Product design for mobility wearable devices for black older adults: bridging usability, inclusion, and engagement through qualitative interviewing
Bibliographic record
Abstract
Black older adults remain largely excluded from the design of mobility wearables, yet their cultural values can shape acceptance and sustained use. We therefore explored the preferences, needs and lived experiences of Canadian Black older adults in co-creating culturally appropriate, AI-powered mobility wearable devices, defined as wearable sensors embedded with artificial intelligence that monitor, analyse, and provide real-time feedback on movement and mobility (e.g. gait, balance, activity). A qualitative descriptive design with conventional content analysis was employed. Twenty community-dwelling Black older adults (11 males, 9 females; 57–89 years) from four Canadian provinces completed 60–90-minute semi-structured videoconference interviews. Audio recordings were transcribed verbatim and analysed thematically through multiple iterative cycles by a multidisciplinary team, with participant member-checking and ongoing reflexive dialogue used to reinforce methodological rigour. Four overarching themes described design priorities: (1) Modular and personalised aesthetics, interchangeable bands, culturally resonant colours and skin-tone-matching casings to support self-expression; (2) Culturally familiar interfaces and voice interactions that feel respectful and welcoming; (3) Ease of use with assistive support—one-click operations and hands-free voice commands accommodating functional limitations; and (4) Privacy and dignity through default data-ownership models enabling user control and community benefit. African-born participants prioritised core health functions, whereas Caribbean-born participants valued aesthetic customisation, underscoring intra-community heterogeneity. Wearables that incorporate the features prioritised by Black older adults, modular styling, culturally familiar interfaces, intuitive controls, and robust privacy safeguards are more likely to be embraced, to support day-to-day mobility, and to narrow inequities in access to assistive technology. Cultural tailoring drives adoption. Designing mobility wearables around the aesthetic and interface preferences of Black older adults can improve initial uptake and long-term adherence to rehabilitation technologies. Modular, personalised hardware respects identity. Interchangeable bands, skin-tone-matched casings, and culturally resonant colours facilitate dignity and self-expression, key elements of person-centred rehabilitation. Intuitive, low-effort controls extend independence. One-tap functions and hands-free voice commands reduce physical and cognitive load, enabling older adults with limited dexterity or vision to manage mobility goals independently. Privacy-by-default builds trust and engagement. User-controlled data ownership and transparent sharing options can strengthen confidence in technology-enabled rehabilitation and support sustained device use.
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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 | 0.030 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| 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".