Digital Interventions for Depression and Anxiety in Older Adults: Protocol for a Systematic Review
Bibliographic record
Abstract
BACKGROUND: There is a high prevalence of older adults experiencing depression and anxiety. In response to heightened demands for mental health interventions that are accessible and affordable, there has been a recent rise in the number of digital mental health interventions (DMHIs) that have been developed and incorporated into mental health treatments. Digital interventions are promising in their ability to provide researchers, medical practitioners, and patients with personalized tools for assessing behavior, consultation, treatment, and care that can be used remotely. Reviews and meta-analyses have shown the benefits of DMHIs for the treatment and prevention of depression, anxiety, and other mental illnesses, but there is still a lack of studies that focus on the benefits and use of DMHIs in the older population. OBJECTIVE: The aim of this systematic review is to investigate the current evidence for the effect of technology-delivered interventions, such as smartphone/tablet applications, remote monitoring and tracking devices, and wearable technology, for the treatment and prevention of depression and anxiety in adults older than 50 years. METHODS: The academic databases SCOPUS, PsycINFO, AgeLine (EBSCO), and Medline (PubMed) will be searched from January 1, 2010, to the date of search commencement to provide a review of existing randomized controlled trial studies. The search will include 3 key concepts: "older adults," "digital intervention," and "depression/anxiety." A set of inclusion criteria will be followed during screening by two reviewers. Data will be extracted to address aims and objectives of the review. The risk of bias for each study will be determined using appropriate tools. If possible, a random-effects meta-analysis will be performed, and the heterogeneity of effect sizes will be calculated. RESULTS: Preliminary searches were conducted in September 2020. The review is anticipated to be completed by April 2021. CONCLUSIONS: The data accumulated in this systematic review will demonstrate the potential benefits of technology-delivered interventions for the treatment of depression and anxiety disorders in older adults. This review will also identify any gaps in current studies of aging and mental health interventions, thereby navigating a way to move forward and paving the path to more accessible and user-friendly digital health interventions for the diverse population of older adults. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42020192532; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020192532. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/22738.
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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 | 0.055 | 0.060 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.019 | 0.019 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.083 | 0.010 |
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".