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The Pennsylvania State University
The Graduate School
UNDERSTANDING INTERVENTION EFFECTS BY CONDUCTING MEDIATION
ANALYSIS OF THE RESULTS OF A FACTORIAL OPTIMIZATION TRIAL
A Thesis in
Human Development and Family Studies
by
Jillian C. Strayhorn
© 2019 Jillian C. Strayhorn
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Master of Science
August 2019
ii
The thesis of Jillian C. Strayhorn was reviewed and approved* by the following:
Linda M. Collins
Distinguished Professor of Human Development and Family Studies
Thesis Adviser
Timothy Brick
Assistant Professor of Human Development and Family Studies
Lisa Gatzke-Kopp
Professor of Human Development and Family Studies
Professor-In-Charge of the Graduate Program
*Signatures are on file in the Graduate School.
iii
Abstract
Introduction. Many behavior change interventions operate by identifying and then targeting
important mediators that are hypothesized to produce change in the outcome of interest
(MacKinnon, 2008). Mediation analysis of an intervention trial’s outcomes provides a way of
evaluating the hypotheses that link an intervention to a mediator and a mediator to an outcome.
In this thesis, we demonstrate a novel approach to mediation analysis that enables the evaluation
of hypotheses about individual intervention components. Using empirical data, we show how
mediation analysis of a factorial optimization trial conducted within the multiphase optimization
strategy framework (MOST; Collins, 2018) can provide insight into why intervention
components were or were not effective. We explore three research questions about mediation in
the factorial optimization trial of Opt-IN, a weight loss intervention for overweight adults
(Pellegrini et al., 2014; Pellegrini et al., 2015). First, can mediation analysis help explain the
significant main effect of Buddy Training in the Opt-IN trial? Second, can mediation analysis
help explain the non-significance of the “High” versus “Low” dose of Coaching Calls in Opt-IN?
Third, can mediation analysis help explain a three-way interaction effect that played an important
role in the Opt-IN decision making? Method. Mediation analyses were performed using
structural equation models with intervention component main effects and interaction effects as
independent variables, a social cognitive or supportive accountability variable as the mediator,
and weight loss as the outcome. Results. The intervention component with a significant main
effect, Buddy Training, had important indirect effects on Weight Change through each of its
target mediators: Self-Efficacy, Self-Regulation, and Facilitation. The intervention component
with a surprisingly non-significant main effect, Coaching Calls, did not appear to successfully
target any of the hypothesized mediators. The important interaction effect appeared to have an
indirect effect on Restraint; this indirect effect appeared to imply an interaction effect on the
mediator. Discussion. Implications for the continual optimization of the Opt-IN intervention are
considered.
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Table of Contents
List of Tables…………………………………………………….v
List of Figures……………………………………………………vi
Introduction………………………………………………………1
Methods…………………………………………………………..10
Results……………………………………………………………24
Discussion………………………………………………………..31
Appendix A: Full Table of Effect Codes, Opt-IN ………………55
Appendix B: All Mediation Analysis Results...…………………59
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List of Tables
Table 1. b weights for the effect of Coaching Calls on the putative mediators
Table 2. Means, standard deviations, and correlations: Individual Timepoints (month in
parentheses)
Table 3. b weights for social cognitive and supportive accountability variables as predictors of
weight change (6-month weight – baseline weight)
Table 4. b weights for the effect of Coaching Calls on the putative mediators
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List of Figures
Figure 1. Intervention model (adapted from MacKinnon, 2008, pg. 39)
Figure 2. Example single mediator model
Figure 3. Participant screening in Opt-IN (Source: Spring et al., presentation at DSMB meeting,
December 2017)
Figure 4. Opt-IN experimental conditions (adapted from Pellegrini et al., 2015)
Figure 5. Example single mediator model, Opt-IN
Figure 6. Buddy×PCP×Text Interaction: Outcome = Change in Restraint
Figure 7. Buddy×PCP×Text Interaction: Outcome = Weight Change
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Introduction
The persistence of behavior-related public health problems – tobacco use (CDC, 2012),
obesity (Ogden et al., 2014), depression and anxiety (Kessler, Chiu, & Demler, 2005), and
interpersonal violence (Acierno, Resnick, & Kilpatrick, 2016), among others – has motivated the
continued development of behavior change interventions. In the United States, the translation of
basic science findings into effective interventions capable of being successfully implemented and
disseminated is one of the priorities of the National Institutes of Health (NIH; Guastaferro &
Collins, 2019). Billions of dollars have been invested into the development of behavioral
interventions, and still there remains urgent need for interventions that are effective, efficient,
economical and immediately scalable (Collins, 2018).
Many behavior change interventions operate using a similar strategy: identifying and then
targeting important behavioral antecedents of the outcome of interest (Collins, Graham, &
Flaherty, 2010). Consider a hypothetical weight loss intervention that identifies self-efficacy for
dieting (Stich et al., 2009) as an important causal factor in weight loss. This intervention makes
the following assumption: participants who believe that they can resist diet-related temptations
will more successfully do so and will lose more weight. To produce the desired outcome,
therefore, the intervention targets—in other words, provides content hypothesized to increase—
self-efficacy for dieting. In this example, self-efficacy for dieting is a mediator of the
intervention’s effect on weight loss.
Testing the Hypotheses Underlying a Behavioral Intervention: Mediation Analysis
The described way of thinking about how a behavioral intervention produces effects is
depicted in Figure 1 (MacKinnon, 2008). Here, “Action Theory” refers to theory about how the
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intervention produces change in the mediator, and “Conceptual Theory” refers to theory about
how the mediator influences the outcome (Chen, 1990).
Mediation analysis offers a method of testing each of these links: the action theory link
and the conceptual theory link. Did the intervention produce change in the mediator (through the
action theory link) as hypothesized? Was the mediator associated with the outcome (through the
conceptual theory link), as hypothesized? An example single mediator model is depicted in
Figure 2. In mediation analysis with a single mediator, the total effect of the intervention
program on the outcome is parsed into: 1) an indirect effect from the intervention program (X) to
the outcome (Y) through the mediator (M), and 2) a direct effect from the intervention program
(X) to the outcome (Y) not through the mediator (M).
In this example, the indirect effect of the intervention on Weight Loss through Self-
Efficacy is the product of the a path (from the intervention to Self-Efficacy) and the b path (from
Self-Efficacy to Weight Loss). The effect of the intervention on Weight Loss not through Self-
Efficacy is the c’ path. In classic mediation terms, this c’ path is known as the direct effect
(MacKinnon, 2008). In the case of a behavior change intervention, a substantial c’ path likely
means that the intervention had some effect on the outcome through some other unmodeled (or
even unmeasured) mediator. The error terms e2 and e3 are the residual terms of two regression
equations: 1) the regression of Weight Loss on both the intervention and Self-Efficacy and 2) the
regression of Self-Efficacy on the intervention, respectively.
The Present Thesis
There is a substantial history behind mediation analysis of the results of randomized
control trials (RCTs; Kraemer & Fairburn, 2002). Performed on the results of an RCT, mediation
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analysis is well equipped to yield evidence about the functioning of an intervention package as a
whole. However, conclusions drawn about mediation of the effect of the intervention package as
a whole may have limited utility, particularly when an intervention contains multiple
intervention components, each targeting its own mediator. Recall the hypothetical weight loss
intervention above. Imagine that instead of targeting only self-efficacy for dieting this
intervention was expanded to include an intervention component targeting supportive
accountability (Mohr, Cujipers, & Lehmen, 2011). Now, one component of the intervention is
hypothesized to target self-efficacy for dieting and another component is hypothesized to target
supportive accountability. Mediation analysis of an RCT of this intervention package would not
be able to distinguish between these different causal pathways; such analysis could evaluate the
whole intervention’s effects on either of these mediators, but it could not evaluate the hypotheses
that connected a particular intervention component to a particular target mediator.
In this thesis, we will consider a novel approach to mediation analysis—one that is
capable of providing evidence at the level of the individual intervention component. In this
approach, which falls within the multiphase optimization strategy (MOST; Collins, 2018)
framework, mediation analysis is performed on the results of a factorial optimization trial of an
intervention, rather than on the results of an RCT. A factorial optimization trial yields one main
effect estimate for each intervention component and one interaction effect for each permutation
of two or more intervention components. Mediation analysis of the results of a factorial
optimization trial enables the evaluation of mediation of each main effect and each interaction
effect.
To our knowledge, mediation analysis of the results of a factorial optimization trial has
previously been considered only by Smith, Coffman, and Zhu (2018), who describe a case study
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of mediation analysis in a 24 factorial experiment. This thesis is intended to build upon the
foundation that Smith, Coffman, and Zhu (2018) provide. We demonstrate mediation analysis of
the results of a factorial optimization trial using the results of the 25 factorial optimization trial of
Opt-IN (Pellegrini et al., 2014; Pellegrini et al., 2015), a 6-month weight loss intervention for
overweight adults. We use these analyses to examine surprising results from the Opt-IN trial,
including: a significant main effect, a non-significant main effect, and an important interaction
effect.
There appears to have been particularly little work done in the study of mediation of
interaction effects in factorial optimization trials. In this thesis we draw upon the literature on
mediated moderation (Morgan-Lopez & MacKinnon, 2006) to explore what the mediation of a
factorial interaction effect might mean. Ultimately, we consider how mediation analysis of the
results of an optimization trial may yield conclusions relevant to the incremental improvement of
Opt-IN and of weight loss interventions more generally.
The Factorial Optimization Trial of Opt-IN
MOST consists of three phases: 1) Preparation, 2) Optimization, and 3) Evaluation. In the
Preparation phase, intervention components are identified and linked with target mediators in a
conceptual model. In the Optimization phase, an optimization trial is conducted to enable the
identification of the intervention components that have important main effects and/or interaction
effects on the outcome. The optimization trial may be carried out with a variety of experimental
designs (Collins, 2018); here, we limit our focus to the 2k factorial experiment. If the
optimization trial identifies a combination of components that meets the pre-defined optimization
criterion, then that set of components is defined as the optimized intervention. If the optimized
intervention is deemed effective enough, then it is tested by RCT in the Evaluation phase.
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One of the fundamental principles of MOST is the continual optimization principle, or
the idea that interventions should be improved systematically and iteratively (Collins, 2018). A
single optimization trial may motivate subsequent optimization trials—whether of the same
intervention, of the intervention supplemented with more components or updated components, or
of a single component within the intervention. From the perspective of the continual optimization
principle, the goal of a single optimization trial is not only to produce an optimized intervention
but also to contribute to the incremental improvement of intervention science more generally.
The decision to optimize Opt-IN was motivated by the fact that, though there are various
weight loss intervention packages that have been deemed effective in RCTs, these intervention
packages are too expensive to be disseminated in full (Pellegrini et al., 2014; Pellegrini et al.,
2015). If one of these prohibitively expensive intervention packages is implemented at all, it is
likely to be modified so that it is less expensive—for example, through the removal of a
component, the reduction of a component’s dosage, or the modification of a component’s
delivery mechanism. However, because the active ingredients of the intervention package are
unclear, any such ad hoc modifications risk altering the intervention to the point that it is no
longer effective. Thus, the primary goal of the Opt-IN project was to develop a weight loss
intervention that is both effective and scalable. The secondary goal was to test the theories
linking certain intervention components with certain target mediators. Both goals were believed
to have important potential to shape the continual optimization of Opt-IN and of other
interventions in the weight loss domain.
In the Preparation phase, the Opt-IN team drew upon both behavior change theory and
current practice to develop the Opt-IN intervention (Pellegrini et al., 2014). Four hypothesized
mediators were selected as targets for Opt-IN’s intervention components. Three of these were
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derived from social cognitive theory (Bandura, 1991): Self-Efficacy, or the belief in one’s
abilities to carry out certain behaviors; Self-Regulation, or the resisting of temptations and
impulses; and Facilitation, or the experience of being exposed to environmental factors that
facilitate certain behaviors. The fourth, Supportive Accountability, or social support that
promotes intervention adherence, was derived from a model of the same name (Mohr, Cujipers,
& Lehmen, 2011). Five experimental intervention components were developed to target these
four hypothesized mediators. Components, mediators, and the outcome, weight loss, were linked
in a conceptual model.
In the Optimization phase, Opt-IN’s five intervention components were operationalized
as two-level factors:
1) Coaching Calls, or telephone-based sessions with a weight-loss coach. These
coaching calls were hypothesized to target the Supportive Accountability mediator.
Given the consensus that holding more weight loss coaching sessions yields better
outcomes, whether coaching is done in person (Wadden et al., 2005) or by phone
(Sherwood et al., 2009), the Coaching Calls factor was constructed with the levels
“Low” and “High” (12 sessions and 24 sessions, respectively).
2) Text Messages, or regular text reminders about physical activity and weight loss
goals. Text messages were hypothesized to target each of the social cognitive
mediators: Self-Efficacy, Self-Regulation, and Facilitation. The Text Messages factor
was constructed with levels “Off” and “On.”
3) Buddy Training, or the provision of training for the weight loss buddies that all
participants selected from their existing social support systems. Buddy training was
hypothesized to target each of the social cognitive mediators: Self-Efficacy, Self-
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Regulation, and Facilitation. The Buddy Training factor was constructed with levels
“Off” and “On.”
4) Meal Replacement Recommendations (Meal), or the provision of meal samples and
recommendations to use meal replacements regularly. This component was
hypothesized to target the social cognitive mediators: Self-Efficacy, Self-Regulation,
and Facilitation. The Meal Replacements factor was constructed with levels “Off”
and “On.”
5) Contact with a Primary Care Physician (PCP), or the communication of updates
about participants’ weight loss goals to their primary care physicians. PCP Contact
was hypothesized to target all four mediators: Supportive Accountability, Self-
Efficacy, Self-Regulation, and Facilitation. The PCP factor was constructed with
levels “Off” and “On.”
These two-level factors were tested in a full 25 factorial optimization trial, which
produced estimates of the main effect of each component and the interactions involving two,
three, four, or all five components. The results were met with some surprise (Spring et al., In
preparation). First, contrary to the team’s expectations, only one of the five experimental
intervention components, Buddy Training, had a significant main effect on Weight Change;
participants who were in a condition with Buddy Training experienced greater weight loss, on
average. Second, there was no significant main effect for Coaching Calls; participants in a
condition with 24 calls performed no better than did participants in a condition with 12 calls.
Third, there was an important, statistically significant interaction effect—a three-way interaction
between Buddy Training, PCP, and Text Messages—that suggested that PCP Contact should be
included in the optimized intervention, even though it did not have a main effect. The resulting
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optimized intervention was: Buddy Training set to “On,” PCP set to “On,” Coaching Calls set to
“Low,” and Text Messages and Meal set to “Off.”
These surprising findings prompted questions about mediation in Opt-IN. Did Buddy
Training have the hypothesized effects on each of the social cognitive mediators? Did the “High”
versus “Low” level of Coaching Calls fail to successfully target supportive accountability—or
maybe, was supportive accountability not associated with weight loss as hypothesized? Can
mediation of the three-way interaction effect between Buddy Training, PCP, and Text Messages
help explain why this interaction played such an important role in the identification of the
optimized intervention?
Research Questions and Hypotheses
Research Question 1: Can the significant main effect of Buddy Training be
understood in terms of indirect effects through the social cognitive mediators? As noted
previously, the Buddy Training component was a priori theorized to target each of the social
cognitive mediators. To investigate these links, we test the following hypotheses:
Hypothesis 1(a): Buddy Training has a significant indirect effect on Weight Change
through Self-Efficacy.
Hypothesis 1(b): Buddy Training has a significant indirect effect on Weight Change
through Self-Regulation.
Hypothesis 1(c): Buddy Training has a significant indirect effect on Weight Change
through Facilitation.
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Research Question 2: Can the ineffectiveness of the “High” versus “Low” level of
Coaching Calls be understood in terms of a disruption in a mediation pathway from
Coaching Calls to Supportive Accountability to Weight Change? The Coaching Calls
component was a priori theorized to target Supportive Accountability. A disruption in a
mediation pathway from Coaching Calls to Supportive Accountability to Weight Change could
mean one of three things:
1) that the mediator, Supportive Accountability, was not associated with Weight Change
(disruption of the b path);
2) that Coaching Calls had no effect on Supportive Accountability (disruption of the a
path);
3) or both: that Coaching Calls had no effect on Supportive Accountability, which was
not associated with Weight Change (disruption of the a and b paths).
If there is no evidence of any such disruption, another possible reason for the relative
ineffectiveness of the “High” versus “Low” level of Coaching Calls is suppression, or the
presence of indirect and direct effects that, operating in opposing directions, cancel each other
out (such that a and b are not disrupted, but c’ is an opposing effect).
To investigate the possibility of disruption, we test the following hypotheses:
Hypothesis 2(a): Supportive Accountability is associated with Weight Change.
Hypothesis 2(b): Coaching Calls has a significant main effect on Supportive
Accountability.
We expect that one or both of these hypotheses will be falsified (suggesting disruption).
If neither is falsified we will also carry out exploratory analyses to investigate the possibility of
10
suppression as an alternative explanation for the relative ineffectiveness of “High” versus “Low”
level of Coaching Calls.
Research Question 3: Can the important three-way interaction between Buddy
Training, PCP, and Text Messages be understood in terms of mediation? There were no a
priori hypotheses about the mediation of interaction effects. We have no reason to expect the
three-way interaction between Buddy Training, PCP, and Text Messages to be mediated by a
specific social cognitive or supportive accountability variable. Still, we expect that the three-way
interaction between Buddy Training, PCP, and Text Messages is mediated by at least one of the
putative mediators. We investigate this possibility and consider what mediation of the three-way
Buddy Training by PCP by Text Messages interaction might mean, conceptually and empirically.
Methods
Participants
The factorial optimization trial of Opt-IN included 562 weight stable adults (82%
females, n = 459) between ages 18 and 60 (M = 38.65; SD = 11.82), with baseline BMIs between
25 and 40 (M = 32.28; SD = 3.57). Baseline weight in BMI units was approximately normally
distributed. The majority of participants identified as White (74.2%, n = 417); the rest, as Black
or African American (15.5%, n = 87), more than one race (5%, n = 28), Asian (3%, n = 16),
American Indian or Alaska Native (<1%, n = 5), Native Hawaiian or Other Pacific Islander
(<1%, n = 1), or Unknown (1.5%, n = 8).
Participants were recruited for the trial via paper and online advertisements and then
screened via online, telephone, and finally in-person screeners. Individuals were screened out for
specific logistical or behavioral reasons, for example if they reported taking certain medications,
met criteria for bulimia or substance abuse (other than nicotine dependence), or reported suicidal
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ideation. Participants were required to obtain a physician’s permission to enroll in the study, and
were also required to nominate a buddy who could be called upon to participate, in person or
remotely. Figure 3 summarizes the participant screening and selection process.
Because of a change in experimental design that occurred part-way through recruitment,
participants were recruited in two cohorts. The original intention was to study the five
intervention components using a fractional factorial design (Pellegrini et al., 2014), a special case
of the factorial experiment that enables the estimation of key effects, including main effects and
lower-order interactions, using fewer experimental conditions (Collins, 2018). However, when
enrollment in the intended sixteen conditions was nearly half complete, it was discovered that,
due to a clerical error, the intended design was not in fact the design being implemented. As
described in Pellegrini et al. (2015), the decision was made to expand the design to a full 25
factorial. The remaining sixteen experimental conditions were added, and the remaining half of
participants were randomized to the new conditions. As a result, the time of randomization
differed across experimental conditions, thus defining the two participant cohorts. In the primary
analysis of the results of the Opt-IN trial (Spring et al., In Preparation), the effects of cohort
were considered and found to be non-significant; there were no apparent differences associated
with the time of randomization. As a result, cohort was not considered further in the following
analyses. The experimental design for the full 25 factorial appears in Figure 4.
In a 2k factorial trial the traditional approach is to code the two-level factors using effect
coding, in which the two levels of each main effect are coded with +1 and -1. Typical practice is
to let +1 represent the “High” or “On” level and -1, the “Low” or “Off” level. To code
interaction terms, the main effect vectors corresponding to the factors involved in the interaction
are multiplied. Effect coding has a number of advantages (Kugler, Dziak, and Trail, 2018); one
12
of these is the fact that the thirty-one main and interaction effects from a 25 factorial experiment
are uncorrelated and orthogonal. A full table of effect codes for the design used in Opt-IN is
provided in Appendix A.
The main effect of each intervention component is calculated by comparing, component
by component, the mean of all conditions set at one factor level (in this case, “On” or “High”) to
the mean of all conditions set at the other factor level (“Off” or “Low”). For Coaching, for
example, the main effect is computed as the difference between two means: that of conditions 9
through 16 and 25 through 32 and that of conditions 1 through 8 and 17 through 24. As can be
seen in Figure 4, conditions 9 through 16 and 25 through 32 are those for which Coaching is set
to “High”; conditions 1 through 8 and 17 through 24 are those for which Coaching is set to
“Low.”
If the effect of one component varies depending on the factor level another component
was set to, there is a two-way interaction; two-way interactions between components are
calculated by comparing the differences in cell means when two components are set to “On” (or
“High”) and when two components are set to “Off” (or “Low”). If a two-way interaction differs
as a function of a third component, there is a three-way interaction. In a 25 factorial trial like that
of Opt-IN, up to a five-way interaction may be calculated. The full set of effects includes: five
main effects, ten two-way interactions, ten three-way interactions, five four-way interactions,
and one five-way interaction.
The following depicts the regression equation for the calculation of each of these main
and interaction effects. All thirty-one effects, the five main effects through the five-way
interaction effect, are included as independent variables.
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E(Weight Change) = b0 + b1(Buddy Training) + b2(Coaching Calls) + b3(PCP) +
b4(Text Messages) + b5(Meal) + b6(B×C) + b7(B×P) + b8(B×T) + b9(B×M)
+ b10(C×P) + … + b31(B×C×P×T×M)
Measures
Anthropometric Measures. Weight was assessed in person using a calibrated beam
balance scale; height, with a stadiometer. If a participant was not able to come in for an in-person
assessment, a self-report of weight was accepted instead. BMI was calculated using the
following formula: weight in pounds/(height in inches)2 x 704.5.
Self-Efficacy. Two types of self-efficacy were assessed: Self-Efficacy for Dieting and
Self-Efficacy for Exercise. Self-Efficacy for Dieting was quantified as the total score of the
Scenario-Based Dieting Self-Efficacy Scale (Stich et al., 2009), which has a reported α of 0.87
and a reported test-retest correlation of r = 0.83 over a 2- to 3-week interval (Stich et al., 2009).
This scale asks participants to rate how likely they are to resist food-related temptations in a
varied collection of scenarios.
Self-efficacy for Exercise was assessed using the 18-item Self-Efficacy of Exercise
Behavior Change measure (Marcus et al., 1992), which prompts participants to report how likely
they are to exercise even when “other things get in the way,” such as inclement weather or
depression.
Self-Regulation. Self-regulation in eating and dieting was measured using the Restraint
and Disinhibition factors of the Three Factor Eating Questionnaire (Stunkard & Messick, 1985).
Both of these factors are conceptualized as distinct but related features of dieting behavior; it is
theorized that “restrained eating” is associated with “counter-regulation” behavior in which a
14
person eats more and not less after an indulgence, such as a milkshake. This is theorized to be
true particularly for those with higher disinhibition, for example due to dysphoric emotions, even
depression (Stunkard & Messick, 1985). Both factors, Restraint and Disinhibition, are
characterized by high internal consistency, with α = 0.90 and α = 0.87, respectively.
Autonomous versus Controlled motivation for self-regulation was assessed using the
subscales of the Treatment Self-Regulation Questionnaire (TSRQ). For the full Controlled
Motivation subscale, α = 0.79; for the full Autonomous Motivation subscale, α = 0.58 (Williams
et al., 1996). At baseline, Controlled Motivation was assessed with 12 items, including “I
decided to enter this weight loss program because people will like me better when I’m thin,” and
Autonomous Motivation was assessed with 6 items, including “I decided to enter this weight loss
program because I really want to makes some changes in my life.” Items were rated using a 7-
point Likert scale (1=not at all true; 7=very true). At follow-up, a shortened version of the TSRQ
specific to “Continued Program Participation” was used; in this version, Controlled Motivation
was assessed with 8 items and Autonomous Motivation was assessed with 5 items.
Supportive Accountability. Therapeutic Alliance was measured using the 20-item
Combined Alliance Short Form- Patient Version (CASF-P), which is reported to have internal
consistency of α =.93 (Hatcher & Barends, 1996). Items of the CASF-P included “I am confident
in my coach’s ability to help me” and “My coach and I are working towards mutually agreed
upon goals.”
Perceived Autonomy Support was measured using an adapted version of the Perceived
Autonomy Support Scale for Exercise Settings (PASSES; α = .92) (Hagger et al., 2007). This 12-
item scale was adapted to the coaching calls context, with items like “I am able to talk to my
coach about the active sports and/or exercise I do in my free time.”
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Facilitation. Facilitation was measured using three items that asked participants to rate
how much the study tools helped them to 1) “eat healthier,” 2) “be more physically active,” and
3) “lose weight.” Ratings were provided on a 5-point Likert scale (1=Not at all; 5=Very much).
A mean score was used to summarize the three items.
Occasions
Data was collected in three waves: at baseline, 3 months, and 6 months. Weight was
measured on all three occasions, as were 6 of the 9 potential mediators: Dieting Self-Efficacy,
Self-Efficacy for Exercise, Restraint, Disinhibition, Autonomous Motivation and Controlled
Motivation. The remaining 3 potential mediators, Therapeutic Alliance, Perceived Autonomy
Support, and Facilitation, were measured on the second and third occasions only, at 3- and 6-
month timepoints. Participants were offered financial incentives to complete the in-person
assessments at 3 and 6 months, including $20 per follow-up and reimbursement for parking.
Participants’ weight loss buddies did not participate in assessments, but those in training were
incentivized up to $40 for completion of trainings and webinars.
Data Analysis
Variables and Occasions. Because the 6-month weight outcome was deemed to have the
greatest relevance for public health (Spring et al., In preparation), this project focuses on
baseline-to-6-month Weight Change. We chose to operationalize our dependent variable as
“Weight Change”: 6-month weight minus baseline weight. As a result, negative Weight Change
indicates successful weight loss; if a person weighed 250 lbs at the beginning of the study and
235 lbs at the end of the study, then that person’s Weight Change was defined as -15.
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Six of the potential mediators were also defined as change scores: Self-Efficacy for
Dieting, Self-Efficacy for Exercise, Restraint, Disinhibition, Autonomous Motivation, and
Controlled Motivation. To compute these six change scores, we subtracted the baseline score
from the 3-month score; for these six mediator change scores, like the weight change score, a
negative sign implies loss and a positive sign implies gain. If that hypothetical person above
reported a Self-Efficacy for Dieting of 20 at baseline and 30 at the 3-month timepoint, then the
person’s Self-Efficacy for Dieting change score will be +10.
The remaining three potential mediators, Therapeutic Alliance, Perceived Autonomy
Support, and Facilitation, were operationalized using the 3-month measures themselves. These
three variables were not measured at baseline; they were specific to the study tools (the coaching,
the app, etc.) and therefore captured change from the pre-intervention timepoint without any
need for subtraction.
Defining the Putative Mediators. Nine social cognitive and supportive accountability
variables were considered as potential mediators. The first step to identifying any of these as a
mediator was to confirm that the variable did predict Weight Change in a regression like the one
that follows. In this example, the potential mediator is the Self-Efficacy for Dieting change score
(3-month score – baseline score). In this example equation, the effect of Self-Efficacy for Dieting
on the weight change score (6-month score – baseline score) is tested while controlling for the
thirty-one main effect and interaction effect terms.
Weight Change = i2 + c’1(Buddy Training) + c’2(Coaching Calls) + c’3(PCP) +
c’4(Text Messages) + c’5(Meal) + c’6(B×C) + c’7(B×P) + c’8(B×T) +
c’9(B×M) + c’10(C×P) + … + c’31(B×C×P×T×M) + b(Self-Efficacy for
Dieting) + e2
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Even if the potential mediator (in this example, Self-Efficacy for Dieting) is
found to predict Weight Change, the variable has still not been tested as a mediator. Only
a single pathway, the b path, has been confirmed. Therefore, if a variable was found to
predict Weight Change, we defined it as a putative mediator and continued to test it in
mediation analyses. If a variable was not found to predict Weight Change, we used that
particular variable as the outcome of a separate 25 factorial analysis to determine whether
it was affected by any of the components. We did not use that variable further as a
putative mediator. We chose to set alpha at 0.05 for the identification of putative
mediators; given the large number of associations to be tested in each single mediator
model, we wanted to err on the side of caution in identifying the mediators to be tested.
Model Selection. We chose to evaluate separate single mediator models for each of the
putative mediators. We included all thirty-one independent variables, the five main effects and
twenty-six interaction effects, in each model. The decision to include all thirty-one independent
variables was motivated by the goal of evaluating the mediation of both main effects and
interaction effects. An example model with Self-Efficacy for Dieting as the mediator is provided
in Figure 5.
The decision to set up separate single-mediator models instead of a multiple mediator
model was motivated by the overarching purpose of the analyses: to test hypotheses linking
specific intervention effects to specific putative mediators. A single mediator model considers
the following question: can change in a particular mediator (or a lack thereof) explain some of
the effects (or lack of effects) of a particular intervention component (or combination of
components)? A single mediator model does not take into account the possible interrelationships
among putative mediators. It cannot speak to the relative roles of different mediating constructs.
18
Instead, a single mediator model provides an important check: did the intervention component
effect target the mediator or mediators it was intended to target?
Because the putative mediators are derived from the 3-month measures and the outcome
is derived from the 6-month measure, the resulting single-mediator models evaluate how change
that occurs over the first 3 months of the study is associated with Weight Change over the full 6
months. For a model with a change score mediator—say, Self-Efficacy for Dieting—mediation
analyses may speak to how a given component produces change in the mediator over 3 months
and how that change over 3 months is associated with Weight Change over the full 6 months. For
a model with a 3-month-score mediator—say, Therapeutic Alliance—mediation analyses may
speak to how a given component produces Therapeutic Alliance at 3 months and how that 3-
month Therapeutic Alliance is associated with Weight Change over the full 6 months. These
models do not take into account any subsequent changes in the mediators that may have occurred
from 3 to 6 months.
Guiding Principles. Given the inherent complexity of a model with thirty-one
independent variables, the following principle guided our analyses: The main and interaction
effects deemed scientifically interesting are those that were identified in the primary outcomes
analyses (Spring et al., In Preparation). We aimed first to replicate these findings, if only
approximately, as the primary outcomes analyses used an autoregressive model instead of
difference scores. We worried less about the possibility of Type I error, as we investigated not
every effect that meets a significance criterion but rather the effects that we previously had
decided were important. We continued to follow the principles that guided decision-making in
the primary outcomes process, including, for example, the idea that interaction effects are more
19
likely to be worth considering if at least one of the components that interaction involves also has
a significant main effect—something Wu & Hamada (2009) referred to as the heredity principle.
Importantly, the mediation analyses that follow are exploratory analyses. Because their
purpose is to examine secondary hypotheses of relevance to decision-making within the
optimization phase, these analyses may be considered from a decision-priority perspective
(Collins, 2018). The priority from this perspective, in contrast to the conclusion-priority
perspective, is less about a statistical conclusion and more about a practical decision. Thus while
we observe hypothesis testing conventions in the analyses that follow, hypothesis testing is not
our primary purpose. Again, the following analyses are exploratory and intended to contribute
information that may be useful in the larger continual optimization process.
Missing Data. The optimization trial of Opt-IN was characterized by a level of attrition
typical of longitudinal intervention research (Hansen, Tobler, & Graham, 1990). At the 3-month
timepoint, participant retention was 90.6 percent; at the 6-month timepoint, 84.3 percent.
Missing data were assumed to be missing at random (MAR) and handled using FIML in the
approach advised by Graham (2003). In each single mediator model the other putative mediators
(not currently being modeled) were included as auxiliary variables; this way, each putative
mediator contributed to the missing data model that was used in all the analyses reported here.
Mediation Analyses. We carried out mediation analysis using the general approach
advised by MacKinnon (2008). Mathematically, the parsing of total, indirect, and direct effects is
accomplished using three general equations (MacKinnon, Fairchild, & Fritz, 2007):
1) Y = i1 + cX + e1
2) Y = i2 + c’X + bM + e2
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3) M = i3 + aX + e3
Equation 1) represents the total effect of X on Y, with intercept i1 and residual e1. In this
equation, the coefficient c quantifies the effect of X on the outcome. Equation 2) expresses Y as a
function of both X and the mediator, M, with intercept i2 and residual e2. In this equation, the
coefficient c’ quantifies the direct effect of X on the outcome, controlling for the mediator, and
the coefficient b quantifies the mediator’s effect on the outcome, controlling for the treatment
effect. Equation 3) expresses the effect of the treatment, X, on the mediator, M, with intercept i3
and residual e3. In this equation, the coefficient a quantifies the effect X on M. As discussed
previously, the indirect effect of X on Y through the M is the product of two paths: from X to M
and from M to Y, controlling for X, or the product ab. The direct effect is the c’ path, or the effect
of X on Y, controlling for M.
In each set of mediation analyses we used the Sobel test (Sobel, 1982) to evaluate the
significance of indirect effects. With this method the a and b paths are assumed to be
independent, and the sample variance estimator (or the multivariate delta method estimator) of
the product ab is sab2 = sa
2�̅�2 + sb2�̅�2 (MacKinnon, Warsi, & Dwyer, 1995). This is one of various
possible approaches to the calculation of indirect effect standard errors; we chose this approach
because it remains widely used and is generally conservative (MacKinnon, Warsi, & Dwyer,
1995).
For the mediation analyses we chose to set alpha at 0.1.
Mediated Moderation. The literature uses the phrase “mediated moderation” to describe
the phenomenon in which an interaction between two variables effects change in a mediator
(Morgan-Lopez & MacKinnon, 2006). In most cases of mediated moderation, one of the two
21
variables in the interaction is a moderator variable that was not experimentally manipulated (for
example, a preexisting risk factor or a subgroup variable, like gender). This differs importantly
from the sort of mediated interaction effect we consider in this thesis—most notably, our
interaction effects result from factorial experimentation versus preexisting differences. Unlike
many interactions considered in the mediated moderation literature, the interaction effects in a
factorial experiment can be uncorrelated (Wu & Hamada, 2009). This occurs when the factorial
experiment is balanced and effect codes are used properly in the factorial ANOVA. Because
correlation among interaction effects has been found to be one of the greatest threats to the
detection of mediated moderation (Morgan-Lopez & MacKinnon, 2006), a factorial experiment
may represent a particularly good case study for the examination of mediated interaction effects.
For the calculation of indirect effects of interactions in this particular case we use the same
multivariate delta method estimator, as demonstrated in the mediated moderation literature
(Morgan-Lopez & MacKinnon, 2006).
Research Question 1
Hypothesis 1(a): . Buddy Training has a significant indirect effect on Weight
Change through Self-Efficacy. We tested this hypothesis by evaluating separate single mediator
models for each of the putative mediators within Self-Efficacy. These models were set up as
structural equation models using Mplus Version 8.0 (Muthén & Muthén, 2017). To investigate
Hypothesis 1(a), we focused specifically on the pathways originating from the Buddy Training
main effect (i.e., where X is Buddy Training). The regression equations of particular relevance to
Hypothesis 1(a) were the following:
1) Weight Change = i2 + c’1(Buddy Training) + c’2(Coaching Calls) + c’3(PCP)
+ c’4(Text Messages) + c’5(Meal) + c’6(B×C) + c’7(B×P) + c’8(B×T) +
22
c’9(B×M) + c’10(C×P) + … + c’31(B×C×P×T×M) + b(Self-Efficacy for
Dieting) + e2
2) Self-Efficacy for Dieting = i3 + a1(Buddy Training) + a2(Coaching Calls) +
a3(PCP) + a4(Text Messages) + a5(Meal) + a6(B×C) + a7(B×P) + a8(B×T) +
a9(B×M) + a10(C×P) + … + a31(B×C×P×T×M) + e3
The coefficients of particular relevance to Hypothesis 1(a) are: c’1, a1, and b. The
intercepts (i2 and i3) and residuals (e2 and e3) are named to match those in the equations from
MacKinnon et al. (2007), above. In this model, as in the rest of the single-mediator models, the
remaining putative mediators were included as auxiliary variables, such that they were taken into
account in the missing data model (Graham, 2003). We considered Hypothesis 1(a) to be
confirmed if Buddy Training was found to have an indirect effect on any one of the putative
mediators within Self-Efficacy.
Hypothesis 1(b): Buddy Training has a significant indirect effect on Weight Change
through Self-Regulation. We tested this hypothesis using the same approach—this time, with
single mediator models for each of the putative mediators within Self-Regulation. We considered
Hypothesis 1(b) to be confirmed if Buddy Training was found to have an indirect effect on any
one of the putative mediators within Self-Regulation.
Hypothesis 1(c): Buddy Training has a significant indirect effect on Weight Change
through Facilitation. We tested this hypothesis by evaluating a single mediator model for each
putative mediator within Facilitation. Importantly, this is a maximum of just one model, with just
one putative mediator, as the mean-scored “Facilitation” variable is the only potential mediator
within Facilitation.
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Research Question 2
Hypothesis 2(a): Supportive Accountability is associated with Weight Change. As
has been discussed, a given social cognitive or supportive accountability variable was defined as
a putative mediator if it was found to be associated with Weight Change. The same analyses that
evaluated variables as putative mediators, therefore, could be used to evaluate this particular
hypothesis. We ran regression models for each of the variables within Supportive
Accountability: one regression of Weight Change on Therapeutic Alliance, controlling for the
thirty-one independent variables, and one regression of Weight Change on Perceived Autonomy
Support, controlling for the thirty-one independent variables.
Hypothesis 2(b): Coaching Calls has a significant main effect on Supportive
Accountability. We tested this hypothesis in two ways. First, if one of the supportive
accountability variables was not found to have an association with weight loss (as described in
Hypothesis 2(a)), we tested the main effect of Coaching Calls on that variable by conducting a 25
factorial analysis with that particular supportive accountability variable as the outcome. Second,
if one of the supportive accountability variables was found to be associated with weight loss
(again, Hypothesis 2(a)), we evaluated the significance of the a path from Coaching Calls to that
supportive accountability variable in a single mediator model.
In the event that Hypothesis 2(a) and Hypothesis 2(b) were both confirmed (and
therefore, there was no evidence of disruption), we planned also to carry out further exploratory
analyses to try to explain the relative ineffectiveness of the “High” versus “Low” level of
Coaching Calls. In particular, we planned to look for evidence of suppression (opposing ab and
c’ effects) in either of the supportive accountability single mediator models.
24
Research Question 3
The mediation of the three-way interaction between Buddy Training, PCP and Text
Messages was investigated using the full set of single-mediator models: one for each of the
putative mediators. In these models we focused on the pathways originating from the Buddy
Training by PCP by Text Messages interaction effect. In particular, we investigated whether this
interaction had a significant indirect effect on Weight Change through any of the putative
mediators.
Results
Preliminary Analyses
Descriptive statistics for Weight Change and the 9 social cognitive and supportive
accountability change scores appear in Table 1. The average participant in Opt-IN lost weight;
the mean Weight Change was -11.08 pounds (SD = 10.57). For the 6 potential mediators that
represent change scores, the sign similarly indicates loss (negative sign) or gain (positive sign).
Notably, the average participant experienced gains and losses in different social cognitive
variables; for example, the mean change in Self-Efficacy for Dieting was +2.45 (SD = 8.31), but
the mean change in Self-Efficacy for Exercise was -4.08 (SD = 14.79).
Weight change was negatively correlated with most of the potential mediators, including
the social cognitive change scores and the supportive accountability variables (Table 1). The
correlation between change in Self-Efficacy for Dieting and Weight Change, for example, was r
= -0.24 (p < 0.05); there was a small but significant negative association between change in Self-
Efficacy for Dieting and Weight Change. The Weight Change data are coded such that more
successful weight loss is more negative. The most desirable outcome, therefore, is a negative
25
number that is large in absolute value: as outcomes, -20 is better than -3; -3 is better than 0; 0 is
better than 3; and 3 is still better than 20. The Self-Efficacy data, meanwhile, are coded such that
positive growth in Self-Efficacy for Dieting is more positive (+20 is better than 0, which is better
than -20). Therefore, the correlation between Self-Efficacy for Dieting and Weight Change
implies that larger (more positive) change in Self-Efficacy for Dieting is associated with smaller
Weight Change. The same association exists between Weight Change and Facilitation (r = -0.36;
p < 0.05), Self-Efficacy for Exercise (r = -0.23; p < 0.05), and, to a still smaller degree,
Therapeutic Alliance (r = -0.17; p < 0.05) and Autonomous Motivation (r = -0.15; p < 0.05).
There was a small, positive association between change in Restraint and Weight Change (r =
0.19; p < 0.05).
The correlations among the single timepoint variables themselves—weight in pounds at 6
months and the social cognitive and supportive accountability variables at 3 months—are
provided in Table 2. The correlations between the 3-month social cognitive and supportive
accountability variables and 6-month weight are quite small; the largest were those for
Autonomous Motivation (r = -0.13; p < 0.05) and Facilitation (r = -0.13; p < 0.05). Still, these
correlations provide some confirmation as to the direction of the associations between these
social cognitive and supportive accountability constructs and weight.
As expected, most of potential mediators were positively correlated with each other
(Tables 1 and 2). The Self-Efficacy for Dieting and Self-Efficacy for Exercise change scores, for
example, had a correlation of r = 0.34 (p < 0.05); Facilitation and Therapeutic Alliance, of r =
0.51 (p < 0.05). The exceptions included Restraint and Controlled Motivation; Restraint, in
particular, was negatively correlated with each potential mediator other than Controlled
Motivation. The correlation between Restraint and the Self-Efficacy for Dieting change score,
26
for example, was r = -0.22 (p < 0.05). The Controlled Motivation change score was negatively
correlated with certain potential mediators and positively correlated with others; most of these
correlations were quite small or nonsignificant, with the exception of the correlation between
Controlled and Autonomous Motivation change scores (r = 0.30, p < 0.05). Recall that
Controlled and Autonomous Motivation are the two subscales of the Treatment Self-Regulation
Questionnaire (TSRQ; Williams et al., 1996).
Putative Mediators
As discussed previously, a social cognitive or supportive accountability variable was
defined as a putative mediator if it was found to predict Weight Change but was otherwise
untested, so far, as a mediator. Seven putative mediators were identified: Self-Efficacy for
Dieting, Self-Efficacy for Exercise, Autonomous Motivation, Restraint, Therapeutic Alliance,
Perceived Autonomy Support and Facilitation. These seven were found to be statistically
significant predictors of Weight Change at α = 0.05, controlling for the thirty-one independent
variables. These results are presented in Table 3. The remaining two potential mediators,
Controlled Motivation and Disinhibition, did not predict Weight Change at α = 0.05 (see Table
3). In these two cases, therefore, there was a disruption of the b path; the supposed mediator, M,
did not in fact predict the outcome, Weight Change, Y.
Research Question 1: Can the significant main effect of Buddy Training be understood in
terms of indirect effects through the social cognitive mediators?
Hypothesis 1(a): Buddy Training has a significant indirect effect on Weight Change
through Self-Efficacy. Self-Efficacy contains two putative mediators: Self-Efficacy for Dieting
and Self-Efficacy for Exercise.
27
Consistent with Hypothesis 1(a), the indirect effect of Buddy Training on Weight Change
through Self-Efficacy for Dieting was statistically significant: -0.284 (SE = 0.119), p = 0.017.
Buddy Training produced a significant increase in Self-Efficacy for Dieting: 0.961 (SE = 0.042),
p = 0.007; this represents the a path, or the effect of the Buddy Training on M. Self-Efficacy for
Dieting was strongly associated with weight loss -0.295 (SE = 0.058), p < 0.001; this represents
the b path, or the effect of M on Y. The effect of Buddy Training on Weight Change not through
this mediator, Self-Efficacy for Dieting, was non-significant: -0.644 (SE = 0.465), p=0.166; this
represents the c’ path.
Contrary to Hypothesis 1(a), the indirect effect of Buddy Training on Weight Change
through Self-Efficacy for Exercise was not significant (-0.037 (SE=0.111), p=0.738). Change in
Self-Efficacy for Exercise predicted Weight Change (the b path: -0.170 (SE = 0.032), p < 0.001)
but Buddy Training was not associated with growth in Self-Efficacy for Exercise (the a path:
0.227 (SE=0.656), p = 0.730). The effect of Buddy Training on Weight Change not through Self-
Efficacy for Exercise (the c’ path), however, was significant: -0.876 (SE = 0.471); p= 0.063.
Hypothesis 1(b): Buddy Training has a significant indirect effect on Weight Change
through Self-Regulation. Self-Regulation contains two putative mediators: Autonomous
Motivation and Restraint.
The indirect effect of Buddy Training on Weight Change through Autonomous
Motivation was significant: -0.130 (SE = 0.076), p=0.089. Autonomous Motivation was a strong
predictor of Weight Change (the b path): -1.425 (SE = 0.040); p < 0.001. Buddy Training, was
more moderately associated with growth in Autonomous Motivation (the a path): 0.091 (SE =
0.049); p=0.064. The effect of Buddy Training on Weight Change not through Autonomous
Motivation (the c’ path) was significant: -0.799 (SE = 0.469); p=0.088.
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The indirect effect of Buddy Training on Weight Change through Restraint was not
significant (0.039 (SE=0.092), p=0.669). Change in Restraint predicted Weight Change (the b
path: 0.813 (SE = 0.171), p < 0.001), but Buddy Training was not associated with change in
Restraint (the a path: -0.006 (SE=0.106), p = 0.957). The effect of Buddy Training on Weight
Change not through Restraint (the c’ path) was not statistically significant: 0.442 (SE = 0.469);
p= 0.345.
Hypothesis 1(c): Buddy Training has a significant indirect effect on Weight Change
through Facilitation.
Facilitation contains a single putative mediator. Consistent with Hypothesis 1(c), the
indirect effect of Buddy Training on Weight Change through this putative mediator, Facilitation,
was statistically significant: -0.377 (SE = 0.167), p=0.024. Buddy Training was associated with
greater Facilitation (the a path, or the effect of Buddy Training on M): 0.090 (SE = 0.039); p =
0.020. Facilitation was strongly associated with weight loss (the b path, or the effect of M on Y):
-4.174 (SE = 0.566); p < 0.001. The effect of Buddy Training on Weight Change not through this
mediator, Facilitation, (the c’ path) was non-significant: -0.552 (SE = 0.488), p=0.218.
29
Research Question 2: Can the ineffectiveness of the “High” versus “Low” level of Coaching
Calls be understood in terms of a disruption in a mediation pathway from Coaching Calls
to Supportive Accountability to Weight Change?
Hypothesis 2(a): Supportive Accountability is associated with Weight Change. This
hypothesis was supported by the preliminary analyses—both constructs within supportive
accountability were found to be significantly associated with Weight Change. Therapeutic
Alliance was strongly associated with Weight Change: -0.164 (SE=0.047), p < 0.001. Perceived
Autonomy Support was also associated with Weight Change: -0.085 (SE = 0.040), p = 0.032.
Because this hypothesis was supported, there is no evidence of a disruption of the b path from
Supportive Accountability to Weight Change.
Hypothesis 2(b): Coaching Calls has a significant main effect on Supportive
Accountability. Because both Therapeutic Alliance and Perceived Autonomy Support were
found to be associated with Weight Change, we tested hypothesis 2(b) by evaluating the a paths
of single mediator models with Therapeutic Alliance and Perceived Autonomy Support as
mediators.
The a path from Coaching Calls to Therapeutic Alliance was not significant: 0.503 (SE =
0.447), p=0.261; neither was the indirect effect ab from Coaching Calls to Weight Change
through Therapeutic Alliance: -0.085 (SE = 0.077), p = 0.272. This suggests a disruption of the a
path: the “High” versus “Low” level of Coaching Calls was not associated with greater
Therapeutic Alliance at 3 months.
The a path from Coaching Calls to Perceived Autonomy Support, however, was
significant: 0.979 (SE = 0.511), p=0.056. It appears, however, that this effect was not large
30
enough to produce a significant indirect effect; the indirect effect of Coaching Calls on Weight
Change through Perceived Autonomy Support was -0.082 (SE=0.0631), p=0.180. This also
suggests a disruption of the a path, in that the magnitude of the effect of Coaching Calls on
Perceived Autonomy Support was too small to produce an indirect effect.
Research Question 3: Can the important three-way interaction between Buddy Training,
PCP, and Text Messages be understood in terms of mediation?
The three-way interaction between Buddy Training, PCP, and Text Messages was
mediated by Restraint. For this interaction effect, the a path was significant: 0.209 (SE = 0.110),
p=0.057. The b path, the association between the Restraint change score and the weight change
score discussed previously, was significant: 0.813 (SE = 0.171), p<0.001. The indirect effect ab
of the Buddy Training by PCP by Text Messages interaction on weight change through Restraint
was also significant: 0.167 (SE=0.087); p=0.054. The c’ path, the Buddy Training by PCP by
Text Messages interaction effect on Weight Change not through Restraint, was also significant:
1.028 (SE=0.0474); p=0.03.
The meaning of some of these pathways changes when the independent variable in
question is an interaction effect. Notably, the a and c’ pathways are interaction effects. In this
case, the a path represents the three-way Buddy Training×PCP×Text Messages interaction effect
on Restraint—the same three-way interaction, but with Restraint as the outcome, not Weight
Change. The c’ path represents the three-way Buddy Training×PCP×Text Messages interaction
effect on Weight Change, controlling for Restraint. The indirect effect is the product of an
interaction effect (a) and the association between Restraint and Weight Change (b).
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The three-way Buddy Training×PCP×Text Messages interaction effect on Restraint, the a
path, can be interpreted through a visual depiction (Figure 6). First, the graph of the three-way
interaction on Restraint demonstrates the basic meaning of a three-way interaction: the two-way
interaction between Buddy Training and PCP differs as a function of whether Text Messages is
set to “Off” or “On.” Because Restraint is positively associated with Weight (Table 2), and
because the Restraint change score is positively associated with Weight Change (the b path;
Table 3), lower Restraint is better. The combination of factor levels that is associated with the
largest decrease in Restraint is: Buddy Training set to “ON,” PCP set to “ON,” and Text
Messages set to “OFF.”
The total three-way Buddy Training×PCP×Text Messages interaction effect on Weight
Change is depicted in Figure 7. In this case as well, the two-way interaction between Buddy
Training and PCP changes as a function of Text Messages. The changes are not precisely the
same as those seen in the interaction effect on Restraint. However, the largest decrease in weight
is still associated with the same three factor levels: Buddy Training set to “ON,” PCP set to
“ON,” and Text Messages set to “OFF.” It seems plausible that the three-way Buddy
Training×PCP×Text Messages interaction effect on Weight Change can be explained in part by
a) a similar interaction effect on Restraint and b) the association between Restraint and Weight
Change.
The complete set of mediation results appears in Appendix B.
Discussion
Mediation analysis of the results of a factorial optimization trial enables the evaluation of
hypotheses about the functioning of individual intervention components. In this thesis we
32
demonstrated one approach to mediation analysis using the results of the optimization trial of
Opt-IN (Pellegrini, 2015; Pellegrini, 2014), a weight loss intervention for overweight adults.
Research Question 1
As hypothesized, Buddy Training had important indirect effects on at least one construct
within each of the social cognitive mediators: Self-Efficacy for Dieting, Autonomous
Motivation, and Facilitation. Buddy Training did not have a significant effect on either of the
remaining two putative social cognitive mediators, Self-Efficacy for Exercise or Restraint.
These results suggest that the Buddy Training component did function as it was
anticipated to function, at least in part. The putative mediators affected by Buddy Training, Self-
Efficacy for Dieting, Autonomous Motivation, and Facilitation, were particularly strongly
associated with weight loss (Table 3). The fact that Buddy Training was successfully able to
target each of these helps to explain its effectiveness.
Interestingly, in two separate cases—Self-Efficacy for Dieting and Facilitation—the
direct effect of Buddy Training on Weight Change became non-significant when the mediator
was included in the model. This finding is counterintuitive. In the traditional interpretation, a
non-significant direct effect means that the effect of Buddy Training on Weight Change not
through the mediator (say, Self-Efficacy for Dieting) is negligible. However, we find,
meanwhile, that the effect of Buddy Training on Weight Change through Facilitation is
significant. And the direct effect in the model with Facilitation as the mediator suggests that the
effect of Buddy Training on Weight Change not through Facilitation is negligible (something we
have already contradicted in the Self-Efficacy for Dieting model). The correlation between
Facilitation and the Self-Efficacy for Dieting change score is positive yet modest: r = 0.21 (p <
0.05). The two are certainly not identical constructs, but they may be importantly interrelated.
33
Theory says little about the expected temporal pattern associated with Self-Efficacy and
Facilitation; perhaps growth in one precedes growth in the other. Subsequent analyses may better
flesh out such relationships. For Opt-IN, the conclusion remains: both Self-Efficacy for Dieting
and Facilitation seem to play important roles in explaining the effectiveness of Buddy Training.
Research Question 2
The fact that Hypothesis 2(a) was supported but Hypothesis 2(b) was not supported
suggests the following: Supportive Accountability is important in its association with Weight
Change, as expected, but the “High” versus “Low” level of the Coaching Calls component did
not increase Supportive Accountability enough to lead to weight loss.
Importantly, the fidelity of the Opt-IN coaches was measured very carefully (Spring et
al., In preparation). Calls were recorded and monitored closely. Therefore, these results cannot
be explained by the idea that coaches might not have stuck to the 12 versus 24 sessions or that
coaches varied in their session times.
To see whether a difference between the two levels of Coaching Calls emerged over the
full 6 months, we also checked whether Coaching Calls had a significant main effect on either of
the Supportive Accountability variables at 6 months; in each case, it did not. The time point at
which Supportive Accountability was measured also cannot provide an alternative explanation
for the results.
Research Question 3
The three-way Buddy Training by PCP by Text Messages interaction effect on Weight
Change suggests that the two-way interaction between Buddy Training and PCP differs as a
function of whether Text Messages is set to “On” or “Off.” In particular, Weight Change is
34
noticeably more negative when both Buddy Training and PCP are set to “On” and Text
Messages is set to “Off.” There could be a number of possible explanations for this finding. For
example, each of the involved components employs some type of exterior social support (support
from a buddy, from a PCP, and from text message reminders); it could be that a certain
saturation point is reached. Perhaps when all three components are set to “On” participants feel
more burdened then supported.
In the described analyses, the three-way interaction between Buddy Training, PCP, and
Text Messages was found to have an indirect effect on Weight Change through Restraint. As
discussed previously, Restraint is a subscale of the Three Factor Eating Questionnaire (Stunkard
& Messick, 1985). It can be interpreted as a tendency toward overly restrictive dieting
behaviors—for example, the setting of rigid rules that, once broken, tend to be violated further
(in what is called “counter-regulation”). Restraint has been found, in this and in previous work
(Stunkard & Messick, 1985), to be associated with less weight loss or even weight gain. For Opt-
IN’s participants the desired change in Restraint over 3 months, therefore, is change in the more
negative direction.
For the three-way interaction between Buddy Training, PCP, and Text Messages, the
indirect effect meant that there was a similar interaction effect along the a path of the single-
mediator model. Interestingly, this interaction effect on Restraint was notably similar to the total
interaction effect on Weight Change. In this case as well, the most desirable outcome—here,
more negative Restraint change—was associated with the following combination of factor levels:
Buddy Training set to “On,” PCP set to “On,” and Text Messages set to “Off.” The three-way
interaction effect observed in the primary outcomes (Spring et al., In Preparation) may be due,
in part, to a) the similar interaction effect on Restraint and b) the association between Restraint
35
and Weight Change. More research is necessary to further understand the range of possibilities
for the mediation of interaction effects.
Implications for Opt-IN
Buddy Training. One of the reasons that the singular effectiveness of Buddy Training
was surprising was the fact that only the training of buddies was manipulated in the Buddy
Training factor. All participants in Opt-IN nominated buddies from their existing social
networks, but training was offered only for the buddies of participants in a Buddy Training
condition. This training consisted of phone calls and webinars (Pellegrini et al., 2014), and
buddies participated to differing degrees. Not all buddies completed all of the training. Still, the
training buddies did receive was enough to produce important main effects on social cognitive
mediators of weight change: Self-Efficacy for Dieting, Autonomous Motivation, and Facilitation.
One possible conclusion, therefore, is that Buddy Training as-delivered was successful enough.
Another possible conclusion is that Buddy Training might more effectively target the
social cognitive mediators if it was successfully delivered at the full dose; a subsequent
optimization trial might test a method of supporting buddies in completing the full training. .
Such an optimization trial could test the Buddy Training component with fidelity components
intended to yield greater buddy participation.
Importantly, however, though these mediation analyses can help to explain why Buddy
Training was effective, the Buddy Training component itself has not been optimized. This
optimization trial does not provide direct evidence that this version of Buddy Training plus
something intended to help Buddy Training better target Self-Efficacy for Exercise would be
more effective. However, given the singular effectiveness of Buddy Training, an optimization
36
trial aimed at improving Buddy Training may be worthwhile. This sort of trial would require
identifying the components that exist within the current version of Buddy Training, adding or
changing components as desired, and then carrying out a new experiment.
Coaching Calls. The finding that 12 sessions performed just as well as 24 stands in
marked contrast to the consensus in public health that higher doses of coaching are better
(Wadden et al., 2005; Sherwood et al., 2009). Continued exploration of this finding will be
useful. As with Buddy Training, one possible response to the findings about the Coaching Calls
factor is to carry out the version of Coaching Calls that this trial identified as best: a version with
12 sessions. The higher dose of Coaching Calls did not add enough to the either of the
Supportive Accountability variables, or to any of the other putative mediators. One possible
conclusion is: 12 sessions is enough.
Another possible response is to try adjusting the Coaching Calls component such that the
link with Supportive Accountability is strengthened. The link between Coaching Calls and
Perceived Autonomy Support, though not enough to yield a significant indirect effect on weight
loss, may mean that the Coaching Calls component could be strengthened such that the effect of
Coaching Calls was greater. However, comparison of the “High” versus “Low” dose of
Coaching Calls shows the potential for negative effects on other putative mediators, like Self-
Efficacy for Exercise (see Table 4). This suggests that the task of adjusting the Coaching Calls
component in the direction of more positive effects is not straightforward. If changes to the
Coaching Calls component were made, the adjusted component would have to be retested in
another optimization trial.
Because every participant in this trial of Opt-IN received at least 12 coaching calls, this
particular optimization trial is not capable of generating evidence about whether having any
37
coaching calls (versus not having coaching calls) contributed to Supportive Accountability. If
this question is of interest in the continual optimization of Opt-IN, a subsequent optimization
trial could also include a factor in which the presence versus absence of Coaching Calls is
manipulated.
One possibility is that a higher dose is not better uniformly, for all types of coaching.
Time on task may be more important for versions of coaching that target certain mediators (like
knowledge or practice) versus other mediators (like supportive accountability). This is an
empirical question; mediation analysis of further optimization trials of interventions that contain
coaching could shed further light on this.
The interaction between Buddy Training, PCP, and Text Messages. The three-way
interaction effects on both Restraint and Weight Change raise the important possibility that
participants may be burdened (or may otherwise experience less success) when too many
components are set to “On.” This provides another important reminder that more is not always
better. When a given component does not function as intended—when it does not target its
intended mediators as expected—the appropriate response is not necessarily to add to the
component or to make it bigger, more complex, or more time-consuming. Still, it is curious that
the combination of Buddy Training and PCP seemed to contribute to participants’ success, both
in terms of Restraint and Weight Change, despite the fact that PCP had no significant main effect
on either Restraint or Weight Change. Subsequent studies should try to replicate this finding.
Implications for Future Analyses of Optimization Trials
A variety of important questions can be addressed through mediation analysis of the
results of a factorial optimization trial, and a variety of approaches may be taken. The approach
38
we have used here—that of sequentially evaluating single-mediator models one-by-one—is a
relatively simple one not without its detractors (VanderWeele & Vansteelandt, 2014). One
objection sometimes raised is the fact that mediators may affect one another. In Opt-IN, most of
the putative mediators are not highly correlated with each other. More importantly, the purpose
of the exploratory mediation analysis of the results of Opt-IN was to perform a check of the
hypothesis that motivated the creation of a given intervention component. Buddy Training, as
has been discussed, was intended to target three categories of mediators: Self-Efficacy, Self-
Regulation, and Facilitation. The question of interest was not: how does Buddy Training
influence each of these, how do these influence each other, and what proportion of Buddy
Training is mediated by each? The question, instead, was: how much validity is there to the
hypothesis that Buddy Training targeted each of these mediators? Various other questions may
be worth considering; the particular sort of question we address in this thesis, we argue, may be
approached using single-mediator structural equation models as a first step.
Another valid approach to the present research questions would have been to use
autoregressive models instead of change score models—that is, to use 3-month and 6-month
measures of the mediator and weight, respectively, controlling for baseline measures. We chose
to use change scores for the mediators (when applicable) and for the outcome so that the results
would be interpretable in terms of change in its simplest meaning. For example, this enabled us
to conclude that Buddy Training produced a change in Self-Efficacy for Dieting, on average.
Another alternative would have been to pare down the number of independent variables
included in the models. For example, this might mean considering a mediation model with only
Buddy Training, Self-Efficacy for Dieting, and Weight Change. However, we chose to include
39
all thirty-one variables in each model for the purpose of evaluating the mediation of multiple
effects, including important interaction effects.
Further work is needed to investigate the meaning of mediated interaction effects in
factorial optimization trials. In this case, the same combination of factor levels that yielded more
desirable Weight Change yielded more desirable change in the mediator, Restraint. This may or
may not be true for every significant a path of a mediated interaction effect. Simulations carried
out in this area may shed light on this and other important questions, like questions concerning
the stability of higher-order interaction effects.
Implications for the Design of Future Optimization Trials
Mediation is a process that unfolds over time (Collins, Graham, & Flaherty, 2010).
Mediation analysis, therefore, makes a number of temporal assumptions. One of these is that the
change that occurs in the mediator precedes the change that occurs in the outcome. Another is
that the measurement of both the mediator(s) and the outcome occurred at the appropriate time
longitudinally, or in a way that was informed by the rate of change in a given variable (e.g., self-
efficacy). A third assumption is that the appropriate number of measurements were taken to
accurately capture the change process. If the anticipated behavior change process is slower,
measurement that occurs less frequently or with fewer measurement occasions may be sufficient;
if the behavior change process is faster, infrequent or inadequate measurement may fail to
capture the change process (Collins et al., 2010).
If mediation analysis of the results of factorial optimization trials is to be maximally
informative, the temporal pattern associated with (or expected to be associated with) the
anticipated effects of each intervention component should be considered when the study is being
40
designed. Just as each mediator should be measured with tools (scales, etc.) that are capable of
identifying and quantifying the hypothesized change, each mediator should also be measured on
enough occasions and with the right timing to accurately capture that change. Successfully
designing an experiment around a given theoretical model of change represents an important
challenge in all longitudinal research (Collins, 2006). However, in a factorial optimization trial
the presence of multiple intervention components, and therefore perhaps multiple theoretical
models of change, can yield important complexity. If two particular intervention components
operate on different timescales but their effects on their respective hypothesized mediators are
measured in the same way, with the same timing, the results may be misleading, for example
such that change is identified in one mediator but missed in the other.
Limitations
On the assessment of temporal processes. There is little consensus as to when or how
often weight should be measured, though it is well understood that weight varies day-to-day—
and even, that the degree of variability in a person’s weight during a weight loss intervention
may be associated with their long-term weight outcome (Feig & Lowe, 2017). Social cognitive
constructs like self-efficacy and self-regulation have also been found to vary, for example as a
function of feedback (Bernacki, Nokes-Malach, & Aleven, 2014). However, in the Opt-IN trial
there were, at most, three measurement occasions (and for some of the putative mediators, just
two). Measuring either weight or the putative mediators on three (or two) occasions is not
frequent enough to capture variability or any sort of non-linear trajectory. The change processes
that we observe in the results of Opt-IN are smoothed, probably considerably.
In the present mediation analyses, change in the mediator is modeled based on the
baseline to 3-month time period. Because the final weight outcome was assessed at 6 months, the
41
3-month time point represents the sole midpoint measurement; we used these 3-month measures
to ensure temporal precedence. We acknowledge that participants’ weights at the 6-month
timepoint could be shaped by changes that occur in the mediators between 3 and 6 months; these
particular analyses cannot speak to that. Instead, these results can be interpreted only in terms of
early change in the mediator, for example: Buddy Training has an effect on changes in Self-
Efficacy for Dieting over the first 3 months of the trial, and these changes are associated with
weight loss. A conclusion like this one is appropriate regardless of the change that may occur in
the mediators from 3 to 6 months.
On confounders of the mediator-outcome relationship. Randomization ensures that
there is no confounding in the relationship between the intervention (X) and the mediator (M) or
between the intervention (X) and the outcome (Y), but it cannot ensure that there are no
confounders in the relationship between M and Y. If there are important confounders of the
mediator-outcome relationship that have not been accounted for, the estimates of indirect and
direct effects may be biased (Valeri & VanderWeele, 2013). This is a limitation of traditional
mediation analysis generally, but it is particularly important if indirect and direct effects are
assumed to explain the entirety of the relationship between the intervention and the outcome. We
do not make this assumption in these analyses.
On the limits of aggregate-level conclusions. It is likely that there is considerable
individual variation in the growth trajectories associated with weight change, supportive
accountability, and the social cognitive constructs. The findings discussed here do not apply to
every individual in the Opt-IN trial—for example, not every person in a Buddy Training
condition who lost weight first experienced growth in Self-Efficacy for Dieting. This is a widely
acknowledged limitation of analyses performed at an aggregate level. Though the results of these
42
mediation analyses can provide important insights into the overall functioning of Opt-IN’s
intervention components, they should not be used to make individual-level predictions.
Future Directions
There is a growing body of work behind the use of a potential outcomes framework in
mediation analysis, particularly given the possibility of confounding in the mediator-outcome
relationship (Imai, Jo, & Stuart, 2011). The use of causal inference in mediation analysis of the
results of factorial optimization trials represents one important direction for future research.
As noted previously, another area for future work involves the continued exploration of
the meaning of mediated interaction effects. The potential to estimate interaction effects is one of
the primary advantages of the factorial experiment (Montgomery, 2013); in MOST, the
estimation of interaction effects among intervention components can contribute important
information not just about a single intervention, but also about the incremental improvement of
behavior change interventions more widely. Accomplishing a better understanding about the
mediation of interaction effects in factorial optimization trials will help intervention scientists
using MOST to derive more valuable information from the results of their optimization trials—
and perhaps, to construct a priori hypotheses about the mediation of interaction effects during the
Preparation Phase.
43
Figures and Tables
44
Condition Coaching Calls PCP Text Messages Meal Buddy Training
1 Low On Off Off Off
2 Low On Off On On
3 Low On On Off On
4 Low On On On Off
5 Low Off Off Off On
6 Low Off Off On Off
7 Low Off On Off Off
8 Low Off On On On
9 High On Off Off Off
10 High On Off On On
11 High On On Off On
12 High On On On Off
13 High Off Off Off On
14 High Off Off On Off
15 High Off On Off Off
16 High Off On On On
17 Low On Off Off Off
18 Low On Off On On
19 Low On On Off On
20 Low On On On Off
21 Low Off Off Off On
22 Low Off Off On Off
23 Low Off On Off Off
24 Low Off On On On
25 High On Off Off Off
26 High On Off On On
27 High On On Off On
28 High On On On Off
29 High Off Off Off On
30 High Off Off On Off
31 High Off On Off Off
32 High Off On On On
Figure 4. Opt-IN experimental conditions (adapted from Pellegrini et al., 2015).
Note. Shading emphasizes the contrast between the two levels of Coaching Calls, “Low” and “High.”
45
Figure 5. Example single mediator model, Opt-IN.
Note. Shading emphasizes the mediation of Buddy Training.
46
47
Table 1
Means, standard deviations, and correlations: Change Scores
Variable M SD 1 2 3 4 5 6 7 8 9
1. Weight
(Δ) -11.07 10.56
2. SE: Diet
(Δ) 2.45 8.31 -.24*
3. SE: Exer
(Δ) -4.08 14.79 -.24* .34*
4. Auton.
(Δ) 0.30 1.12 -.15* .11* .22*
5. Control.
(Δ) 0.45 1.17 .06 -.16* .01 .30*
6. Restraint
(Δ) -1.66 2.53 .20* -.22* -.09* -.16* .06
7. Disinhib
(Δ) 0.37 2.14 -.09 .36* .19* .05 -.11* -.09*
8. Ther.
Alliance 70.68 10.53 -.17* .18* .17* .28* -.01 -.11* .11*
9. Auton
Support. 71.86 12.09 -.11* .08 .15* .28* .01 -.08 .05 .74*
10. Facil. 3.84 0.90 -.36* .21* .29* .41* .11* -.17* .15* .51* .44*
Note. M and SD are used to represent mean and standard deviation, respectively. * indicates p < .05. SE: Diet = Self-
Efficacy for Dieting change score. SE: Exer. = Self-Efficacy for Exercise. Auton = Autonomous Motivation.
Control. = Controlled Motivation. Disinhib = Disinhibition. Ther Alliance = Therapeutic Alliance. Auton Support =
Perceived Autonomy Support. Facil = Facilitation. (Δ) indicates change score: 3-month score – baseline score.
48
Table 2
Means, standard deviations, and correlations: Individual Timepoints (month in parentheses)
Note. M and SD are used to represent mean and standard deviation, respectively. * indicates p < .05. SE: Diet =
Self-Efficacy for Dieting. SE: Exer. = Self-Efficacy for Exercise. Auton = Autonomous Motivation. Control. =
Controlled Motivation. Disinhib = Disinhibition. Ther Alliance = Therapeutic Alliance. Auton Support = Perceived
Autonomy Support. Facil = Facilitation. (Month) of measurement shown in parentheses.
Variable M SD 1 2 3 4 5 6 7 8 9
1. Weight
(6) 186.30 30.53
2. SE: Diet
(3) 35.94 8.53 -.03
3. SE: Exer
(3) 58.41 15.04 -.10* .49*
4. Restraint
(3) 8.52 2.05 .14* -.25* -.15*
5. Disinhib
(3) 7.19 2.57 .02 .60* .30* -.08
6. Auton
(3) 6.11 0.91 -.13* .21* .26* -.13* .14*
7. Control
(3) 3.29 1.16 .10* -.16* -.09* .06 -.23* .18*
8. Ther
Alliance (3) 70.68 10.53 .03 .28* .25* -.14* .19* .48* .01
9. Auton
Support (3) 71.86 12.09 .03 .18* .25* -.10* .10* .41* .01 .74*
10. Facil
(3) 3.84 0.90 -.13* .39* .37* -.27* .27* .55* .04 .51* .44*
49
Table 3
b weights for social cognitive and supportive accountability variables as predictors of weight change
(6-month weight – baseline weight)
Variable b SE b p
Self-Efficacy for Dieting (Δ) -0.295 0.058 <0.001
Self-Efficacy for Exercise (Δ) -0.170 0.032 <0.001
Autonomous Motivation (Δ) -1.425 0.040 0.001
Controlled Motivation (Δ) 0.503 0.430 0.242
Restraint (Δ) 0.813 0.171 <0.001
Disinhibition (Δ) -0.465 0.248 0.061
Therapeutic Alliance -0.164 0.047 0.001
Perceived Autonomy Support -0.085 0.040 0.032
Facilitation -4.174 0.566 <0.001
Note. (Δ) indicates change score: 3-month score – baseline score.
50
Table 4
b weights for the effect of Coaching Calls on the putative mediators
Variable b SE b p
Self-Efficacy for Dieting (Δ) -0.416 0.356 0.243
Self-Efficacy for Exercise (Δ) -1.147 0.638 0.072
Autonomous Motivation (Δ) 0.002 0.049 0.970
Restraint (Δ) 0.126 0.107 0.238
Therapeutic Alliance 0.518 0.447 0.247
Perceived Autonomy Support 0.979 0.511 0.056
Facilitation 0.050 0.039 0.194
Note. * indicates p < 0.05. (Δ) indicates change score: 3-month score – baseline score.
51
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Appendix A. Full Table of Effect Codes, Opt-IN
56
Condition Coaching PCP Text Meal Buddy CxP CxT CxM CxB PxT PxM PxT TxM TxB MxB
1 -1 1 -1 -1 -1 -1 1 1 1 -1 -1 -1 1 1 1
2 -1 1 -1 1 1 -1 1 -1 -1 -1 1 1 -1 -1 1
3 -1 1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1
4 -1 1 1 1 -1 -1 -1 -1 1 1 1 -1 1 -1 -1
5 -1 -1 -1 -1 1 1 1 1 -1 1 1 -1 1 -1 -1
6 -1 -1 -1 1 -1 1 1 -1 1 1 -1 1 -1 1 -1
7 -1 -1 1 -1 -1 1 -1 1 1 -1 1 1 -1 -1 1
8 1 -1 1 1 1 -1 1 1 1 -1 -1 -1 1 1 1
9 1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 1 1 1
10 1 1 -1 1 1 1 -1 1 1 -1 1 1 -1 -1 1
11 1 1 1 -1 1 1 1 -1 1 1 -1 1 -1 1 -1
12 1 1 1 1 -1 1 1 1 -1 1 1 -1 1 -1 -1
13 1 -1 -1 -1 1 -1 -1 -1 1 1 1 -1 1 -1 -1
14 1 -1 -1 1 -1 -1 -1 1 -1 1 -1 1 -1 1 -1
15 1 -1 1 -1 -1 -1 1 -1 -1 -1 1 1 -1 -1 1
16 -1 -1 1 1 1 1 -1 -1 -1 -1 -1 -1 1 1 1
17 -1 1 -1 -1 -1 -1 1 1 1 -1 -1 -1 1 1 1
18 -1 1 -1 1 1 -1 1 -1 -1 -1 1 1 -1 -1 1
19 -1 1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1
20 -1 1 1 1 -1 -1 -1 -1 1 1 1 -1 1 -1 -1
21 -1 -1 -1 -1 1 1 1 1 -1 1 1 -1 1 -1 -1
22 -1 -1 -1 1 -1 1 1 -1 1 1 -1 1 -1 1 -1
23 -1 -1 1 -1 -1 1 -1 1 1 -1 1 1 -1 -1 1
24 1 -1 1 1 1 -1 1 1 1 -1 -1 -1 1 1 1
25 1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 1 1 1
26 1 1 -1 1 1 1 -1 1 1 -1 1 1 -1 -1 1
27 1 1 1 -1 1 1 1 -1 1 1 -1 1 -1 1 -1
28 1 1 1 1 -1 1 1 1 -1 1 1 -1 1 -1 -1
29 1 -1 -1 -1 1 -1 -1 -1 1 1 1 -1 1 -1 -1
30 1 -1 -1 1 -1 -1 -1 1 -1 1 -1 1 -1 1 -1
31 1 -1 1 -1 -1 -1 1 -1 -1 -1 1 1 -1 -1 1
32 1 -1 1 1 1 -1 1 1 1 -1 -1 -1 1 1 1
57
CPT CPM CPB CTM CTB CMB PTM PTB PMB TMB CPTM CPTB CTMB CPMB PTMB CPTMB
1 1 1 -1 -1 -1 1 1 1 -1 -1 -1 1 -1 -1 1
1 -1 -1 1 1 -1 -1 -1 1 -1 1 -1 1 -1 -1 1
-1 1 -1 1 -1 1 -1 1 -1 -1 1 1 1 1 -1 1
-1 -1 1 -1 1 1 1 -1 -1 -1 -1 1 1 1 -1 1
-1 -1 1 -1 1 1 -1 1 1 1 1 -1 -1 -1 -1 1
-1 1 -1 1 -1 1 1 -1 1 1 -1 -1 -1 -1 -1 1
1 -1 -1 1 1 -1 1 1 -1 1 -1 1 -1 1 -1 1
-1 -1 -1 1 1 1 -1 -1 -1 1 -1 -1 1 -1 -1 -1
-1 -1 -1 1 1 1 1 1 1 -1 1 1 -1 1 -1 -1
-1 1 1 -1 -1 1 -1 -1 1 -1 -1 1 -1 1 -1 -1
1 -1 1 -1 1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1
1 1 -1 1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1
1 1 -1 1 -1 -1 -1 1 1 1 -1 1 1 1 -1 -1
1 -1 1 -1 1 -1 1 -1 1 1 1 1 1 1 -1 -1
-1 1 1 -1 -1 1 1 1 -1 1 1 -1 1 -1 -1 -1
1 1 1 -1 -1 -1 -1 -1 -1 1 1 1 -1 1 -1 1
1 1 1 -1 -1 -1 1 1 1 -1 -1 -1 1 -1 -1 1
1 -1 -1 1 1 -1 -1 -1 1 -1 1 -1 1 -1 -1 1
-1 1 -1 1 -1 1 -1 1 -1 -1 1 1 1 1 -1 1
-1 -1 1 -1 1 1 1 -1 -1 -1 -1 1 1 1 -1 1
-1 -1 1 -1 1 1 -1 1 1 1 1 -1 -1 -1 -1 1
-1 1 -1 1 -1 1 1 -1 1 1 -1 -1 -1 -1 -1 1
1 -1 -1 1 1 -1 1 1 -1 1 -1 1 -1 1 -1 1
-1 -1 -1 1 1 1 -1 -1 -1 1 -1 -1 1 -1 -1 -1
-1 -1 -1 1 1 1 1 1 1 -1 1 1 -1 1 -1 -1
-1 1 1 -1 -1 1 -1 -1 1 -1 -1 1 -1 1 -1 -1
1 -1 1 -1 1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1
1 1 -1 1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1
1 1 -1 1 -1 -1 -1 1 1 1 -1 1 1 1 -1 -1
1 -1 1 -1 1 -1 1 -1 1 1 1 1 1 1 -1 -1
-1 1 1 -1 -1 1 1 1 -1 1 1 -1 1 -1 -1 -1
58
-1 -1 -1 1 1 1 -1 -1 -1 1 -1 -1 1 -1 -1 -1
59
Appendix B. All Mediation Analysis Results.
C = Coaching Calls
T = Text Messages
B = Buddy Training
M = Meal
P = PCP
Mediator Variable Estimate
Type Estimate S.E. Est./S.E. p-
value
Facilitation COACHING CALLS
Total 0.407 0.473 0.861 0.389
Facilitation COACHING CALLS
Indirect -0.21 0.165 -1.274 0.203
Facilitation COACHING CALLS
Direct 0.618 0.448 1.378 0.168
Facilitation TEXT MESSAGES
Total 0.397 0.47 0.846 0.398
Facilitation TEXT MESSAGES
Indirect -0.246 0.165 -1.495 0.135
Facilitation TEXT MESSAGES
Direct 0.644 0.444 1.45 0.147
Facilitation BUDDY TRAINING
Total -0.929 0.472 -1.966 0.049
Facilitation BUDDY TRAINING
Indirect -0.377 0.167 -2.256 0.024
Facilitation BUDDY TRAINING
Direct -0.552 0.448 -1.232 0.218
Facilitation MEAL Total 0.015 0.474 0.031 0.975
Facilitation MEAL Indirect 0.022 0.162 0.138 0.89
Facilitation MEAL Direct -0.008 0.446 -0.017 0.986
Facilitation PCP Total 0.022 0.47 0.047 0.962
Facilitation PCP Indirect -0.146 0.161 -0.912 0.362
Facilitation PCP Direct 0.169 0.442 0.382 0.703
Facilitation CXT Total -0.447 0.472 -0.948 0.343
Facilitation CXT Indirect -0.007 0.162 -0.041 0.967
Facilitation CXT Direct -0.441 0.444 -0.992 0.321
Facilitation CXB Total -0.34 0.471 -0.722 0.47
Facilitation CXB Indirect -0.009 0.162 -0.053 0.957
60
Facilitation CXB Direct -0.332 0.444 -0.747 0.455
Facilitation CXM Total -0.236 0.474 -0.498 0.618
Facilitation CXM Indirect -0.1 0.163 -0.614 0.539
Facilitation CXM Direct -0.136 0.449 -0.303 0.762
Facilitation CXP Total -0.268 0.472 -0.568 0.57
Facilitation CXP Indirect -0.049 0.162 -0.305 0.761
Facilitation CXP Direct -0.219 0.444 -0.493 0.622
Facilitation TXB Total 0.471 0.474 0.994 0.32
Facilitation TXB Indirect 0.01 0.162 0.061 0.951
Facilitation TXB Direct 0.461 0.447 1.033 0.302
Facilitation TXM Total 0.375 0.471 0.796 0.426
Facilitation TXM Indirect 0.142 0.162 0.874 0.382
Facilitation TXM Direct 0.234 0.444 0.526 0.599
Facilitation TXP Total -0.267 0.473 -0.565 0.572
Facilitation TXP Indirect -0.194 0.165 -1.178 0.239
Facilitation TXP Direct -0.073 0.45 -0.162 0.872
Facilitation BXM Total -0.006 0.472 -0.013 0.99
Facilitation BXM Indirect -0.055 0.163 -0.338 0.735
Facilitation BXM Direct 0.049 0.444 0.111 0.912
Facilitation BXP Total -0.675 0.474 -1.426 0.154
Facilitation BXP Indirect -0.02 0.162 -0.126 0.899
Facilitation BXP Direct -0.655 0.446 -1.468 0.142
Facilitation MXP Total -0.032 0.474 -0.068 0.946
Facilitation MXP Indirect -0.056 0.162 -0.349 0.727
Facilitation MXP Direct 0.024 0.446 0.055 0.956
Facilitation CXTXB Total -0.028 0.472 -0.059 0.953
Facilitation CXTXB Indirect 0.03 0.163 0.184 0.854
Facilitation CXTXB Direct -0.058 0.445 -0.13 0.896
Facilitation CXTXM Total -0.101 0.47 -0.215 0.83
Facilitation CXTXM Indirect -0.142 0.162 -0.88 0.379
Facilitation CXTXM Direct 0.041 0.44 0.094 0.926
Facilitation CXTXP Total -0.24 0.475 -0.505 0.613
Facilitation CXTXP Indirect 0.06 0.162 0.369 0.712
Facilitation CXTXP Direct -0.3 0.447 -0.671 0.502
Facilitation CXBXM Total 0.04 0.475 0.084 0.933
61
Facilitation CXBXM Indirect 0.074 0.163 0.453 0.65
Facilitation CXBXM Direct -0.034 0.446 -0.076 0.94
Facilitation CXBXP Total 0.303 0.475 0.639 0.522
Facilitation CXBXP Indirect -0.069 0.162 -0.428 0.669
Facilitation CXBXP Direct 0.373 0.447 0.834 0.405
Facilitation CXMXP Total 0.765 0.474 1.613 0.107
Facilitation CXMXP Indirect 0.068 0.164 0.416 0.677
Facilitation CXMXP Direct 0.697 0.447 1.559 0.119
Facilitation TXBXM Total -0.333 0.474 -0.702 0.482
Facilitation TXBXM Indirect 0.076 0.162 0.469 0.639
Facilitation TXBXM Direct -0.409 0.445 -0.919 0.358
Facilitation TXBXP Total 1.052 0.473 2.222 0.026
Facilitation TXBXP Indirect 0.113 0.161 0.698 0.485
Facilitation TXBXP Direct 0.939 0.447 2.101 0.036
Facilitation TXMXP Total -0.214 0.471 -0.454 0.65
Facilitation TXMXP Indirect 0.018 0.163 0.109 0.913
Facilitation TXMXP Direct -0.231 0.443 -0.522 0.602
Facilitation BXMXP Total 0.276 0.472 0.585 0.558
Facilitation BXMXP Indirect 0.112 0.163 0.685 0.493
Facilitation BXMXP Direct 0.164 0.445 0.369 0.712
Facilitation CTBM Total -0.133 0.474 -0.282 0.778
Facilitation CTBM Indirect -0.222 0.164 -1.355 0.175
Facilitation CTBM Direct 0.089 0.446 0.2 0.842
Facilitation CTBP Total -1.199 0.473 -2.538 0.011
Facilitation CTBP Indirect -0.184 0.166 -1.112 0.266
Facilitation CTBP Direct -1.015 0.445 -2.278 0.023
Facilitation CTMP Total 0.32 0.473 0.676 0.499
Facilitation CTMP Indirect 0.148 0.162 0.913 0.361
Facilitation CTMP Direct 0.172 0.445 0.386 0.699
Facilitation CBMP Total -0.537 0.472 -1.138 0.255
Facilitation CBMP Indirect -0.19 0.166 -1.139 0.255
Facilitation CBMP Direct -0.348 0.444 -0.782 0.434
Facilitation TBMP Total 0.022 0.473 0.047 0.963
Facilitation TBMP Indirect 0.078 0.162 0.481 0.63
Facilitation TBMP Direct -0.056 0.445 -0.125 0.9
62
Facilitation CTBMP Total -0.315 0.47 -0.67 0.503
Facilitation CTBMP Indirect -0.269 0.167 -1.605 0.108
Facilitation CTBMP Direct -0.046 0.451 -0.103 0.918
SELF-EFFICACY, DIETING
COACHING CALLS
Total 0.407 0.473 0.86 0.39
SELF-EFFICACY, DIETING
COACHING CALLS
Indirect 0.123 0.108 1.137 0.255
SELF-EFFICACY, DIETING
COACHING CALLS
Direct 0.284 0.463 0.614 0.539
SELF-EFFICACY, DIETING
TEXT MESSAGES
Total 0.397 0.47 0.846 0.398
SELF-EFFICACY, DIETING
TEXT MESSAGES
Indirect 0.081 0.106 0.769 0.442
SELF-EFFICACY, DIETING
TEXT MESSAGES
Direct 0.316 0.458 0.69 0.49
SELF-EFFICACY, DIETING
BUDDY TRAINING
Total -0.929 0.472 -1.966 0.049
SELF-EFFICACY, DIETING
BUDDY TRAINING
Indirect -0.284 0.119 -2.392 0.017
SELF-EFFICACY, DIETING
BUDDY TRAINING
Direct -0.644 0.465 -1.384 0.166
SELF-EFFICACY, DIETING
MEAL Total 0.014 0.474 0.03 0.976
SELF-EFFICACY, DIETING
MEAL Indirect 0.027 0.106 0.255 0.798
SELF-EFFICACY, DIETING
MEAL Direct -0.013 0.463 -0.027 0.978
SELF-EFFICACY, DIETING
PCP Total 0.023 0.47 0.049 0.961
SELF-EFFICACY, DIETING
PCP Indirect -0.088 0.107 -0.819 0.413
SELF-EFFICACY, DIETING
PCP Direct 0.111 0.457 0.242 0.809
SELF-EFFICACY, DIETING
CXT Total -0.447 0.472 -0.948 0.343
SELF-EFFICACY, DIETING
CXT Indirect 0.04 0.106 0.381 0.703
SELF-EFFICACY, DIETING
CXT Direct -0.488 0.461 -1.057 0.291
63
SELF-EFFICACY, DIETING
CXB Total -0.34 0.471 -0.722 0.471
SELF-EFFICACY, DIETING
CXB Indirect -0.024 0.105 -0.224 0.822
SELF-EFFICACY, DIETING
CXB Direct -0.316 0.461 -0.687 0.492
SELF-EFFICACY, DIETING
CXM Total -0.236 0.474 -0.498 0.618
SELF-EFFICACY, DIETING
CXM Indirect -0.065 0.105 -0.619 0.536
SELF-EFFICACY, DIETING
CXM Direct -0.171 0.463 -0.369 0.712
SELF-EFFICACY, DIETING
CXP Total -0.268 0.472 -0.568 0.57
SELF-EFFICACY, DIETING
CXP Indirect 0.01 0.106 0.092 0.927
SELF-EFFICACY, DIETING
CXP Direct -0.278 0.46 -0.604 0.546
SELF-EFFICACY, DIETING
TXB Total 0.471 0.474 0.994 0.32
SELF-EFFICACY, DIETING
TXB Indirect 0.141 0.11 1.281 0.2
SELF-EFFICACY, DIETING
TXB Direct 0.331 0.465 0.71 0.478
SELF-EFFICACY, DIETING
TXM Total 0.375 0.471 0.796 0.426
SELF-EFFICACY, DIETING
TXM Indirect -0.038 0.106 -0.357 0.721
SELF-EFFICACY, DIETING
TXM Direct 0.413 0.46 0.897 0.369
SELF-EFFICACY, DIETING
TXP Total -0.267 0.473 -0.565 0.572
SELF-EFFICACY, DIETING
TXP Indirect 0.107 0.108 0.997 0.319
SELF-EFFICACY, DIETING
TXP Direct -0.374 0.461 -0.812 0.417
SELF-EFFICACY, DIETING
BXM Total -0.006 0.472 -0.013 0.99
64
SELF-EFFICACY, DIETING
BXM Indirect -0.134 0.107 -1.245 0.213
SELF-EFFICACY, DIETING
BXM Direct 0.128 0.461 0.277 0.782
SELF-EFFICACY, DIETING
BXP Total -0.675 0.474 -1.425 0.154
SELF-EFFICACY, DIETING
BXP Indirect -0.001 0.105 -0.013 0.99
SELF-EFFICACY, DIETING
BXP Direct -0.674 0.462 -1.457 0.145
SELF-EFFICACY, DIETING
MXP Total -0.032 0.474 -0.067 0.946
SELF-EFFICACY, DIETING
MXP Indirect 0.02 0.106 0.192 0.848
SELF-EFFICACY, DIETING
MXP Direct -0.052 0.463 -0.113 0.91
SELF-EFFICACY, DIETING
CXTXB Total -0.028 0.472 -0.06 0.953
SELF-EFFICACY, DIETING
CXTXB Indirect -0.083 0.107 -0.776 0.438
SELF-EFFICACY, DIETING
CXTXB Direct 0.055 0.46 0.119 0.905
SELF-EFFICACY, DIETING
CXTXM Total -0.101 0.47 -0.215 0.83
SELF-EFFICACY, DIETING
CXTXM Indirect 0 0.105 0.004 0.997
SELF-EFFICACY, DIETING
CXTXM Direct -0.101 0.46 -0.221 0.825
SELF-EFFICACY, DIETING
CXTXP Total -0.24 0.475 -0.505 0.613
SELF-EFFICACY, DIETING
CXTXP Indirect -0.067 0.106 -0.632 0.527
SELF-EFFICACY, DIETING
CXTXP Direct -0.173 0.465 -0.372 0.71
SELF-EFFICACY, DIETING
CXBXM Total 0.04 0.475 0.085 0.933
SELF-EFFICACY, DIETING
CXBXM Indirect -0.228 0.115 -1.984 0.047
65
SELF-EFFICACY, DIETING
CXBXM Direct 0.269 0.465 0.578 0.563
SELF-EFFICACY, DIETING
CXBXP Total 0.304 0.475 0.64 0.522
SELF-EFFICACY, DIETING
CXBXP Indirect -0.085 0.107 -0.798 0.425
SELF-EFFICACY, DIETING
CXBXP Direct 0.389 0.465 0.837 0.403
SELF-EFFICACY, DIETING
CXMXP Total 0.765 0.474 1.614 0.107
SELF-EFFICACY, DIETING
CXMXP Indirect -0.072 0.106 -0.677 0.498
SELF-EFFICACY, DIETING
CXMXP Direct 0.837 0.462 1.811 0.07
SELF-EFFICACY, DIETING
TXBXM Total -0.333 0.474 -0.702 0.483
SELF-EFFICACY, DIETING
TXBXM Indirect -0.046 0.106 -0.431 0.667
SELF-EFFICACY, DIETING
TXBXM Direct -0.287 0.463 -0.62 0.535
SELF-EFFICACY, DIETING
TXBXP Total 1.052 0.473 2.222 0.026
SELF-EFFICACY, DIETING
TXBXP Indirect 0.094 0.107 0.88 0.379
SELF-EFFICACY, DIETING
TXBXP Direct 0.957 0.464 2.064 0.039
SELF-EFFICACY, DIETING
TXMXP Total -0.214 0.471 -0.454 0.65
SELF-EFFICACY, DIETING
TXMXP Indirect -0.015 0.105 -0.139 0.89
SELF-EFFICACY, DIETING
TXMXP Direct -0.199 0.459 -0.433 0.665
SELF-EFFICACY, DIETING
BXMXP Total 0.276 0.472 0.585 0.558
SELF-EFFICACY, DIETING
BXMXP Indirect 0.047 0.107 0.441 0.659
SELF-EFFICACY, DIETING
BXMXP Direct 0.229 0.461 0.497 0.619
66
SELF-EFFICACY, DIETING
CTBM Total -0.133 0.474 -0.282 0.778
SELF-EFFICACY, DIETING
CTBM Indirect 0.053 0.106 0.496 0.62
SELF-EFFICACY, DIETING
CTBM Direct -0.186 0.463 -0.402 0.688
SELF-EFFICACY, DIETING
CTMP Total 0.32 0.473 0.676 0.499
SELF-EFFICACY, DIETING
CTMP Indirect -0.077 0.106 -0.723 0.47
SELF-EFFICACY, DIETING
CTMP Direct 0.397 0.461 0.86 0.39
SELF-EFFICACY, DIETING
CTBP Total -1.199 0.473 -2.538 0.011
SELF-EFFICACY, DIETING
CTBP Indirect -0.001 0.105 -0.01 0.992
SELF-EFFICACY, DIETING
CTBP Direct -1.198 0.461 -2.597 0.009
SELF-EFFICACY, DIETING
CBMP Total -0.538 0.472 -1.139 0.255
SELF-EFFICACY, DIETING
CBMP Indirect -0.035 0.106 -0.336 0.737
SELF-EFFICACY, DIETING
CBMP Direct -0.502 0.46 -1.091 0.275
SELF-EFFICACY, DIETING
TBMP Total 0.023 0.473 0.049 0.961
SELF-EFFICACY, DIETING
TBMP Indirect 0.141 0.108 1.31 0.19
SELF-EFFICACY, DIETING
TBMP Direct -0.118 0.465 -0.254 0.799
SELF-EFFICACY, DIETING
CTBMP Total -0.315 0.47 -0.67 0.503
SELF-EFFICACY, DIETING
CTBMP Indirect -0.016 0.106 -0.148 0.882
SELF-EFFICACY, DIETING
CTBMP Direct -0.299 0.46 -0.652 0.515
SELF-EFFICACY, EXERCISE
COACHING CALLS
Total 0.487 0.499 0.976 0.329
67
SELF-EFFICACY, EXERCISE
COACHING CALLS
Indirect 0.19 0.124 1.533 0.125
SELF-EFFICACY, EXERCISE
COACHING CALLS
Direct 0.297 0.485 0.612 0.54
SELF-EFFICACY, EXERCISE
TEXT MESSAGES
Total 0.493 0.513 0.961 0.336
SELF-EFFICACY, EXERCISE
TEXT MESSAGES
Indirect -0.023 0.119 -0.196 0.845
SELF-EFFICACY, EXERCISE
TEXT MESSAGES
Direct 0.516 0.498 1.036 0.3
SELF-EFFICACY, EXERCISE
BUDDY TRAINING
Total -0.913 0.485 -1.884 0.06
SELF-EFFICACY, EXERCISE
BUDDY TRAINING
Indirect -0.037 0.111 -0.335 0.738
SELF-EFFICACY, EXERCISE
BUDDY TRAINING
Direct -0.876 0.471 -1.86 0.063
SELF-EFFICACY, EXERCISE
MEAL Total -0.008 0.486 -0.017 0.986
SELF-EFFICACY, EXERCISE
MEAL Indirect 0.207 0.123 1.683 0.092
SELF-EFFICACY, EXERCISE
MEAL Direct -0.215 0.476 -0.451 0.652
SELF-EFFICACY, EXERCISE
PCP Total 0.046 0.495 0.092 0.927
SELF-EFFICACY, EXERCISE
PCP Indirect 0.013 0.11 0.119 0.905
SELF-EFFICACY, EXERCISE
PCP Direct 0.033 0.487 0.067 0.947
SELF-EFFICACY, EXERCISE
CXT Total -0.461 0.512 -0.9 0.368
SELF-EFFICACY, EXERCISE
CXT Indirect -0.078 0.111 -0.704 0.481
SELF-EFFICACY, EXERCISE
CXT Direct -0.383 0.499 -0.767 0.443
SELF-EFFICACY, EXERCISE
CXB Total -0.337 0.499 -0.676 0.499
SELF-EFFICACY, EXERCISE
CXB Indirect 0.093 0.109 0.849 0.396
68
SELF-EFFICACY, EXERCISE
CXB Direct -0.43 0.486 -0.884 0.376
SELF-EFFICACY, EXERCISE
CXM Total -0.323 0.502 -0.644 0.519
SELF-EFFICACY, EXERCISE
CXM Indirect -0.085 0.114 -0.742 0.458
SELF-EFFICACY, EXERCISE
CXM Direct -0.239 0.486 -0.491 0.624
SELF-EFFICACY, EXERCISE
CXP Total -0.334 0.499 -0.671 0.502
SELF-EFFICACY, EXERCISE
CXP Indirect 0.086 0.117 0.732 0.464
SELF-EFFICACY, EXERCISE
CXP Direct -0.42 0.486 -0.864 0.388
SELF-EFFICACY, EXERCISE
TXB Total 0.543 0.492 1.104 0.27
SELF-EFFICACY, EXERCISE
TXB Indirect -0.026 0.114 -0.233 0.816
SELF-EFFICACY, EXERCISE
TXB Direct 0.57 0.474 1.202 0.23
SELF-EFFICACY, EXERCISE
TXM Total 0.32 0.487 0.657 0.511
SELF-EFFICACY, EXERCISE
TXM Indirect 0.006 0.111 0.056 0.955
SELF-EFFICACY, EXERCISE
TXM Direct 0.314 0.476 0.659 0.51
SELF-EFFICACY, EXERCISE
TXP Total -0.294 0.498 -0.59 0.555
SELF-EFFICACY, EXERCISE
TXP Indirect -0.116 0.118 -0.984 0.325
SELF-EFFICACY, EXERCISE
TXP Direct -0.178 0.486 -0.367 0.714
SELF-EFFICACY, EXERCISE
BXM Total 0.03 0.509 0.058 0.953
SELF-EFFICACY, EXERCISE
BXM Indirect -0.106 0.115 -0.919 0.358
SELF-EFFICACY, EXERCISE
BXM Direct 0.135 0.495 0.274 0.784
69
SELF-EFFICACY, EXERCISE
BXP Total -0.705 0.51 -1.382 0.167
SELF-EFFICACY, EXERCISE
BXP Indirect 0.049 0.111 0.447 0.655
SELF-EFFICACY, EXERCISE
BXP Direct -0.755 0.495 -1.524 0.128
SELF-EFFICACY, EXERCISE
MXP Total -0.034 0.498 -0.068 0.946
SELF-EFFICACY, EXERCISE
MXP Indirect -0.126 0.113 -1.111 0.267
SELF-EFFICACY, EXERCISE
MXP Direct 0.092 0.486 0.189 0.85
SELF-EFFICACY, EXERCISE
CXTXB Total -0.114 0.486 -0.236 0.814
SELF-EFFICACY, EXERCISE
CXTXB Indirect 0.023 0.114 0.202 0.84
SELF-EFFICACY, EXERCISE
CXTXB Direct -0.138 0.475 -0.289 0.772
SELF-EFFICACY, EXERCISE
CXTXM Total -0.134 0.511 -0.262 0.793
SELF-EFFICACY, EXERCISE
CXTXM Indirect -0.175 0.115 -1.522 0.128
SELF-EFFICACY, EXERCISE
CXTXM Direct 0.041 0.497 0.083 0.934
SELF-EFFICACY, EXERCISE
CXTXP Total -0.275 0.49 -0.562 0.574
SELF-EFFICACY, EXERCISE
CXTXP Indirect -0.177 0.121 -1.464 0.143
SELF-EFFICACY, EXERCISE
CXTXP Direct -0.099 0.481 -0.205 0.837
SELF-EFFICACY, EXERCISE
CXBXM Total -0.016 0.488 -0.032 0.974
SELF-EFFICACY, EXERCISE
CXBXM Indirect -0.013 0.107 -0.125 0.901
SELF-EFFICACY, EXERCISE
CXBXM Direct -0.002 0.481 -0.005 0.996
SELF-EFFICACY, EXERCISE
CXBXP Total 0.261 0.48 0.544 0.586
70
SELF-EFFICACY, EXERCISE
CXBXP Indirect -0.076 0.113 -0.674 0.5
SELF-EFFICACY, EXERCISE
CXBXP Direct 0.338 0.464 0.727 0.467
SELF-EFFICACY, EXERCISE
CXMXP Total 0.772 0.492 1.568 0.117
SELF-EFFICACY, EXERCISE
CXMXP Indirect 0.106 0.118 0.893 0.372
SELF-EFFICACY, EXERCISE
CXMXP Direct 0.666 0.476 1.399 0.162
SELF-EFFICACY, EXERCISE
TXBXM Total -0.4 0.472 -0.848 0.397
SELF-EFFICACY, EXERCISE
TXBXM Indirect -0.095 0.111 -0.859 0.39
SELF-EFFICACY, EXERCISE
TXBXM Direct -0.305 0.459 -0.665 0.506
SELF-EFFICACY, EXERCISE
TXBXP Total 1.121 0.493 2.274 0.023
SELF-EFFICACY, EXERCISE
TXBXP Indirect -0.013 0.112 -0.116 0.908
SELF-EFFICACY, EXERCISE
TXBXP Direct 1.134 0.48 2.363 0.018
SELF-EFFICACY, EXERCISE
TXMXP Total -0.138 0.489 -0.283 0.777
SELF-EFFICACY, EXERCISE
TXMXP Indirect -0.058 0.107 -0.537 0.591
SELF-EFFICACY, EXERCISE
TXMXP Direct -0.081 0.485 -0.167 0.868
SELF-EFFICACY, EXERCISE
BXMXP Total 0.292 0.48 0.608 0.543
SELF-EFFICACY, EXERCISE
BXMXP Indirect 0.102 0.112 0.908 0.364
SELF-EFFICACY, EXERCISE
BXMXP Direct 0.19 0.467 0.407 0.684
SELF-EFFICACY, EXERCISE
CTBM Total -0.089 0.49 -0.182 0.855
SELF-EFFICACY, EXERCISE
CTBM Indirect -0.079 0.117 -0.674 0.501
71
SELF-EFFICACY, EXERCISE
CTBM Direct -0.011 0.477 -0.022 0.982
SELF-EFFICACY, EXERCISE
CTMP Total 0.33 0.492 0.671 0.502
SELF-EFFICACY, EXERCISE
CTMP Indirect -0.109 0.112 -0.968 0.333
SELF-EFFICACY, EXERCISE
CTMP Direct 0.439 0.48 0.914 0.361
SELF-EFFICACY, EXERCISE
CTBP Total -1.13 0.484 -2.336 0.019
SELF-EFFICACY, EXERCISE
CTBP Indirect -0.118 0.112 -1.055 0.291
SELF-EFFICACY, EXERCISE
CTBP Direct -1.012 0.469 -2.156 0.031
SELF-EFFICACY, EXERCISE
CBMP Total -0.562 0.484 -1.161 0.246
SELF-EFFICACY, EXERCISE
CBMP Indirect -0.032 0.11 -0.29 0.772
SELF-EFFICACY, EXERCISE
CBMP Direct -0.53 0.473 -1.12 0.263
SELF-EFFICACY, EXERCISE
TBMP Total 0.051 0.498 0.103 0.918
SELF-EFFICACY, EXERCISE
TBMP Indirect 0.071 0.114 0.62 0.535
SELF-EFFICACY, EXERCISE
TBMP Direct -0.02 0.484 -0.04 0.968
SELF-EFFICACY, EXERCISE
CTBMP Total -0.215 0.502 -0.428 0.669
SELF-EFFICACY, EXERCISE
CTBMP Indirect -0.09 0.12 -0.754 0.451
SELF-EFFICACY, EXERCISE
CTBMP Direct -0.125 0.482 -0.258 0.796
Restraint COACHING CALLS
Total 0.407 0.477 0.854 0.393
Restraint COACHING CALLS
Indirect 0.103 0.09 1.138 0.255
Restraint COACHING CALLS
Direct 0.305 0.47 0.648 0.517
72
Restraint TEXT MESSAGES
Total 0.397 0.477 0.833 0.405
Restraint TEXT MESSAGES
Indirect 0.039 0.087 0.447 0.655
Restraint TEXT MESSAGES
Direct 0.358 0.47 0.763 0.446
Restraint BUDDY TRAINING
Total -0.929 0.477 -1.948 0.051
Restraint BUDDY TRAINING
Indirect -0.004 0.087 -0.046 0.963
Restraint BUDDY TRAINING
Direct -0.925 0.469 -1.972 0.049
Restraint MEAL Total 0.015 0.478 0.031 0.976
Restraint MEAL Indirect 0.066 0.088 0.748 0.454
Restraint MEAL Direct -0.051 0.47 -0.11 0.913
Restraint PCP Total 0.023 0.477 0.048 0.962
Restraint PCP Indirect -0.125 0.092 -1.354 0.176
Restraint PCP Direct 0.147 0.471 0.312 0.755
Restraint CXT Total -0.447 0.477 -0.938 0.348
Restraint CXT Indirect 0.183 0.098 1.878 0.06
Restraint CXT Direct -0.631 0.472 -1.336 0.182
Restraint CXB Total -0.34 0.477 -0.713 0.476
Restraint CXB Indirect -0.043 0.088 -0.486 0.627
Restraint CXB Direct -0.298 0.469 -0.634 0.526
Restraint CXM Total -0.236 0.477 -0.495 0.621
Restraint CXM Indirect 0.137 0.093 1.473 0.141
Restraint CXM Direct -0.373 0.47 -0.794 0.427
Restraint CXP Total -0.268 0.478 -0.562 0.574
Restraint CXP Indirect -0.169 0.096 -1.759 0.079
Restraint CXP Direct -0.099 0.473 -0.21 0.834
Restraint TXB Total 0.471 0.477 0.988 0.323
Restraint TXB Indirect 0.09 0.09 1.005 0.315
Restraint TXB Direct 0.381 0.47 0.812 0.417
Restraint TXM Total 0.375 0.477 0.786 0.432
Restraint TXM Indirect 0.075 0.089 0.842 0.4
Restraint TXM Direct 0.3 0.47 0.639 0.523
73
Restraint TXP Total -0.267 0.477 -0.56 0.575
Restraint TXP Indirect 0.106 0.091 1.167 0.243
Restraint TXP Direct -0.373 0.47 -0.794 0.427
Restraint BXM Total -0.006 0.478 -0.012 0.99
Restraint BXM Indirect -0.125 0.092 -1.358 0.175
Restraint BXM Direct 0.119 0.471 0.253 0.8
Restraint BXP Total -0.675 0.477 -1.415 0.157
Restraint BXP Indirect -0.091 0.09 -1.011 0.312
Restraint BXP Direct -0.585 0.469 -1.245 0.213
Restraint MXP Total -0.032 0.477 -0.067 0.947
Restraint MXP Indirect 0.03 0.087 0.344 0.731
Restraint MXP Direct -0.062 0.469 -0.132 0.895
Restraint CXTXB Total -0.028 0.477 -0.059 0.953
Restraint CXTXB Indirect -0.106 0.09 -1.168 0.243
Restraint CXTXB Direct 0.078 0.471 0.165 0.869
Restraint CXTXM Total -0.101 0.478 -0.212 0.832
Restraint CXTXM Indirect -0.039 0.088 -0.448 0.654
Restraint CXTXM Direct -0.062 0.47 -0.132 0.895
Restraint CXTXP Total -0.24 0.477 -0.503 0.615
Restraint CXTXP Indirect -0.018 0.087 -0.21 0.833
Restraint CXTXP Direct -0.222 0.469 -0.472 0.637
Restraint CXBXM Total 0.04 0.477 0.084 0.933
Restraint CXBXM Indirect 0.033 0.087 0.382 0.703
Restraint CXBXM Direct 0.007 0.469 0.014 0.989
Restraint CXBXP Total 0.303 0.477 0.636 0.525
Restraint CXBXP Indirect 0.149 0.094 1.585 0.113
Restraint CXBXP Direct 0.155 0.471 0.328 0.743
Restraint CXMXP Total 0.765 0.477 1.603 0.109
Restraint CXMXP Indirect 0.178 0.097 1.839 0.066
Restraint CXMXP Direct 0.587 0.471 1.245 0.213
Restraint TXBXM Total -0.333 0.478 -0.697 0.486
Restraint TXBXM Indirect 0.132 0.093 1.423 0.155
Restraint TXBXM Direct -0.465 0.47 -0.989 0.323
Restraint TXBXP Total 1.052 0.477 2.204 0.027
Restraint TXBXP Indirect 0.168 0.096 1.755 0.079
74
Restraint TXBXP Direct 0.884 0.471 1.877 0.061
Restraint TXMXP Total -0.214 0.477 -0.448 0.654
Restraint TXMXP Indirect 0.059 0.088 0.673 0.501
Restraint TXMXP Direct -0.273 0.47 -0.581 0.561
Restraint BXMXP Total 0.276 0.477 0.579 0.563
Restraint BXMXP Indirect 0.212 0.101 2.103 0.035
Restraint BXMXP Direct 0.064 0.472 0.136 0.892
Restraint CTBM Total -0.133 0.478 -0.279 0.78
Restraint CTBM Indirect 0.004 0.087 0.048 0.962
Restraint CTBM Direct -0.138 0.47 -0.293 0.77
Restraint CTMP Total 0.32 0.477 0.671 0.502
Restraint CTMP Indirect 0.012 0.087 0.135 0.893
Restraint CTMP Direct 0.308 0.469 0.657 0.511
Restraint CTBP Total -1.199 0.477 -2.515 0.012
Restraint CTBP Indirect -0.017 0.087 -0.194 0.846
Restraint CTBP Direct -1.182 0.469 -2.521 0.012
Restraint CBMP Total -0.537 0.477 -1.127 0.26
Restraint CBMP Indirect 0.117 0.091 1.285 0.199
Restraint CBMP Direct -0.655 0.47 -1.393 0.164
Restraint TBMP Total 0.023 0.477 0.047 0.962
Restraint TBMP Indirect -0.095 0.09 -1.055 0.292
Restraint TBMP Direct 0.117 0.469 0.25 0.802
Restraint CTBMP Total -0.315 0.477 -0.66 0.509
Restraint CTBMP Indirect 0.148 0.094 1.578 0.115
Restraint CTBMP Direct -0.463 0.472 -0.983 0.326
Therapeutic Alliance COACHING CALLS
Total 0.407 0.473 0.86 0.39
Therapeutic Alliance COACHING CALLS
Indirect -0.085 0.077 -1.099 0.272
Therapeutic Alliance COACHING CALLS
Direct 0.492 0.471 1.046 0.296
Therapeutic Alliance TEXT MESSAGES
Total 0.397 0.47 0.846 0.398
Therapeutic Alliance TEXT MESSAGES
Indirect -0.02 0.074 -0.267 0.79
75
Therapeutic Alliance TEXT MESSAGES
Direct 0.417 0.464 0.899 0.369
Therapeutic Alliance BUDDY TRAINING
Total -0.929 0.472 -1.966 0.049
Therapeutic Alliance BUDDY TRAINING
Indirect -0.082 0.077 -1.069 0.285
Therapeutic Alliance BUDDY TRAINING
Direct -0.846 0.467 -1.814 0.07
Therapeutic Alliance MEAL Total 0.015 0.474 0.031 0.975
Therapeutic Alliance MEAL Indirect 0.041 0.074 0.561 0.575
Therapeutic Alliance MEAL Direct -0.027 0.467 -0.058 0.954
Therapeutic Alliance PCP Total 0.023 0.47 0.048 0.961
Therapeutic Alliance PCP Indirect -0.064 0.076 -0.844 0.398
Therapeutic Alliance PCP Direct 0.087 0.464 0.187 0.851
Therapeutic Alliance CXT Total -0.447 0.472 -0.948 0.343
Therapeutic Alliance CXT Indirect -0.086 0.078 -1.103 0.27
Therapeutic Alliance CXT Direct -0.362 0.467 -0.774 0.439
Therapeutic Alliance CXB Total -0.34 0.471 -0.722 0.47
Therapeutic Alliance CXB Indirect 0.037 0.074 0.497 0.619
Therapeutic Alliance CXB Direct -0.377 0.465 -0.81 0.418
Therapeutic Alliance CXM Total -0.236 0.474 -0.498 0.618
Therapeutic Alliance CXM Indirect -0.148 0.081 -1.827 0.068
Therapeutic Alliance CXM Direct -0.088 0.471 -0.188 0.851
Therapeutic Alliance CXP Total -0.268 0.472 -0.568 0.57
Therapeutic Alliance CXP Indirect -0.057 0.075 -0.766 0.444
Therapeutic Alliance CXP Direct -0.211 0.465 -0.453 0.651
Therapeutic Alliance TXB Total 0.471 0.474 0.994 0.32
Therapeutic Alliance TXB Indirect 0.081 0.076 1.062 0.288
Therapeutic Alliance TXB Direct 0.391 0.468 0.835 0.404
Therapeutic Alliance TXM Total 0.375 0.471 0.796 0.426
Therapeutic Alliance TXM Indirect 0.088 0.075 1.167 0.243
Therapeutic Alliance TXM Direct 0.287 0.464 0.619 0.536
Therapeutic Alliance TXP Total -0.267 0.473 -0.565 0.572
Therapeutic Alliance TXP Indirect -0.109 0.079 -1.388 0.165
Therapeutic Alliance TXP Direct -0.158 0.466 -0.339 0.735
Therapeutic Alliance BXM Total -0.006 0.472 -0.013 0.99
76
Therapeutic Alliance BXM Indirect -0.168 0.088 -1.903 0.057
Therapeutic Alliance BXM Direct 0.162 0.465 0.348 0.728
Therapeutic Alliance BXP Total -0.675 0.474 -1.426 0.154
Therapeutic Alliance BXP Indirect 0.021 0.074 0.287 0.774
Therapeutic Alliance BXP Direct -0.696 0.468 -1.489 0.137
Therapeutic Alliance MXP Total -0.032 0.474 -0.067 0.946
Therapeutic Alliance MXP Indirect -0.021 0.073 -0.28 0.78
Therapeutic Alliance MXP Direct -0.011 0.467 -0.025 0.98
Therapeutic Alliance CXTXB Total -0.028 0.472 -0.059 0.953
Therapeutic Alliance CXTXB Indirect 0.048 0.075 0.637 0.524
Therapeutic Alliance CXTXB Direct -0.076 0.468 -0.161 0.872
Therapeutic Alliance CXTXM Total -0.101 0.47 -0.215 0.83
Therapeutic Alliance CXTXM Indirect -0.062 0.074 -0.838 0.402
Therapeutic Alliance CXTXM Direct -0.039 0.462 -0.085 0.932
Therapeutic Alliance CXTXP Total -0.24 0.475 -0.505 0.613
Therapeutic Alliance CXTXP Indirect -0.005 0.073 -0.068 0.946
Therapeutic Alliance CXTXP Direct -0.235 0.469 -0.501 0.616
Therapeutic Alliance CXBXM Total 0.04 0.475 0.084 0.933
Therapeutic Alliance CXBXM Indirect -0.025 0.073 -0.348 0.728
Therapeutic Alliance CXBXM Direct 0.065 0.468 0.14 0.889
Therapeutic Alliance CXBXP Total 0.303 0.475 0.639 0.523
Therapeutic Alliance CXBXP Indirect -0.044 0.073 -0.596 0.551
Therapeutic Alliance CXBXP Direct 0.347 0.467 0.743 0.457
Therapeutic Alliance CXMXP Total 0.765 0.474 1.613 0.107
Therapeutic Alliance CXMXP Indirect -0.022 0.074 -0.305 0.76
Therapeutic Alliance CXMXP Direct 0.787 0.468 1.683 0.092
Therapeutic Alliance TXBXM Total -0.333 0.474 -0.702 0.482
Therapeutic Alliance TXBXM Indirect 0.076 0.074 1.017 0.309
Therapeutic Alliance TXBXM Direct -0.408 0.467 -0.874 0.382
Therapeutic Alliance TXBXP Total 1.052 0.473 2.222 0.026
Therapeutic Alliance TXBXP Indirect 0.002 0.073 0.028 0.977
Therapeutic Alliance TXBXP Direct 1.05 0.467 2.249 0.025
Therapeutic Alliance TXMXP Total -0.214 0.471 -0.454 0.65
Therapeutic Alliance TXMXP Indirect 0.142 0.087 1.626 0.104
Therapeutic Alliance TXMXP Direct -0.356 0.468 -0.76 0.447
77
Therapeutic Alliance BXMXP Total 0.276 0.472 0.585 0.559
Therapeutic Alliance BXMXP Indirect 0.041 0.073 0.557 0.577
Therapeutic Alliance BXMXP Direct 0.235 0.465 0.506 0.613
Therapeutic Alliance CTBM Total -0.133 0.474 -0.281 0.778
Therapeutic Alliance CTBM Indirect 0.085 0.077 1.104 0.27
Therapeutic Alliance CTBM Direct -0.218 0.469 -0.465 0.642
Therapeutic Alliance CTBP Total -1.199 0.473 -2.537 0.011
Therapeutic Alliance CTBP Indirect -0.127 0.081 -1.574 0.115
Therapeutic Alliance CTBP Direct -1.072 0.465 -2.304 0.021
Therapeutic Alliance CTMP Total 0.32 0.473 0.676 0.499
Therapeutic Alliance CTMP Indirect 0.016 0.074 0.22 0.826
Therapeutic Alliance CTMP Direct 0.304 0.467 0.65 0.515
Therapeutic Alliance CBMP Total -0.537 0.472 -1.138 0.255
Therapeutic Alliance CBMP Indirect -0.02 0.074 -0.271 0.787
Therapeutic Alliance CBMP Direct -0.517 0.466 -1.11 0.267
Therapeutic Alliance TBMP Total 0.023 0.473 0.048 0.962
Therapeutic Alliance TBMP Indirect 0.218 0.092 2.363 0.018
Therapeutic Alliance TBMP Direct -0.195 0.468 -0.417 0.676
Therapeutic Alliance CTBMP Total -0.315 0.47 -0.67 0.503
Therapeutic Alliance CTBMP Indirect -0.011 0.074 -0.144 0.885
Therapeutic Alliance CTBMP Direct -0.305 0.464 -0.656 0.512
Autonomous Motiv. COACHING CALLS
Total 0.407 0.473 0.861 0.389
Autonomous Motiv. COACHING CALLS
Indirect -0.003 0.07 -0.038 0.97
Autonomous Motiv. COACHING CALLS
Direct 0.41 0.468 0.876 0.381
Autonomous Motiv. TEXT MESSAGES
Total 0.397 0.47 0.846 0.397
Autonomous Motiv. TEXT MESSAGES
Indirect 0.046 0.072 0.64 0.522
Autonomous Motiv. TEXT MESSAGES
Direct 0.351 0.465 0.755 0.45
Autonomous Motiv. BUDDY TRAINING
Total -0.929 0.472 -1.967 0.049
Autonomous Motiv. BUDDY TRAINING
Indirect -0.13 0.076 -1.699 0.089
78
Autonomous Motiv. BUDDY TRAINING
Direct -0.799 0.469 -1.704 0.088
Autonomous Motiv. MEAL Total 0.015 0.474 0.031 0.976
Autonomous Motiv. MEAL Indirect -0.033 0.072 -0.453 0.65
Autonomous Motiv. MEAL Direct 0.047 0.469 0.101 0.92
Autonomous Motiv. PCP Total 0.023 0.47 0.048 0.962
Autonomous Motiv. PCP Indirect -0.1 0.075 -1.323 0.186
Autonomous Motiv. PCP Direct 0.122 0.467 0.262 0.793
Autonomous Motiv. CXT Total -0.447 0.472 -0.948 0.343
Autonomous Motiv. CXT Indirect 0.055 0.071 0.775 0.439
Autonomous Motiv. CXT Direct -0.502 0.468 -1.073 0.283
Autonomous Motiv. CXB Total -0.34 0.471 -0.722 0.47
Autonomous Motiv. CXB Indirect 0.154 0.084 1.845 0.065
Autonomous Motiv. CXB Direct -0.495 0.47 -1.052 0.293
Autonomous Motiv. CXM Total -0.236 0.474 -0.499 0.618
Autonomous Motiv. CXM Indirect -0.029 0.071 -0.41 0.682
Autonomous Motiv. CXM Direct -0.207 0.469 -0.442 0.659
Autonomous Motiv. CXP Total -0.268 0.472 -0.569 0.569
Autonomous Motiv. CXP Indirect 0.001 0.07 0.015 0.988
Autonomous Motiv. CXP Direct -0.269 0.467 -0.578 0.564
Autonomous Motiv. TXB Total 0.471 0.474 0.994 0.32
Autonomous Motiv. TXB Indirect 0.027 0.071 0.381 0.703
Autonomous Motiv. TXB Direct 0.444 0.468 0.95 0.342
Autonomous Motiv. TXM Total 0.375 0.471 0.796 0.426
Autonomous Motiv. TXM Indirect -0.045 0.072 -0.633 0.527
Autonomous Motiv. TXM Direct 0.421 0.466 0.903 0.366
Autonomous Motiv. TXP Total -0.267 0.473 -0.565 0.572
Autonomous Motiv. TXP Indirect -0.085 0.074 -1.151 0.25
Autonomous Motiv. TXP Direct -0.182 0.467 -0.389 0.697
Autonomous Motiv. BXM Total -0.006 0.472 -0.013 0.99
Autonomous Motiv. BXM Indirect -0.007 0.07 -0.107 0.915
Autonomous Motiv. BXM Direct 0.001 0.467 0.003 0.998
Autonomous Motiv. BXP Total -0.675 0.474 -1.426 0.154
Autonomous Motiv. BXP Indirect 0.049 0.07 0.692 0.489
Autonomous Motiv. BXP Direct -0.724 0.469 -1.544 0.123
79
Autonomous Motiv. MXP Total -0.032 0.474 -0.067 0.946
Autonomous Motiv. MXP Indirect -0.031 0.071 -0.438 0.662
Autonomous Motiv. MXP Direct -0.001 0.469 -0.002 0.998
Autonomous Motiv. CXTXB Total -0.028 0.472 -0.059 0.953
Autonomous Motiv. CXTXB Indirect -0.003 0.07 -0.041 0.967
Autonomous Motiv. CXTXB Direct -0.025 0.468 -0.054 0.957
Autonomous Motiv. CXTXM Total -0.101 0.47 -0.215 0.83
Autonomous Motiv. CXTXM Indirect 0.003 0.07 0.044 0.965
Autonomous Motiv. CXTXM Direct -0.104 0.465 -0.224 0.823
Autonomous Motiv. CXTXP Total -0.24 0.475 -0.505 0.614
Autonomous Motiv. CXTXP Indirect 0.02 0.071 0.275 0.784
Autonomous Motiv. CXTXP Direct -0.259 0.47 -0.552 0.581
Autonomous Motiv. CXBXM Total 0.04 0.475 0.084 0.933
Autonomous Motiv. CXBXM Indirect -0.079 0.072 -1.107 0.268
Autonomous Motiv. CXBXM Direct 0.119 0.474 0.252 0.801
Autonomous Motiv. CXBXP Total 0.303 0.475 0.639 0.523
Autonomous Motiv. CXBXP Indirect -0.026 0.071 -0.372 0.71
Autonomous Motiv. CXBXP Direct 0.33 0.469 0.703 0.482
Autonomous Motiv. CXMXP Total 0.765 0.474 1.613 0.107
Autonomous Motiv. CXMXP Indirect 0.013 0.07 0.192 0.848
Autonomous Motiv. CXMXP Direct 0.752 0.469 1.603 0.109
Autonomous Motiv. TXBXM Total -0.333 0.474 -0.703 0.482
Autonomous Motiv. TXBXM Indirect 0.103 0.079 1.307 0.191
Autonomous Motiv. TXBXM Direct -0.436 0.472 -0.924 0.355
Autonomous Motiv. TXBXP Total 1.052 0.473 2.223 0.026
Autonomous Motiv. TXBXP Indirect 0.05 0.07 0.709 0.478
Autonomous Motiv. TXBXP Direct 1.002 0.471 2.129 0.033
Autonomous Motiv. TXMXP Total -0.214 0.471 -0.454 0.65
Autonomous Motiv. TXMXP Indirect 0.039 0.071 0.546 0.585
Autonomous Motiv. TXMXP Direct -0.253 0.466 -0.542 0.587
Autonomous Motiv. BXMXP Total 0.276 0.472 0.585 0.559
Autonomous Motiv. BXMXP Indirect 0.04 0.071 0.568 0.57
Autonomous Motiv. BXMXP Direct 0.236 0.467 0.505 0.614
Autonomous Motiv. CTBM Total -0.133 0.474 -0.281 0.779
Autonomous Motiv. CTBM Indirect -0.128 0.08 -1.608 0.108
80
Autonomous Motiv. CTBM Direct -0.005 0.466 -0.01 0.992
Autonomous Motiv. CTMP Total 0.32 0.473 0.676 0.499
Autonomous Motiv. CTMP Indirect 0 0.07 0.001 0.999
Autonomous Motiv. CTMP Direct 0.32 0.468 0.684 0.494
Autonomous Motiv. CTBP Total -1.199 0.473 -2.538 0.011
Autonomous Motiv. CTBP Indirect -0.017 0.07 -0.24 0.81
Autonomous Motiv. CTBP Direct -1.182 0.468 -2.525 0.012
Autonomous Motiv. CBMP Total -0.537 0.472 -1.139 0.255
Autonomous Motiv. CBMP Indirect 0.041 0.071 0.58 0.562
Autonomous Motiv. CBMP Direct -0.579 0.468 -1.236 0.216
Autonomous Motiv. TBMP Total 0.023 0.473 0.048 0.962
Autonomous Motiv. TBMP Indirect -0.059 0.072 -0.817 0.414
Autonomous Motiv. TBMP Direct 0.082 0.468 0.175 0.861
Autonomous Motiv. CTBMP Total -0.315 0.47 -0.67 0.503
Autonomous Motiv. CTBMP Indirect -0.042 0.072 -0.591 0.555
Autonomous Motiv. CTBMP Direct -0.273 0.466 -0.584 0.559
Autonomy Support COACHING CALLS
Total 0.407 0.473 0.861 0.39
Autonomy Support COACHING CALLS
Indirect -0.082 0.061 -1.342 0.18
Autonomy Support COACHING CALLS
Direct 0.489 0.48 1.018 0.308
Autonomy Support TEXT MESSAGES
Total 0.397 0.47 0.846 0.398
Autonomy Support TEXT MESSAGES
Indirect 0.033 0.044 0.744 0.457
Autonomy Support TEXT MESSAGES
Direct 0.364 0.467 0.78 0.436
Autonomy Support BUDDY TRAINING
Total -0.929 0.472 -1.966 0.049
Autonomy Support BUDDY TRAINING
Indirect -0.043 0.047 -0.903 0.367
Autonomy Support BUDDY TRAINING
Direct -0.886 0.469 -1.888 0.059
Autonomy Support MEAL Total 0.015 0.474 0.031 0.975
Autonomy Support MEAL Indirect 0.043 0.048 0.898 0.369
Autonomy Support MEAL Direct -0.029 0.473 -0.061 0.951
81
Autonomy Support PCP Total 0.023 0.47 0.048 0.961
Autonomy Support PCP Indirect -0.097 0.064 -1.506 0.132
Autonomy Support PCP Direct 0.119 0.471 0.254 0.8
Autonomy Support CXT Total -0.447 0.472 -0.948 0.343
Autonomy Support CXT Indirect -0.012 0.043 -0.285 0.776
Autonomy Support CXT Direct -0.435 0.47 -0.926 0.354
Autonomy Support CXB Total -0.34 0.471 -0.722 0.47
Autonomy Support CXB Indirect 0.047 0.05 0.939 0.348
Autonomy Support CXB Direct -0.388 0.47 -0.825 0.409
Autonomy Support CXM Total -0.236 0.474 -0.498 0.618
Autonomy Support CXM Indirect -0.094 0.059 -1.59 0.112
Autonomy Support CXM Direct -0.142 0.475 -0.3 0.764
Autonomy Support CXP Total -0.268 0.472 -0.568 0.57
Autonomy Support CXP Indirect -0.039 0.046 -0.85 0.395
Autonomy Support CXP Direct -0.229 0.47 -0.487 0.626
Autonomy Support TXB Total 0.471 0.474 0.994 0.32
Autonomy Support TXB Indirect 0.055 0.05 1.108 0.268
Autonomy Support TXB Direct 0.416 0.471 0.883 0.377
Autonomy Support TXM Total 0.375 0.471 0.796 0.426
Autonomy Support TXM Indirect 0.077 0.055 1.39 0.164
Autonomy Support TXM Direct 0.298 0.471 0.633 0.526
Autonomy Support TXP Total -0.267 0.473 -0.565 0.572
Autonomy Support TXP Indirect -0.057 0.05 -1.145 0.252
Autonomy Support TXP Direct -0.21 0.47 -0.447 0.655
Autonomy Support BXM Total -0.006 0.472 -0.013 0.99
Autonomy Support BXM Indirect -0.092 0.06 -1.524 0.128
Autonomy Support BXM Direct 0.086 0.468 0.183 0.855
Autonomy Support BXP Total -0.675 0.474 -1.426 0.154
Autonomy Support BXP Indirect 0.014 0.043 0.314 0.753
Autonomy Support BXP Direct -0.689 0.472 -1.459 0.145
Autonomy Support MXP Total -0.032 0.474 -0.067 0.946
Autonomy Support MXP Indirect 0.008 0.043 0.176 0.86
Autonomy Support MXP Direct -0.04 0.472 -0.084 0.933
Autonomy Support CXTXB Total -0.028 0.472 -0.059 0.953
Autonomy Support CXTXB Indirect 0.007 0.043 0.171 0.864
82
Autonomy Support CXTXB Direct -0.035 0.471 -0.075 0.94
Autonomy Support CXTXM Total -0.101 0.47 -0.215 0.83
Autonomy Support CXTXM Indirect -0.047 0.046 -1.028 0.304
Autonomy Support CXTXM Direct -0.054 0.466 -0.116 0.907
Autonomy Support CXTXP Total -0.24 0.475 -0.505 0.613
Autonomy Support CXTXP Indirect 0.028 0.044 0.629 0.529
Autonomy Support CXTXP Direct -0.268 0.472 -0.567 0.571
Autonomy Support CXBXM Total 0.04 0.475 0.084 0.933
Autonomy Support CXBXM Indirect 0.003 0.043 0.068 0.946
Autonomy Support CXBXM Direct 0.037 0.473 0.078 0.937
Autonomy Support CXBXP Total 0.304 0.475 0.64 0.522
Autonomy Support CXBXP Indirect 0.011 0.043 0.248 0.804
Autonomy Support CXBXP Direct 0.293 0.473 0.619 0.536
Autonomy Support CXMXP Total 0.765 0.474 1.613 0.107
Autonomy Support CXMXP Indirect 0.013 0.043 0.309 0.757
Autonomy Support CXMXP Direct 0.752 0.472 1.594 0.111
Autonomy Support TXBXM Total -0.333 0.474 -0.702 0.482
Autonomy Support TXBXM Indirect 0.056 0.049 1.135 0.256
Autonomy Support TXBXM Direct -0.388 0.472 -0.823 0.41
Autonomy Support TXBXP Total 1.052 0.473 2.222 0.026
Autonomy Support TXBXP Indirect 0.003 0.043 0.076 0.94
Autonomy Support TXBXP Direct 1.049 0.471 2.226 0.026
Autonomy Support TXMXP Total -0.214 0.471 -0.454 0.65
Autonomy Support TXMXP Indirect 0.048 0.049 0.984 0.325
Autonomy Support TXMXP Direct -0.262 0.468 -0.559 0.576
Autonomy Support BXMXP Total 0.276 0.472 0.585 0.558
Autonomy Support BXMXP Indirect 0.041 0.045 0.897 0.37
Autonomy Support BXMXP Direct 0.235 0.468 0.502 0.615
Autonomy Support CTBM Total -0.133 0.474 -0.281 0.778
Autonomy Support CTBM Indirect 0.031 0.045 0.696 0.486
Autonomy Support CTBM Direct -0.164 0.472 -0.348 0.728
Autonomy Support CTBP Total -1.199 0.473 -2.538 0.011
Autonomy Support CTBP Indirect -0.091 0.058 -1.576 0.115
Autonomy Support CTBP Direct -1.108 0.47 -2.36 0.018
Autonomy Support CTMP Total 0.32 0.473 0.676 0.499
83
Autonomy Support CTMP Indirect 0.04 0.047 0.849 0.396
Autonomy Support CTMP Direct 0.28 0.471 0.595 0.552
Autonomy Support CBMP Total -0.537 0.472 -1.138 0.255
Autonomy Support CBMP Indirect -0.003 0.043 -0.071 0.943
Autonomy Support CBMP Direct -0.534 0.47 -1.137 0.256
Autonomy Support TBMP Total 0.023 0.473 0.048 0.962
Autonomy Support TBMP Indirect 0.072 0.053 1.344 0.179
Autonomy Support TBMP Direct -0.049 0.473 -0.104 0.917
Autonomy Support CTBMP Total -0.315 0.47 -0.67 0.503
Autonomy Support CTBMP Indirect -0.025 0.045 -0.559 0.576
Autonomy Support CTBMP Direct -0.29 0.469 -0.618 0.536
