Understanding Older Adults Motivations and Preferences for a Medication Adherence App

As the percentage of older adults with hypertension continues to increase, medication adherence remains low. However, medication adherence can potentially be improved through the use of medication reminder apps. Medication reminder apps contain numerous features that enable older adults to remember to take their medication, such as providing alerts to take their medication, reminding them when to refill their prescription, and more. Despite being aware of these apps, many older adults lack the motivation needed to use them continuously. We recruited 9 participants (60 years or older) who currently take medication for a chronic condition. Using a mixed-methods approach, we gathered quantitative survey data using the TechSAge Demographic Background, Motivation, and Behavior Change Technique Questionnaires). Qualitative data were gathered through a semi-structured interview that asked questions about general motivations and preferences in addition to engaging participants in co-designing a medication adherence app. Results from the interview were analyzed through a thematic analysis that identified comprehension and preferences of older adults in medication reminder app usage. We tested five different intrinsic motivation factors, and results indicate older adults are most motivated intrinsically due to perceived choice, perceived competence, value/usefulness, effort/importance, and pressure/tension. We also tested five factors of extrinsic motivation, and results indicate older adults are most motivated extrinsically due to introjected regulation, reward-driven, external regulation, compliance, and identification. These data provide insights to guide the design of medication reminder apps to support older adults in the self-management of their chronic conditions.

Physical distancing messages targeting youth on the social media accounts of Canadian public health entities and the use of behavioral change techniques

Introduction Physical distancing (PD) is an important public health strategy to reduce the transmission of COVID-19 and has been promoted by public health authorities through social media. Although youth have a tendency to engage in high-risk behaviors that could facilitate COVID-19 transmission, there is limited research on the characteristics of PD messaging targeting this population on social media platforms with which youth frequently engage. This study examined social media posts created by Canadian public health entities (PHEs) with PD messaging aimed at youth and young adults aged 16–29 years and reported behavioral change techniques (BCTs) used in these posts. Methods A content analysis of all social media posts of Canadian PHEs from Facebook, Twitter, Instagram and YouTube were conducted from April 1st to May 31st, 2020. Posts were classified as either implicitly or explicitly targeting youth and young adults. BCTs in social media posts were identified and classified based on Behavior Change Technique Taxonomy version 1 (BCTTv1). Frequency counts and proportions were used to describe the data. Results In total, 319 youth-targeted PD posts were identified. Over 43% of the posts originated from Ontario Regional public health units, and 36.4 and 32.6% of them were extracted from Twitter and Facebook, respectively. Only 5.3% of the total posts explicitly targeted youth. Explicit posts were most frequent from federal PHEs and posted on YouTube. Implicit posts elicited more interactions than explicit posts regardless of jurisdiction level or social media format. Three-quarters of the posts contained at least one BCT, with a greater portion of BCTs found within implicit posts (75%) than explicit posts (52.9%). The most common BCTs from explicit posts were instructions on how to perform a behavior (25.0%) and restructuring the social environment (18.8%). Conclusions There is a need for more PD messaging that explicitly targets youth. BCTs should be used when designing posts to deliver public health messages and social media platforms should be selected depending on the target population.

Physical activity prediction using fitness data: Challenges and issues

In the new healthcare transformations, individuals are encourage to maintain healthy life based on their food diet and physical activity routine to avoid risk of serious disease. One of the recent healthcare technologies to support self health monitoring is wearable device that allow individual play active role on their own healthcare. However, there is still questions in terms of the accuracy of wearable data for recommending physical activity due to enormous fitness data generated by wearable devices. In this study, we conducted a literature review on machine learning techniques to predict suitable physical activities based on personal context and fitness data. We categorize and structure the research evidence that has been publish in the area of machine learning techniques for predicting physical activities using fitness data. We found 10 different models in Behavior Change Technique (BCT) and we selected two suitable models which are Fogg Behavior Model (FBM) and Trans-theoretical Behavior Model (TTM) for predicting physical activity using fitness data. We proposed a conceptual framework which consists of personal fitness data, combination of TTM and FBM to predict the suitable physical activity based on personal context. This study will provide new insights in software development of healthcare technologies to support personalization of individuals in managing their own health.

Changing the Home Visiting Research Paradigm: Models’ Perspectives on Behavioral Pathways and Intervention Techniques to Promote Good Birth Outcomes

Background: The US is scaling up evidence-based home visiting to promote health equity in expectant families and families with young children. Persistently small average effects for full models argue for a shift in the research paradigm to understand what interventions within full models work best, for which families, in which contexts, why, and how. Historically, the complexity and proprietary nature of most evidence-based models have been barriers to such research. To address this, stakeholders are building the Precision Paradigm, a common framework and language to define and test interventions and their mediators and moderators. This observational study applied portions of the Paradigm to describe models' intended behavioral pathways to good birth outcomes and their stance on home visitors' use of specific intervention techniques to promote families' progress along intended pathways.Methods: Five of the six eligible evidence-based models participated. Model representatives independently completed three structured surveys focused on 41 intended behavioral pathways and 23 behavior change technique categories. Survey data were used to describe: the number and nature of models' intended behavioral pathways to good birth outcomes; the behavior change techniques they required or recommended home visitors to use; the relative emphasis given to required/recommended techniques; the compatibility of other techniques with their model; and the consistency of their stance on techniques across intended pathways.Results: Models defined 31.6 intended pathways on average but varied in their number of intended pathways (range 16-41) and the nature of such pathways. They varied in the number of technique categories they required (mean 3.2, range 0-8.9) or recommended (mean 10.6, range 1.5-20.0) that home visitors use. That said, all models rated nearly all technique categories as at least compatible with their model, even if not explicitly required or recommended. Within intended pathways, models varied in the relative emphasis to give to specific techniques. Models varied in the consistency of their requirements and recommendations for using specific techniques across intended pathways.Conclusions: The Precision Paradigm is a promising resource to accelerate innovative cross-model research to clarify which interventions within home visiting work best for which families, in which contexts, why and how.

Abstract 13773: Applied Behavior Change Techniques in Self-care Interventions for Patients With a Chronic Condition

Introduction: Self-care requires behavior change in patients and healthcare providers play a pivotal role in supporting those changes. Examination of the behavior change techniques used by providers in self-care interventions can provide insights into how self-care behavior is enhanced in patients with a chronic condition. Objective: To quantify the behavior change techniques used to enhance healthy behavior in self-care interventions for patients with a chronic condition. Methods: Studies from a scoping review (n=233) were reviewed to identify behavior change techniques used in studies testing self-care interventions in adults with nine chronic conditions, most of which were CVDs. All studies were published between January 2008 and January 2019. Nine techniques from the Behavior Change Technique Taxonomy V1 (with 93 consensually agreed, distinct techniques) were selected for review because they are commonly used by a broad range of disciplines and have previously been shown to influence behavior change. Results: The 233 studies represent 59,950 patients, mean age 59.5 (±8.3) years, 44% female. Study sample sizes ranged from 24 to 2,445 patients. Most studies focused on DM2 (n=85; 36%), HTN (n=32; 14%) or HF (n=27; 12%). Most interventions targeted only patients (n=207; 89%). Goal setting (n=113; 48%) and problem solving (n=92; 40%) were the behavior change techniques used predominantly. Action planning, feedback and review of behavioral goals were used in 46-60 (20%-26%) studies and most commonly in patients with DM2 or CAD and rarely in patients with HTN or HF. Information about health consequences was used in 46 (20%) studies. Social support (n=17; 7%) and reminders (n=13; 6%) were rarely used. Conclusions: Few behavior change techniques were specified in the published self-care intervention trials. In future research, behavior change techniques and the associated mechanisms of actions need to be specified to support self-care intervention research.

Applying the Behavior Change Technique Taxonomy to Four Multicomponent Childhood Obesity Interventions

Applying the Behavior Change Technique Taxonomy has the potential to facilitate identification of effective childhood obesity intervention components. This article evaluates the feasibility of coding Childhood Obesity Prevention and Treatment Consortium interventions and compares reliability between external taxonomy-familiar coders and internal intervention-familiar coders. After training, coder pairs independently coded prespecified portions of intervention materials. An adjudication process was used to explore coding discrepancies. Reliability between internal and external coders was moderate (prevalence and bias-adjusted kappa .38 to .55). Reliability for specific target behaviors varied with substantial agreement for physical activity (.63 to .76) and moderate for dietary intake (.44 to .63). Applying the taxonomy to these interventions was feasible, but agreement was modest. Coding discrepancies highlight the importance of refining coding to capture the complexities of childhood obesity interventions, which often engage multiple recipients (e.g., parents and/or children) and address multiple behaviors (e.g., diet, physical activity, screen time).

Implementation Intentions for Exercise and Physical Activity: Who Do They Work For? A Systematic Review

Background: As a behavior change technique, implementation intentions are a cost-effective strategy to promote physical activity and exercise. This technique helps individuals plan when and where they will do a behavior. However, previous research on implementation intentions and physical activity has shown heterogeneous outcomes. Methods: The primary aim of this review was to systematically review the literature on the social cognitive variables that moderate the mediating effects of implementation intentions on physical activity and exercise. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, 22 papers yielded 24 studies. Of the 24 reviewed studies, 1 was cross-sectional, 12 were randomized control trials, 10 were longitudinal, and 1 was secondary data from a randomized control trial. Results: Habit strength, self-concordance, self-efficacy, perceived behavioral control, and intention were identified as moderating variables. Conditions for effectiveness for implementation intentions were observed. Implementation intentions are an effective behavior change technique for individuals who have preexisting intentions and strong self-efficacy. Conclusions: Implementation intentions may be a valuable behavior change technique for certain individuals. For self-efficacious individuals who desire to be physically active, implementation intentions can help translate intentions into behavior.