Exploring the Perspectives and Experiences of Older Adults With Asthma and Chronic Obstructive Pulmonary Disease Toward Mobile Health: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The use of mobile health (mHealth) in asthma and chronic obstructive pulmonary disease (COPD) is growing, and as the population ages, a greater number of older adults stand to benefit from mHealth-enhanced airway disease care. Though older adults are a heterogeneous population of health technology users, older age represents a potential barrier to health technology adoption, and there is currently a lack of knowledge on how older age influences mHealth use in asthma and COPD. OBJECTIVE: In this qualitative study, we sought to explore the experiences and perspectives of adults who were aged 65 years and older with asthma and COPD toward mHealth use. METHODS: Semistructured individual interviews were conducted with adults who were aged 65 years and older with asthma or COPD and owned a smartphone. Applying phenomenological methodology, we analyzed interview transcripts in order to develop themes and propose an essential experience of mHealth use among older adults with airway disease. We then summarized our qualitative findings and proposed strategies to leverage our results in order to guide future research and implementation efforts targeting older adults' use of airway mHealth. RESULTS: Twenty participants (mean age 79.8, SD 4.4 years) were interviewed. Participants described a central tension between (1) the perception that mHealth could help maintain independence throughout aging and (2) an apprehension toward the ways in which mHealth could negatively affect established health care experiences. Several elements of these 2 themes are absent from previous research focusing on younger adults with asthma and COPD. The individual elements of these 2 themes informed potential strategies to optimize future older adults' use of asthma and COPD mHealth tools. CONCLUSIONS: Focusing on the perspectives and experiences of older adults with asthma and COPD in their use of mHealth identified novel understandings of health technology use in this important demographic in need of greater care. These lessons were translated into potential strategies that will need to be objectively evaluated in future airway mHealth research, development, and implementation efforts.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".