Virtual delivery of improvisational movement and social engagement interventions in the IMOVE trial during the COVID-19 pandemic
Bibliographic record
Abstract
Background: IMOVE evaluated the contributions of movement and social engagement to quality of life, brain network connectivity, and motor and social-emotional functioning in people with early-stage Alzheimer's disease participating with a caregiver. In response to COVID-19 restrictions, a pilot study was conducted to assess integrity of key elements of the intervention and feasibility of virtual intervention delivery. Methods: Participants in the parent study were randomized to one of 4 study conditions (Movement Group [MG], Movement Alone [MA], Social Group [SG], or Usual Care [UC; control]). To test virtual adaptations of each condition, groups of three participant-caregiver dyads (6 individuals) who had completed the parent trial participated in virtual adaptation classes. We adopted an engineering-inspired, rapid refinement model to optimize virtual interventions on the dimensions of social connectedness, fun, and physical exertion. After completing one iteration, participants gave feedback and adjustments were made to the intervention. This process was repeated until no further adjustments were needed. Results: The MA arm easily transitioned to virtual format. The virtual MG intervention required the most iterations, with participants reporting needs for additional technology support, higher level of physical exertion, and stronger social connection. The virtual SG intervention reported good social connection, but needed additional technology instruction and measures to promote equal participation. Conclusions: Our pilot study results underscore the feasibility of delivering remote social and/or dance interventions for older adults and provide a useful road map for other research teams interested in increasing their reach by adapting in-person group behavioral interventions for remote delivery.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".