyscognition, depression, and anxiety), we previously identified four unique symptom clusters. The purpose of this study was to examine the stability of fibromyalgia symptom clusters between baseline and 2-year follow-up. Women with a diagnosis of fibromyalgia completed the Brief Pain Inventory, Profile of Mood States, Medical Outcomes Study Sleep measure, Multidimensional Fatigue Inventory, Multiple Ability Self-Report Questionnaire, Revised Fibromyalgia Impact Questionnaire, and the 36-Item Short Form Survey Instrument at baseline. Follow-up measures were completed approximately 2 years later. The hierarchical agglomerative clustering algorithm previously developed was applied; agreement between baseline and follow-up was assessed with the κ statistic. Among 433 participants, the mean age was 56 (range 20–85) years. The median Revised Fibromyalgia Impact Questionnaire total score was 57 (range 8–96). More than half of participants (58%) remained in the same cluster at follow-up as at baseline, which represented moderate agreement between baseline and follow-up (κ = 0.44, 95% confidence interval (CI) 0.37–0.50). Only two patients changed from high symptom intensity to low symptom intensity; similarly, only three moved from low to high. Fibromyalgia patients classified into four unique symptom clusters based on the key domains of pain, fatigue, sleep disturbance, function, stiffness, dyscognition, depression, and anxiety showed moderate stability in cluster assignment after 2 years. Few patients moved between the two extremes of severity, and it was slightly more common to move to a lower symptom level than to worsen. Not applicable.

In wireless packet data systems such as the Enhanced General Packet Radio Service (EGPRS), selective ARQ is used for error recovery over a radio link. In earlier work, the radio link control (RLC) performance is typically characterized in terms of the throughput and the size of the higher layer protocol data units (PDUs) or the packets to be transferred is ignored. We propose that the link adaptation be based on user perceived delay instead of the throughput

To evaluate the use of a wearable activity tracker and brief feedback in the workplace to motivate employees to improve activity. A total of 135 adult participants were randomized to 1 of 3 groups: control group (blinded to their step activity), intervention group (received counseling based on their step count), or no step-tracking device group. Participants were recruited from June 27, 2016, through February 21, 2018. Most of the 135 participants were women (84%), with a mean ± SD age of 42.6±10.1 years. Most participants (96%) completed 11 of the 12 weeks of step counts. Comparing treatment groups at week 12 (end of treatment), the intervention group (vs the control group) had significantly more steps (644.8; P<.01), had an 11.1% increase in step count from baseline (P<.01), was more likely to achieve goal (odds ratio=1.73; P=.02), increased distance traveled per week (0.46 miles; P<.01) and calories burned (90.6; P<.01), and had a decrease in some bioelectrical impedance measurements over time, including a greater loss in body fat mass (-0.90 kg; P=.01), percentage fat (-0.96; P<.01), and visceral fat level (-0.60; P<.01). Finally, the intervention group indicated significantly greater satisfaction with their assigned randomization (89% vs 77%; P=.01) and greater confidence in the effectiveness of their activity tracker (P<.01). Brief counseling accompanied by use of a step-counting device can improve workplace activity, which, in turn, can increase steps and decrease body fat, including visceral fat. clinicaltrials.gov Identifier: NCT02794727.