Secondary Data Analysis

Secondary data analysis is the re-examination of existing datasets, such as transcripts or survey results, to generate new insights beyond the original purpose of data collection. In co-design studies, it is often applied in the Discover phase to identify user needs, anticipate potential barriers, and inform design priorities, ensuring that lessons learned from previous projects contribute to more user-centred and effective digital health interventions.

Application Example​

In a study developing Empower@Home, a web-delivered cognitive behavioural therapy (CBT) program for homebound older adults with depression, researchers employed secondary data analysis in the Discover phase to identify user needs and potential usability barriers. The analysis of qualitative data from a previous project highlighted several recurring problems experienced by older participants, including hard-to-read text, small clickable areas, confusing navigation pathways, complex menu options, excessive information load, and difficulties with text entry.

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By drawing on existing data, the team uncovered critical usability challenges without conducting new large-scale data collection. These findings guided the initial design priorities of Empower@Home, ensuring that accessibility and ease of use were built into the intervention from the outset. (Xiang et al., 2023)