Design of an mHealth application for winter mobility for mobility device users
Bibliographic record
Abstract
There is limited evidence on the strategies, resources, and tools shown to improve winter mobility and community participation. This paper describes a multifaceted approach taken to develop an mHealth application that provides information, resources, and strategies to facilitate winter mobility for mobility device users, service providers, community organisations, and researchers. The study was conducted in three phases: (1) A scoping review of peer-reviewed and grey literature was completed to identify literature that reported on tools, strategies, resources, and recommendations used to promote winter mobility; (2) Online asynchronous focus groups were conducted to identify the type of content that mobility device users wanted to include in the web-based application; and (3) A prototype mHealth application was developed based on the findings from the previous phases. Using a rapid prototyping process that included stakeholder review through an online survey, four cycles of application design and development were undertaken. The scoping review identified 23 peer-reviewed studies and limited grey literature on winter mobility strategies, resources and recommendations. Twenty-four participants from across Canada engaged in one of five focus groups. Focus group analysis led to the development of the content categories for the mHealth application. The initial prototype application developed was reviewed by; 27 mobility device users, 16 health care providers, and seven consumer organisation representatives identified areas of strength and further refinement in regard to application design. The approach used in this study provided a method to develop an application based on the ideas, needs, and interests of a variety of stakeholders. Once fully developed, the application has the potential to fill the gaps related to the lack of a unified collection of winter mobility strategies and resources, and open the dialogue on methods to improve winter participation among mobility device users.IMPLICATIONS FOR REHABILITATIONDespite winter conditions being a common challenge among mobility device users, there is an absence of an organised approach towards helping individuals manage their winter mobility needs.As the development and usage of mHealth applications continues to increase, it is valuable to use methods of designing applications based on the ideas, needs, and interests of a variety of stakeholders.Development of a framework for collating information on winter mobility strategies and resources is the first step towards launching an mHealth application. Despite winter conditions being a common challenge among mobility device users, there is an absence of an organised approach towards helping individuals manage their winter mobility needs. As the development and usage of mHealth applications continues to increase, it is valuable to use methods of designing applications based on the ideas, needs, and interests of a variety of stakeholders. Development of a framework for collating information on winter mobility strategies and resources is the first step towards launching an mHealth application.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".