The Experts below are selected from a list of 16104 Experts worldwide ranked by ideXlab platform
Corneel Vandelanotte - One of the best experts on this subject based on the ideXlab platform.
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are Health Behavior Change interventions that use online social networks effective a systematic review
Journal of Medical Internet Research, 2014Co-Authors: Carol Maher, Lucy K Lewis, Katia Ferrar, Ilse De Bourdeaudhuij, Simon J Marshall, Corneel VandelanotteAbstract:Background: The dramatic growth of Web 2.0 technologies and online social networks offers immense potential for the delivery of Health Behavior Change campaigns. However, it is currently unclear how online social networks may best be harnessed to achieve Health Behavior Change. Objective: The intent of the study was to systematically review the current level of evidence regarding the effectiveness of online social network Health Behavior interventions. Methods: Eight databases (Scopus, CINAHL, Medline, ProQuest, EMBASE, PsycINFO, Cochrane, Web of Science and Communication & Mass Media Complete) were searched from 2000 to present using a comprehensive search strategy. Study eligibility criteria were based on the PICOS format, where “population” included child or adult populations, including Healthy and disease populations; “intervention” involved Behavior Change interventions targeting key modifiable Health Behaviors (tobacco and alcohol consumption, dietary intake, physical activity, and sedentary Behavior) delivered either wholly or in part using online social networks; “comparator” was either a control group or within subject in the case of pre-post study designs; “outcomes” included Health Behavior Change and closely related variables (such as theorized mediators of Health Behavior Change, eg, self-efficacy); and “study design” included experimental studies reported in full-length peer-reviewed sources. Reports of intervention effectiveness were summarized and effect sizes (Cohen’s d and 95% confidence intervals) were calculated wherever possible. Attrition (percentage of people who completed the study), engagement (actual usage), and fidelity (actual usage/intended usage) with the social networking component of the interventions were scrutinized. Results: A total of 2040 studies were identified from the database searches following removal of duplicates, of which 10 met inclusion criteria. The studies involved a total of 113,988 participants (ranging from n=10 to n=107,907). Interventions included commercial online Health social network websites (n=2), research Health social network websites (n=3), and multi-component interventions delivered in part via pre-existing popular online social network websites (Facebook n=4 and Twitter n=1). Nine of the 10 included studies reported significant improvements in some aspect of Health Behavior Change or outcomes related to Behavior Change. Effect sizes for Behavior Change ranged widely from −0.05 (95% CI 0.45-0.35) to 0.84 (95% CI 0.49-1.19), but in general were small in magnitude and statistically non-significant. Participant attrition ranged from 0-84%. Engagement and fidelity were relatively low, with most studies achieving 5-15% fidelity (with one exception, which achieved 105% fidelity). Conclusions: To date there is very modest evidence that interventions incorporating online social networks may be effective; however, this field of research is in its infancy. Further research is needed to determine how to maximize retention and engagement,
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Are Health Behavior Change Interventions That Use Online Social Networks Effective? A Systematic Review.
Journal of Medical Internet Research, 2014Co-Authors: Emily Brindal, Sitwat Langrial, Carol A Maher, Lucy K Lewis, Katia Ferrar, Simon Marshall, Derek Foster, Ilse De Bourdeaudhuij, Corneel VandelanotteAbstract:Background: The dramatic growth of Web 2.0 technologies and online social networks offers immense potential for the delivery of Health Behavior Change campaigns. However, it is currently unclear how online social networks may best be harnessed to achieve Health Behavior Change. Objective: The intent of the study was to systematically review the current level of evidence regarding the effectiveness of online social network Health Behavior interventions. Methods: Eight databases (Scopus, CINAHL, Medline, ProQuest, EMBASE, PsycINFO, Cochrane, Web of Science and Communication & Mass Media Complete) were searched from 2000 to present using a comprehensive search strategy. Study eligibility criteria were based on the PICOS format, where “population” included child or adult populations, including Healthy and disease populations; “intervention” involved Behavior Change interventions targeting key modifiable Health Behaviors (tobacco and alcohol consumption, dietary intake, physical activity, and sedentary Behavior) delivered either wholly or in part using online social networks; “comparator” was either a control group or within subject in the case of pre-post study designs; “outcomes” included Health Behavior Change and closely related variables (such as theorized mediators of Health Behavior Change, eg, self-efficacy); and “study design” included experimental studies reported in full-length peer-reviewed sources. Reports of intervention effectiveness were summarized and effect sizes (Cohen’s d and 95% confidence intervals) were calculated wherever possible. Attrition (percentage of people who completed the study), engagement (actual usage), and fidelity (actual usage/intended usage) with the social networking component of the interventions were scrutinized. Results: A total of 2040 studies were identified from the database searches following removal of duplicates, of which 10 met inclusion criteria. The studies involved a total of 113,988 participants (ranging from n=10 to n=107,907). Interventions included commercial online Health social network websites (n=2), research Health social network websites (n=3), and multi-component interventions delivered in part via pre-existing popular online social network websites (Facebook n=4 and Twitter n=1). Nine of the 10 included studies reported significant improvements in some aspect of Health Behavior Change or outcomes related to Behavior Change. Effect sizes for Behavior Change ranged widely from −0.05 (95% CI 0.45-0.35) to 0.84 (95% CI 0.49-1.19), but in general were small in magnitude and statistically non-significant. Participant attrition ranged from 0-84%. Engagement and fidelity were relatively low, with most studies achieving 5-15% fidelity (with one exception, which achieved 105% fidelity). Conclusions: To date there is very modest evidence that interventions incorporating online social networks may be effective; however, this field of research is in its infancy. Further research is needed to determine how to maximize retention and engagement, whether Behavior Change can be sustained in the longer term, and to determine how to exploit online social networks to achieve mass dissemination. Specific recommendations for future research are provided.
Wayne F Velicer - One of the best experts on this subject based on the ideXlab platform.
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Automated Indexing of Internet Stories for Health Behavior Change: Weight Loss Attitude Pilot Study
Journal of Medical Internet Research, 2014Co-Authors: Ramesh Manuvinakurike, Wayne F Velicer, Timothy W. BickmoreAbstract:Background: Automated Health Behavior Change interventions show promise, but suffer from high attrition and disuse. The Internet abounds with thousands of personal narrative accounts of Health Behavior Change that could not only provide useful information and motivation for others who are also trying to Change, but an endless source of novel, entertaining stories that may keep participants more engaged than messages authored by interventionists. Objective: Given a collection of relevant personal Health Behavior Change stories gathered from the Internet, the aim of this study was to develop and evaluate an automated indexing algorithm that could select the best possible story to provide to a user to have the greatest possible impact on their attitudes toward changing a targeted Health Behavior, in this case weight loss. Methods: An indexing algorithm was developed using features informed by theories from Behavioral medicine together with text classification and machine learning techniques. The algorithm was trained using a crowdsourced dataset, then evaluated in a 2×2 between-subjects randomized pilot study. One factor compared the effects of participants reading 2 indexed stories vs 2 randomly selected stories, whereas the second factor compared the medium used to tell the stories: text or animated conversational agent. Outcome measures included Changes in self-efficacy and decisional balance for weight loss before and after the stories were read. Results: Participants were recruited from a crowdsourcing website (N=103; 53.4%, 55/103 female; mean age 35, SD 10.8 years; 65.0%, 67/103 precontemplation; 19.4%, 20/103 contemplation for weight loss). Participants who read indexed stories exhibited a significantly greater increase in self-efficacy for weight loss compared to the control group ( F 1,107 =5.5, P =.02). There were no significant effects of indexing on Change in decisional balance ( F 1,97 =0.05, P =.83) and no significant effects of medium on Change in self-efficacy ( F 1,107 =0.04, P =.84) or decisional balance ( F 1,97 =0.78, P =.38). Conclusions: Personal stories of Health Behavior Change can be harvested from the Internet and used directly and automatically in interventions to affect participant attitudes, such as self-efficacy for changing Behavior. Such approaches have the potential to provide highly tailored interventions that maximize engagement and retention with minimal intervention development effort. [J Med Internet Res 2014;16(12):e285]
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the benefits and challenges of multiple Health Behavior Change in research and in practice
Preventive Medicine, 2010Co-Authors: Judith J Prochaska, Claudio R. Nigg, Wayne F Velicer, Bonnie Spring, James O. ProchaskaAbstract:Objective The major chronic diseases are caused by multiple risks, yet the science of multiple Health Behavior Change (MHBC) is at an early stage, and factors that facilitate or impede scientists' involvement in MHBC research are unknown. Benefits and challenges of MHBC interventions were investigated to strengthen researchers' commitment and prepare them for challenges.
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evaluating theories of Health Behavior Change a hierarchy of criteria applied to the transtheoretical model
Applied Psychology, 2008Co-Authors: James O. Prochaska, Julie A Wright, Wayne F VelicerAbstract:The most common criteria recommended by philosophers of science for evaluating theories were organised within a hierarchy ranging from the least to the most risky tests for theories of Health Behavior Change. The hierarchy progressed across: (1) Clarity; (2) Consistency; (3) Parsimony; (4) Testable; (5) Predictive Power; (6) Explanatory Power; (7) Productivity; (8) Generalisable; (9) Integration; (10) Utility; (11) Efficacy; and (12) Impact. The hierarchy was applied to the Transtheoretical Model (TTM) as an example of a Health Behavior Change theory. The application was from the perspective of critics and advocates of TTM. Examples of basic and applied research challenging and supporting TTM across the hierarchy of criteria are presented. The goal is to provide a model for comparing alternative theories and to evaluate progress across the hierarchy within a particular theory. As theories meet criteria at each step in the hierarchy, the research and applications they generate can have increasing impacts on the science and practice of Health Behavior Change.
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strengths and weaknesses of Health Behavior Change programs on the internet
Journal of Health Psychology, 2003Co-Authors: Kerry E Evers, Janice M Prochaska, Marymargaret Driskell, Carol O Cummins, Wayne F VelicerAbstract:Full reviews were conducted on 37 public websites on Health Behavior Change for disease prevention and management. All had at least four of five of the `5A's for effective Health Behavior Change tr...
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the transtheoretical model of Health Behavior Change
American Journal of Health Promotion, 1997Co-Authors: James O. Prochaska, Wayne F VelicerAbstract:The transtheoretical model posits that Health Behavior Change involves progress through six stages of Change: precontemplation, contemplation, preparation, action, maintenance, and termination. Ten processes of Change have been identified for producing progress along with decisional balance, self-efficacy, and temptations. Basic research has generated a rule of thumb for at-risk populations: 40% in precontemplation, 40% in contemplation, and 20% in preparation. Across 12 Health Behaviors, consistent patterns have been found between the pros and cons of changing and the stages of Change. Applied research has demonstrated dramatic improvements in recruitment, retention, and progress using stage-matched interventions and proactive recruitment procedures. The most promising outcomes to date have been found with computer-based individualized and interactive interventions. The most promising enhancement to the computer-based programs are personalized counselors. One of the most striking results to date for stag...
James O. Prochaska - One of the best experts on this subject based on the ideXlab platform.
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a review of multiple Health Behavior Change interventions for primary prevention
American Journal of Lifestyle Medicine, 2011Co-Authors: Judith J Prochaska, James O. ProchaskaAbstract:Most individuals engage in multiple unHealthy lifestyle Behaviors with the potential for negative Health consequences. Yet most Health promotion research has addressed risk factors as categorically separate entities, and little is known about how to effectively promote multiple Health Behavior Change (MHBC). This review summarizes the recent literature (January 2004 to December 2009) on randomized clinical trials evaluating MHBC interventions for primary prevention. Combining all the studies across all the reviews, fewer than 150 studies were identified. This is a fraction of the number of trials conducted on changing individual Behavioral risks. Three primary Behavioral clusters dominated: (1) the energy balance Behaviors of physical activity and diet; (2) addictive Behaviors like smoking and other drugs; and (3) disease-related Behaviors, specifically cardiovascular disease (CVD) and cancer related. Findings were largely disappointing for studies of diet and physical activity, particularly with youth. T...
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the benefits and challenges of multiple Health Behavior Change in research and in practice
Preventive Medicine, 2010Co-Authors: Judith J Prochaska, Claudio R. Nigg, Wayne F Velicer, Bonnie Spring, James O. ProchaskaAbstract:Objective The major chronic diseases are caused by multiple risks, yet the science of multiple Health Behavior Change (MHBC) is at an early stage, and factors that facilitate or impede scientists' involvement in MHBC research are unknown. Benefits and challenges of MHBC interventions were investigated to strengthen researchers' commitment and prepare them for challenges.
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evaluating theories of Health Behavior Change a hierarchy of criteria applied to the transtheoretical model
Applied Psychology, 2008Co-Authors: James O. Prochaska, Julie A Wright, Wayne F VelicerAbstract:The most common criteria recommended by philosophers of science for evaluating theories were organised within a hierarchy ranging from the least to the most risky tests for theories of Health Behavior Change. The hierarchy progressed across: (1) Clarity; (2) Consistency; (3) Parsimony; (4) Testable; (5) Predictive Power; (6) Explanatory Power; (7) Productivity; (8) Generalisable; (9) Integration; (10) Utility; (11) Efficacy; and (12) Impact. The hierarchy was applied to the Transtheoretical Model (TTM) as an example of a Health Behavior Change theory. The application was from the perspective of critics and advocates of TTM. Examples of basic and applied research challenging and supporting TTM across the hierarchy of criteria are presented. The goal is to provide a model for comparing alternative theories and to evaluate progress across the hierarchy within a particular theory. As theories meet criteria at each step in the hierarchy, the research and applications they generate can have increasing impacts on the science and practice of Health Behavior Change.
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the transtheoretical model of Health Behavior Change
American Journal of Health Promotion, 1997Co-Authors: James O. Prochaska, Wayne F VelicerAbstract:The transtheoretical model posits that Health Behavior Change involves progress through six stages of Change: precontemplation, contemplation, preparation, action, maintenance, and termination. Ten processes of Change have been identified for producing progress along with decisional balance, self-efficacy, and temptations. Basic research has generated a rule of thumb for at-risk populations: 40% in precontemplation, 40% in contemplation, and 20% in preparation. Across 12 Health Behaviors, consistent patterns have been found between the pros and cons of changing and the stages of Change. Applied research has demonstrated dramatic improvements in recruitment, retention, and progress using stage-matched interventions and proactive recruitment procedures. The most promising outcomes to date have been found with computer-based individualized and interactive interventions. The most promising enhancement to the computer-based programs are personalized counselors. One of the most striking results to date for stag...
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A criterion measurement model for Health Behavior Change
Addictive Behaviors, 1996Co-Authors: Wayne F Velicer, Joseph S. Rossi, James O. Prochaska, Carlo C. DiclementeAbstract:Abstract Researchers in the field of Health Behavior Change have traditionally relied on a univariate criterion measure to evaluate the efficacy of an intervention. Such measures have superficial face validity but suffer from a number of problems: (a) lack of precise definitions: (b) poor statistical power; and (c) a lack of meaningfulness for some aspects of the problem. As an alternative, a theoretical model is developed that attempts to define more appropriate multivariate sets of dependent variables for the study of Health Behavior Change. The model involves three separate constructs: Positive Evaluation Strength, Negative Evaluation Strength, and Habit Strength. The pattern of Change for each construct is described across four stages of Change: Precontemplation, Contemplation, Action, and Maintenance. For each construct, two thresholds are proposed representing the ability of the environment to modify the construct. Four tests of the model are provided from existing data sets. First, a structural model analysis was used to test if the proposed measurement model adequately fits the data. Second, a dynamic typology approach produced profiles of Change that are consistent with the model. Third, a time series analysis provided support for the assumed model. Fourth, longitudinal, five-wave panel design was employed to test if the relation between the two cognitive variables (Pros and Cons) and the Behavioral measure (Habit Strength) was consistent with the model. Implications for alternative intervention strategies are discussed.
Liliana Laranjo - One of the best experts on this subject based on the ideXlab platform.
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Social Media and Health Behavior Change
Participatory Health Through Social Media, 2020Co-Authors: Liliana LaranjoAbstract:Abstract Chronic diseases are nowadays the leading cause of mortality and morbidity worldwide, being largely promoted by unHealthy lifestyle Behaviors. Indeed, sedentariness, poor diet, smoking, and alcohol abuse are common risk factors for cardiovascular disease and other chronic conditions, accounting for a great amount of Healthcare costs. Behavioral informatics interventions have the potential to assist individuals (both patients and Healthy consumers) in modifying Behaviors to improve physical, mental, or Behavioral Health. In particular, social media interventions have several advantages, including wide accessibility across geographical barriers, ubiquity, and cost-efficiency. Nowadays, the growth in social networking sites brings new opportunities to disseminate public Health interventions and promote large improvements in the field of Health Behavior Change. This chapter discusses the importance of Health Behavior Change and the numerous theories, models, and frameworks that attempt to explain its processes, as well as provides possible avenues for the use of social media in Behavior Change interventions.
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the influence of social networking sites on Health Behavior Change a systematic review and meta analysis
Journal of the American Medical Informatics Association, 2015Co-Authors: Liliana Laranjo, Amael Arguel, Ana Luisa Neves, Aideen Gallagher, Ruth Kaplan, Nathan J Mortimer, Guilherme A MendesAbstract:Objective Our aim was to evaluate the use and effectiveness of interventions using social networking sites (SNSs) to Change Health Behaviors. Materials and methods Five databases were scanned using a predefined search strategy. Studies were included if they focused on patients/consumers, involved an SNS intervention, had an outcome related to Health Behavior Change, and were prospective. Studies were screened by independent investigators, and assessed using Cochrane's ‘risk of bias’ tool. Randomized controlled trials were pooled in a meta-analysis. Results The database search retrieved 4656 citations; 12 studies (7411 participants) met the inclusion criteria. Facebook was the most utilized SNS, followed by Health-specific SNSs, and Twitter. Eight randomized controlled trials were combined in a meta-analysis. A positive effect of SNS interventions on Health Behavior outcomes was found (Hedges’ g 0.24; 95% CI 0.04 to 0.43). There was considerable heterogeneity (I2 = 84.0%; T2 = 0.058) and no evidence of publication bias. Discussion To the best of our knowledge, this is the first meta-analysis evaluating the effectiveness of SNS interventions in changing Health-related Behaviors. Most studies evaluated multi-component interventions, posing problems in isolating the specific effect of the SNS. Health Behavior Change theories were seldom mentioned in the included articles, but two particularly innovative studies used ‘network alteration’, showing a positive effect. Overall, SNS interventions appeared to be effective in promoting Changes in Health-related Behaviors, and further research regarding the application of these promising tools is warranted. Conclusions Our study showed a positive effect of SNS interventions on Health Behavior-related outcomes, but there was considerable heterogeneity. Protocol registration The protocol for this systematic review is registered at with the number CRD42013004140.
Guilherme A Mendes - One of the best experts on this subject based on the ideXlab platform.
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the influence of social networking sites on Health Behavior Change a systematic review and meta analysis
Journal of the American Medical Informatics Association, 2015Co-Authors: Liliana Laranjo, Amael Arguel, Ana Luisa Neves, Aideen Gallagher, Ruth Kaplan, Nathan J Mortimer, Guilherme A MendesAbstract:Objective Our aim was to evaluate the use and effectiveness of interventions using social networking sites (SNSs) to Change Health Behaviors. Materials and methods Five databases were scanned using a predefined search strategy. Studies were included if they focused on patients/consumers, involved an SNS intervention, had an outcome related to Health Behavior Change, and were prospective. Studies were screened by independent investigators, and assessed using Cochrane's ‘risk of bias’ tool. Randomized controlled trials were pooled in a meta-analysis. Results The database search retrieved 4656 citations; 12 studies (7411 participants) met the inclusion criteria. Facebook was the most utilized SNS, followed by Health-specific SNSs, and Twitter. Eight randomized controlled trials were combined in a meta-analysis. A positive effect of SNS interventions on Health Behavior outcomes was found (Hedges’ g 0.24; 95% CI 0.04 to 0.43). There was considerable heterogeneity (I2 = 84.0%; T2 = 0.058) and no evidence of publication bias. Discussion To the best of our knowledge, this is the first meta-analysis evaluating the effectiveness of SNS interventions in changing Health-related Behaviors. Most studies evaluated multi-component interventions, posing problems in isolating the specific effect of the SNS. Health Behavior Change theories were seldom mentioned in the included articles, but two particularly innovative studies used ‘network alteration’, showing a positive effect. Overall, SNS interventions appeared to be effective in promoting Changes in Health-related Behaviors, and further research regarding the application of these promising tools is warranted. Conclusions Our study showed a positive effect of SNS interventions on Health Behavior-related outcomes, but there was considerable heterogeneity. Protocol registration The protocol for this systematic review is registered at with the number CRD42013004140.
