scholarly journals Effects of Mobile Health Prompts on Self-Monitoring and Exercise Behaviors Following a Diabetes Prevention Program: Secondary Analysis From a Randomized Controlled Trial (Preprint)

2018 ◽  
Author(s):  
Megan M MacPherson ◽  
Kohle J Merry ◽  
Sean R Locke ◽  
Mary E Jung
Keyword(s):  
Mobile Health ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Secondary Analysis ◽  
Diabetes Prevention Program ◽  
Exercise Behavior ◽  
Moderate Intensity ◽  
User Engagement ◽  
Self Monitoring ◽  
Exercise Behaviors

BACKGROUND A number of mobile health (mHealth) apps exist that focus specifically on promoting exercise behavior. To increase user engagement, prompts, such as text messages, emails, or push notifications, are often used. To date, little research has been done to understand whether, and for how long, these prompts influence exercise behavior. OBJECTIVE This study aimed to assess the impact of prompts on mHealth self-monitoring and self-reported exercise in the days following a prompt and whether these effects differ based on exercise modality. METHODS Of the possible 99 adults at risk for developing type II diabetes who participated in a diabetes prevention program, 69 were included in this secondary analysis. Participants were randomly assigned to 1 of the following 2 exercise conditions: high-intensity interval training or moderate-intensity continuous training. In the year following a brief, community-based diabetes prevention program involving counseling and supervised exercise sessions, all participants self-monitored their daily exercise behaviors on an mHealth app in which they were sent personalized prompts at varying frequencies. mHealth self-monitoring and self-reported exercise data from the app were averaged over 1, 3, 5, and 7 days preceding and following a prompt and subsequently compared using t tests. RESULTS In the year following the diabetes prevention program, self-monitoring (t68=6.82; P<.001; d=0.46) and self-reported exercise (t68=2.16; P=.03; d=0.38) significantly increased in the 3 days following a prompt compared with the 3 days preceding. Prompts were most effective in the first half of the year, and there were no differences in self-monitoring or self-reported exercise behaviors between exercise modalities (P values >.05). In the first half of the year, self-monitoring was significant in the 3 days following a prompt (t68=8.61; P<.001; d=0.60), and self-reported exercise was significant in the 3 days (t68=3.7; P<.001; d=0.37), 5 days (t67=2.15; P=.04; d=0.14), and 7 days (t68=2.46; P=.02; d=0.15) following a prompt, whereas no significant changes were found in the second half of the year. CONCLUSIONS This study provides preliminary evidence regarding the potential influence of prompts on mHealth self-monitoring and self-reported exercise and the duration for which prompts may be effective as exercise behavior change tools. Future studies should determine the optimal prompting frequency for influencing self-reported exercise behaviors. Optimizing prompt frequency can potentially reduce intervention costs and promote user engagement. Furthermore, it can encourage consumers to self-monitor using mHealth technology while ensuring prompts are sent when necessary and effective. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR2-10.2196/11226

scholarly journals Effects of Mobile Health Prompts on Self-Monitoring and Exercise Behaviors Following a Diabetes Prevention Program: Secondary Analysis From a Randomized Controlled Trial

10.2196/12956 ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. e12956 ◽  
Author(s):  
Megan M MacPherson ◽  
Kohle J Merry ◽  
Sean R Locke ◽  
Mary E Jung
Keyword(s):  
Mobile Health ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Secondary Analysis ◽  
Diabetes Prevention Program ◽  
Exercise Behavior ◽  
Moderate Intensity ◽  
User Engagement ◽  
Self Monitoring ◽  
Exercise Behaviors

Background A number of mobile health (mHealth) apps exist that focus specifically on promoting exercise behavior. To increase user engagement, prompts, such as text messages, emails, or push notifications, are often used. To date, little research has been done to understand whether, and for how long, these prompts influence exercise behavior. Objective This study aimed to assess the impact of prompts on mHealth self-monitoring and self-reported exercise in the days following a prompt and whether these effects differ based on exercise modality. Methods Of the possible 99 adults at risk for developing type II diabetes who participated in a diabetes prevention program, 69 were included in this secondary analysis. Participants were randomly assigned to 1 of the following 2 exercise conditions: high-intensity interval training or moderate-intensity continuous training. In the year following a brief, community-based diabetes prevention program involving counseling and supervised exercise sessions, all participants self-monitored their daily exercise behaviors on an mHealth app in which they were sent personalized prompts at varying frequencies. mHealth self-monitoring and self-reported exercise data from the app were averaged over 1, 3, 5, and 7 days preceding and following a prompt and subsequently compared using t tests. Results In the year following the diabetes prevention program, self-monitoring (t68=6.82; P<.001; d=0.46) and self-reported exercise (t68=2.16; P=.03; d=0.38) significantly increased in the 3 days following a prompt compared with the 3 days preceding. Prompts were most effective in the first half of the year, and there were no differences in self-monitoring or self-reported exercise behaviors between exercise modalities (P values >.05). In the first half of the year, self-monitoring was significant in the 3 days following a prompt (t68=8.61; P<.001; d=0.60), and self-reported exercise was significant in the 3 days (t68=3.7; P<.001; d=0.37), 5 days (t67=2.15; P=.04; d=0.14), and 7 days (t68=2.46; P=.02; d=0.15) following a prompt, whereas no significant changes were found in the second half of the year. Conclusions This study provides preliminary evidence regarding the potential influence of prompts on mHealth self-monitoring and self-reported exercise and the duration for which prompts may be effective as exercise behavior change tools. Future studies should determine the optimal prompting frequency for influencing self-reported exercise behaviors. Optimizing prompt frequency can potentially reduce intervention costs and promote user engagement. Furthermore, it can encourage consumers to self-monitor using mHealth technology while ensuring prompts are sent when necessary and effective. International Registered Report Identifier (IRRID) DERR2-10.2196/11226

Weight and Physical Activity: a 12-month follow-up of a digital diabetes prevention program (VP Transform for Prediabetes) in adults with prediabetes (Preprint)

2020 ◽  
Author(s):  
Ryan Batten ◽  
Meshari F Alwashmi ◽  
Gerald Mugford ◽  
Misa Muccio ◽  
Angele Besner ◽  
...  
Keyword(s):  
Physical Activity ◽  
Type 2 Diabetes ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Secondary Analysis ◽  
Diabetes Prevention Program ◽  
Global Scale ◽  
Lifestyle Modifications ◽  
Behavior Tracking

BACKGROUND The prevalence of diabetes increasingly rapidly. Previous research has demonstrated the efficacy of a diabetes prevention program (DPP) in lifestyle modifications which can prevent or delay the onset of type 2 diabetes among individuals at-risk. Digital DPPs have the potential to utilize technology, in conjunction with behavior change science, to prevent prediabetes on a national and global scale OBJECTIVE The aim of this study was to investigate the effects of a digital DPP (VP Transform for Prediabetes) on weight loss and physical activity among participants who had completed twelve months of the program. METHODS This study was a secondary analysis of retrospective data of adults with prediabetes who were enrolled in VP Transform for Prediabetes for 12 months of the program. The program incorporates interactive mobile computing, remote monitoring, an evidence-based curriculum, behavior tracking tools, health coaching and online peer support to prevent or delay the onset of type 2 diabetes. Analysis included data that were collected at baseline and after 12 months of the VP Transform for Prediabetes DPP. RESULTS The sample (N=1,095) comprised people with prediabetes who completed 12 months of the VP Transform for Prediabetes program. Participants included 67.7% female, with a mean age of 53.6 (SD 9.75). On average, participants decreased their weight by 10.9 pounds (5.5%) and increased their physical activity by 91.2 minutes per week. CONCLUSIONS These results suggest that VP Transform for Prediabetes is effective at preventing type 2 diabetes through significant reduction in body weight and increase of physical activity. Furthermore, these results suggest that the DPP remains effective 12 months after beginning the program. A prospective, controlled clinical study is warranted to validate these findings.

Combining supervised run interval training or moderate-intensity continuous training with the diabetes prevention program on clinical outcomes

2019 ◽  
Vol 119 (7) ◽  
pp. 1503-1512
Author(s):  
Nicole M. Gilbertson ◽  
Joan A. Mandelson ◽  
Kathryn Hilovsky ◽  
Jeremy D. Akers ◽  
Trent A. Hargens ◽  
...  
Keyword(s):  
Clinical Outcomes ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Interval Training ◽  
Diabetes Prevention Program ◽  
Moderate Intensity ◽  
Continuous Training

scholarly journals A Digital Diabetes Prevention Program (Transform) for Adults With Prediabetes: Secondary Analysis (Preprint)

2019 ◽  
Author(s):  
Meshari F Alwashmi ◽  
Gerald Mugford ◽  
Waseem Abu-Ashour ◽  
Misa Nuccio
Keyword(s):  
Type 2 Diabetes ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Secondary Analysis ◽  
Diabetes Prevention Program ◽  
Global Scale ◽  
Lifestyle Modifications ◽  
Exercise Frequency ◽  
Behavior Tracking

BACKGROUND The prevalence of diabetes is increasing among adults globally. Research has demonstrated that a diabetes prevention program (DPP), which focuses on developing and maintaining health-promoting lifestyle modifications, can prevent or delay the onset of type 2 diabetes among at-risk individuals. The implementation of a digitally adapted DPP has the potential to prevent prediabetes on a national and global scale by using technology and behavior change science. OBJECTIVE This study aimed to investigate the effects of a novel digital therapeutic DPP (Transform) on weight loss, body mass index (BMI), exercise frequency, and work absenteeism. METHODS This study was a secondary analysis of retrospective data of adults with prediabetes who were enrolled in the Transform DPP from December 2016 to December 2017. The program incorporates interactive mobile computing, remote monitoring, an evidence-based curriculum, behavior tracking tools, health coaching, and online peer support to prevent or delay the onset of type 2 diabetes. The analysis included data that were collected at baseline and after 4 months of the Transform DPP. RESULTS The sample (N=273) comprised people with prediabetes who completed 4 months of the Transform program. Participants included 70.3% women, with a mean age of 54.0 (SD 11.2) years. On average, participants decreased their weight by 13.3 lbs (6.5%) and their BMI by 1.9 kg/m2. On average, participants increased their exercise frequency by 1.7 days per week, and absenteeism was reduced by almost half a day per month. CONCLUSIONS These results suggest that the digital therapeutic DPP (Transform) is effective at preventing type 2 diabetes through a significant reduction in body weight and an increase of physical activity. A prospective, controlled clinical study is warranted to validate these findings.

scholarly journals How is the Behavior Change Technique Content of the NHS Diabetes Prevention Program Understood by Participants? A Qualitative Study of Fidelity, With a Focus on Receipt

2021 ◽  
Author(s):  
Lisa M Miles ◽  
Rhiannon E Hawkes ◽  
David P French
Keyword(s):  
Problem Solving ◽  
Qualitative Study ◽  
Behavior Change ◽  
Prevention Program ◽  
Behavioral Interventions ◽  
Diabetes Prevention ◽  
Group Discussion ◽  
Diabetes Prevention Program ◽  
Action Planning ◽  
Self Monitoring

Abstract Background The National Health Service (NHS) Diabetes Prevention Program (DPP) is a nationally implemented behavioral intervention for adults at high risk of developing Type 2 diabetes in England, based on a program specification that stipulates inclusion of 19 specific behavior change techniques (BCTs). Previous work has identified drift in fidelity from these NHS England specifications through providers’ program manuals, training, and delivery, especially in relation to BCTs targeting self-regulatory processes. Purpose This qualitative study investigates intervention receipt, i.e., how the self-regulatory BCT content of the NHS-DPP is understood by participants. Methods Twenty participants from eight NHS-DPP locations were interviewed; topics included participants’ understanding of self-monitoring of behavior, goal setting, feedback, problem solving, and action planning. Transcripts were analyzed thematically using the framework method. Results There was a wide variation in understanding among participants for some BCTs, as well as between BCTs. Participants described their understanding of “self-monitoring of behaviors” with ease and valued BCTs focused on outcomes (weight loss). Some participants learned how to set appropriate behavioral goals. Participants struggled to recall “action planning” or “problem solving” or found these techniques challenging to understand, unless additional support was provided (e.g., through group discussion). Conclusions Participants’ lack of understanding of some self-regulatory BCTs is consistent with the drift across fidelity domains previously identified from NHS design specifications. Behavioral interventions should build-in necessary support for participants to help them understand some BCTs such as action planning and problem solving. Alternatively, these self-regulatory BCTs may be intrinsically difficult to use for this population.

scholarly journals A Digital Diabetes Prevention Program (Transform) for Adults With Prediabetes: Secondary Analysis

JMIR Diabetes ◽  
10.2196/13904 ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. e13904 ◽  
Author(s):  
Meshari F Alwashmi ◽  
Gerald Mugford ◽  
Waseem Abu-Ashour ◽  
Misa Nuccio
Keyword(s):  
Prevention Program ◽  
Diabetes Prevention ◽  
Secondary Analysis ◽  
Diabetes Prevention Program

scholarly journals Drivers of weight loss in a CDC-recognized digital diabetes prevention program

2020 ◽  
Vol 8 (1) ◽  
pp. e001132 ◽  
Author(s):  
Stefanie L Painter ◽  
Wei Lu ◽  
Jennifer Schneider ◽  
Roberta James ◽  
Bimal Shah
Keyword(s):  
Weight Loss ◽  
Regression Analysis ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Diabetes Prevention Program ◽  
Self Monitoring ◽  
Using Data ◽  
Predictors Of Weight Loss ◽  
Design And Methods ◽  
The Impact

IntroductionTo investigate the impact of the digital Livongo Diabetes Prevention Program (DPP) on weight at 12 months, understand participants’ self-monitoring behaviors associated with greater weight loss, and evaluate the impact of coaching interactions on more frequent self-monitoring behaviors.Research design and methodsA retrospective analysis was performed using data from 2037 participants enrolled in the Livongo DPP who completed lesson 1 and recorded a starting weight during 2016–2017. Self-monitoring behaviors, including weigh-ins, food logging, activity, and coach–participant interactions, were analyzed at 6 and 12 months. Subgroup analysis was conducted based on those who were highly engaged versus those minimally engaged. Multiple regression analysis was performed using demographic, self-monitoring, and lesson attendance data to determine predictors of weight loss at 12 months and coaching impact on self-monitoring.ResultsParticipants had a mean age of 50 years (SD ±12), with a starting weight of 94 kg (SD ±21), were college-educated (78%), and were female (74%). Overall, participants lost on average 5.1% of their starting weight. Highly engaged participants lost 6.6% of starting body weight, with 25% losing ≥10% at 12 months. Logistic regression analysis showed each submitted food log was associated with 0.23 kg (p<0.05) weight loss, each lesson completed was associated with 0.14 kg (p<0.05) weight loss, and a week of 150 active minutes was associated with 0.1 kg (p<0.01) weight loss. One additional coach–participant message each week was associated with 1.4 more food logs per week, 1.6% increase in weeks with four or more weigh-ins, and a 2.7% increase in weeks with 150 min of activity.ConclusionsFood logging had the largest impact on weight loss, followed by lesson engagement and physical activity. Future studies should examine further opportunities to deliver nutrition-based content to increase and sustain weight loss for DPP.

Translating the diabetes prevention program into a hospital-based weight loss program.

2008 ◽  
Vol 27 (1, Suppl) ◽  
pp. S91-S98 ◽  
Author(s):  
Sherry L. Pagoto ◽  
Lyle Kantor ◽  
Jamie S. Bodenlos ◽  
Mitchell Gitkind ◽  
Yunsheng Ma
Keyword(s):  
Weight Loss ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Diabetes Prevention Program ◽  
Weight Loss Program
Sign in / Sign up

Export Citation Format

Share Document