Weight Loss Apps


Concierge Medicine & Obesity: Bringing Patients and Doctors Together

Obesity is a global health concern, with more than 39% of the population being classified as overweight (Sharpe et al., 2017). Unfortunately, the majority of conventional weight loss programs seem to be ineffective as one-third of patients regain weight in the same year of intervention.

Nevertheless, with the rapid advancements in digital health, e-interventions prove to be sufficient tools in healthcare. Weight loss apps, in particular, are becoming more and more beneficial to:

  • Help patients lose weight and sustain a healthy diet and lifestyle in the long term.
  • Increase patients’ motivation, physical activity, and emotional well-being.
  • Reduce the risk of fatal health outcomes (e.g., type 2 diabetes, heart diseases, and stroke).
  • Decrease delays and costs associated with conventional weight loss programs.

Concierge Medicine & Obesity: Bringing Patients and Doctors Together

Since many patients, especially those living in the westernized society, report having a smartphone with a data plan, mHealth apps have become an inseparable part of healthcare practices (Hurkmans et al., 2018). At the same time, although there’s an abundance of weight loss apps available online, users worldwide report lack of personalized approach. To improve patient outcomes and user satisfaction, Qolty helps doctors create the most effective weight loss app, which will suit their research and healthcare management goals. Note that customization options can support a wide range of patients’ personal targets, such as weight reduction, energy intake, and levels of physical activity. Qolty can help you integrate a wide range of customized options and measures, analyze data in real time 24/7, provide feedback and reinforcement to users, and create an engaging and easy to navigate interface at no cost. Apps can be available for different operating systems, such as Android and iOS. Some essential features include:

  • Personalized analysis of weight, age, habits, and goals. Based on patients’ individual information, a weight loss plan can be established, with daily calorie needs. In fact, calorie counting can help people reduce their energy intake and lose weight (Champagne et al., 2011). Participants can log their daily food intake into the app and get reports on their food intake, as well as graphs on their weight changes. Note that in case users know the total number of calories they ate, there can be a quick feature which can help them add the total number instead of all the details about their meal.
  • Digital advice for a dietary plan. The app can pull information about foods, brands, and restaurant dishes from a large database. It can even help users scan their products and save their searches. Scanning products during shopping can also be incorporated, including allergens and hidden products (e.g., high-fructose corn syrup). In addition, the app can allow users to take pictures of their meals and portion size. Note that digital imaging methods (e.g., snap options) regarding food intake and calorie counting has proved to be effective (Martin et al., 2014).
  • Complete analyses of calorie, fat, and protein consumption, as well as vitamins and minerals. A good weight loss app should integrate a complete library on nutrition, which can be verified by experts. To support long-term healthy habits, an engaging weight loss app can even contain a wide range of alternative homemade recipes (Hurkmans et al., 2018). Note that eating out has been associated with high energy intake and body weight (Bezerra et al., 2012)
  • Biometric data analysis. An effective weight loss app should share information about BMI, cholesterol levels, blood pressure, sleep patterns, and other vital indicators that have an impact on health. In fact, digital interventions prove to be successful not only for weight loss interventions but across multiple health domains (Sharpe et al., 2017). Thus, different apps can be synchronized, and information combined (e.g., alcohol intake and calories), to provide a full medical picture.
  • Digital advice on physical activity. In fact, a study conducted by Hurkmans asked participants to be active 30 minutes a day, five days a week on a moderate-intensity level or three days on a high-intensity level. Demos and pictures of suggested exercises can be included. The app can be synchronized with wearable devices. Note that wearables, which measure miles, steps, heart rate, etc., increase patients’ self-efficacy and improve health outcomes (Gualtieri et al., 2016).
  • Self-monitoring as a major behavioral component. People can monitor their progress and visualize their journey towards a healthier life. Changes can be assessed over certain periods of time to motivate participants. Snapshot options of a patient’s body, including fat body percentage, can also be implemented to help them visualize changes. Avatar possibilities (e.g., pictures of users without certain pounds) can support action planning and decrease the risk of relapse (Hurkmans et al., 2018).
  • Help button. Apps give participants and experts the chance to stay in touch 24/7. Since tailored approach can motivate people, participants can easily share their data or seek advice online, saving time from commuting, appointments, and costs associated with face-to-face interventions. Note that the financial burden is a major obstacle for any weight loss treatments (Vois et al., 2017).
  • Support system. Having a support system is vital. Community chat features, success stories, options to connect with friends, and so on and on are essential. Research shows that online social support is a major motivator in weight loss programs (Hwang et al., 2011). A study conducted by Hwang and colleagues revealed that 87.6% of participants lost weight thanks to social support.
  • Research emphasizes the importance of reinforcement (mainly positive reinforcement) in weight loss interventions. Points, pacts, challenges, badges, and financial reinforcement – incentives can motivate users and prevent relapse. A study conducted by Voils and colleagues (2017) showed that incentives delivered on a variable ratio schedule are highly effective. Note that the incentives varied between $0 to $30, which was uploaded to a gift card. The research team conducted a 2×2 factorial planning study, which involved reinforcement for dietary self-monitoring and interim weight loss. The design included four groups: one group received incentives for self-monitoring, and weight loss, one for daily self-monitoring, another for weight loss, and the fourth group did not receive any incentives. The results proved the importance of reinforcement.

Weight Loss Apps & User Engagement: Patients Are the Core of Medicine

Weight loss apps integrate numerous measures and customization options. Apart from all the features Qolty can help you incorporate into your weight loss app and research, mHealth apps have numerous benefits:

  • Data is accurate and available 24/7, and advice and feedback are personalized.
  • Weight loss apps balance an abundance of information and the readability of features to help patients navigate and visualize data effectively.
  • Push-on notifications and updates can be beneficial reminders, which can lead to action planning and healthy choices.
  • The interactive interface and behavioral concepts can improve user engagement.
  • Online support is available. Note that a button that leads to a Facebook page can help users gain more information and social support.
  • Obstacles related to human error, delays in recruitment, and costs are eliminated.

The potential of mHeath apps is mind-blowing. Hurkmans and colleagues concluded that in fact, weight loss programs could be completed via mHealth apps. Since many people own a smartphone, apps are easily accessible and well-accepted.

So, if you have an innovative idea about a weight loss program, let us help you build a sophisticated weight loss app with a wide range of integrated tools – with the sole purpose to benefit your research and improve your patients’ well-being. Do not forget that we can help you recruit participants and analyze data effectively.

ConductScience Digital Health can help you fight obesity and join the future of mHealth.


Bezerra, I. Curioni, C., & Sichieri, R. (2012).  Association between eating out of home and body weight. Nutrition Reviews, 70 (2), p. 65-79.

Champagne, C., Broyles, S., Moran, L., Cash, K., Levy, E., Lin, P., Batch, B., Lien, L., Funk., K., Dalcin, A., Loria, C., & Myers, V. (2011). Dietary intakes associated with successful weight loss and maintenance during the Weight Loss Maintenance Trial. Journal of the American Dietic Association, 111(12), p. 1826–1835.

Gualtieri, L., Rosenbluth, S., & Phillips, J. (2016). Can a Free Wearable Activity Tracker Change Behavior? The Impact of Trackers on Adults in a Physician-Led Wellness Group. JMIR Res Protoc, 5(4).

Hurkmans, E., Matthys, C., Bogaerts, A., Scheys, L., Devloo, K., & Seghers, J. (2018). Face-to-Face Versus Mobile Versus Blended Weight Loss Program: Randomized Clinical Trial. JMIR Mhealth and Uhealth, 6(1).

Hwang, K., Ottenbacher, A., Green, A., Cannon-Diehl, R., Richardson, O., Bernstam, E., & Thomas, E. (2010). Social support in an Internet weight loss community. International Journal of Medical Informatics, 79(1), p. 5–13.

Martin, C., Nicklas, T., Gunturk, B., Correa, J., Allen, R., & Champagne, C. (2014). Measuring food intake with digital photography. Hum Nutr Diet, 27, p. 72–81.

O’Neil, P., Miller-Kovach, K., Tuerk, P., Becker, L., Wadden, T., Fujioka, K., Hollander, P., Kushner, R., Timothy Garvey, W., Rubino, D., Malcolm, R., Weiss, D., Raum, W., Salyer, J., Hermayer, K., Rost, S., Veliko, J., & Sora, N. (2016). Randomized controlled trial of a nationally available weight control program tailored for adults with type 2 diabetes. Obesity (Silver Spring), 24(11), p. 2269-2277

Sharpe, E., Karasouli, E., & Meyer, C. (2017). Examining Factors of Engagement with Digital Interventions for Weight Management: Rapid Review. JMIR Research Protocols, 6(10).

Voils, C., Levince, E., Giersch, J., Pendergast, J., Hale, S., McVay, M., Reed, S., Yancy, W., Bennett, G., Strawbridge, E., White, Al., & Shaw, R. (2017). Study protocol for Log2Lose: A feasibility randomized controlled trial to evaluate financial incentives for dietary self-monitoring and interim weight loss in adults with obesity. Contemporary Clinical Trials, 65.

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