Currently submitted to: JMIR Formative Research
Date Submitted: Nov 18, 2025
Open Peer Review Period: Dec 9, 2025 - Feb 3, 2026
(closed for review but you can still tweet)
NOTE: This is an unreviewed Preprint
Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).
Peer review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a "Peer Review Me" button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.
Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).
Final version: If our system detects a final peer-reviewed "version of record" (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.
Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.
Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Feasibility and Preliminary Usability Assessment of an Self-Monitoring Platform application for Brain Tumor Patients: A Pilot Study Toward Digital Early Warning Systems
ABSTRACT
Background:
Postoperative follow-up after brain tumor surgery is typically limited to intermittent clinic visits, leaving subtle neurological or general deterioration between visits underrecognized. Digital self-monitoring platforms may help fill this gap, but evidence in neuro-oncology is scarce, particularly regarding how patient-reported symptom trajectories can feed into future artificial intelligence (AI)–driven early warning systems.
Objective:
To evaluate the feasibility, usage patterns, and preliminary usability of a smartphone/web-based self-monitoring system for patients after brain tumor surgery, and to explore simple rule-based digital alerts as a first step toward an AI-based early warning framework.
Methods:
We conducted a single-center prospective pilot study including adults discharged after brain tumor surgery who had access to a smartphone and could use a web app. Participants completed brief symptom surveys consisting of 51 binary items across seven symptom domains, with an automatically calculated daily total score and score-history visualization. Feasibility was assessed by enrollment, retention, submission counts, and submission rates. Four interpretable alert rules based on current score, short-term worsening, new-onset symptom combinations, and persistence across domains were evaluated using each patient’s last three submissions as the analytic unit. Clinical deterioration was defined a priori as objective decline in performance status, new neurological deficit, radiologic progression, or clinically significant laboratory changes. Rule performance metrics and bootstrap confidence intervals were computed. Usability and acceptability were evaluated using the System Usability Scale (SUS) and additional adherence-related items.
Results:
Of 64 enrolled patients, 30 with ≥3 submissions formed the analysis cohort (median age 57 years; 43% malignant tumors); six (20%) experienced clinical deterioration during follow-up. Patients contributed a median of 8.5 submissions (mean 19.0) at 1.7 surveys/week on average, indicating sustained but heterogeneous engagement. The best-performing rule, based on net short-term score increase, achieved an AUROC of 0.88 with sensitivity 0.83, specificity 0.92, and accuracy 0.90 on the last-window dataset, outperforming rules based solely on current score or multi-domain persistence. Among 23 app users who completed the SUS, the mean score was 84.0, reflecting high perceived usability; higher-frequency users reported stronger perceived usefulness and habit-driven use.
Conclusions:
This pilot study demonstrates that a smartphone/web-based self-monitoring platform for brain tumor patients is feasible and well accepted, and that simple, transparent rules applied to longitudinal symptom scores can capture early signals of clinical deterioration. These findings support further development of integrated, AI-assisted digital early warning systems that combine patient-reported trajectories with clinical and physiological data to enhance postoperative neurosurgical care.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.