Date of Award
Department of Computer Science
Health data collection poses unique challenges in rural areas of the developing world. mHealth systems that are used by health workers to collect data in remote rural regions should also record contextual information to increase confidence in the fidelity of the collected data. We built a user-friendly, mobile health-data collection system using wireless medical sensors that interface with an Android application. The data-collection system was designed to support minimally trained, non-clinical health workers to gather data about blood pressure and body weight using off-the-shelf medical sensors. This system comprises a blood-pressure cuff, a weighing scale and a portable point-of-sales printer. With this system, we introduced a new method to record contextual information associated with a blood-pressure reading using a tablet’s touchscreen and accelerometer. This contextual information can be used to verify that a patient’s lower arm remained well-supported and stationary during her blood-pressure measurement. This method can allow mHealth applications to guide untrained patients (or health workers) in measuring blood pressure correctly. Usability is a particularly important design and deployment challenge in remote, rural areas, given the limited resources for technology training and support. We conducted a field study to assess our system’s usability in rural India, where we logged health worker interactions with the app’s interface using an existing usability toolkit. Researchers analyzed logs from this toolkit to evaluate the app’s user experience and quantify specific usability challenges in the app. We have recorded experiential notes from the field study in this document. We present four contributions to future mHealth projects in this document: > We describe a method for measuring lower-arm stillness and support during a blood-pressure measurement, using an off-the-shelf Android tablet. > We evaluate our method for measuring lower-arm stillness with a preliminary user study of 12 subjects and found that our method can distinguish stationary arms from different types of lower-arm movement with 90% accuracy. > We conduct an experiential study with 28 participants and three app operators. In this study, we evaluate our system’s field usability by deploying it in rural India. > We provide a quantitative usability analysis of our mobile-data-collection app’s interface using an existing usability toolkit.
Murthy, Rima Narayana, "mCollector: Sensor-enabled health-data collection system for rural areas in the developing world" (2015). Dartmouth College Master’s Theses. 22.