CoChat
Sign Up
Cited by 144Mobile Health and mHealth ApplicationsHealth, Technology, Consumer BehaviorTechnology Adoption and User Behaviour

The DOI matches its Crossref record; 75 of the 77 references it cites were found in a registry or an index.

Factors influencing mHealth adoption and its impact on mental well-being during COVID-19 pandemic: A SEM-ANN approach

Open access
Alam, Mirza Mohammad Didarul, Alam, Mohammad Zahedul, Rahman, Syed Abidur, Taghizadeh, Seyedeh Khadijeh·2021·Journal of Biomedical Informatics
DOI 10.1016/j.jbi.2021.103722PMC PMC8970651
Open this paper in CoChat — free accountOpen PDF

Abstract

The objectives of this study are to examine the factors affecting the intention and actual usage behavior on mHealth adoption, investigate the effect of actual usage behavior of mHealth on mental well-being of the end-users, and investigate the moderating role of self-quarantine on the intention-actual usage of mHealth under the coronavirus disease (COVID-19) pandemic situation. The required primary data were gathered from the end-users of mHealth in Bangladesh. Using the Unified Theory of Acceptance and Use of Technology (UTAUT2), this study has confirmed that performance expectancy, effort expectancy, social influence, hedonic motivation, and facilitating conditions have a positive influence on behavioral intention whereas health consciousness has an impact on both intention and actual usage behavior. mHealth usage behavior has an affirmative and meaningful effect on the mental well-being of the service users. Moreover, self-quarantine has strong influence on actual usage behavior but does not moderate the intention-behavior relationship. In addition, due to the existence of a non-linearity problem in the data set, the Artificial Neural Network (ANN) approach was engaged to sort out relatively significant predictors acquired from Structural Equation Modeling (SEM). However, this study contributes to the emergent mHealth literature by revealing how the use of the mHealth services elevates the quality of patients' mental well-being under this pandemic situation.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.jbi.2021.103722

Cites

75 of 75 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 77 references

Research works printed but not linked: 3

  1. Alam, 2014, Factors affecting consumers’ adoption of mobile banking in Bangladesh: an empirical study, TNI J. Bus. Administ. Languages, 2, 31
  2. Bhatt, 2020, Importance of digitech adoption for providing efficient healthcare services during COVID-19, Int. J. Emerg. Technol., 11, 1
  3. H. Hong, Scale development for measuring health consciousness: Re-conceptualization, in: Paper presented at the 12th Annual International Public Relations Research Conference: Research that Matters to the Practice, Coral Gables, Florida, 2009.

Cited by

Sorted by citations

Items 101–144 of 144

Related

See all

Metadata

Authors
Alam, M. M. D., Alam, M. Z., Rahman, S. A., & Taghizadeh, S. K.
Affiliations
  • United International University (BD)
  • Sultan Qaboos University (OM)
Corresponding author
Alam, Mirza Mohammad Didarul
ORCID
Venue
Journal of Biomedical Informatics
Publisher
Elsevier BV
Published
2021-04
Volume
116
Pages
103722
ISSN
1532-0464 (ISSN-L)
DOI
10.1016/j.jbi.2021.103722
PMC
PMC8970651
OpenAlex
W3093020079
Cited by
144
References
77
Citations by year
2026: 16 · 2025: 26 · 2024: 37 · 2023: 29 · 2022: 27 · 2021: 9
FWCI
28.65
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Biomedical Informatics (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
Keywords
mHealth adoption, psychological well-being, COVID-19 pandemic, self-quarantine, UTAUT2, behavioral intention, health consciousness, performance expectancy, effort expectancy, social influence, hedonic motivation, facilitating conditions, moderating effect
MeSH terms
Latent Class Analysis, COVID-19, SARS-CoV-2, Adolescent, Adult, Bangladesh, Female, Humans, Male, Medical Informatics, Mental Health, Middle Aged, Patient Acceptance of Health Care, Quarantine, Surveys and Questionnaires, Health Behavior, Neural Networks, Computer, Telemedicine, Intention, Cell Phone, Young Adult, Pandemics, Mobile Applications

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review