Evidence map›Paper›PMID 35401996›Full record

ReviewDigital health

The effectiveness of automated digital health solutions at successfully managing obesity and obesity-associated disorders: A PICO-structured investigation.

James Christopher Price, Heitor Oliveira Santos, Allain Amador Bueno

Open access · goldAbstract readReview
In one paragraph

Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
3.1field-weighted citation impact, top 9% 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

10 citing papers in PubMed, 11 citations in OpenAlex.

  1. An exploratory study to understand how rurality status and demographic characteristics are associated with enrollment, engagement, and retention in a digital health intervention targeting the Appalachian region.The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2025
    Trial
  2. Review
  3. Review
  4. Article
  5. Article
  6. Observational
  7. Review
  8. Review
  9. eHealth in obesity care.Clinical medicine (London, England) · 2023
    Article
  10. Review
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

3 authors at 2 institutions in 2 countries.

James Christopher PriceCollege of Health, Life and Environmental Sciences, University of Worcester, UK.ORCID https://orcid.org/0000-0002-7587-2415
Heitor Oliveira SantosSchool of Medicine, Federal University of Uberlândia (UFU), Uberlandia, Minas Gerais, Brazil.
Allain Amador BuenoCollege of Health, Life and Environmental Sciences, University of Worcester, UK.ORCID https://orcid.org/0000-0002-9456-8558
University of Worcester · GBUniversidade Federal de Uberlândia · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Most adults in the UK and USA are classified as overweight or obese. Recent studies suggest that the prevalence of obesity has further increased during the SARS-CoV-2 pandemic and associated lockdowns. Digital technologies may be effective at managing obesity and related comorbidities, a potential further justified by social isolation and distancing circumstances. This review of published literature employed a Patient-Intervention-Comparison-Outcome structured approach on the use of digital solutions to determine the effectiveness of their use in the management and treatment of obesity, hypertension, and type 2 diabetes and included commercially available, automated devices and applications that did not require intervention from a clinician. Our search covered studies published between January 2004 and February 2019, and 18 papers were included in the final analysis. The digital solutions reviewed were smartphone applications, wearable activity trackers, and 'digital medicine offerings' (DMO), including ingestible sensors and wearable patches. This study found that not all interventions were effective at encouraging the lifestyle changes required for the management of obesity. Smartphone applications requiring interaction from the patient appeared to be more effective at encouraging engagement with treatment interventions than more passive wearable activity trackers. Automated feedback from smartphone applications was effective at managing type 2 diabetes, while DMO were effective at reducing blood pressure. With the advancement of new technologies alongside a rapid increase in the prevalence of obesity and associated disorders, further studies comparing the various technologies available in larger sample populations for longer periods would help determine the most cost-effective preventive and therapeutic strategies.

Indexed as

diabetesdigital healthdigital medicine offeringshypertensionobesitysmartphone applicationswearable technology

Identifiers

PMID35401996
PMCPMC8990694
OpenAlexW4223583727

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.