Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

40 Years Theory and Model at Wageningen UR

"Theorie en model" zo luidde de titel van de inaugurele rede van CT de Wit (1968). Reden genoeg voor een (theoretische) terugblik op zijn wer

Awareness of lifestyle and colorectal cancer risk:findings from the BeWEL study

It is estimated that 47% of colorectal cancers (CRC) could be prevented by appropriate lifestyles. This study aimed to identify awareness of the causes of CRC in patients who had been diagnosed with a colorectal adenoma through the Scottish Bowel Screening Programme and subsequently enrolled in an intervention trial (using diet and physical activity education and behavioural change techniques) (BeWEL). At baseline and 12-month follow-up, participants answered an open-ended question on factors influencing CRC development. Of the 329 participants at baseline, 40 (12%) reported that they did not know any risk factors and 36 (11%) failed to identify specific factors related to diet and activity. From a potential knowledge score of 1 to 6, the mean score was 1.5 (SD 1.1, range 0 to 5) with no difference between intervention and control groups. At follow-up, the intervention group had a significantly greater knowledge score and better weight loss, diet, and physical activity measures than the control group. Awareness of relevant lifestyle factors for CRC remains low in people at increased risk of the disease. Opportunities within routine NHS screening to aid the capability (including knowledge of risk factors) of individuals to make behavioural changes to reduce CRC risk deserve exploration.Additional co-author: The BeWEL team. The BeWEL Team consists of Shaun Treweek, Fergus Daly, Jill Belch, Jackie Rodger, Alison Kirk, Anne Ludbrook, Petra Rauchhaus, Patricia Norwood, Joyce Thompson, and Jane Wardle

Using the Intervention Mapping and Behavioral Intervention Technology Frameworks: Development of an mHealth Intervention for Physical Activity and Sedentary Behavior Change

Few interventions to promote physical activity (PA) adapt dynamically to changes in individuals' behavior. Interventions targeting determinants of behavior are linked with increased effectiveness and should reflect changes in behavior over time. This article describes the application of two frameworks to assist the development of an adaptive evidence-based smartphone-delivered intervention aimed at influencing PA and sedentary behaviors (SB). Intervention mapping was used to identify the determinants influencing uptake of PA and optimal behavior change techniques (BCTs). Behavioral intervention technology was used to translate and operationalize the BCTs and its modes of delivery. The intervention was based on the integrated behavior change model, focused on nine determinants, consisted of 33 BCTs, and included three main components: (1) automated capture of daily PA and SB via an existing smartphone application, (2) classification of the individual into an activity profile according to their PA and SB, and (3) behavior change content delivery in a dynamic fashion via a proof-of-concept application. This article illustrates how two complementary frameworks can be used to guide the development of a mobile health behavior change program. This approach can guide the development of future mHealth programs