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Smartloss: A Personalized Mobile Health Intervention for Weight Management and Health Promotion.

Martin CK, Gilmore LA, Apolzan JW, Myers CA, Thomas DM, Redman LM - JMIR Mhealth Uhealth (2016)

Bottom Line: Behavior change theory (eg, the theory of planned behavior) can be effectively encapsulated into mobile phone-based health improvement programs, which is fostered by the ability of mobile phones and related devices to collect and transmit objective data in near real time and for health care or research professionals and clients to communicate easily.The clinician dashboard allows for efficient remote monitoring of all clients simultaneously, which may further increase adherence, personalization of treatment, treatment fidelity, and efficacy.Evidence of the efficacy of the SmartLoss approach has been reported previously.

View Article: PubMed Central - HTML - PubMed

Affiliation: Pennington Biomedical Research Center, Baton Rouge, LA, United States.

ABSTRACT

Background: Synonymous with increased use of mobile phones has been the development of mobile health (mHealth) technology for improving health, including weight management. Behavior change theory (eg, the theory of planned behavior) can be effectively encapsulated into mobile phone-based health improvement programs, which is fostered by the ability of mobile phones and related devices to collect and transmit objective data in near real time and for health care or research professionals and clients to communicate easily.

Objective: To describe SmartLoss, a semiautomated mHealth platform for weight loss.

Methods: We developed and validated a dynamic energy balance model that determines the amount of weight an individual will lose over time if they are adherent to an energy intake prescription. This model was incorporated into computer code that enables adherence to a prescribed caloric prescription determined from the change in body weight of the individual. Data from the individual are then used to guide personalized recommendations regarding weight loss and behavior change via a semiautomated mHealth platform called SmartLoss, which consists of 2 elements: (1) a clinician dashboard and (2) a mobile phone app. SmartLoss includes and interfaces with a network-connected bathroom scale and a Bluetooth-connected accelerometer, which enables automated collection of client information (eg, body weight change and physical activity patterns), as well as the systematic delivery of preplanned health materials and automated feedback that is based on client data and is designed to foster prolonged adherence with body weight, diet, and exercise goals. The clinician dashboard allows for efficient remote monitoring of all clients simultaneously, which may further increase adherence, personalization of treatment, treatment fidelity, and efficacy.

Results: Evidence of the efficacy of the SmartLoss approach has been reported previously. The present report provides a thorough description of the SmartLoss Virtual Weight Management Suite, a professionally programmed platform that facilitates treatment fidelity and the ability to customize interventions and disseminate them widely.

Conclusions: SmartLoss functions as a virtual weight management clinic that relies upon empirical weight loss research and behavioral theory to promote behavior change and weight loss.

No MeSH data available.


Related in: MedlinePlus

Panel A: A screenshot from the clinician dashboard for a hypothetical client. The SmartLoss intervention generates a SmartGraph that shows the predicted course of weight change for an individual assuming adherence to the energy intake target (yellow line), which can be either an increase or decrease in daily energy intake. The upper and lower bounds around the predicted weights (dashed blue lines) create a “weight zone” or “zone of adherence” and participants are considered adherent if their body weight (purple line) falls within the zone over time. Also, intervention strategies are delivered based on the participant’s actual weight in relation to this zone, and the graph is available to participants and health care or research professionals at any time via the clinician dashboard and app. Panel B: The SmartGraph is automatically delivered to participants smartphones at regular intervals (e.g., once per day, after a new body weight is received by the dashboard). Panel C: A “details view” in the app allows participants to see their weights over time in addition to their predicted weights, the weights associated with the upper and lower bounds of the adherence zone, and a color coded flag denoting if they were in or out of the zone (these same data are also available via the clinician dashboard).
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figure2: Panel A: A screenshot from the clinician dashboard for a hypothetical client. The SmartLoss intervention generates a SmartGraph that shows the predicted course of weight change for an individual assuming adherence to the energy intake target (yellow line), which can be either an increase or decrease in daily energy intake. The upper and lower bounds around the predicted weights (dashed blue lines) create a “weight zone” or “zone of adherence” and participants are considered adherent if their body weight (purple line) falls within the zone over time. Also, intervention strategies are delivered based on the participant’s actual weight in relation to this zone, and the graph is available to participants and health care or research professionals at any time via the clinician dashboard and app. Panel B: The SmartGraph is automatically delivered to participants smartphones at regular intervals (e.g., once per day, after a new body weight is received by the dashboard). Panel C: A “details view” in the app allows participants to see their weights over time in addition to their predicted weights, the weights associated with the upper and lower bounds of the adherence zone, and a color coded flag denoting if they were in or out of the zone (these same data are also available via the clinician dashboard).

Mentions: The functionality of the mathematical models is demonstrated on the Weight Loss Calculator website (Figure 1) [40]. As shown in Figure 1, to use the models the user is required to enter sex, age, weight, and height. The user then can elect to either reduce or increase their calorie intake to view the effect of the energy balance prescription on their body weight. The example in Figure 1 is for a 50-year-old female who is 65 inches tall, weighs 200 pounds, and wishes to reduce her intake by 500 kcal/day. This example is for a client wishing to diet and not dramatically increase levels of exercise, yet exercise-only interventions and interventions that include both energy restriction and high levels of exercise can be accommodated, as detailed in the following section. In such cases, the weight graphs provide a proxy measure of adherence to the energy balance prescription, which promotes an energy imbalance of a specified size through changes to diet and exercise behaviors. As demonstrated in the output, this patient’s weight will decrease by 17.4 pounds or 8.7% over 12 months if adherent to her new energy intake level. This website is free to use, and the SmartLoss intervention relies on these mathematical models to generate a SmartGraph, which shows the predicted course of weight change for the individual as well as the acceptable upper and lower limits of the projected weight change for the chosen energy intake target. As demonstrated in Figure 2, the upper and lower bounds of the weight loss projection present a proxy for adherence to the energy balance prescription, which, in our example, is induced by reducing energy intake. The individual is considered adherent if, throughout the course of the weight management program, the change in body weight falls between the upper and lower bounds, which is termed the “weight zone” or “zone of adherence.” Intervention strategies, including personalized feedback, are delivered based on the actual weight of the individual in relation to this zone [21]. One of the strengths of this approach is the ability to objectively quantify adherence to diet and exercise recommendations based on observed body weight. As described herein, the SmartGraph is automatically created by SmartLoss and updated with new weight data in near real time, and it is viewable by the client and health care or research professional on the SmartLoss clinician dashboard and the app.


Smartloss: A Personalized Mobile Health Intervention for Weight Management and Health Promotion.

Martin CK, Gilmore LA, Apolzan JW, Myers CA, Thomas DM, Redman LM - JMIR Mhealth Uhealth (2016)

Panel A: A screenshot from the clinician dashboard for a hypothetical client. The SmartLoss intervention generates a SmartGraph that shows the predicted course of weight change for an individual assuming adherence to the energy intake target (yellow line), which can be either an increase or decrease in daily energy intake. The upper and lower bounds around the predicted weights (dashed blue lines) create a “weight zone” or “zone of adherence” and participants are considered adherent if their body weight (purple line) falls within the zone over time. Also, intervention strategies are delivered based on the participant’s actual weight in relation to this zone, and the graph is available to participants and health care or research professionals at any time via the clinician dashboard and app. Panel B: The SmartGraph is automatically delivered to participants smartphones at regular intervals (e.g., once per day, after a new body weight is received by the dashboard). Panel C: A “details view” in the app allows participants to see their weights over time in addition to their predicted weights, the weights associated with the upper and lower bounds of the adherence zone, and a color coded flag denoting if they were in or out of the zone (these same data are also available via the clinician dashboard).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4834738&req=5

figure2: Panel A: A screenshot from the clinician dashboard for a hypothetical client. The SmartLoss intervention generates a SmartGraph that shows the predicted course of weight change for an individual assuming adherence to the energy intake target (yellow line), which can be either an increase or decrease in daily energy intake. The upper and lower bounds around the predicted weights (dashed blue lines) create a “weight zone” or “zone of adherence” and participants are considered adherent if their body weight (purple line) falls within the zone over time. Also, intervention strategies are delivered based on the participant’s actual weight in relation to this zone, and the graph is available to participants and health care or research professionals at any time via the clinician dashboard and app. Panel B: The SmartGraph is automatically delivered to participants smartphones at regular intervals (e.g., once per day, after a new body weight is received by the dashboard). Panel C: A “details view” in the app allows participants to see their weights over time in addition to their predicted weights, the weights associated with the upper and lower bounds of the adherence zone, and a color coded flag denoting if they were in or out of the zone (these same data are also available via the clinician dashboard).
Mentions: The functionality of the mathematical models is demonstrated on the Weight Loss Calculator website (Figure 1) [40]. As shown in Figure 1, to use the models the user is required to enter sex, age, weight, and height. The user then can elect to either reduce or increase their calorie intake to view the effect of the energy balance prescription on their body weight. The example in Figure 1 is for a 50-year-old female who is 65 inches tall, weighs 200 pounds, and wishes to reduce her intake by 500 kcal/day. This example is for a client wishing to diet and not dramatically increase levels of exercise, yet exercise-only interventions and interventions that include both energy restriction and high levels of exercise can be accommodated, as detailed in the following section. In such cases, the weight graphs provide a proxy measure of adherence to the energy balance prescription, which promotes an energy imbalance of a specified size through changes to diet and exercise behaviors. As demonstrated in the output, this patient’s weight will decrease by 17.4 pounds or 8.7% over 12 months if adherent to her new energy intake level. This website is free to use, and the SmartLoss intervention relies on these mathematical models to generate a SmartGraph, which shows the predicted course of weight change for the individual as well as the acceptable upper and lower limits of the projected weight change for the chosen energy intake target. As demonstrated in Figure 2, the upper and lower bounds of the weight loss projection present a proxy for adherence to the energy balance prescription, which, in our example, is induced by reducing energy intake. The individual is considered adherent if, throughout the course of the weight management program, the change in body weight falls between the upper and lower bounds, which is termed the “weight zone” or “zone of adherence.” Intervention strategies, including personalized feedback, are delivered based on the actual weight of the individual in relation to this zone [21]. One of the strengths of this approach is the ability to objectively quantify adherence to diet and exercise recommendations based on observed body weight. As described herein, the SmartGraph is automatically created by SmartLoss and updated with new weight data in near real time, and it is viewable by the client and health care or research professional on the SmartLoss clinician dashboard and the app.

Bottom Line: Behavior change theory (eg, the theory of planned behavior) can be effectively encapsulated into mobile phone-based health improvement programs, which is fostered by the ability of mobile phones and related devices to collect and transmit objective data in near real time and for health care or research professionals and clients to communicate easily.The clinician dashboard allows for efficient remote monitoring of all clients simultaneously, which may further increase adherence, personalization of treatment, treatment fidelity, and efficacy.Evidence of the efficacy of the SmartLoss approach has been reported previously.

View Article: PubMed Central - HTML - PubMed

Affiliation: Pennington Biomedical Research Center, Baton Rouge, LA, United States.

ABSTRACT

Background: Synonymous with increased use of mobile phones has been the development of mobile health (mHealth) technology for improving health, including weight management. Behavior change theory (eg, the theory of planned behavior) can be effectively encapsulated into mobile phone-based health improvement programs, which is fostered by the ability of mobile phones and related devices to collect and transmit objective data in near real time and for health care or research professionals and clients to communicate easily.

Objective: To describe SmartLoss, a semiautomated mHealth platform for weight loss.

Methods: We developed and validated a dynamic energy balance model that determines the amount of weight an individual will lose over time if they are adherent to an energy intake prescription. This model was incorporated into computer code that enables adherence to a prescribed caloric prescription determined from the change in body weight of the individual. Data from the individual are then used to guide personalized recommendations regarding weight loss and behavior change via a semiautomated mHealth platform called SmartLoss, which consists of 2 elements: (1) a clinician dashboard and (2) a mobile phone app. SmartLoss includes and interfaces with a network-connected bathroom scale and a Bluetooth-connected accelerometer, which enables automated collection of client information (eg, body weight change and physical activity patterns), as well as the systematic delivery of preplanned health materials and automated feedback that is based on client data and is designed to foster prolonged adherence with body weight, diet, and exercise goals. The clinician dashboard allows for efficient remote monitoring of all clients simultaneously, which may further increase adherence, personalization of treatment, treatment fidelity, and efficacy.

Results: Evidence of the efficacy of the SmartLoss approach has been reported previously. The present report provides a thorough description of the SmartLoss Virtual Weight Management Suite, a professionally programmed platform that facilitates treatment fidelity and the ability to customize interventions and disseminate them widely.

Conclusions: SmartLoss functions as a virtual weight management clinic that relies upon empirical weight loss research and behavioral theory to promote behavior change and weight loss.

No MeSH data available.


Related in: MedlinePlus