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Record W4400407544 · doi:10.2196/50847

Internet-Based Supportive Interventions for Family Caregivers of People With Dementia: Randomized Controlled Trial

2024· article· en· W4400407544 on OpenAlexvenueno aff
Yan Xie, Shanshan Shen, Caixia Liu, Huilan Guan, J. Zhang, Wanqi Yu

Bibliographic record

VenueJMIR Aging · 2024
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Key Research and Development Program of China
KeywordsDementiaPreprintPsychological interventionRandomized controlled trialThe InternetGerontologyFamily caregiversPsychologyMedicinePsychiatryWorld Wide WebComputer scienceInternal medicineDisease

Abstract

fetched live from OpenAlex

Background: As dementia progresses, patients exhibit various psychological and behavioral symptoms, imposing a significant burden on families and society, including behavioral and psychological symptoms of dementia. However, caregivers lack professional care knowledge and skills, making it difficult for them to effectively cope with the diverse challenges of caregiving. Therefore, it is necessary to provide caregivers with professional knowledge and skills guidance. Objective: This study aimed to analyze the impact of internet-based training on behavioral and psychological symptoms of dementia in patients, and explore how this training model affects the caregiving abilities and caregiving burden of the family caregivers of patients with dementia. Methods: Using a consecutive enrollment method, the Department of Geriatrics at Zhejiang Hospital (Zhejiang, China) recruited 72 informal caregivers of patients with dementia. These caregivers were randomly divided into an intervention group and a control group, with 36 participants in each group. The intervention group underwent caregiver skill training via a web-based platform, whereas the control group initially received face-to-face follow-up guidance and was subsequently offered web-based training after 6 months. To assess the effectiveness of the intervention program, we used the Neuropsychiatric Inventory Questionnaire (NPI-Q), the Chinese version of the Zarit Burden Interview (CZBI), and the Sense of Competence in Dementia Care Staff Scale (SCIDS) for evaluations conducted before the intervention, 3 months after the intervention, and 6 months after the intervention. Results: Between July 2019 and December 2020, a total of 66 patients successfully completed the intervention and follow-up. After 6 months of intervention, the NPI-Q score of the intervention group was 3.18 (SD 3.81), the CZBI score was 10.97 (SD 5.43), and the SCIDS score was 71.88 (SD 4.78). The NPI-Q score of the control group was 8.09 (SD 8.52), the CZBI score was 30.30 (SD 13.05), and the SCIDS score was 50.12 (SD 9.10). There were statistically significant differences in NPI-Q (P=.004), CZBI (P<.001), and SCIDS scores (P<.001) between the intervention group and the control group. Repeated measures analysis of variance showed that compared with before the intervention, there were statistically significant differences in CZBI (P<.001) and SCIDS (P<.001) scores 3 months after the intervention, while the difference in NPI-Q (P=.11) scores was not significant. The total scores of NPI-Q (P<.001), CZBI (P<.001), and SCIDS (P<.001) were significantly improved 6 months after the intervention. In addition, the results of the covariance analysis showed that after excluding the time effect, the web-based training intervention significantly reduced the NPI-Q score (-2.79, 95% CI -4.38 to -1.19; P<.001) of patients with dementia and the CZBI score (-13.52, 95% CI -15.87 to -11.16; P<.001) of caregivers, while increasing the SCIDS score (12.24, 95% CI 9.02-15.47; P<.001). Conclusions: Internet-based training could significantly reduce the level of behavioral symptoms in older patients with dementia and alleviate the burden on caregivers, enhancing their caregiving abilities. Our results confirmed the effectiveness and feasibility of web-based training, which was of great significance in providing caregiving knowledge training for informal caregivers of persons with dementia.

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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0010.000

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 head0.023
GPT teacher head0.349
Teacher spread0.326 · 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 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

Citations17
Published2024
Admission routes1
Has abstractyes

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