Human-Centered Design of Mobile Health Apps for Older Adults: Systematic Review and Narrative Synthesis
Bibliographic record
Abstract
BACKGROUND: The world is aging. The number of older patients is on the rise, and along with it comes the burden of noncommunicable diseases, both clinical and economic. Attempts with mobile health (mHealth) have been made to remedy the situation with promising outcomes. Researchers have adopted human-centered design (HCD) in mHealth creation to ensure those promises become a reality. OBJECTIVE: This systematic review aims to explore existing literature on relevant primary research and case studies to (1) illustrate how HCD can be used to create mHealth solutions for older adults and (2) summarize the overall process with recommendations specific to the older population. METHODS: We conducted a systematic review to address the study objectives. IEEE Xplore, Medline via Ovid, PubMed, and Scopus were searched for HCD research of mHealth solutions for older adults. Two independent reviewers then included the papers if they (1) were written in English, (2) included participants equal to or older than 60 years old, (3) were primary research, and (4) reported about mHealth apps and their HCD developments from start to finish. The 2 reviewers continued to assess the included studies' qualities using the Mixed Methods Appraisal Tool (MMAT). A narrative synthesis was then carried out and completed. RESULTS: Eight studies passed the eligibility criteria: 5 were mixed methods studies and 3 were case studies. Some studies were about the same mHealth projects with a total of 5 mHealth apps. The included studies differed in HCD goals, target groups, and details of their HCD methodologies. The HCD process was explored through narrative synthesis in 4 steps according to the International Standardization Organization (ISO) standard 9241-210: (1) understand and specify the context of use, (2) specify the user requirements, (3) produce design solutions to meet these requirements, and (4) evaluate the designs against requirements. The overall process and recommendations unique to older adults are summarized logically with structural order and time order based on the Minto pyramid principle and ISO 9241-210. CONCLUSIONS: Findings show that HCD can be used to create mHealth solutions for older adults with positive outcomes. This review has also summarized practical HCD steps and additional suggestions based on existing literature in the subfield. However, evidence-based results are still limited because most included studies lacked details about their sampling methods and did not set objective and quantifiable goals, leading to failure to draw significant conclusions. More studies of HCD application on mHealth for older adults with measurable design goals and rigorous research strategy are warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".