Designing a Mobile e-Coaching App for Immigrant Informal Caregivers: Qualitative Study Using the Persuasive System Design Model
Bibliographic record
Abstract
BACKGROUND: Informal caregivers are vital in caring for their family and friends at home who may have illnesses or disabilities. In particular, the demands for caregiving can be even more challenging for those with limited resources, support systems, and language barriers, such as immigrant informal caregivers. They face complex challenges in providing care for their relatives. These challenges can be related to sociocultural diversity, language barriers, and health care system navigation. Acknowledging the global context of the increasing number of immigrants is essential in designing inclusive mobile health apps. OBJECTIVE: This study aims to investigate the needs of immigrant informal caregivers in Sweden and discuss the application of the Persuasive System Design Model (PSDM) to develop an e-coaching prototype. By addressing the unique challenges faced by immigrant informal caregivers, this study will contribute to the development of more effective and inclusive mobile health apps. METHODS: The participants were considered immigrants and included in the study if they and their parents were born outside of Sweden. Through various channels, such as the National Association of Relatives, rehabilitation departments at municipalities, and immigrant groups, we recruited 13 immigrant informal caregivers. These immigrant informal caregivers were primarily women aged 18 to 40 years. Most participants belonged to the Middle Eastern region whereas some were from North Africa. However, all of them spoke Arabic. We used semistructured interviews to gather data from the participants in Arabic, which were translated into English. Data were analyzed using thematic analysis and discussed in relation to the extended PSDM. The needs of the caregivers were compared with the description of persuasive design principles, and a design principle was chosen based on the match. The PSDM was extended if the need description did not match any principles. Several brainstorming and prototyping sessions were conducted to design the mobile e-coaching app. RESULTS: Immigrant informal caregivers have various needs in their caregiving role. They reported a need for training on the illness and future caregiving needs, assistance with understanding the Swedish language and culture, and help with accessing internet-based information and services. They also required recognition and appreciation for their efforts, additional informal support, and easy access to health care services, which can be important for their mental health. The PSDM was adapted to the informal caregiving context by adding "facilitating conditions" and "verbal encouragement" as additional persuasive design principles. This study also presents the subsequent mobile e-coaching app for immigrant informal caregivers in Sweden. CONCLUSIONS: This study revealed important immigrant informal caregivers' needs based on which design suggestions for a mobile e-coaching app were presented. We also proposed an adapted PSDM, for the informal caregiving context. The adapted PSDM can be further used to design digital interventions for caregiving.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".