Emerging technologies to measure neighborhood conditions in public health: implications
      for interventions and next steps

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Abstract

Adverse neighborhood conditions play an important role beyond individual characteristics. There is increasing interest in identifying specific characteristics of the social and built environments adversely affecting health outcomes. Most research has assessed aspects of such exposures via self-reported instruments or census data. Potential threats in the local environment may be subject to short-term changes that can only be measured with more nimble technology. The advent of new technologies may offer new opportunities to obtain geospatial data about neighborhoods that may circumvent the limitations of traditional data sources. This overview describes the utility, validity and reliability of selected emerging technologies to measure neighborhood conditions for public health applications. It also describes next steps for future research and opportunities for interventions. The paper presents an overview of the literature on measurement of the built and social environment in public health (Google Street View, webcams, crowdsourcing, remote sensing, social media, unmanned aerial vehicles, and lifespace) and location-based interventions. Emerging technologies such as Google Street View, social media, drones, webcams, and crowdsourcing may serve as effective and inexpensive tools to measure the ever-changing environment. Georeferenced social media responses may help identify where to target intervention activities, but also to passively evaluate their effectiveness. Future studies should measure exposure across key time points during the life-course as part of the exposome paradigm and integrate various types of data sources to measure environmental contexts. By harnessing these technologies, public health research can not only monitor populations and the environment, but intervene using novel strategies to improve the public health.

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Harnessing Context Sensing to Develop a Mobile Intervention for Depression

Michelle Burns, Mark Begale, Jennifer Duffecy … (2011)

Background Mobile phone sensors can be used to develop context-aware systems that automatically detect when patients require assistance. Mobile phones can also provide ecological momentary interventions that deliver tailored assistance during problematic situations. However, such approaches have not yet been used to treat major depressive disorder. Objective The purpose of this study was to investigate the technical feasibility, functional reliability, and patient satisfaction with Mobilyze!, a mobile phone- and Internet-based intervention including ecological momentary intervention and context sensing. Methods We developed a mobile phone application and supporting architecture, in which machine learning models (ie, learners) predicted patients’ mood, emotions, cognitive/motivational states, activities, environmental context, and social context based on at least 38 concurrent phone sensor values (eg, global positioning system, ambient light, recent calls). The website included feedback graphs illustrating correlations between patients’ self-reported states, as well as didactics and tools teaching patients behavioral activation concepts. Brief telephone calls and emails with a clinician were used to promote adherence. We enrolled 8 adults with major depressive disorder in a single-arm pilot study to receive Mobilyze! and complete clinical assessments for 8 weeks. Results Promising accuracy rates (60% to 91%) were achieved by learners predicting categorical contextual states (eg, location). For states rated on scales (eg, mood), predictive capability was poor. Participants were satisfied with the phone application and improved significantly on self-reported depressive symptoms (betaweek = –.82, P < .001, per-protocol Cohen d = 3.43) and interview measures of depressive symptoms (betaweek = –.81, P < .001, per-protocol Cohen d = 3.55). Participants also became less likely to meet criteria for major depressive disorder diagnosis (bweek = –.65, P = .03, per-protocol remission rate = 85.71%). Comorbid anxiety symptoms also decreased (betaweek = –.71, P < .001, per-protocol Cohen d = 2.58). Conclusions Mobilyze! is a scalable, feasible intervention with preliminary evidence of efficacy. To our knowledge, it is the first ecological momentary intervention for unipolar depression, as well as one of the first attempts to use context sensing to identify mental health-related states. Several lessons learned regarding technical functionality, data mining, and software development process are discussed. Trial Registration Clinicaltrials.gov NCT01107041; http://clinicaltrials.gov/ct2/show/NCT01107041 (Archived by WebCite at http://www.webcitation.org/60CVjPH0n)

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Can virtual streetscape audits reliably replace physical streetscape audits?

Robin Kearns, Karen Witten, Simon Opit … (2010)

There is increasing recognition that the neighborhood-built environment influences health outcomes, such as physical activity behaviors, and technological advancements now provide opportunities to examine the neighborhood streetscape remotely. Accordingly, the aims of this methodological study are to: (1) compare the efficiencies of physically and virtually conducting a streetscape audit within the neighborhood context, and (2) assess the level of agreement between the physical (criterion) and virtual (test) audits. Built environment attributes associated with walking and cycling were audited using the New Zealand Systematic Pedestrian and Cycling Environment Scan (NZ-SPACES) in 48 street segments drawn from four neighborhoods in Auckland, New Zealand. Audits were conducted physically (on-site) and remotely (using Google Street View) in January and February 2010. Time taken to complete the audits, travel mileage, and Internet bandwidth used were also measured. It was quicker to conduct the virtual audits when compared with the physical audits (χ = 115.3 min (virtual), χ = 148.5 min (physical)). In the majority of cases, the physical and virtual audits were within the acceptable levels of agreement (ICC ≥ 0.70) for the variables being assessed. The methodological implication of this study is that Google Street View is a potentially valuable data source for measuring the contextual features of neighborhood streets that likely impact on health outcomes. Overall, Google Street View provided a resource-efficient and reliable alternative to physically auditing the attributes of neighborhood streetscapes associated with walking and cycling. Supplementary data derived from other sources (e.g., Geographical Information Systems) could be used to assess the less reliable streetscape variables.

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The Public Health Exposome: A Population-Based, Exposure Science Approach to Health Disparities Research

Paul Juarez, Patricia Matthews-Juarez, Darryl Hood … (2014)

The lack of progress in reducing health disparities suggests that new approaches are needed if we are to achieve meaningful, equitable, and lasting reductions. Current scientific paradigms do not adequately capture the complexity of the relationships between environment, personal health and population level disparities. The public health exposome is presented as a universal exposure tracking framework for integrating complex relationships between exogenous and endogenous exposures across the lifespan from conception to death. It uses a social-ecological framework that builds on the exposome paradigm for conceptualizing how exogenous exposures “get under the skin”. The public health exposome approach has led our team to develop a taxonomy and bioinformatics infrastructure to integrate health outcomes data with thousands of sources of exogenous exposure, organized in four broad domains: natural, built, social, and policy environments. With the input of a transdisciplinary team, we have borrowed and applied the methods, tools and terms from various disciplines to measure the effects of environmental exposures on personal and population health outcomes and disparities, many of which may not manifest until many years later. As is customary with a paradigm shift, this approach has far reaching implications for research methods and design, analytics, community engagement strategies, and research training.