Feasibility of PRIME: A Cognitive Neuroscience-Informed Mobile App Intervention to Enhance Motivated Behavior and Improve Quality of Life in Recent Onset Schizophrenia
Bibliographic record
Abstract
BACKGROUND: Despite improvements in treating psychosis, schizophrenia remains a chronic and debilitating disorder that affects approximately 1% of the US population and costs society more than depression, dementia, and other medical illnesses across most of the lifespan. Improving functioning early in the course of illness could have significant implications for long-term outcome of individuals with schizophrenia. Yet, current gold-standard treatments do not lead to clinically meaningful improvements in outcome, partly due to the inherent challenges of treating a population with significant cognitive and motivational impairments. The rise of technology presents an opportunity to develop novel treatments that may circumvent the motivational and cognitive challenges observed in schizophrenia. OBJECTIVE: The purpose of this study was two-fold: (1) to evaluate the feasibility and acceptability of implementing a Personalized Real-Time Intervention for Motivation Enhancement (PRIME), a mobile app intervention designed to target reward-processing impairments, enhance motivation, and thereby improve quality of life in recent onset schizophrenia, and (2) evaluate the empirical benefits of using an iterative, user-centered design (UCD) process. METHODS: We conducted two design workshops with 15 key stakeholders, followed by a series of in-depth interviews in collaboration with IDEO, a design and innovation firm. The UCD approach ultimately resulted in the first iteration of PRIME, which was evaluated by 10 RO participants. Results from the Stage 1 participants were then used to guide the next iteration that is currently being evaluated in an ongoing RCT. Participants in both phases were encouraged to use the app daily with a minimum frequency of 1/week over a 12-week period. RESULTS: The UCD process resulted in the following feature set: (1) delivery of text message (short message service, SMS)-based motivational coaching from trained therapists, (2) individualized goal setting in prognostically important psychosocial domains, (3) social networking via direct peer-to-peer messaging, and (4) community "moments feed" to capture and reinforce rewarding experiences and goal achievements. Users preferred an experience that highlighted several of the principles of self-determination theory, including the desire for more control of their future (autonomy and competence) and an approach that helps them improve existing relationships (relatedness). IDEO, also recommended an approach that was casual, friendly, and nonstigmatizing, which is in line with the recovery model of psychosis. After 12-weeks of using PRIME, participants used the app, on average, every other day, were actively engaged with its various features each time they logged in and retention and satisfaction was high (20/20, 100% retention, high satisfaction ratings). The iterative design process lead to a 2- to 3-fold increase in engagement from Stage 1 to Stage 2 in almost each aspect of the platform. CONCLUSIONS: These results indicate that the neuroscience-informed mobile app, PRIME, is a feasible and acceptable intervention for young people with schizophrenia.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".