The necessity for sustainable intervention effects: lessons-learned from an experience sampling intervention for spousal carers of people with dementia
Bibliographic record
Abstract
Objectives Caring for a person with dementia can be challenging over the years. To support family carers throughout their entire caregiving career, interventions with a sustained effectivity are needed. A novel 6-week mobile health (mHealth) intervention using the experience sampling method (ESM) showed positive effects on carers’ well-being over a period of 2 months after the intervention. In this study, the effects after 6 months of the selfsame intervention were examined to evaluate the sustainability of positive intervention effects.Method The 6-week mHealth intervention consisted of an experimental group (ESM self-monitoring and personalized feedback), a pseudo-experimental group (ESM self-monitoring without feedback), and a control group (providing regular care without ESM self-monitoring or feedback). Carers’ sense of competence, mastery, and psychological complaints (depression, anxiety and perceived stress) were evaluated pre- and post-intervention as well as at two follow-up time points. The present study focuses on the 6-month follow-up data (n = 50).Results Positive intervention effects on sense of competence, perceived stress, and depressive symptoms were not sustained over 6-month follow-up.Conclusion The benefits of this mHealth intervention for carers of people living with dementia were not sustained over a long time. Similarly, other psychosocial interventions for carers of people with dementia rarely reported long-lasting effects. In order to sustainably contribute to carers’ well-being, researchers and clinicians should continuously ensure flexible adjustment of the intervention and consider additional features such as ad-hoc counseling options and booster sessions. In this regard, mHealth interventions can offer ideally suited and unique opportunities.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".