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Record W2615076621 · doi:10.2196/mhealth.6638

Effect of a Counseling Session Bolstered by Text Messaging on Self-Selected Health Behaviors in College Students: A Preliminary Randomized Controlled Trial

2017· article· en· W2615076621 on OpenAlexvenueno aff
Janice Sandrick, D. Tracy, Arn H. Eliasson, Ashley Roth, Jeffrey S. Bartel, Melanie Simko, Tracy Bowman, K. Harouse-Bell, Mariam Kashani, Marina Vernalis

Bibliographic record

VenueJMIR mhealth and uhealth · 2017
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsSession (web analytics)Randomized controlled trialText messagingPsychologyCollege healthMedical educationApplied psychologyClinical psychologyMultimediaMedicineComputer scienceFamily medicineInternet privacyWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: The college experience is often the first time when young adults live independently and make their own lifestyle choices. These choices affect dietary behaviors, exercise habits, techniques to deal with stress, and decisions on sleep time, all of which direct the trajectory of future health. There is a need for effective strategies that will encourage healthy lifestyle choices in young adults attending college. OBJECTIVE: This preliminary randomized controlled trial tested the effect of coaching and text messages (short message service, SMS) on self-selected health behaviors in the domains of diet, exercise, stress, and sleep. A second analysis measured the ripple effect of the intervention on health behaviors not specifically selected as a goal by participants. METHODS: Full-time students aged 18-30 years were recruited by word of mouth and campuswide advertisements (flyers, posters, mailings, university website) at a small university in western Pennsylvania from January to May 2015. Exclusions included pregnancy, eating disorders, chronic medical diagnoses, and prescription medications other than birth control. Of 60 participants, 30 were randomized to receive a single face-to-face meeting with a health coach to review results of behavioral questionnaires and to set a health behavior goal for the 8-week study period. The face-to-face meeting was followed by SMS text messages designed to encourage achievement of the behavioral goal. A total of 30 control subjects underwent the same health and behavioral assessments at intake and program end but did not receive coaching or SMS text messages. RESULTS: The texting app showed that 87.31% (2187/2505) of messages were viewed by intervention participants. Furthermore, 28 of the 30 intervention participants and all 30 control participants provided outcome data. Among intervention participants, 22 of 30 (73%) showed improvement in health behavior goal attainment, with the whole group (n=30) showing a mean improvement of 88% (95% CI 39-136). Mean improvement in any behavioral domains was not seen in the control group. Intervention participants also increased their exercise significantly compared with controls, regardless of their self-selected goal category. The increased exercise was paralleled by significantly lower fasting glucose levels. CONCLUSIONS: The health coaching session plus tailored SMS text messages improved self-selected health behaviors with a modest ripple effect to include unselected health behaviors. TRIAL REGISTRATION: Clinicaltrials.gov NCT02476604; https://clinicaltrials.gov/ct2/show/NCT02476604 (Archived by WebCite at http://www.webcitation.org/6qAAryS5t).

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0130.001

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 head0.026
GPT teacher head0.467
Teacher spread0.440 · 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 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

Citations27
Published2017
Admission routes1
Has abstractyes

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