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Inductive Content Analysis of Nurses’ Interpretation of Health-Smart Home Data: Implications for Clinical Decision-Making
ABSTRACT
Background:
Health-smart home technologies offer real-time sensor-based monitoring of older adults, allowing for early detection of health changes. How clinicians interpret and utilize this data, particularly in visualized formats such as bar, line, and pie graphs, remains underexplored.
Objective:
This study aimed to examine how nurses interpret health smart home (HSH)-generated data visualisations and their clinical implications. Specifically, it investigated how nurses engage with bar, line, and pie graphs displaying HSH sensor data, identifying key patterns in their analysis of patient activity, sleep, and mobility, as well as challenges that may impact clinical decision-making.
Methods:
Using a qualitative descriptive methodology and inductive content analysis approach with a quantitative component, we analysed nurses’ qualitative interpretations of existing health-smart home data from 3 older adults living with ambient whole-home sensing. Nurses provided structured written feedback on visualised trends in activity, sleep, and mobility patterns.
Results:
The findings highlight both opportunities and challenges of using sensor-derived health data in older adults’ care. Nurses identified key patterns in sleep, mobility, and home engagement, but interpretation difficulties, such as unclear sleep metrics and lack of clinical context, hindered decision-making. Nurses preferred bar and line graphs over pie charts for interpreting these data. Survey results show a statistically significant difference in how nurses rated different graph types (χ²(2) = 17.11, p = 0.00019), with pie charts rated significantly lower than both bar and line graphs (p < 0.001 and p = 0.0082, respectively). These findings underscore the need for improved data visualisation and integration to enhance clinical utility.
Conclusions:
Findings indicate that nurses were able to provide accurate interpretations of the sensor-based data. However, there is a need for improved visualisation techniques and clinician training to optimize health-smart home data for early intervention. Standardized approaches to data representation could enhance nurses' ability to detect and act on subtle yet important information about older adults’ health changes occurring in home settings. Clinical Trial: N/A
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