Attitudes and Preferences on the Use of Mobile Health Technology and Health Games for Self-Management: Interviews With Older Adults on Anticoagulation Therapy
Bibliographic record
Abstract
BACKGROUND: Older adults are at substantial risk for cardiovascular disorders that may require anticoagulation therapy. Those on warfarin therapy report dissatisfaction and reduced quality of life (QOL) resulting from the treatment. Advances in the area of mobile health (mHealth) technology have resulted in the design and development of new patient-centric models for the provision of personalized health care services to improve care delivery. However, there is a paucity of research examining the effectiveness of mHealth tools on knowledge, attitudes, and patient satisfaction with treatment, as well as self-management, adherence to therapy, and QOL in older adults with chronic illness conditions requiring long-term warfarin therapy. OBJECTIVE: The objective of the study was to explore the attitudes and preferences of older adults on warfarin therapy regarding the use of mHealth technology and health games to gain skills for self-management. METHODS: We conducted group and individual interviews with patients (60 years or older) on warfarin therapy at two anticoagulation clinics affiliated with an academic medical center. We held 4 group and 2 individual interviews, resulting in 11 patient participants and 2 family caregiver participants. We used structured questions on three topic areas including medication self-management strategies, mHealth technology use, and health games for exercise. We demonstrated some commercial health apps related to medication management, vitamin K content of food, and a videogame for balance exercise. Discussions were audiotaped and transcribed verbatim. Common themes were drawn using content analysis. RESULTS: The participants reported awareness of the importance of staying on schedule with warfarin therapy. They also acknowledged that negative experiences of friends or family members who were taking warfarin influenced their desire to keep on schedule with warfarin therapy. In addition, the participants expressed that the use of mHealth technology may be helpful for medication management. They also expressed the need for family support in the use of health technology devices. Moreover, the participants discussed concerns and challenges to use health technology and health games, and provided suggestions on ways to make mHealth technology and health games elder-friendly. CONCLUSIONS: These findings indicate that our older adults on warfarin therapy are interested in mHealth technology specific to warfarin medication management and health games. Further research needs to be done to validate these findings. Elder-friendly designs, technology support, and physical safety using mHealth technology may be useful in this population. These findings can be used to inform a larger study to design and test an elder-centered mHealth technology in this target population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".