Wearable cameras can reduce dietary under-reporting: doubly labelled water validation of a camera-assisted 24 h recall

Preliminary research has suggested that wearable cameras may reduce under-reporting of energy intake (EI) in self-reported dietary assessment. The aim of the present study was to test the validity of a wearable camera-assisted 24 h dietary recall against the doubly labelled water (DLW) technique. Total energy expenditure (TEE) was assessed over 15 d using the DLW protocol among forty adults (n 20 males, age 35 (sd 17) years, BMI 27 (sd 4) kg/m2 and n 20 females, age 28 (sd 7) years, BMI 22 (sd 2) kg/m2). EI was assessed using three multiple-pass 24 h dietary recalls (MP24) on days 2-4, 8-10 and 13-15. On the days before each nutrition assessment, participants wore an automated wearable camera (SenseCam (SC)) in free-living conditions. The wearable camera images were viewed by the participants following the completion of the dietary recall, and their changes in self-reported intakes were recorded (MP24+SC). TEE and EI assessed by the MP24 and MP24+SC methods were compared. Among men, the MP24 and MP24+SC measures underestimated TEE by 17 and 9%, respectively (P< 0.001 and P= 0.02). Among women, these measures underestimated TEE by 13 and 7%, respectively (P< 0.001 and P= 0.004). The assistance of the wearable camera (MP24+SC) reduced the magnitude of under-reporting by 8% for men and 6% for women compared with the MP24 alone (P< 0.001 and P< 0.001). The increase in EI was predominantly from the addition of 265 unreported foods (often snacks) as revealed by the participants during the image review. Wearable cameras enhance the accuracy of self-report by providing passive and objective information regarding dietary intake. High-definition image sensors and increased imaging frequency may improve the accuracy further.

Social desirability bias in the reporting of alcohol consumption: a randomized trial

OBJECTIVE: To investigate reporting of alcohol consumption, we manipulated the contexts of questions in ways designed to induce social desirability bias. METHOD: We undertook a two-arm, parallel-group, individually randomized trial at an Australian public university. Students were recruited by email to a web-based "Research Project on Student Health Behavior." Respondents answered nine questions about their physical activity, diet, and smoking. They were unknowingly randomized to a group presented with either (A) three questions about their alcohol consumption or (B) seven questions about their alcohol dependence and problems (under a prominent header labeled "Alcohol Use Disorders Identification Test"), followed by the same three alcohol consumption questions from (A). RESULTS: A total of 3,594 students (mean age = 27, SD = 10) responded and were randomized: 1,778 to Group A and 1,816 to Group B. Outcome measures were the number of days they drank alcohol, the typical number of drinks they consumed per drinking day, and the number of days they consumed six or more drinks. The primary analysis included participants with any alcohol consumption in the preceding 4 weeks (1,304 in Group A; 1,340 in Group B) using between-group, two-tailed t tests. RESULTS: In Groups A and B, respectively, means (and SDs) of the number of days drinking were 5.89 (5.92) versus 6.06 (6.12), p = .49; typical number of drinks per drinking day: 4.02 (3.87) versus 3.82 (3.76), p = .17; and number of days consuming six or more drinks: 1.69 (2.94) versus 1.67 (3.25), p = .56. CONCLUSIONS: We could not reject the null hypothesis because earlier questions about alcohol dependence and problems showed no sign of biasing the respondents' subsequent reports of alcohol consumption. These data support the validity of university students' reporting of alcohol consumption in web-based studies.

Interactive data and retail investor decision-making: an experimental study

The results of an experimental study of retail investors' use of eXtensible Business Reporting Language tagged (interactive) data and PDF format for making investment decisions are reported. The main finding is that data format made no difference to participants' ability to locate and integrate information from statement footnotes to improve investment decisions. Interactive data were perceived by participants as quick and ‘accurate’, but it failed to facilitate the identification of the adjustment needed to make the ratios accurate for comparison. An important implication is that regulators and software designers should work to reduce user reliance on the comparability of ratios generated automatically using interactive data.

Digital business reporting standards: mapping the battle in France

Government agencies use information technology extensively to collect business data for regulatory purposes. Data communication standards form part of the infrastructure with which businesses must conform to survive. We examine the development of, and emerging competition between, two open business reporting data standards adopted by government bodies in France; Electronic Data Interchange for Administration, Commerce and Transport (EDIFACT) (incumbent) and eXtensible Business Reporting Language (XBRL) (challenger). The research explores whether an incumbent may be displaced in a setting in which the contest is unresolved. Latour's translation map is applied to trace the enrollments and detours in the battle. We find that regulators play an important role as allies in the development of the standards. The antecedent networks in which the standards are located embed strong beliefs that become barriers to collaboration and fuel the battle. One of the key differentiating attitudes is whether speed is more important than legitimacy. The failure of collaboration encourages competition. The newness of XBRL's technology just as regulators need to respond to an economic crisis and its adoption by French regulators not using EDIFACT create an opportunity for the challenger to make significant network gains over the longer term. ANT also highlights the importance of the preservation of key components of EDIFACT in ebXML.

The SEC's retail investor 2.0: interactive data and the rise of calculative accountability

The Securities and Exchange Commission (SEC) in the United States mandated a new digital reporting system for US companies in late 2008. The new generation of information provision has been dubbed by Chairman Cox, ‘interactive data’ (SEC, 2006a). Despite the promise of its name, we find that in the development of the project retail investors are invoked as calculative actors rather than engaged in dialogue. Similarly, the potential for the underlying technology to be applied in ways to encourage new forms of accountability appears to be forfeited in the interests of enrolling company filers.We theorise the activities of the SEC and in particular its chairman at the time, Christopher Cox, over a three year period, both prior to and following the ‘credit crisis’. We argue that individuals and institutions play a central role in advancing the socio-technical project that is constituted by interactive data. We adopt insights from ANT (Callon, 1986, Latour, 1987 and Latour, 2005b) and governmentality (Miller, 2008 and Miller and Rose, 2008) to show how regulators and the proponents of the technology have acted as spokespersons for the interactive data technology and the retail investor. We examine the way in which calculative accountability has been privileged in the SEC's construction of the retail investor as concerned with atomised, quantitative data (Kamuf, 2007, Roberts, 2009 and Tsoukas, 1997). We find that the possibilities for the democratising effects of digital information on the Internet has not been realised in the interactive data project and that it contains risks for the very investors the SEC claims to seek to protect.

Persistent and contemporaneous effects of job stressors on mental health: a study testing multiple analytic approaches across 13 waves of annually collected cohort data

OBJECTIVES: This study investigated the extent that psychosocial job stressors had lasting effects on a scaled measure of mental health. We applied econometric approaches to a longitudinal cohort to: (1) control for unmeasured individual effects; (2) assess the role of prior (lagged) exposures of job stressors on mental health and (3) the persistence of mental health.

METHODS: We used a panel study with 13 annual waves and applied fixed-effects, first-difference and fixed-effects Arellano-Bond models. The Short Form 36 (SF-36) Mental Health Component Summary score was the outcome variable and the key exposures included: job control, job demands, job insecurity and fairness of pay.

RESULTS: Results from the Arellano-Bond models suggest that greater fairness of pay (β-coefficient 0.34, 95% CI 0.23 to 0.45), job control (β-coefficient 0.15, 95% CI 0.10 to 0.20) and job security (β-coefficient 0.37, 95% CI 0.32 to 0.42) were contemporaneously associated with better mental health. Similar results were found for the fixed-effects and first-difference models. The Arellano-Bond model also showed persistent effects of individual mental health, whereby individuals' previous reports of mental health were related to their reporting in subsequent waves. The estimated long-run impact of job demands on mental health increased after accounting for time-related dynamics, while there were more minimal impacts for the other job stressor variables.

CONCLUSIONS: Our results showed that the majority of the effects of psychosocial job stressors on a scaled measure of mental health are contemporaneous except for job demands where accounting for the lagged dynamics was important.

Text2PreventCVD: Protocol for a systematic review and individual participant data meta-analysis of text-message based interventions for the prevention of cardiovascular diseases.

Introduction Text message interventions have been shown to be effective in prevention and management of several non-communicable disease risk factors. However, the extent to which their effects might vary in different participants and settings is uncertain. We aim to conduct a systematic review and individual participant data (IPD) meta-analysis of randomised clinical trials examining text message interventions aimed to prevent cardiovascular diseases (CVD) through modification of cardiovascular risk factors (CVRFs). Methods and analysis Systematic review and IPD meta-analysis will be conducted according to Preferred Reporting Items for Systematic review and Meta-Analysis of IPD (PRISMA-IPD) guidelines. Electronic database of published studies (MEDLINE, EMBASE, PsycINFO and Cochrane Library) and international trial registries will be searched to identify relevant randomised clinical trials. Authors of studies meeting the inclusion criteria will be invited to join the IPD meta-analysis group and contribute study data to the common database. The primary outcome will be the difference between intervention and control groups in blood pressure at 6-month follow-up. Key secondary outcomes include effects on lipid parameters, body mass index, smoking levels and self-reported quality of life. If sufficient data is available, we will also analyse blood pressure and other secondary outcomes at 12 months. IPD meta-analysis will be performed using a one-step approach and modelling data simultaneously while accounting for the clustering of the participants within studies. This study will use the existing data to assess the effectiveness of text message-based interventions on CVRFs, the consistency of any effects by participant subgroups and across different healthcare settings. Ethics and dissemination Ethical approval was obtained for the individual studies by the trial investigators from relevant local ethics committees. This study will include anonymised data for secondary analysis and investigators will be asked to check that this is consistent with their existing approvals. Results will be disseminated via scientific forums including peer-reviewed publications and presentations at international conferences.

Guidelines for developing and reporting machine learning predictive models in biomedical research : a multidisciplinary view

BACKGROUND: As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs.

OBJECTIVE: To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence.

METHODS: A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method.

RESULTS: The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models.

CONCLUSIONS: A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community.

FTO genotype and weight loss: systematic review and meta-analysis of 9563 individual participant data from eight randomised controlled trials

OBJECTIVE:  To assess the effect of the FTO genotype on weight loss after dietary, physical activity, or drug based interventions in randomised controlled trials.

DESIGN:  Systematic review and random effects meta-analysis of individual participant data from randomised controlled trials.

DATA SOURCES:  Ovid Medline, Scopus, Embase, and Cochrane from inception to November 2015.

ELIGIBILITY CRITERIA FOR STUDY SELECTION:  Randomised controlled trials in overweight or obese adults reporting reduction in body mass index, body weight, or waist circumference by FTO genotype (rs9939609 or a proxy) after dietary, physical activity, or drug based interventions. Gene by treatment interaction models were fitted to individual participant data from all studies included in this review, using allele dose coding for genetic effects and a common set of covariates. Study level interactions were combined using random effect models. Metaregression and subgroup analysis were used to assess sources of study heterogeneity.

RESULTS:  We identified eight eligible randomised controlled trials for the systematic review and meta-analysis (n=9563). Overall, differential changes in body mass index, body weight, and waist circumference in response to weight loss intervention were not significantly different between FTO genotypes. Sensitivity analyses indicated that differential changes in body mass index, body weight, and waist circumference by FTO genotype did not differ by intervention type, intervention length, ethnicity, sample size, sex, and baseline body mass index and age category.

CONCLUSIONS:  We have observed that carriage of the FTO minor allele was not associated with differential change in adiposity after weight loss interventions. These findings show that individuals carrying the minor allele respond equally well to dietary, physical activity, or drug based weight loss interventions and thus genetic predisposition to obesity associated with the FTO minor allele can be at least partly counteracted through such interventions.