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Record W1546752049 · doi:10.2196/resprot.4134

Usage and Users of Online Self-Management Programs for Adult Patients With Atopic Dermatitis and Food Allergy: An Explorative Study

2015· article· en· W1546752049 on OpenAlexvenueno aff
Harmieke van Os‐Medendorp, Ilse van Leent- de Wit, Marjolein de Bruin‐Weller, André C. Knulst

Bibliographic record

VenueJMIR Research Protocols · 2015
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsnot available
FundersInnovatiefonds ZorgverzekeraarsDeakin University
KeywordsAtopic dermatitisMedicineDescriptive statisticsQuality of life (healthcare)Self-managementRegimenFamily medicineNursingComputer scienceStatisticsImmunologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Two online self-management programs for patients with atopic dermatitis (AD) or food allergy (FA) were developed with the aim of helping patients cope with their condition, follow the prescribed treatment regimen, and deal with the consequences of their illness in daily life. Both programs consist of several modules containing information, personal stories by fellow patients, videos, and exercises with feedback. Health care professionals can refer their patients to the programs. However, the use of the program in daily practice is unknown. OBJECTIVE: The aim of this study was to explore the use and characteristics of users of the online self-management programs "Living with eczema," and "Living with food allergy," and to investigate factors related to the use of the trainings. METHODS: A cross-sectional design was carried out in which the outcome parameters were the number of log-ins by patients, the number of hits on the system's core features, disease severity, quality of life, and domains of self-management. Descriptive statistics were used to summarize sample characteristics and to describe number of log-ins and hits per module and per functionality. Correlation and regression analyses were used to explore the relation between the number of log-ins and patient characteristics. RESULTS: Since the start, 299 adult patients have been referred to the online AD program; 173 logged in for at least one occasion. Data from 75 AD patients were available for analyses. Mean number of log-ins was 3.1 (range 1-11). Linear regression with the number of log-ins as dependent variable showed that age and quality of life contributed most to the model, with betas of .35 ( P=.002) and .26 (P=.05), respectively, and an R(2) of .23. Two hundred fourteen adult FA patients were referred to the online FA training, 124 logged in for at least one occasion and data from 45 patients were available for analysis. Mean number of log-ins was 3.0 (range 1-11). Linear regression with the number of log-ins as dependent variable revealed that adding the self-management domain "social integration and support" to the model led to an R(2) of .13. The modules with information about the disease, diagnosis, and treatment were most visited. Most hits were on the information parts of the modules (55-58%), followed by exercises (30-32%). CONCLUSIONS: The online self-management programs "Living with eczema" and "Living with food allergy" were used by patients in addition to the usual face-to-face care. Almost 60% of all referred patients logged in, with an average of three log-ins. All modules seemed to be relevant, but there is room for improvement in the use of the training. Age, quality of life, and lower social integration and support were related to the use of the training, but only part of the variance in use could be explained by these variables.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.165
GPT teacher head0.469
Teacher spread0.305 · 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 designObservational
Domainnot available
GenreMethods

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

Citations14
Published2015
Admission routes1
Has abstractyes

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