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Record W4205195164 · doi:10.2196/33082

Text Messages and Financial Incentives to Increase Physical Activity in Adolescents With Prediabetes and Type 2 Diabetes: Web-Based Group Interviews to Inform Intervention Design

2021· article· en· W4205195164 on OpenAlexvenueno aff
Mary Ellen Vajravelu, Talia A. Hitt, NaDea Mak, Aliya Edwards, Jonathan A. Mitchell, Lisa A. Schwartz, Andrea Kelly, Sandra Amaral

Bibliographic record

VenueJMIR Diabetes · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of Health
KeywordsPrediabetesType 2 diabetesOverweightIncentivePopulationPsychologyMedicineIntervention (counseling)Framing (construction)ObesityNursingDiabetes mellitusInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Physical activity is a major component of treatment for adolescents with obesity and prediabetes or type 2 diabetes; however, sedentary behavior remains pervasive. An SMS text message-based intervention paired with financial incentives may be an effective way to promote physical activity in this population. OBJECTIVE: This study aims to obtain end-user feedback on SMS text message content and assess the acceptability of a planned SMS text messaging intervention with financial incentives to motivate youth with prediabetes or type 2 diabetes to increase physical activity. METHODS: Adolescents with overweight or obesity and prediabetes or type 2 diabetes who attended a large academic pediatric endocrinology clinic were recruited to participate in group interviews (2-4/group) via videoconferencing. Participants were asked to share their thoughts on the use of SMS text messages and financial incentives to remind and motivate them to be more physically active. They rated and provided feedback on specific messages to be used in clinical trials. Participants were also asked about their personal experience with rewards to motivate behavior change and their anticipated reactions to rewards provided for goal attainment (gain-framing) versus those provided and then taken away if a goal was not met (loss-framing). The interviews were conducted by 2 trained interviewers and a note-taker. Content analysis was used to explore themes. RESULTS: (all >95th percentile for age and sex). Most participants were non-Hispanic Black (14/20, 70%) and 10% (2/20) were Hispanics. Participants frequently cited near-continuous smartphone use and agreed that SMS text messages would serve as good reminders to be physically active, but the consensus about the need for short messages was strong. Favorable content included references to what they were likely to be doing when messages were sent (eg, homework or watching television) and messages that were upbeat or informative. Specific physical activity suggestions were rated favorably. Attitudes toward financial incentives varied, with differing opinions about whether loss-framed incentives would be motivating or discouraging. Many participants highlighted the role of intrinsic, rather than extrinsic, motivation in achieving and sustaining behavior change. CONCLUSIONS: The engagement of adolescents with obesity and diabetes or prediabetes allowed for the refinement of SMS text messages for our planned intervention, with an emphasis on short, upbeat, relatable, and informative messages. Although an SMS text messaging intervention using financial incentives to motivate youth with prediabetes or type 2 diabetes to be more physically active is theoretically acceptable, the impact on actual activity levels in this population requires prospective evaluation in a clinical trial.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.369
Teacher spread0.341 · 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 teacher head, not a consensus.

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

Citations7
Published2021
Admission routes1
Has abstractyes

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