Publications

Mobile health (mHealth) technology presents opportunities to enhance chronic illness management, which is especially relevant for persons living with HIV (PLWH). Since mHealth technology comprises evolving and adaptable hardware and software, it provides many challenging design problems. To address this challenge, our methods were guided by the Information System Research (ISR) framework. This paper focuses on the Design Cycle of the ISR framework in which we used user-centered distributed information design methods and participatory action research methods to inform the design of a mobile application (app) for PLWH. In the first design session, participants (N=5) identified features that are optimal for meeting the treatment and management needs of PLWH. In the second design session, participants (N=6) were presented with findings from the first design session and pictures of existing apps. Findings from the Design Cycle will be evaluated with usability inspection methods. Using a systematic approach has the potential to improve mHealth functionality and use and subsequent impact.

Developing gross and fine motor skills and expressing complex emotion is critical for child development. We introduce "StorySense", an eBook-integrated mobile app prototype that can sense face and sound topologies and identify movement and expression to promote children's motor skills and emotional developmental. Currently, most interactive eBooks on mobile devices only leverage "low-motor" interaction (i.e. tapping or swiping). Our app senses a greater breath of motion (e.g. clapping, snapping, and face tracking), and dynamically alters the storyline according to physical responses in ways that encourage the performance of predetermined motor skills ideal for a child's gross and fine motor development. In addition, our app can capture changes in facial topology, which can later be mapped using the Facial Action Coding System (FACS) for later interpretation of emotion. StorySense expands the human computer interaction vocabulary for mobile devices. Potential clinical applications include child development, physical therapy, and autism.

ClinicalTrials.gov presents great opportunities for analyzing commonalities in clinical trial target populations to facilitate knowledge reuse when designing eligibility criteria of future trials or to reveal potential systematic biases in selecting population subgroups for clinical research. Towards this goal, this paper presents a novel data resource for enabling such analyses. Our method includes two parts: (1) parsing and indexing eligibility criteria text; and (2) mining common eligibility features and attributes of common numeric features (e.g., A1c). We designed and built a database called "Commonalities in Target Populations of Clinical Trials" (COMPACT), which stores structured eligibility criteria and trial metadata in a readily computable format. We illustrate its use in an example analytic module called CONECT using COMPACT as the backend. Type 2 diabetes is used as an example to analyze commonalities in the target populations of 4,493 clinical trials on this disease.

BACKGROUND

With 40% of the world's population relying on solid fuel, household air pollution (HAP) represents a major preventable risk factor for COPD (chronic obstructive pulmonary disease). Meta-analyses have confirmed this relationship; however, constituent studies are observational, with virtually none measuring exposure directly.

OBJECTIVES

We estimated associations between HAP exposure and respiratory symptoms and lung function in young, nonsmoking women in rural Guatemala, using measured carbon monoxide (CO) concentrations in exhaled breath and personal air to assess exposure.

METHODS

The Randomized Exposure Study of Pollution Indoors and Respiratory Effects (RESPIRE) Guatemala study was a trial comparing respiratory outcomes among 504 women using improved chimney stoves versus traditional cookstoves. The present analysis included 456 women with data from postintervention surveys including interviews at 6, 12, and 18 months (respiratory symptoms) and spirometry and CO (ppm) in exhaled breath measurements. Personal CO was measured using passive diffusion tubes at variable times during the study. Associations between CO concentrations and respiratory health were estimated using random intercept regression models.

RESULTS

Respiratory symptoms (cough, phlegm, wheeze, or chest tightness) during the previous 6 months were positively associated with breath CO measured at the same time of symptom reporting and with average personal CO concentrations during the follow-up period. CO in exhaled breath at the same time as spirometry was associated with lower lung function [average reduction in FEV1 (forced expiratory volume in 1 sec) for a 10% increase in CO was 3.33 mL (95% CI: -0.86, -5.81)]. Lung function measures were not significantly associated with average postintervention personal CO concentrations.

CONCLUSIONS

Our results provide further support for the effects of HAP exposures on airway inflammation. Further longitudinal research modeling continuous exposure to particulate matter against lung function will help us understand more fully the impact of HAP on COPD.

T2-weighted (T2w) sequences are commonly relied upon in magnetic resonance imaging protocols for the detection of brain lesions in dogs. Previously, the effect of fluid suppression via fluid-attenuated inversion recovery (FLAIR) has been compared to T2-weighting with mixed results. Short tau inversion recovery (STIR) has been reported to increase the detection of some CNS lesions in people. The purpose of the current study was to evaluate the effect of fat suppression on brain parenchymal contrast resolution and lesion detection in dogs. We compared three sequences: T2w images, STIR, and T2w FLAIR with chemical fat suppression (T2-FLAIR-FS) in dogs with meningoencephalitis. Dogs with meningoencephalitis and dogs with idiopathic epilepsy were retrospectively identified and anonymized. Evaluators recorded the presence or absence of lesions within 12 predetermined brain regions on randomized sequences, viewing and scoring each sequence individually. Additionally, signal-to-noise ratios, contrast-to-noise ratios, and relative contrast (RC) were measured in a reference population. Short tau inversion recovery sequences had the highest RC between gray and white matter. While descriptively more lesions were identified by evaluators on T2-FLAIR-FS images, there was no statistical difference in the relative sensitivity of lesion detection between the sequences. Nor was there a statistical difference in false lesion detection within our reference population. Short tau inversion recovery may be favored for enhanced anatomic contrast depiction in brain imaging. No benefit of the inclusion of a fat-suppressed T2-FLAIR sequence was found.