scholarly journals Hand Washing and Related Cognitions Following a Brief Behavior Change Intervention During the COVID-19 Pandemic: a Pre-Post Analysis

2021 ◽  
Author(s):  
Jan Keller ◽  
Dominika Kwasnicka ◽  
Lea O. Wilhelm ◽  
Noemi Lorbeer ◽  
Theresa Pauly ◽  
...  
Keyword(s):  
Behavior Change ◽  
Self Efficacy ◽  
Action Planning ◽  
Hand Washing ◽  
Future Research ◽  
Convenience Sample ◽  
Daily Diaries ◽  
Self Monitoring ◽  
Behavior Change Interventions ◽  
Registration Number

Abstract Background Effective hand washing (for at least 20 s, with water and soap) is one of the health behaviors protecting against infection transmissions. Behavior change interventions supporting the initiation and maintenance of hand washing are crucial to prevent infection transmissions. Based on the Health Action Process Approach, the aim of this research was to conduct a pre-post analysis of hand washing and related cognitions (i.e., intention, self-efficacy, self-monitoring), measured up to 100 days following an intervention. Methods A convenience sample of N = 123 participants (age: M = 23.96 years; SD = 5.82; 80% women) received a brief intervention (key behavior change techniques: information about health consequences of hand washing; action planning) and responded to daily diaries and questionnaires up to a 100-day follow-up. Two-level models were used to analyze data of n = 89 participants who provided longitudinal data. Results Hand washing and self-monitoring increased, whereas intention and self-efficacy decreased over time. Only self-monitoring was a consistent positive correlate of hand washing on a between-person level. Conclusions Hand washing and self-monitoring considerably increased over several weeks following the intervention. Future research testing the intervention against a control condition is needed to rule out that changes in behavior and cognitions might have been prompted by completing the daily diaries. Trial Registration German Clinical Trials Register; https://www.drks.de; registration number: DRKS00022067.

scholarly journals Peer-Based Social Media Features in Behavior Change Interventions: Systematic Review (Preprint)

2017 ◽  
Author(s):  
Sheik Mohammad Roushdat Ally Elaheebocus ◽  
Mark Weal ◽  
Leanne Morrison ◽  
Lucy Yardley
Keyword(s):  
Systematic Review ◽  
Social Media ◽  
Behavior Change ◽  
Behavioral Interventions ◽  
Selection Process ◽  
Future Research ◽  
Behavioral Risk ◽  
Lack Of Information ◽  
Behavior Change Interventions ◽  
High Level

BACKGROUND Incorporating social media features into digital behavior change interventions (DBCIs) has the potential to contribute positively to their success. However, the lack of clear design principles to describe and guide the use of these features in behavioral interventions limits cross-study comparisons of their uses and effects. OBJECTIVE The aim of this study was to provide a systematic review of DBCIs targeting modifiable behavioral risk factors that have included social media features as part of their intervention infrastructure. A taxonomy of social media features is presented to inform the development, description, and evaluation of behavioral interventions. METHODS Search terms were used in 8 databases to identify DBCIs that incorporated social media features and targeted tobacco smoking, diet and nutrition, physical activities, or alcohol consumption. The screening and review process was performed by 2 independent researchers. RESULTS A total of 5264 articles were screened, and 143 articles describing a total of 134 studies were retained for full review. The majority of studies (70%) reported positive outcomes, followed by 28% finding no effects with regard to their respective objectives and hypothesis, and 2% of the studies found that their interventions had negative outcomes. Few studies reported on the association between the inclusion of social media features and intervention effect. A taxonomy of social media features used in behavioral interventions has been presented with 36 social media features organized under 7 high-level categories. The taxonomy has been used to guide the analysis of this review. CONCLUSIONS Although social media features are commonly included in DBCIs, there is an acute lack of information with respect to their effect on outcomes and a lack of clear guidance to inform the selection process based on the features’ suitability for the different behaviors. The proposed taxonomy along with the set of recommendations included in this review will support future research aimed at isolating and reporting the effects of social media features on DBCIs, cross-study comparisons, and evaluations.

scholarly journals Behavior Change Techniques in Wrist-Worn Wearables to Promote Physical Activity: Content Analysis (Preprint)

2020 ◽  
Author(s):  
Peter Düking ◽  
Marie Tafler ◽  
Birgit Wallmann-Sperlich ◽  
Billy Sperlich ◽  
Sonja Kleih
Keyword(s):  
Physical Activity ◽  
Content Analysis ◽  
Behavior Change ◽  
Goal Setting ◽  
Wearable Sensors ◽  
Action Planning ◽  
Noncommunicable Diseases ◽  
Behavior Change Techniques ◽  
Self Monitoring ◽  
Current Behavior

BACKGROUND Decreasing levels of physical activity (PA) increase the incidences of noncommunicable diseases, obesity, and mortality. To counteract these developments, interventions aiming to increase PA are urgently needed. Mobile health (mHealth) solutions such as wearable sensors (wearables) may assist with an improvement in PA. OBJECTIVE The aim of this study is to examine which behavior change techniques (BCTs) are incorporated in currently available commercial high-end wearables that target users’ PA behavior. METHODS The BCTs incorporated in 5 different high-end wearables (Apple Watch Series 3, Garmin Vívoactive 3, Fitbit Versa, Xiaomi Amazfit Stratos 2, and Polar M600) were assessed by 2 researchers using the BCT Taxonomy version 1 (BCTTv1). Effectiveness of the incorporated BCTs in promoting PA behavior was assessed by a content analysis of the existing literature. RESULTS The most common BCTs were goal setting (behavior), action planning, review behavior goal(s), discrepancy between current behavior and goal, feedback on behavior, self-monitoring of behavior, and biofeedback. Fitbit Versa, Garmin Vívoactive 3, Apple Watch Series 3, Polar M600, and Xiaomi Amazfit Stratos 2 incorporated 17, 16, 12, 11, and 11 BCTs, respectively, which are proven to effectively promote PA. CONCLUSIONS Wearables employ different numbers and combinations of BCTs, which might impact their effectiveness in improving PA. To promote PA by employing wearables, we encourage researchers to develop a taxonomy specifically designed to assess BCTs incorporated in wearables. We also encourage manufacturers to customize BCTs based on the targeted populations.

scholarly journals Baby Buddy App for Breastfeeding and Behavior Change: Retrospective Study of the App Using the Behavior Change Wheel (Preprint)

2020 ◽  
Author(s):  
Loretta M Musgrave ◽  
Alison Baum ◽  
Nilushka Perera ◽  
Caroline SE Homer ◽  
Adrienne Gordon
Keyword(s):  
Behavior Change ◽  
Self Efficacy ◽  
Mobile App ◽  
World Health ◽  
Future Research ◽  
Potential Health ◽  
Behavior Change Techniques ◽  
Barriers And Enablers ◽  
Behavior Change Wheel ◽  
The Uk

BACKGROUND Breastfeeding plays a major role in the health of mothers and babies and has the potential to positively shape an individual’s life both in the short and long term. In the United Kingdom (UK), although 81% of women initiate breastfeeding, only 1% of women breastfeed exclusively to 6 months as recommended by the World Health Organization. In the UK, women who are socially disadvantaged and younger are less likely to breastfeed at 6 to 8 weeks postpartum. One strategy that aims to improve these statistics is the Baby Buddy app, which has been designed and implemented by the UK charity Best Beginnings to be a universal intervention to help reduce health inequalities, including those in breastfeeding. OBJECTIVE This study aimed to retrospectively examine the development of Baby Buddy by applying the Behavior Change Wheel (BCW) framework to understand how it might increase breastfeeding self-efficacy, knowledge, and confidence. METHODS Retrospective application of the BCW was completed after the app was developed and embedded into maternity services. A three-stage process evaluation used triangulation methods and formalized tools to gain an understanding of the potential mechanisms and behaviors used in apps that are needed to improve breastfeeding rates in the UK. First, we generated a behavioral analysis by mapping breastfeeding barriers and enablers onto the Capability, Opportunity, and Motivation-Behavior (COM-B) system using documents provided by Best Beginnings. Second, we identified the intervention functions and policy categories used. Third, we linked these with the behavior change techniques identified in the app breastfeeding content using the Behavior Change Techniques Taxonomy (BCTTv1). RESULTS Baby Buddy is a well-designed platform that could be used to change breastfeeding behaviors. Findings from stage one showed that Best Beginnings had defined breastfeeding as a key behavior requiring support and demonstrated a thorough understanding of the context in which breastfeeding occurs, the barriers and enablers of breastfeeding, and the target actions needed to support breastfeeding. In stage two, Best Beginnings had used intervention and policy functions to address the barriers and enablers of breastfeeding. In stage three, Baby Buddy had been assessed for acceptability, practicability, effectiveness, affordability, safety, and equity. Several behavior change techniques that could assist women with decision making around breastfeeding (eg, information about health consequences and credible sources) and possibly affect attitudes and self-efficacy were identified. Of the 39 videos in the app, 19 (49%) addressed physical capabilities related to breastfeeding and demonstrated positive breastfeeding behaviors. CONCLUSIONS Applying a theoretical framework retrospectively to a mobile app is possible and results in useful information to understand potential health benefits and to inform future development. Future research should assess which components and behavioral techniques in the app are most effective in changing behavior and supporting breastfeeding.

scholarly journals Known Knowns and Known Unknowns on Behavior Change Interventions and Mechanisms of Action

2020 ◽  
Author(s):  
Martin S Hagger ◽  
Susette Moyers ◽  
Kaylyn McAnally ◽  
Lauren Mckinley
Keyword(s):  
Behavior Change ◽  
Behavioral Intervention ◽  
Mechanisms Of Action ◽  
Intervention Research ◽  
Current Evidence ◽  
Future Research ◽  
Study Quality ◽  
Behavior Change Techniques ◽  
Behavior Change Interventions ◽  
Known Unknowns

Systematic reviews and meta-analyses play an important role in summarizing current research on the efficacy of behavior change interventions and their mechanisms of action. The reviews in this special issue represent a ‘step change’ in evaluating current evidence on behavior change interventions and mechanisms. This article outlines the findings and emerging issues identified in the reviews (‘known knowns’), and summarizes evidence gaps to be addressed in future research (‘known unknowns’). Findings indicate that tests of mechanisms of behavior change interventions are not routinely conducted in primary studies and research syntheses; reviews and studies do not sufficiently account for study quality; substantive variability exists in descriptions of intervention content and putative mediators implicated in their mechanisms of action; limited data is available on the efficacy of many behavior change techniques; and moderators of intervention effects and mechanisms are seldom taken into account. Possible solutions include testing effects of isolated behavior change techniques and mechanisms of action; routine evaluation of study quality in behavioral intervention research; development of an evidence base linking behavior change techniques with theory-based constructs that comprise mechanisms of action; adoption of fit-for-purpose methods for synthesizing behavioral intervention mechanisms of action; and routine testing of moderators in intervention research.

Factors Related to Healthy Diet and Physical Activity in Hospital-Based Clinical Nurses

2014 ◽  
Vol 19 (3) ◽  
Author(s):  
Nancy Albert ◽  
Robert Butler ◽  
Jeanne Sorrell
Keyword(s):  
Physical Activity ◽  
Self Efficacy ◽  
Healthy Diet ◽  
Future Research ◽  
Activity Levels ◽  
Cross Sectional ◽  
Convenience Sample ◽  
Clinical Nurses ◽  
Study Results ◽  
Diet And Physical Activity

Hospitals often promote healthy lifestyles, but little is known about nurses’ actual diet and physical activity. Greater understanding about these lifestyle choices for clinical nurses may improve existing hospital-based programs and/or create desirable services. This article discusses a study that considered diet and physical activity of clinical nurses, using elements of Pender’s self-care theory as a conceptual framework. Study methods included a cross-sectional, correlational design and a convenience sample of 278 nurses who worked on units with 24 hours/day and seven days-per-week responsibilities. Participants completed diet and exercise questionnaires about perceptions of attitudes and opinions, barriers, diet benefits/exercise motivators, self-efficacy, and locus of control, and personal and work characteristics. Diet and activity categories were created. Study results demonstrated that over 50% of nurses had moderately healthy diets but were insufficiently active. Healthy diet and physical activity levels were associated with higher self-efficacy, more diet benefits and physical activity motivators, fewer perceived barriers, and confidence in body image. The article discussion and conclusion sections note areas for future research and suggest that focused interventions that address benefits, motivators, and self-efficacy may increase participation in hospital-based programs and enhance healthy lifestyle for hospital-based clinical nurses.

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

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Barbara Riegel ◽  
Heleen Westland ◽  
Paolo Iovino ◽  
Ingrid Barelds ◽  
Joyce Bruins Slot ◽  
...  
Keyword(s):  
Behavior Change ◽  
Chronic Condition ◽  
Healthcare Providers ◽  
Self Care ◽  
Action Planning ◽  
Future Research ◽  
Behavior Change Techniques ◽  
Care Intervention ◽  
Intervention Trials ◽  
Behavior Change Technique

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.

scholarly journals Behavior change is not one size fits all: psychosocial phenotypes of childhood obesity prevention intervention participants

2018 ◽  
Vol 8 (5) ◽  
pp. 799-807 ◽  
Author(s):  
Marissa Burgermaster ◽  
Isobel Contento ◽  
Pamela Koch ◽  
Lena Mamykina
Keyword(s):  
Childhood Obesity ◽  
Behavior Change ◽  
Obesity Prevention ◽  
Future Research ◽  
Psychosocial Characteristics ◽  
Childhood Obesity Prevention ◽  
Prevention Intervention ◽  
Target Behaviors ◽  
Behavior Change Interventions ◽  
Successful Behavior

Abstract Variability in individuals’ responses to interventions may contribute to small average treatment effects of childhood obesity prevention interventions. But, neither the causes of this individual variability nor the mechanism by which it influences behavior are clear. We used qualitative methods to characterize variability in students’ responses to participating in a childhood obesity prevention intervention and psychosocial characteristics related to the behavior change process. We interviewed 18 students participating in a school-based curriculum and policy behavior change intervention. Descriptive coding, summary, and case-ordered descriptive meta-matrices were used to group participants by their psychosocial responses to the intervention and associated behavior changes. Four psychosocial phenotypes of responses emerged: (a) Activated—successful behavior-changers with strong internal supports; (b) Inspired—motivated, but not fully successful behavior-changers with some internal supports, whose taste preferences and food environment overwhelmed their motivation; (c) Reinforced—already practiced target behaviors, were motivated, and had strong family support; and (d) Indifferent—uninterested in behavior change and only did target behaviors if family insisted. Our findings contribute to the field of behavioral medicine by suggesting the presence of specific subgroups of participants who respond differently to behavior change interventions and salient psychosocial characteristics that differentiate among these phenotypes. Future research should examine the utility of prospectively identifying psychosocial phenotypes for improving the tailoring of nutrition behavior change interventions.

scholarly journals Behavior change techniques in mobile applications for sedentary behavior

2018 ◽  
Vol 4 ◽  
pp. 205520761878579 ◽  
Author(s):  
Emily E Dunn ◽  
Heather L Gainforth ◽  
Jennifer E Robertson-Wilson
Keyword(s):  
Physical Activity ◽  
Behavior Change ◽  
Sedentary Behavior ◽  
Mobile Applications ◽  
Health Behavior Change ◽  
Behavior Change Techniques ◽  
Self Monitoring ◽  
Behavior Change Interventions ◽  
And Behavior ◽  
Keyword Searches

Objective Mobile applications (apps) are increasingly being utilized in health behavior change interventions. To determine the presence of underlying behavior change mechanisms, apps for physical activity have been coded for behavior change techniques (BCTs). However, apps for sedentary behavior have yet to be assessed for BCTs. Thus, the purpose of the present study was to review apps designed to decrease sedentary time and determine the presence of BCTs. Methods Systematic searches of the iTunes App and Google Play stores were completed using keyword searches. Two reviewers independently coded free ( n = 36) and paid ( n = 14) app descriptions using a taxonomy of 93 BCTs (December 2016–January 2017). A subsample ( n = 4) of free apps were trialed for one week by the reviewers and coded for the presence of BCTs (February 2017). Results In the free and paid app descriptions, only 10 of 93 BCTs were present with a mean of 2.42 BCTs (range 0–6) per app. The BCTs coded most frequently were “prompts/cues” ( n = 43), “information about health consequences” ( n = 31), and “self-monitoring of behavior” ( n = 17). For the four free apps that were trialed, three additional BCTs were coded that were not coded in the descriptions: “graded tasks,” “focus on past successes,” and “behavior substitution.” Conclusions These sedentary behavior apps have fewer BCTs compared with physical activity apps and traditional (i.e., non-app) physical activity and healthy eating interventions. The present study sheds light on the behavior change potential of sedentary behavior apps and provides practical insight about coding for BCTs in apps.

scholarly journals Measuring Engagement in eHealth and mHealth Behavior Change Interventions: Viewpoint of Methodologies (Preprint)

2017 ◽  
Author(s):  
Camille E Short ◽  
Ann DeSmet ◽  
Catherine Woods ◽  
Susan L Williams ◽  
Carol Maher ◽  
...  
Keyword(s):  
Behavior Change ◽  
Self Report ◽  
Sensor Data ◽  
Future Research ◽  
Snowball Sampling ◽  
System Usage ◽  
Behavior Change Interventions ◽  
Wide Range ◽  
Methodological Considerations ◽  
Usage Data

UNSTRUCTURED Engagement in electronic health (eHealth) and mobile health (mHealth) behavior change interventions is thought to be important for intervention effectiveness, though what constitutes engagement and how it enhances efficacy has been somewhat unclear in the literature. Recently published detailed definitions and conceptual models of engagement have helped to build consensus around a definition of engagement and improve our understanding of how engagement may influence effectiveness. This work has helped to establish a clearer research agenda. However, to test the hypotheses generated by the conceptual modules, we need to know how to measure engagement in a valid and reliable way. The aim of this viewpoint is to provide an overview of engagement measurement options that can be employed in eHealth and mHealth behavior change intervention evaluations, discuss methodological considerations, and provide direction for future research. To identify measures, we used snowball sampling, starting from systematic reviews of engagement research as well as those utilized in studies known to the authors. A wide range of methods to measure engagement were identified, including qualitative measures, self-report questionnaires, ecological momentary assessments, system usage data, sensor data, social media data, and psychophysiological measures. Each measurement method is appraised and examples are provided to illustrate possible use in eHealth and mHealth behavior change research. Recommendations for future research are provided, based on the limitations of current methods and the heavy reliance on system usage data as the sole assessment of engagement. The validation and adoption of a wider range of engagement measurements and their thoughtful application to the study of engagement are encouraged.

scholarly journals Determinants of Fasting in Patients with Cancer : A Longitudinal Study Using an Integrated Behavior Change Model

2021 ◽  
Author(s):  
Sébastien Mas ◽  
Mathieu Gourlan ◽  
Paquito Bernard ◽  
Florence Cousson-Gélie
Keyword(s):  
Behavior Change ◽  
Autonomy Support ◽  
Action Planning ◽  
Autonomous Motivation ◽  
Subjective Norm ◽  
Initial Time ◽  
Future Research ◽  
Change Model ◽  
Patients With Cancer ◽  
Past Behavior

Fasting is currently not recommended during cancer treatment. Despite this, a meaningful proportion of patients with cancer have positive perceptions of fasting, or practice fasting during their treatment. The integrated behavior change model (IBCM) combines variables and hypotheses derived from the theory of planned behavior, the self-determination theory, and the health action process approach to explain health behavior. Based on the IBCM, the present study examined determinants of fasting in patients with cancer. A total of 114 patients (M = 52 years, 108 women) completed an online questionnaire measuring past behavior, action planning, intention, attitudes, subjective norm, perceived behavioral control (PBC), autonomous motivation, controlled motivation, amotivation, and perceived autonomy support at an initial time point and informed one month later to what extent they had performed fasting in the last 30 days. Hypotheses were tested using a Bayesian path analysis. The model tested confirmed most of the hypothesized paths of the IBCM. Autonomous motivation was significantly directly linked to intention, while attitude and subjective norm were not significantly linked to intention. PBC was significantly indirectly linked to fasting through intention and action planning. Past behavior was significantly indirectly linked to actual fasting through some variables of the IBCM, with a significant strong link between past behavior and autonomous motivation. Autonomous motivation appears to play a central role in the practice of fasting among patients with cancer. Future research should apply the IBCM to fasting among various populations and with a longer period of follow-up.

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