PROutine: a feasibility study assessing surveillance of electronic patient reported outcomes and adherence via smartphone app in advanced cancer
Bibliographic record
Abstract
BACKGROUND: In advanced cancer, quality of life (QoL) is a major treatment goal. In order to achieve this, the identification of suffering by screening for patient-reported-outcomes (PROs, i.e., symptoms) is of utmost importance. The use of paper-pencil questionnaires is associated with significant shortcomings due to missing data, recall bias and transcription errors. Other than that, the electronic recording of PROs by mobile Health (mHealth) offers a number of advantages. The aim of this study was to test whether the routine assessment of PROs via a newly developed smartphone application (MeQoL®) is feasible. METHODS: A prospective, uncontrolled, multi-center, feasibility trial was performed in adult outpatients with advanced, solid cancer. Patients under anti-cancer therapy and with regular outpatient visits were eligible. Patients daily recorded the degree of perceived distress (NCCN Distress Thermometer®), pain intensity {average and worst [numerical rating scale (NRS), 0-10]}, the number of breakthrough pain episodes (BPE) and ten questions from a modified version of the Edmonton Symptom Assessment Scale (ESAS). Weekly, five questions concerning different domains of QoL from the short-form 8 (SF-8) questionnaire were obtained. Also, patients recorded the intake of their opioid rescue medication. According to the main scope of the trial (feasibility), no primary endpoint was defined. Rather, the following main feasibility criteria were assessed: missing data, drop-out- and acceptance-rate, patient satisfaction, patients' judgement of practicability, patients' and physicians' suggestions for improvement and basic clinical and demographic data of the participating patients. The study was registered in the German Clinical Trials Register (ID: DRKS00008761). RESULTS: In three German cancer centers, 40 patients {female: 28 (70%); average age, 57 years [range, 27-73 years; standard deviation (SD), 12]} were included. As three devices were lost on transport, 37 devices could be evaluated. The median investigation period per device was 99.5 days (SD, 31). Patient adherence in using the smartphone app to document their distress and symptoms was high and missing data were low: In median daily reviews were performed on 70 (SD, 29) of these days (70%) and median weekly recordings were 13 weeks (87%). Most often, patients recorded symptom intensity (89%, MIDOS) and distress (85%, NCCN thermometer). On feedback forms, patients reported a good to very good user friendliness of MeQoL® and a high motivation to use this tool again. CONCLUSIONS: Even though participants were asked to record PROs rather frequently (daily), missing data were low and patient satisfaction was high. Having in mind the findings of other working groups, such routine implementation of mHealth solutions may substantially improve outcomes of cancer therapy and increase the value of trials' findings. For the individual patient, MeQoL® allows for monitoring adherence to pharmacotherapy and can facilitate patient guidance.
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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 | 0.001 | 0.001 |
| 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".