8 Few interventions to promote physical activity (PA) adapt dynamically to changes in 9 individuals' behavior. Interventions targeting determinants of behavior are linked with 10 increased effectiveness and should reflect changes in behavior over time. This paper 11 describes the application of two frameworks to assist the development of an adaptive 12 evidence-based smartphone-delivered intervention aimed at influencing PA and sedentary 13 behaviors (SB). Intervention Mapping was used to identify the determinants influencing 14 uptake of PA and optimal behavior change techniques (BCTs). Behavioral Intervention 15 Technology was used to translate and operationalise the BCTs and its modes of delivery. The 16 intervention was based on the Integrated Behavior Change Model, focussed on nine 17 determinants, consisted of 33 BCTs, and included three main components: 1) automated 18 capture of daily PA and SB via an existing smartphone application, 2) classification of the 19 individual into an activity profile according to their PA and SB, 3) behavior change content 20 delivery in a dynamic fashion via a proof-of concept application. This paper illustrates how 21 two complementary frameworks can be used to guide the development of a mobile health 22 behavior change program. This approach can guide the development of future mHealth 23 programs. 24 Keywords: Intervention design; Intervention mapping; Behavioral intervention 25 technology; Physical activity; Sedentary behavior; Integrated behavior change model 26 27 USING INTERVENTION MAPPING AND BIT

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There is a long history of repeatable and comparable evaluation in Information Retrieval (IR). However, thus far, no shared test collection exists that has been designed to support interactive lifelog retrieval. In this paper we introduce the LSC2018 collection, that is designed to evaluate the performance of interactive retrieval systems. We describe the features of the dataset and we report on the outcome of the first Lifelog Search Challenge (LSC), which used the dataset in an interactive competition at ACM ICMR 2018.

Abstract-Smart-phones are becoming our constant companions, they are with us all of the time, being used for calling, web surfing, apps, music listening, TV viewing, social networking, buying, gaming, and a myriad of other uses. Smart-phones are a technology that knows us much better than most of us could imagine. Based on our usage and the fact that we are never far away from our smart phones, they know where we go, who we interact with, what information we consume, and with a little clever software, they can know what we are doing and even why we are doing it. They are beginning to know us better than we know ourselves. In this work we present SenseSeer a generic mobile-cloud-based mobile Lifelogging framework. This framework supports customisable analytic services for sensing the person, understanding the semantics of life activities and the easy deployment of analytic tools and novel interfaces. At present, SenseSeer supports services in many domains, such as personal health monitoring, location tracking, lifestyle analysis and tourism focused applications. This work demonstrate the design principles of SenseSeer and three of its services: My Health, My Location and My Social Activity.

Test collections have a long history of supporting repeatable and comparable evaluation in Information Retrieval (IR). However, thus far, no shared test collection exists for IR systems that are designed to index and retrieve multimodal lifelog data. In this paper we introduce the first test collection for personal lifelog data, which has been employed for the NTCIR12-Lifelog task. In this paper, the requirements for the test collection are motivated, the process of creating the test collection is described, along with an overview of the test collection. Finally suggestions are given for possible applications of the test collection.