Preliminary Findings from an Augmented Reality (AR) App Delivering Recovery-Oriented Cognitive Therapy for Negative Symptoms in Schizophrenia
Bibliographic record
Abstract
Abstract Background Negative symptoms are a primary driver of poor outcomes in schizophrenia spectrum disorders (SSD), but there are no FDA-approved medications or FDA-cleared therapeutics targeting negative symptoms in schizophrenia. NST-SPARK is a novel digital therapeutic targeting negative symptoms in SSD. It is a smartphone application delivering recovery-oriented cognitive therapy (CT-R), via gamified augmented reality (AR) experiences, to provide experiential learning aimed at dismantling maladaptive beliefs. In this study, we assessed a prototype (NST-SPARK v.1.5) in 20 participants with SSD and clinically significant negative symptoms. NST-SPARK v.1.5 delivers a single therapeutic module over a 1-week period. The primary objective was to determine the acceptability and feasibility of this approach. Secondary objectives were to generate descriptive findings for changes in defeatist beliefs, self-esteem, and attitudes toward goal-oriented activities. Methods Recruitment and all study procedures were completed online. Twenty participants with schizophrenia or schizoaffective disorder were enrolled, with a range of demographic and socioeconomic status and treatment settings. Participants completed self-reports on the acceptability and feasibility of NST-SPARK v.1.5 and provided open-ended feedback through a semi-structured interview. Self-report scales on defeatist beliefs, self-esteem, and attitudes toward goal-oriented activities were completed before and after participants were introduced to NST-SPARK, and then again at a 1-week follow-up. Results In general, participants found NST-SPARK v.1.5 to be feasible and acceptable, responding with an average response of “Agree”, indicating that the intervention was found to meet with the participants’ approval and seemed implementable. Almost all participants (19 of 20) used the app on their own prior to the 1-week follow up despite not being incentivized to do so. In addition, participants responded to open-ended feedback questions in a generally positive way. We also observed shifts in defeatist beliefs (Cohen’s d = 0.12), self-esteem (Cohen’s d = -0.21), and attitudes toward goal attainment consistent (intention: Cohen’s d = 0.13; confidence: Cohen’s d = 0.35) with intended improvements in these targeted areas. Participants were able to make substantive progress toward identified goals in 90% of cases. Conclusions This preliminary, single-arm, unblinded study of a single-module prototype for NST-SPARK found that the approach is generally acceptable and feasible for people with SSD and negative symptoms. Engagement of the intended target of defeatist beliefs was supported by our findings but require confirmation in future randomized controlled trials. Overall, NST-SPARK is based on a promising approach and further development is warranted.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".