Using a Collaborative Research Approach to Develop an Interdisciplinary Research Agenda for the Study of Mobile Health Interventions for Older Adults
Bibliographic record
Abstract
BACKGROUND: Seniors with chronic diseases are often called on to self-manage their conditions. Mobile health (mHealth) tools may be a useful strategy to help seniors access health information at the point of decision-making, receive real-time feedback and coaching, and monitor health conditions. However, developing successful mHealth interventions for seniors presents many challenges. One of the key challenges is to ensure the scope of possible research questions includes the diverse views of seniors, experts and the stakeholder groups who support seniors as they manage chronic disease. OBJECTIVE: Our primary objective was to present a case-study of a collaborative research approach to the development of an interdisciplinary research agenda. Our secondary objectives were to report on the results of a nominal group technique (NGT) approach used generate research questions and to assess the success of including non-academic researchers to enrich the scope, priority, and total number of possible research questions. METHODS: We invited researchers and stakeholders to participate in a full day meeting that included rapid-style presentations by researchers, health care professionals, technology experts, patients and community groups followed by group discussions. An NGT was used to establish group consensus on the following question: In your opinion, what research needs to be done to better understand the effectiveness, usability and design of mobile health apps and devices for older adults? RESULTS: Overall, the collaborative approach was a very successful strategy to bring together a diverse group of participants with the same end goal. The 32 participants generated 119 items in total. The top three research questions that emerged from the NGT were related to adoption, the need for high quality tools and the digital divide. Strong sub-themes included privacy and security, engagement and design. The NGT also helped us include the perspectives information from non-academic researchers that would not have been captured if the process had been limited to the research team. CONCLUSIONS: Developing ways for patients and other stakeholders to have a voice when it comes to developing patient awareness as related to mHealth may guide future research into engagement, ownership, usability and design. It is our intention that our paper be used and adapted by other researchers to engage small or vulnerable populations often excluded from mHealth research and design.
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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 | 0.272 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.020 | 0.026 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.007 | 0.031 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".