A Guide to the SPHERE 100 Homes Study Dataset

The SPHERE project has developed a multi-modal sensor platform for health and behavior monitoring in residential environments. So far, the SPHERE platform has been deployed for data collection in approximately 50 homes for duration up to one year. This technical document describes the format and the expected content of the SPHERE dataset(s) under preparation. It includes a list of some data quality problems (both known to exist in the dataset(s) and potential ones), their workarounds, and other information important to people working with the SPHERE data, software, and hardware. This document does not aim to be an exhaustive descriptor of the SPHERE dataset(s); it also does not aim to discuss or validate the potential scientific uses of the SPHERE data

Modelling Patient Behaviour Using IoT Sensor Data: a Case Study to Evaluate Techniques for Modelling Domestic Behaviour in Recovery from Total Hip Replacement Surgery

The UK health service sees around 160,000 total hip or knee replacements every year and this number is expected to rise with an ageing population. Expectations of surgical outcomes are changing alongside demographic trends, whilst aftercare may be fractured as a result of resource limitations. Conventional assessments of health outcomes must evolve to keep up with these changing trends. Health outcomes may be assessed largely by self-report using Patient Reported Outcome Measures (PROMs), such as the Oxford Hip or Oxford Knee Score, in the months up to and following surgery. Though widely used, many PROMs have methodological limitations and there is debate about how to interpret results and definitions of clinically meaningful change. With the development of a home-monitoring system, there is opportunity to characterise the relationship between PROMs and behaviour in a natural setting and to develop methods of passive monitoring of outcome and recovery after surgery. In this paper, we discuss the motivation and technology used in long-term continuous observation of movement, sleep and domestic routine for healthcare applications, such as the HEmiSPHERE project for hip and knee replacement patients. In this case study, we evaluate trends evident in data of two patients, collected over a 3-month observation period post-surgery, by comparison with scores from PROMs for sleep and movement quality, and by comparison with a third control home. We find that accelerometer and indoor localisation data correctly highlight long-term trends in sleep and movement quality and can be used to predict sleep and wake times and measure sleep and wake routine variance over time, whilst indoor localisation provides context for the domestic routine and mobility of the patient. Finally, we discuss a visual method of sharing findings with healthcare professionals

Data standardization

With data rapidly becoming the lifeblood of the global economy, the ability to improve its use significantly affects both social and private welfare. Data standardization is key to facilitating and improving the use of data when data portability and interoperability are needed. Absent data standardization, a “Tower of Babel” of different databases may be created, limiting synergetic knowledge production. Based on interviews with data scientists, this Article identifies three main technological obstacles to data portability and interoperability: metadata uncertainties, data transfer obstacles, and missing data. It then explains how data standardization can remove at least some of these obstacles and lead to smoother data flows and better machine learning. The Article then identifies and analyzes additional effects of data standardization. As shown, data standardization has the potential to support a competitive and distributed data collection ecosystem and lead to easier policing in cases where rights are infringed or unjustified harms are created by data-fed algorithms. At the same time, increasing the scale and scope of data analysis can create negative externalities in the form of better profiling, increased harms to privacy, and cybersecurity harms. Standardization also has implications for investment and innovation, especially if lock-in to an inefficient standard occurs. The Article then explores whether market-led standardization initiatives can be relied upon to increase welfare, and the role governmental-facilitated data standardization should play, if at all

Household Smoking Bans in Ohio

Background: Clean indoor air ordinances are being rapidly adopted across the United States to protect persons in public places from exposure to environmental tobacco smoke. The private sphere can be partially protected by adopting a household smoking ban. Objective: To analyze the prevalence and adoption patterns of household smoking bans in Ohio. Design: The 2008 Ohio Family Health Survey collected data using random-digit-dialing methodology and cell phone sampling from more than 50,000 Ohio households that provided sociodemographic and health behavior data for analysis. Respondent, household, neighborhood and regional level data were examined to determine the prevalence of adopting a total household smoking ban. Basic descriptive statistics and chi square analyses were used to determine if there were differences in ban adoption by select characteristics. Results: The variables most closely associated with the adoption of household smoking bans included higher respondent education level, and the presence of children and other adults in a household. Being a current smoker was most negatively related to the adoption of a household smoking ban. Conclusions: Public health officials have done an excellent job promoting the adoption of household smoking bans. It may now be necessary to refocus future campaigns to target those populations that have lower household smoking ban adoption rates, namely those in rural Appalachia, blacks, and smokers.No embarg