Self-Management Skills and Applied Behavior Analysis

Erhard, Patricio; Wong, Travis; Barnett, Monique; Falcomata, Terry S.; Lang, Russell

doi:10.1007/978-3-030-88538-0_41

Cited by 7 publications

(3 citation statements)

References 61 publications

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“…Self-management was defined as “the personal application of behavior-change tactics that produces a desired change in behavior” [ 66 ]. Through self-management interventions, individuals learn to identify occurrences of their own target responding, accurately self-recording the target response, self-evaluating their behavior, and self-delivering reinforcement as a consequence [ 67 ].…”

Section: Discussionmentioning

confidence: 99%

The Impact of Digital Self-Monitoring of Weight on Improving Diabetes Clinical Outcomes: Quasi-Randomized Study

Fundoiano-Hershcovitz,

Ritholz,

Horwitz

et al. 2024

J Med Internet Res

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Background The management of type 2 diabetes (T2D) and obesity, particularly in the context of self-monitoring, remains a critical challenge in health care. As nearly 80% to 90% of patients with T2D have overweight or obesity, there is a compelling need for interventions that can effectively manage both conditions simultaneously. One of the goals in managing chronic conditions is to increase awareness and generate behavioral change to improve outcomes in diabetes and related comorbidities, such as overweight or obesity. There is a lack of real-life evidence to test the impact of self-monitoring of weight on glycemic outcomes and its underlying mechanisms. Objective This study aims to assess the efficacy of digital self-monitoring of weight on blood glucose (BG) levels during diabetes management, investigating whether the weight changes may drive glucose fluctuations. Methods In this retrospective, real-world quasi-randomized study, 50% of the individuals who regularly used the weight monitoring (WM) feature were propensity score matched with 50% of the users who did not use the weight monitoring feature (NWM) based on demographic and clinical characteristics. All the patients were diagnosed with T2D and tracked their BG levels. We analyzed monthly aggregated data 6 months before and after starting their weight monitoring. A piecewise mixed model was used for analyzing the time trajectories of BG and weight as well as exploring the disaggregation effect of between- and within-patient lagged effects of weight on BG. Results The WM group exhibited a significant reduction in BG levels post intervention (P<.001), whereas the nonmonitoring group showed no significant changes (P=.59), and both groups showed no differences in BG pattern before the intervention (P=.59). Furthermore, the WM group achieved a meaningful decrease in BMI (P<.001). Finally, both within-patient (P<.001) and between-patient (P=.008) weight variability was positively associated with BG levels. However, 1-month lagged back BMI was not associated with BG levels (P=.36). Conclusions This study highlights the substantial benefits of self-monitoring of weight in managing BG levels in patients with diabetes, facilitated by a digital health platform, and advocates for the integration of digital self-monitoring tools in chronic disease management. We also provide initial evidence of testing the underlying mechanisms associated with BG management, underscoring the potential role of patient empowerment.

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Section: Discussionmentioning

confidence: 99%

The Impact of Digital Self-Monitoring of Weight on Improving Diabetes Clinical Outcomes: Quasi-Randomized Study

Fundoiano-Hershcovitz,

Ritholz,

Horwitz

et al. 2024

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Self-management was defined as "the personal application of behavior-change tactics that produces a desired change in behavior" [66]. Through self-management interventions, individuals learn to identify occurrences of their own target responding, accurately self-recording the target response, self-evaluating their behavior, and self-delivering reinforcement as a consequence [67].…”

Section: Principal Findingsmentioning

confidence: 99%

The Impact of Digital Self-Monitoring of Weight on Improving Diabetes Clinical Outcomes: Quasi-Randomized Study (Preprint)

Fundoiano-Hershcovitz,

Ritholz,

Horwitz

et al. 2023

Preprint

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BACKGROUND The management of type 2 diabetes (T2D) and obesity, particularly in the context of self-monitoring, remains a critical challenge in health care. As nearly 80% to 90% of patients with T2D have overweight or obesity, there is a compelling need for interventions that can effectively manage both conditions simultaneously. One of the goals in managing chronic conditions is to increase awareness and generate behavioral change to improve outcomes in diabetes and related comorbidities, such as overweight or obesity. There is a lack of real-life evidence to test the impact of self-monitoring of weight on glycemic outcomes and its underlying mechanisms. OBJECTIVE This study aims to assess the efficacy of digital self-monitoring of weight on blood glucose (BG) levels during diabetes management, investigating whether the weight changes may drive glucose fluctuations. METHODS In this retrospective, real-world quasi-randomized study, 50% of the individuals who regularly used the weight monitoring (WM) feature were propensity score matched with 50% of the users who did not use the weight monitoring feature (NWM) based on demographic and clinical characteristics. All the patients were diagnosed with T2D and tracked their BG levels. We analyzed monthly aggregated data 6 months before and after starting their weight monitoring. A piecewise mixed model was used for analyzing the time trajectories of BG and weight as well as exploring the disaggregation effect of between- and within-patient lagged effects of weight on BG. RESULTS The WM group exhibited a significant reduction in BG levels post intervention (P<.001), whereas the nonmonitoring group showed no significant changes (P=.59), and both groups showed no differences in BG pattern before the intervention (P=.59). Furthermore, the WM group achieved a meaningful decrease in BMI (P<.001). Finally, both within-patient (P<.001) and between-patient (P=.008) weight variability was positively associated with BG levels. However, 1-month lagged back BMI was not associated with BG levels (P=.36). CONCLUSIONS This study highlights the substantial benefits of self-monitoring of weight in managing BG levels in patients with diabetes, facilitated by a digital health platform, and advocates for the integration of digital self-monitoring tools in chronic disease management. We also provide initial evidence of testing the underlying mechanisms associated with BG management, underscoring the potential role of patient empowerment.

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“…These procedures can be utilized at a household level to reduce portion sizes to the recommended serving. Further, behavior analysts can work with individuals using common self-management strategies that have been used with other populations (e.g., autism; Erhard et al, 2022) or organizational behavior management (Ferguson & Rivera, 2022). This can include targets, such as meal planning for nutritionally well-balanced meals and purchasing readily available fruits and vegetables.…”

Section: Food Sectormentioning

confidence: 99%

Climate Change and Six Americas: What Can Behavior Analysts Do?

Meshes

Kamau²,

Summers

et al. 2022

Behav. Soc. Iss.

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Climate change, directly impacted by human behavior, has been investigated and evaluated across disciplines. The Six Americas was developed as a segmentation tool to communicate effectively with the United States population about climate change across a spectrum from those likely to act in opposition to climate change mitigation strategies to those actively seeking to remediate the climate change effects. Behavior analysts offer unique skills to intervene at the individual level effectively. Behavior analysts will benefit from learning about this conceptual model and its tools, particularly to inform intervention across the spectrum of the Six Americas. This paper will cover a background of the Six Americas and suggestions on how to intervene for these different segments at the individual level, followed by a brief review of the existing effective literature, particularly regarding changing behavior in the food, energy, and transportation sectors. Specifically, behavior analytic interventions will be suggested for a population concerned about climate change who may also be posed for action. Finally, we will provide suggestions to guide behavior analysts to intervene with those disengaged or actively dismissive of the threats posed by climate change.

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Self-Management Skills and Applied Behavior Analysis

Cited by 7 publications

References 61 publications

The Impact of Digital Self-Monitoring of Weight on Improving Diabetes Clinical Outcomes: Quasi-Randomized Study

The Impact of Digital Self-Monitoring of Weight on Improving Diabetes Clinical Outcomes: Quasi-Randomized Study

The Impact of Digital Self-Monitoring of Weight on Improving Diabetes Clinical Outcomes: Quasi-Randomized Study (Preprint)

Climate Change and Six Americas: What Can Behavior Analysts Do?

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