| Literature DB >> 31099338 |
Georgios Paslakis1,2, Josefine Fischer-Jacobs3, Lars Pape4,5, Mario Schiffer6,7, Raoul Gertges5, Uwe Tegtbur5,8, Tanja Zimmermann3, Mariel Nöhre3,5, Martina de Zwaan3,5.
Abstract
BACKGROUND: There has been an incremental increase in the use of technology in health care delivery. Feasibility, acceptability, and efficacy of interventions based on internet technologies are supported by a growing body of evidence.Entities:
Keywords: eHealth; electronic medical records; health care delivery; internet; online psychotherapy; teleconference; telemedicine
Mesh:
Year: 2019 PMID: 31099338 PMCID: PMC6542248 DOI: 10.2196/12416
Source DB: PubMed Journal: J Med Internet Res ISSN: 1438-8871 Impact factor: 5.428
Sociodemographics of study participants as a cohort.
| Variables | Survey participants, n (%) | |
| Male | 1100 (45.10) | |
| Female | 1339 (54.90) | |
| 18-24 | 196 (8.04) | |
| 25-34 | 372 (15.25) | |
| 35-44 | 423 (17.34) | |
| 45-54 | 472 (19.36) | |
| 55-64 | 483 (19.80) | |
| 65+ | 493 (20.21) | |
| <12 | 1926 (78.97) | |
| ≥12 | 513 (21.03) | |
| 0 to <1000 | 662 (27.71) | |
| 1000 to <2500 | 1487 (62.24) | |
| ≥2500 | 240 (10.05) | |
| <5000 | 351 (14.39) | |
| 5000 to <50,000 | 1028 (42.15) | |
| ≥50,000 | 1060 (43.46) | |
Figure 1Internet use depending on age and educational level in a survey of adults from the general population. A significant interaction between age and educational level is found.
Linear regression analysis to predict internet use based on sex, age, income, population size, and educational level.
| Variable | Nonstandardized coefficient | Standardized coefficient beta | |||
| Regression coefficient B (SEa) | 95% CI | ||||
| Constant | 4.09 (.18) | 3.74 to 4.44 | 22.81 | <.001 | |
| Sex | .11 (.06) | –0.002 to 0.23 | .03 | 1.93 | .054 |
| Age | –.50 (.02) | –0.54 to –0.47 | –.49 | –28.17 | <.001 |
| Education | .63 (.07) | 0.49 to 0.77 | .16 | 8.94 | <.001 |
| Income | .39 (.05) | 0.29 to 0.49 | .14 | 7.66 | <.001 |
| Population | .14 (.04) | 0.06 to 0.22 | .06 | 3.45 | .001 |
aSE: standard error.
Sociodemographic characteristics of internet users and nonusers.
| Variables | Internet users, n (%) | Internet nonusers, n (%) | dfa | Chi-square | ||
| Male | 861 (45.56) | 235 (43.68) | ||||
| Female | 1029 (54.44) | 303 (56.32) | ||||
| 18-24 | 185 (9.79) | 10 (1.86) | ||||
| 25-34 | 347 (18.36) | 22 (4.09) | ||||
| 35-44 | 391 (20.69) | 31 (5.76) | ||||
| 45-54 | 413 (21.85) | 59 (10.97) | ||||
| 55-64 | 357 (18.89) | 122 (22.67) | ||||
| 65+ | 197 (10.42) | 294 (54.65) | ||||
| <12 | 1410 (74.60) | 509 (94.61) | ||||
| ≥12 | 480 (25.40) | 29 (5.39) | ||||
| 0 to <1000 | 486 (26.34) | 169 (31.70) | ||||
| 1000 to <2500 | 1141 (61.84) | 342 (64.17) | ||||
| ≥2500 | 218 (11.82) | 22 (4.13) | ||||
| <5000 | 261 (13.81) | 88 (16.35) | ||||
| 5000 to <50,000 | 790 (41.80) | 231 (42.94) | ||||
| ≥50,000 | 839 (44.39) | 219 (40.71) | ||||
adf: degree of freedom.
Figure 2Distribution of age groups in noninternet users (a) compared to internet users (b). Within each of the groups users versus nonusers, age groups had a significant main effect. Age groups also differed between the groups.
Figure 3Comparisons of educational levels in noninternet users (a) compared to internet users (b). Within each of the groups users versus nonusers, educational level was significantly different with regard to the total years of education. Educational level also differed between the groups.
Willingness of adult internet users (n=1836) to consider the use of internet technologies within a medical context of consultation or treatment and their actual experiences with technologies of this kind within this specific context.
| Variable | Yes, n (%) | No, n (%) |
| Would use email to schedule visits | 1257 (68.46) | 579 (31.54) |
| Have used email to schedule visits | 358 (22.31) | 1247 (77.69) |
| Would use email to report symptoms | 798 (43.42) | 1040 (56.58) |
| Have used email to report symptoms | 64 (4.04) | 1519 (95.96) |
| Would use videoconferencing with their physician | 667 (36.02) | 1185 (63.98) |
| Have used videoconferencing with their physician | 14 (0.89) | 1561 (99.11) |
| Would use videoconferencing with more than 1 physician at the same time | 614 (33.14) | 1239 (66.86) |
| Have used videoconferencing with more than 1 physician at the same time | 16 (1.02) | 1559 (98.98) |
| Would use videoconferencing for psychotherapy | 377 (20.35) | 1476 (79.65) |
| Have used videoconferencing for psychotherapy | 19 (1.21) | 1554 (98.79) |
| Would use electronic medical records | 876 (47.38) | 973 (52.62) |
| Have used electronic medical records | 22 (1.40) | 1547 (98.60) |
| Would use apps | 866 (46.71) | 988 (53.29) |
| Have used apps | 34 (2.20) | 1508 (97.80) |