Evidence map›Paper›PMID 34185014›Full record

SynthesisJournal of medical Internet research2021

Health Recommender Systems: Systematic Review.

Robin De Croon, Leen Van Houdt, Nyi Nyi Htun, Gregor Štiglic, Vero Vanden Abeele, Katrien Verbert

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing 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

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

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Observational
  20. 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.

Robin De CroonDepartment of Computer Science, KU Leuven, Leuven, Belgium.ORCID 0000-0002-1329-156X
Leen Van HoudtDepartment of Computer Science, KU Leuven, Leuven, Belgium.ORCID 0000-0002-5584-1902
Nyi Nyi HtunDepartment of Computer Science, KU Leuven, Leuven, Belgium.ORCID 0000-0001-9604-4056
Gregor ŠtiglicFaculty of Health Sciences, University of Maribor, Maribor, Slovenia.ORCID 0000-0002-0183-8679
Vero Vanden AbeeleDepartment of Computer Science, KU Leuven, Leuven, Belgium.ORCID 0000-0002-3031-9579
Katrien VerbertDepartment of Computer Science, KU Leuven, Leuven, Belgium.ORCID 0000-0001-6699-7710

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealth recommender systems (HRSs) offer the potential to motivate and engage users to change their behavior by sharing better choices and actionable knowledge based on observed user behavior.

objectiveWe aim to review HRSs targeting nonmedical professionals (laypersons) to better understand the current state of the art and identify both the main trends and the gaps with respect to current implementations.

methodsWe conducted a systematic literature review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and synthesized the results. A total of 73 published studies that reported both an implementation and evaluation of an HRS targeted to laypersons were included and analyzed in this review.

resultsRecommended items were classified into four major categories: lifestyle, nutrition, general health care information, and specific health conditions. The majority of HRSs use hybrid recommendation algorithms. Evaluations of HRSs vary greatly; half of the studies only evaluated the algorithm with various metrics, whereas others performed full-scale randomized controlled trials or conducted in-the-wild studies to evaluate the impact of HRSs, thereby showing that the field is slowly maturing. On the basis of our review, we derived five reporting guidelines that can serve as a reference frame for future HRS studies. HRS studies should clarify who the target user is and to whom the recommendations apply, what is recommended and how the recommendations are presented to the user, where the data set can be found, what algorithms were used to calculate the recommendations, and what evaluation protocol was used.

conclusionsThere is significant opportunity for an HRS to inform and guide health actions. Through this review, we promote the discussion of ways to augment HRS research by recommending a reference frame with five design guidelines.

Indexed as

AlgorithmsLife StyleHumanseHealthevaluationguidelineshealthhealth carehealth recommender systemslaypersonmobile phonepatientrecommendation systemrecommenderrecommender techniquesystematic reviewuser interface

Identifiers

PMID34185014
PMCPMC8278303

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.