Dataset Information

Families Improving Together (FIT) for weight loss: a resource for translation of a positive climate-based intervention into community settings.

ABSTRACT: Climate-based weight loss interventions, or those that foster a nurturing family environment, address important ecological influences typically ignored by the traditional biomedical treatments. Promoting a climate characterized by positive communication, autonomy support, and parental warmth supports adolescents in making healthy behavioral changes. In addition, encouraging these skills within the family may have additional benefits of improved family functioning and other mental and physical health outcomes. Although several programs have identified essential elements and established the evidence base for the efficacy of these interventions, few have offered resources for the translation of these constructs from theoretical concepts to tangible practice. This paper provides strategies and resources utilized in the Families Improving Together (FIT) for weight loss randomized controlled trial to create a warm, supportive climate characterized by positive communication within the parent-child relationship. Detailed descriptions of how Project FIT emphasized these constructs through facilitator training, intervention curriculum, and process evaluation are provided as a resource for clinical and community interventions. Researchers are encouraged to provide resources to promote translation of evidence-based interventions for programs aiming to utilize a positive climate-based family approach for lifestyle modification.

SUBMITTER: Law LH

PROVIDER: S-EPMC7543080 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Families Improving Together (FIT) for weight loss: a resource for translation of a positive climate-based intervention into community settings.

Law Lauren H LH Wilson Dawn K DK St George Sara M SM Kitzman Heather H Kipp Colby J CJ

Translational behavioral medicine 20201001 4

Climate-based weight loss interventions, or those that foster a nurturing family environment, address important ecological influences typically ignored by the traditional biomedical treatments. Promoting a climate characterized by positive communication, autonomy support, and parental warmth supports adolescents in making healthy behavioral changes. In addition, encouraging these skills within the family may have additional benefits of improved family functioning and other mental and physical he ...[more]

PMID: 31167022

Similar Datasets

Project description:BackgroundAlthough weight loss is an important primary outcome in obesity interventions, family-based interventions may have cascading ripple effects that extend to other aspects of health and well-being. Identifying these secondary benefits may be useful for understanding how to best engage underserved African American families in weight loss. The present research examines whether African American adolescents and parents perceive secondary benefits from participating in a family-based weight-loss intervention, including secondary health, social, or cognitive benefits.MethodsQualitative data were obtained from families participating in the group-based intervention of the Families Improving Together (FIT) for Weight Loss trial. During the final week of the face-to-face motivational and family-based intervention program, families completed a guided open-ended group discussion about changes they experienced from participating (14 groups, N = 41 adolescents and 41 parents). Sessions were audiotaped, transcribed, and coded by independent pairs of raters using both inductive and deductive approaches. Guided by the multi-theoretical framework for the FIT trial, some themes were determined prior to coding using a deductive approach, including: (a) health outcomes (e.g., monitoring strategies for diet and physical activity), (b) social outcomes (e.g., involvement in family support, group support, autonomy support, family bonding, positive communication) and (c) cognitive outcomes (e.g., expression of self-confidence through self-efficacy, self-regulation, establishment of long-term goals). In addition to these pre-determined themes, the coding process included an inductive assessment, allowing for unexpected themes to surface as well around positive self-talk, relapse prevention, and monitoring strategies for different types of weight-related behaviors.ResultsAcross both adolescents and parents, the cognitive outcomes were the most frequently discussed outcomes, including self-regulation, monitoring strategies for diet, establishing long-term goals, and ultimate relapse prevention. Parents made a greater number of comments about the social outcomes, including family support, group support, self-efficacy, and family connectedness, whereas adolescents made a greater number of comments about positive family communication.ConclusionsThe results provide preliminary support for the positive secondary effects of weight loss programs on improving both cognitive and social well-being in underserved African American adolescents.Trial registrationClinicalTrials.gov # NCT01796067. https://clinicaltrials.gov/ct2/show/NCT01796067?term=NCT01796067&rank=1 The trial was registered on February 21, 2013 and the first participant was enrolled July 12, 2013.

Project description:BackgroundWeb-based tailored interventions offer rich opportunities for improved access to and personalization of behavioral interventions. However, despite the promise of this approach, the engagement and underrepresentation of minority groups remain major issues.ObjectiveThis study evaluated whether engagement (log-in status and log-in duration) with different types of tailored behavioral content from the Families Improving Together for weight loss web-based intervention was associated with changes in moderate to vigorous physical activity (MVPA) among African American families with overweight or obesity.MethodsParent-adolescent dyads were randomized to a web-based tailored intervention or web-based health education comparison program. The web-based intervention (N=119) was completed by parents and targeted 6 weight-related behaviors to support their adolescent children's weight loss goals (session contents included energy balance, fast food, fruits and vegetables, physical activity [PA], sedentary behavior, and sweetened beverages). MVPA was measured using accelerometers at baseline and after the intervention.ResultsUsing a hierarchical approach, the log-in status and duration for each web-based session were used to evaluate the additive effects of engagement with different types of tailored behavioral content on MVPA after the web-based intervention. Among parents, logging in to the PA session was not associated with greater MVPA (B=-12.561, 95% CI -18.759 to -6.367), but MVPA increased with greater log-in duration for the PA (B=0.008, 95% CI 0.004-0.012) and sedentary behavior (B= 0.008, 95% CI 0.004-0.012) sessions. These results suggest that parents who logged in to the PA session had lower MVPA, but MVPA increased with greater log-in duration for the PA and sedentary behavior sessions. These associations remained even after accounting for engagement with other content sessions. However, these engagement effects did not translate to the adolescents.ConclusionsThe results of this study highlight the need to disentangle the impact of engagement with different tailored content to improve the efficacy of tailored web-based interventions, especially for promoting PA in African American families.Trial registrationClinicalTrials.gov NCT01796067; https://clinicaltrials.gov/ct2/show/NCT01796067.

Project description:BACKGROUND:Globally, preterm birth is the leading cause of neonatal death with estimated prevalence and associated mortality highest in low- and middle-income countries (LMICs). Accurate identification of preterm infants is important at the individual level for appropriate clinical intervention as well as at the population level for informed policy decisions and resource allocation. As early prenatal ultrasound is commonly not available in these settings, gestational age (GA) is often estimated using newborn assessment at birth. This approach assumes last menstrual period to be unreliable and birthweight to be unable to distinguish preterm infants from those that are small for gestational age (SGA). We sought to leverage machine learning algorithms incorporating maternal factors associated with SGA to improve accuracy of preterm newborn identification in LMIC settings. METHODS AND FINDINGS:This study uses data from an ongoing obstetrical cohort in Lusaka, Zambia that uses early pregnancy ultrasound to estimate GA. Our intent was to identify the best set of parameters commonly available at delivery to correctly categorize births as either preterm (<37 weeks) or term, compared to GA assigned by early ultrasound as the gold standard. Trained midwives conducted a newborn assessment (<72 hours) and collected maternal and neonatal data at the time of delivery or shortly thereafter. New Ballard Score (NBS), last menstrual period (LMP), and birth weight were used individually to assign GA at delivery and categorize each birth as either preterm or term. Additionally, machine learning techniques incorporated combinations of these measures with several maternal and newborn characteristics associated with prematurity and SGA to develop GA at delivery and preterm birth prediction models. The distribution and accuracy of all models were compared to early ultrasound dating. Within our live-born cohort to date (n = 862), the median GA at delivery by early ultrasound was 39.4 weeks (IQR: 38.3-40.3). Among assessed newborns with complete data included in this analysis (n = 468), the median GA by ultrasound was 39.6 weeks (IQR: 38.4-40.3). Using machine learning, we identified a combination of six accessible parameters (LMP, birth weight, twin delivery, maternal height, hypertension in labor, and HIV serostatus) that can be used by machine learning to outperform current GA prediction methods. For preterm birth prediction, this combination of covariates correctly classified >94% of newborns and achieved an area under the curve (AUC) of 0.9796. CONCLUSIONS:We identified a parsimonious list of variables that can be used by machine learning approaches to improve accuracy of preterm newborn identification. Our best-performing model included LMP, birth weight, twin delivery, HIV serostatus, and maternal factors associated with SGA. These variables are all easily collected at delivery, reducing the skill and time required by the frontline health worker to assess GA. TRIAL REGISTRATION:ClinicalTrials.gov Identifier: NCT02738892.

Dataset Information

Families Improving Together (FIT) for weight loss: a resource for translation of a positive climate-based intervention into community settings.

Publications

Families Improving Together (FIT) for weight loss: a resource for translation of a positive climate-based intervention into community settings.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets