Evidence map›Paper›PMID 35623020›Full record

SynthesisJCO clinical cancer informatics2022

How Is Quality of mHealth Interventions for Cancer Survivors Defined and Described? An Umbrella Review.

Timothy Tune, Shaun Goh, Patricia A H Williams, Bogda Koczwara

Abstract readSystematic Review
In one paragraph

Synthesis in JCO clinical cancer informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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. Article
  2. Optimizing the impact of supportive cancer care digital health interventions: considerations for design, development, and evaluation.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025
    Review
  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

4 authors.

Timothy TuneCollege of Medicine and Public Health, Flinders University, Adelaide, Australia.ORCID 0000-0003-2452-4944
Shaun GohCollege of Medicine and Public Health, Flinders University, Adelaide, Australia.ORCID 0000-0002-9506-8133
Patricia A H WilliamsCollege of Science and Engineering, Flinders Digital Health Research Centre, Adelaide, Australia.ORCID 0000-0003-3479-3523
Bogda KoczwaraCollege of Medicine and Public Health, Flinders University, Adelaide, Australia.ORCID 0000-0002-1201-1642

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAlthough there are commonly accepted criteria of what defines quality of health care including cancer care, less is known about what defines quality of mHealth interventions in health care. The aim of this review was to identify how quality of mHealth interventions for cancer survivors is described and measured.

methodsCINAHL, EmCare, JBI, Medline, SCOPUS, and ProQuest databases from January 2008 to January 2020 were searched. Review papers with search terms related to mobile devices, quality, and cancer relevant to adults with cancer were included. Interventions needed to consist of mHealth technologies, such as mobile applications or short message service, or wearable devices. Title and abstract screening, full-text screening, and data extraction were performed independently by two reviewers. Conflicts were resolved by a third reviewer. Reviews were evaluated for coverage of quality according to six metrics defined by the Institute of Medicine: patient-centeredness, equitability, safety, effectiveness, timeliness, and efficiency. Any additional quality items were recorded. A Measurement Tool to Assess systematic Reviews (AMSTAR) was used to rate the quality of the reviews included.

resultsThe initial search yielded 766 papers with seven systematic reviews meeting the eligibility criteria. Four papers were of AMSTAR moderate quality, with three of low quality. The median number of quality metrics reported in a review was two (the range was 1-4). Efficacy and safety and timeliness and efficiency were most reported (n = 4), followed by usability (n = 3), equitability and access (n = 2), privacy and security (n = 2), and patient-centeredness (n = 2).

conclusionThere is great variability in how quality of mHealth interventions is defined with no reviews addressing all quality metrics. A comprehensive approach to measure quality of mHealth interventions is needed.

Indexed as

Cancer SurvivorsMobile ApplicationsNeoplasmsTelemedicineText MessagingAdultDelivery of Health CareHumansUnited States

Identifiers

PMID35623020
PMCPMC9225666

What OpenQuestion holds

Textmetadata
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.