Mobile technologies are becoming ubiquitous in the world, changing the way we communicate and provide patient care and services. Some of the most compelling benefits of mobile technologies are in the areas of disease prevention, health management, and care delivery. For all the advances that are occurring in mobile health, its full potential for older adults is only starting to emerge. Yet, existing mobile health applications have design flaws that may limit usability by older adults. The aim of this paper is to review barriers and identify knowledge gaps where more research is needed to improve the accessibility of mobile health use in aging populations. The same observations might apply to those who are not elderly, including individuals suffering from severe mental or medical illnesses.

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For better user satisfaction and business effectiveness, Click-Through Rate (CTR) prediction is one of the most important tasks in Ecommerce. It is often the case that users' interests different from their past routines may emerge or impressions such as promotional items may burst in a very short period. In essence, such changes relate to item evolution problem, which has not been investigated by previous studies. The state-of-the-art methods in the sequential recommendation, which use simple user behaviors, are incapable of modeling these changes sufficiently. It is because, in the user behaviors, outdated interests may exist and the popularity of an item over time is not well represented. To address these limitations, we introduce time-aware item behaviors for addressing the recommendation of emerging preference. The time-aware item behavior for an item is a set of users who interact with this item with timestamps. The rich interaction information of users for an item may help to model its evolution. In this work, we propose a CTR prediction model TIEN based on the time-aware item behavior. In TIEN, by leveraging the interaction time intervals, information of similar users in a short time interval helps identify the emerging user interest of the target user. By using the sequential time intervals, the item's popularity over time can be captured in evolutionary item dynamics. Noisy users who interact with items accidentally are further eliminated thus learning robust personalized item dynamics. To the best of our knowledge, this is the first study to the item evolution problem for E-commerce CTR prediction. We conduct extensive experiments on five real-world CTR prediction datasets. The results show that the TIEN model consistently achieves remarkable improvements to the state-of-the-art methods.

For better user experience and business effectiveness, Click-Through Rate (CTR) prediction has been one of the most important tasks in E-commerce. Although extensive CTR prediction models have been proposed, learning good representation of items from multimodal features is still less investigated, considering an item in E-commerce usually contains multiple heterogeneous modalities. Previous works either concatenate the multiple modality features, that is equivalent to giving a fixed importance weight to each modality; or learn dynamic weights of different modalities for different items through technique like attention mechanism. However, a problem is that there usually exists common redundant information across multiple modalities. The dynamic weights of different modalities computed by using the redundant information may not correctly reflect the different importance of each modality. To address this, we explore the complementarity and redundancy of modalities by considering modality-specific and modality-invariant features differently. We propose a novel Multimodal Adversarial Representation Network (MARN) for the CTR prediction task. A multimodal attention network first calculates the weights of multiple modalities for each item according to its modality-specific features. Then a multimodal adversarial network learns modalityinvariant representations where a double-discriminators strategy is introduced. Finally, we achieve the multimodal item representations by combining both modality-specific and modality-invariant representations. We conduct extensive experiments on both public and industrial datasets, and the proposed method consistently achieves remarkable improvements to the state-of-the-art methods. Moreover, the approach has been deployed in an operational E-commerce system and online A/B testing further demonstrates the effectiveness.

Purpose This study described and evaluated the rapid recruitment of elderly Chinese into clinical research at the Mount Sinai Alzheimer’s Disease Research Center (MSADRC). Design and Methods Methods of publicizing the study included lectures to local senior centers/churches and publications in local Chinese newspapers. The amount of time and success of these methods were evaluated. A “go to them” model of evaluation was employed to enable participants to complete the study visit at locations where they were comfortable. Results From January to December 2015, we recruited 98 participants aged ≥ 65 who primarily speak Mandarin/Cantonese and reside in New York. The mean age and years of education was 73.93±6.34 and 12.79±4.58, respectively. The majority of participants were female (65.3%) and primarily Mandarin speaking (53.1%). Of all enrollees, 54.1% were recruited from community lectures, 29.6% through newspapers, 10.2% through word of mouth, and 6.1% from our clinical services. 40.8% of participants underwent evaluations at the MSADRC, 44.9% at local senior centers/churches, and 14.3% at home. Implications Given that the majority of our participants had low English proficiency, the use of bilingual recruiters probably allowed us to overcome the language barrier, facilitating recruitment. Our “go to them” model of evaluation is another important factor contributing to our successful recruitment.