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Optimising use of patient generated health data through analysis of their glucose meter results

A 2018/2019 Summer Studentship research project

If we have a better understanding of patterns of self-testing and how this relates to overall control as assessed by the HbA1c test, this may allow patients to make better use of all their stored glucose data for diabetes self-management, not ‘just’ their day-to-day results.  New Zealand is uniquely placed to undertake this research as we have a single brand, sole supply arrangement for patients’ glucose meters and test strips. This ensures there is homogeneity in glucose meter type and associated software; this scenario is not available for researchers in other countries.

Student: Kacey Gritt
Supervisors: Associate Professor Helen Lunt, Helen Heenan (RN), Dr Huan Chan (MB ChB FRACP)
Sponsor: TBC

Student prerequisites

The student will interact with 200+ outpatients and their personal diabetes self-monitoring technology.

They must therefore:

  1. Like interacting with patients, and
  2. Be comfortable manipulating technology-derived data.

How to apply

Contact the first supervisor, Associate Professor Helen Lunt, to express your interest:

Tel +64 27 433 3508

Project brief


Patients on insulin need to undertake regular ‘finger stick’ glucose tests, to manage their diabetes. These ‘finger stick’ derived data are stored on a memory glucose meter. In New Zealand, the meter and test strips are provided free to patients by PHARMAC, thus there are no financial barriers to self-testing. These PHARMAC funded meters hold ≤1,000 date and time stamped glucose values. These data are downloaded routinely at the diabetes outpatient clinic, for discussion with the attending clinician. Could these date and time stamped glucose data then be ‘recycled’ and used to better effect? We undertook a feasibility study in 2017/18, enrolling of 34 patients with type 1 diabetes. This showed that there was a tight correlation between individual patient’s glucose values and HbA1c (glycated haemoglobin, a blood test which measures three months of glucose control), thus finger stick results might provide a good representation of overall glucose control.  (See also the diagram in the Methodology section).


We aim to determine which diabetes population subgroups (type 1 or type 2 diabetes, frequent finger stick testers versus less frequent testers etc) generate glucose finger stick data that can be used as a predictor of overall glucose control.


Patients on insulin will be identified prior to their routine diabetes clinic visit and asked if they are prepared to spend an extra 10–15 minutes at clinic, signing a consent sheet and completing a short questionnaire. Their routine glucose meter download will be done in a way that allows subsequent ‘manipulation’ of their date and time stamped glucose data, using a CSV file.

Student researcher’s component of the study

The student will work with the diabetes outpatient team, embedding their clinical research project into routine clinic workflow. They will interview 200 patients, undertaking a short questionnaire and also download glucose meter data into a CSV file. The student will be responsible for organizing data in a spreadsheet and linking this with data such as clinical characteristics (e.g. type of diabetes) and HbA1c.

This project builds on a previous feasibility study, aimed at checking that each step of data collection and subsequent analysis is feasible. For students interested in statistical analysis, the project also provides the opportunity to work with the biostatistician who analysed results from the previous feasibility study.