MétaCan
Menu
Back to cohort
Record W2756301480 · doi:10.2196/jmir.7896

Components and Outcomes of Internet-Based Interventions for Caregivers of Older Adults: Systematic Review

2017· review· en· W2756301480 on OpenAlexafffund
Cassioppée Guay, Claudine Auger, Louise Demers, W. Ben Mortenson, William C. Miller, Dominique Gélinas-Bronsard, Sara Ahmed

Bibliographic record

VenueJournal of Medical Internet Research · 2017
Typereview
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsMcGill University Health CentreInternational Collaboration On Repair DiscoveriesUniversity of British ColumbiaGF Strong Rehabilitation CentreInstitut Universitaire de Gériatrie de MontréalUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
FundersCanadian Institutes of Health Research
KeywordsPsychological interventionThe InternetQuality of life (healthcare)Family caregiversPsychologyInternet privacyGerontologyMedicineComputer scienceNursingWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: When trying to access interventions to improve their well-being and quality of life, family caregivers face many challenges. Internet-based interventions provide new and accessible opportunities to remotely support them and can contribute to reducing their burden. However, little is known about the link existing between the components, the use of behavior change techniques, and the outcomes of these Internet-based interventions. OBJECTIVE: This study aimed to provide an update on the best available evidence about the efficacy of Internet-based interventions for caregivers of older adults. Specifically, the components and the use of behavior change techniques and how they impact on the efficacy of the intervention were sought. METHODS: A systematic review searched primary source studies published between 2000 and 2015. Included studies were scored with a high level of evidence by independent raters using the GRADE criteria and reported caregiver-specific outcomes about interventions delivered through the Internet for caregivers of people aged 50 years and older. A narrative synthesis identified intervention components (eg, content, multimedia use, interactive online activities, and provision of support), behavior change techniques, and caregiver outcomes (eg, effects on stressors, mediators, and psychological health). The risk of bias within the included studies was assessed. RESULTS: A total of 2338 articles were screened and 12 studies describing 10 Internet-based interventions were identified. Seven of these interventions led to statistically significant improvements in caregiver outcomes (eg, reducing depression or anxiety, n=4). These efficacious interventions used interactive components, such as online exercises and homework (n=4) or questionnaires on health status (n=2) and five of them incorporated remote human support, either by professionals or peers. The most frequently used behavior change techniques included in efficacious interventions were provision of social support (n=6) and combinations of instructions to guide behavior change and barrier identification (n=5). The design and aim of the included studies did not permit determining exactly which component and/or behavior change technique was more efficacious in producing positive outcomes in caregivers. The risk for selection bias was low for all the studies, and low to high for performance, detection, and attrition biases. CONCLUSIONS: In sum, Internet-based interventions that incorporate professional and social support, and provide instructions to change behavior and problem solve in an interactive manner appear to lead to positive outcomes in caregivers. Studies isolating the specific effect of components are needed to improve our understanding of the underlying mechanism of action.

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)0.010
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.054
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.008
Bibliometrics0.0100.008
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.483
GPT teacher head0.601
Teacher spread0.118 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations99
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Medical Internet ResearchSame topicFamily and Patient Care in Intensive Care UnitsFrench-language works237,207