Gaining insights from mixed methods data integration. Use of a triangulation protocol in the iPOPP pilot trial.

Talk Code: 
Daniel Herron
Daniel Herron, Elaine Nicolls, Emma Healey, Carolyn Chew-Graham, John McBeth, Clare Jinks (on behalf of the iPOPP team)
Author institutions: 
Keele University, Midlands Partnership NHS Foundation Trust, University of Manchester


Development and evaluation of complex interventions requires mixed methods data collection and analysis. Despite the existence of methods to integrate data analysis, data sets are often analysed and findings reported separately. Integrated analysis adds value to research by identifying where data agree, complement each other or disagree (and thus contradict each other). The process enables a more complete analysis that contributes to the validity of the results and adds strength to conclusions that are made. The iPOPP pilot trial evaluated the feasibility and acceptability of a walking intervention for older adults with chronic musculoskeletal pain delivered by Health Care Assistants (HCAs) in primary care. Five datasets were available for analysis: qualitative interviews with trial participants (n=20), qualitative interviews with HCAs delivering iPOPP (n=4), audio-recorded iPOPP consultations (n=18), quantitative pilot trial data (case report forms n=47), HCA pre and post-training questionnaires (n=5 HCAs trained). We needed a method to integrate analysis in order to draw robust conclusions about feasibility and acceptability and any changes required to the iPOPP intervention or trial processes.


A triangulation protocol was used when all datasets had been analysed. Initial key findings statements were generated from each dataset by four researchers (working independently) who had analysed them. The initial key finding statements were then compared across all datasets by all researchers (working independently) and a final list produced through discussion. For each key finding, pairwise comparisons were then independently undertaken by two researchers to compare data from each data set. Comparisons were categorised as agreement, partial agreement, dissonance, silence or not applicable. Consensus was agreed through discussion where comparisons had been categorised differently.


Preliminary analysis produced 65 initial finding statements across all five datasets, reducing to 29 after identifying duplicate key findings. A total of 290 paired comparisons were made across data sets. We will present examples of agreement, partial agreement and disagreement from the pairwise comparisons that are linked to our feasibility objectives. Examples are: agreement that intervention components (pedometers, activity diary) motivated older adults with chronic pain to walk; agreement that interventions were acceptable; agreement and disagreement that intervention fidelity was good; and, agreement that HCAs found it challenging to set goals with participants who perceived themselves already active.


This study is novel as we have used a triangulation protocol within a primary care pilot trial in order to integrate mixed methods analysis and provide robust transparent findings and conclusions about acceptability and feasibility of a new walking intervention for older adults with chronic musculoskeletal pain. The integrated analysis enables deeper understanding of findings which go beyond those available from analysis of single datasets in isolation.

Submitted by: 
Clare Jinks
Funding acknowledgement: 
The iPOPP pilot trial was funded by Arthritis Research UK. Clare Jinks is part-funded by the National Institute for Health Research (NIHR) Collaborations for Leadership in Applied Health Research and Care West Midlands. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.