User-Centered Mobile Applications for Stroke Survivors (MAPPS): A Mixed-Methods Study of Patient Preferences.

OBJECTIVE: Investigate stroke survivors' (SS) preferences for a hypothetical mHealth app for post-stroke care and to study the influence of demographic variables on these preferences. DESIGN: Mixed-methods, sequential, observational study. SETTING: Focus groups (phase 1) were conducted to identify SS perceptions and knowledge of mHealth applications (apps). Using grounded theory approach, recurring themes were identified. A multiple-choice questionnaire of 5 desired app features was generated using these themes and mailed to SS (national survey, phase 2). SS' demographics and perceived usefulness (yes/no) for each feature were recorded. In-person usability testing (phase 3) was conducted to identify areas of improvement in user interfaces of existing apps. Summative telephone interviews (phase 4) were conducted for final impressions supplementary to national survey. PARTICIPANTS: SS aged >18 years recruited from study hospital, national stroke association database, stroke support and advocacy groups. Non-English speakers and those unable to communicate were excluded. INTERVENTIONS: None. MAIN OUTCOME MEASURES: (1) Percentage of SS (phase 2) identifying proposed app features to be useful. (2) Influence of age, sex, race, education, and time since stroke on perceived usefulness. RESULTS: Ninety-six SS participated in focus groups. High cost, complexity, and lack of technical support were identified as barriers to adoption of mHealth apps. In the national survey (n=1194), ability to track fitness and diet (84%) and communication (70%) were the most and least useful features, respectively. Perceived usefulness was higher among younger SS (P<.001 to .006) and SS of color (African American and Hispanic) (ORs 1.73-4.41). Simple design and accommodation for neurologic deficits were main recommendations from usability testing. CONCLUSIONS: SS are willing to adopt mHealth apps that are free of cost and provide technical support. Apps for SS should perform multiple tasks and be of simple design. Greater interest for the app's features among SS of color may provide opportunities to address health inequities.

Computerized cognitive performance assessments in the Brooklyn Cognitive Impairments in Health Disparities Pilot Study.

INTRODUCTION: Detecting cognitive impairment in diverse, health disparities communities is an urgent health care priority. METHODS: The Brooklyn Cognitive Impairments in Health Disparities Pilot Study investigated quantitative aspects and liking of a computerized cognitive performance assessment, Cognigram, among individuals ≥ 40 years in traditional and nontraditional primary care settings. RESULTS: Cognigram was piloted in the Emergency Department, Family Medicine, and Geriatric Psychiatry clinics: 58 adults (23 men, 35 women), 67.9 ± 9.8 years (range 43-91), completed the Cognigram and 5-item liking survey. The observed liking range was 2 to maximum score 5 (67% scored 4-5; no sex or age differences). DISCUSSION: The Cognigram was well liked in waiting rooms of primary care settings. Assistance from a trained adult and clinic endorsement were keys to success. How the Cognigram performs in a geographically compact, population-dense global setting, such as Brooklyn with high vascular disease risk and a plethora of health disparities, is being tested.

Medical Mobile Applications for Stroke Survivors and Caregivers.

BACKGROUND: Recent studies estimate nearly half of the US population can access mobile medical applications (apps) on their smartphones. The are no systematic data available on apps focused on stroke survivors/caregivers. OBJECTIVE: To identify apps (a) designed for stroke survivors/caregivers, (b) dealing with a modifiable stroke risk factor (SRF), or (c) were developed for other purposes but could potentially be used by stroke survivors/caregivers. METHODS: A systematic review of the medical apps in the US Apple iTunes store was conducted between August 2013 and January 2016 using 18 predefined inclusion/exclusion criteria. SRFs considered were: diabetes, hypertension, smoking, obesity, atrial fibrillation, and dyslipidemia. RESULTS: Out of 30,132 medical apps available, 843 (2.7%) eligible apps were identified. Of these apps, (n = 74, 8.7%) apps were specifically designed for stroke survivors/caregivers use and provided the following services: language/speech therapy (n = 28, 37%), communication with aphasic patients (n = 19, 25%), stroke risk calculation (n = 11, 14%), assistance in spotting an acute stroke (n = 8, 10%), detection of atrial fibrillation (n = 3, 4%), direction to nearby emergency room (n = 3, 4%), physical rehabilitation (n = 3, 4%), direction to the nearest certified stroke center (n = 1, < 2%), and visual attention therapy (n = 1, <2%). 769 apps identified that were developed for purposes other than stroke. Of these, the majority (n = 526, 68%) addressed SRFs. CONCLUSIONS: Over 70 medical apps exist to specifically support stroke survivors/caregivers and primarily targeted language and communication difficulties. Apps encompassing most stroke survivor/caregiver needs could be developed and tested to ensure the issues faced by these populations are being adequately addressed.

Does the Magnitude of the Electrocardiogram QT Interval Dispersion Predict Stroke Outcome?

BACKGROUND: QT dispersion, maximal interlead difference in QT interval on 12-lead electrocardiogram (ECG), measures cardiac repolarization abnormalities. Data are conflicting whether QT dispersion predicts adverse outcome in acute ischemic stroke (AIS) patients. Our objective is to determine if QT dispersion predicts: (1) short-term clinical outcome in AIS, and (2) stroke location (insular versus noninsular cortex). METHODS: Admission ECGs from 412 consecutive patients with acute stroke symptoms from 2 university-based stroke centers were reviewed. QT dispersion was measured. A neuroradiologist reviewed brain imaging for insular cortex involvement. Favorable clinical outcomes at discharge were modified Rankin Scale (mRS) score of 0-1, discharge National Institutes of Health Stroke Scale (NIHSS) score less than 2, and discharge to home. Multiple logistic regressions were performed for each outcome measure and to determine the association between insular infarct and QT dispersion. RESULTS: Of 145 subjects in the final analysis, median age was 65 years (interquartile range [IQR] 56-75), male patients were 38%, black patients were 68%, median QT dispersion was 78 milliseconds (IQR 59-98), and median admission NIHSS score was 4 (IQR 2-6). QT dispersion did not predict short-term clinical outcome for mRS score (odds ratio [OR] = 1.001, 95% confidence interval [CI] .99-1.01, P = .85), NIHSS at discharge (OR = .994, 95% CI .98-1.01, P = .30), or discharge disposition (OR = 1.001, 95% CI .99-1.01, P = .81). Insular cortex involvement did not correlate with QT dispersion magnitude (OR = 1.009, 95% CI .99-1.02, P = .45). CONCLUSIONS: We could not demonstrate that QT dispersion is useful in predicting short-term clinical outcome at discharge in AIS. Further, the magnitude of QT dispersion did not predict insular cortical stroke location.

To Treat or Not to Treat?

Background and Purpose- The 2015 updated US Food and Drug Administration alteplase package insert altered several contraindications. We thus explored clinical factors influencing alteplase treatment decisions for patients with minor stroke. Methods- An expert panel selected 7 factors to build a series of survey vignettes: National Institutes of Health Stroke Scale (NIHSS), NIHSS area of primary deficit, baseline functional status, previous ischemic stroke, previous intracerebral hemorrhage, recent anticoagulation, and temporal pattern of symptoms in first hour of care. We used a fractional factorial design (150 vignettes) to provide unconfounded estimates of the effect of all 7 main factors, plus first-order interactions for NIHSS. Surveys were emailed to national organizations of neurologists, emergency physicians, and colleagues. Physicians were randomized to 1 of 10 sets of 15 vignettes, presented randomly. Physicians reported the subjective likelihood of giving alteplase on a 0 to 5 scale; scale categories were anchored to 6 probabilities from 0% to 100%. A conjoint statistical analysis was applied. Results- Responses from 194 US physicians yielded 156 with complete vignette data: 74% male, mean age 46, 80% neurologists. Treatment mean probabilities for individual vignettes ranged from 6% to 95%. Treatment probability increased from 24% for NIHSS score =1 to 41% for NIHSS score =5. The conjoint model accounted for 25% of total observed response variance. In contrast, a model accounting for all possible interactions accounted for 30% variance. Four of the 7 factors accounted jointly for 58% of total relative importance within the conjoint model: previous intracerebral hemorrhage (18%), recent anticoagulation (17%), NIHSS (13%), and previous ischemic stroke (10%). Conclusions- Four main variables jointly account for only a small fraction (<15%) of the total variance related to deciding to treat with intravenous alteplase, reflecting high variability and complexity. Future studies should consider other variables, including physician characteristics.