Utilizing a Culturally-Modified Smartphone App to Increase Engagement in Depression Treatment among Chinese Americans: A Pilot Study
Bibliographic record
Abstract
Background: As smartphone technology has become ubiquitous in the US society, recent research indicates there is a high rate of smartphone ownership and medical apps use among ethnic minorities. Although the actual effectiveness of medical apps is largely unknown, emerging data has shown interest and feasibility of utilizing smartphone apps to reduce health disparities and improve engagement with the health care system among low-income minorities. Objective: This is a pilot study to evaluate the feasibility of using a smartphone application featuring a culturally-validated Chinese Bilingual version of the Patient Health Questionnaire (CB-PHQ-9) and Tai-Chi mindfulness intervention among Chinese American with depression. We hypothesize that Chinese American outpatients will be able to download the app to their personal smartphone and use it for 30 days. We further hypothesize that the culturally-customized screening tool and mindfulness intervention delivered through a smartphone app will increase the engagement to seek depression treatment among Chinese Americans. Methods: A total of 25 participants will be recruited from outpatient psychiatry and primary care clinics at Beth Israel Deaconess Medical Center (BIDMC) located in Boston, Massachusetts. Eligibility requirements include Chinese ethnicity, fluency in either English or Mandarin Chinese, and a baseline score of 10 or higher on the PHQ-9. Participants will be instructed to use the Tai-Chi mindfulness exercise and then complete the CB-PHQ-9 once a day during a 30-day period. At the 30th day follow-up visit, a 10-minute semi-structured verbal interview and a written survey containing the System Usability Scale (SUS) will be administered to each participant to collect user feedback. The data of daily app login, CB-PHQ-9 scores, and self-reported physical location will be automatically recorded by the app. Results: Preliminary results will be available by the time of the CHC 2017 meeting. The IRB of this pilot study is currently under review at BIDMC. Conclusions: Preliminary results will be available by the time of the CHC 2017 meeting. The IRB of this pilot study is currently under review at BIDMC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".