Evidence map›Paper›PMID 35632195›Full record

SynthesisSensors (Basel, Switzerland)2022

Smartphone as a Disease Screening Tool: A Systematic Review.

Jeban Chandir Moses, Sasan Adibi, Nilmini Wickramasinghe, Lemai Nguyen, Maia Angelova, Sheikh Mohammed Shariful Islam

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
7.8field-weighted citation impact, top 2% of its field
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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Advances in E-Health and Mobile Health Monitoring.Sensors (Basel, Switzerland) · 2022
    Article
  12. Observational
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 at 2 institutions in 1 country.

Jeban Chandir MosesSchool of Information Technology, Deakin University, Burwood, VIC 3125, Australia.ORCID 0000-0002-4628-8850
Sasan AdibiSchool of Information Technology, Deakin University, Burwood, VIC 3125, Australia.
Nilmini WickramasingheIverson Health Innovation Research Institute, Swinburne University of Technology, Hawthorn, VIC 3122, Australia.ORCID 0000-0002-1314-8843
Lemai NguyenDepartment of Information Systems and Business Analytics, Deakin Business School, Deakin University, 221 Burwood Highway, Burwood, VIC 3125, Australia.ORCID 0000-0003-3695-7245
Maia AngelovaSchool of Information Technology, Deakin University, Burwood, VIC 3125, Australia.ORCID 0000-0002-0931-0916
Sheikh Mohammed Shariful IslamInstitute for Physical Activity and Nutrition (IPAN), Deakin University, Burwood, VIC 3125, Australia.ORCID 0000-0001-7926-9368
Deakin University · AUSwinburne University of Technology · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Disease screening identifies a disease in an individual/community early to effectively prevent or treat the condition. COVID-19 has restricted hospital visits for screening and other healthcare services resulting in the disruption of screening for cancer, diabetes, and cardiovascular diseases. Smartphone technologies, coupled with built-in sensors and wireless technologies, enable the smartphone to function as a disease-screening and monitoring device with negligible additional costs and potentially higher quality results. Thus, we sought to evaluate the use of smartphone applications for disease screening and the acceptability of this technology in the medical and healthcare sectors. We followed a systematic review process using four databases, including Medline Complete, Web of Science, Embase, and Proquest. We included articles published in English examining smartphone application utilisation in disease screening. Further, we presented and discussed the primary outcomes of the research articles and their statistically significant value. The initial search yielded 1046 studies for the initial title and abstract screening. Of the 105 articles eligible for full-text screening, we selected nine studies and discussed them in detail under four main categories: an overview of the literature reviewed, participant characteristics, disease screening, and technology acceptance. According to our objective, we further evaluated the disease-screening approaches and classified them as clinically administered screening (33%, n = 3), health-worker-administered screening (33%, n = 3), and home-based screening (33%, n = 3). Finally, we analysed the technology acceptance among the users and healthcare practitioners. We observed a significant statistical relationship between smartphone applications and standard clinical screening. We also reviewed user acceptance of these smartphone applications. Hence, we set out critical considerations to provide equitable healthcare solutions without barriers when designing, developing, and deploying smartphone solutions. The findings may increase research opportunities for the evaluation of smartphone solutions as valid and reliable screening solutions.

Indexed as

COVID-19Mobile ApplicationsText MessagingDelivery of Health CareHumansSmartphonechronic diseasedisease screeningmobile solutionssmartphone applicationstechnologytechnology acceptance

Identifiers

PMID35632195
PMCPMC9145643
OpenAlexW4280646277

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.