A phase II trial of nivolumab followed by ipilimumab and nivolumab in advanced non-clear-cell renal cell carcinoma.

OBJECTIVE: To evaluate the efficacy of sequential treatment with ipilimumab and nivolumab following progression on nivolumab monotherapy in individuals with advanced, non-clear-cell renal cell carcinoma (nccRCC). MATERIALS AND METHODS: UNISoN (ANZUP1602; NCT03177239) was an open-label, single-arm, phase 2 clinical trial that recruited adults with immunotherapy-naïve, advanced nccRCC. Participants received nivolumab 240 mg i.v. two-weekly for up to 12 months (Part 1), followed by sequential addition of ipilimumab 1 mg/kg three-weekly for four doses to nivolumab if disease progression occurred during treatment (Part 2). The primary endpoint was objective tumour response rate (OTRR) and secondary endpoints included duration of response (DOR), progression-free (PFS) and overall survival (OS), and toxicity (treatment-related adverse events). RESULTS: A total of 83 participants were eligible for Part 1, including people with papillary (37/83, 45%), chromophobe (15/83, 18%) and other nccRCC subtypes (31/83, 37%); 41 participants enrolled in Part 2. The median (range) follow-up was 22 (16-30) months. In Part 1, the OTRR was 16.9% (95% confidence interval [CI] 9.5-26.7), the median DOR was 20.7 months (95% CI 3.7-not reached) and the median PFS was 4.0 months (95% CI 3.6-7.4). Treatment-related adverse events were reported in 71% of participants; 19% were grade 3 or 4. For participants who enrolled in Part 2, the OTRR was 10%; the median DOR was 13.5 months (95% CI 4.8-19.7) and the median PFS 2.6 months (95% CI 2.2-3.8). Treatment-related adverse events occurred in 80% of these participants; 49% had grade 3, 4 or 5. The median OS was 24 months (95% CI 16-28) from time of enrolment in Part 1. CONCLUSIONS: Nivolumab monotherapy had a modest effect overall, with a few participants experiencing a long DOR. Sequential combination immunotherapy by addition of ipilimumab in the context of disease progression to nivolumab in nccRCC is not supported by this study, with only a minority of participants benefiting from this strategy.

Development of a key performance indicator for breast cancer in Queensland, Australia.

PURPOSE: Using population-based data for women diagnosed with stage I-III breast cancer, our aim was to examine the impact of time to treatment completion on survival and to identify factors associated with treatment delay. METHODS: This retrospective study used clinical and treatment data from the Queensland Oncology Repository. Time from diagnosis to completing surgery, chemotherapy and radiation therapy identified a cut-off of 37 weeks as the optimal threshold for completing treatment. Logistic regression was used to identify factors associated with the likelihood of completing treatment > 37 weeks. Overall (OS) and breast cancer-specific survival (BCSS) were examined using Cox proportional hazards models. RESULTS: Of 8279 women with stage I-III breast cancer, 31.9% completed treatment > 37 weeks. Apart from several clinical factors, being Indigenous (p = 0.002), living in a disadvantaged area (p = 0.003) and receiving ≥ two treatment modalities within the public sector (p < 0.001) were associated with an increased likelihood of completing treatment > 37 weeks. The risk of death from any cause was about 40% higher for women whose treatment went beyond 37 weeks (HR 1.37, 95%CI 1.16-1.61), a similar result was observed for BCSS. Using the surgery + chemotherapy + radiation pathway, a delay of > 6.9 weeks from surgery to starting chemotherapy was significantly associated with poorer survival (p = 0.001). CONCLUSIONS: Several sociodemographic and system-related factors were associated with a greater likelihood of treatment completion > 37 weeks. We are proposing a key performance indicator for the management of early breast cancer where a facility should have > 90% of patients with a time from surgery to adjuvant chemotherapy < 6.9 weeks.

Supermarket Transaction Records In Dietary Evaluation: the STRIDE study: validation against self-reported dietary intake.

OBJECTIVE: Scalable methods are required for population dietary monitoring. The Supermarket Transaction Records In Dietary Evaluation (STRIDE) study compares dietary estimates from supermarket transactions with an online FFQ. DESIGN: Participants were recruited in four waves, accounting for seasonal dietary variation. Purchases were collected for 1 year during and 1 year prior to the study. Bland-Altman agreement and limits of agreement (LoA) were calculated for energy, sugar, fat, saturated fat, protein and sodium (absolute and relative). SETTING: This study was partnered with a large UK retailer. PARTICIPANTS: Totally, 1788 participants from four UK regions were recruited from the retailer's loyalty card customer database, according to breadth and frequency of purchases. Six hundred and eighty-six participants were included for analysis. RESULTS: The analysis sample were mostly female (72 %), with a mean age of 56 years (sd 13). The ratio of purchases to intakes varied depending on amounts purchased and consumed; purchases under-estimated intakes for smaller amounts on average, but over-estimated for larger amounts. For absolute measures, the LoA across households were wide, for example, for energy intake of 2000 kcal, purchases could under- or over-estimate intake by a factor of 5; values could be between 400 kcal and 10000 kcal. LoA for relative (energy-adjusted) estimates were smaller, for example, for 14 % of total energy from saturated fat, purchase estimates may be between 7 % and 27 %. CONCLUSIONS: Agreement between purchases and intake was highly variable, strongest for smaller loyal households and for relative values. For some customers, relative nutrient purchases are a reasonable proxy for dietary composition indicating utility in population-level dietary research.

Trends in surgery and adjuvant treatment for early-stage breast cancer: a population-based study in Queensland, Australia.

PURPOSE: Our aim was to describe variations in the treatment of early-stage breast cancer and to examine factors associated with disease-specific survival (DSS). METHODS: The study used linked data for 24,190 women with a T1 (≤ 20 mm) breast cancer who underwent surgery from 2005 to 2019. Multivariate logistic regression was used to model predictors of receiving breast-conserving surgery (BCS) compared to mastectomy and a multinomial model was used to examine factors associated with type(s) of treatment received. RESULTS: Overall, 70.3% had BCS, with a reduced likelihood of BCS observed for younger women (p < 0.001), rural residence, (p < 0.001), socioeconomic disadvantage (p = 0.004), higher tumour grade (p < 0.001) and surgery in a public versus private hospital (p < 0.001). Compared to women who received BCS and radiation therapy (RT), those having mastectomy alone or mastectomy plus RT were more likely to be younger (p < 0.001), live in a rural area (p < 0.001), have higher-grade tumours (p < 0.001) and positive lymph nodes (p < 0.001). Overall 5-year survival was 95.3% and breast cancer-specific survival was 98.3%. Highest survival was observed for women having BCS and RT and lowest for those having mastectomy and RT (p < 0.001). CONCLUSION: Our results indicate some variation in the management of early-stage breast cancer. Lower rates of BCS were observed for rural and disadvantaged women and for those treated in a public or low-volume hospital. Whilst survival was high for this cohort, differences in tumour biology likely explain the differences in survival according to treatment type.

Objectively measuring the association between the built environment and physical activity: a systematic review and reporting framework.

BACKGROUND: Objective measures of built environment and physical activity provide the opportunity to directly compare their relationship across different populations and spatial contexts. This systematic review synthesises the current body of knowledge and knowledge gaps around the impact of objectively measured built environment metrics on physical activity levels in adults (≥ 18 years). Additionally, this review aims to address the need for improved quality of methodological reporting to evaluate studies and improve inter-study comparability though the creation of a reporting framework. METHODS: A systematic search of the literature was conducted following the PRISMA guidelines. After abstract and full-text screening, 94 studies were included in the final review. Results were synthesised using an association matrix to show overall association between built environment and physical activity variables. Finally, the new PERFORM ('Physical and Environmental Reporting Framework for Objectively Recorded Measures') checklist was created and applied to the included studies rating them on their reporting quality across four key areas: study design and characteristics, built environment exposures, physical activity metrics, and the association between built environment and physical activity. RESULTS: Studies came from 21 countries and ranged from two days to six years in duration. Accelerometers and using geographic information system (GIS) to define the spatial extent of exposure around a pre-defined geocoded location were the most popular tools to capture physical activity and built environment respectively. Ethnicity and socio-economic status of participants were generally poorly reported. Moderate-to-vigorous physical activity (MVPA) was the most common metric of physical activity used followed by walking. Commonly investigated elements of the built environment included walkability, access to parks and green space. Areas where there was a strong body of evidence for a positive or negative association between the built environment and physical activity were identified. The new PERFORM checklist was devised and poorly reported areas identified, included poor reporting of built environment data sources and poor justification of method choice. CONCLUSIONS: This systematic review highlights key gaps in studies objectively measuring the built environment and physical activity both in terms of the breadth and quality of reporting. Broadening the variety measures of the built environment and physical activity across different demographic groups and spatial areas will grow the body and quality of evidence around built environment effect on activity behaviour. Whilst following the PERFORM reporting guidance will ensure the high quality, reproducibility, and comparability of future research.

A foresight whole systems obesity classification for the English UK biobank cohort.

BACKGROUND: The number of people living with obesity or who are overweight presents a global challenge, and the development of effective interventions is hampered by a lack of research which takes a joined up, whole system, approach that considers multiple elements of the complex obesity system together. We need to better understand the collective characteristics and behaviours of those who are overweight or have obesity and how these differ from those who maintain a healthy weight. METHODS: Using the UK Biobank cohort we develop an obesity classification system using k-means clustering. Variable selection from the UK Biobank cohort is informed by the Foresight obesity system map across key domains (Societal Influences, Individual Psychology, Individual Physiology, Individual Physical Activity, Physical Activity Environment). RESULTS: Our classification identifies eight groups of people, similar in respect to their exposure to known drivers of obesity: 'Younger, urban hard-pressed', 'Comfortable, fit families', 'Healthy, active and retirees', 'Content, rural and retirees', 'Comfortable professionals', 'Stressed and not in work', 'Deprived with less healthy lifestyles' and 'Active manual workers'. Pen portraits are developed to describe the characteristics of these different groups. Multinomial logistic regression is used to demonstrate that the classification can effectively detect groups of individuals more likely to be living with overweight or obesity. The group identified as 'Comfortable, fit families' are observed to have a higher proportion of healthy weight, while three groups have increased relative risk of being overweight or having obesity: 'Active manual workers', 'Stressed and not in work' and 'Deprived with less healthy lifestyles'. CONCLUSIONS: This paper presents the first study of UK Biobank participants to adopt this obesity system approach to characterising participants. It provides an innovative new approach to better understand the complex drivers of obesity which has the potential to produce meaningful tools for policy makers to better target interventions across the whole system to reduce overweight and obesity.

Opportunities to improve behavioral health crisis response: Results of a large urban county's community status assessment.

A comprehensive community status assessment of an Ohio urban county's crisis response (CR) system explored the experiences of its behavioral health services' clients and providers to surface themes characterizing the system's responsiveness and identifying opportunities for improvements. Forty-eight focus groups and two online surveys were conducted. Data were analyzed using qualitative content analysis and descriptive statistics. The greatest areas of needed improvement ascertained by this effort are in increased CR system resources, more efficient use of resources, and capacity enhancements in nine areas: the mobile crisis team, CR protocols, psychiatric inpatient and crisis stabilization beds, stabilization admission for eligible persons, stabilization services for in-crisis but admission-ineligible persons, continuity of care, research into child versus adult CR systems, Provider Emergency Support Program, and first responder crisis intervention training. The assessment provides a foundation for the county to identify further opportunities for system scale-up.

The impact of body mass index on breast cancer incidence among women at increased risk: an observational study from the International Breast Intervention Studies.

BACKGROUND: We investigated the association between body mass index (BMI) and breast cancer risk in women at increased risk of breast cancer receiving tamoxifen or anastrozole compared with placebo using data from the International Breast Cancer Intervention Studies [IBIS-I (tamoxifen) and IBIS-II (anastrozole)]. METHODS: Baseline BMI was calculated from nurse assessed height and weight measurements for premenopausal (n = 3138) and postmenopausal (n = 3731) women in IBIS-I and postmenopausal women in IBIS-II (n = 3787). The primary endpoint was any breast cancer event (invasive and ductal carcinoma in situ). We used Cox proportional hazards regression to calculate hazard ratios (HRs) for risk after adjustment for covariates. RESULTS: There were 582 (IBIS-I) and 248 (IBIS-II) breast cancer events [median follow-up = 16.2 years (IQR 14.4-17.7) and 10.9 years (IQR 8.8-13.0), respectively]. In adjusted analysis, women with a higher BMI had an increased breast cancer risk in both IBIS-I [HR = 1.06 per 5 kg/m2 (0.99-1.15), p = 0.114] and in IBIS-II [HR per 5 kg/m2 = 1.21 (1.09-1.35), p < 0.001]. In IBIS-I, the association between BMI and breast cancer risk was positive in postmenopausal women [adjusted HR per 5 kg/m2 = 1.14 (1.03-1.26), p = 0.01] but not premenopausal women [adjusted HR per 5 kg/m2 = 0.97 (0.86-1.09), p = 0.628]. There was no interaction between BMI and treatment group for breast cancer risk in either IBIS-I (p = 0.62) or IBIS-II (p = 0.55). CONCLUSIONS: Higher BMI is associated with greater breast cancer risk in postmenopausal women at increased risk of the disease, but no effect was observed in premenopausal women. The lack of interaction between BMI and treatment group on breast cancer risk suggests women are likely to experience benefit from preventive therapy regardless of their BMI. Trial registration Both trials were registered [IBIS-I: ISRCTN91879928 on 24/02/2006, retrospectively registered ( http://www.isrctn.com/ISRCTN91879928 ); IBIS-II: ISRCTN31488319 on 07/01/2005, retrospectively registered ( http://www.isrctn.com/ISRCTN31488319 )].

Can a data driven obesity classification system identify those at risk of severe COVID-19 in the UK Biobank cohort study?

COVID-19 is a disease that has been shown to have outcomes that vary by certain socio-demographic and socio-economic groups. It is increasingly important that an understanding of these outcomes should be derived not from the consideration of one aspect, but by a more multi-faceted understanding of the individual. In this study use is made of a recent obesity driven classification of participants in the United Kingdom Biobank (UKB) to identify trends in COVID-19 outcomes. This classification is informed by a recently created obesity systems map, and the COVID-19 outcomes are: undertaking a test, a positive test, hospitalisation and mortality. It is demonstrated that the classification is able to identify meaningful differentials in these outcomes. This more holistic approach is recommended for identification and prioritisation of COVID-19 risk and possible long-COVID determination.

Clinical presentation and outcomes of COVID-19 following hematopoietic cell transplantation and cellular therapy.

BACKGROUND: One year into the pandemic, published data on hematopoietic cell transplantation (HCT) recipients with coronavirus disease 2019 (COVID-19) remain limited. METHODS: Single-center retrospective cohort study of adult HCT recipients with polymerase chain reaction (PCR)-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. RESULTS: Twenty-eight consecutive transplantation and cellular therapy patients (autologous, n = 12; allogeneic, n = 15; chimeric antigen receptor T-cell therapy [CAR-T], n = 1) with COVID-19 were identified. The median age was 57 years. The median time from HCT to COVID-19 diagnosis was 656 days (interquartile range [IQR], 33-1274). Patients were followed for a median of 59 days (IQR, 40-88). Among assessable patients (n = 19), 10 (53%) had documented virological clearance; median time to clearance was 34 days (range, 21-56). Out of 28, 12 (43%), 6 (21%), and 10 (36%) patients had mild, moderate, and severe/critical disease, respectively. Overall mortality was 25%, nearly identical for autologous and allogeneic HCT, and exclusively seen in hospitalized patients, older than 50 years of age with severe COVID-19. None of the patients with mild (n = 12) or moderate (n = 6) COVID-19 died whereas 7/10 patients (70%) with severe/critical COVID-19 died (P = .0001). Patients diagnosed with COVID-19 within 12 months of HCT exhibited higher mortality (57% vs 14%; P = .04). All-cause 30-day mortality (n = 4) was 14%. A higher proportion of patients who died within 30 days of COVID-19 diagnosis (3/4) were receiving ≥2 immunosuppressants, compared with patients who survived beyond 30 days after COVID-19 diagnosis (2/24; 75% vs. 8%; P = .01). CONCLUSIONS: Mortality in COVID-19 HCT patients is higher than that of the age-comparable general population and largely dependent on age, disease severity, timing from HCT, and intensity of immunosuppression.

Understanding Barriers to Novel Data Linkages: Topic Modeling of the Results of the LifeInfo Survey.

BACKGROUND: Novel consumer and lifestyle data, such as those collected by supermarket loyalty cards or mobile phone exercise tracking apps, offer numerous benefits for researchers seeking to understand diet- and exercise-related risk factors for diseases. However, limited research has addressed public attitudes toward linking these data with individual health records for research purposes. Data linkage, combining data from multiple sources, provides the opportunity to enhance preexisting data sets to gain new insights. OBJECTIVE: The aim of this study is to identify key barriers to data linkage and recommend safeguards and procedures that would encourage individuals to share such data for potential future research. METHODS: The LifeInfo Survey consulted the public on their attitudes toward sharing consumer and lifestyle data for research purposes. Where barriers to data sharing existed, participants provided unstructured survey responses detailing what would make them more likely to share data for linkage with their health records in the future. The topic modeling technique latent Dirichlet allocation was used to analyze these textual responses to uncover common thematic topics within the texts. RESULTS: Participants provided responses related to sharing their store loyalty card data (n=2338) and health and fitness app data (n=1531). Key barriers to data sharing identified through topic modeling included data safety and security, personal privacy, requirements of further information, fear of data being accessed by others, problems with data accuracy, not understanding the reason for data linkage, and not using services that produce these data. We provide recommendations for addressing these issues to establish the best practice for future researchers interested in using these data. CONCLUSIONS: This study formulates a large-scale consultation of public attitudes toward this kind of data linkage, which is an important first step in understanding and addressing barriers to participation in research using novel consumer and lifestyle data.

Clustering Accelerometer Activity Patterns from the UK Biobank Cohort.

Many researchers are beginning to adopt the use of wrist-worn accelerometers to objectively measure personal activity levels. Data from these devices are often used to summarise such activity in terms of averages, variances, exceedances, and patterns within a profile. In this study, we report the development of a clustering utilising the whole activity profile. This was achieved using the robust clustering technique of k-medoids applied to an extensive data set of over 90,000 activity profiles, collected as part of the UK Biobank study. We identified nine distinct activity profiles in these data, which captured both the pattern of activity throughout a week and the intensity of the activity: "Active 9 to 5", "Active", "Morning Movers", "Get up and Active", "Live for the Weekend", "Moderates", "Leisurely 9 to 5", "Sedate" and "Inactive". These patterns are differentiated by sociodemographic, socioeconomic, and health and circadian rhythm data collected by UK Biobank. The utility of these findings are that they sit alongside existing summary measures of physical activity to provide a way to typify distinct activity patterns that may help to explain other health and morbidity outcomes, e.g., BMI or COVID-19. This research will be returned to the UK Biobank for other researchers to use.

Evidence from big data in obesity research: international case studies.

BACKGROUND/OBJECTIVE: Obesity is thought to be the product of over 100 different factors, interacting as a complex system over multiple levels. Understanding the drivers of obesity requires considerable data, which are challenging, costly and time-consuming to collect through traditional means. Use of 'big data' presents a potential solution to this challenge. Big data is defined by Delphi consensus as: always digital, has a large sample size, and a large volume or variety or velocity of variables that require additional computing power (Vogel et al. Int J Obes. 2019). 'Additional computing power' introduces the concept of big data analytics. The aim of this paper is to showcase international research case studies presented during a seminar series held by the Economic and Social Research Council (ESRC) Strategic Network for Obesity in the UK. These are intended to provide an in-depth view of how big data can be used in obesity research, and the specific benefits, limitations and challenges encountered. METHODS AND RESULTS: Three case studies are presented. The first investigated the influence of the built environment on physical activity. It used spatial data on green spaces and exercise facilities alongside individual-level data on physical activity and swipe card entry to leisure centres, collected as part of a local authority exercise class initiative. The second used a variety of linked electronic health datasets to investigate associations between obesity surgery and the risk of developing cancer. The third used data on tax parcel values alongside data from the Seattle Obesity Study to investigate sociodemographic determinants of obesity in Seattle. CONCLUSIONS: The case studies demonstrated how big data could be used to augment traditional data to capture a broader range of variables in the obesity system. They also showed that big data can present improvements over traditional data in relation to size, coverage, temporality, and objectivity of measures. However, the case studies also encountered challenges or limitations; particularly in relation to hidden/unforeseen biases and lack of contextual information. Overall, despite challenges, big data presents a relatively untapped resource that shows promise in helping to understand drivers of obesity.

Rapid, quantitative therapeutic screening for Alzheimer's enzymes enabled by optimal signal transduction with transistors.

We show that commercially sourced n-channel silicon field-effect transistors (nFETs) operating above their threshold voltage with closed loop feedback to maintain a constant channel current allow a pH readout resolution of (7.2 ± 0.3) × 10-3 at a bandwidth of 10 Hz, or ≈3-fold better than the open loop operation commonly employed by integrated ion-sensitive field-effect transistors (ISFETs). We leveraged the improved nFET performance to measure the change in solution pH arising from the activity of a pathological form of the kinase Cdk5, an enzyme implicated in Alzheimer's disease, and showed quantitative agreement with previous measurements. The improved pH resolution was realized while the devices were operated in a remote sensing configuration with the pH sensing element off-chip and connected electrically to the FET gate terminal. We compared these results with those measured by using a custom-built dual-gate 2D field-effect transistor (dg2DFET) fabricated with 2D semi-conducting MoS2 channels and a signal amplification of 8. Under identical solution conditions the nFET performance approached the dg2DFETs pH resolution of (3.9 ± 0.7) × 10-3. Finally, using the nFETs, we demonstrated the effectiveness of a custom polypeptide, p5, as a therapeutic agent in restoring the function of Cdk5. We expect that the straight-forward modifications to commercially sourced nFETs demonstrated here will lower the barrier to widespread adoption of these remote-gate devices and enable sensitive bioanalytical measurements for high throughput screening in drug discovery and precision medicine applications.

Validation of the Oxford WebQ Online 24-Hour Dietary Questionnaire Using Biomarkers.

The Oxford WebQ is an online 24-hour dietary questionnaire that is appropriate for repeated administration in large-scale prospective studies, including the UK Biobank study and the Million Women Study. We compared the performance of the Oxford WebQ and a traditional interviewer-administered multiple-pass 24-hour dietary recall against biomarkers for protein, potassium, and total sugar intake and total energy expenditure estimated by accelerometry. We recruited 160 participants in London, United Kingdom, between 2014 and 2016 and measured their biomarker levels at 3 nonconsecutive time points. The measurement error model simultaneously compared all 3 methods. Attenuation factors for protein, potassium, total sugar, and total energy intakes estimated as the mean of 2 applications of the Oxford WebQ were 0.37, 0.42, 0.45, and 0.31, respectively, with performance improving incrementally for the mean of more measures. Correlation between the mean value from 2 Oxford WebQs and estimated true intakes, reflecting attenuation when intake is categorized or ranked, was 0.47, 0.39, 0.40, and 0.38, respectively, also improving with repeated administration. These correlations were similar to those of the more administratively burdensome interviewer-based recall. Using objective biomarkers as the standard, the Oxford WebQ performs well across key nutrients in comparison with more administratively burdensome interviewer-based 24-hour recalls. Attenuation improves when the average value is taken over repeated administrations, reducing measurement error bias in assessment of diet-disease associations.

Creating a long-term future for big data in obesity research.

Big data are part of the future in obesity research. The ESRC funded Strategic Network for Obesity has together generated a series of papers, published in the International Journal for Obesity illustrating various aspects of their utility, in particular relating to the large social and environmental drivers of obesity. This article is the final part of the series and reflects upon progress to date and identifies four areas that require attention to promote the continued role of big data in research. We additionally include a 'getting started with big data' checklist to encourage more obesity researchers to engage with alternative data resources.

Validity of an online 24-h recall tool (myfood24) for dietary assessment in population studies: comparison with biomarkers and standard interviews.

BACKGROUND: Online dietary assessment tools can reduce administrative costs and facilitate repeated dietary assessment during follow-up in large-scale studies. However, information on bias due to measurement error of such tools is limited. We developed an online 24-h recall (myfood24) and compared its performance with a traditional interviewer-administered multiple-pass 24-h recall, assessing both against biomarkers. METHODS: Metabolically stable adults were recruited and completed the new online dietary recall, an interviewer-based multiple pass recall and a suite of reference measures. Longer-term dietary intake was estimated from up to 3 × 24-h recalls taken 2 weeks apart. Estimated intakes of protein, potassium and sodium were compared with urinary biomarker concentrations. Estimated total sugar intake was compared with a predictive biomarker and estimated energy intake compared with energy expenditure measured by accelerometry and calorimetry. Nutrient intakes were also compared to those derived from an interviewer-administered multiple-pass 24-h recall. RESULTS: Biomarker samples were received from 212 participants on at least one occasion. Both self-reported dietary assessment tools led to attenuation compared to biomarkers. The online tools resulted in attenuation factors of around 0.2-0.3 and partial correlation coefficients, reflecting ranking intakes, of approximately 0.3-0.4. This was broadly similar to the more administratively burdensome interviewer-based tool. Other nutrient estimates derived from myfood24 were around 10-20% lower than those from the interviewer-based tool, with wide limits of agreement. Intraclass correlation coefficients were approximately 0.4-0.5, indicating consistent moderate agreement. CONCLUSIONS: Our findings show that, whilst results from both measures of self-reported diet are attenuated compared to biomarker measures, the myfood24 online 24-h recall is comparable to the more time-consuming and costly interviewer-based 24-h recall across a range of measures.

Can big data solve a big problem? Reporting the obesity data landscape in line with the Foresight obesity system map.

BACKGROUND: Obesity research at a population level is multifaceted and complex. This has been characterised in the UK by the Foresight obesity systems map, identifying over 100 variables, across seven domain areas which are thought to influence energy balance, and subsequent obesity. Availability of data to consider the whole obesity system is traditionally lacking. However, in an era of big data, new possibilities are emerging. Understanding what data are available can be the first challenge, followed by an inconsistency in data reporting to enable adequate use in the obesity context. In this study we map data sources against the Foresight obesity system map domains and nodes and develop a framework to report big data for obesity research. Opportunities and challenges associated with this new data approach to whole systems obesity research are discussed. METHODS: Expert opinion from the ESRC Strategic Network for Obesity was harnessed in order to develop a data source reporting framework for obesity research. The framework was then tested on a range of data sources. In order to assess availability of data sources relevant to obesity research, a data mapping exercise against the Foresight obesity systems map domains and nodes was carried out. RESULTS: A reporting framework was developed to recommend the reporting of key information in line with these headings: Background; Elements; Exemplars; Content; Ownership; Aggregation; Sharing; Temporality (BEE-COAST). The new BEE-COAST framework was successfully applied to eight exemplar data sources from the UK. 80% coverage of the Foresight obesity systems map is possible using a wide range of big data sources. The remaining 20% were primarily biological measurements often captured by more traditional laboratory based research. CONCLUSIONS: Big data offer great potential across many domains of obesity research and need to be leveraged in conjunction with traditional data for societal benefit and health promotion.

How has big data contributed to obesity research? A review of the literature.

There has been growing interest in the potential of 'big data' to enhance our understanding in medicine and public health. Although there is no agreed definition of big data, accepted critical components include greater volume, complexity, coverage and speed of availability. Much of these data are 'found' (as opposed to 'made'), in that they have been collected for non-research purposes, but could include valuable information for research. The aim of this paper is to review the contribution of 'found' data to obesity research to date, and describe the benefits and challenges encountered. A narrative review was conducted to identify and collate peer-reviewed research studies. Database searches conducted up to September 2017 found original studies using a variety of data types and sources. These included: retail sales, transport, geospatial, commercial weight management data, social media, and smartphones and wearable technologies. The narrative review highlights the variety of data uses in the literature: describing the built environment, exploring social networks, estimating nutrient purchases or assessing the impact of interventions. The examples demonstrate four significant ways in which 'found' data can complement conventional 'made' data: firstly, in moving beyond constraints in scope (coverage, size and temporality); secondly, in providing objective, quantitative measures; thirdly, in reaching hard-to-access population groups; and lastly in the potential for evaluating real-world interventions. Alongside these opportunities, 'found' data come with distinct challenges, such as: ethical and legal questions around access and ownership; commercial sensitivities; costs; lack of control over data acquisition; validity; representativeness; finding appropriate comparators; and complexities of data processing, management and linkage. Despite widespread recognition of the opportunities, the impact of 'found' data on academic obesity research has been limited. The merit of such data lies not in their novelty, but in the benefits they could add over and above, or in combination with, conventionally collected data.

Is adolescent body mass index and waist circumference associated with the food environments surrounding schools and homes? A longitudinal analysis.

BACKGROUND: There has been considerable interest in the role of access to unhealthy food options as a determinant of weight status. There is conflict across the literature as to the existence of such an association, partly due to the dominance of cross-sectional study designs and inconsistent definitions of the food environment. The aim of our study is to use longitudinal data to examine if features of the food environment are associated to measures of adolescent weight status. METHODS: Data were collected from secondary schools in Leeds (UK) and included measurements at school years 7 (ages 11/12), 9 (13/14), and 11 (15/16). Outcome variables, for weight status, were standardised body mass index and standardised waist circumference. Explanatory variables included the number of fast food outlets, supermarkets and 'other retail outlets' located within a 1 km radius of an individual's home or school, and estimated travel route between these locations (with a 500 m buffer). Multi-level models were fit to analyse the association (adjusted for confounders) between the explanatory and outcome variables. We also examined changes in our outcome variables between each time period. RESULTS: We found few associations between the food environment and measures of adolescent weight status. Where significant associations were detected, they mainly demonstrated a positive association between the number of amenities and weight status (although effect sizes were small). Examining changes in weight status between time periods produced mainly non-significant or inconsistent associations. CONCLUSIONS: Our study found little consistent evidence of an association between features of the food environment and adolescent weight status. It suggests that policy efforts focusing on the food environment may have a limited effect at tackling the high prevalence of obesity if not supported by additional strategies.