Evidence map›Paper›PMID 30145623›Full record

ArticleJournal of behavioral medicine2019

Can the artificial intelligence technique of reinforcement learning use continuously-monitored digital data to optimize treatment for weight loss?

Evan M Forman, Stephanie G Kerrigan, Meghan L Butryn, Adrienne S Juarascio, Stephanie M Manasse, Santiago Ontañón, Diane H Dallal, Rebecca J Crochiere, Danielle Moskow

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of behavioral medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
5.3field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 73 citations in OpenAlex.

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  19. Data-driven Interpretable Policy Construction for Personalized Mobile Health.IEEE International Conference on Digital Health. IEEE International Conference on Digital Health · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Evan M FormanDepartment of Psychology, WELL Center, Drexel University, Stratton Hall, 3141 Chestnut Street, Philadelphia, PA, 19104, USA. evan.forman@drexel.edu.
Stephanie G KerriganDepartment of Psychology, WELL Center, Drexel University, Stratton Hall, 3141 Chestnut Street, Philadelphia, PA, 19104, USA.
Meghan L ButrynDepartment of Psychology, WELL Center, Drexel University, Stratton Hall, 3141 Chestnut Street, Philadelphia, PA, 19104, USA.
Adrienne S JuarascioDepartment of Psychology, WELL Center, Drexel University, Stratton Hall, 3141 Chestnut Street, Philadelphia, PA, 19104, USA.
Stephanie M ManasseCenter for Weight, Eating, and Lifestyle Sciences (WELL Center), Drexel University, 3201 Chestnut Street, Philadelphia, PA, 19104, USA.
Santiago OntañónDepartment of Computer Science, Drexel University, 3401 Market Street, Philadelphia, PA, 19104, USA.
Diane H DallalDepartment of Psychology, WELL Center, Drexel University, Stratton Hall, 3141 Chestnut Street, Philadelphia, PA, 19104, USA.
Rebecca J CrochiereDepartment of Psychology, WELL Center, Drexel University, Stratton Hall, 3141 Chestnut Street, Philadelphia, PA, 19104, USA.
Danielle MoskowDepartment of Psychology, WELL Center, Drexel University, Stratton Hall, 3141 Chestnut Street, Philadelphia, PA, 19104, USA.
Drexel University · US

Funding

Addressing Weight History to Improve Behavioral Treatments for Bulimia NervosaK23MH105680 · NIMH · DREXEL UNIVERSITY · PI JUARASCIO, ADRIENNE SARAH · 2015 to 2018
$644k
NIMH NIH HHS K23 MH105680
6 · The paper itself

Abstract

Behavioral weight loss (WL) trials show that, on average, participants regain lost weight unless provided long-term, intensive-and thus costly-intervention. Optimization solutions have shown mixed success. The artificial intelligence principle of "reinforcement learning" (RL) offers a new and more sophisticated form of optimization in which the intensity of each individual's intervention is continuously adjusted depending on patterns of response. In this pilot, we evaluated the feasibility and acceptability of a RL-based WL intervention, and whether optimization would achieve equivalent benefit at a reduced cost compared to a non-optimized intensive intervention. Participants (n = 52) completed a 1-month, group-based in-person behavioral WL intervention and then (in Phase II) were randomly assigned to receive 3 months of twice-weekly remote interventions that were non-optimized (NO; 10-min phone calls) or optimized (a combination of phone calls, text exchanges, and automated messages selected by an algorithm). The Individually-Optimized (IO) and Group-Optimized (GO) algorithms selected interventions based on past performance of each intervention for each participant, and for each group member that fit into a fixed amount of time (e.g., 1 h), respectively. Results indicated that the system was feasible to deploy and acceptable to participants and coaches. As hypothesized, we were able to achieve equivalent Phase II weight losses (NO = 4.42%, IO = 4.56%, GO = 4.39%) at roughly one-third the cost (1.73 and 1.77 coaching hours/participant for IO and GO, versus 4.38 for NO), indicating strong promise for a RL system approach to weight loss and maintenance.

Indexed as

Artificial IntelligenceText MessagingWeight Reduction ProgramsAdultAgedBehavior TherapyFemaleHumansLongitudinal StudiesMaleMiddle AgedObesityReinforcement, PsychologyWeight LossArtificial intelligenceBehavioral treatmentLifestyle modificationObesityOptimizationWeight loss

Identifiers

PMID30145623
PMCPMC6524648
OpenAlexW2888461849

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.