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Record W2904362253 · doi:10.2196/11664

Neighborhood Deprivation and the Effectiveness of Mobile Health Coaching to Improve Periconceptional Nutrition and Lifestyle in Women: Survey in a Large Urban Municipality in the Netherlands

2018· article· en· W2904362253 on OpenAlexvenueno aff
Dionne V. Gootjes, Matthijs R. van Dijk, Maria P.H. Koster, Sten P. Willemsen, Eric A.P. Steegers, Régine P.M. Steegers‐Theunissen

Bibliographic record

VenueJMIR mhealth and uhealth · 2018
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsnot available
FundersAchmea
KeywordsCoachingPregnancymHealthHealth coachingEnvironmental healthMedicineGerontologyPsychologyNursingPsychological intervention

Abstract

fetched live from OpenAlex

BACKGROUND: In 2011, we launched the Smarter Pregnancy mobile health (mHealth) coaching program, which has shown to effectively improve inadequate nutrition and lifestyle behaviors in women before and during pregnancy. It is known that in deprived neighborhoods, risk factors for adverse pregnancy outcomes like inadequate nutrition and lifestyle behaviors accumulate. However, it has not yet been investigated whether the Smarter Pregnancy program is equally effective in women living in deprived neighborhoods. OBJECTIVE: This paper aimed to study the associations between neighborhood deprivation and improvement of inadequate nutrition and lifestyle behaviors of women who were either contemplating pregnancy or already pregnant and subscribed to the Smarter Pregnancy program. METHODS: We performed an additional analysis on data from women who used the Smarter Pregnancy program from 2011 to 2016. The program comprised 24 weeks of coaching on 5 nutrition and lifestyle behaviors, of which adequate intakes or lifestyle behaviors were defined as an intake of 200 grams or above of vegetables, 2 pieces of fruit, daily folic acid supplement use of 400 µg per day, and no smoking or alcohol consumption. Neighborhood deprivation was determined according to the status scores of the Netherlands Institute for Social Research. Logistic regression analyses and generalized estimating equation models were used to assess the associations between the neighborhood status score (NSS) and the improvement of inadequate nutrition and lifestyle behaviors, taking into account the behaviors at baseline. We adjusted the analyses for maternal age, body mass index, geographic origin, pregnancy status, and participation as a couple. RESULTS: Of the 2554 women included, 521 participated with their male partner. Overall, daily vegetable intake was most frequently inadequate at the start of the program (77.72, 1985/2554). Women with a higher NSS (ie, nondeprived neighborhood) smoked less often (adjusted odds ratio [OR] 0.85; 95% CI 0.77-0.93), consumed alcohol more often (adjusted OR 1.14, 95% CI 1.04-1.24), and were less likely to complete the 24 weeks of coaching (OR 0.91, 95% CI 0.88-0.95) compared with women who lived in a neighborhood with a low NSS (ie, deprived). In the total group, the relative improvement of inadequate nutrition and lifestyle behaviors after 24 weeks of coaching was between 26% and 64%. NSS was negatively associated with this improvement, indicating that women with a higher NSS were less likely to improve inadequate nutrition and lifestyle behaviors, especially vegetable intake (adjusted OR 0.89, 95% CI 0.82-0.97). CONCLUSIONS: The Smarter Pregnancy mHealth coaching program empowers women to improve inadequate nutrition and lifestyle behaviors. Unexpectedly, the program seemed more effective in women living in deprived neighborhoods. It is important to unravel differences in needs and behaviors of specific target groups to further tailor the mHealth program on the basis of demographic characteristics like neighborhood deprivation.

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)0.003
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.277

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.031
GPT teacher head0.382
Teacher spread0.351 · 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".

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Citations35
Published2018
Admission routes1
Has abstractyes

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