Evidence map›Paper›PMID 35355685›Full record

ArticleFrontiers in digital health2022

MCMTC: A Pragmatic Framework for Selecting an Experimental Design to Inform the Development of Digital Interventions.

Inbal Nahum-Shani, John J Dziak, David W Wetter

Abstract read
In one paragraph

Article in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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  6. Optimizing the impact of supportive cancer care digital health interventions: considerations for design, development, and evaluation.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025
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  7. Article
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  16. Hybrid Experimental Designs for Intervention Development: What, Why, and How.Advances in methods and practices in psychological science
    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

3 authors.

Inbal Nahum-ShaniInsitute for Social Research, University of Michigan, Ann Arbor, MI, United States.
John J DziakEdna Bennett Pierce Prevention Research Center, The Pennsylvania State University, State College, PA, United States.
David W WetterHuntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States.

Funding

Pilot and Mentoring CoreP50DA054039 · NIDA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LINDA M COLLINS, SUSAN A MURPHY · 2021 to 2026
$18.2M
Pilot, Mentoring, and Professional Development CoreP50DA039838 · NIDA · PENNSYLVANIA STATE UNIVERSITY, THE · PI COLLINS, LINDA M · 2015 to 2019
$13.9M
Novel Methods for Intensive Longitudinal Data in SMART Studies of Drug Abuse and HIVR01DA039901 · NIDA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ALMIRALL, DANIEL, NAHUM-SHANI, INBAL BILLIE · 2015 to 2024
$5.4M
Novel use of mHealth data to identify states of vulnerability and receptivity to JITAIs SupplementU01CA229437 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI NAHUM-SHANI, INBAL BILLIE, WETTER, DAVID W · 2018 to 2022
$2.8M
NCI NIH HHS U01 CA229437NIDA NIH HHS P50 DA039838NIDA NIH HHS P50 DA054039NIDA NIH HHS R01 DA039901
6 · The paper itself

Abstract

Advances in digital technologies have created unprecedented opportunities to deliver effective and scalable behavior change interventions. Many digital interventions include multiple components, namely several aspects of the intervention that can be differentiated for systematic investigation. Various types of experimental approaches have been developed in recent years to enable researchers to obtain the empirical evidence necessary for the development of effective multiple-component interventions. These include factorial designs, Sequential Multiple Assignment Randomized Trials (SMARTs), and Micro-Randomized Trials (MRTs). An important challenge facing researchers concerns selecting the right type of design to match their scientific questions. Here, we propose MCMTC - a pragmatic framework that can be used to guide investigators interested in developing digital interventions in deciding which experimental approach to select. This framework includes five questions that investigators are encouraged to answer in the process of selecting the most suitable design: (1) Multiple-component intervention: Is the goal to develop an intervention that includes multiple components; (2) Component selection: Are there open scientific questions about the selection of specific components for inclusion in the intervention; (3) More than a single component: Are there open scientific questions about the inclusion of more than a single component in the intervention; (4) Timing: Are there open scientific questions about the timing of component delivery, that is when to deliver specific components; and (5) Change: Are the components in question designed to address conditions that change relatively slowly (e.g., over months or weeks) or rapidly (e.g., every day, hours, minutes). Throughout we use examples of tobacco cessation digital interventions to illustrate the process of selecting a design by answering these questions. For simplicity we focus exclusively on four experimental approaches-standard two- or multi-arm randomized trials, classic factorial designs, SMARTs, and MRTs-acknowledging that the array of possible experimental approaches for developing digital interventions is not limited to these designs.

Indexed as

adaptive interventionsdigital interventionsfactorial designsjust in time adaptive interventionsMicro-Randomized Trial (MRT)Sequential Multiple Assignment Randomized Trial (SMART)

Identifiers

PMID35355685
PMCPMC8959436

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.