Losing weight and maintaining that loss is a complex endeavor, influenced by a variety of factors ranging from dietary choices to behavioral strategies. With the rise of mobile health applications, there is growing interest in leveraging technology to support weight loss efforts. This article explores the effectiveness of different weight loss approaches, including the role of behavioral strategies, the potential of mobile apps, and the impact of macronutrient composition on body composition and cardiometabolic health.
The Role of Behavioral Strategies in Weight Loss
Behavioral strategies play a crucial role in weight loss interventions. Among these strategies, self-monitoring and goal setting are common features in weight loss apps. However, adherence to self-monitoring is often low, especially among individuals with motivational challenges. A comprehensive set of behavioral strategies is needed in weight loss apps to increase relevance, utility, and impact in people at varying levels of adherence and motivation.
One behavioral strategy that is leveraged in virtually every visit of behavioral weight loss interventions and is specifically used to deal with adherence and motivational issues is problem-solving. Problem-solving is a counseling technique used to help an individual identify barriers to behavior change and generate solutions to be iteratively attempted until barriers are overcome. The counselor works through five steps with the patient, including (1) identifying a significant barrier to behavior change, (2) brainstorming a list of solutions with the patient, (3) having the patient select a solution he/she would be willing to try over the next week, (4) scheduling a time to attempt the solution, and (5) evaluating the outcome and trying additional solutions until the problem is solved.
Problem-solving has been shown to be effective as a standalone intervention for weight loss maintenance and is a strong predictor of weight loss outcomes. A problem-solving app can be designed to address a wide range of weight loss barriers.
The Habit App: Automating Problem-Solving for Weight Loss
To address the limitations of traditional weight loss apps, the Habit app was developed to automate the problem-solving process for common weight loss barriers. The app was designed to facilitate the five-step problem-solving process, guiding users through identifying challenges, brainstorming solutions, selecting a solution to try, making a plan, and following up on their progress.
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Habit App Development
To develop a database of weight loss problems, a steering committee of clinicians was queried, and problem-solving sessions with patients were conducted. Solutions were derived in problem-solving sessions and by the investigative team, who have extensive experience in behavioral weight loss counseling. An algorithm was then designed to ensure solutions provided by the app were tailored to user characteristics.
Eleven counselors (4 dietitians, 5 psychologists, 1 Master’s-level counselor, and 1 health educator) with experience counseling patients for weight management and practicing at UMass Memorial Medical Center composed the steering committee and were asked to name the most common problems patients experience when it comes to diet and exercise. A total of 77 problems were identified.
Adults with obesity (N=30; female: 27/30, 90%; age: mean 47, SD 13 years; body mass index [BMI]: mean 35.9, SD 4.2 kg/m2; non-Hispanic white: 23/30, 77%) were recruited via ads to participate in a single session of problem solving with a weight loss counselor. Adults were eligible if they had BMI between 30 and 45 kg/m2 and were currently trying to lose weight. Each participant attended a 1-hour session with a weight loss counselor in which the problem-solving session of the Diabetes Prevention Protocol Lifestyle Intervention was administered. These sessions followed the five-step problem-solving process described previously. Each participant was asked to discuss one diet and one exercise problem, and the counselor cycled through the process for each and came up with 10 solutions for each problem. These sessions generated 60 problems and 600 solutions, although many were duplicates.
A total of 137 responses for problems (77 from steering committee, 60 from patients) were reviewed by the investigative team who removed duplicates and infrequent responses and classified the remaining into nine diet and six exercise problem categories.
Each problem was accompanied by a set of one to five questions regarding user and lifestyle characteristics that would eliminate as many irrelevant solutions for the user as possible. The user and lifestyle characteristics that had relevance to multiple problems were queried during the profile setup. User characteristics included, but were not limited to, employment status, parental status, medical conditions, and climate.
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Habit App Features
The home screen of the Habit app gives users the choice to update their profile, address a diet or exercise challenge (“problem solving”), view solutions they currently have scheduled, or complete a weekly check-in. Participants did not prefer the terms “problems,” “barriers,” or “solutions,” but rather suggested “challenges” and “habits.” Therefore, language according to their preferences was adopted.
The Habit app prompts users to set up a profile in which they enter their height, weight, goals, current exercise habits and preferences, employment status, parental status, and notification preferences. Users can indicate how often they prefer to be reminded to weigh in. Fitbit users can log in using their Fitbit credentials and transfer data logged by the Fitbit device or entered in the Fitbit app, which allowed Habit to remind the user to problem solve if they exceeded their calorie goal. The Habit profile page includes a weight graph and lists the participant’s current list of habits.
For the “identify the problem” step, users can choose a diet or exercise challenge. Once selected, a list of challenges appears. Once they choose a challenge, they are asked to further specify the challenge by answering one to five questions, depending on the challenge selected. Participants also have the option of adding habits to the app if the habit they would like to work on is not included in the app’s database. A screen appears with a list of solutions, referred to as “habits” in the app. For each, the user can click “more info” to be linked to an online article that elaborates on the importance of the habit and how to implement it. For the “pick a solution to try,” the user selects “build my habit” for the habit they want to try. A list of days of the week and hours of the day is presented, and the app prompts the user to set reminders to implement the habit. A reminder notification occurs on the selected time and days until the user deactivates the notification. All scheduled habits are viewable by clicking the My Habits button on the main page. Users can delete the reminders here and view all current habits and all habits in their history. Each week, the user will be prompted to complete a weekly check-in, which asks the user to enter their current weight, to indicate all scheduled habits they successfully accomplished for the week, to indicate all the habits they would like to work on for the coming week, and to select new habits. Those indicating a desire to select new habits are brought back to the main page to enter a challenge.
Feasibility Testing of the Habit App
Two sequential single-arm pilot studies were conducted to examine the feasibility, acceptability, and use of the Habit app. Changes in problem-solving skills and weight over 8 weeks were described. Identical recruitment, screening, inclusion and exclusion criteria, and measures were used for pilots 1 and 2. Intervention refinements were made for pilot 2 based on pilot 1 results.
Results from both pilots show acceptable use of the Habit app over 8 weeks with on average two to three uses per week, the recommended rate of use. Acceptability ratings were mixed such that 54% (13/24) and 73% (11/15) of participants found the diet solutions helpful and 71% (17/24) and 80% (12/15) found setting reminders for habits helpful in pilots 1 and 2, respectively. In both pilots, participants lost significant weight (P=.005 and P=.03, respectively).
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The Impact of Macronutrient Composition on Weight Loss
In addition to behavioral strategies and mobile apps, the composition of the diet plays a significant role in weight loss effectiveness. A growing body of evidence suggests that during caloric restriction, a low-fat diet (<30% fat), higher in protein (HP) and lower in carbohydrate, compared with a conventionally recommended high carbohydrate (HC), low-fat diet may offer a number of advantages. These include improving body composition by attenuating the loss of fat-free mass (FFM) and/or increasing body fat mass (FM) loss and reducing cardiovascular disease risk factors including insulin sensitivity and the blood lipid profile.
One study investigated the long-term effects of two, low-fat, hypocaloric diets differing in carbohydrate:protein ratio (HP vs HC), on body composition and cardiometabolic health in overweight and obese males.
Study Design and Methods
One hundred and twenty-three overweight or obese males were recruited by public advertisement. Participants were excluded if they had a body mass index <27 or >40 kg m−2, were aged <20 or >65 years, had diabetes or uncontrolled hypertension; a history of gastrointestinal, renal, coronary, metabolic or hepatic disease or a malignancy; were taking hypoglycaemic medication or drugs which affect insulin sensitivity, or were smokers.
In a parallel study design, participants were blocked, matched for age and body mass index, then randomised by the trial coordinators using computer-generated random number allocation to consume either an energy-restricted HP, low-fat diet (HP, n=59) or an isocaloric high carbohydrate, low-fat diet (HC, n=64) for 52 weeks.
The dietary patterns were isocaloric and moderate energy restricted (7 MJ per day energy intake). The planned macronutrient profiles of the diets were: HP diet; protein 35% (142 g, ∼1.30 g kg−1 per day), carbohydrate 40% (135 g), fat 25% (total 53 g, saturated 14 g). HC diet; protein 17% (88 g, ∼0.85 g kg−1 per day), carbohydrate 58% (198 g), fat 25% (total 51 g, saturated 14 g) that was designed to reflect current conventional dietary recommendations.
Participants met individually with a qualified dietician at baseline, and every 2 weeks during the first 12 weeks of the study and monthly thereafter. During these visits, participants received detailed dietary prescription, meal planning advice, and recipe information. To further facilitate dietary compliance, the dietary patterns were structured into quantities of daily foods and presented as a food checklist.
Study Results
After 52 weeks both groups had similar reductions in body weight, FM and waist circumference. Participants who consumed the HP diet lost less FFM. After 52 weeks both groups had a similar increase in HDL cholesterol, and reductions in total cholesterol, low-density lipoprotein cholesterol, triglycerides, glucose, insulin, blood pressure and C-reactive protein.
Study Discussion
This study showed in overweight and obese men, consuming of a low-fat, energy-restricted diet with a higher protein intake improves body composition to a greater extent compared with an isocaloric HC diet. A HP and HC diet similarly reduced body weight and improved a number of cardiometabolic risk factors.
The HP diet group lost less FFM such that the FFM percentage contribution to overall weight loss in HP was only just over half that of the HC diet group (21% vs 35%). The mitigation of FFM reduction in HP was anticipated to some degree, given the level of protein intake in HP (∼1.24 g kg−1 per day).
The Broader Context of Weight Loss Research
The effectiveness of weight loss strategies is a topic of ongoing research, with numerous studies exploring different approaches and their impact on various health outcomes.
The Role of Physical Activity
Physical activity is widely recognized as an important component of weight management and overall health.
The Impact of Weight Stigma
Weight stigma, bias, and discrimination have received increasing attention among researchers and the public. People who experience weight discrimination are more likely to gain more weight over time than people with obesity who don’t describe these kinds of experiences.
The Use of Medications for Weight Loss
Medications for weight loss have also gained significant attention, with new drugs being developed and studied for their effectiveness in treating obesity and related conditions.
The Importance of Personalized Approaches
Given the complexity of weight loss and the variability in individual responses to different strategies, personalized approaches that take into account individual preferences, lifestyles, and health conditions are likely to be most effective.