An active learning approach to home heating in the smart grid

A key issue for the realization of the smart grid vision is the implementation of effective demand-side management. One possible approach involves exposing dynamic energy prices to end-users. In this paper, we consider a resulting problem on the user’s side: how to adaptively heat a home given dynamic prices. The user faces the challenge of having to react to dynamic prices in real time, trading off his comfort with the costs of heating his home to a certain temperature. We propose an active learning approach to adjust the home temperature in a semiautomatic way. Our algorithm learns the user’s preferences over time and automatically adjusts the temperature in real-time as prices change. In addition, the algorithm asks the user for feedback once a day. To find the best query time, the algorithm solves an optimal stopping problem. Via simulations, we show that our algorithm learns users’ preferences quickly, and that using the expected utility loss as the query criterion outperforms standard approaches from the active learning literature

Impact of high‐intensity interval training including Indigenous narratives on adolescents’ mental health: a cluster‐randomised controlled trial

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Additional file 1: of Assessing the sustained impact of a school-based obesity prevention program for adolescent boys: the ATLAS cluster randomized controlled trial

CONSORT checklist. (DOCX 23 kb

Additional file 2: Table S2. of Promoting physical activity among adolescent girls: the Girls in Sport group randomized trial

Perceptions of girls regarding changes that occurred in their school as a result of Girls in Sport. Responses from girls in the intervention schools were compared with those from the control schools (N = 1241). (DOCX 111 kb

Additional file 1: Table S1. of Promoting physical activity among adolescent girls: the Girls in Sport group randomized trial

Girls in Sport Criteria Table. (DOCX 20 kb

Additional file 1: of A systematic review and meta-analysis of moderate-to-vigorous physical activity levels in secondary school physical education lessons

A record of the search strategy used for each database. (DOCX 24 kb

Measurement properties of smartphone approaches to assess key lifestyle behaviours: protocol of a systematic review

Background: Six core behavioural risk factors (poor diet, physical activity, sedentary behaviour, alcohol misuse, smoking and unhealthy sleep patterns) have been identified as strong determinants of chronic disease, such as cardiovascular disease, diabetes and cancers. Smartphones have the potential to provide a real-time, pervasive, unobtrusive and cost-effective way to measure health behaviours and deliver instant feedback to users. Despite this, validity of using smartphones to measure these six key behaviours is largely unknown. The proposed systematic review aims to address this gap by identifying existing smartphone-based approaches to measure these health behaviours and critically appraising, comparing and summarizing the quality of their measurement properties. Methods: A systematic search of the Ovid MEDLINE, Embase (Elsevier), Cochrane Library (Wiley), PsychINFO (EBSCOhost), CINAHL (EBSCOHost), Web of Science (Clarivate), SPORTDiscus (EBSCOhost) and IEEE Xplore Digital Library databases will be conducted from January 2007 to March 2020. Eligible studies will be those written in English that measure at least one of the six health behaviours of interest via a smartphone and report on at least one measurement property. The primary outcomes will be validity, reliability and/or responsiveness of these measurement approaches. A secondary outcome will be the feasibility (e.g. user burden, usability and cost) of identified approaches. No restrictions will be placed on the participant population or study design. Two reviewers will independently screen studies for eligibility, extract data and assess the risk of bias. The study methodological quality (or bias) will be appraised using an appropriate tool. Our results will be described in a narrative synthesis. If feasible, random effects meta-analysis will be conducted where appropriate.Discussion: The results from this review will provide important information about the types of smartphone-based approaches currently available to measure the core behavioural risk factors for chronic disease and the quality of their measurement properties. It will allow recommendations on the most suitable and effective measures of these lifestyle behaviours using smartphones. Valid and reliable measurement of these behaviours and risk factor opens the door to targeted and real-time delivery of health behaviour interventions, providing unprecedented opportunities to offset the trajectory toward chronic disease