This evidence map focuses on the effectiveness of different categories of mobile health (mHealth) interventions for the self-management of chronic health conditions as assessed in 99 systematic reviews, as well as the strength of evidence underlying those findings. Read more about Using This Map and overarching Research Insights gained from this map. For the best map interaction experience, we recommend viewing this evidence map on desktop-sized devices in a Chrome, Safari, or Firefox browser.

ECRI investigators performed the strength of evidence assessment. Definitions for strength of evidence terms are as follows:
High – We are very confident in the stated direction of effect.
Moderate – We are moderately confident in the stated direction of effect, but a possibility exists that the true effect is substantially different from the estimate.
Low – Our confidence in the stated direction of effect is limited; the true effect may be substantially different from the estimate.
Very low – We have very little confidence in the stated direction of effect; the true effect is likely to be substantially different from the estimate.
For further information, please refer to the Methods section of the accompanying Summary Report.
The ECRI Institute-Penn Medicine Evidence-based Practice Center developed this map for PCORI.

Using This Map

Axes: The x-axis (horizontal axis along the bottom) depicts the effect (which ranges from no effect to unclear effect to a positive effect). The y-axis (vertical axis on the right) displays the strength of the evidence, classified as very low to high. (Definitions of these terms are provided in the Notes above).

Bubbles: Different bubble colors represent different categories of mHealth interventions (e.g., text messaging, mobile applications). This allows for the creation of packed bubbles (e.g., multiple smaller colored bubbles within a lighter shaded circle), which describe specific subsets of mHealth interventions that are available for a given mHealth functionality and chronic condition.

Hover over a given bubble to obtain more detailed information about the evidence available for that chronic condition category. For example, for a positive effect and moderate strength of evidence, there are 12 systematic reviews of text messaging, eight systematic reviews of mobile applications, one systematic review of wearable devices, and one systematic review of other mHealth interventions (for a total of 14 unique systematic reviews, as some reviews mentioned more than one type of mHealth modality). Hovering also allows one to click on links to the systematic review abstracts in PubMed.

Filters: The filters above the map allow for customization of the presented data in the map by inclusion of vulnerable populations, age range, outcome assessed, functionality of the mHealth intervention, and type of chronic condition.

Greater insights related to effectiveness of the mHealth interventions may be achieved by filtering by single chronic condition category; however, it is important to note that multiple subcategories may be present within these overarching groupings, which may complicate the interpretation of findings. For example, if the filter for mental disorders is chosen, it appears that mobile applications have moderate strength of evidence to support both a positive as well as an unclear effect.

Clicking on the links to the individual systematic reviews cited in each bubble reveals at least one reason for the potential discrepancy: one review evaluated mobile applications in posttraumatic stress disorder, whereas the other looked at illicit drug and alcohol dependence.

Research Insights

  • Most systematic reviews had “unclear” findings, meaning that the efficacy of the mHealth interventions is uncertain, and the strength of evidence was largely rated as “low.” The general pattern of findings was similar across different chronic conditions, age groups, outcomes, technologies, and technology functions.
  • For some chronic conditions, the strength of evidence and the potential effect varied substantially among systematic reviews. Explanations include differences in:
    • Study inclusion criteria 
    • Instruments used to assess the risk of bias 
    • Outcomes evaluated
    • Specific chronic conditions (e.g., different mental disorders) evaluated.

Posted: March 6, 2019

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