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 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 | 0.000 |
| 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 | 0.000 |
| 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".