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Background Little is known about how individuals engage over time with smartphone app interventions and whether this engagement predicts health outcomes. Objective In the context of a randomized trial comparing 2 smartphone apps for smoking cessation, this study aimed to determine distinct groups of smartphone app log-in trajectories over a 6-month period, their association with smoking cessation outcomes at 12 months, and baseline user characteristics that predict data-driven trajectory group membership. Methods Functional clustering of 182 consecutive days of smoothed log-in data from both arms of a large (N=2415) randomized trial of 2 smartphone apps for smoking cessation (iCanQuit and QuitGuide) was used to identify distinct trajectory groups. Logistic regression was used to determine the association of group membership with the primary outcome of 30-day point prevalence of smoking abstinence at 12 months. Finally, the baseline characteristics associated with group membership were examined using logistic and multinomial logistic regression. The analyses were conducted separately for each app. Results For iCanQuit, participants were clustered into 3 groups: “1-week users” (610/1069, 57.06%), “4-week users” (303/1069, 28.34%), and “26-week users” (156/1069, 14.59%). For smoking cessation rates at the 12-month follow-up, compared with 1-week users, 4-week users had 50% higher odds of cessation (30% vs 23%; odds ratio [OR] 1.50, 95% CI 1.05-2.14; P=.03), whereas 26-week users had 397% higher odds (56% vs 23%; OR 4.97, 95% CI 3.31-7.52; P<.001). For QuitGuide, participants were clustered into 2 groups: “1-week users” (695/1064, 65.32%) and “3-week users” (369/1064, 34.68%). The difference in the odds of being abstinent at 12 months for 3-week users versus 1-week users was minimal (23% vs 21%; OR 1.16, 95% CI 0.84-1.62; P=.37). Different baseline characteristics predicted the trajectory group membership for each app. Conclusions Patterns of 1-, 3-, and 4-week smartphone app use for smoking cessation may be common in how people engage in digital health interventions. There were significantly higher odds of quitting smoking among 4-week users and especially among 26-week users of the iCanQuit app. To improve study outcomes, strategies for detecting users who disengage early from these interventions (1-week users) and proactively offering them a more intensive intervention could be fruitful.

A unique self-standing membrane composed of hierarchical thermoplastic polyurethane (TPU)/polyacrylonitrile (PAN) fibers is prepared by the electrospinning technique, followed by a simple dip-coating process. Fe3O4 nanoparticles are uniformly anchored on TPU/PAN fibers during the electrospinning process, enabling the membrane to achieve effective electromagnetic interference shielding (EMI SE) performance. Such a hybrid membrane has a high magnetization of 18.9 emu/g. When MXene (Ti3C2Tx) layers are further loaded on the TPU/PAN/Fe3O4NPs hybrid membrane, its EMI SE performance in the X band can exceed 30 dB due to the hydrogen bonds generated between the macromolecular chain of PAN and the functional group (Tx) on the surface of MXene. Simultaneously, the interfacial attraction between MXene and the TPU/PAN/Fe3O4NPs substrate is enhanced. The EMI SE mechanism of the hybrid membrane indicates that this film has great potential in the fields of wearable devices and flexible materials.

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This paper analyzes a parareal approach based on discontinuous Galerkin (DG) method for the time-dependent Stokes equations. A class of primal discontinuous Galerkin methods, namely variations of interior penalty methods, are adopted for the spatial discretization in the parareal algorithm (we call it parareal DG algorithm). We study three discontinuous Galerkin methods for the time-dependent Stokes equations, and the optimal continuous in time error estimates for the velocities and pressure are derived. Based on these error estimates, the proposed parareal DG algorithm is proved to be unconditionally stable and bounded by the error of discontinuous Galerkin discretization after a finite number of iterations. Finally, some numerical experiments are conducted which confirm our theoretical results, meanwhile, the efficiency of the parareal DG algorithm can be seen through a parallel experiment.