| Literature DB >> 33252340 |
Florian Dittrich1,2, David Alexander Back3, Anna Katharina Harren4, Stefan Landgraeber1, Felix Reinecke5, Sebastian Serong1, Sascha Beck6.
Abstract
BACKGROUND: In the course of digitization, smartphones are affecting an increasing number of areas of users' lives, giving them almost ubiquitous access to the internet and other web applications. Mobile health (mHealth) has become an integral part of some areas of patient care. In contrast to other disciplines, routine integration of mobile devices in orthopedics and trauma surgery in Germany is still in its infancy.Entities:
Keywords: communication; mHealth; medicine; orthopedics; smartphone; surveys and questionnaires; technology; trauma surgery
Year: 2020 PMID: 33252340 PMCID: PMC7735902 DOI: 10.2196/14787
Source DB: PubMed Journal: JMIR Form Res ISSN: 2561-326X
Characteristics of the DGOUa members (N=10,487) and survey respondents (N=189) with regard to age.
| Age groups (years) | Values | |
|
| ||
|
| <35 | 1413 (13.47) |
|
| 36-45 | 3091 (29.47) |
|
| 46-55 | 2644 (25.21) |
|
| 56-65 | 1937 (18.47) |
|
| >65 | 1402 (13.37) |
|
| ||
|
| <30 | 30 (15.87) |
|
| 30-39 | 93 (49.21) |
|
| 40-49 | 41 (21.69) |
|
| 50-59 | 16 (8.47) |
|
| >60 | 9 (4.76) |
aDGOU: Society for Orthopaedics and Trauma. Status as of February 2020. Source: DGOU Office.
Characteristics of the DGOUa members (N=10,487) and survey respondents (N=206) with regard to area of activity.
| Area of activity | DGOU, n (%) | Survey respondents, n (%) |
| Higher education | 1624 (15.49) | 0 (0) |
| Resident physician | 2686 (25.61) | 93 (45.15) |
| Consultant | 1093 (10.42) | 41 (19.90) |
| Senior consultant | 2932 (27.96) | 67 (32.52) |
| Other medical employment | 1702 (16.23) | 5 (2.42) |
aDGOU: Society for Orthopaedics and Trauma. Status as of February 2020. Source: DGOU Office.
Figure 1Usage behavior of smartphones and medical apps in orthopedics and trauma surgery (N=206).
Figure 2Functions or features important to the survey participants in future medical apps (indicated via responses to multiple-choice questions; N=194).
Figure 3Participants’ responses regarding the greatest benefits of using of medical apps (indicated via responses to multiple-choice questions; N=201).
Figure 4Participants’ responses regarding the risks that can arise from the regular use of medical apps in the future (indicated via responses to multiple-choice questions; N=189).
Statistical analysis of respondents’ answers to the question “Do you use your smartphone on a regular basis in your clinical routine?”a
|
| B | SE | Wald ( | P | Exp (B) |
| Medical qualification | –0.42 | 0.33 | 1.60 (1) | .21 | 0.66 |
| Ageb | –0.09 | 0.03 | 9.16 (1) | .002 | 0.91 |
| Future potentialb | 3.81 | 1.12 | 11.67 (1) | .001 | 45.22 |
| App costsb | 0.54 | 0.20 | 7.06 (1) | .01 | 1.72 |
| Constant | 0.69 | 1.49 | 0.22 (1) | .64 | 2.00 |
aχ24=73.4; P<.001. The model explains 52.3% (Nagelkerke R2) of the variance and correctly classifies 88.2% of the cases.
bValues are significant for age, future potential, and app costs at P<.002, P<.001, and P<.01, respectively.
Statistical analysis of respondents’ answers to the question “Do you use medical apps on a regular basis in your clinical routine?”a
|
| B | SE | Wald ( | P | Exp (B) |
| Medical qualification | –0.15 | 0.27 | 0.31 (1) | .58 | 0.86 |
| Ageb | –0.10 | 0.03 | 12.00 (1) | .001 | 0.90 |
| Future potentialb | 2.33 | 0.89 | 6.87 (1) | .011 | 10.32 |
| App costs | 0.26 | 0.16 | 2.56 (1) | .11 | 1.30 |
| Apps offered | 0.62 | 0.44 | 1.98 (1) | .16 | 1.86 |
| Constant | 1.63 | 1.32 | 1.53 (1) | .22 | 5.12 |
aχ24=51.4; P<.001. The model explains 36.6% (Nagelkerke R2) of the variance and correctly classifies 76.9% of cases.
bValues are significant for age and future potential at P<.001 and P<.01, respectively.
Statistical analysis of respondents’ answers to the question “Do you think the increased use of smartphones in clinical routine will be productive in the future?”a
|
| B | SE | Wald ( |
| Exp(B) |
| Medical qualification | –0.06 | 0.56 | 0.01 (1) | .92 | 0.94 |
| Age | –0.00 | 0.05 | 0.00 (1) | .96 | 1.00 |
| App costsb | 0.71 | 0.30 | 5.67 (1) | .02 | 2.03 |
| App offer | –1.30 | 0.87 | 2.24 (1) | .13 | 0.27 |
| Clinical smartphone usageb | 5.01 | 1.73 | 8.37 (1) | .004 | 150.30 |
| Clinical app usage | –0.52 | 1.49 | 0.12 (1) | .73 | 0.59 |
| Constant | –0.91 | 2.74 | 0.11 (1) | .74 | 0.40 |
aχ24=60.4; P<.001. The model explains 67.0% (Nagelkerke R2) of the variance and correctly classifies 95.2% of the cases.
bValues are significant for app costs and clinical smartphone usage at P<.02 and P<.004, respectively.
Statistical analysis of respondents’ answers to the question “Do you use your smartphone on a regular basis for medical research?”a
|
| B | SE | Wald ( |
| Exp (B) |
| Medical qualification | –0.09 | 0.24 | 0.14 (1) | .71 | 0.91 |
| Ageb | –0.08 | 0.02 | 11.78 (1) | .001 | 0.93 |
| Constant | 4.26 | 0.77 | 31.03 (1) | .001 | 71.05 |
aχ24=21.5; P<.001. The model explains 16.2% (Nagelkerke R2) of the variance and correctly classifies 76.2% of the cases.
bValues are significant for age at P<.001.
Figure 5Future innovative usage scenarios of apps in daily clinical routines requiring the implementation of smartphones as the central information and communication medium. (1) Collection of patient-related data via smartphones (wearables). (2) Data storage into databases communicating with additional information systems. (3) Data processing by AI (including determination of risk factors or patterns, conducting interdisciplinary case discussions, and facilitating data backflow to the patient (individualized therapy recommendations and patient monitoring). (4) Treatment recommendations (exchange/communicate information with other physicians; eg, digitized guidelines for antibiotic treatment).