scholarly journals Using the Habit App for Weight Loss Problem Solving: Development and Feasibility Study (Preprint)

2018 ◽  
Author(s):  
Sherry Pagoto ◽  
Bengisu Tulu ◽  
Emmanuel Agu ◽  
Molly E Waring ◽  
Jessica L Oleski ◽  
...  
Keyword(s):  
Weight Loss ◽  
Problem Solving ◽  
Mobile Apps ◽  
Mobile App ◽  
Behavioral Weight Loss ◽  
Problem Solving Therapy ◽  
Pilot Studies ◽  
Self Monitoring ◽  
Problem Solving Skills ◽  
Automated Delivery

BACKGROUND Reviews of weight loss mobile apps have revealed they include very few evidence-based features, relying mostly on self-monitoring. Unfortunately, adherence to self-monitoring is often low, especially among patients with motivational challenges. 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 has been successfully implemented in depression mobile apps, but not yet in weight loss apps. OBJECTIVE This study describes the development and feasibility testing of the Habit app, which was designed to automate problem-solving therapy for weight loss. METHODS Two iterative single-arm pilot studies were conducted to evaluate the feasibility and acceptability of the Habit app. In each pilot study, adults who were overweight or obese were enrolled in an 8-week intervention that included the Habit app plus support via a private Facebook group. Feasibility outcomes included retention, app usage, usability, and acceptability. Changes in problem-solving skills and weight over 8 weeks are described, as well as app usage and weight change at 16 weeks. 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). In neither pilot was an effect on problem-solving skills observed (P=.62 and P=.27, respectively). CONCLUSIONS Problem-solving therapy for weight loss is feasible to implement in a mobile app environment; however, automated delivery may not impact problem-solving skills as has been observed previously via human delivery. CLINICALTRIAL ClinicalTrials.gov NCT02192905; https://clinicaltrials.gov/ct2/show/NCT02192905 (Archived by WebCite at http://www.webcitation.org/6zPQmvOF2)

scholarly journals Comparing Self-Monitoring Strategies for Weight Loss in a Smartphone App: Randomized Controlled Trial (Preprint)

2018 ◽  
Author(s):  
Michele L Patel ◽  
Christina M Hopkins ◽  
Taylor L Brooks ◽  
Gary G Bennett
Keyword(s):  
Weight Loss ◽  
Randomized Controlled Trial ◽  
Controlled Trial ◽  
Mobile App ◽  
Self Report ◽  
Smartphone App ◽  
Weight Loss Intervention ◽  
Behavioral Weight Loss ◽  
Self Monitoring ◽  
Randomized Controlled

BACKGROUND Self-monitoring of dietary intake is a valuable component of behavioral weight loss treatment; however, it declines quickly, thereby resulting in suboptimal treatment outcomes. OBJECTIVE This study aimed to examine a novel behavioral weight loss intervention that aims to attenuate the decline in dietary self-monitoring engagement. METHODS GoalTracker was an automated randomized controlled trial. Participants were adults with overweight or obesity (n=105; aged 21-65 years; body mass index, BMI, 25-45 kg/m2) and were randomized to a 12-week stand-alone weight loss intervention using the MyFitnessPal smartphone app for daily self-monitoring of either (1) both weight and diet, with weekly lessons, action plans, and feedback (Simultaneous); (2) weight through week 4, then added diet, with the same behavioral components (Sequential); or (3) only diet (App-Only). All groups received a goal to lose 5% of initial weight by 12 weeks, a tailored calorie goal, and automated in-app reminders. Participants were recruited via online and offline methods. Weight was collected in-person at baseline, 1 month, and 3 months using calibrated scales and via self-report at 6 months. We retrieved objective self-monitoring engagement data from MyFitnessPal using an application programming interface. Engagement was defined as the number of days per week in which tracking occurred, with diet entries counted if ≥800 kcal per day. Other assessment data were collected in-person via online self-report questionnaires. RESULTS At baseline, participants (84/100 female) had a mean age (SD) of 42.7 (11.7) years and a BMI of 31.9 (SD 4.5) kg/m2. One-third (33/100) were from racial or ethnic minority groups. During the trial, 5 participants became ineligible. Of the remaining 100 participants, 84% (84/100) and 76% (76/100) completed the 1-month and 3-month visits, respectively. In intent-to-treat analyses, there was no difference in weight change at 3 months between the Sequential arm (mean −2.7 kg, 95% CI −3.9 to −1.5) and either the App-Only arm (−2.4 kg, −3.7 to −1.2; P=.78) or the Simultaneous arm (−2.8 kg, −4.0 to −1.5; P=.72). The median number of days of self-monitoring diet per week was 1.9 (interquartile range [IQR] 0.3-5.5) in Sequential (once began), 5.3 (IQR 1.8-6.7) in Simultaneous, and 2.9 (IQR 1.2-5.2) in App-Only. Weight was tracked 4.8 (IQR 1.9-6.3) days per week in Sequential and 5.1 (IQR 1.8-6.3) days per week in Simultaneous. Engagement in neither diet nor weight tracking differed between arms. CONCLUSIONS Regardless of the order in which diet is tracked, using tailored goals and a commercial mobile app can produce clinically significant weight loss. Stand-alone digital health treatments may be a viable option for those looking for a lower intensity approach. CLINICALTRIAL ClinicalTrials.gov NCT03254953; https://clinicaltrials.gov/ct2/show/NCT03254953 (Archived by WebCite at http://www.webcitation.org/72PyQrFjn).

scholarly journals A systematic review of the use of dietary self-monitoring in behavioral weight-loss interventions: delivery, intensity and effectiveness

2021 ◽  
pp. 1-85
Author(s):  
Margaret Raber ◽  
Yue Liao ◽  
Anne Rara ◽  
Susan M. Schembre ◽  
Kate J. Krause ◽  
...  
Keyword(s):  
Systematic Review ◽  
Weight Loss ◽  
Implementation Strategies ◽  
Mobile App ◽  
Behavioral Weight Loss ◽  
Self Monitoring ◽  
Monitoring Strategies ◽  
Weight Loss Outcomes ◽  
Monitoring Protocols ◽  
Weight Loss Interventions

Abstract Objective: To identify dietary self-monitoring implementation strategies in behavioral weight loss interventions. Design: We conducted a systematic review of eight databases and examined 59 weight loss intervention studies targeting adults with overweight/obesity that used dietary self-monitoring. Setting: NA Participants: NA Results: We identified self-monitoring implementation characteristics, effectiveness of interventions in supporting weight loss, and examined weight loss outcomes among higher and lower intensity dietary self-monitoring protocols. Included studies utilized diverse self-monitoring formats (paper, website, mobile app, phone) and intensity levels (recording all intake or only certain aspects of diet). We found the majority of studies using high and low intensity self-monitoring strategies demonstrated statistically significant weight loss in intervention groups compared to control groups. Conclusions: Based on our findings, lower and higher intensity dietary self-monitoring may support weight loss, but variability in adherence measures and limited analysis of weight loss relative to self-monitoring usage limits our understanding of how these methods compare to each other.

scholarly journals Long-Term Evaluation of Counselor Efficiency at Providing Feedback in a Technology-Based Behavioral Weight Loss Intervention (Preprint)

2020 ◽  
Author(s):  
Margaret Fahey ◽  
Robert C. Klesges ◽  
Mehmet Kocak ◽  
Leslie Gladney ◽  
Gerald W. Talcott ◽  
...  
Keyword(s):  
Weight Loss ◽  
Electronic Mail ◽  
Detailed Examination ◽  
Monitoring Data ◽  
Weight Loss Intervention ◽  
Behavioral Weight Loss ◽  
Self Monitoring ◽  
Future Technology ◽  
Study Implementation ◽  
Weight Loss Interventions

BACKGROUND Feedback for participants’ self-monitoring is a crucial, and costly, component of technology-based weight loss interventions. Detailed examination of interventionist time when reviewing and providing feedback for online self-monitoring data is unknown. OBJECTIVE Study purpose was to longitudinally examine time counselors spent providing feedback on participant self-monitoring data (i.e., diet, physical activity, weight) in a 12-month technology-based weight loss intervention. We hypothesized that counselors would deliver feedback to participants more quickly over time. METHODS Time counselors (N=10) spent reviewing and providing feedback to participants via electronic mail (e-email) was longitudinally examined for all counselors across the three years of study implementation. Descriptives were observed for counselor feedback duration across counselors by 12 annual quarters (i.e., three-month periods). Differences in overall duration times by each consecutive annual quarter were analyzed using Wilcoxon-Mann-Whitney tests. RESULTS There was a decrease in counselor feedback duration from first to second quarter [Mean (M) = 53 to 46 minutes], and from second to third (M= 46 to 30). A trend suggested a decrease from third to fourth quarters (M = 30 to 26), but no changes were found in subsequent quarters. Consistent with hypothesis, counselors increased their efficiency in providing feedback. Across 12-months, mean time counselors needed to review participant self-monitoring and provide feedback decreased from 53 to 26 minutes. CONCLUSIONS Counselors needed increasingly less time to review online self-monitoring data and provide feedback after the initial nine months of study implementation. Results inform counselor costs for future technology-based behavioral weight loss interventions. For example, regardless of increasing counselor efficiency, 25-30 minutes per feedback message is a high cost for interventions. One possibility for reducing costs would be generating computer-automated feedback. CLINICALTRIAL NCT02063178

scholarly journals Opportunities for Mobile App–Based Adherence Support for Children With Tuberculosis in South Africa

10.2196/19154 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e19154
Author(s):  
Rachel M Morse ◽  
Hanlie Myburgh ◽  
David Reubi ◽  
Ava E Archey ◽  
Leletu Busakwe ◽  
...  
Keyword(s):  
South Africa ◽  
Treatment Adherence ◽  
Mobile Apps ◽  
Mobile App ◽  
Tuberculosis Treatment ◽  
Mobile Technologies ◽  
Patient Treatment ◽  
Pilot Studies ◽  
Adherence Support ◽  
Positive Treatment

Tuberculosis is the number one infectious cause of death globally. Young children, generally those younger than 5 years, are at the highest risk of progressing from tuberculosis infection to tuberculosis disease and of developing the most severe forms of tuberculosis. Most current tuberculosis drug formulations have poor acceptability among children and require consistent adherence for prolonged periods of time. These challenges complicate children’s adherence to treatment and caregivers’ daily administration of the drugs. Rapid developments in mobile technologies and apps present opportunities for using widely available technology to support national tuberculosis programs and patient treatment adherence. Pilot studies have demonstrated that mobile apps are a feasible and acceptable means of enhancing children’s treatment adherence for other chronic conditions. Despite this, no mobile apps that aim to promote adherence to tuberculosis treatment have been developed for children. In this paper, we draw on our experiences carrying out research in clinical pediatric tuberculosis studies in South Africa. We present hypothetical scenarios of children’s adherence to tuberculosis medication to suggest priorities for behavioral and educational strategies that a mobile app could incorporate to address some of the adherence support gaps faced by children diagnosed with tuberculosis. We argue that a mobile app has the potential to lessen some of the negative experiences that children associate with taking tuberculosis treatment and to facilitate a more positive treatment adherence experience for children and their caregivers.

The Impact of Self-monitoring of Blood Glucose on a Behavioral Weight Loss Intervention for Patients With Type 2 Diabetes

2012 ◽  
Vol 39 (3) ◽  
pp. 397-405 ◽  
Author(s):  
Lisa M. McAndrew ◽  
Melissa A. Napolitano ◽  
Leonard M. Pogach ◽  
Karen S. Quigley ◽  
Kerri Leh Shantz ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Weight Loss ◽  
Blood Glucose ◽  
Weight Loss Intervention ◽  
Behavioral Weight Loss ◽  
Self Monitoring ◽  
Behavioral Weight Loss Intervention ◽  
The Impact

Discrepancies Between Clinician and Participant Intervention Adherence Ratings Predict Percent Weight Change During a Six-Month Behavioral Weight Loss Intervention

2021 ◽  
Vol 11 (4) ◽  
pp. 1006-1014
Author(s):  
Michael P Berry ◽  
Elisabeth M Seburg ◽  
Meghan L Butryn ◽  
Robert W Jeffery ◽  
Melissa M Crane ◽  
...  
Keyword(s):  
Weight Loss ◽  
Weight Change ◽  
Weight Control ◽  
Calorie Intake ◽  
Behavioral Weight Loss ◽  
Weight Loss Treatment ◽  
Self Monitoring ◽  
Weight Control Behaviors ◽  
Behavioral Weight Loss Intervention ◽  
Behavioral Weight Loss Treatment

Abstract Background Individuals receiving behavioral weight loss treatment frequently fail to adhere to prescribed dietary and self-monitoring instructions, resulting in weight loss clinicians often needing to assess and intervene in these important weight control behaviors. A significant obstacle to improving adherence is that clinicians and clients sometimes disagree on the degree to which clients are actually adherent. However, prior research has not examined how clinicians and clients differ in their perceptions of client adherence to weight control behaviors, nor the implications for treatment outcomes. Purpose In the context of a 6-month weight-loss treatment, we examined differences between participants and clinicians when rating adherence to weight control behaviors (dietary self-monitoring; limiting calorie intake) and evaluated the hypothesis that rating one’s own adherence more highly than one’s clinician would predict less weight loss during treatment. Methods Using clinician and participant-reported measures of self-monitoring and calorie intake adherence, each assessed using a single item with a 7- or 8-point scale, we characterized discrepancies between participant and clinician adherence and examined associations with percent weight change over 6 months using linear mixed-effects models. Results Results indicated that ratings of adherence were higher when reported by participants and supported the hypothesis that participants who provided higher adherence ratings relative to their clinicians lost less weight during treatment (p < 0.001). Conclusions These findings suggest that participants in weight loss treatment frequently appraise their own adherence more highly than their clinicians and that participants who do so to a greater degree tend to lose less weight.

scholarly journals Binge eating predicts adherence to digital self-monitoring during behavioral weight loss

2020 ◽  
Vol 39 ◽  
pp. 101448
Author(s):  
Mary K. Martinelli ◽  
Laura D'Adamo ◽  
Meghan L. Butryn
Keyword(s):  
Weight Loss ◽  
Binge Eating ◽  
Behavioral Weight Loss ◽  
Self Monitoring

scholarly journals Online and other ICT-based Training Tools for Problem-solving Skills

2016 ◽  
Vol 11 (06) ◽  
pp. 35
Author(s):  
Athanasios Drigas ◽  
Maria Karyotaki
Keyword(s):  
Problem Solving ◽  
Learning Environments ◽  
Cognitive Processes ◽  
Metacognitive Skills ◽  
Dynamic Learning ◽  
Self Monitoring ◽  
Problem Solving Skills ◽  
Social Dynamic ◽  
Key Factor ◽  
Real World Learning

Problem-solving requires creative skills, critical thinking as well the ability to implement ideas and theories in practical ways. Moreover, interactive and self-managed problem-solving experiences promote students’ motivation as expressed through the developmental progression of learners’ metacognitive skills, such as self-monitoring and self-reinforcement. Effective learning based on constructivist didactics, encompassing self-organized learning in combination with active and creative problem-solving in collaborative settings, advances students’ concomitant cognitive and meta-cognitive processes. Hence, students’ co-construction of knowledge embodied in social dynamic learning environments, such as school-based tasks leverage the semantic relationships rising from exercising, verifying and testing of knowledge through information sharing and discussion. Future studies should focus on designing interactive, adaptable, ill-defined, real-world learning environments to elicit students’ cognitive and meta-cognitive processes as a key factor for the effective training of problem-solving skills.

scholarly journals Describing Transitions in Adherence to Physical Activity Self-monitoring and Goal Attainment in an Online Behavioral Weight Loss Program: Secondary Analysis of a Randomized Controlled Trial (Preprint)

2021 ◽  
Author(s):  
Melissa Lee Stansbury ◽  
Jean R Harvey ◽  
Rebecca A Krukowski ◽  
Christine A Pellegrini ◽  
Xuewen Wang ◽  
...  
Keyword(s):  
Physical Activity ◽  
Weight Loss ◽  
Goal Attainment ◽  
Vigorous Physical Activity ◽  
Secondary Analysis ◽  
Individual Characteristics ◽  
Behavioral Weight Loss ◽  
Self Monitoring ◽  
Program Goal ◽  
Monitoring Behavior

BACKGROUND Standard behavioral weight loss interventions often set uniform physical activity (PA) goals and promote PA self-monitoring; however, adherence remains a challenge and recommendations may not accommodate all individuals. Identifying patterns of PA goal attainment and self-monitoring behavior will offer a deeper understanding of how individuals adhere to different types of commonly prescribed PA recommendations (ie., minutes of moderate-to-vigorous physical activity [MVPA] and daily steps) and guide future recommendations for improved intervention effectiveness. OBJECTIVE This study examined weekly patterns of adherence to steps-based and minutes-based PA goals and self-monitoring behavior during a 6-month online behavioral weight loss intervention. METHODS Participants were prescribed weekly PA goals for steps (7,000 to 10,000 steps/day) and minutes of MVPA (50 to 200 minutes/week) as part of a lifestyle program. Goals gradually increased during the initial 2 months, followed by 4 months of fixed goals. PA was self-reported daily on the study website. For each week, participants were categorized as “adherent” if they self-monitored their PA and met the program PA goal, “suboptimally adherent” if they self-monitored but did not meet the program goal, or “nonadherent” if they did not self-monitor. The probability of transitioning into a less adherent status was examined using multinomial logistic regression. RESULTS Individuals (N=212) were predominantly middle-aged females with obesity, and 31.6% self-identified as a racial/ethnic minority. Initially, 34.4% were categorized as “adherent” to steps-based goals (51.9% “suboptimally adherent” and 13.7% “nonadherent”), and there was a high probability of either remaining “suboptimally adherent” from week-to-week or transitioning to a “nonadherent” status. On the other hand, 70.3% of individuals started out “adherent” to minutes-based goals (16.0% “suboptimally adherent” and 13.7% “nonadherent”), with “suboptimally adherent” seen as the most variable status. During the graded goal phase, individuals were more likely to transition to a less adherent status for minutes-based goals (OR 1.39, 95% CI 1.31-1.48) compared to steps-based goals (OR 1.24, 95% CI 1.17-1.30); however, no differences were seen during the fixed goal phase (minutes-based goals: OR 1.06, 95% CI 1.05, 1.08 versus steps-based goals: OR 1.07, 95% CI 1.05, 1.08). CONCLUSIONS States of vulnerability to poor PA adherence can emerge rapidly and early in obesity treatment. There is a window of opportunity within the initial two months to bring more people towards “adherent” behavior, especially those who fail to meet the prescribed goals but engage in self-monitoring. While this study describes the probability of adhering to steps-based and minutes-based targets, it will be prudent to determine how individual characteristics and contextual states relate to these behavioral patterns, which can inform how best to adapt interventions. CLINICALTRIAL This study was a secondary analysis of a pre-registered randomized trial (Trial Registration: ClinicalTrials.gov NCT02688621).

Sign in / Sign up

Export Citation Format

Share Document