The Effectiveness of Web-Based vs. Non-Web-Based Interventions: A Meta-Analysis of Behavioral Change Outcomes

Abstract

BackgroundA primary focus of self-care interventions for chronic illness is the encouragement of an individual's behavior change necessitating knowledge sharing, education, and understanding of the condition. The use of the Internet to deliver Web-based interventions to patients is increasing rapidly. In a 7-year period (1996 to 2003), there was a 12-fold increase in MEDLINE citations for “Web-based therapies.” The use and effectiveness of Web-based interventions to encourage an individual's change in behavior compared to non-Web-based interventions have not been substantially reviewed.ObjectiveThis meta-analysis was undertaken to provide further information on patient/client knowledge and behavioral change outcomes after Web-based interventions as compared to outcomes seen after implementation of non-Web-based interventions.MethodsThe MEDLINE, CINAHL, Cochrane Library, EMBASE, ERIC, and PSYCHInfo databases were searched for relevant citations between the years 1996 and 2003. Identified articles were retrieved, reviewed, and assessed according to established criteria for quality and inclusion/exclusion in the study. Twenty-two articles were deemed appropriate for the study and selected for analysis. Effect sizes were calculated to ascertain a standardized difference between the intervention (Web-based) and control (non-Web-based) groups by applying the appropriate meta-analytic technique. Homogeneity analysis, forest plot review, and sensitivity analyses were performed to ascertain the comparability of the studies.ResultsAggregation of participant data revealed a total of 11,754 participants (5,841 women and 5,729 men). The average age of participants was 41.5 years. In those studies reporting attrition rates, the average drop out rate was 21% for both the intervention and control groups. For the five Web-based studies that reported usage statistics, time spent/session/person ranged from 4.5 to 45 minutes. Session logons/person/week ranged from 2.6 logons/person over 32 weeks to 1008 logons/person over 36 weeks. The intervention designs included one-time Web-participant health outcome studies compared to non-Web participant health outcomes, self-paced interventions, and longitudinal, repeated measure intervention studies. Longitudinal studies ranged from 3 weeks to 78 weeks in duration. The effect sizes for the studied outcomes ranged from -.01 to .75. Broad variability in the focus of the studied outcomes precluded the calculation of an overall effect size for the compared outcome variables in the Web-based compared to the non-Web-based interventions. Homogeneity statistic estimation also revealed widely differing study parameters (Qw16 = 49.993, P ≤ .001). There was no significant difference between study length and effect size. Sixteen of the 17 studied effect outcomes revealed improved knowledge and/or improved behavioral outcomes for participants using the Web-based interventions. Five studies provided group information to compare the validity of Web-based vs. non-Web-based instruments using one-time cross-s...

How reliable is internet-based self-reported identity, socio-demographic and obesity measures in European adults?

“…By 2015, 85 % of the EU population are predicted to be internet users (Eurostat 2012) and internet-based interventions are increasing. The degree of behavioural change achievable via internet-based interventions is similar to (Steele et al 2009;Weigold et al 2013), or potentially greater than (Wantland et al 2004), those conducted face-to-face.…”

Weight loss—there is an app for that! But does it adhere to evidence-informed practices?

“…These practices and the indicators of adherence were as follows: (1) assess one's weight (scored on whether the app provided a means of calculating one's body mass index with an interpretation of what that means), (2) eat a diet rich in fruits and vegetables (scored on whether the app recommended a certain number of daily servings fruits and vegetables or allowed users to track their number of servings each day), (3) perform regular physical activity (scored on whether the app recommended a certain amount of physical each day per week), (4) drink water instead of juice or soda (scored on whether the app recommended a certain number of daily serving of water or allowed users to track their daily servings of water), (5) keep a food diary (scored on whether the app allowed users to track the daily food consumption), (6) maintain calorie balance of in vs. out (scored on whether the app allowed for users to calculate the number of calorie needed in order to meet desired weight loss/maintenance goals given one's activity level), (7) weight loss of 1 to 2 lb a week (scored on whether the app recommended weight loss goals of 1 to 2 lb/week), (8) portion control (scored on whether the app described or illustrated portions sizes or let users look up nutritional information according to portion size), (9) read nutrition labels (scored on whether the app recommended reading labels, described how to properly read labels, or let users look up nutritional information for food items), (10) track your weight (scored on whether the app provided a means to track weight over time), (11) keep a physical activity journal (scored on whether an app provided a means to track daily physical activity), (12) plan meals (scored on whether the app recommended users plan their meals, had a tool for menu planning, or a way to search recipes), and (13) seek social support (scored on whether the app allowed users access to social support components like message boards, chat rooms, email an expert, or a networking component like Twitter).…”

“…However, research on apps for these and other health promotion purposes has not kept pace with technological innovations, and their efficacy is yet to be determined [11]. While little is known about the efficacy of smartphone apps, a systematic review of the literature on Internet-based approaches concluded that Internet-based approaches are efficacious for improving behavioral outcomes, including in the arena of weight loss [12,13]. Many of the reasons that make Internet approaches promising may apply to smartphone apps.…”

“…The treatment effect sizes of Internet-based approaches are not large, but their potential to reach a large audience could result in a meaningful public health impact [14]. Some of the reasons that Internet platforms are efficacious may be due to ability to provide a tailored experience along with the ease of self-monitoring [12,15,16]. Mobile apps have the additional appeal of being portable and available to a person regard-Implications Practice: Although popular among the lay public, many smartphone apps may lack comprehensive evidence-informed recommendations or practices for healthy weight control, so practitioners need to discuss the utility of these apps with patients if patients are using them as a supplement to treatment.…”

A systematic review of eHealth cancer prevention and control interventions: new technology, same methods and designs?

“…efficacy, and clinical outcomes [4][5][6][7][8]. While substantial progress has been made, few efficacious EHIs are adopted or sustained in real-world settings beyond the scope of the research project [5].…”