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.

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.

Development of a new assessment tool for cervical myelopathy using hand-tracking sensor: Part 1: validity and reliability.

PURPOSE: To assess the reliability and validity of a hand motion sensor, Leap Motion Controller (LMC), in the 15-s hand grip-and-release test, as compared against human inspection of an external digital camera recording. METHODS: Fifty healthy participants were asked to fully grip-and-release their dominant hand as rapidly as possible for two trials with a 10-min rest in-between, while wearing a non-metal wrist splint. Each test lasted for 15 s, and a digital camera was used to film the anterolateral side of the hand on the first test. Three assessors counted the frequency of grip-and-release (G-R) cycles independently and in a blinded fashion. The average mean of the three was compared with that measured by LMC using the Bland-Altman method. Test-retest reliability was examined by comparing the two 15-s tests. RESULTS: The mean number of G-R cycles recorded was: 47.8 ± 6.4 (test 1, video observer); 47.7 ± 6.5 (test 1, LMC); and 50.2 ± 6.5 (test 2, LMC). Bland-Altman indicated good agreement, with a low bias (0.15 cycles) and narrow limits of agreement. The ICC showed high inter-rater agreement and the coefficient of repeatability for the number of cycles was ±5.393, with a mean bias of 3.63. CONCLUSIONS: LMC appears to be valid and reliable in the 15-s grip-and-release test. This serves as a first step towards the development of an objective myelopathy assessment device and platform for the assessment of neuromotor hand function in general. Further assessment in a clinical setting and to gauge healthy benchmark values is warranted.

Development of a new assessment tool for cervical myelopathy using hand-tracking sensor: Part 2: normative values.

PURPOSE: To set a baseline measurement of the number of hand flexion-extension cycles and analyse the degree of motion in young healthy individuals, measured by leap motion controller (LMC), besides describing gender and dominant hand differences. METHODS: Fifty healthy participants were asked to fully grip-and-release their dominant hand as rapidly as possible for a maximum of 3 min or until subjects fatigued, while wearing a non-metal wrist splint. Participants also performed a 15-s grip-and-release test. An assessor blindly counted the frequency of grip-and-release cycles and magnitude of motion from the LMC data. RESULTS: The mean number of the 15-s G-R cycles recorded by LMC was: 47.7 ± 6.5 (test 1, LMC); and 50.2 ± 6.5 (test 2, LMC). In the 3-min test, the total number of hand flexion-extension cycles and the degree of motion decreased as the person fatigued. However, the decline in frequency preceded that of motion's magnitude. The mean frequency of cycles per 10-s interval decreased from 35.4 to 26.6 over the 3 min. Participants reached fatigue from 59.38 s; 43 participants were able to complete the 3-min test. CONCLUSIONS: Normative values of the frequency of cycles and extent of motion for young healthy individuals, aged 18-35 years, are provided. Future work is needed to establish values in a wider age range and in a clinical setting.

The monetary value of diets consumed by British adults: an exploration into sociodemographic differences in individual-level diet costs.

OBJECTIVE: To describe the diet costs of adults in the National Diet and Nutrition Study (NDNS) and explore patterns in costs according to sociodemographic indicators. DESIGN: Cross-sectional diet diary information was matched to a database of food prices to assign a cost to each food or non-alcoholic beverage consumed. Daily diet costs were calculated, as well as costs per 10 MJ to improve comparability across differing energy requirements. Costs were compared between categories of sociodemographic variables and health behaviours. Multivariable regression assessed the effects of each variable on diet costs after adjustment. SETTING: The NDNS is a rolling dietary survey, recruiting a representative UK sample each year. The study features data from 2008-2010. SUBJECTS: Adults aged 19 years or over were included. The sample consisted of 1014 participants. RESULTS: The geometric mean daily diet cost was £2·89 (95 % CI £2·81, £2·96). Energy intake and daily diet cost were strongly associated. The mean energy-adjusted cost was £4·09 (95 % CI £4·01, £4·18) per 10 MJ. Energy-adjusted costs differed significantly between many subgroups, including by sex and household income. Multivariable regression found significant effects of sex, qualifications and occupation (costs per 10 MJ only), as well as equivalized household income, BMI and fruit and vegetable consumption on diet costs. CONCLUSIONS: This is the first time that monetary costs have been applied to the diets of NDNS adults. The findings suggest that certain subgroups in the UK - for example those on lower incomes - consume diets of lower monetary value. Observed differences were mostly in the directions anticipated.

Running and Knee Osteoarthritis: A Systematic Review and Meta-analysis.

BACKGROUND: Osteoarthritis (OA) is a chronic condition characterized by pain, impaired function, and reduced quality of life. A number of risk factors for knee OA have been identified, such as obesity, occupation, and injury. The association between knee OA and physical activity or particular sports such as running is less clear. Previous reviews, and the evidence that informs them, present contradictory or inconclusive findings. PURPOSE: This systematic review aimed to determine the association between running and the development of knee OA. STUDY DESIGN: Systematic review and meta-analysis. METHODS: Four electronic databases were searched, along with citations in eligible articles and reviews and the contents of recent journal issues. Two reviewers independently screened the titles and abstracts using prespecified eligibility criteria. Full-text articles were also independently assessed for eligibility. Eligible studies were those in which running or running-related sports (eg, triathlon or orienteering) were assessed as a risk factor for the onset or progression of knee OA in adults. Relevant outcomes included (1) diagnosis of knee OA, (2) radiographic markers of knee OA, (3) knee joint surgery for OA, (4) knee pain, and (5) knee-associated disability. Risk of bias was judged by use of the Newcastle-Ottawa scale. A random-effects meta-analysis was performed with case-control studies investigating arthroplasty. RESULTS: After de-duplication, the search returned 1322 records. Of these, 153 full-text articles were assessed; 25 were eligible, describing 15 studies: 11 cohort (6 retrospective) and 4 case-control studies. Findings of studies with a diagnostic OA outcome were mixed. Some radiographic differences were observed in runners, but only at baseline within some subgroups. Meta-analysis suggested a protective effect of running against surgery due to OA: pooled odds ratio 0.46 (95% CI, 0.30-0.71). The I2 was 0% (95% CI, 0%-73%). Evidence relating to symptomatic outcomes was sparse and inconclusive. CONCLUSION: With this evidence, it is not possible to determine the role of running in knee OA. Moderate- to low-quality evidence suggests no association with OA diagnosis, a positive association with OA diagnosis, and a negative association with knee OA surgery. Conflicting results may reflect methodological heterogeneity. More evidence from well-designed, prospective studies is needed to clarify the contradictions.