One factor may determine the best way to lose weight
Fitness routines need to be tailored to you based on one key characteristic, a new study suggests.
For some people, just saying you need to exercise isn't enough to get moving. Some flock to group fitness classes for motivation. Others turn to mobile apps and notification reminders to break a sweat, or pledge to exercise with a friend or family member.
But according to a new study, one factor influences how well any of these strategies work for you: personality type.
In the study, published Wednesday in the journal PLOS ONE, researchers find that the success of different strategies for boosting physical activity varies according to people's personality, demographic, psychological, and behavioral characteristics.
The study broke personality types into three groups: extroverted and motivated, less active and less social, or less motivated and at-risk. For all three groups, a competition-based strategy worked better to boost physical activity than strategies based on collaboration or social support.
But there were some key differences. The extroverted and motivated group did best in a competitive program, but they didn't maintain their activity level after the program ended. People who were less active to begin with and who reported less social support, however, performed the best with a program with competitive, collaborative, and supportive social incentives.
The research suggests fitness programs should not only be targeted to individuals' activity and weight-loss goals, but also factor in their personality, psychology, and social networks.
"It's challenging to change behaviors, especially in the long term," study co-author Shirley Chen, a hospitalist at the Mount Sinai Health System, tells Inverse.
"A lot of strategies such as offering different incentives and using technology are promising but could be much more impactful if we understood how to match them to those individuals who are more likely to benefit," she says.
Working it out — Historically, personality-based research has been confined to psychology and the social sciences, Chen says.
"There is strong evidence that personality is linked to decision-making, coping styles, and many health behaviors," Chen notes. "So it's likely that personality could play a role in determining how people respond to health behavior interventions, but there hasn't been much research in this area so far."
To evaluate the role of personality traits in promoting activity, Chen and her team reanalyzed data from the 2019 Step Up trial, a randomized clinical trial including 602 American adults classified as overweight or obese (with a body mass index of more than 25). The group were all employees at Deloitte, a large U.S. consulting firm, which limits the ability to generalize the results to a wider population.
Participants were randomized to take part in either the control condition or one of three behavioral interventions to boost physical activity:
- Supportive intervention: Participants identified a friend or family member who could support the participant at the start of the study and received weekly reports on the participant’s performance.
- Competitive intervention: Researchers divided participants into groups of three and used a weekly leaderboard email to foster competition.
- Collaborative intervention: Researchers also divided participants into groups of three, but a designated member was selected each day to represent the team through their step activity.
Across a 24-week period, scientists measured physical activity through daily step counts captured by wrist sensors.
Mapping motivation — The 2019 trial showed that across the board, a competition-based strategy to boost activity worked better than strategies based on collaboration or social support.
Chen and her team wondered whether the findings held true when participants were broken down by personality, psychological, demographic, and behavioral characteristics.
When the group initially enrolled in the study, they completed a sociodemographic survey and tests to assess their personality and certain key characteristics, including their risk-taking behavior, grit, social support, exercise self-efficacy, mood, self-reported health status, sleep quality, and dietary patterns.
Based on their answers, the scientists split participants into three categories: extroverted and motivated, less active and less social, or less motivated and at-risk.
By reanalyzing the trial data, the researchers found the competition-based strategy was effective in boosting physical activity for extroverted and motivated participants, but these participants were less likely to stay active after the program ended.
Meanwhile, competition, collaboration, and social support-based strategies were all effective for less active and less social participants, who all stayed active after the program concluded. None of the strategies were effective for less motivated and at-risk participants.
Essentially, if you want to exercise and lose weight, you need to take your personality into account to give yourself the best chance of sticking to a fitness routine.
"Personality traits have been both positively and negatively associated with many health behaviors including exercise but aren't typically considered when designing wellness programs and health behavior interventions," Chen says.
"In our study, we included personality as a key component of distinguishing different behavioral phenotypes and found that these phenotypes responded very differently to a physical activity program."
Chen and her team are still hammering out which fitness routines and behaviors are best for the different personality types. But for now, it's crucial to recognize there aren't any one-size-fits-all solutions for everyone to become more active, the researchers say.
"It's very important to develop successful approaches to help people change health behaviors," Chen stresses. "Whether it's being physically active or taking medications regularly, health behavior change is key to staying healthy and preventing disease and also to managing disease and avoiding complications."
Abstract: Participants often vary in their response to behavioral interventions, but methods to identify groups of participants that are more likely to respond are lacking. In this secondary analysis of a randomized clinical trial, we used baseline characteristics to group participants into distinct behavioral phenotypes and evaluated differential responses to a physical activity intervention. Latent class analysis was used to segment participants based on baseline participant data including demographics, validated measures of psychosocial variables, and physical activity behavior. The trial included 602 adults from 40 U.S. states with body mass index ≥25 who were randomized to control or one of three gamification interventions (supportive, collaborative, or competitive) to increase physical activity. Daily step counts were monitored using a wearable device for a 24-week intervention with 12 weeks of follow-up. The model segmented participants into three classes named for key defining traits: Class 1, extroverted and motivated; Class 2, less active and less social; Class 3, less motivated and at-risk. Adjusted regression models were used to test for differences in intervention response relative to control within each behavioral phenotype. In Class 1, only participants in the competitive arm increased their mean daily steps during the intervention (adjusted difference, 945; 95% CI, 352–1537; P = .002), but it was not sustained during follow-up. In Class 2, participants in all three gamification arms significantly increased their mean daily steps compared to control during the intervention (supportive arm adjusted difference 1172; 95% CI, 363–1980; P = .005; collaborative arm adjusted difference 1119; 95% CI, 319– 1919; P = .006; competitive arm adjusted difference 1179; 95% CI, 400–1957; P = .003) and all three had sustained impact during follow-up. In Class 3, none of the interventions had a significant effect on physical activity. Three behavioral phenotypes were identified, each with a different response to the interventions. This approach could be used to better target behavioral interventions to participants that are more likely to respond to them.