Experiences With Developing and Using Vital Sign Telemonitoring to Support Mobile Nursing in Rural Regions: Feasibility and Usability Study
Bibliographic record
Abstract
BACKGROUND: Modern information and communication technology has the potential to support mobile care in rural regions such as the Alpine region, which is characterized by long distances or even physically unreachable areas. OBJECTIVE: This study investigated the potential of supporting mobile nursing organizations in rural regions with the use of mobile telemonitoring systems in a case study setting. METHODS: As a subproject of the European Union-funded project INTESI, the VITAl parameter MOnitoring (VITAMO) project gathered stakeholders' requirements for telemonitoring support of mobile care in rural regions and then developed and implemented a prototype system that was used for a 3-month test period with a local nursing organization in Austria. Log analysis, surveys, and interviews were used to evaluate the system according to the Technology Acceptance Model. The focus was technology assessment and user satisfaction of both patients and nurses. RESULTS: Participants were provided Bluetooth devices to measure blood pressure, body weight, and blood glucose and to track activity. They also received a tablet with a mobile internet connection to see the results. The nurses were able to access the results remotely. Regularly executed speed tests and log analysis demonstrated the availability of high-speed mobile internet in the rural test region. Log analysis, surveys, and interviews revealed the suitability of the technology environment and showed that the system was easy to use and potentially useful. The perceived usefulness for supporting mobile care was rated meaningfully low, and the frequency of nurses using the tool declined continuously over the field test period. Further group discussions investigated this issue. CONCLUSIONS: While the technology environment with mobile internet, Bluetooth devices, and smart vital sign monitoring devices was adequate and suitable to support mobile nursing in rural regions, the potential benefit for the nursing organization could not be confirmed. Further analysis revealed that operational care processes did not follow a well-defined care strategy. Technology has the potential to leverage the available environment for developing meaningful solutions. These experiences could contribute to further investigations that need to identify and analyze existing mobile care processes at an organizational level.
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) | 0.000 | 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".