Roschelle Fritz

Using Smart Homes to Detect and Analyze Health Events

Smart homes offer an unprecedented opportunity to unobtrusively monitor human behavior in everyday environments and to determine whether relationships exist between behavior and health changes. Behavior change detection (BCD) can be used to identify changes that accompany health events, which can potentially save lives.

Detecting Health and Behavior Change by Analyzing Smart Home Sensor Data

Smart home environments offer an unprecedented opportunity to unobtrusively monitor human behavior. Sensor data collected from smart homes can be labeled using activity recognition to help determine whether relationships exist between behavior in the home and health changes. To detect and analyze behavior changes that accompany health events, we introduce the behavior change detection (BCD) approach. BCD detects activity timing and duration changes between windows of time, determines the significance of the detected changes, and analyzes the nature of the changes. We demonstrate our approach using two case studies for older adults living in smart homes who experienced major health events, including cancer treatment and insomnia. Our algorithm detects behavior changes consistent with the medical literature for these cases. The results suggest the changes can be automatically detected using BCD. The proposed smart home, activity recognition algorithms, and change detection approach are useful data mining techniques for understanding the behavioral effects of major health conditions.

Research Education: Findings of a Study of Teaching–Learning Research Using Multiple Analytical Perspectives

This multimethod, qualitative study provides results for educators of nursing doctoral students to consider. Combining the expertise of an empirical analytical researcher (who uses statistical methods) and an interpretive phenomenological researcher (who uses hermeneutic methods), a course was designed that would place doctoral students in the midst of multiparadigmatic discussions while learning fundamental research methods. Field notes and iterative analytical discussions led to patterns and themes that highlight the value of this innovative pedagogical application. Using content analysis and interpretive phenomenological approaches, together with one of the students, data were analyzed from field notes recorded in real time over the period the course was offered. This article describes the course and the study analysis, and offers the pedagogical experience as transformative. A link to a sample syllabus is included in the article. The results encourage nurse educators of doctoral nursing students to focus educational practice on multiple methodological perspectives.

Data Collection via In-Depth Email Interviewing: Lessons From the Field

This methods article is a reflection on the use of in-depth email interviewing in a qualitative descriptive study. The use of emailing to conduct interviews is thought to be an effective way to collect qualitative data. Building on current methodological literature in qualitative research regarding in-depth email interviewing, we move the conversation toward elicitation of quality data and management of multiple concurrent email interviews. Excerpts are shared from a field journal that was kept throughout one study, with commentary on developing insights. Valuable lessons learned include the importance of (a) logistics and timing related to the management of multiple concurrent email interviews, (b) language and eliciting the data, (c) constructing the email, and (d) processing text-based data and preparing transcripts. Qualitative researchers seeking deeply reflective answers and geographically diverse samples may wish to consider using in-depth email interviews.