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Attitudes and Barriers to Healthy Diet and Physical Activity: A Latent Profile Analysis.

ABSTRACT: Healthy diet and physical activity (PA) prevent and reduce chronic disease. Social cognitive theory delineates multiple attitudes and barriers that influence these behaviors. Understanding covariation in these attitudes and barriers is complex. We examined whether individuals could be grouped into a small number of categories that are easier to study. Interviews were conducted with 982 adults from two low-income, predominantly African American neighborhoods in the same city. Social cognitive constructs, including self-efficacy, social norms, and internal and external barriers to diet and exercise, and walking were self-reported. We measured moderate to vigorous physical activity with accelerometers and diet with 24-hour recalls. We conducted a latent profile analysis of attitudes and barriers to diet and PA and identified four classes: (a) moderate diet and negative exercise attitudes, where participants were roughly average on dietary attitudes but reported exercise-related challenges, including lower social support, outcome expectancies, physical functioning, and self-efficacy; (b) few barriers and benefits of healthy diet and exercise, where participants reported fewer barriers and lower outcome expectancies for diet and PA; (c) moderate overall attitudes, where participants had average scores on most indicators but below-average exercise self-efficacy and slightly more exercise barriers; and (d) positive overall attitudes, characterized by more positive attitudes toward both diet and PA across most domains, particularly regarding self-efficacy to overcome exercise barriers. These profiles could inform efforts to tailor individual-level interventions for diet and PA of persons at high risk of chronic diseases.

SUBMITTER: Vaughan CA

PROVIDER: S-EPMC5794658 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Attitudes and Barriers to Healthy Diet and Physical Activity: A Latent Profile Analysis.

Vaughan Christine A CA Ghosh-Dastidar Madhumita M Dubowitz Tamara T

Health education & behavior : the official publication of the Society for Public Health Education 20170817 3

Healthy diet and physical activity (PA) prevent and reduce chronic disease. Social cognitive theory delineates multiple attitudes and barriers that influence these behaviors. Understanding covariation in these attitudes and barriers is complex. We examined whether individuals could be grouped into a small number of categories that are easier to study. Interviews were conducted with 982 adults from two low-income, predominantly African American neighborhoods in the same city. Social cognitive con ...[more]

PMID: 28817966

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Project description:Purpose:To investigate whether adolescents cluster into profiles based on where they accumulate moderate-to-vigorous physical activity (MVPA), if overall MVPA differs across profiles, and if walking to school and participant and neighborhood characteristics explain profile membership. Methods:Adolescents (N=528; mean age=14.12±1.44; 50% girls) wore accelerometers and Global Positioning Systems (GPS) trackers for 3.9±1.5 days to assess MVPA minutes in five locations: at home, at school, in home neighborhood, in school neighborhood, and other. Walking to school and participant characteristics were assessed by questionnaire, and neighborhood environment by Geographic Information Systems (GIS). Latent profile analysis (LPA) was used to identify profiles/groups of participants based on accumulation of physical activity across the five locations. Mixed-effects regression tested differences in overall MVPA, walking to school, and other characteristics across profiles. Results:Four initial profiles emerged: one Insufficiently Active profile and three "Active" profiles (Active Around School, Active Home Neighborhood, and Active Other Locations). The Insufficiently Active profile emerging from the first LPA (90% of participants) was further separated into four profiles in a second LPA: Insufficiently Active, and three additional "Active" profiles (Moderately-Active Around School, Moderately-Active Home Neighborhood, and Active At Home). Those in the six Active profiles had more overall MVPA (41.1-92.7 minutes/day) than those in the two Insufficiently Active profiles (34.5-38.3 minutes/day). Variables that differed across profiles included walking to school, sports/athletic ability, and neighborhood walkability. Conclusions:Although most participants did not meet the MVPA guideline, the six Active profiles showed the places in which many adolescents were able to achieve the 60-minute/day guideline. The home and school neighborhood (partly through walking to school), "other" locations, and to a lesser extent the home, appeared to be key sources for physical activity that distinguished active from insufficiently active adolescents. Finding the right match between the individual and physical activity source/location may be a promising strategy for increasing active travel and MVPA in adolescents.

Project description:BackgroundPhysical activity is associated with improved physical and mental health among children. However, physical activity declines and sedentary time increases with age, and large proportions of older children do not meet the recommended hour per day of moderate-to-vigorous-intensity physical activity (MVPA). The aim of this paper is to identify profiles of children based on the complex relationship between physical activity and sedentary time at ages 6 and 9 and explore how those profiles are associated with other covariates and how they change over time.MethodsValid accelerometer data were collected for 1132 children aged 6 and 1121 at age 9, with 565 children with data at both ages. We calculated the proportions of total wear time spent in sedentary, light and MVPA activity on both weekdays and weekends. Latent profile (class) analysis was applied separately to the two age groups to identify activity profiles. We then used latent transition analysis to explore transitions between profiles at the two time points.ResultsWe identified five profiles of activity at age 6 and six profiles at age 9. Although profiles were not directly equivalent, five classes captured similar patterns at both ages and ranged from very active to inactive. At both ages, active profiles, where the majority achieved the recommended MVPA guidelines, were more likely to be active at weekends than on weekdays. There was substantial movement between classes, with strongest patterns of movement to classes with no change or a decrease in MVPA. Transition between classes was associated with sex, BMI z-score, screen-viewing and participation in out-of-school activities.ConclusionsThis paper is the first to apply latent profile analysis to the physical activity of UK children as they move through primary school. Profiles were identified at ages 6 and 9, reflecting different weekday and weekend patterns of physical activity and sedentary time. There was substantial movement between profiles between ages 6 and 9, mostly to no change or less active profiles. Weekend differences suggest that greater focus on how weekend activity contributes to an average of 60 min per day of MVPA across the week may be warranted.

Project description:BackgroundThis systematic review aimed to evaluate AI chatbot characteristics, functions, and core conversational capacities and investigate whether AI chatbot interventions were effective in changing physical activity, healthy eating, weight management behaviors, and other related health outcomes.MethodsIn collaboration with a medical librarian, six electronic bibliographic databases (PubMed, EMBASE, ACM Digital Library, Web of Science, PsycINFO, and IEEE) were searched to identify relevant studies. Only randomized controlled trials or quasi-experimental studies were included. Studies were screened by two independent reviewers, and any discrepancy was resolved by a third reviewer. The National Institutes of Health quality assessment tools were used to assess risk of bias in individual studies. We applied the AI Chatbot Behavior Change Model to characterize components of chatbot interventions, including chatbot characteristics, persuasive and relational capacity, and evaluation of outcomes.ResultsThe database search retrieved 1692 citations, and 9 studies met the inclusion criteria. Of the 9 studies, 4 were randomized controlled trials and 5 were quasi-experimental studies. Five out of the seven studies suggest chatbot interventions are promising strategies in increasing physical activity. In contrast, the number of studies focusing on changing diet and weight status was limited. Outcome assessments, however, were reported inconsistently across the studies. Eighty-nine and thirty-three percent of the studies specified a name and gender (i.e., woman) of the chatbot, respectively. Over half (56%) of the studies used a constrained chatbot (i.e., rule-based), while the remaining studies used unconstrained chatbots that resemble human-to-human communication.ConclusionChatbots may improve physical activity, but we were not able to make definitive conclusions regarding the efficacy of chatbot interventions on physical activity, diet, and weight management/loss. Application of AI chatbots is an emerging field of research in lifestyle modification programs and is expected to grow exponentially. Thus, standardization of designing and reporting chatbot interventions is warranted in the near future.Systematic review registrationInternational Prospective Register of Systematic Reviews (PROSPERO): CRD42020216761 .

Dataset Information

Attitudes and Barriers to Healthy Diet and Physical Activity: A Latent Profile Analysis.

Publications

Attitudes and Barriers to Healthy Diet and Physical Activity: A Latent Profile Analysis.

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