Evidence map›Paper›PMID 34223610›Full record

ArticleHealth promotion international2022

Predicting COVID-19 health behaviour initiation, consistency, interruptions and discontinuation among social media users in Nigeria.

Eke Kalu Oyeoku, Felix Olajide Talabi, David Oloyede, Aiyesimoju Ayodeji Boluwatife, Verlumun Celestine Gever, Ibe Ebere

Abstract read
In one paragraph

Article in Health promotion international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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.

Eke Kalu OyeokuDepartment of Education Foundations, University of Nigeria, Nsukka, Nigeria.
Felix Olajide TalabiDepartment of Mass Communication, Redeemers' University, Osun State, Nigeria.
David OloyedeDepartment of Mass Communication, Adeleke University, Ede Osun State, Nigeria.
Aiyesimoju Ayodeji BoluwatifeDepartment of Mass Communication, Joseph Ayo Babalola University, Ikeji-Arakeji, Osun State, Nigeria.
Verlumun Celestine GeverDepartment of Mass Communication, University of Nigeria, Nsukka, Nigeria.
Ibe EbereDepartment of Science Education, University of Nigeria, Nsukka, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Literature on the predictors of health behaviour initiation, interruption, consistency and discontinuation is scarce. This study sought to fill this gap by suggesting a model that takes into account these variables. Using the structural equation modelling, we surveyed 470 social media users in Nigeria and found that perceived vulnerability, perceived seriousness; benefits to action, self-efficacy and cues to action significantly predict health behaviour initiation. Also, the predictors of health interruption were found to be perceived vulnerability, perceived seriousness, benefits to action, challenges to action and perceived realness. The predictors of health behaviour consistency were found to be perceived vulnerability, perceived seriousness, benefits to action, perceived realness, self-efficacy and cues to action. Finally, the predictors of health behaviour discontinuation were found to be perceived vulnerability, perceived seriousness, perception on realness, behaviour fatigue and behaviour discomfort. We explored the implication of these results on health promotion and made suggestions for further studies.

Indexed as

COVID-19Social MediaHealth BehaviorHumansNigeriaSARS-CoV-2COVID-19health behaviourhealth belief modelmodellingsocial media Nigeria

Identifiers

PMID34223610
PMCPMC8344883

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.