Personal goal setting eHealth component associated with improved weight loss at 6 months: A mixed methods secondary analysis

• Published in: Digital health Vol. 10; p. 20552076241277351
• Main Authors: Hurley, Lex, Nezami, Brooke T, Sciamanna, Christopher, Tate, Deborah F
• Format: Journal Article
• Language: English
• Published: United States SAGE Publishing 01-01-2024
• Subjects: weight loss; goal setting; Digital health; engagement; eHealth
• ISSN: 2055-2076; 2055-2076
• DOI: 10.1177/20552076241277351

Goal setting is a behavior change technique associated with improved change in outcomes. Digital (eHealth, mHealth) behavior change interventions often prescribe all goals with no opportunity for participants to create and track their own; thus, little is known about the types of goals participants create for themselves and their impacts on behavioral outcomes. This analysis describes the goals created by participants using an optional personal goal-setting component and evaluates the association between participant goal creation and weight loss in an eHealth adult weight loss intervention.
This represents a mixed methods QUANT-qual design to understand the types of goals users create for themselves and their impacts on behavior change outcomes. Qualitative codes were applied for the topic, behavior/outcome focus, adherence to SMART criteria, and repetition with count summaries. Quantitative analyses applied regression modeling to determine if the number of goals set was associated with the 6-month weight change, controlling for covariates.
Participants (n = 363) set an average of 23.4 goals (SD = 22.7) over 6 months. Those who reached at least 5% weight loss set significantly more goals than those who lost between 1% and 4.99% or who lost <1% or gained weight (
's < 0.0001). Setting more personal goals was associated with significant weight loss reduction at 6 months, controlling for covariates (
's < 0.05).
Greater use of a personal goal-setting feature was associated with improved weight loss outcomes among active users. This can be a low-investment addition to digital behavior change interventions to contribute to improved outcomes.