An Individual Cognitive Stimulation Therapy App for People With Dementia and Their Carers: Protocol for a Feasibility Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: There is a need for more resources to support the cognition and quality of life of people with dementia. The individual cognitive stimulation therapy (iCST) app aims to provide cognitive stimulation and social interaction to people with dementia and carers through interactive touchscreen technology. The iCST app has been developed according to the principles of CST and iCST, which have previously shown to improve the cognition and quality of life of people with dementia and benefit the relationship between the person with dementia and his/her carer. The iCST app has also shown to improve the quality of the carer's life. OBJECTIVE: The aim of this study is to evaluate the usability of the iCST app intervention and the feasibility of conducting a full-scale randomized controlled trial (RCT) to assess the clinical effectiveness of the iCST app intervention compared to that of treatment-as-usual for people with mild-to-moderate dementia. METHODS: We aim to recruit 60 people with mild-to-moderate dementia and their informal carers as dyads in a multi-center feasibility RCT with a treatment-as-usual control group. Both parties must be able to provide informed consent and participate in the intervention. Dyads will complete a baseline assessment that will include cognition and quality of life measures and they will subsequently be randomized (1:1) to the iCST app intervention in addition to usual care or to usual care only. All participants will be followed up at 5 weeks and at 11 weeks after the baseline assessments. A range of feasibility outcomes will be assessed, including recruitment and retention rates, intervention fidelity and usability, and acceptability of the outcome measures. A sample of the experimental group will be invited to a semistructured posttrial interview to further examine the experience of using the iCST app. RESULTS: This study received funding in May 2015 and obtained ethical approval in March 2018. Data collection began in November 2018 and was completed in March 2020 with a total of 61 dyads recruited. Data analyses are in progress and the final results are expected to be available in the spring of 2021. CONCLUSIONS: This study will investigate whether it is feasible to conduct a full-scale RCT to evaluate the clinical effectiveness of the iCST app in comparison to that of usual care alone. In addition, this study will examine the usability of the iCST app. The data will provide information on potential modifications to be made to the intervention, study design, and study process. TRIAL REGISTRATION: ClinicalTrials.gov NCT03282877; https://clinicaltrials.gov/ct2/show/NCT03282877. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24628.
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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 | 0.043 | 0.030 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.094 | 0.016 |
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".