A meta-analysis of computer-tailored interventions for health behavior change.
Ontology highlight
ABSTRACT: Objective
Computer-tailored interventions have become increasingly common for facilitating improvement in behaviors related to chronic disease and health promotion. A sufficient number of outcome studies from these interventions are now available to facilitate the quantitative analysis of effect sizes, permitting moderator analyses that were not possible with previous systematic reviews.Method
The present study employs meta-analytic techniques to assess the mean effect for 88 computer-tailored interventions published between 1988 and 2009 focusing on four health behaviors: smoking cessation, physical activity, eating a healthy diet, and receiving regular mammography screening. Effect sizes were calculated using Hedges g. Study, tailoring, and demographic moderators were examined by analyzing between-group variance and meta-regression.Results
Clinically and statistically significant overall effect sizes were found across each of the four behaviors. While effect sizes decreased after intervention completion, dynamically tailored interventions were found to have increased efficacy over time as compared with tailored interventions based on one assessment only. Study effects did not differ across communication channels nor decline when up to three behaviors were identified for intervention simultaneously.Conclusion
This study demonstrates that computer-tailored interventions have the potential to improve health behaviors and suggests strategies that may lead to greater effectiveness of these techniques.
SUBMITTER: Krebs P
PROVIDER: S-EPMC2939185 | biostudies-literature | 2010 Sep-Oct
REPOSITORIES: biostudies-literature
Publications
A meta-analysis of computer-tailored interventions for health behavior change.
Krebs Paul P Prochaska James O JO Rossi Joseph S JS
Preventive medicine 20100615 3-4
<h4>Objective</h4>Computer-tailored interventions have become increasingly common for facilitating improvement in behaviors related to chronic disease and health promotion. A sufficient number of outcome studies from these interventions are now available to facilitate the quantitative analysis of effect sizes, permitting moderator analyses that were not possible with previous systematic reviews.<h4>Method</h4>The present study employs meta-analytic techniques to assess the mean effect for 88 com ...[more]
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Project description:BackgroundIncreasing physical activity is important in the promotion of better health. Computer-tailored behavior change programs have shown promise in changing lifestyle risk factors.PurposeTo provide a narrative systematic review describing the range of evidence on 'second' and 'third' generation computer-tailored primary prevention interventions for physical activity, to determine their effectiveness and key characteristics of success. Unlike previous reviews, this review used specific criteria to measure the external validity of studies, was exclusive to primary prevention interventions in which tailoring was generated through an expert system, and excluded first generation computer-tailored interventions.MethodsComputer-tailored intervention studies published from January 1996-2008 were identified through a search of five databases: Medline; Embase; PsycINFO; CINAHL; and All EBM Reviews and by examining reference lists of relevant articles.ResultsSeventeen articles were included, describing the evaluation of 16 interventions, ten of which found significant positive effects of the computer-tailored interventions on physical activity or weight reduction outcomes.ConclusionThe evidence of effectiveness for computer-tailored physical activity interventions is inconclusive. They have potential to reach large groups of people however there is uncertainty whether reported effects are generalizable and sustained.
| S-EPMC2700068 | biostudies-literature
Project description:A physically active lifestyle provides innumerable benefits; yet, few individuals are physically active enough to reap those benefits. Tailored physical activity interventions may address low rates of physical activity by offering individualized strategies that consider a person's characteristics, needs, preferences, and/or context, rather than the traditional one-size-fits-all approach. However, the tailoring methodology is in its nascency, and an understanding of how best to develop such interventions is needed. In this commentary, we identify future directions to enhance the impact of tailored interventions designed to increase physical activity participation. A multi-country collaborative was established to review the literature and discuss an agenda for future research. Two overarching research opportunities are suggested for improving the development of tailored, behavioral physical activity interventions: (a) optimize the engagement of diverse knowledge users in intervention co-design and (b) examine ethical considerations that may impact the use of technology to support tailored physical activity delivery. Specifically, there is a need for better reporting and evaluation of knowledge user involvement alongside targeting diversity in the inclusion of knowledge users. Furthermore, while technology boasts many opportunities to increase the scale and precision of interventions, examinations of how it impacts recipients' experiences of and participation in tailored interventions are needed to ensure the benefits of technology use outweigh the risks. A better understanding of these research areas will help ensure that the diverse needs of individuals are met, technology is appropriately used to support tailoring, and ultimately it improves the effectiveness of tailored physical activity interventions.
| S-EPMC8672936 | biostudies-literature
Project description:Background:Self-monitoring (SM) of diet and tailored feedback (TF) have been suggested as tools for changing dietary behavior. New technologies allow users to monitor behavior remotely, potentially improving reach, adherence, and outcomes. Objective:We conducted a systematic literature review and meta-analysis to address the following question: are remotely delivered standalone (i.e., no human contact) interventions that use SM or TF effective in changing eating behaviors? Design:Five databases were searched in October 2016 (updated in September 2017). Only randomized controlled trials published after 1990 were included. Trials could include any adult population with no history of disordered eating which delivered an SM or TF intervention without direct contact and recorded actual dietary consumption as an outcome. Three assessors independently screened the search results. Two reviewers extracted the study characteristics, intervention details, and outcomes, and assessed risk of bias using the Cochrane tool. Results were converted to standardized mean differences and incorporated into a 3-level (individuals and outcomes nested in studies) random effects meta-analysis. Results:Twenty-six studies containing 21,262 participants were identified. The majority of the studies were judged to be unclear or at high risk of bias. The meta-analysis showed dietary improvement in the intervention group compared to the control group with a standardized mean difference of 0.17 (95% CI: 0.10, 0.24; P < 0.0001). The I2 statistic for the meta-analysis was 0.77, indicating substantial heterogeneity in results. A "one study removed" sensitivity analysis showed that no single study excessively influenced the results. Conclusions:Standalone interventions containing self-regulatory methods have a small but significant effect on dietary behavior, and integrating these elements could be important in future interventions. However, there was substantial variation in study results that could not be explained by the characteristics we explored, and there were risk-of-bias concerns with the majority of studies.
| S-EPMC5875102 | biostudies-literature
Project description:BackgroundDigital behavior change interventions (DBCIs) appear to reduce alcohol consumption, but greater understanding is needed of their mechanisms of action.PurposeTo describe the behavior change techniques (BCTs) used in DBCIs and examine whether individual BCTs, the inclusion of more BCTs or more Control Theory congruent BCTs is associated with increased effectiveness.MethodsForty-one randomized control trials were extracted from a Cochrane review of alcohol reduction DBCIs and coded for up to 93 BCTs using an established and reliable method. Random effects unadjusted and adjusted meta-regression models were performed to assess associations between BCTs and intervention effectiveness.ResultsInterventions used a mean of 9.1 BCTs (range 1-22), 23 different BCTs were used in four or more trials. Trials that used "Behavior substitution" (-95.112 grams per week [gpw], 95% CI: -162.90, -27.34), "Problem solving" (-45.92 gpw, 95% CI: -90.97, -0.87) and "Credible source" (-32.09 gpw, 95% CI: -60.64, -3.55) were significantly associated with greater alcohol reduction than trials without these BCTs. The "Behavior substitution" result should be treated as preliminary because it was reported in only four trials, three of which were conducted by the same research group. "Feedback" was used in 98% of trials (n = 41); other Control Theory congruent BCTs were used less frequently: for example, "Goal setting" 43% (n = 18) and "Self-monitoring" 29%, (n = 12).Conclusions"Behavior substitution," "Problem solving," and "Credible source" were associated with greater alcohol reduction. Many BCTs were used infrequently in DBCIs, including BCTs with evidence of effectiveness in other domains, such as "Self-monitoring" and "Goal setting."
| S-EPMC6361280 | biostudies-literature
Project description:ContextProlonged high salt (sodium) intake can increase the risk of hypertension and cardiovascular disease. Behavioral interventions may help reduce sodium intake at the population level.ObjectiveThe effectiveness of behavior change interventions to reduce sodium intake in adults was investigated in this systematic review and meta-analysis.Data sourceThe PubMed, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, and EMBASE databases were searched.Data extractionNarrative synthesis and random-effects meta-analyses were used to determine intervention efficacy. A total of 61 trials (46 controlled trials and 15 quasi-experimental studies) were included.ResultsBehavior change interventions resulted in significant improvements in salt consumption behavior (eg, decrease in purchase of salty foods; increase in use of salt substitutes), leading to reductions in sodium intake as measured by urinary sodium in 32 trials (N = 7840 participants; mean difference, -486.19 mg/d [95%CI, -669.44 to -302.95]; P < 0.001; I2 = 92%) and dietary sodium in 19 trials (N = 3750 participants; mean difference -399.86 mg/d [95%CI, -581.51 to -218.20]; P < 0.001; I2 = 96%), equivalent to a reduction of >1 g of salt intake daily. Effects were not significantly different based on baseline sodium intakes, blood pressure status, disease status, the use of behavior change theories, or the main method of intervention delivery (ie, online vs face-to-face).ConclusionBehavior change interventions are effective at improving salt consumption practices and appear to reduce salt intake by >1 g/d.Systematic review registrationPROSPERO registration no. CRD42020185639.
| S-EPMC8907486 | biostudies-literature
Project description:BackgroundEfforts have multiplied in the past decade to underline the importance of pain management. For both acute and chronic pain management, various barriers generate considerable treatment accessibility issues, thereby providing an opportunity for alternative intervention formats to be implemented. Several systematic reviews on Web-based interventions with a large emphasis on chronic pain and cognitive behavioral therapy have been recently conducted to explore the influence of these interventions on pain management However, to our knowledge, the specific contribution of tailored Web-based interventions for pain management has not been described and their effect on pain has not been evaluated.ObjectiveThe primary aim of this systematic review was to answer the following research question: What is the effect of tailored Web-based pain management interventions for adults on pain intensity compared with usual care, face-to-face interventions, and standardized Web-based interventions? A secondary aim was to examine the effects of these interventions on physical and psychological functions.MethodsWe conducted a systematic review of articles published from January 2000 to December 2015. We used the DerSimonian-Laird random effects models with 95% confidence intervals to calculate effect estimates for all analyses. We calculated standardized mean differences from extracted means and standard deviations, as outcome variables were measured on different continuous scales. We evaluated 5 different outcomes: pain intensity (primary outcome), pain-related disability, anxiety, depression, and pain catastrophizing. We assessed effects according to 3 time intervals: short term (<1 month), medium term (1-6 months), and long term (6-12 months).ResultsAfter full-text review, we excluded 31 articles, resulting in 17 eligible studies. Only 1 study concerned acute pain and was removed from the meta-analysis, resulting in 16 studies available for quantitative assessment. Compared with standard care or a waiting list, tailored Web-based intervention showed benefits immediately after, with small effect sizes (<0.40) for pain intensity (10 randomized controlled trials [RCTs], n=1310, P=.003) and pain-related disability (6 RCTs, n=953, P<.001). No other improvements were observed at follow-up in the medium and long terms. Compared with the active control group, no improvements were found for the primary outcome (pain intensity) or any of the outcomes except for a small effect size on pain catastrophizing (2 RCTs, n=333, P<.001) immediately after the intervention.ConclusionsTailored Web-based interventions did not prove to be more efficacious than standardized Web-based interventions in terms of pain intensity, pain-related disability, anxiety, and depression. An interesting finding was that some efficacy was shown on pain catastrophizing compared with active control interventions. Considering the diversity of approaches used in tailored Web-based interventions for chronic pain management, their efficacy is yet to be explored. Moreover, their contribution to acute pain management is embryonic.Trial registrationInternational prospective register of systematic reviews (PROSPERO): CRD42015027669; http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42015027669 (Archived by WebCite at http://www. webcitation.org/6uneWAuyR).
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Project description:BackgroundPrevious systematic reviews of digital eating disorder interventions have demonstrated effectiveness at improving symptoms of eating disorders; however, our understanding of how these interventions work and what contributes to their effectiveness is limited. Understanding the behavior change techniques (BCTs) that are most commonly included within effective interventions may provide valuable information for researchers and developers. Establishing whether these techniques have been informed by theory will identify whether they target those mechanisms of action that have been identified as core to changing eating disorder behaviors. It will also evaluate the importance of a theoretical approach to digital intervention design.ObjectiveThis study aims to define the BCTs within digital self-management interventions or minimally guided self-help interventions for adults with eating disorders that have been evaluated within randomized controlled trials. It also assessed which of the digital interventions were grounded in theory and the range of modes of delivery included.MethodsA literature search identified randomized controlled trials of digital intervention for the treatment of adults with eating disorders with minimal therapist support. Each digital intervention was coded for BCTs using the established BCT Taxonomy v1; for the application of theory using an adapted version of the theory coding scheme (TCS); and for modes of delivery using the Mode of Delivery Ontology. A meta-analysis evaluated the evidence that any individual BCT moderated effect size or that other potential factors such as the application of theory or number of modes of delivery had an effect on eating disorder outcomes.ResultsDigital interventions included an average of 14 (SD 2.6; range 9-18) BCTs. Self-monitoring of behavior was included in all effective interventions, with Problem-solving, Information about antecedents, Feedback on behavior, Self-monitoring of outcomes of behavior, and Action planning identified in >75% (13/17) of effective interventions. Social support and Information about health consequences were more evident in effective interventions at follow-up compared with postintervention measurement. The mean number of modes of delivery was 4 (SD 1.6; range 2-7) out of 12 possible modes, with most interventions (15/17, 88%) being web based. Digital interventions that had a higher score on the TCS had a greater effect size than those with a lower TCS score (subgroup differences: χ21=9.7; P=.002; I²=89.7%) within the meta-analysis. No other subgroup analyses had statistically significant results.ConclusionsThere was a high level of consistency in terms of the most common BCTs within effective interventions; however, there was no evidence that any specific BCT contributed to intervention efficacy. The interventions that were more strongly informed by theory demonstrated greater improvements in eating disorder outcomes compared to waitlist or treatment-as-usual controls. These results can be used to inform the development of future digital eating disorder interventions.Trial registrationPROSPERO CRD42023410060; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=410060.
| S-EPMC11327638 | biostudies-literature
Project description:BackgroundMobile apps are being widely used for delivering health interventions, with their ubiquitous access and sensing capabilities. One such use is the delivery of interventions for healthy eating behavior.ObjectiveThe aim of this study was to provide a comprehensive view of the literature on the use of mobile interventions for eating behavior change. We synthesized the studies with such interventions and mapped out their input methods, interventions, and outcomes.MethodsWe conducted a scoping literature search in PubMed/MEDLINE, Association for Computing Machinery Digital Library, and PsycINFO databases to identify relevant papers published between January 2013 and April 2018. We also hand-searched relevant themes of journals in the Journal of Medical Internet Research and registered protocols. Studies were included if they provided and assessed mobile-based interventions for dietary behavior changes and/or health outcomes.ResultsThe search resulted in 30 studies that we classified by 3 main aspects: input methods, mobile-based interventions, and dietary behavior changes and health outcomes. First, regarding input methods, 5 studies allowed photo/voice/video inputs of diet information, whereas text input methods were used in the remaining studies. Other than diet information, the content of the input data in the mobile apps included user's demographics, medication, health behaviors, and goals. Second, we identified 6 categories of intervention contents, that is, self-monitoring, feedback, gamification, goal reviews, social support, and educational information. Although all 30 studies included self-monitoring as a key component of their intervention, personalized feedback was a component in 18 studies, gamification was used in 10 studies, goal reviews in 5 studies, social support in 3 studies, and educational information in 2 studies. Finally, we found that 13 studies directly examined the effects of interventions on health outcomes and 12 studies examined the effects on dietary behavior changes, whereas only 5 studies observed the effects both on dietary behavior changes and health outcomes. Regarding the type of studies, although two-thirds of the included studies conducted diverse forms of randomized control trials, the other 10 studies used field studies, surveys, protocols, qualitative interviews, propensity score matching method, and test and reference method.ConclusionsThis scoping review identified and classified studies on mobile-based interventions for dietary behavior change as per the input methods, nature of intervention, and outcomes examined. Our findings indicated that dietary behavior changes, although playing a mediating role in improving health outcomes, have not been adequately examined in the literature. Dietary behavior change as a mechanism for the relationship between mobile-based intervention and health outcomes needs to be further investigated. Our review provides guidance for future research in this promising mobile health area.
| S-EPMC6360385 | biostudies-literature
Project description:Health risk behaviors are leading contributors to morbidity, premature mortality associated with chronic diseases, and escalating health costs. However, traditional interventions to change health behaviors often have modest effects, and limited applicability and scale. To better support health improvement goals across the care continuum, new approaches incorporating various smart technologies are being utilized to create more individualized digital behavior change interventions (DBCIs). The purpose of this study is to identify context-aware DBCIs that provide individualized interventions to improve health. A systematic review of published literature (2013-2020) was conducted from multiple databases and manual searches. All included DBCIs were context-aware, automated digital health technologies, whereby user input, activity, or location influenced the intervention. Included studies addressed explicit health behaviors and reported data of behavior change outcomes. Data extracted from studies included study design, type of intervention, including its functions and technologies used, behavior change techniques, and target health behavior and outcomes data. Thirty-three articles were included, comprising mobile health (mHealth) applications, Internet of Things wearables/sensors, and internet-based web applications. The most frequently adopted behavior change techniques were in the groupings of feedback and monitoring, shaping knowledge, associations, and goals and planning. Technologies used to apply these in a context-aware, automated fashion included analytic and artificial intelligence (e.g., machine learning and symbolic reasoning) methods requiring various degrees of access to data. Studies demonstrated improvements in physical activity, dietary behaviors, medication adherence, and sun protection practices. Context-aware DBCIs effectively supported behavior change to improve users' health behaviors.
| S-EPMC8158169 | biostudies-literature
Project description:BackgroundDigital health interventions are efficacious in health-promoting behaviors (eg, healthy eating and regular physical activity) that mitigate health risks and menopausal symptoms in midlife. However, integrated evidence-based knowledge about the mechanisms of change in these interventions is unclear.ObjectiveThis systematic review aimed to evaluate studies on behavior change techniques (BCTs) and mechanisms of change in digital health interventions aimed at promoting health-enhancing behaviors in midlife women (aged 40-65 years).MethodsA systematic literature search of the electronic databases PubMed, Web of Science, PsycINFO, and Cochrane Central Register of Controlled Trials in the Cochrane Library was conducted. In total, 2 independent reviewers selected the studies for inclusion, extracted data, and completed BCT mapping of eligible studies. The mechanism of action and intervention functions of eligible studies were evaluated using the behavior change wheel framework. Reporting of psychological theory use within these interventions was explored using the Theory Coding Scheme. Mode of delivery, psychological theory, and BCTs were presented as descriptive statistics.ResultsIn total, 13 interventions (including 1315 women) reviewed used 13 (SD 4.30, range 6-21) BCTs per intervention on average. The "Shaping knowledge" and "Repetition and substitution" behavior change categories were used most frequently, with 92% (12/13) of the interventions implementing at least one of the BCTs from these 2 categories. Only 13.98% (169/1209) of the 93 available BCTs were used, with "Instructions on behaviour" most frequently used (12/13, 92%). The behavior change wheel mapping suggests that half of the intervention content aimed to increase "Capability" (49/98, 50% of the intervention strategies), "Motivation" (41/98, 42%), and "Opportunity" (8/98, 8%). "Behavioural Regulation" was the most frequently used mechanism of action (15/98, 15%), followed by increasing "Knowledge" (13/98, 13%) and "Cognitive and Interpersonal skills" (10/98, 10%). A total of 78% (7/9) of the intervention functions were used in the studies to change behavior, primarily through "Enablement" (60/169, 35.5%), whereas no study used "Restriction" or "Modelling" functions. Although 69% (9/13) of the interventions mentioned a psychological theory or model, most (10/13, 77%) stated or suggested rather than demonstrated the use of a theoretical base, and none reported explicit links between all BCTs within the intervention and the targeted theoretical constructs. Technological components were primarily based on web-based (9/13, 69%) modes of delivery, followed by phone or SMS text message (8/13, 62%) and wearables (7/13, 54%).ConclusionsThe findings of this review indicate an overall weak use of theory, low levels of treatment fidelity, insignificant outcomes, and insufficient description of several interventions to support the assessment of how specific BCTs were activated. Thus, the identified limitations in the current literature provide an opportunity to improve the design of lifestyle health-enhancing interventions for women in midlife.Trial registrationPROSPERO CRD42021259246; https://tinyurl.com/4ph74a9u.
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