Mechanisms of Action in a Behavioral Weight-Management Program: Latent Growth Curve Analysis

A Pilot Goal-Oriented Episodic Future Thinking Weight Loss Intervention for Low-Income Overweight or Obese Young Mothers

BACKGROUND

Episodic future thinking (EFT) has shown efficacy in laboratory settings. We conducted a pilot goal-oriented EFT (GoEFT) intervention in a real-world setting to help low-income overweight or obese mothers lose weight. This paper presents intervention acceptability and efficacy.

METHODS

The study used a single-group, before-after design. During the 3-week intervention, participants (N = 15) completed weekly web-based lessons and online health coaching sessions to manage stress and emotion, eat healthier, and be more physically active. Participants completed online surveys at baseline and immediately after the intervention. They also completed an interview to evaluate intervention acceptability. We applied paired t-tests to evaluate efficacy and used content analysis to discover interview themes.

RESULTS

Participants consistently identified the intervention as acceptable, noting the usefulness of pre-written goals, GoEFT strategies, and goal progress evaluations. The intervention effectively promoted weight loss (d = -0.69), fruit and vegetable intake (d = 0.45-0.49), and emotion control (d = 0.71). It also reduced fat (d = -0.51) and added sugar intake (d = -0.48) and alleviated stress (d = -0.52). Moreover, the intervention increased autonomous motivation (d = 0.75-0.88) and self-efficacy (d = 0.46-0.61).

CONCLUSION

The GoEFT intervention was acceptable to participants, showing strong preliminary efficacy.

Sensitivity analyses for data missing at random versus missing not at random using latent growth modelling: a practical guide for randomised controlled trials

Background

Missing data are ubiquitous in randomised controlled trials. Although sensitivity analyses for different missing data mechanisms (missing at random vs. missing not at random) are widely recommended, they are rarely conducted in practice. The aim of the present study was to demonstrate sensitivity analyses for different assumptions regarding the missing data mechanism for randomised controlled trials using latent growth modelling (LGM).

Methods

Data from a randomised controlled brief alcohol intervention trial was used. The sample included 1646 adults (56% female; mean age = 31.0 years) from the general population who had received up to three individualized alcohol feedback letters or assessment-only. Follow-up interviews were conducted after 12 and 36 months via telephone. The main outcome for the analysis was change in alcohol use over time. A three-step LGM approach was used. First, evidence about the process that generated the missing data was accumulated by analysing the extent of missing values in both study conditions, missing data patterns, and baseline variables that predicted participation in the two follow-up assessments using logistic regression. Second, growth models were calculated to analyse intervention effects over time. These models assumed that data were missing at random and applied full-information maximum likelihood estimation. Third, the findings were safeguarded by incorporating model components to account for the possibility that data were missing not at random. For that purpose, Diggle-Kenward selection, Wu-Carroll shared parameter and pattern mixture models were implemented.

Results

Although the true data generating process remained unknown, the evidence was unequivocal: both the intervention and control group reduced their alcohol use over time, but no significant group differences emerged. There was no clear evidence for intervention efficacy, neither in the growth models that assumed the missing data to be at random nor those that assumed the missing data to be not at random.

Conclusion

The illustrated approach allows the assessment of how sensitive conclusions about the efficacy of an intervention are to different assumptions regarding the missing data mechanism. For researchers familiar with LGM, it is a valuable statistical supplement to safeguard their findings against the possibility of nonignorable missingness.

Trial registration

The PRINT trial was prospectively registered at the German Clinical Trials Register (DRKS00014274, date of registration: 12th March 2018).