Research Papers Internet Impact On Society Heart

1. Ziglio E, Currie C, Rasmussen VB. (2004). The WHO cross-national study of health behavior in school aged children from 35 countries: findings from 2001–2002. J School Health, 74 (6): 204– 206. [PubMed]

2. WHO (2001). Services for prevention and management of genetic disorders and birth defect in developing countries (Farhud DD. As committee member) (WHO/HGN/WAOPB-D/99.1).

3. Karimi M, Heidarnia A, Ghofranipur F. (2010). Effective factors on using medication in aging by using healthy believe. J Arak Med Uni, 14 (5); 70 78.

4. IRNA (2013). Iran as a second country in using drugs in Asia. Available at: Accessed: 15 Sep 2014.

5. Mozaffarian D, Hao T, Rimm EB, Willett W, Hu FB. (2011). Changes in diet and life style and long term weight gain in women & men. N Eng Med J, 364: 2392–2404. [PMC free article][PubMed]

6. Dunn AL, Anderson RE, Jakicic JM. (1998). Lifestyle physical activity interventions: history, short and long term effects and recommendations. Am J Preven Med, 15 (4): 398– 412. [PubMed]

7. C3 collaborating for health (2011). The benefits of physical activity for health and wellbeing. Available at: Accessed: 1 Oct 2014.

8. Farhud DD, Malmir M, Khanahmadi M. (2015). Happiness as a healthy life style. Iranian Academy of Medical Science. (In press)

9. Farhud DD, Tahavorgar A. (2013). Melatonin hormone, metabolism & its clinical effects: a review. Iran J Endocrinol Metabol, 15 (2): 221– 236 (In Persian).

10. Ebrat news (2013). Frequency of using cigarette in adolescents: awareness of authority. . Accessed: 1 Oct 2014

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Noncommunicable diseases (NCDs) are the leading cause of mortality and morbidity globally, accounting for 65% of deaths and 54% of disability‐adjusted life‐years in 2010.1, 2 Suboptimal lifestyle is the major cause of NCDs, including poor diet, physical inactivity, tobacco, adiposity, and excess alcohol.2 Clearly, novel interventions to improve lifestyle and prevent NCDs are urgently required. In recent years, the evidence for effectiveness of conventional clinic‐based education3 and policy4 approaches to improve lifestyle has been systematically evaluated. In comparison, the effectiveness of more novel information and communication technologies, such as Internet and mobile applications, to improve lifestyle is not well established. Such technologies are particularly promising because of potential for scalability, low cost, use in multiple settings including middle‐ and low‐income nations, and opportunities for real‐time modifications and improvements. Numerous small trials have been reported, but their findings have not been systematically reviewed.

To understand and compare the effectiveness of novel technologies for behavior change across diverse lifestyle targets, we systematically investigated, summarized, and graded the evidence for effectiveness of such interventions to improve diet, adiposity, tobacco, physical inactivity, and excess alcohol.


Search Strategy and Study Selection

Following Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines, we systematically searched PubMed for all interventional trials (randomized, quasi‐experimental) and prospective observational cohorts evaluating the effect of novel information and communication technologies on diet habits, adiposity, physical inactivity, tobacco (ie, smoking cessation), and excess alcohol among adults. Eligible technologies included those based on Internet, mobile (text messages, cellphone calls, smartphone apps), personal digital assistant, and social media applications, as well personal sensors (eg, pedometers, accelerometers). The full search strategy including search terms is provided in Datas S1 through S4.

Studies were excluded if cross‐sectional, ecological, or lab experiments (hypothetical situations); conducted among people with underlying prevalent disease related to the study outcome (eg, cardiovascular disease, except diabetes); only evaluating intervention feasibility or acceptability; or only evaluating changes in knowledge, awareness, or attitude. We also excluded studies with fewer than 50 subjects, or duration <1 week. Additionally, we excluded studies published prior to 1990 (because of our focus on novel technologies) or conducted only among children (because of our focus on NCDs). For diet and adiposity, our searches identified several prior systematic reviews on information and communication technologies; for these targets, we included prior identified investigations and searched for additional original articles published after the time period of search of these reports (after January 2011). Titles and abstracts of all identified articles were screened by 1 investigator; and full texts were reviewed by 1 investigator after 10% of the articles were reviewed independently and in duplicate by 2 investigators until 100% concordance was achieved.

Data Extraction

Using a standardized electronic format, data were extracted by 1 investigator on first author name, publication year, study location, design, population characteristics (sample size, age, race, sex, education), intervention characteristics (description, components, duration), study outcomes (description, assessment method), and intervention effectiveness (effect measure, uncertainty estimates). To ensure accuracy and quality of data extraction, data from 10% of studies were extracted by 2 investigators in independently and in duplicate.

Assessment of Effectiveness

In some studies, the technology intervention was compared to usual care or minimal intervention (eg, printed leaflets); and in other studies, to intensive, non‐technology‐based, behavioral interventions (eg, standard‐of care clinical counseling). Thus, the various “control groups” received mixed interventions with varying degrees of intensity. For studies including a usual care or minimal intervention control group, intervention effectiveness was evaluated by comparing the change in lifestyle target between the 2 groups. These studies provide direct evidence for effectiveness of the intervention, compared to usual care.

For studies having a control group receiving more intensive behavioral interventions (and for quasi‐experimental studies with no control), we evaluated intervention effectiveness based on change in the target lifestyle from pre‐ to postintervention within the intervention group. This provides better comparability of findings to studies with minimal intervention controls. However, because such results could be confounded by participation (Hawthorne effect), we also reviewed and summarized the findings from the comparison groups whenever relevant.

We generally separately evaluated each lifestyle target. Because many studies jointly evaluated diet and adiposity, these 2 lifestyle targets were considered and reviewed together.

Evidence Grading

Two investigators reviewed and graded the evidence independently and in duplicate, based on the American Heart Association criteria for evidence grading (Table 1).5 Briefly, Class of recommendation (I, IIa, IIb, III) was determined based on the consistency of evidence for benefits and effectiveness of the intervention; and Level of evidence (A, B, C) was determined based on the number and types of studies (eg, clinical trials, nonrandomized studies) used to assess the intervention.

This study did not meet the definition of human subjects research because no identifiable private information was obtained for this research.

Table 1.Definition of Classes and Levels of Evidence Used in This Reporta


Diet and Adiposity

Of 3602 abstracts screened, 65 original articles met inclusion criteria (Figure 1). These included 47 randomized controlled trials (RCTs) and 18 quasi‐experimental studies. Thirty‐seven studies were conducted in the United States; 26 in other high‐income countries (Australia, United Kingdom, New Zealand, Belgium, The Netherlands, Austria, Korea, Japan, Germany); and 2 in middle‐income countries (Iran, Brazil).6 Settings included community, worksite, university/college, hospital/clinic, church, health club, and online populations. Study durations ranged from 1 week (examining effects of an Internet intervention on fruit intake7) to 37 months (examining effects of an Internet intervention on weight loss).8 Most studies had durations between 6 weeks and 6 months; only 10 studies had durations >1 year. Details on the intervention strategies and dietary and adiposity outcomes are provided in Table 2.

Figure 1.

Screening and selection process of studies evaluating the effectiveness of information and communication technology interventions to improve diet and adiposity.

We also identified 5 prior systematic reviews evaluating relevant studies published prior to January 2011, each including from 7 to 36 studies.9, 10, 11, 12, 13 Table 3 summarizes the characteristics and findings of these reviews.

Of 35 studies (22 RCTs, 13 quasi‐experimental) assessing Internet interventions and adiposity, 24 (69%) reported significant improvements following the intervention (Table 2). Findings were similar, limited only to RCTs, with reduced adiposity in 13 of 22 (59%) trials. In studies reporting significant weight reduction, the magnitude of weight change ranged from 1 to 6 kg after 6 months of follow‐up. Of 9 studies comparing Internet interventions with higher intensity conventional interventions (rather than usual care/minimal intervention), 4 reported significantly higher weight reduction in the Internet intervention group, 4 showed both interventions were equally effective in reducing adiposity, and 1 showed no significant effect in either of the intervention groups. In a meta‐analysis of 23 RCTs evaluating the effect of the Internet component of weight loss programs, using the Internet resulted in 0.68 kg (95% CI: 0.08, 1.29 kg) additional weight reduction over a period of 3 to 30 months.9 However, stratified analysis suggested that such interventions were effective when used in combination with in‐person counseling (−1.93 kg; 95% CI: −2.71, −1.15 kg), rather than as a substitute for that (−0.19 kg, 95% CI: −0.87, 0.49 kg).

Twenty studies (15 RCTs, 5 quasi‐experimental studies) evaluated Internet interventions and diet. Fourteen (10 RCTs and 4 quasi‐experimental; 70%) found significant dietary improvements following the intervention. Effect sizes varied due to heterogeneity in dietary targets. As an example, the intake of fruit, the most common dietary target across studies, increased by ≈1 serving/day. Five of these 20 studies compared Internet intervention with higher‐intensity conventional interventions: 2 showed significantly greater dietary effects in the Internet group, 2 showed both interventions were equally effective, and 1 showed no significant effect in either intervention arm.

Of 3 studies (2 RCTs, 1 quasi‐experimental) evaluating mobile interventions and adiposity, 2 found significant reductions in adiposity.14, 15 Two RCTs assessed mobile‐based interventions and fruit/vegetable intake; each found significant improvement (by 2 and 4 servings/day)14, 16 One of these compared a mobile intervention to an established conventional intervention, finding a greater effect in the mobile intervention group.14 In a prior systematic review of 14 mobile trials (2007–2010) focused on text messages and lasting from 2 weeks to 12 months (Table 3), 11 studies reported significant improvements in weight loss–related outcomes, whereas no significant change was reported in calorie intake or consumption of sugar‐sweetened beverages in 4 trials evaluating diet.10

Fourteen RCTs and 2 quasi‐experimental studies evaluated combined Internet/mobile interventions and adiposity. Most (13 of 16, 81%) reported significant reduction in adiposity. Of 6 studies that compared combined interventions with conventional interventions, 4 reported significantly higher effect on adiposity in the combined intervention group. Of 5 RCTs evaluating combined interventions and diet, 3 found significant improvement in dietary intake.17, 18, 19, 20 Of 2 studies assessing combined interventions and conventional interventions, 1 reported significantly higher effect on diet in the combined intervention group and 1 reported no effect in either intervention group.

Table 2.Effectiveness of Novel Information and Communication Technologies for Improving Diet and Adipositya, b, c, d
Table 3.Characteristics of Prior Systematic Reviews on Effectiveness of Information and Communication Technology Interventions to Improve Diet and Adipositya

Physical Activity

Of 2855 abstracts screened, 55 RCTs and 16 quasi‐experimental studies were identified (Figure 2). Thirty‐three studies were from the United States, 35 from other high‐income countries (Australia, Austria, Belgium, Canada, Denmark, Finland, Germany, New Zealand, Norway, Scotland, Switzerland, The Netherlands, United Kingdom), and 3 from middle‐income countries (Brazil, Taiwan). Studies were conducted in different settings including worksites, colleges, hospitals, churches, and in online communities. Study durations ranged from 1 week to 5 years, with most lasting between 6 weeks and 6 months (Table 4).

Figure 2.

Screening and selection process of studies evaluating the effectiveness of information and communication technology to improve physical activity.

Of the 33 Internet interventions (29 RCTs and 4 quasi‐experimental), 29 (88%) reported significant improvement in physical activity (Table 4). Among RCTs, 25 of 29 (86%) found improved physical activity after the intervention. Of 2 RCTs comparing Internet interventions with conventional interventions, 1 reported significantly higher physical activity among participants receiving the Internet intervention, and 1 reported no significant improvement in either of the intervention groups.

The measure and magnitude of effect sizes varied across the studies reporting statistically significant effects. For example, in studies evaluating total duration of physical activity, the effect size ranged from 1.5 to 153 minutes/week. The difference of 153 minutes was reported in a 6‐month study in which participants received immediate individually tailored computer‐generated motivational messages after completing a monthly online questionnaire.21 In studies evaluating frequency of physical activity, the effect sizes ranged from 1 to 1.2 days per week. In studies that used the odds ratio (OR) of meeting a physical activity recommendation as the effect measure, the effect sizes were between 1.3 and 1.5.

Nineteen studies (13 RCTs and 6 quasi‐experimental studies) evaluated personal sensor (pedometer) interventions alone or along with educational materials, classes, or behavioral change techniques including goal setting. Of these, 15 (79%) reported significant positive effects. In these studies, the increase in step count from baseline was between 900 and 4500 steps/day. Examples of other types of effect sizes reported include the following: 97 minutes/week increase in leisure‐time physical activity22; 32% reduction in percentage of sedentary participants23; 95 minutes/day reduction in sitting time24; and 2‐day per week increase in walking days.24 In general, studies that included behavioral change techniques were more effective. Of 5 RCTs that compared sensor interventions with conventional interventions, 2 reported significantly higher step counts and leisure walking with sensor interventions, 2 only found significant within‐group improvements (from baseline) in physical activity, and 1 reported no significant within‐ or between‐group difference in step count.

Six studies (5 RCTs and 1 quasi‐experimental) assessed mobile interventions: 3 text message interventions, 2 smartphone applications, and 1 automated voice response. Of these, 2 of 3 utilizing text messages and all interventions involving smartphone applications and automated voice response were effective.

Five RCTs and 5 quasi‐experimental studies evaluated combined interventions (eg, Internet and sensors). Of these, 7 (70%) demonstrated significant improvements: 5 reported increased step count (1000–2600 steps/day); 1, increased 7‐day walking (90 minutes/week)25; 1, increased odds of meeting physical activity recommendations (OR: 1.7).26

Table 4.Effectiveness of Information and Communication Technology for Improving Physical Activitya, b, c


Of 1182 screened articles, 41 met inclusion criteria (Figure 3): 17 from the United States, 22 from other high‐income countries (United Kingdom, Australia, Germany, Norway, New Zealand, The Netherlands, Switzerland), and 2 from middle‐income nations (Turkey, Thailand). Study settings included worksites, academic institutions, communities, general clinical practice, and online populations. Primary outcomes were generally the prevalence of abstinence (eg, 7‐ or 30‐day abstinence) at different time‐points (eg, 1, 3, 6 months). Abstinence assessment methods ranged from self‐report (N=29 studies) to breath carbon monoxide (N=6) and salivary cotinine (N=3). Duration of interventions ranged from 4 weeks to 2 years, with only 4 studies lasting longer than 1 year (Table 5).

Figure 3.

Screening and selection process of studies evaluating the effectiveness of information and communication technology for tobacco.

Of 22 studies (21 RCTs, 1 prospective cohort) assessing Internet interventions, 17 reported significant increase in abstinence (Table 5). In studies reporting benefits, the OR for 7‐day abstinence at 6 months ranged from 1.6 (95% CI: 1.1, 2.4) in an Internet worksite smoking cessation program in the United States to 2.7 (95% CI: 1.8, 4.0) utilizing email counseling in Switzerland. The OR did not consistently vary with longer durations of follow‐up, either within or between studies. Of 6 studies comparing Internet interventions with conventional interventions, 4 reported significantly greater effect in an Internet group and 2 reported significant within‐group changes with no significant difference between intervention arms.

Seven studies (6 RCTs, 1 quasi‐experimental) evaluated mobile phone text‐messaging smoking cessation programs. Of these, only 2 (29%) reported benefits with OR of 7‐day abstinence ranging from 1.3 (95% CI: 1.2, 1.5) in a UK‐based study to 2.2 (95% CI: 1.8, 2.7) in a study conducted in New Zealand. We did not identify any study comparing mobile interventions with conventional interventions.

Of 8 RCTs‐assessed computer‐based software, 4 (50%) reported significant improvements in abstinence, with ORs ranging from 1.1 to 1.6. The intervention generally included a questionnaire to assess smoking behavior, which was then used as a basis to provide tailored smoking cessation advice.

Table 5.Effectiveness of Information and Communication Technology for Smoking Cessationa, b, c
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