Care Me Too, a Mobile App for Engaging Chinese Immigrant Caregivers in Self-Care: Qualitative Usability Study
Bibliographic record
Abstract
BACKGROUND: Caregiving and self-care are challenging for Chinese immigrants in the United States due to limited accessible support and resources. Few interventions exist to assist Chinese immigrant caregivers in better performing self-care. To address this gap in the literature, our team developed the Care Me Too app to engage Chinese immigrant caregivers in self-care and conducted a user experience test to assess its usability and acceptability. OBJECTIVE: This paper aims to report the results of the app's usability and acceptability testing with Chinese immigrant caregivers and to solicit participants' feedback of the app design and functions. METHODS: A total of 22 Mandarin-speaking Chinese caregivers participated in the study, which consisted of 2 parts: the in-lab testing and the 1-week at-home testing. In-depth face-to-face interviews and follow-up phone interviews were used to assess user experience of the app's usability and acceptability and to solicit feedback for app design and functions. Directed content analysis was used to analyze the qualitative data. RESULTS: Among the 22 participants, the average age was 60.5 (SD 8.1) years, ranging from 46 to 80 years; 17 (77%) participants were women and 14 (64%) had an associate degree or higher. Participants reported uniformly positive ratings of the usability and acceptability of the app and provided detailed suggestions for app improvement. We generated guidelines for mobile health (mHealth) app designs targeting immigrant caregivers, including weighing flexibility versus majority preferences, increasing text sizes, using colors effectively, providing engaging and playful visual designs and functions, simplifying navigation, simplifying the log-in process, improving access to and the content on the help document, designing functions to cater to the population's context, and ensuring offline access. CONCLUSIONS: The main contribution of this study is the improved understanding of Chinese caregivers' user experiences with a language-appropriate mHealth app for a population that lacks accessible caregiving and self-care resources and support. It is recommended that future researchers and app designers consider the proposed guidelines when developing mHealth apps for their population to enhance user experience and harness mHealth's value.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".