| Literature DB >> 34912474 |
Abstract
The goal of this study was to develop and use a questionnaire in order to analyse the effects of eHealth apps on patient care using Jordanian population. A two-stage cross-sectional research was conducted. A questionnaire was developed in the beginning to evaluate its consistency and legitimacy using Cronbach's alpha coefficient, a multitrait connection atmosphere; the multivariate technique is component examination. In the study's another phase, correlation and regression are used to determine the influence of eHealth apps on patient care. The five major axes of the final surveys were healthcare efficiency, teaching, notices, consultation, and follow-up. Individuals from diverse demographic aspects, such as gender, age, job experience, and education level, have no differing perspectives on cell phone use in their amenities, according to a staff's viewpoint evaluation. In general, mobile health applications had a good influence on health services and healthcare, which would be an important setting for the operative use of mobile headphones in public policy; such a background would affect in workers' intents to practice and adopt mHealth.Entities:
Year: 2021 PMID: 34912474 PMCID: PMC8668363 DOI: 10.1155/2021/7611686
Source DB: PubMed Journal: Appl Bionics Biomech ISSN: 1176-2322 Impact factor: 1.781
Figure 1The conceptual framework of the study.
Demographic data of questionnaire respondents.
| Variable | Category | % |
|---|---|---|
| Gender of the respondents | Male | 71.0 |
| Female | 29.0 | |
|
| ||
| Marital status of the respondents | Single | 56.1 |
| Married | 43.9 | |
|
| ||
| Age of the respondents | 18-25 years | 29.8 |
| 25-35 years | 53.4 | |
| 35-50 years | 16.8 | |
| Above 50 | 0.0 | |
|
| ||
| Qualification of respondents | Graduation | 40.2 |
| Master | 39.9 | |
| PhD | 1.5 | |
| Other | 18.4 | |
Figure 2Gender distribution.
Variable descriptive analysis.
|
| Minimum | Maximum | Mean | Std. deviation | |
|---|---|---|---|---|---|
| Effectiveness of health services | 100 | 1.00 | 4.67 | 3.4172 | .94798 |
| Education | 100 | 1.00 | 4.67 | 3.4104 | .93936 |
| Consultation | 100 | 1.00 | 4.67 | 3.3742 | .90135 |
| Notification services | 100 | 1.00 | 5.00 | 3.4921 | 1.08387 |
| Patient healthcare | 100 | 1.00 | 5.00 | 4.3915 | .92207 |
| Valid | 100 |
Factor loadings, normality, and reliability analysis results.
| Factor loadings | Normality (skewness) | Cronbach's alpha | |
|---|---|---|---|
| Effectiveness of health services | 0.994 | 0.561 | 0.80 |
| Education | 0.976 | 0.48 | |
| Consultation | 0.922 | 0.627 | |
| Notification services | 0.937 | 0.482 | |
| Patient healthcare | 0.987 | 0.499 |
Correlation analysis results.
| Effectiveness of health services | Education | Consultation | Notification services | Patient healthcare | |
|---|---|---|---|---|---|
| Effectiveness of health services | 1 | ||||
| Education | .889∗∗ | ||||
| Consultation | .785∗∗ | .764∗∗ | |||
| Notification services | .924∗∗ | .897∗∗ | .785∗∗ | ||
| Patient healthcare | .928∗∗ | .897∗∗ | .777∗∗ | .930∗∗ | 1 |
Regression analysis results.
| Information quality | |||||
|---|---|---|---|---|---|
|
| Std. error | Beta |
| Sig. | |
| Effectiveness of health services | 0.824 | 0.112 | 1.045 | 8.842 | 0.000 |
| Education | 0.565 | 0.122 | 0.897 | 8.408 | 0.000 |
| Consultation | 0.808 | 0.041 | 0.831 | 19.884 | 0.000 |
| Notification services | 0.923 | 0.213 | 0.239 | 7.3113 | 0.000 |