Smartband-based smoking detection and real-time brief mindfulness intervention: findings from a feasibility clinical trial.

BACKGROUND: Smartbands can be used to detect cigarette smoking and deliver real time smoking interventions. Brief mindfulness interventions have been found to reduce smoking. OBJECTIVE: This single arm feasibility trial used a smartband to detect smoking and deliver brief mindfulness exercises. METHODS: Daily smokers who were motivated to reduce their smoking wore a smartband for 60 days. For 21 days, the smartband monitored, detected and notified the user of smoking in real time. After 21 days, a 'mindful smoking' exercise was triggered by detected smoking. After 28 days, a 'RAIN' (recognize, allow, investigate, nonidentify) exercise was delivered to predicted smoking. Participants received mindfulness exercises by text message and online mindfulness training. Feasibility measures included treatment fidelity, adherence and acceptability. RESULTS: Participants (N=155) were 54% female, 76% white non-Hispanic, and treatment starters (n=115) were analyzed. Treatment fidelity cutoffs were met, including for detecting smoking and delivering mindfulness exercises. Adherence was mixed, including moderate smartband use and low completion of mindfulness exercises. Acceptability was mixed, including high helpfulness ratings and mixed user experiences data. Retention of treatment starters was high (81.9%). CONCLUSIONS: Findings demonstrate the feasibility of using a smartband to track smoking and deliver quit smoking interventions contingent on smoking.

Smartband-Based Automatic Smoking Detection and Real-time Mindfulness Intervention: Protocol for a Feasibility Trial.

BACKGROUND: Smoking is the leading cause of preventable death in the United States. Smoking cessation interventions delivered by smartphone apps are a promising tool for helping smokers quit. However, currently available smartphone apps for smoking cessation have not exploited their unique potential advantages to aid quitting. Notably, few to no available apps use wearable technologies, most apps require users to self-report their smoking, and few to no apps deliver treatment automatically contingent upon smoking. OBJECTIVE: This pilot trial tests the feasibility of using a smartband and smartphone to monitor and detect smoking and deliver brief mindfulness interventions in real time to reduce smoking. METHODS: Daily smokers (N=100, ≥5 cigarettes per day) wear a smartband for 60 days to monitor and detect smoking, notify them about their smoking events in real time, and deliver real-time brief mindfulness exercises triggered by detected smoking events or targeted at predicted smoking events. Smokers set a quit date at 30 days. A three-step intervention to reduce smoking is tested. First, participants wear a smartband to monitor and detect smoking, and notify them of smoking events in real time to bring awareness to smoking and triggers for 21 days. Next, a "mindful smoking" exercise is triggered by detected smoking events to bring a clear recognition of the actual effects of smoking for 7 days. Finally, after their quit date, a "RAIN" (recognize, allow, investigate, nonidentification) exercise is delivered to predicted smoking events (based on the initial 3 weeks of tracking smoking data) to help smokers learn to work mindfully with cravings rather than smoke for 30 days. The primary outcomes are feasibility measures of treatment fidelity, adherence, and acceptability. The secondary outcomes are smoking rates at end of treatment. RESULTS: Recruitment for this trial started in May 2021 and will continue until November 2021 or until enrollment is completed. Data monitoring and management are ongoing for enrolled participants. The final 60-day end of treatment data is anticipated in January 2022. We expect that all trial results will be available in April 2022. CONCLUSIONS: Findings will provide data and information on the feasibility of using a smartband and smartphone to monitor and detect smoking and deliver real-time brief mindfulness interventions, and whether the intervention warrants additional testing for smoking cessation. TRIAL REGISTRATION: ClinicalTrials.gov NCT03995225; https://clinicaltrials.gov/ct2/show/NCT03995225. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/32521.

The Opioid Epidemic and Primary Headache Disorders: A Nationwide Population-Based Study.

Introduction The opioid epidemic has been linked to several other health problems, but its impact on headache disorders has not been well studied. We performed a population-based study looking at the prevalence of opioid use in headache disorders and its impact on outcomes compared to non-abusers with headaches. Methodology We performed a cross-sectional analysis of the Nationwide Inpatient Sample (years 2008-2014) in adults hospitalized for primary headache disorders (migraine, tension-type headache [TTH], and cluster headache [CH]) using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes. We performed weighted analyses using the chi-square test, Student's t-test, and Cochran-Armitage trend test. Multivariate survey logistic regression analysis with weighted algorithm modelling was performed to evaluate morbidity, disability, and discharge disposition. Among US hospitalizations during 2013-2014, regression analysis was performed to evaluate the odds of having opioid abuse among headache disorders. Results A total of 5,627,936 headache hospitalizations were present between 2008 and 2014 of which 3,098,542 (55.06%), 113,332 (2.01%), 26,572 (0.47%) were related to migraine, TTH, and CH, respectively. Of these headache hospitalizations, 128,383 (2.28%) patients had abused opioids. There was a significant increase in the prevalence trend of opioid abuse among patients with headache disorders from 2008 to 2014. The prevalence of migraine (63.54% vs. 54.86%), TTH (2.29% vs. 2.01%), and CH (0.59% vs. 0.47%) was also higher among opioid abusers than non-abusers (p<0.0001). Opioid abusers with headaches were more likely to be younger (43 years old vs. 50 years old), men (30.17% vs. 24.78%), white (80.83% vs. 73.29%), Medicaid recipients (30.15% vs. 17.03%), and emergency admissions (85.4% vs. 78.51%) as compared to opioid non-abusers with headaches (p<0.0001). Opioid abusers with headaches had higher prevalence and odds of morbidity (4.06% vs. 3.70%; adjusted odds ratio [aOR]: 1.48; 95% CI: 1.39-1.59), severe disability (28.14% vs. 22.43%; aOR: 1.58; 95% CI: 1.53-1.63), and discharge to non-home location (17.13% vs. 18.41%; aOR: 1.35; 95% CI: 1.30-1.40) as compared to non-abusers. US hospitalizations in years 2013-2014 showed the migraine (OR: 1.61; 95% CI: 1.57-1.66), TTH (OR: 1.43; 95% CI: 1.22-1.66), and CH (OR: 1.34; 95% CI: 1.01-1.78) were linked with opioid abuse. Conclusion Through this study, we found that the prevalence of migraine, TTH, and CH was higher in opioid abusers than non-abusers. Opioid abusers with primary headache disorders had higher odds of morbidity, severe disability, and discharge to non-home location as compared to non-abusers.

Age-Adjusted Risk Factors Associated with Mortality and Mechanical Ventilation Utilization Amongst COVID-19 Hospitalizations-a Systematic Review and Meta-Analysis.

The increasing COVID-19 cases in the USA have led to overburdening of healthcare in regard to invasive mechanical ventilation (IMV) utilization as well as mortality. We aim to identify risk factors associated with poor outcomes (IMV and mortality) of COVID-19 hospitalized patients. A meta-analysis of observational studies with epidemiological characteristics of COVID-19 in PubMed, Web of Science, Scopus, and medRxiv from December 1, 2019 to May 31, 2020 following MOOSE guidelines was conducted. Twenty-nine full-text studies detailing epidemiological characteristics, symptoms, comorbidities, complications, and outcomes were included. Meta-regression was performed to evaluate effects of comorbidities, and complications on outcomes using a random-effects model. The pooled correlation coefficient (r), 95% CI, and OR were calculated. Of 29 studies (12,258 confirmed cases), 17 reported IMV and 21 reported deaths. The pooled prevalence of IMV was 23.3% (95% CI: 17.1-30.9%), and mortality was 13% (9.3-18%). The age-adjusted meta-regression models showed significant association of mortality with male (r: 0.14; OR: 1.15; 95% CI: 1.07-1.23; I 2: 95.2%), comorbidities including pre-existing cerebrovascular disease (r: 0.35; 1.42 (1.14-1.77); I 2: 96.1%), and chronic liver disease (r: 0.08; 1.08 (1.01-1.17); I 2: 96.23%), complications like septic shock (r: 0.099; 1.10 (1.02-1.2); I 2: 78.12%) and ARDS (r: 0.04; 1.04 (1.02-1.06); I 2: 90.3%), ICU admissions (r: 0.03; 1.03 (1.03-1.05); I 2: 95.21%), and IMV utilization (r: 0.05; 1.05 (1.03-1.07); I 2: 89.80%). Similarly, male (r: 0.08; 1.08 (1.02-1.15); I 2: 95%), comorbidities like pre-existing cerebrovascular disease (r: 0.29; 1.34 (1.09-1.63); I 2:93.4%), and cardiovascular disease (r: 0.28; 1.32 (1.1-1.58); I 2: 89.7%) had higher odds of IMV utilization. COVID-19 patients with comorbidities including cardiovascular disease, cerebrovascular disease, and chronic liver disease had poor outcomes. Diabetes and hypertension had higher prevalence but no association with mortality and IMV. Our study results will be helpful in right allocation of resources towards patients who need them the most.

Stroke-Associated Pneumonia: A Retrospective Study of Risk Factors and Outcomes.

INTRODUCTION: Pneumonia is the most common complication after stroke, but our knowledge on risk factors and predictors of stroke-associated pneumonia (SAP) is limited. We sought to evaluate the predictors and outcomes of SAP among acute ischemic stroke (AIS) hospitalizations. METHODS: This is a cross-sectional study of the Nationwide Inpatient Sample database from the year 2003 to 2014. We identified adult hospitalizations with AIS using International Classification of Diseases, ninth revision, clinical modification (ICD-9-CM) codes. The SAP was identified by the presence of a secondary diagnosis of hospital-acquired pneumonia and ventilator-associated pneumonia. Multivariable survey logistic regression models were utilized to evaluate the predictors of SAP. RESULTS: Overall, 4,224,924 AIS hospitalizations were identified, of which 149,169 (3.53%) had SAP. The prevalence of SAP decreased from 3.72% in 2003 to 3.17% in 2014 (P<0.0001). Mortality [17.12% vs. 4.77%; adjusted odds ratio (aOR): 1.71; P<0.0001] and morbidity (22.53% vs. 3.28%; aOR: 1.86; P<0.0001) were markedly elevated in SAP group compare to non-SAP group. The significant risk factors of pneumonia among AIS hospitalization were nasogastric tube (aOR: 1.21; P=0.0179), noninvasive mechanical ventilation (aOR: 1.65; P<0.0001), invasive mechanical ventilation (aOR: 4.09; P<0.0001), length of stay between 1 to 2 weeks (aOR: 1.99; P<0.0001), >2 weeks (aOR: 3.90; P<0.0001), hemorrhagic conversion (aOR: 1.17; P=0.0002), and epilepsy (aOR: 1.09; P=0.0009). Other concurrent comorbidities which increased the risk of SAP among AIS patients were acquired immune deficiency syndrome (aOR: 1.88; P<0.0001), alcohol abuse (aOR: 1.60; P=0.0006), deficiency anemia (aOR: 1.26; P<0.0001), heart failure (aOR: 1.62; P<0.0001), pulmonary disease (aOR: 1.73; P<0.0001), diabetes (aOR: 1.29; P=0.0288), electrolyte disorders (aOR: 1.50; P<0.0001), paralysis (aOR: 1.22; P<0.0001), pulmonary circulation disorders (aOR: 1.22; P<0.0001), renal failure (aOR: 1.12; P<0.0001), coagulopathy (aOR: 1.13; P=0.0006), and weight loss (aOR: 1.39; P<0.0001). CONCLUSION: Our data underline the considerable epidemiological and prognostic impact of SAP in patients with AIS leading to higher mortality, morbidity, length of stay, and hospital cost despite advancements in care.

A Comprehensive Assessment of Vascular and Nonvascular Risk Factors Associated with Migraine.

INTRODUCTION:  Migraine is a chronic disabling neurological disease, with an estimated expense of $15-20 million/year. Several studies with a small number of patients have studied risk factors for migraine such as cardiovascular disorders, stroke, smoking, demographic, and genetic factors but this is the first comprehensive study for evaluation of vascular and nonvascular risk factors. It is important to evaluate all the risk factors that help to prevent the healthcare burden related to migraine.  Methodology: We performed a retrospective cross-sectional analysis of the Nationwide Inpatient Sample (NIS) (years 2013-2014) in adult (>18-years old) hospitalizations in the United States. Migraine patients were identified using ICD-9-CM code to determine the demographic characteristics, vascular, and nonvascular risk factors. Univariate analysis was performed using the chi-square test and a multivariate survey logistic regression analysis was performed to identify the prevalence of the risk factors and evaluate the odds of prevalence of risk factors amongst migraine patients compared to nonmigraine patients, respectively. RESULTS:  On weighted analysis, after removing missing data of age, gender and race, from years 2013 to 2014, of the total 983,065 (1.74%) migraine patients were identified. We found that younger (median age 48-years vs. 60-years), female (82.1% vs. 58.5%; p<0.0001), white population (76.8% vs. 70.5%; p<0.0001), and privately insured (41.1% vs. 27.4%; p<0.0001) patients were more likely to have migraine than others. Cerebral atherosclerosis, diabetes mellitus, ischemic heart disease, atrial fibrillation, and alcohol abuse were not significantly associated with migraine. Migraineurs had higher odds of having hypertension [odds ratio (OR): 1.44; 95% confidence interval (CI): 1.43-1.46; 44.49% vs. 52.84%], recent transient ischemic attack (TIA) (OR: 3.13; 95%CI: 3.02-3.25; 1.74% vs. 0.67%), ischemic stroke (OR: 1.40; 95%CI: 1.35-1.45; 2.06% vs. 1.97%), hemorrhagic stroke (OR: 1.11; 95%CI: 1.04-1.19; 0.49% vs. 0.46%), obesity (OR: 1.46; 95%CI: 1.44-1.48; 19.20% vs. 13.56%), hypercholesterolemia (OR: 1.33; 95%CI: 1.30-1.36; 5.75% vs. 5.54%), substance abuse (OR: 1.51; 95%CI: 1.48-1.54; 7.88% vs. 4.88%), past or current consumption of tobacco (OR: 1.40; 95%CI: 1.38-1.41; 31.02% vs. 27.39%), AIDS (OR: 1.13; 95%CI: 1.04-1.24; 0.33% vs. 0.41%), hypocalcemia (OR: 1.09; 95%CI: 1.03-1.14; 0.77% vs. 0.89%), and vitamin D deficiency (OR: 1.93; 95%CI: 1.88-1.99; 2.47% vs. 1.37%) than patients without migraine. Female patients were at a higher risk of migraine (OR: 3.02; 95%CI: 2.98-3.05) than male. CONCLUSION:  In this study, we have identified significant risk factors for migraine hospitalizations. Early identification of these risk factors may improve the risk stratification in migraine patients.