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Record W4403247913 · doi:10.2196/58306

The Effectiveness of Video Animations as a Tool to Improve Health Information Recall for Patients: Systematic Review

2024· review· en· W4403247913 on OpenAlexaboutno aff
Steffen Hansen, Tue Secher Jensen, Anne Schmidt, Janni Strøm, Peter Vistisen, Mette Terp Høybye

Bibliographic record

VenueJournal of Medical Internet Research · 2024
Typereview
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintRecallComputer scienceWorld Wide WebMultimediaPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Access to clear and comprehensible health information is crucial for patient empowerment, leading to improved self-care, adherence to treatment plans, and overall health outcomes. Traditional methods of information delivery, such as written documents and oral communication, often result in poor memorization and comprehension. Recent innovations, such as animation videos, have shown promise in enhancing patient understanding, but comprehensive investigations into their effectiveness across various health care settings are lacking. OBJECTIVE: This systematic review aims to investigate the effectiveness of animation videos on health information recall in adult patients across diverse health care sectors, comparing their impact to usual information delivery methods on short-term and long-term recall of health information. METHODS: We conducted systematic searches in PubMed, CINAHL, and Embase databases, supplemented by manual searches of reference lists. Included studies were randomized controlled trials involving adult participants (≥18 years) that focused on the use of animation videos to provide health information measured against usual information delivery practice. There were no language restrictions. Out of 2 independent reviewers screened studies, extracted data, and assessed the risk of bias using the Revised Cochrane risk-of-bias tool for randomized trials (RoB2), Covidence was used to handle screening and risk of bias process. A narrative synthesis approach was applied to present results. RESULTS: A total of 15 randomized controlled trials-3 in the United States, 2 in France, 2 in Australia, 2 in Canada, and 1 in the United Kingdom, Japan, Singapore, Brazil, Austria, and Türkiye, respectively-met the inclusion criteria, encompassing 2,454 patients across various health care settings. The majority of studies (11/15, 73%) reported statistically significant improvements in health information recall when animation videos were used, compared with usual care. Animation videos ranged from 1 to 15 minutes in duration with the most common length ranging from 1 to 8 minutes (10/15) and used various styles including 2D cartoons, 3D computers, and whiteboard animations. Most studies (12/15) assessed information recall immediately after intervention, with only 3 studies including longer follow-up periods. Most studies exhibited some concerns related to the risk of bias, particularly in domains related to deviations from intended interventions and selection of reported results. CONCLUSIONS: Animation videos appear to significantly improve short-term recall of health information among adult patients across various health care settings compared with usual care. This suggests that animation videos could be a valuable tool for informing patients in different health care settings. However, further research is needed to explore the long-term efficacy of these interventions, their impact on diverse populations, and how different animation styles might affect information recall. Future studies should also address methodological limitations identified in current research, including the use of validated outcome measures and longer follow-up periods. TRIAL REGISTRATION: PROSPERO CRD42022380016; http://crd.york.ac.uk/prospero/display_record.php?RecordID=380016.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.152
metaresearch head score (Gemma)0.141
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.444
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1520.141
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.125
GPT teacher head0.605
Teacher spread0.480 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations35
Published2024
Admission routes1
Has abstractyes

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