Evidence map›Paper›PMID 39265164›Full record

ReviewJMIR public health and surveillance2024

Leveraging mHealth Technologies for Public Health.

Pedro Elkind Velmovitsky, Merna Kirolos, Paulo Alencar, Scott Leatherdale, Donald Cowan, Plinio Pelegrini Morita

Abstract readReview
In one paragraph

Review in JMIR public health and surveillance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Pedro Elkind VelmovitskySchool of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.ORCID 0000-0001-7539-3193
Merna KirolosSchool of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.ORCID 0009-0007-8270-5459
Paulo AlencarDavid R Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada.ORCID 0009-0006-2885-0009
Scott LeatherdaleSchool of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.ORCID 0000-0001-5926-3065
Donald CowanDavid R Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada.ORCID 0000-0001-7468-0879
Plinio Pelegrini MoritaSchool of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.ORCID 0000-0001-9515-6478

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional public health surveillance efforts are generally based on self-reported data. Although well validated, these methods may nevertheless be subjected to limitations such as biases, delays, and costs or logistical challenges. An alternative is the use of smart technologies (eg, smartphones and smartwatches) to complement self-report indicators. Having embedded sensors that provide zero-effort, passive, and continuous monitoring of health variables, these devices generate data that could be leveraged for cases in which the data are related to the same self-report metric of interest. However, some challenges must be considered when discussing the use of mobile health technologies for public health to ensure digital health equity, privacy, and best practices. This paper provides, through a review of major Canadian surveys and mobile health studies, an overview of research involving mobile data for public health, including a mapping of variables currently collected by public health surveys that could be complemented with self-report, challenges to technology adoption, and considerations on digital health equity, with a specific focus on the Canadian context. Population characteristics from major smart technology brands-Apple, Fitbit, and Samsung-and demographic barriers to the use of technology are provided. We conclude with public health implications and present our view that public health agencies and researchers should leverage mobile health data while being mindful of the current barriers and limitations to device use and access. In this manner, data ecosystems that leverage personal smart devices for public health can be put in place as appropriate, as we move toward a future in which barriers to technology adoption are decreasing.

Indexed as

Public HealthTelemedicineCanadaDigital HealthHumansappsdigital public healthmHealthmobile healthmobile phonepopulation healthpublic healthself-reportsmart technologysurveillancesurveyswearables

Identifiers

PMID39265164
PMCPMC11427850

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.