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Record W4381432415 · doi:10.2196/46269

Acceptability and Feasibility of a Socially Enhanced, Self-Guided, Positive Emotion Regulation Intervention for Caregivers of Individuals With Dementia: Pilot Intervention Study

2023· article· en· W4381432415 on OpenAlexvenueno aff
Ian Kwok, Emily G. Lattie, Dershung Yang, Amanda Summers, Veronika Grote, Paul D. Cotten, Judith T. Moskowitz

Bibliographic record

VenueJMIR Aging · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Institute on AgingNorthwestern UniversitySmall Business Innovation Research
KeywordsFacilitatorPsychological interventionIntervention (counseling)PsychologyDementiaUsabilitySocial supportClinical psychologyApplied psychologyMedicinePsychotherapistSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Background The responsibilities of being a primary caregiver for a loved one with dementia can produce significant stress for the caregiver, leading to deleterious outcomes for the caregiver’s physical and psychological health. Hence, researchers are developing eHealth interventions to provide support for caregivers. Members of our research team previously developed and tested a positive emotion regulation intervention that we delivered through videoconferencing, in which caregiver participants would meet one-on-one with a trained facilitator. Although proven effective, such delivery methods have limited scalability because they require significant resources in terms of cost and direct contact hours. Objective This study aimed to conduct a pilot test of a socially enhanced, self-guided version of the positive emotion regulation intervention, Social Augmentation of Self-Guided Electronic Delivery of the Life Enhancing Activities for Family Caregivers (SAGE LEAF). Studies have shown that social presence or the perception of others in a virtual space is associated with enhanced learning and user satisfaction. Hence, the intervention leverages various social features (eg, discussion boards, podcasts, videos, user profiles, and social notifications) to foster a sense of social presence among participants and study team members. Methods Usability, usefulness, feasibility, and acceptability data were collected from a pilot test in which participants (N=15) were given full access to the SAGE LEAF intervention over 6 weeks and completed preintervention and postintervention assessments (10/15, 67%). Preliminary outcome measures were also collected, with an understanding that no conclusions about efficacy could be made, because our pilot study did not have a control group and was not sufficiently powered. Results The results suggest that SAGE LEAF is feasible, with participants viewing an average of 72% (SD 42%) of the total available intervention web pages. In addition, acceptability was found to be good, as demonstrated by participants’ willingness to recommend the SAGE LEAF program to a friend or other caregiver. Applying Pearson correlational analyses, we found moderate, positive correlation between social presence scores and participants’ willingness to recommend the program to others (r9=0.672; P=.03). We also found positive correlation between social presence scores and participants’ perceptions about the overall usefulness of the intervention (r9=0.773; P=.009). This suggests that participants’ sense of social presence may be important for the feasibility and acceptability of the program. Conclusions In this pilot study, the SAGE LEAF intervention demonstrates potential for broad dissemination for dementia caregivers. We aim to incorporate participant feedback about how the social features may be improved in future iterations to enhance usability and to further bolster a sense of social connection among participants and study staff members. Next steps include partnering with dementia clinics and other caregiver-serving organizations across the United States to conduct a randomized controlled trial to evaluate the effectiveness of the intervention.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.389
Teacher spread0.350 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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