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Record W4229079868 · doi:10.2196/27389

The Value of Tracking Data on the Behavior of Patients Who Have Undergone Bariatric Surgery: Explorative Study

2022· article· en· W4229079868 on OpenAlexvenueno aff
Dirk P. A. Versteegden, Magaly van Himbeeck, Anne Wil Burghoorn, Peter Lövei, Eva Deckers, Jos-Marien Jansen, Simon W. Nienhuijs

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsReferralContext (archaeology)Psychological interventionApplied psychologyQualitative propertyPsychologyMedicineMedical educationComputer scienceHuman–computer interactionNursing

Abstract

fetched live from OpenAlex

BACKGROUND: To maintain the benefits of a bariatric procedure, patients have to change their lifestyle permanently. This happens within a context of coresponsibilities of health care professionals and their social support system. However, most interventions are focused on the patient as an individual. In this explorative pilot study, behavioral, contextual, and experiential data were gathered to obtain insight on coresponsibility. OBJECTIVE: The aim of this study is to explore the use of trackers by patients who have undergone bariatric surgery in a data-enabled design approach. METHODS: Behavioral and contextual data on the households of patients who have undergone bariatric surgery were explored using a smartphone with an interactive user interface (UI), weight scale, activity bracelet, smart socket, accelerometer motion sensor, and event button to find examples of opportunities for future interventions. RESULTS: A total of 6 households were monitored. Approximately 483,000 data points were collected, and the participants engaged in 1483 conversations with the system. Examples were found using different combinations of data types, which provided the obesity team a better understanding of patient behaviors and their support system, such as a referral to a family coach instead of a dietician. Another finding regarding the partners was, for example, that the conversational UI system facilitated discussion about the support structure by asking for awareness. CONCLUSIONS: An intelligent system using a combination of quantitative data gathered by data tracking products in the home environment and qualitative data gathered by app-enhanced short conversations, as well as face-to-face interviews, is useful for an improved understanding of coresponsibilities in the households of patients who have undergone bariatric surgery. The examples found in this explorative study so far encourage research in this field.

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)0.035
metaresearch head score (Gemma)0.075
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.075
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.003
Scholarly communication0.0020.003
Open science0.0010.003
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 head0.193
GPT teacher head0.424
Teacher spread0.231 · 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
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
Published2022
Admission routes1
Has abstractyes

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Same venueJMIR Formative ResearchSame topicBariatric Surgery and OutcomesFrench-language works237,207