Digitizing Non-Invasive Neuromodulation Trials: Scoping Review, Process Mapping, and Recommendations from a Delphi Panel
Bibliographic record
Abstract
Abstract Although relatively costly and non-scalable, non-invasive neuromodulation interventions are treatment alternatives for neuropsychiatric disorders. The recent developments of highly-deployable transcranial electric stimulation (tES) systems, combined with mobile-Health technologies, could be incorporated in digital trials to overcome methodological barriers and increase equity of access. We convened 61 highly-productive specialists and contacted 8 tES companies to assess 71 issues related to tES digitalization readiness, and processes, barriers, advantages, and opportunities for implementing tES digital trials. Delphi-based recommendations (>60% agreement) were provided. Device appraisal showed moderate digitalization readiness, with high safety and the possibility of trial implementation, but low connectivity. Panelists recognized the potential of tES for scalability, generalizability, and leverage of digital trials processes; although they reached no consensus about aspects regarding methodological biases. We further propose and discuss a conceptual framework for exploiting shared aspects between mobile-Health tES technologies with digital trials methodology to drive future efforts for digitizing tES trials. Graphical Abstract. Consensus Roadmap (A) Recruitment process . The study procedure started with defining the components of the research problem by the core research team. After defining the problems, two different sets of participants (the steering committee (SC) including key leaders of the field identified by the core team and the expert panel (EP) as a more diverse group of experts identified based on the number of publications based on a systematic review) were identified and were invited to participate in a Delphi study. The study facilitators (first and last authors) led the communications with the SC to design the initial questionnaire through an iterative approach. (B) Evidence synthesis: To collect the available evidence, companies producing portable tES (ptES) devices were contacted, based on the companies suggested by the SC and EP to provide details about the available devices. For mapping methodological processes of digitizing tES trials, two distinct strategies were performed and embedded into the questionnaire, namely SIPOC (Suppliers, Inputs, Process, Outputs, and Customer) and SWOT (Strengths, Weaknesses, Opportunities, and Threats) assessment were performed and embedded into the questionnaire. (C) Consensus development: In the next phase, the questionnaire was validated and finalized via collecting and summarizing opinions. Afterward, the SC and EP responded to the final questionnaire, and results were analyzed providing a list of recommendations for running tES digital trials based on a pre-registered consensus threshold.
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 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 | 0.576 | 0.605 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.030 | 0.017 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".