Evidence map›Paper›PMID 37426036›Full record

ArticleProceedings of the ... American Control Conference. American Control Conference

Idiographic Dynamic Modeling for Behavioral Interventions with Mixed Data Partitioning and Discrete Simultaneous Perturbation Stochastic Approximation.

Rachael T Kha, Daniel E Rivera, Predrag Klasnja, Eric Hekler

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the ... American Control Conference. American Control Conference. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Article
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

4 authors.

Rachael T KhaR. T. Kha and D. E. Rivera are with the Control Systems Engineering Lab (CSEL) in the School for Engineering of Matter, Transport and Energy at Arizona State University, Tempe, AZ 85281 USA.
Daniel E RiveraR. T. Kha and D. E. Rivera are with the Control Systems Engineering Lab (CSEL) in the School for Engineering of Matter, Transport and Energy at Arizona State University, Tempe, AZ 85281 USA.
Predrag KlasnjaP. Klasnja is with the Division of Biomedical and Health Informatics, School of Information, University of Michigan, Ann Arbor, MU 48109 USA.
Eric HeklerE. Hekler is with the Center for Wireless & Population Health Systems, Univeristy of California, San Diego (UCSD), La Jolla, CA 92093 USA.

Funding

Optimizing Individualized and Adaptive mHealth Interventions via Control Systems Engineering MethodsR01CA244777 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI HEKLER, ERIC, RIVERA, DANIEL E · 2020 to 2024
$3.8M
Operationalizing Behavioral Theory for mHealth: Dynamics, Context, and PersonalizationU01CA229445 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI KLASNJA, PREDRAG, MARLIN, BENJAMIN M. · 2018 to 2022
$2.1M
SCH: Control Systems Engineering for Counteracting Notification Fatigue: An Examination of Health Behavior Change R01LM013107 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI HEKLER, ERIC, KLASNJA, PREDRAG · 2019 to 2022
$995k
NCI NIH HHS R01 CA244777NCI NIH HHS U01 CA229445NLM NIH HHS R01 LM013107
6 · The paper itself

Abstract

This paper presents the use of discrete simultaneous perturbation stochastic approximation (DSPSA) as a routine method to efficiently determine features and parameters of idiographic (i.e. single subject) dynamic models for personalized behavioral interventions using various partitions of estimation and validation data. DSPSA is demonstrated as a valuable method to search over model features and regressor orders of AutoRegressive with eXogenous input estimated models using participant data from

Identifiers

PMID37426036
PMCPMC10327578

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Registered trials

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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.