MOST from a Conceptual Perspective: Module 1 study notes

The lesson defines the three building blocks used everywhere else in the course.

Interventions may be behavioral (changing cognitions or behavior), biobehavioral (behavioral strategy plus medical or biological components such as a prescription drug), biomedical (primarily medical or biological, often with behavioral aspects), or social-structural (changing behavior by changing the environment through the legal system, the built environment, etc.). These are not discrete categories; the point is not to label interventions but to see the range. An intervention may target any level or several: individual, family, school, neighbourhood, clinic, and beyond. Examples given: treatment of depression; school-based drug abuse prevention; a smartphone app for adolescents with Type 1 diabetes on an insulin pump; installing sidewalks and lighting and upgrading parks to encourage physical activity.

Types of intervention components: parts of intervention content (e.g. curriculum topics in drug abuse prevention; training in different weight-loss strategies); features that promote compliance/adherence/engagement (reminder calls or texts; gamification of app-delivered interventions); features aimed at improving fidelity (enhanced training for delivery personnel; resources such as a staff FAQ chat window).

Illustrated by the smartwatch example: in a weight-loss optimization study, participants wear a smartwatch that counts steps. If step-counting is included only as a way of measuring an outcome variable and is definitely going to be omitted when the intervention goes to scale, it is not an intervention component. If it is being considered for inclusion in the intervention when it goes to scale, it is a componentand being a component does not preclude step count also being used as an outcome.

Lesson 2: Definitions of Key Concepts, Part II

Defines the RCT and the classical treatment package approach, via Scenario 1.

Lesson 3: What Knowledge Can and Cannot Be Obtained from an ERCT

Evaluating a multicomponent intervention via an ERCT is not wrong, “absolutely nothing” is wrong with it. The limits are about what the ERCT can and cannot tell you.

Candidate component	Levels
Motivational interviewing	not included / included (no / yes)
Peer mentoring	not included / included (no / yes)
Text message support	not included / included (no / yes)
Mindfulness meditation	not included / included (no / yes)
Behavioral skills training	low intensity / high intensity

What an ERCT that finds a significant effect will NOT reveal: which components make positive contributions to the overall effect; whether the inclusion of one component has an impact on the effect of another; whether a component's contribution justifies its cost; how to make the intervention more effective, affordable, scalable and efficient.

What an ERCT that finds a non-significant effect will NOT reveal: whether any components are worth retaining; whether one component had a negative effect that offset the positive effects of others; specifically what went wrong and how to do better next time.

Lesson 4: Comparison of the Classical Treatment Package and Engineering Approaches

Introduces Scenario 2, the truck leaf spring manufacturing problem (from Pignatiello & Ramberg, 1985, in Wu & Hamada, 2011), to show how an engineer would approach optimizationand why the classical package approach fails as engineering.

Component	Levels (lower / higher)
Furnace temperature	lower / higher
Heating time	shorter / longer
Transfer time on conveyor belt	shorter / longer
Hold down time in high-pressure press	shorter / longer
Quench oil temperature range	lower / higher

If an industrial engineer used the classical treatment package approach: combine all the hypothesized “better” settings (higher furnace temperature + longer heating time + longer conveyor time + longer press time + higher quench oil temperature), compare this package to the old process, and run post-hoc analyses. Why engineers usually do not: if the new process is better, it doesn't indicate which components made a difference; if it is not better, it doesn't indicate which (if any) components improved anything; when repeated, there is no guarantee of systematic incremental improvement, so it is not a good long-run strategy; and it does not take cost or other constraints into account.

The fired-engineer dialogue: the engineer proudly reports springs that are nearly identical, “you need a microscope to see the differences in length!” The plant owners ask the per-spring cost: $100,000. The springs retail for about $50 and manufacturing cannot exceed $40/spring. “We won't be using your manufacturing process, and YOU ARE FIRED.” The constraint on manufacturing cost (no more than $40/spring) should have been established much earlier. The engineer's job is to produce the least variability in length without exceeding $40, which is usually not the best possible process in an absolute sense.

Back to Scenario 1: if behavioral scientists thought like engineers: start with a clear goal including constraints (e.g. best expected outcome obtainable for no more than $300 per participant); design and conduct an efficient experiment to gather needed information on the candidate components; then optimize by selecting from the candidate components/component levels to balance effectiveness and cost; only then evaluate the optimized intervention in an ERCT if indicated.

Lesson 5: The Concept of Optimization

Three points about the definition. (1) Optimization is an ongoing process, not an endpointthe definition says “an intervention” (best obtainable within constraints), not “the intervention”; it is always possible to improve further (e.g. after optimizing for $300/participant, examine new components to get a more effective intervention still under $300). (2) Key constraints: optimization becomes complicated with too many constraints, so identify the most important ones. (3) Fundamental tension: with constraints operating (and there are always constraints), outcomes cannot be as good as they would be without constraints; “constraints require sacrifices, such as not including all the components under consideration. Optimization is about making these sacrifices strategically.”

Desideratum	Definition (verbatim)	Note
Effectiveness	“The extent to which the intervention does more good than harm under real-world conditions.”	Flay (1986)
Affordability	“The extent to which the intervention is effective without exceeding budgetary constraints.”	Called “economy” in Collins (2018) and other publications
Scalability	“The extent to which the intervention can be implemented widely with fidelity.”	Implemented with fidelity = implemented exactly as evaluated, with no ad hoc modifications
Efficiency	“The extent to which the intervention avoids wasting time, money, or other valuable resources.”	An efficient intervention has no inactive components

Optimization vs evaluation. Optimization requires examination of the performance of individual components, which needs experimental designs other than the ERCT (later in the course: the factorial experiment). Evaluation requires comparison of an intervention package to a control or comparison condition, “the ERCT is the way to do this.” Both matter; they are not the same.

Lesson 6: The Three Phases of MOST

Defines the three phases and the two principles, and contrasts the classical and MOST pipelines visually.

What MOST is / is not: MOST is designed to make the best use of available research resources; it is very new and still an open area. It is not an off-the-shelf procedure identical for every application (implementation varies considerably); it is not any particular experimental design.

Preparation phase: devise a detailed conceptual model (or revise an existing one), “a model of the process to be intervened upon, showing explicitly how each candidate component is hypothesized to impact this process”; conduct any pilot testing of components or the optimization trial; identify the optimization objective (the goal of the optimization, e.g. the best expected outcome attainable for $300 or less per participant). Covered in Module 2.

Optimization phase: conduct one or more optimization trials - “an experiment conducted to gather some of the information needed for optimization.” Designs include the factorial experiment (the most common), fractional factorial experiment, sequential multiple-assignment randomized trial (SMART), micro-randomized trial, and system identification (from control engineering). Fractional factorial designs, SMARTs and micro-randomized trials are all in the family of factorial experiments. Then use the data, possibly with cost data, to identify the intervention that best meets the optimization objective.

Evaluation phase: confirm effectiveness of the optimized intervention via an ERCT, typically a standard two-arm trial (intervention vs wait-list control, no treatment, current standard of care, or another appropriate comparator).

Figure 1: The two pipelines (Scenario 1)

Figure 2: The three phases of MOST

Knowledge check

MULTIPLE CHOICE: Which one of the following correctly lists the phases of MOST?

☐ Screening, refining, and confirming. (This is incorrect. Screening, refining, and confirming were the phases that appeared in the early literature on intervention optimization. The current version of MOST has been reconceptualized to be more consistent with how intervention scientists conduct research.)
☐ Preparation, refining, and evaluation. (This is incorrect. Refining is not one of the phases of MOST (although it did appear in the early literature on intervention optimization).)
☑ Preparation, optimization, and evaluation.
☐ Preparation, pilot testing, and evaluation. (This is incorrect. Pilot testing is a part of the preparation phase of MOST, not a distinct phase. Optimization trials are not pilot studies.)

MULTIPLE RESPONSE: Which of the following statements are correct? Select all that apply.

☑ According to the resource management principle, it is necessary to select the most efficient experimental design for the optimization trial.
☐ According to the resource management principle, after an optimization trial has been conducted the next step should always be the evaluation phase of MOST.
☑ According to the continual optimization principle, an effective intervention can always be improved further.
☑ According to the continual optimization principle, improving an intervention starts with reconsideration of the conceptual model.

Feedback when incorrect: This is incorrect. According to the resource management principle, after an optimization trial has been conducted the next step may or may not be an ERCT, depending on the results of the optimization trial.

Lesson 7: Why MOST is a Different Way of Thinking

Pulls the module together as a set of differences in perspective between the classical approach and MOST. The course notes these matter when communicating with intervention scientists (e.g. grant reviewers) unfamiliar with MOST.

Dimension	Classical approach	MOST
Overall objective	To develop an intervention that demonstrates a statistically and clinically significant effect.	To develop an intervention that both achieves EASE (strategic balance of effectiveness, affordability, scalability, efficiency) and demonstrates a statistically and clinically significant effect.
Next steps after pilot testing	Assemble the intervention and evaluate it as a package in an ERCT.	Conduct an optimization trial and identify the optimized intervention.
Experimental designs used	Primarily the ERCT and its variants.	A wide variety of designs; the optimization trial design is selected by the resource management principle (very often in the factorial family); evaluation uses primarily the ERCT and its variants.
Examining effectiveness of individual components	Primarily via post-hoc analyses on ERCT data.	Primarily via direct experimental manipulation of components.
Examining interactions among components	Not possible, the ERCT does not enable it.	Wherever feasible, an experimental design is selected that enables it.
Inert or counterproductive components	Generally tolerated, as long as overall effectiveness of the package is demonstrated.	Generally not tolerated, considerable effort is spent to identify and remove them, because they reduce efficiency.
Scalability and sustainability	Usually dealt with after evaluation, sometimes via ad hoc modifications.	The intervention is built for immediate scalability and long-term sustainability, by identifying constraints (e.g. cost) and working toward acceptable effectiveness within them.

Knowledge check

MULTIPLE RESPONSE: Which of the following statements correctly state differences in perspective between the classical approach and MOST? Select all that apply.

☑ In the classical approach inert or counterproductive components are generally tolerated, whereas these are generally not tolerated in MOST.
☐ The classical approach relies primarily on the ERCT, whereas MOST requires the use of a factorial experiment.
☑ In the classical approach, scalability is considered after an intervention has been evaluated, whereas in MOST scalability is considered from the beginning of intervention development.
☐ The objective of the classical approach is to develop an intervention that demonstrates a statistically and clinically significant effect, whereas the objective of MOST is to identify an optimized intervention.
☐ In the classical approach pilot testing is done in the usual way, whereas in MOST pilot testing is done via the optimization trial.

Feedback when incorrect: The classical approach relies primarily on the ERCT, whereas MOST requires the use of a factorial experiment.This is incorrect. MOST does not require the use of a factorial experiment; instead, any of a wide variety of experimental designs, including but not limited to the factorial experiment, are possibilities in the optimization phase. The ERCT is typically used in the evaluation phase. The objective of the classical approach is to develop an intervention that demonstrates a statistically and clinically significant effect, whereas the objective of MOST is to identify an optimized intervention. This is incorrect. The objective of MOST is to identify an optimized intervention that demonstrates a statistically and clinically significant effect. In the classical approach pilot testing is done in the usual way, whereas in MOST pilot testing is done via the optimization trial. This is incorrect. In MOST pilot testing is done in the preparation phase, in essentially the same manner as in the classical treatment package approach. Optimization trials are fully powered and carefully controlled experiments, not pilot studies.

Module quiz (self-test)

Reveal the 7 quiz items with answers

MULTIPLE RESPONSE: Which of the following could be considered an example of an intervention component? Select all that apply.

☑ A prescription for antidepressant medication.
☑ A bus pass for participants to provide transportation to intervention sessions.
☑ A web site with FAQ for program staff.
☐ A gift card provided as incentive for completion of research surveys.

Feedback when incorrect: This is incorrect. A gift card provided to incentivize completion of research surveys is included for research purposes only, and will not be a part of the intervention when it goes to scale. Therefore, it cannot be considered an intervention component.

MULTIPLE RESPONSE: Which of the following is something a well-conducted ERTC cannot tell us? Select all that apply.

☑ Whether the inclusion of one component has an impact on the effect of another.
☑ Whether there is a positive or negative interaction between components.
☐ Whether an intervention performs better than a control.
☑ Whether all components are needed to produce an overall treatment effect.
☑ Whether any individual component had a negative effect.

Feedback when incorrect: This is incorrect. The purpose of a well-conducted ERCT is to determine whether an intervention performs better than a control.

MULTIPLE RESPONSE: Which of the following statements are consistent with the definition of intervention optimization covered in this lesson? Select all that apply.

☐ Optimization is the process of identifying the intervention that provides the best possible outcome that can be obtained.
☑ Optimization involves consideration of constraints.
☑ Optimization is an ongoing process.
☑ Optimization involves striving for an efficient intervention.

Feedback when incorrect: This is incorrect. Optimization is not about identifying the intervention that provides the best possible outcome; rather, it is about balancing effectiveness against other considerations.

MULTIPLE CHOICE: Which one of the following statements is correct?

☐ Intervention EASE is the process of maximizing effectiveness, affordability, scalability, and efficiency. (This is incorrect. EASE does not involve maximizing all four of these attributes of an intervention simultaneously.)
☐ Intervention EASE is a strategic balance of efficiency against affordability, scalability, and effectiveness. (This is incorrect. EASE does not involve balancing efficiency against affordability and scalability.)
☐ Intervention EASE is the process of achieving the greatest degree of effectiveness attainable. (This is incorrect. EASE does not involve achieving the greatest degree of effectiveness attainable in an absolute sense.)
☑ Intervention EASE is a strategic balance of effectiveness against affordability, scalability, and efficiency. (That's right!)

MULTIPLE CHOICE: Which one of the following correctly lists the phases of MOST?

☐ Screening, refining, and confirming. (This is incorrect. Screening, refining, and confirming were the phases that appeared in the early literature on intervention optimization. The current version of MOST has been reconceptualized to be more consistent with how intervention scientists conduct research.)
☐ Preparation, refining, and evaluation. (This is incorrect. Refining is not one of the phases of MOST (although it did appear in the early literature on intervention optimization).)
☑ Preparation, optimization, and evaluation.
☐ Preparation, pilot testing, and evaluation. (This is incorrect. Pilot testing is a part of the preparation phase of MOST, not a distinct phase. Optimization trials are not pilot studies.)

MULTIPLE RESPONSE: Which of the following statements correctly state differences in perspective between the classical approach and MOST? Select all that apply.

☑ In the classical approach inert or counterproductive components are generally tolerated, whereas these are generally not tolerated in MOST.
☐ The classical approach relies primarily on the ERCT, whereas MOST requires the use of a factorial experiment.
☑ In the classical approach, scalability is considered after an intervention has been evaluated, whereas in MOST scalability is considered from the beginning of intervention development.
☐ The objective of the classical approach is to develop an intervention that demonstrates a statistically and clinically significant effect, whereas the objective of MOST is to identify an optimized intervention.
☐ In the classical approach pilot testing is done in the usual way, whereas in MOST pilot testing is done via the optimization trial.

MULTIPLE RESPONSE: Which of the following statements are correct? Select all that apply.

☑ According to the resource management principle, it is necessary to select the most efficient experimental design for the optimization trial.
☐ According to the resource management principle, after an optimization trial has been conducted the next step should always be the evaluation phase of MOST.
☑ According to the continual optimization principle, an effective intervention can always be improved further.
☑ According to the continual optimization principle, improving an intervention starts with reconsideration of the conceptual model.

Synthesis (my own reading: not part of the course)

For readers who work with the 2021 MRC/NIHR framework for developing and evaluating complex interventions, Module 1 is best read as the optimization layer the MRC framework gestures at but does not operationalise. The MRC framework's iterative phases (develop, identify, feasibility, evaluate, implement) map onto MOST's preparation (developing the conceptual model and candidate components), optimization (efficient experiments before committing to a full trial), and evaluation (the confirmatory trial). MOST's insistence on estimating individual and combined component contributions is the answer to a classic MRC-style question: a complex intervention evaluated as a package leaves you unable to say which active ingredients produced the effect, exactly the post-hoc-analysis trap Lesson 3 describes.

For readers who use the APEASE criteria (Affordability, Practicality, Effectiveness, Acceptability, Side-effects/safety, Equity) from behaviour-change intervention design, EASE is a subset of the same family of implementation-feasibility concerns, stripped to the four that most directly constrain optimisation. Two notes for translation: APEASE's Affordability and Effectiveness align closely with EASE's; APEASE's additional criteria (acceptability, side-effects, equity) are exactly the kind of “other constraints” the MOST definition leaves open for the investigator to name in the optimization objective. That is not a defect, it is the definition's deliberate flexibility. The course's own caution applies to both frameworks: define your constraints explicitly and keep the number of key constraints small.

One methodological flag worth carrying forward: the course repeatedly stresses that MOST “is not any particular experimental design” and that the resource management principle governs design choice. That sets up Module 3's claim that factorial-family designs are often the efficient choice: not a dogma. The conceptual course also quietly updates Collins (2018): “economy” becomes affordability, and the early literature's phase labels (screening, refining, confirming) have been reconceptualised to the current preparation/optimization/evaluation. Quote the current labels.

Glossary

Sources

Video list

Prepared 2 Sep 2026. Notes paraphrase the narration unless marked as verbatim quotes; figures are redrawn from the course slides. Slide decks and the full question JSON are in the module package.

MOST from a Conceptual Perspective: Module 1

Lesson 1: Definitions of Key Concepts, Part I

Knowledge check

Lesson 2: Definitions of Key Concepts, Part II

Lesson 3: What Knowledge Can and Cannot Be Obtained from an ERCT

Knowledge check

Lesson 4: Comparison of the Classical Treatment Package and Engineering Approaches

Lesson 5: The Concept of Optimization

Knowledge check

Lesson 6: The Three Phases of MOST

Figure 1: The two pipelines (Scenario 1)

Figure 2: The three phases of MOST

Knowledge check

Lesson 7: Why MOST is a Different Way of Thinking

Knowledge check

Module quiz (self-test)

Synthesis (my own reading: not part of the course)

Glossary

Sources

Video list