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1.
Mol Nutr Food Res ; 68(5): e2300338, 2024 Mar.
Article in English | MEDLINE | ID: mdl-38308150

ABSTRACT

SCOPE: Various lifestyle and sociodemographic factors have been associated with risk factors for type 2 diabetes (T2D). However, their combined associations with T2D risk factors have been studied much less. MATERIALS AND RESULTS: This study investigates cross-sectional associations of lifestyle patterns with T2D risk factors among 2925 adults at increased risk participating in the Stop Diabetes study. Lifestyle patterns are determined using principal component analysis (PCA) with several lifestyle and sociodemographic factors. The associations of lifestyle patterns with measures of glucose and lipid metabolism and serum metabolites analyzed by nuclear magnetic resonance (NMR) spectroscopy are studied using linear regression analysis. "Healthy eating" pattern is associated with better glucose and insulin metabolism, more favorable lipoprotein and fatty acid profiles and lower serum concentrations of metabolites related to inflammation, insulin resistance, and T2D. "High socioeconomic status and low physical activity" pattern is associated with increased serum concentrations of branched-chain amino acids, as are "Meat and poultry" and "Sleeping hours" patterns. "Snacks" pattern is associated with lower serum concentrations of ketone bodies. CONCLUSIONS: Our results show, in large scale primary care setting, that healthy eating is associated with better glucose and lipid metabolism and reveal novel associations of lifestyle patterns with metabolites related to glucose metabolism.


Subject(s)
Diabetes Mellitus, Type 2 , Adult , Humans , Diabetes Mellitus, Type 2/metabolism , Glucose , Lipid Metabolism , Finland/epidemiology , Cross-Sectional Studies , Life Style
2.
Stud Health Technol Inform ; 302: 1009-1010, 2023 May 18.
Article in English | MEDLINE | ID: mdl-37203555

ABSTRACT

Type 2 diabetes (T2D) can be prevented or delayed through a healthy lifestyle. Digital behavior change interventions (DBCIs) may offer cost-effective and scalable means to support lifestyle changes. This study investigated associations between user engagement with a habit-formation-based DBCI, the BitHabit app, and changes in T2D risk factors over 12 months in 963 participants at risk of T2D. User engagement was characterized by calculating use metrics from the BitHabit log data. User ratings were used as a subjective measure of engagement. The use metrics and user ratings were the strongest associated with improvements in diet quality. Weak positive associations were observed between the use metrics and changes in waist circumference and body mass index. No associations were found with changes in physical activity, fasting plasma glucose, or plasma glucose two hours after an oral glucose tolerance test. To conclude, increased use of the BitHabit app can have beneficial impacts on T2D risk factors, especially on diet quality.


Subject(s)
Diabetes Mellitus, Type 2 , Humans , Diabetes Mellitus, Type 2/prevention & control , Blood Glucose , Life Style , Exercise , Risk Factors
3.
Lancet Reg Health Eur ; 24: 100527, 2023 Jan.
Article in English | MEDLINE | ID: mdl-36620354

ABSTRACT

Background: No real-world randomised controlled trials (RCTs) have explored the effectiveness of lifestyle interventions based on multiple behaviour change theories and using combined digital and group-based face-to-face delivery to improve risk factors for type 2 diabetes (T2D). Methods: We conducted a one-year, multi-centre, unblinded, pragmatic RCT in primary healthcare using the habit formation, self-determination, and self-regulation theories among 2907 adults aged 18-74 years at increased T2D risk randomised into a digital lifestyle intervention group (DIGI, n = 967), a combined digital and group-based lifestyle intervention group (DIGI+GROUP, n = 971), and a control group receiving usual care (CONTROL, n = 969). We collected data on primary outcomes (diet quality by Healthy Diet Index [HDI], physical activity, body weight, fasting plasma glucose, 2-hour plasma glucose) and secondary outcomes (sedentary time, waist circumference, fasting plasma insulin) using digital questionnaires, clinical examinations, fasting blood tests, and 2-hour oral glucose tolerance tests. Main statistical analyses were performed using linear mixed-effects models adjusted for age, sex, and province. This RCT was registered with ClinicalTrials.gov, NCT03156478. Findings: The 2907 participants assigned were recruited between March 1st, 2017, and February 28th, 2018. Diet quality improved more (3·2 vs. 1·4 HDI points, p<0·001 for difference between groups, p'<0·001 for group*time interaction) and waist circumference tended to decrease more (-1·8 vs. -1·3 cm, p = 0·028, p' = 0·068) in DIGI+GROUP than in CONTROL. Fasting insulin tended to increase in CONTROL but not in DIGI (1·0 vs. 0·0 mU/L, p = 0·033, p' = 0·054) or in DIGI+GROUP (1·0 vs. 0·5 mU/L, p = 0·042, p' = 0·054). Good adherence to DIGI and DIGI+GROUP (≥median of 501 habits/year in DIGI, ≥5 of all 6 sessions in GROUP) was associated with improved diet quality and good adherence to DIGI with increased physical activity and decreased sedentary time. Interpretation: A lifestyle intervention based on multiple behaviour change theories and combined digital and group-based face-to-face delivery improves diet quality and tends to decrease abdominal adiposity and prevent an increase in insulin resistance. Good adherence improves the results of the interventions. Funding: Strategic Research Council at Academy of Finland, Academy of Finland, Novo Nordisk Foundation, and Finnish Diabetes Research foundation.

4.
J Med Internet Res ; 24(2): e31530, 2022 02 24.
Article in English | MEDLINE | ID: mdl-35200147

ABSTRACT

BACKGROUND: Digital health interventions may offer a scalable way to prevent type 2 diabetes (T2D) with minimal burden on health care systems by providing early support for healthy behaviors among adults at increased risk for T2D. However, ensuring continued engagement with digital solutions is a challenge impacting the expected effectiveness. OBJECTIVE: We aimed to investigate the longitudinal usage patterns of a digital healthy habit formation intervention, BitHabit, and the associations with changes in T2D risk factors. METHODS: This is a secondary analysis of the StopDia (Stop Diabetes) study, an unblinded parallel 1-year randomized controlled trial evaluating the effectiveness of the BitHabit app alone or together with face-to-face group coaching in comparison with routine care in Finland in 2017-2019 among community-dwelling adults (aged 18 to 74 years) at an increased risk of T2D. We used longitudinal data on usage from 1926 participants randomized to the digital intervention arms. Latent class growth models were applied to identify user engagement trajectories with the app during the study. Predictors for trajectory membership were examined with multinomial logistic regression models. Analysis of covariance was used to investigate the association between trajectories and 12-month changes in T2D risk factors. RESULTS: More than half (1022/1926, 53.1%) of the participants continued to use the app throughout the 12-month intervention. The following 4 user engagement trajectories were identified: terminated usage (904/1926, 46.9%), weekly usage (731/1926, 38.0%), twice weekly usage (208/1926, 10.8%), and daily usage (83/1926, 4.3%). Active app use during the first month, higher net promoter score after the first 1 to 2 months of use, older age, and better quality of diet at baseline increased the odds of belonging to the continued usage trajectories. Compared with other trajectories, daily usage was associated with a higher increase in diet quality and a more pronounced decrease in BMI and waist circumference at 12 months. CONCLUSIONS: Distinct long-term usage trajectories of the BitHabit app were identified, and individual predictors for belonging to different trajectory groups were found. These findings highlight the need for being able to identify individuals likely to disengage from interventions early on, and could be used to inform the development of future adaptive interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT03156478; https://clinicaltrials.gov/ct2/show/NCT03156478. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12889-019-6574-y.


Subject(s)
Diabetes Mellitus, Type 2 , Adolescent , Adult , Aged , Diabetes Mellitus, Type 2/prevention & control , Diet , Habits , Health Behavior , Humans , Life Style , Middle Aged , Young Adult
5.
Nutrients ; 13(11)2021 Nov 11.
Article in English | MEDLINE | ID: mdl-34836283

ABSTRACT

Eating competence (EC) is characterized by positive attitudes towards food and eating, having regular meals, eating a variety of foods, and internally regulated eating. We investigated the associations of changes in EC with changes in lifestyle, anthropometrics and biomarkers of glucose and lipid metabolism in 2291 adults at increased risk of type 2 diabetes as part of the StopDia study conducted in primary healthcare. EC and diet quality were assessed with validated digital questionnaires. During the intervention, the participants received either (1) the digital lifestyle intervention, (2) the combined digital and face-to-face group-based lifestyle intervention, or (3) standard care. EC increased among the participants independent of the intervention type. Increase in EC was associated with an increase in diet quality, high-density lipoprotein (HDL) cholesterol, and with a decrease in body mass index and waist circumference, regardless of baseline EC. Of the subdomains of EC, the contextual skills, food acceptance and eating attitudes were associated with various of these changes. Our results thus suggest that EC could be a potential target in lifestyle interventions aiming to improve the cardiometabolic health of people at type 2 diabetes risk.


Subject(s)
Cardiovascular Diseases/complications , Cardiovascular Diseases/prevention & control , Diabetes Mellitus, Type 2/complications , Diabetes Mellitus, Type 2/prevention & control , Diet , Eating , Feeding Behavior , Adiposity , Biomarkers , Body Mass Index , Delivery of Health Care , Exercise , Finland , Glucose , Humans , Life Style , Lipid Metabolism , Meals , Obesity/prevention & control , Overweight , Risk Assessment , Risk Factors , Surveys and Questionnaires
6.
Article in English | MEDLINE | ID: mdl-33670967

ABSTRACT

Lack of tools to evaluate the quality of diet impedes dietary counselling in healthcare. We constructed a scoring for a validated food intake questionnaire, to measure the adherence to a healthy diet that prevents type 2 diabetes (T2D). The Healthy Diet Index (HDI) consists of seven weighted domains (meal pattern, grains, fruit and vegetables, fats, fish and meat, dairy, snacks and treats). We studied the correlations of the HDI with nutrient intakes calculated from 7-day food records among 52 men and 25 women, and associations of HDI with biomarkers and anthropometrics among 645 men and 2455 women. The HDI correlated inversely with total fat (Pearson's r = -0.37), saturated fat (r = -0.37), monounsaturated fat (r = -0.37), and the glycaemic index of diet (r = -0.32) and positively with carbohydrates (r = 0.23), protein (r = 0.25), fibre (r = 0.66), magnesium (r = 0.26), iron (r = 0.25), and vitamin D (r = 0.27), (p < 0.05 for all). In the linear regression model adjusted for BMI and age, HDI is associated inversely with waist circumference, concentrations of fasting and 2-h glucose and triglycerides in men and women, total and LDL cholesterol in women, and fasting insulin in men (p < 0.05 for all). The HDI proved to be a valid tool to measure adherence to a health-promoting diet and to support individualised dietary counselling.


Subject(s)
Diabetes Mellitus, Type 2 , Diet, Healthy , Animals , Cross-Sectional Studies , Delivery of Health Care , Diabetes Mellitus, Type 2/prevention & control , Diet , Female , Humans , Male , Vegetables
7.
Nutrients ; 12(1)2019 Dec 30.
Article in English | MEDLINE | ID: mdl-31905938

ABSTRACT

A healthy diet prevents type 2 diabetes but is often difficult to adhere to. This cross-sectional study aimed to investigate whether eating competence is associated with diet or risk factors and prevalence of type 2 diabetes in individuals screened for type 2 diabetes risk. Eating competence is an indicator of food acceptance, positive attitudes, internal regulation and contextual skills related to food and eating. In total, 3147 Finnish adults aged 18-74 at an increased risk for type 2 diabetes identified via online risk screening participated in the baseline examinations of the Stop Diabetes (StopDia) study. The participants filled out the digital questionnaire on food intake, physical activity and sleep, and the Satter Eating Competence Inventory 2.0TM (ecSI 2.0TM). In addition, anthropometric and laboratory measurements were performed at primary healthcare centres. Eating competent individuals (37%, classified by ecSI 2.0TM) had a better quality of diet (p < 0.05 for all). Additionally, eating competence was associated with a lower prevalence of previously undiagnosed type 2 diabetes, abdominal obesity, metabolic syndrome and hypertriglyceridaemia, and with better insulin sensitivity (p < 0.05 for all). However, these associations, except for metabolic syndrome, were at least partly mediated by body mass index. Eating competence is associated with a healthy diet and could, thus, in the long term, support the prevention of type 2 diabetes.


Subject(s)
Diabetes Mellitus, Type 2/prevention & control , Diet, Healthy , Feeding Behavior , Insulin Resistance/physiology , Obesity/etiology , Adult , Aged , Cross-Sectional Studies , Eating , Female , Finland , Food Preferences , Health Behavior , Humans , Male , Middle Aged , Risk Factors
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