mHealth Technologies for Palliative Care Patients at the Interface of In-Patient to Outpatient Care: Protocol of Feasibility Study Aiming to Early Predict Deterioration of Patient’s Health Status
Bibliographic record
Abstract
BACKGROUND: Palliative care patients are a particularly vulnerable population and one of the critical phases in patients' trajectories is discharge from specialized in-patient palliative care into outpatient care, where availability of a palliative care infrastructure is highly variable. A relevant number of potentially avoidable readmissions and emergency visits of palliative patients is observed due to rapid exacerbation of symptoms indicating the need for a closer patient monitoring. In the last years, different mHealth technology applications have been evaluated in many different patient groups. OBJECTIVE: The aim of our study is to test feasibility of a remote physical and social tracking system in palliative care patients. METHODS: A feasibility study with explorative, descriptive study design, comprised of 3 work packages. From the wards of the Clinic of Radiation-Oncology at the University Hospital Zurich, including the specialized palliative care ward, 30 patients will be recruited and will receive a mobile phone and a tracking bracelet before discharge. The aim of work package A is to evaluate if severely ill patients accept to be equipped with a tracking bracelet and a mobile phone (by semiquantitative questionnaires and guideline interviews). Work package B evaluates the technical feasibility and quality of the acquired electronic health data. Work package C will demonstrate whether physical activity parameters, such as step count, sleep duration, social activity patterns like making calls, and vital signs (eg, heart rate) do correlate with subjective health data and can serve as indicator to early detect and predict changes in patients' health status. Activity parameters will be extracted from the mobile phone's and wristband's sensor data using signal processing methods. Subjective health data is captured via electronic version of visual analog scale and Distress Thermometer as well as the European Organization for Research and Treatment of Cancer - Quality of Life Questionnaire C30 in paper version. RESULTS: Enrollment began in February 2017. First study results will be reported in the middle of 2018. CONCLUSIONS: Our project will deliver relevant data on patients' acceptance of activity and social tracking and test the correlation between subjective symptom assessment and objective activity in the vulnerable population of palliative care patients. The proposed study is meant to be preparatory work for an intervention study to test the effect of wireless monitoring of palliative care patients on symptom control and quality of life.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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 source (direct Gemma or distilled Codex), 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".