Mobile Technology Utilization Among Patients From Diverse Cultural and Linguistic Backgrounds Attending Cardiac Rehabilitation in Australia: Descriptive, Case-Matched Comparative Study
Bibliographic record
Abstract
BACKGROUND: Barriers to attending cardiac rehabilitation (CR), including cultural and linguistic differences, may be addressed by recent technological developments. However, the feasibility of using these approaches in culturally and linguistically diverse patients is yet to be determined. OBJECTIVE: This study aims to assess the use of mobile technologies and features, as well as confidence in utilization across patients speaking different languages at home (ie, English, Mandarin Chinese, and a language other than English and Mandarin [other]) and are both eligible and physically suitable for CR. In addition, the study aims to determine the sociodemographic correlates of the mobile technology/feature use, including language spoken at home in the three groups mentioned above. METHODS: This is a descriptive, case matched, comparative study. Age and gender-matched patients speaking English, Mandarin and other languages (n=30/group) eligible for CR were surveyed for their mobile technology and mobile feature use. RESULTS: 'Participants had a mean age of 66.7 years (SD 13, n=90, range 46-95), with 53.3% (48/90) male. The majority (82/90, 91.1%) used at least one technology device, with 87.8% (79/90) using mobile devices, the most common being smartphones (57/90, 63.3%), tablets (28/90, 31.1%), and text/voice-only phones (24/90, 26.7%). More English-speaking participants used computers than Mandarin or "other" language speaking participants (P=.003 and .02) and were more confident in doing so compared to Mandarin-speaking participants (P=.003). More Mandarin-speaking participants used smartphones compared with "other" language speaking participants (P=.03). Most commonly used mobile features were voice calls (77/82, 93.9%), text message (54/82, 65.9%), the internet (39/82, 47.6%), email (36/82, 43.9%), and videoconferencing (Skype or FaceTime [WeChat or QQ] 35/82, 42.7%). Less Mandarin-speaking participants used emails (P=.001) and social media (P=.007) than English-speaking participants. Speaking Mandarin was independently associated with using smartphone, emails, and accessing the web-based medication information (OR 7.238, 95% CI 1.262-41.522; P=.03, OR 0.089, 95% CI 0.016-0.490; P=.006 and OR 0.191, 95% CI 0.037-0.984; P=.05). CONCLUSIONS: This study reveals a high usage of mobile technology among CR patients and provides further insights into differences in the technology use across CALD patients in Australia. The findings of this study may inform the design and implementation of future technology-based CR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".