Accepted for/Published in: JMIR Diabetes
Date Submitted: Mar 23, 2021
Date Accepted: Oct 31, 2021
Date Submitted to PubMed: Nov 16, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Patient-generated health data analytics on ambulatory behaviours of people living with T2D: A Scoping Review
ABSTRACT
Background:
Complications due to Type 2 Diabetes (T2D) can be mitigated through proper self-management which can positively change health behaviours. Technological tools are available to help people living with T2D manage their condition and such tools provide a large repository for patient-generated health data (PGHD). Analytics can provide insights about the ambulatory behaviours of people living with T2D.
Objective:
The objective of this review was to investigate analytical insights can be derived through PGHD with respect to ambulatory behaviours of people living with T2D.
Methods:
A scoping review using the Arksey & O’Malley framework was conducted in which a comprehensive search of the literature was conducted by two reviewers. Three electronic databases (PubMed, IEEE, ACM) were searched using keywords associated with diabetes, behaviours, and analytics. Several rounds of screening using predetermined inclusion and exclusion criteria were conducted and studies were selected. Critical examination took place through a descriptive-analytical narrative method and data extracted from the studies was classified into thematic categories. These categories reflect the findings of this study as per our objective.
Results:
We identified 43 studies that met the inclusion criteria for this review. While 70% of the studies examined PGHD independently, 30% of the studies combined PGHD with other data sources. The majority of these studies used machine learning algorithms to perform their analysis. Themes identified through this review include 1) predicting diabetes / obesity, 2) factors that contribute to diabetes / obesity, 3) insights from social media & online forums, 4) predicting glycemia, 5) improved adherence / outcomes, 6) analysis of sedentary behaviours, 7) deriving behavioural patterns, 8) discovering clinical findings, and 9) developing design principles.
Conclusions:
The increased volume and availability of PGHD has the potential to derive analytical insights regarding the ambulatory behaviours of people living with T2D. From the literature, we determined that analytics can predict outcomes and identify granular behavioural patterns from PGHD. This review determined the broad range of insights that can be examined through PGHD, that would not be available through other data sources.
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