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Record W4293509984 · doi:10.2196/38656

Lessons Learned From an Effectiveness Evaluation of Inlife, a Web-Based Social Support Intervention for Caregivers of People With Dementia: Randomized Controlled Trial

2022· article· en· W4293509984 on OpenAlexaffvenue
Hannah Liane Christie, Alieske E.H. Dam, Martin P.J. van Boxtel, Sebastian Köhler, Frans R.J. Verhey, Marjolein de Vugt

Bibliographic record

VenueJMIR Aging · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSocial supportRandomized controlled trialLonelinessDementiaPsychological interventionFeelingPsychologySupport groupMedicineQuality of life (healthcare)Clinical psychologyNursingPsychiatrySocial psychologyDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Informal care for people with dementia not only affects the well-being of the primary caregiver but also changes their roles and interactions with the social environment. New online interventions might facilitate access to social support. Recently, an online social support platform, Inlife, was developed in the Netherlands and aims to enhance social support and positive interactions in informal support networks. OBJECTIVE: This study aimed to evaluate the effectiveness of Inlife for caregivers of people with dementia. METHODS: ) served as the primary endpoint to evaluate the results for the primary and secondary outcome variables evaluated with online self-report questionnaires. The primary outcomes included feelings of caregiver competence and perceived social support. The secondary outcomes included received support, feelings of loneliness, psychological complaints (eg, anxiety, stress), and quality of life. RESULTS: No significant improvements were demonstrated for the intervention group (n=48) relative to the control group (n=48) for the primary outcomes (feeling of carer competence: b=-0.057, 95% CI -0.715 to 0.602, P=.87; perceived social support: b=-15.877, 95% CI -78.284 to 46.530, P=.62) or any secondary outcome. This contrasts with our qualitative findings showing the potential of Inlife to facilitate the care process in daily life. Adherence was not optimal for all Inlife users. Additional per-protocol and sensitivity analyses also revealed no beneficial results for high active Inlife users or specific subgroups. Inlife users were more active when part of a larger network. CONCLUSIONS: Researchers should be modest regarding the effectiveness of online caregiver interventions in terms of quantitative measures of well-being and quality of life. Nevertheless, online tools have the potential to facilitate the caregiver process in daily life. Lessons learned include the importance of harnessing the power of human interaction in eHealth, making use of the user's social capital, and the need to develop research methods that can identify benefits in daily life that are ecologically valid for caregivers. TRIAL REGISTRATION: Netherlands Trial Register NTR6131; https://trialsearch.who.int/Trial2.aspx?TrialID=NTR6131. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-017-2097-y.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.032
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.042
GPT teacher head0.402
Teacher spread0.360 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations25
Published2022
Admission routes2
Has abstractyes

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