The Bigger Picture of Digital Interventions for Pain, Anxiety and Stress: A Systematic Review of 1200+ Controlled Trials
Bibliographic record
Abstract
Abstract The aim of this systematic scoping review was to gain a better understanding of research trends in digital mental health care. We focused on comorbid conditions: depression, anxiety, and pain–which continue to affect an estimated 20% of world population and require complex and continuous social and medical care provisions. We searched all randomized controlled trials on PubMed until May 2021 for any articles that used a form of information and communication technology (ICT) in relation to primary outcomes anxiety, pain, depression, or stress. From 1285 articles that satisfied the inclusion criteria, 890 were randomized trials with nearly 70% satisfactory outcomes. For depression and anxiety, the most frequently reported, were web-based, or mobile apps used for self-monitoring, and guided interventions. For pain, VR-based interventions or games were more prevalent, especially as tools for distraction, or as stimuli for mechanistic studies of pain or anxiety. We discuss gaps in knowledge and challenges that relate to the human factors in digital health applications, and underline the need for a practical and conceptual framework for capturing and reporting such variations.
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 | 0.019 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".