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The aim of this pilot randomized controlled trial LI-PAD is to identify whether a 6-month intervention approach to individually optimize lifestyle behavior, physical activity, and diet, is feasible and leads to larger improvements in body weight, cardiovascular disease risk factors, and health-related quality of life compared to simple written lifestyle advice, in individuals with overweight or obesity.
The intervention group will be offered individual support for lifestyle behavioral change (precision health) and the control group will be offered written lifestyle advice, following national recommendations. In total, 60 population-based participants and 60 controls from the Gothenburg area, aged 45-65 years, will be recruited.
Individualized lifestyle Intervention with Physical Activity and Diet as precision health in individuals with overweight: a 6-month pilot randomized controlled trial (LI-PAD)
BACKGROUND Individuals with obesity and type 2 diabetes have an increased risk of cardiovascular disease (CVD). Lifestyle changes with physical activity (PA) and diet have been associated with temporary improvements in these risk factors. However, sustained behavior changes are difficult to achieve. In Sweden and globally, the built environment promotes overweight and obesity through high availability of cheap ultra-processed energy-dense foods and drinks and low possibility of physical activity. Although the recommendation is to apply an individualized approach, few studies have applied this in clinical practice. PA on prescription, using an individualized approach, has been partly implemented into Swedish health care, resulting in short- and long-term favorable effects of PA. However, an individualized approach to modifying diet has not yet been incorporated, despite evidence showing that interventions targeting PA and diet together have a greater effect on obesity. Another crucial limitation is that PA and diet recommendations are not developed for individual application.
Previous research has shown that it is important to consider individual adaption to medical conditions (e.g. risk profile, symptoms, comorbidities) and to psychosocial factors (e.g. preferences, barriers-facilitators, readiness-to-change). It is also know that individuals require support for behavioral change (e.g., counseling/coaching, group activities, education, nudging). Furthermore, there is a link between unhealthy food environments, unhealthy food consumptions, and food-related diseases, but results vary. Previous intervention studies have shown that an unhealthy food environment is a barrier of adherence to intended behavioral changes. However, it is unclear which components are particularly important, how much of each of them should be included, and whether freedom of choice is a more efficient option compared to offering a more standardized solution as in most randomized controlled trials (RCTs).
To prescribe optimal PA for the underlying disease, the investigators are currently developing accelerometer-based PA measures and recommendations adapted to individual fitness. The optimal diet prescription targets weight reduction, primarily as body fat, by restriction energy intake below the energy need. Although rapid early weight loss is important for sustained effects, too great energy restriction (due to unrealistic goals or applying a standard treatment) results in increased hunger and reduced metabolism, not leading to larger weight reduction when clinically applied. Research supports up to 10% weight loss at 6 months by applying a ~500 kcal/d reduction of energy intake below energy need. Energy need is most accurately determined with the doubly labeled water method but is too expensive for clinical use. An alternative method commonly used is to determine resting energy expenditure (REE), calculated from individual characteristics such as body weight, and multiplied with a factor for the PA level. However, REE determined using body weight is not accurate in obesity, and the most optimal equation considering body composition is too imprecise at an individual level. REE based on measured oxygen (O2) and carbon dioxide (CO2) exchange is preferred and can also be used to monitor the respiratory quotient (RQ=CO2/O2), which is an indicator of fat metabolism. Measured O2 and CO2 are inexpensive and can be used together with measured food intake for more individualized diet prescription and to track the effects of the intervention on metabolism and use of body fat.
The present study represents a unique progress of lifestyle intervention programs away from the more standardized randomized controlled trials to approach precision health:1) adaption to medical conditions and psychosocial factors, 2) individualized PA and diet advice based on individual and environmental measurement, 3) aids for achieving goals using education, skills training and supports (Figure 1). Although individual adaptions to medical conditions and psychosocial factors are already performed in health care, individualization of PA, diet, and support for behavioral change has rarely been implemented and evaluated. This individualization requires additional resources in health care. Therefore, it is important to determine the benefits of this approach and in future studies also follow up with cost-benefit analyses.
Previous received funding from the Swedish Heart-Lung Foundation for two successive research projects, "Aerobic fitness for cardiovascular health - a Swedish CArdioPulmonary bioimage study (SCAPIS)" targeting improved physical activity recommendations (20180379) and "Individualized physical activity recommendations for cardiovascular health: a SCAPIS program in precision health" (20210270),is the foundation for refined individualized exercise prescriptions, using the individual fitness levels. Knowledge from this research forms an important part of our current individualized lifestyle intervention program.
OBJECTIVES - PARADIGM AND HYPOTHESES This pilot study aims to identify whether an approach to optimize lifestyle behavior interventions with PA and diet at an individual environmental level leads to larger improvements in body weight, CVD risk factors, and health-related quality of life compared to simple lifestyle advice, and is feasible, in overweight individuals. The hypothesis is that the precision health approach is feasible and superior to simple lifestyle advice. The knowledge gained will be used to design a larger long-term intervention study. The paradigm "one size fits all" is not optimal, why our paradigm shift towards precision health is required.
Work plan - Overview LI-PAD is a randomized controlled trial with two arms (Figure 2). The intervention group will be offered individual precision health (Figure 1) and the control group usual care, defined as written lifestyle advice based on the general recommendations for diet and PA. In total, 60 population-based participants and 60 controls from the Gothenburg area will be recruited. LI-PAD includes outcome evaluation on change in body weight (primary outcome), risk factors for CVD, PA, and diet, and quality of life (secondary), process evaluation of feasibility, and identification of barriers and facilitators from qualitative data.
PARTICIPANTS Men and women aged 45-65 years will be recruited from the census register in the Gothenburg area and invited by mail. Included are individuals with a body mass index (BMI) of ≥28 and equal to or less than 34. Individuals with known coronary artery disease (clinical symptoms/earlier event) or other contraindications such as inability to understand language or unable to perform lifestyle interventions, will be excluded.
OUTCOME MEASURES (Figure 2)
PRIMARY OUTCOME (all participants) Weight reduction from 0 to 6 months.
SECONDARY OUTCOMES (all participants)
ADDITIONAL OUTCOMES (intervention group)
INTERVENTION GROUP Figure 1 presents a detailed description of the Lifestyle Intervention with Physical Activity and Diet (LI-PAD) for precision health.
CONTROL GROUP - one size fits all (Figure 2) The control group will receive usual care defined as simple lifestyle advice based on the general recommendations for diet and PA, including a healthy and varied diet, at least 300 mins/week of medium-intensity aerobic PA, and resistance training 2 times per week.
STATISTICAL ANALYSES Multilevel mixed modeling for repeated measures will be applied, to determine variation both at group and individual levels. Both continuous and categorical variables will be included, therefore both linear and logistic analyses will be employed. Some of the measures may include multiple interrelated categories (e.g., physical activity intensity categories). Therefore, regression models considering multicollinearity will be used (e.g., partial least square modeling). To map interrelationships between intervention components and outcome measures, statistical analyses can be expanded to structural equation modeling with or without partial least square regression for repeated measurement. Peer protocol analyses will be applied.
The primary intervention target is the reduction of body weight. The evidence-based and clinically relevant weight change has been determined to be 5-10% at 6 months. In the previous Look Action for Health in Diabetes (AHEAD) study, the mean (SD) % weight change up to one year in the intervention group was 8.5%. The corresponding value in the control group was 0.6 (10)%. Look AHEAD is one of the most comprehensive, and evaluated lifestyle behavior interventions with diet and physical activity. Data from the previous Look AHEAD study were used to estimate the sample size of each group in the present study:
To detect a mean (standard deviation, SD) reduction of 5 (10)% in the intervention group relative to the control group (effect size) with 80% power would include at least 60 participants in each group.
Our previous extensive research in the field of physical activity and cardiovascular health-epidemiology, measurement, methodology (accelerometry, fitness tests) and clinical aspects are all relevant to the present application. Specifically, the investigators have improved the processing of accelerometer data to useful measures of PA and the statistics to analyze more complex accelerometer data for group differences, individual development over time, and associations with measures of CVD risk. Based on this research, the investigators are currently developing accelerometer-based PA measures and recommendations adapted to individual fitness.
COLLABORATORS AND RESEARCH NETWORKS The affiliated research environment offers expertise in methodological, epidemiological, and clinical research. Collaborations and research networks include the SCAPIS, national network for quality and research (SWEDEHEART), and HPI study networks, the Swedish School of Exercise and Sports Science (GIH) as well as the Department of Food and Nutrition, and Sport Science (IKI), with IKI adding knowledge and skills in health promotion and lifestyle behavior change to the project. Group training will be led by physiotherapists at Sahlgrenska University Hospital/Östra.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Individualized physical activity and diet | Experimental | The intervention group will receive (offered) an optimal prescription of physical activity and diet based on medical (comorbidities, risk profile, medications, symptoms) and psycho-social (preferences for physical activities and diet, barriers-facilitators, social support, readiness to change, accelerometry and predicted VO2-max data (Ekblom Bak ergometer test), resting energy expenditure and knowledge and skills (lifestyle education school for achieving knowledge and skills in handling change in diet and physical activity in daily life) and support (health promotors, behavioral change app, food diary app, face-to-face, phone/online coaching, physical activity (aerobic training, strength training) in group and nudging. |
|
| Standard of care | No Intervention | The control group will receive (be offered) usual care defined as written lifestyle advices based on the general recommendations for diet (Nordic Nutrition Recommendations, 2012. Integrating nutrition and physical activity. Copenhagen: Nordic Council of Ministers, 2014) and PA (World Health Organization. World Health Organization guidelines on physical activity and sedentary behavior, 2020), including a healthy and varied diet, aiming at 300 mins/week of medium intensity aerobic PA and strength training two times per week. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Individualized physical activity and diet | Behavioral | Precision health |
|
| Measure | Description | Time Frame |
|---|---|---|
| Change in body weight 1 | Reduction in body weight (kg) in percent will be identified by using Lidén weighing scale. | From baseline to 6 months |
| Change in body weight 2 | Reduction in body weight (kg) in percent will be identified by using Lidén weighing scale. Less weight at the 1-month follow-up than at baseline is a better outcome than vice versa. | From baseline to 1 month (intervention group) |
| Change in body weight 3 | Reduction in body weight (kg) in percent will be identified by using Lidén weighing scale. Less weight at the 3-month follow-up than at the 1-month follow-up is a better outcome than vice versa. | From 1 month to 3 months (intervention group) |
| Change in body weight 4 | Reduction in body weight (kg) in percent will be identified by using Lidén weighing scale. Less weight at the 6-month follow-up than at the 3-month follow-up is a better outcome than vice versa. | From 3 months to 6 months (intervention group) |
| Measure | Description | Time Frame |
|---|---|---|
| Length in cm | Length will be combined with height to report body mass index (BMI) in kg/m^2. | From baseline to 6 months |
| Weight in kilograms | Weight will be combined with height to report body mass index (BMI) in kg/m^2. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Mats Börjesson, Professor | Sahlgrenska University Hospital (and University of Gothenburg) | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Centre for lifestyle intervention | Gothenburg | Sweden |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 17363746 | Background | Look AHEAD Research Group; Pi-Sunyer X, Blackburn G, Brancati FL, Bray GA, Bright R, Clark JM, Curtis JM, Espeland MA, Foreyt JP, Graves K, Haffner SM, Harrison B, Hill JO, Horton ES, Jakicic J, Jeffery RW, Johnson KC, Kahn S, Kelley DE, Kitabchi AE, Knowler WC, Lewis CE, Maschak-Carey BJ, Montgomery B, Nathan DM, Patricio J, Peters A, Redmon JB, Reeves RS, Ryan DH, Safford M, Van Dorsten B, Wadden TA, Wagenknecht L, Wesche-Thobaben J, Wing RR, Yanovski SZ. Reduction in weight and cardiovascular disease risk factors in individuals with type 2 diabetes: one-year results of the look AHEAD trial. Diabetes Care. 2007 Jun;30(6):1374-83. doi: 10.2337/dc07-0048. Epub 2007 Mar 15. | |
| 23796131 |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot | Yes | No | No | Study Protocol | Apr 12, 2024 | Apr 12, 2024 | Prot_000.pdf |
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| ID | Term |
|---|---|
| D050177 | Overweight |
| ID | Term |
|---|---|
| D044343 | Overnutrition |
| D009748 | Nutrition Disorders |
| D009750 | Nutritional and Metabolic Diseases |
| D001835 | Body Weight |
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| ID | Term |
|---|---|
| D004032 | Diet |
| ID | Term |
|---|---|
| D009747 | Nutritional Physiological Phenomena |
| D000066888 | Diet, Food, and Nutrition |
| D010829 | Physiological Phenomena |
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| From baseline to 6 months |
| Cardiovascular disease risk factor change - Body Mass Index (BMI) | Reduction in BMI will be identified by using Lidén weighing scale and seca length gauge and the formula: BMI = kg/m2 where kg is a person's weight in kilograms and m2 is their height in metres squared. BMI <18.5: underweight. BMI 18.5-24.9: normal weight. BMI ≥25.0: overweight. BMI ≥30.0: obesity (World Health Organization). | From baseline to 6 months |
| Cardiovascular disease risk factor change - Waist-to-hip ratio 1 | Waist-hip-ratio will be identified by using a measure and by dividing waist circumference (cm) at its narrowest point by hip circumference at the widest point (cm). A reduction in waist-hip ratio is a better outcome than vice versa. | From baseline to 6 months |
| Cardiovascular disease risk factor change - Waist-to-hip ratio 2 | Waist-to-hip ratio will be identified by using a measure and by dividing waist circumference (cm) at its narrowest point by hip circumference at the widest point (cm). A reduction in waist-hip ratio is a better outcome than vice versa. | From baseline to 1 month (intervention group) |
| Cardiovascular disease risk factor change - Waist-to-hip-ratio 3 | Waist-to-hip ratio will be identified by using a measure and by dividing waist circumference (cm) at its narrowest point by hip circumference at the widest point (cm). A reduction in waist-hip ratio is a better outcome than vice versa. | From 1 month to 3 months (intervention group) |
| Cardiovascular disease risk factor change - Waist-hip-ratio 4 | Waist-to-hip ratio will be identified by using a measure and by dividing waist circumference (cm) at its narrowest point by hip circumference at the widest point (cm). A reduction in waist-hip ratio is a better outcome than vice versa. | From 3 months to 6 months (intervention group) |
| Cardiovascular disease risk factor change - Blood pressure 1 | Systolic and diastolic blood pressure (mm Hg) will be identified by using Welch Allyn Blood Pressure Monitors. A reduction i blood pressure levels is a better outcome than vice versa. | From baseline to 6 months |
| Cardiovascular disease risk factor change - Blood pressure 2 | Systolic and diastolic blood pressure (mm Hg) will be identified by using Welch Allyn Blood Pressure Monitors. A reduction i blood pressure levels is a better outcome than vice versa. | From baseline to 1 month (intervention group) |
| Cardiovascular disease risk factor change - Blood pressure 3 | Systolic and diastolic blood pressure (mm Hg) will be identified by using Welch Allyn Blood Pressure Monitors. A reduction i blood pressure levels is a better outcome than vice versa. | From 1 month to 3 months (intervention group) |
| Cardiovascular disease risk factor change - Blood pressure 4 | Systolic and diastolic blood pressure (mm Hg) will be identified by using Welch Allyn Blood Pressure Monitors. A reduction i blood pressure levels is a better outcome than vice versa. | From 3 month to 6 months (intervention group) |
| Cardiovascular disease risk factor change - Long-term blood glucose control using glycated haemoglobin (HbA1c) | Reduction of HbA1C values will be identified by blood samples. A normal HbA1C will be below 42 mmol/mol (below 6%). Prediabetes will be referred to 42-47 mmol/mol and 6.0 to 6.4%. Diabetes will be identified as a HbA1C of 48 mmol/mol or over and 6.5%. | From baseline to 6 months |
| Cardiovascular disease risk factor change - Serum (total) Cholesterol (S-Cholesterol) | Reduction of S-Cholesterol will be identified by blood samples. A S-cholesterol below 200 mg/dL is ideal, from 200 to 239 mg/dL is borderline high and 240 mg/dL and over is high. | From baseline to 6 months |
| Cardiovascular disease risk factor change - High density lipoprotein (HDL) | Rise of HDL levels will be identified by blood samples. A value of 50+ mg/dL indicates less risk | From baseline to 6 months. |
| Cardiovascular disease risk factor change - Low densitiy lipoprotein (LDL) | A reduction in LDL levels will be identified by blood samples. LDL should be below 130 mg/dL. | From baseline to 6 months. |
| Cardiovascular disease risk factor change - Triglycerides | A decrease in mg/dL will be identified by blood samples. A healthy level for adult is below 150 mg/dL. | From baseline to 6 months. |
| Cardiovascular disease risk factor change - Aerobic fitness (predicted V02 max) | An increase in predicted maximal oxygen uptake capacity will be identified by using the Ekblom-Bak test. | From baseline to 6 months. |
| Cardiovascular disease risk factor change - Physical activity level | An increase in physical activity time spent at a higher intensity level will be identified using accelerometry data. | From baseline to 6 months |
| Change in functional lower extremity muscle strength | Number of sit-to-stand test during 30 s. A higher number means a better outcome. | From baseline to 6 months |
| Change in upper extremity muscle strength - Hand grip strength | The mean hand grip strength in kg for the right and left hands will be registered using the Jamar hand dynamometer. Participants will be asked to perform three attempts per hand (alternating between left and right hand). The participants will be seated, and the performance will be completed with the elbow in 90° flexion and with a straight wrist. The more kg, the better the outcome. | From baseline to 6 months |
| Change in muscle strength - Shoulder flexion | Shoulder flexion -The participants will be asked to move a dumbbell (3 kg for men, 2 kg for women) up and down with a straight arm between 0 and 90 degrees of shoulder flexion. A metronome set at 40 beats/minute, i.e., 20 contractions/minute, will keep everyone at the same pace. The test will be interrupted if the participant does not reach 90 degrees of shoulder flexion, performs the movement with a flexed elbow, or cannot keep up the pace. The maximal number of repetitions will be recorded. The more repetitions, the better the outcome. | From baseline to 6 months |
| Change in muscle strength - Ankle Plantar | Standing Heel-Rise Test for Ankle Plantar - The number of maximal heel lifts for each leg with the participant standing on a 10° wedge will be recorded, with one lift every other second (i.e., 30 heel rises/min) using a metronome. The participants will be allowed to contact the wall for balance, and on each rise, the head must touch a marking on a calibrated measuring stick. The opposing foot will be elevated slightly above the floor. The test will be terminated if the knee on the tested leg is bent or if the research subject is unable to perform the test.The higher number of heel lifts, the better the outcome. | From baseline to 6 months |
| Cardiovascular disease risk factor change - Healthy food intake | Healthy food intake will be measured using the Meal-Q web-based food frequency questionnaire. Healthy food intake is determined with a Healthy Food Index (HFI) as an aggregated composite score. The selected food items are important indicators of a healthy food intake behavior (vegetables, fruits and berries, nuts, vegetable oils, fatty fish). The higher the score of HFI, the better the outcome. | From baseline to 6 months |
| Change in resting energy expenditure 1 | Resting energy expenditure (REE) will be determined from measured oxygen uptake and the carbon dioxide during 20 minutes at rest in a lying position. The first 5 minutes of data will be excluded from the analysis. The 2-5 minutes with the lowest data and the lowest data variation, with a variation <10%, will be used in the analysis. Energy expenditure will be calculated as: REE (kcal/) = 3.9 x O2 (L) + 1.1 x CO2 (L). REE is connected to energy intake. A too restrictive intake with the aim of reducing body weight may lower REE and counteract metabolism of body fat and weight reduction. Physical activity may increase muscle mass and consequently increase REE. Unchanged or increased REE is a positive outcome. | From baseline to 6 months |
| Change in resting energy expenditure 2 | Resting energy expenditure (REE) will be determined from measured oxygen uptake and the carbon dioxide during 20 minutes at rest in a lying position. The first 5 minutes of data will be excluded from the analysis. The 2-5 minutes with the lowest data and the lowest data variation, with a variation <10%, will be used in the analysis. Energy expenditure will be calculated as: REE (kcal/) = 3.9 x O2 (L) + 1.1 x CO2 (L). REE is connected to energy intake. A too restrictive intake with the aim of reducing body weight may lower REE and counteract metabolism of body fat and weight reduction. Physical activity may increase muscle mass and consequently increase REE. Unchanged or increased REE is a positive outcome. | From baseline to 1 month (intervention group) |
| Change in resting energy expenditure 3 | Resting energy expenditure (REE) will be determined from measured oxygen uptake and the carbon dioxide during 20 minutes at rest in a lying position. The first 5 minutes of data will be excluded from the analysis. The 2-5 minutes with the lowest data and the lowest data variation, with a variation <10%, will be used in the analysis. Energy expenditure will be calculated as: REE (kcal/) = 3.9 x O2 (L) + 1.1 x CO2 (L). REE is connected to energy intake. A too restrictive intake with the aim of reducing body weight may lower REE and counteract metabolism of body fat and weight reduction. Physical activity may increase muscle mass and consequently increase REE. Unchanged or increased REE is a positive outcome. | From 1 month to 3 months (intervention group) |
| Change in resting energy expenditure 4 | Resting energy expenditure (REE) will be determined from measured oxygen uptake and the carbon dioxide during 20 minutes at rest in a lying position. The first 5 minutes of data will be excluded from the analysis. The 2-5 minutes with the lowest data and the lowest data variation, with a variation <10%, will be used in the analysis. Energy expenditure will be calculated as: REE (kcal/) = 3.9 x O2 (L) + 1.1 x CO2 (L). REE is connected to energy intake. A too restrictive intake with the aim of reducing body weight may lower REE and counteract metabolism of body fat and weight reduction. Physical activity may increase muscle mass and consequently increase REE. Unchanged or increased REE is a positive outcome. | From 3 months to 6 months (intervention group) |
| Change in respiratory quotient 1 | Oxygen uptake (O2) and carbon dioxide production (CO2) will be measured during 20 minutes at rest in a lying position. The first 5 minutes of data will be excluded from the analysis. The 2-5 minutes with the lowest data and the lowest data variation, with a variation <10%, will be used in the analysis. Respiratory quotient (RQ) is determined as the quotient CO2/O2 and indicates the proportion carbohydrate, fat and protein used as energy substrate. A value of 1.0 indicates only carbohydrate used and a value of 0.7 indicates only fat used. With a mixed food intake, the normal value range between 0.8-0.9. If an increase fat metabolism is induced by energy restriction, the RQ will be reduced. A reduced or maintained lower RQ (below 0.8) is a positive outcome. | From baseline to 6 months |
| Change in respiratory quotient 2 | Oxygen uptake (O2) and carbon dioxide production (CO2) will be measured during 20 minutes at rest in a lying position. The first 5 minutes of data will be excluded from the analysis. The 2-5 minutes with the lowest data and the lowest data variation, with a variation <10%, will be used in the analysis. Respiratory quotient (RQ) is determined as the quotient CO2/O2 and indicates the proportion carbohydrate, fat and protein used as energy substrate. A value of 1.0 indicates only carbohydrate used and a value of 0.7 indicates only fat used. With a mixed food intake, the normal value range between 0.8-0.9. If an increase fat metabolism is induced by energy restriction, the RQ will be reduced. A reduced or maintained lower RQ (below 0.8) is a positive outcome. | From baseline to 1 month (intervention group) |
| Change in respiratory quotient 3 | Oxygen uptake (O2) and carbon dioxide production (CO2) will be measured during 20 minutes at rest in a lying position. The first 5 minutes of data will be excluded from the analysis. The 2-5 minutes with the lowest data and the lowest data variation, with a variation <10%, will be used in the analysis. Respiratory quotient (RQ) is determined as the quotient CO2/O2 and indicates the proportion carbohydrate, fat and protein used as energy substrate. A value of 1.0 indicates only carbohydrate used and a value of 0.7 indicates only fat used. With a mixed food intake, the normal value range between 0.8-0.9. If an increase fat metabolism is induced by energy restriction, the RQ will be reduced. A reduced or maintained lower RQ (below 0.8) is a positive outcome. | From 1 month to 3 months (intervention group) |
| Change in respiratory quotient 4 | Oxygen uptake (O2) and carbon dioxide production (CO2) will be measured during 20 minutes at rest in a lying position. The first 5 minutes of data will be excluded from the analysis. The 2-5 minutes with the lowest data and the lowest data variation, with a variation <10%, will be used in the analysis. Respiratory quotient (RQ) is determined as the quotient CO2/O2 and indicates the proportion carbohydrate, fat and protein used as energy substrate. A value of 1.0 indicates only carbohydrate used and a value of 0.7 indicates only fat used. With a mixed food intake, the normal value range between 0.8-0.9. If an increase fat metabolism is induced by energy restriction, the RQ will be reduced. A reduced or maintained lower RQ (below 0.8) is a positive outcome. | From 3 months to 6 months (intervention group) |
| Change in health-related quality of life - Proportions of problems in five dimensions | HRQoL will be assessed using the EuroQoL five dimensions (5D) and three levels (3L), i.e.,the EQ-5D-3L questionnaire.The proportions of "no problems", "moderate problems" and "extreme problems and the EQ-5D-3L value index for each of the five dimensions will be registered for baseline and the 6-month follow-up. The higher proportion of participants with no problems, the better the outcome. The EuroQol Group Association comprises an international network of multidisciplinary researchers. | From baseline to 6 months |
| Change in health-related quality of life - Index score | The health state of each participant will be converted into the EuroQoL: EQ-5 dimensions and 3 levels (i.e., the EQ-5D-3L) value index, a score between 0 and 1. A higher index score means a better outcome as an index score of 0 means death and an index score of 1 means complete health. Values less than 0 indicate health states worse than death. The EuroQol Group Association comprises an international network of multidisciplinary researchers. | From baseline to 6 months |
| Change in health-related quality of life - Visual analog score of present health status | The participants will also be asked to score their self-perceived present health status using the EuroQoL Visual Analog Scale (i.e. the EQ-VAS). The EQ-VAS is a vertical scale ranging from 0, "The worst health you can imagine" to 100, "The best health you can imagine". That is, a higher score means a better outcome. The EuroQol Group Association comprises an international network of multidisciplinary researchers. | From baseline to 6 months |
| Change in health-related quality of life - Health status categories | The EuroQoL Visual Analog Scale (i.e., the EQ-VAS) scores will also be presented related to health status categories (very poor health, poor health, fair health, good health, and very good health).The higher EQ-VAS score, the better the outcome. The EuroQol Group Association comprises an international network of multidisciplinary researchers. | From baseline to 6 months |
| Change in self-reported physical activity level | Self-reported physical activity level will be assessed using the Saltin-Grimby Physical Activity Level Scale (i.e., the SGPALS). The SGPALS is a self-reporting scale with response categories that ranges from 1 to 4. A higher values refers to a higher level of self-reported physical activity level. A higher value at the 6-month follow-up than at the baseline is a better outcome than vice versa. | From baseline to 6 months |
| Change in exposure pattern to the food environment | Outlet density and quality will be recorded within the individual food environment using Global Positioning System (GPS) and Geographical Information System (GIS). Outlet quality refers to type of outlet, e.g. fast-food restaurants, full-service restaurants, bakeries, coffee shops, supermarkets, convenience stores, department stores, gas stations, fruit- and vegetable stores and markets. The individual food environment will cover 1) neighborhood area, 2) daily path area, and 3) work/school area. The individual will have a movement pattern within the individual food environment connected with a specific exposure to the different outlets. Change in individual exposure pattern will be used as outcome measure. The change in the individual exposure pattern will be related to change in the food pattern determined with the Meal-Q food frequency questionnaire. A change in the exposure pattern that is related to a healthier food pattern is a positive outcome of the study. | From baseline to 6 months |
| Change in exposure pattern to the built environment | The built environment density and quality will be recorded within the individual food environment using Global Positioning System (GPS) and Geographical Information System (GIS). Density and quality refers to number and type of conditions promoting a physical activity, e.g. green areas, in- and outdoor gyms, places for spontaneous sport, walking and bike lanes, and other recreational spaces. The individual built environment will cover 1) neighborhood area, 2) daily path area, and 3) work/school area. The individual will have a movement pattern within the individual built environment connected with a specific exposure to the different conditions. Change in individual exposure pattern will be used as outcome measure. The change in the individual exposure pattern will be related to change in physical activity level. A change in the exposure pattern that is related to a increased physical activity level is a positive outcome of the study. | From baseline to 6 months |
| Process evaluation measure 1 - Extent to which intervention was implemented as planned | The intervention offers:
The process evaluation measure 1 determines the number of these components implemented as planned. | During the 6 months intervention (intervention group) |
| Process evaluation measure 2 - Number of sessions of each intervention component delivered |
Process evaluation measure 2 quantifies the number of sessions delivered of each of 7 intervention components. | During the 6 months intervention (intervention group) |
| Process evaluation measure 3 - Number of sessions of each intervention component received |
Process evaluation measure 3 quantifies the number of sessions delivered of each of 7 intervention components that each participant took part of. | During the 6 months intervention (intervention group) |
| Process evaluation measure 4 - Satisfaction with individual lifestyle counselling | Process evaluation measure 4 attempts to provide an overall view of the participant satisfaction with the individual lifestyle counselling. It combines categorial with open-ended (free text) questions to determine:
The questions provide together an overall degree of satisfaction with the individual counseling and contribution to behavioral change and weight reduction. | After 6 months intervention (intervention group) |
| Process evaluation measure 5 - Satisfaction with the lifestyle school | Process evaluation measure 5 attempts to provide an overall view of the participant satisfaction with the lifestyle school. It combines categorial with open-ended (free text) questions to determine:
The questions provide together an overall degree of satisfaction with the lifestyle school and contribution to behavioral change and weight reduction. | After 6 months intervention (intervention group) |
| Process evaluation measure 6A - Perceived facilitators | A qualitative interview with open-ended questions will be performed at the end of the intervention to identify facilitators for lifestyle behavior change and weight reduction. | After 6 months intervention (intervention group) |
| Process evaluation measure 6B - Perceived barriers | A qualitative interview with open-ended questions will be performed at the end of the intervention to identify facilitators for lifestyle behavior change and weight reduction. | After 6 months intervention (intervention group) |
| Process evaluation measure 7 - Reach | Process evaluation measure 7 assesses the participation rate at each step of the study: 1) Recruitment; 2) Screening; 3) Baseline; 4) Medical evaluation; 5) Allocation; 6) Intervention period (intervention component and sessions, measurement sessions); 7) 6 months followup measurements. It also assesses characteristics of completers at each step versus non-completers. | From start of recruitment to end of 6 months followup measurements |
| Background |
| Look AHEAD Research Group; Wing RR, Bolin P, Brancati FL, Bray GA, Clark JM, Coday M, Crow RS, Curtis JM, Egan CM, Espeland MA, Evans M, Foreyt JP, Ghazarian S, Gregg EW, Harrison B, Hazuda HP, Hill JO, Horton ES, Hubbard VS, Jakicic JM, Jeffery RW, Johnson KC, Kahn SE, Kitabchi AE, Knowler WC, Lewis CE, Maschak-Carey BJ, Montez MG, Murillo A, Nathan DM, Patricio J, Peters A, Pi-Sunyer X, Pownall H, Reboussin D, Regensteiner JG, Rickman AD, Ryan DH, Safford M, Wadden TA, Wagenknecht LE, West DS, Williamson DF, Yanovski SZ. Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes. N Engl J Med. 2013 Jul 11;369(2):145-54. doi: 10.1056/NEJMoa1212914. Epub 2013 Jun 24. |
| 28214525 | Background | Thom G, Lean M. Is There an Optimal Diet for Weight Management and Metabolic Health? Gastroenterology. 2017 May;152(7):1739-1751. doi: 10.1053/j.gastro.2017.01.056. Epub 2017 Feb 15. |
| Background | Hassmén P, Wisén A, Hagströmer M. [Individanpassad rådgivning om fysisk aktivitet]. In: Dohrn I, Jansson E, Börjesson M, Hagström M, editors. FYSS20211, 4th ed. Stockholm: Läkartidningens Förlag; 2021. p. 169-79. |
| 36315564 | Background | Lundqvist S, Cider A, Larsson MEH, Hagberg L, Bjork MP, Borjesson M. The effects of a 5-year physical activity on prescription (PAP) intervention in patients with metabolic risk factors. PLoS One. 2022 Oct 31;17(10):e0276868. doi: 10.1371/journal.pone.0276868. eCollection 2022. |
| Background | Nordic Council of Ministers. Nordic Nutrition Recommendations 2012. Integrating nutrition and physical activity. Copenhagen: Nordic Council of Ministers; 2014. |
| 33652168 | Background | Pratt M. What's new in the 2020 World Health Organization Guidelines on Physical Activity and Sedentary Behavior? J Sport Health Sci. 2021 May;10(3):288-289. doi: 10.1016/j.jshs.2021.02.004. Epub 2021 Feb 27. No abstract available. |
| Background | Börjesson M. [Riskbedömning vid fysisk aktivitet]. In: Dorn I, Jansson E, Börjesson M, Hagströmer M, editors. FYSS2021, 4th ed. Stockholm: Läkartidningens Förlag; 2021. |
| Background | Kallings L, Eriksson M, Lundqvist S, Dohrn I. [Fysisk aktivitet på recept - FaR]. In: Dohrn I, Jansson E, Börjesson M, Hagströmer M, editors. FYSS2021, 4th ed. Stockholm: Läkartidningens Förlag; 2021. p. 142-51. |
| 24222017 | Background | Jensen MD, Ryan DH, Apovian CM, Ard JD, Comuzzie AG, Donato KA, Hu FB, Hubbard VS, Jakicic JM, Kushner RF, Loria CM, Millen BE, Nonas CA, Pi-Sunyer FX, Stevens J, Stevens VJ, Wadden TA, Wolfe BM, Yanovski SZ, Jordan HS, Kendall KA, Lux LJ, Mentor-Marcel R, Morgan LC, Trisolini MG, Wnek J, Anderson JL, Halperin JL, Albert NM, Bozkurt B, Brindis RG, Curtis LH, DeMets D, Hochman JS, Kovacs RJ, Ohman EM, Pressler SJ, Sellke FW, Shen WK, Smith SC Jr, Tomaselli GF; American College of Cardiology/American Heart Association Task Force on Practice Guidelines; Obesity Society. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Circulation. 2014 Jun 24;129(25 Suppl 2):S102-38. doi: 10.1161/01.cir.0000437739.71477.ee. Epub 2013 Nov 12. No abstract available. |
| 32057825 | Background | Most J, Redman LM. Impact of calorie restriction on energy metabolism in humans. Exp Gerontol. 2020 May;133:110875. doi: 10.1016/j.exger.2020.110875. Epub 2020 Feb 11. |
| 20846198 | Background | McDoniel SO, Hammond RS. A 24-week randomised controlled trial comparing usual care and metabolic-based diet plans in obese adults. Int J Clin Pract. 2010 Oct;64(11):1503-1511. doi: 10.1111/j.1742-1241.2010.02464.x. |
| 26923904 | Background | Madden AM, Mulrooney HM, Shah S. Estimation of energy expenditure using prediction equations in overweight and obese adults: a systematic review. J Hum Nutr Diet. 2016 Aug;29(4):458-76. doi: 10.1111/jhn.12355. Epub 2016 Feb 29. |
| 25771930 | Background | Miles-Chan JL, Dulloo AG, Schutz Y. Fasting substrate oxidation at rest assessed by indirect calorimetry: is prior dietary macronutrient level and composition a confounder? Int J Obes (Lond). 2015 Jul;39(7):1114-7. doi: 10.1038/ijo.2015.29. Epub 2015 Mar 16. |
| 18617080 | Background | Hollis JF, Gullion CM, Stevens VJ, Brantley PJ, Appel LJ, Ard JD, Champagne CM, Dalcin A, Erlinger TP, Funk K, Laferriere D, Lin PH, Loria CM, Samuel-Hodge C, Vollmer WM, Svetkey LP; Weight Loss Maintenance Trial Research Group. Weight loss during the intensive intervention phase of the weight-loss maintenance trial. Am J Prev Med. 2008 Aug;35(2):118-26. doi: 10.1016/j.amepre.2008.04.013. |
| 33917778 | Background | Cooney C, Daly E, McDonagh M, Ryan L. Evaluation of Measured Resting Metabolic Rate for Dietary Prescription in Ageing Adults with Overweight and Adiposity-Based Chronic Disease. Nutrients. 2021 Apr 8;13(4):1229. doi: 10.3390/nu13041229. |
| 26589125 | Background | Grimby G, Borjesson M, Jonsdottir IH, Schnohr P, Thelle DS, Saltin B. The "Saltin-Grimby Physical Activity Level Scale" and its application to health research. Scand J Med Sci Sports. 2015 Dec;25 Suppl 4:119-25. doi: 10.1111/sms.12611. |
| 27752306 | Background | Nybacka S, Berteus Forslund H, Wirfalt E, Larsson I, Ericson U, Warensjo Lemming E, Bergstrom G, Hedblad B, Winkvist A, Lindroos AK. Comparison of a web-based food record tool and a food-frequency questionnaire and objective validation using the doubly labelled water technique in a Swedish middle-aged population. J Nutr Sci. 2016 Oct 3;5:e39. doi: 10.1017/jns.2016.29. eCollection 2016. |
| 16720129 | Background | Compher C, Frankenfield D, Keim N, Roth-Yousey L; Evidence Analysis Working Group. Best practice methods to apply to measurement of resting metabolic rate in adults: a systematic review. J Am Diet Assoc. 2006 Jun;106(6):881-903. doi: 10.1016/j.jada.2006.02.009. |
| 34537993 | Background | Fridolfsson J, Arvidsson D, Andersen LB, Thorsson O, Wollmer P, Rosengren B, Karlsson MK, Dencker M. Physical activity spectrum discriminant analysis-A method to compare detailed patterns between groups. Scand J Med Sci Sports. 2021 Dec;31(12):2333-2342. doi: 10.1111/sms.14052. Epub 2021 Sep 23. |
| 24307184 | Background | Look AHEAD Research Group. Eight-year weight losses with an intensive lifestyle intervention: the look AHEAD study. Obesity (Silver Spring). 2014 Jan;22(1):5-13. doi: 10.1002/oby.20662. |
| 26092476 | Background | Whitehead AL, Julious SA, Cooper CL, Campbell MJ. Estimating the sample size for a pilot randomised trial to minimise the overall trial sample size for the external pilot and main trial for a continuous outcome variable. Stat Methods Med Res. 2016 Jun;25(3):1057-73. doi: 10.1177/0962280215588241. Epub 2015 Jun 19. |
| 32085652 | Background | Fridolfsson J, Borjesson M, Ekblom-Bak E, Ekblom O, Arvidsson D. Stronger Association between High Intensity Physical Activity and Cardiometabolic Health with Improved Assessment of the Full Intensity Range Using Accelerometry. Sensors (Basel). 2020 Feb 18;20(4):1118. doi: 10.3390/s20041118. |
| 41840656 | Derived | Persson CU, Arvidsson D, Fridolfsson J, Bergstrom G, Lundberg CE, Borjesson M. An individualised Lifestyle Intervention with Physical Activity and Diet in individuals with overweight and obesity (LI-PAD)-study protocol of a 6-month randomised controlled study 1a. Trials. 2026 Mar 16;27(1):232. doi: 10.1186/s13063-026-09606-6. |
| D012816 |
| Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |