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Record W3156818268 · doi:10.2196/26152

Efficacy of Zemedy, a Mobile Digital Therapeutic for the Self-management of Irritable Bowel Syndrome: Crossover Randomized Controlled Trial

2021· article· en· W3156818268 on OpenAlexvenueaboutno aff
Melissa G. Hunt, Sofia Miguez, Benji Dukas, Obinna Onwude, Sarah White

Bibliographic record

VenueJMIR mhealth and uhealth · 2021
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsnot available
Fundersnot available
KeywordsIrritable bowel syndromeRandomized controlled trialMedicinePhysical therapyQuality of life (healthcare)mHealthPsychoeducationAnxietyProgressive muscle relaxationeHealthGuided imageryCognitive behavioral therapyMcGill Pain QuestionnairePsychological interventionInternal medicinePsychiatryVisual analogue scaleHealth care

Abstract

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Background Patients with irritable bowel syndrome (IBS) experience abdominal pain, altered bowel habits, and defecation-related anxiety, which can result in reduced productivity and impaired health-related quality of life (HRQL). Cognitive behavioral therapy (CBT) has been shown to reduce symptoms of IBS and to improve HRQL, but access to qualified therapists is limited. Smartphone-based digital therapeutic interventions have potential to increase access to guided CBT at scale, but require careful study to assess their benefits and risks. Objective The aim of this study was to test the efficacy of a novel app, Zemedy, as a mobile digital therapeutic that delivers a comprehensive CBT program to individuals with IBS. Methods This was a crossover randomized controlled trial. Participants were recruited online and randomly allocated to either immediate treatment (n=62) or waitlist control (n=59) groups. The Zemedy app consists of 8 modules focusing on psychoeducation, relaxation training, exercise, the cognitive model of stress management, applying CBT to IBS symptoms, reducing avoidance through exposure therapy, behavioral experiments, and information about diet. Users interact with a chatbot that presents the information and encourages specific plans, homework, and exercises. The treatment was fully automated, with no therapist involvement or communication. At baseline and after 8 weeks, participants were asked to complete the battery of primary (Irritable Bowel Syndrome Quality of Life [IBS-QOL], Gastrointestinal Symptom Rating Scale [GSRS]) and secondary (Fear of Food Questionnaire [FFQ], Visceral Sensitivity Index [VSI], Gastrointestinal Cognition Questionnaire [GI-COG], Depression Anxiety Stress Scale [DASS], and Patient Health Questionnaire-9 [PHQ-9]) outcome measures. Waitlist controls were then offered the opportunity to crossover to treatment. All participants were assessed once more at 3 months posttreatment. Results Both intention-to-treat and completer analyses at posttreatment revealed significant improvement for the immediate treatment group compared to the waitlist control group on both primary and secondary outcome measures. Gains were generally maintained at 3 months posttreatment. Scores on the GSRS, IBS-QoL, GI-COG, VSI, and FFQ all improved significantly more in the treatment group (F1,79=20.49, P<.001, Cohen d=1.01; F1,79=20.12, P<.001, d=1.25; F1,79=34.71, P<.001, d=1.47; F1,79=18.7, P<.001, d=1.07; and F1,79=12.13, P=.001, d=0.62, respectively). Depression improved significantly as measured by the PHQ-9 (F1,79=10.5, P=.002, d=1.07), and the DASS Depression (F1,79=6.03, P=.02, d=.83) and Stress (F1,79=4.47, P=.04, d=0.65) subscales in the completer analysis but not in the intention-to-treat analysis. The impact of treatment on HRQL was mediated by reductions in catastrophizing and visceral sensitivity. Conclusions Despite its relatively benign physical profile, IBS can be an extraordinarily debilitating condition. Zemedy is an effective modality to deliver CBT for individuals with IBS, and could increase accessibility of this evidence-based treatment. Trial Registration ClinicalTrials.gov NCT04170686; https://www.clinicaltrials.gov/ct2/show/NCT04170686

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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)0.002
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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0140.001

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 head0.025
GPT teacher head0.345
Teacher spread0.320 · 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

Citations103
Published2021
Admission routes2
Has abstractyes

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