Association of genetic liability for psychiatric disorders with accelerometer-assessed physical activity in the UK Biobank.

Levels of activity are often affected in psychiatric disorders and can be core symptoms of illness. Advances in technology now allow the accurate assessment of activity levels but it remains unclear whether alterations in activity arise from shared risk factors for developing psychiatric disorders, such as genetics, or are better explained as consequences of the disorders and their associated factors. We aimed to examine objectively-measured physical activity in individuals with psychiatric disorders, and assess the role of genetic liability for psychiatric disorders on physical activity. Accelerometer data were available on 95,529 UK Biobank participants, including measures of overall mean activity and minutes per day of moderate activity, walking, sedentary activity, and sleep. Linear regressions measured associations between psychiatric diagnosis and activity levels, and polygenic risk scores (PRS) for psychiatric disorders and activity levels. Genetic correlations were calculated between psychiatric disorders and different types of activity. Having a diagnosis of schizophrenia, bipolar disorder, depression, or autism spectrum disorders (ASD) was associated with reduced overall activity compared to unaffected controls. In individuals without a psychiatric disorder, reduced overall activity levels were associated with PRS for schizophrenia, depression, and ASD. ADHD PRS was associated with increased overall activity. Genetic correlations were consistent with PRS findings. Variation in physical activity is an important feature across psychiatric disorders. Whilst levels of activity are associated with genetic liability to psychiatric disorders to a very limited extent, the substantial differences in activity levels in those with psychiatric disorders most likely arise as a consequences of disorder-related factors.

Sedentary Behavior and Chronic Disease: Mechanisms and Future Directions.

BACKGROUND: Recent updates to physical activity guidelines highlight the importance of reducing sedentary time. However, at present, only general recommendations are possible (ie, "Sit less, move more"). There remains a need to investigate the strength, temporality, specificity, and dose-response nature of sedentary behavior associations with chronic disease, along with potential underlying mechanisms. METHODS: Stemming from a recent research workshop organized by the Sedentary Behavior Council themed "Sedentary behaviour mechanisms-biological and behavioural pathways linking sitting to adverse health outcomes," this paper (1) discusses existing challenges and scientific discussions within this advancing area of science, (2) highlights and discusses emerging areas of interest, and (3) points to potential future directions. RESULTS: A brief knowledge update is provided, reflecting upon current and evolving thinking/discussions, and the rapid accumulation of new evidence linking sedentary behavior to chronic disease. Research "action points" are made at the end of each section-spanning from measurement systems and analytic methods, genetic epidemiology, causal mediation, and experimental studies to biological and behavioral determinants and mechanisms. CONCLUSION: A better understanding of whether and how sedentary behavior is causally related to chronic disease will allow for more meaningful conclusions in the future and assist in refining clinical and public health policies/recommendations.

Utility of passive photography to objectively audit built environment features of active transport journeys: an observational study.

BACKGROUND: Active transport can contribute to physical activity accumulation and improved health in adults. The built environment is an established associate of active transport behaviours; however, assessment of environmental features encountered during journeys remains challenging. The purpose of this study was to examine the utility of wearable cameras to objectively audit and quantify environmental features along work-related walking and cycling routes. METHODS: A convenience sample of employed adults was recruited in New Zealand, in June 2011. Participants wore a SenseCam for all journeys over three weekdays and completed travel diaries and demographic questionnaires. SenseCam images for work-related active transport journeys were coded for presence of environmental features hypothesised to be related to active transport. Differences in presence of features by transport mode and in participant-reported and SenseCam-derived journey duration were determined using two-sample tests of proportion and an independent samples t-test, respectively. RESULTS: Fifteen adults participated in the study, yielding 1749 SenseCam images from 30 work-related active transport journeys for coding. Significant differences in presence of features were found between walking and cycling journeys. Almost a quarter of images were uncodeable due to being too dark to determine features. There was a non-significant tendency for respondents to under-report their journey duration. CONCLUSION: This study provides proof of concept for the use of the SenseCam to capture built environment data in real time that may be related to active transportation. Further work is required to test and refine coding methodologies across a range of settings, travel behaviours, and demographic groups.

Using wearable cameras to categorise type and context of accelerometer-identified episodes of physical activity.

BACKGROUND: Accelerometers can identify certain physical activity behaviours, but not the context in which they take place. This study investigates the feasibility of wearable cameras to objectively categorise the behaviour type and context of participants' accelerometer-identified episodes of activity. METHODS: Adults were given an Actical hip-mounted accelerometer and a SenseCam wearable camera (worn via lanyard). The onboard clocks on both devices were time-synchronised. Participants engaged in free-living activities for 3 days. Actical data were cleaned and episodes of sedentary, lifestyle-light, lifestyle-moderate, and moderate-to-vigorous physical activity (MVPA) were identified. Actical episodes were categorised according to their social and environmental context and Physical Activity (PA) compendium category as identified from time-matched SenseCam images. RESULTS: There were 212 days considered from 49 participants from whom SenseCam images and associated Actical data were captured. Using SenseCam images, behaviour type and context attributes were annotated for 386 (out of 3017) randomly selected episodes (such as walking/transportation, social/not-social, domestic/leisure). Across the episodes, 12 categories that aligned with the PA Compendium were identified, and 114 subcategory types were identified. Nineteen percent of episodes could not have their behaviour type and context categorized; 59% were outdoors versus 39% indoors; 33% of episodes were recorded as leisure time activities, with 33% transport, 18% domestic, and 15% occupational. 33% of the randomly selected episodes contained direct social interaction and 22% were in social situations where the participant wasn't involved in direct engagement. CONCLUSION: Wearable camera images offer an objective method to capture a spectrum of activity behaviour types and context across 81% of accelerometer-identified episodes of activity. Wearable cameras represent the best objective method currently available to categorise the social and environmental context of accelerometer-defined episodes of activity in free-living conditions.

Using the SenseCam to improve classifications of sedentary behavior in free-living settings.

BACKGROUND: Studies have shown relationships between important health outcomes and sedentary behavior, independent of physical activity. There are known errors in tools employed to assess sedentary behavior. Studies of accelerometers have been limited to laboratory environments. PURPOSE: To assess a broad range of sedentary behaviors in free-living adults using accelerometers and a Microsoft SenseCam that can provide an objective observation of sedentary behaviors through first person-view images. METHODS: Participants were 40 university employees who wore a SenseCam and Actigraph accelerometer for 3-5 days. Images were coded for sitting and standing posture and 12 activity types. Data were merged and aggregated to a 60-second epoch. Accelerometer counts per minute (cpm) of <100 were compared with coded behaviors. Sensitivity and specificity analyses were performed. Data were collected in June and July 2011 and analyzed in April 2012. RESULTS: TV viewing, other screen use, and administrative activities were correctly classified by the 100-cpm cutpoint. However, standing behaviors also fell under this threshold, and driving behaviors exceeded it. Multiple behaviors occurred simultaneously. A nearly 30-minute per day difference was found in sedentary behavior estimates based on the accelerometer versus the SenseCam. CONCLUSIONS: Researchers should be aware of the strengths and weaknesses of the 100-cpm accelerometer cutpoint for identifying sedentary behavior. The SenseCam may be a useful tool in free-living conditions to better understand health behaviors such as sitting.

The smartphone as a platform for wearable cameras in health research.

BACKGROUND: The Microsoft SenseCam, a small camera that is worn on the chest via a lanyard, increasingly is being deployed in health research. However, the SenseCam and other wearable cameras are not yet in widespread use because of a variety of factors. It is proposed that the ubiquitous smartphones can provide a more accessible alternative to SenseCam and similar devices. PURPOSE: To perform an initial evaluation of the potential of smartphones to become an alternative to a wearable camera such as the SenseCam. METHODS: In 2012, adults were supplied with a smartphone, which they wore on a lanyard, that ran life-logging software. Participants wore the smartphone for up to 1 day and the resulting life-log data were both manually annotated and automatically analyzed for the presence of visual concepts. The results were compared to prior work using the SenseCam. RESULTS: In total, 166,000 smartphone photos were gathered from 47 individuals, along with associated sensor readings. The average time spent wearing the device across all users was 5 hours 39 minutes (SD=4 hours 11 minutes). A subset of 36,698 photos was selected for manual annotation by five researchers. Software analysis of these photos supports the automatic identification of activities to a similar level of accuracy as for SenseCam images in a previous study. CONCLUSIONS: Many aspects of the functionality of a SenseCam largely can be replicated, and in some cases enhanced, by the ubiquitous smartphone platform. This makes smartphones good candidates for a new generation of wearable sensing devices in health research, because of their widespread use across many populations. It is envisioned that smartphones will provide a compelling alternative to the dedicated SenseCam hardware for a number of users and application areas. This will be achieved by integrating new types of sensor data, leveraging the smartphone's real-time connectivity and rich user interface, and providing support for a range of relatively sophisticated applications.

An ethical framework for automated, wearable cameras in health behavior research.

Technologic advances mean automated, wearable cameras are now feasible for investigating health behaviors in a public health context. This paper attempts to identify and discuss the ethical implications of such research, in relation to existing guidelines for ethical research in traditional visual methodologies. Research using automated, wearable cameras can be very intrusive, generating unprecedented levels of image data, some of it potentially unflattering or unwanted. Participants and third parties they encounter may feel uncomfortable or that their privacy has been affected negatively. This paper attempts to formalize the protection of all according to best ethical principles through the development of an ethical framework. Respect for autonomy, through appropriate approaches to informed consent and adequate privacy and confidentiality controls, allows for ethical research, which has the potential to confer substantial benefits on the field of health behavior research.

Evaluating the feasibility of measuring travel to school using a wearable camera.

BACKGROUND: The school journey is often studied in relation to health outcomes in children and adolescents. Self-report is the most common measurement tool. PURPOSE: To investigate the error on self-reported journey duration in adolescents, using a wearable digital camera (Microsoft SenseCam). METHODS: During March-May 2011, participants (n=17; aged 13-15 years) from four schools wore wearable cameras to and from school for 1 week. The device automatically records time-stamped, first-person point-of-view images, without any action from the wearer. Participants also completed a researcher-administered self-report travel survey over the same period. Analysis took place in November 2011. Within- and between-subjects correlation coefficients and Bland-Altman 95% limits of agreement were derived, accounting for the multiple observations per individual. RESULTS: Self-report data were collected for 150 journey stages and SenseCam data for 135 (90%) of these. The within-subjects correlation coefficient for journey duration was 0.89 (95% CI=0.84, 0.93). The between-subjects correlation coefficient was 0.92 (95% CI=0.79, 0.97). The mean difference (bias) between methods at the whole sample level was small (10 seconds per journey, 95% CI= -33, 53). The wide limits of agreement (± 501 seconds, 95% CI= -491, 511) reveal large random error. CONCLUSIONS: Compared to direct observation from images, self-reported journey duration is accurate at the mean group level but imprecise at the level of the individual participant.

Remote real-time monitoring of subsurface landfill gas migration.

The cost of monitoring greenhouse gas emissions from landfill sites is of major concern for regulatory authorities. The current monitoring procedure is recognised as labour intensive, requiring agency inspectors to physically travel to perimeter borehole wells in rough terrain and manually measure gas concentration levels with expensive hand-held instrumentation. In this article we present a cost-effective and efficient system for remotely monitoring landfill subsurface migration of methane and carbon dioxide concentration levels. Based purely on an autonomous sensing architecture, the proposed sensing platform was capable of performing complex analytical measurements in situ and successfully communicating the data remotely to a cloud database. A web tool was developed to present the sensed data to relevant stakeholders. We report our experiences in deploying such an approach in the field over a period of approximately 16 months.

Automatically assisting human memory: a SenseCam browser.

SenseCams have many potential applications as tools for lifelogging, including the possibility of use as a memory rehabilitation tool. Given that a SenseCam can log hundreds of thousands of images per year, it is critical that these be presented to the viewer in a manner that supports the aims of memory rehabilitation. In this article we report a software browser constructed with the aim of using the characteristics of memory to organise SenseCam images into a form that makes the wealth of information stored on SenseCam more accessible. To enable a large amount of visual information to be easily and quickly assimilated by a user, we apply a series of automatic content analysis techniques to structure the images into "events", suggest their relative importance, and select representative images for each. This minimises effort when browsing and searching. We provide anecdotes on use of such a system and emphasise the need for SenseCam images to be meaningfully sorted using such a browser.

Automatically augmenting lifelog events using pervasively generated content from millions of people.

In sensor research we take advantage of additional contextual sensor information to disambiguate potentially erroneous sensor readings or to make better informed decisions on a single sensor's output. This use of additional information reinforces, validates, semantically enriches, and augments sensed data. Lifelog data is challenging to augment, as it tracks one's life with many images including the places they go, making it non-trivial to find associated sources of information. We investigate realising the goal of pervasive user-generated content based on sensors, by augmenting passive visual lifelogs with "Web 2.0" content collected by millions of other individuals.

Correlating multimodal physical sensor information with biological analysis in ultra endurance cycling.

The sporting domain has traditionally been used as a testing ground for new technologies which subsequently make their way into the public domain. This includes sensors. In this article a range of physical and biological sensors deployed in a 64 hour ultra-endurance non-stop cycling race are described. A novel algorithm to estimate the energy expenditure while cycling and resting during the event are outlined. Initial analysis in this noisy domain of "sensors in the field" are very encouraging and represent a first with respect to cycling.