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Background Advances in mobile health (mHealth) have enabled systematic and continuous management of patients with chronic diseases. Objective We developed a smartphone-based mHealth system and aimed to evaluate its effects on health behavior management and risk factor control in stroke patients. Methods With a multifaceted stroke aftercare management system that included exercise, medication, and educational materials, we performed a 12-week single-arm intervention among eligible poststroke patients in the stroke clinic from September to December 2016. The intervention consisted of (1) regular blood pressure (BP), blood glucose, and physical activity measurements; (2) stroke education; (3) an exercise program; (4) a medication program; and (5) feedback on reviewing of records by clinicians. Clinical assessments consisted of the stroke awareness score, Beck Depression Inventory-II (BDI), EuroQol-5 Dimensions (EQ-5D), and BP at visit 1 (baseline), visit 2 (4 weeks), and visit 3 (12 weeks). Temporal differences in the parameters over 12 weeks were investigated with repeated-measures analysis of variance. Changes in medication adherence at visit 1-2 (from visit 1 to visit 2) and visit 2-3 (from visit 2 to visit 3) were compared. System satisfaction was evaluated with a self-questionnaire using a 5-point Likert scale at visit 3. Results The study was approved by the Institutional Review Board in September 2016, and participants were enrolled from September to December 2016. Among the 110 patients enrolled for the study, 99 were included in our analyses. The mean stroke awareness score (baseline: 59.6 [SD 18.1]; 4 weeks: 67.6 [SD 16.0], P<.001; 12 weeks: 74.7 [SD 14.0], P<.001) and BDI score (baseline: 12.7 [SD 10.1]; 4 weeks: 11.2 [SD 10.2], P=.01; 12 weeks: 10.7 [SD 10.2], P<.001) showed gradual improvement; however, no significant differences were found in the mean EQ-5D score (baseline: 0.66 [SD 0.33]; 4 weeks: 0.69 [SD 0.34], P=.01; 12 weeks: 0.69 [SD 0.34], P<.001). Twenty-six patients who had uncontrolled BP at baseline had −13.92 mmHg (P=.001) and −6.19 mmHg (P<.001) reductions on average in systolic and diastolic BP, respectively, without any antihypertensive medication change. Medication compliance was better at visit 2-3 (60.9% [SD 37.2%]) than at visit 1-2 (47.8% [SD 38.7%], P<.001). Conclusions Awareness of stroke, depression, and BP was enhanced when using the smartphone-based mHealth system. Emerging mHealth techniques have potential as new nonpharmacological secondary prevention methods because of their ubiquitous access, near real-time responsiveness, and comparatively lower cost.

Background: We aimed to evaluate covert brain infarction (CBI), frequently encountered during the diagnostic work-up of acute ischemic stroke, as a risk factor for stroke recurrence in patients with atrial fibrillation (AF). Methods: For this prospective cohort study, from patients with acute ischemic stroke hospitalized at 14 centers between 2017 and 2019, we enrolled AF patients without history of stroke or transient ischemic attack and divided them into the CBI (+) and CBI (−) groups. The 2 groups were compared regarding the 1-year cumulative incidence of recurrent ischemic stroke and all-cause mortality using the Fine and Gray subdistribution hazard model with nonstroke death as a competing risk and the Cox frailty model, respectively. Each CBI lesion was also categorized into either embolic-appearing (EA) or non-EA pattern CBI. Adjusted hazard ratios and 95% CIs of any CBI, EA pattern CBI only, non-EA pattern CBI only, and both CBIs were estimated. Results: Among 1383 first-ever stroke patients with AF, 578 patients (41.8%) had CBI. Of these 578 with CBI, EA pattern CBI only, non-EA pattern CBI only, and both CBIs were 61.8% (n=357), 21.8% (n=126), and 16.4% (n=95), respectively. The estimated 1-year cumulative incidence of recurrent ischemic stroke was 5.2% and 1.9% in the CBI (+) and CBI (−) groups, respectively ( P =0.001 by Gray test). CBI increased the risk of recurrent ischemic stroke (adjusted hazard ratio [95% CI], 2.91 [1.44–5.88]) but did not the risk of all-cause mortality (1.32 [0.97–1.80]). The EA pattern CBI only and both CBIs elevated the risk of recurrent ischemic stroke (2.76 [1.32–5.77] and 5.39 [2.25–12.91], respectively), while the non-EA pattern only did not (1.44 [0.40–5.16]). Conclusions: Our study suggests that AF patients with CBI might have increased risk of recurrent stroke. CBI could be considered when estimating the stroke risk in patients with AF.

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BACKGROUND AND PURPOSE: Data on SAH after M2 mechanical thrombectomy are limited. We aimed to determine the prevalence of sulcal SAH after mechanical thrombectomy for M2 occlusion, its associated predictors, and the resulting clinical outcome. MATERIALS AND METHODS:The study retrospectively reviewed the data of patients with acute ischemic stroke who underwent mechanical thrombectomy for isolated M2 occlusion. The patients were divided into 2 groups according to the presence of sulcal SAH after M2 mechanical thrombectomy. Angiographic and clinical outcomes were compared. Multivariable analysis was performed to identify independent predictors of sulcal SAH and unfavorable outcome (90-day mRS, 3-6).RESULTS: Of the 209 enrolled patients, sulcal SAH was observed in 33 (15.8%) patients. The sulcal SAH group showed a higher rate of distal M2 occlusion (69.7% versus 22.7%), a higher of rate of superior division occlusion (63.6% versus 43.8%), and a higher M2 angulation (median, 128°versus 106°) than the non-sulcal SAH group. Of the 33 sulcal SAH cases, 23 (66.7%) were covert without visible intraprocedural contrast extravasation.