Implementing a Digital Physical Activity Intervention for Older Adults: Qualitative Study
Bibliographic record
Abstract
Background: Physical activity (PA) in older adults can prevent, treat, or offset symptoms and deterioration from various health conditions and help maintain independence. However, most older adults are insufficiently active. Digital interventions have the potential for high reach at low cost. Objective: This paper reports on the implementation of "Active Lives," a digital intervention developed specifically for older adults. Methods: This study had a qualitative design. The implementation team approached a range of National Health Service, public health, community, and third-sector organizations in the United Kingdom to offer Active Lives to as large and diverse groups of older adults as possible. Alongside real-world implementation activities, research was conducted to explore what supports and inhibits the implementation of a digital intervention for PA in older adults. Data collection involved interviews with implementation partners (n=15) and the implementation team (n=3) plus extensive field notes from stakeholder communications. Inductive thematic analysis was used to analyze the data. Results: Five broad themes were developed, capturing implementation barriers and facilitators. These were (1) complex and opaque networks and influencers, (2) forming an understanding of Active Lives and its fit, (3) a landscape of competition and conflicting interests, (4) navigating unclear approval processes, and (5) shifting strategies: small and effortful to high reach and passive. Identifying key decision makers proved arduous, consuming significant time and resources, and proposals from enthusiastic implementation partners often proved impractical or overly burdensome. Health care professionals demonstrated a comprehensive understanding of the potential benefits of digital interventions in alleviating operational burdens and improving patient care. However, stakeholders from disparate sectors held reservations about digital intervention and had different views on the best approaches to supporting PA among older adults. This discord was exacerbated by conflicts with existing local initiatives, such as group exercise programs, which occasionally hindered the implementation of Active Lives. Furthermore, bureaucratic hurdles within National Health Service trust approval processes acted as formidable obstacles, dampening progress and resolve, highlighting the need for guidance in identifying sustainable and scalable practices. Conclusions: The findings highlight important implementation challenges to digital PA interventions for older adults such as bureaucratic barriers and alignment with ongoing initiatives. This research emphasizes the necessity for strategic direction and multilevel guidance to efficiently implement digital interventions for PA among community-dwelling older adults.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".