Language Matters when Describing Weight Loss Goals

Obesity affects millions of individuals worldwide and is associated with a significantly increased risk for cardiovascular and metabolic diseases. A study publishing June 16th in the open access journal PLOS Digital Health by Annabell Ho at Noom, Inc. New York, United States, suggests that while setting a weight-loss goal, analytical language was associated with greater weight loss success and a lower likelihood of attrition.

Outcomes for behavioral interventions treating obesity vary widely, with some individuals dropping off the program before they receive the full intervention. Yet the factors contributing to attrition or weight loss are poorly understood. To better understand how language may affect weight loss and program attrition, researchers conducted a retrospective study of 1,350 Noom Weight - an app-based weight management program - users who paid to participate in a 16-week program. Each participant set an initial goal and interacted with a coach to provide more detail about their weight loss goals. The researchers then analyzed the language using an automated text analysis program and calculated weight loss as well as weight loss and the dropout rate by analyzing program activity data.

The authors found that in goal striving conversations, such as talking to a coach about efforts to pursue a goal, analytical versus present-focused language was associated with greater weight loss and lower likelihood of attrition. While these findings may be useful, the study did not examine other related variables, for example the effects of education level or English proficiency on goal-setting language. Future studies should focus on the factors mediating the relationship between language and outcomes to confirm exactly why analytical language is helpful.

According to the authors, "Our results are among the first to identify individuals' language, which has not been studied much previously, as relevant and informative for understanding weight loss and dropout. This raises directions for future research to improve intervention development and ascertain whether language is informative in other lifestyle behavior change interventions."

Ho adds, "Using analytical language, for example analyzing what’s important and why, predicts more weight loss and less program attrition on a digital weight loss program. On the other hand, using words that are more self-focused or present-focused like ‘I’ and ‘me’ predict less weight loss and more attrition."

Ho AS, Behr H, Mitchell ES, Yang Q, Lee J, May CN, et al.
Goal language is associated with attrition and weight loss on a digital program: Observational study.
PLOS Digit Health 1(6): e0000050. 2022. doi: 10.1371/journal.pdig.0000050

Most Popular Now

Unlocking the 10 Year Health Plan

The government's plan for the NHS is a huge document. Jane Stephenson, chief executive of SPARK TSL, argues the key to unlocking its digital ambitions is to consider what it...

Alcidion Grows Top Talent in the UK, wit…

Alcidion has today announced the addition of three new appointments to their UK-based team, with one internal promotion and two external recruits. Dr Paul Deffley has been announced as the...

AI can Find Cancer Pathologists Miss

Men assessed as healthy after a pathologist analyses their tissue sample may still have an early form of prostate cancer. Using AI, researchers at Uppsala University have been able to...

AI, Full Automation could Expand Artific…

Automated insulin delivery (AID) systems such as the UVA Health-developed artificial pancreas could help more type 1 diabetes patients if the devices become fully automated, according to a new review...

How AI could Speed the Development of RN…

Using artificial intelligence (AI), MIT researchers have come up with a new way to design nanoparticles that can more efficiently deliver RNA vaccines and other types of RNA therapies. After training...

MIT Researchers Use Generative AI to Des…

With help from artificial intelligence, MIT researchers have designed novel antibiotics that can combat two hard-to-treat infections: drug-resistant Neisseria gonorrhoeae and multi-drug-resistant Staphylococcus aureus (MRSA). Using generative AI algorithms, the research...

AI Hybrid Strategy Improves Mammogram In…

A hybrid reading strategy for screening mammography, developed by Dutch researchers and deployed retrospectively to more than 40,000 exams, reduced radiologist workload by 38% without changing recall or cancer detection...

New Training Year Starts at Siemens Heal…

In September, 197 school graduates will start their vocational training or dual studies in Germany at Siemens Healthineers. 117 apprentices and 80 dual students will begin their careers at Siemens...

Penn Developed AI Tools and Datasets Hel…

Doctors treating kidney disease have long depended on trial-and-error to find the best therapies for individual patients. Now, new artificial intelligence (AI) tools developed by researchers in the Perelman School...

Are You Eligible for a Clinical Trial? C…

A new study in the academic journal Machine Learning: Health discovers that ChatGPT can accelerate patient screening for clinical trials, showing promise in reducing delays and improving trial success rates. Researchers...

New AI Tool Addresses Accuracy and Fairn…

A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases in datasets used to train machine-learning algorithms...

Global Study Reveals How Patients View M…

How physicians feel about artificial intelligence (AI) in medicine has been studied many times. But what do patients think? A team led by researchers at the Technical University of Munich...