Collecting Food and Drink Intake Data With Voice Input: Development, Usability, and Acceptability Study.

Background: Voice-based systems such as Amazon Alexa may be useful for collecting self-reported information in real time from participants of epidemiology studies using verbal input. In epidemiological research studies, self-reported data tend to be collected using short, infrequent questionnaires, in which the items require participants to select from predefined options, which may lead to errors in the information collected and lack of coverage. Voice-based systems give the potential to collect self-reported information "continuously" over several days or weeks.

A multi-sensor dataset with annotated activities of daily living recorded in a residential setting.

SPHERE is a large multidisciplinary project to research and develop a sensor network to facilitate home healthcare by activity monitoring, specifically towards activities of daily living. It aims to use the latest technologies in low powered sensors, internet of things, machine learning and automated decision making to provide benefits to patients and clinicians. This dataset comprises data collected from a SPHERE sensor network deployment during a set of experiments conducted in the 'SPHERE House' in Bristol, UK, during 2016, including video tracking, accelerometer and environmental sensor data obtained by volunteers undertaking both scripted and non-scripted activities of daily living in a domestic residence.

GLU: a software package for analysing continuously measured glucose levels in epidemiology.

Continuous glucose monitors (CGM) record interstitial glucose levels 'continuously', producing a sequence of measurements for each participant (e.g. the average glucose level every 5 min over several days, both day and night).

Activities of Daily Living Ontology for Ubiquitous Systems: Development and Evaluation.

Ubiquitous eHealth systems based on sensor technologies are seen as key enablers in the effort to reduce the financial impact of an ageing society. At the heart of such systems sit activity recognition algorithms, which need sensor data to reason over, and a ground truth of adequate quality used for training and validation purposes. The large set up costs of such research projects and their complexity limit rapid developments in this area.

Presenteeism in a Dutch hand eczema population-a cross-sectional survey.

Background: Presenteeism (attending work despite complaints and ill health, which should prompt rest and absence) has been overlooked in the field of hand eczema.

Objectives: To examine the 1-year prevalence of presenteeism related to hand eczema in a population of hand eczema patients who visited a tertiary referral centre. Secondary objectives: to identify intrinsic/extrinsic reasons for presenteeism and to evaluate associated factors.

Physical activity phenotyping with activity bigrams, and their association with BMI.

Background: Analysis of physical activity usually focuses on a small number of summary statistics derived from accelerometer recordings: average counts per minute and the proportion of time spent in moderate-vigorous physical activity or in sedentary behaviour. We show how bigrams, a concept from the field of text mining, can be used to describe how a person's activity levels change across (brief) time points. These variables can, for instance, differentiate between two people spending the same time in moderate activity, where one person often stays in moderate activity from one moment to the next and the other does not.

The Work Role Functioning Questionnaire v2.0 Showed Consistent Factor Structure Across Six Working Samples.

Objective The Work Role Functioning Questionnaire v2.0 (WRFQ) is an outcome measure linking a persons' health to the ability to meet work demands in the twenty-first century. We aimed to examine the construct validity of the WRFQ in a heterogeneous set of working samples in the Netherlands with mixed clinical conditions and job types to evaluate the comparability of the scale structure.

Machine learning to assist risk-of-bias assessments in systematic reviews.

Background: Risk-of-bias assessments are now a standard component of systematic reviews. At present, reviewers need to manually identify relevant parts of research articles for a set of methodological elements that affect the risk of bias, in order to make a risk-of-bias judgement for each of these elements. We investigate the use of text mining methods to automate risk-of-bias assessments in systematic reviews.

MR-PheWAS: hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization.

Observational cohort studies can provide rich datasets with a diverse range of phenotypic variables. However, hypothesis-driven epidemiological analyses by definition only test particular hypotheses chosen by researchers. Furthermore, observational analyses may not provide robust evidence of causality, as they are susceptible to confounding, reverse causation and measurement error.

Identifying employees at risk for job loss during sick leave.

Purpose: To examine the associations between medical, work-related, organizational and sociodemographic factors and job loss during sick leave in a Dutch population of 4132 employees on sick leave.

Methods: Data were assessed by occupational health physicians (OHPs) on sociodemographic, medical, work-related and organizational factors. Odds ratios for job loss were calculated in logistic regression models.

Factors associated with first return to work and sick leave durations in workers with common mental disorders.

Background: Associations are examined between socio-demographic, medical, work-related and organizational factors and the moment of first return to work (RTW) (within or after 6 weeks of sick leave) and total sick leave duration in sick leave spells due to common mental disorders.

Methods: Data are derived from a Dutch database, build to provide reference data for sick leave duration for various medical conditions. The cases in this study were entered in 2004 and 2005 by specially trained occupational health physicians, based on the physician's assessment of medical and other factors.

Determinants of sick-leave duration: a tool for managers?

Aims: To provide managers with tools to manage episodes of sick-leave of their employees, the influence of factors such as age, gender, duration of tenure, working full-time or part-time, cause and history of sick-leave, salary and education on sick-leave duration was studied.

Method: In a cross-sectional study, data derived from the 2005 sick-leave files of a Dutch university were examined. Odds ratios of the single risk factors were calculated for short spells (or=91 days) of sick-leave.