Cyber-kids!

02.04.2014 09:33

Cyber-kids!

20.03.2014 13:25

10 Reasons Why Handheld Devices Should Be Banned for Children Under the Age of 12

Posted: 03/06/2014 3:35 pm EST Updated: 03/09/2014 3:59 pm EDT Print Article

MORE: Hild Technology Handhelds Educational Technology Child Violence Child Depression Child Anxiety Child Aggression ADHD Screen Sense Technology Addiction Autism Learning Difficulties Child Technology Guidelines Sleep Deprivation Parents News

The American Academy of Pediatrics and the Canadian Society of Pediatrics state infants aged 0-2 years should not have any exposure to technology, 3-5 years be restricted to one hour per day, and 6-18 years restricted to 2 hours per day (AAP 2001/13, CPS 2010). Children and youth use 4-5 times the recommended amount of technology, with serious and often life threatening consequences (Kaiser Foundation 2010, Active Healthy Kids Canada 2012). Handheld devices (cell phones, tablets, electronic games) have dramatically increased the accessibility and usage of technology, especially by very young children (Common Sense Media, 2013). As a pediatric occupational therapist, I'm calling on parents, teachers and governments to ban the use of all handheld devices for children under the age of 12 years. Following are 10 research-based reasons for this ban. Please visit zonein.ca to view the Zone'in Fact Sheet for referenced research.

1. Rapid brain growth

Between 0 and 2 years, infant's brains triple in size, and continue in a state of rapid development to 21 years of age (Christakis 2011). Early brain development is determined by environmental stimuli, or lack thereof. Stimulation to a developing brain caused by overexposure to technologies (cell phones, internet, iPads, TV), has been shown to be associated with executive functioning and attention deficit, cognitive delays, impaired learning, increased impulsivity and decreased ability to self-regulate, e.g. tantrums (Small 2008, Pagini 2010).

2. Delayed Development

Technology use restricts movement, which can result in delayed development. One in three children now enter school developmentally delayed, negatively impacting literacy and academic achievement (HELP EDI Maps 2013). Movement enhances attention and learning ability (Ratey 2008). Use of technology under the age of 12 years is detrimental to child development and learning (Rowan 2010).

3. Epidemic Obesity

TV and video game use correlates with increased obesity (Tremblay 2005). Children who are allowed a device in their bedrooms have 30% increased incidence of obesity (Feng 2011). One in four Canadian, and one in three U.S. children are obese (Tremblay 2011). 30% of children with obesity will develop diabetes, and obese individuals are at higher risk for early stroke and heart attack, gravely shortening life expectancy (Center for Disease Control and Prevention 2010). Largely due to obesity, 21st century children may be the first generation many of whom will not outlive their parents (Professor Andrew Prentice, BBC News 2002).

4. Sleep Deprivation

60% of parents do not supervise their child's technology usage, and 75% of children are allowed technology in their bedrooms (Kaiser Foundation 2010). 75% of children aged 9 and 10 years are sleep deprived to the extent that their grades are detrimentally impacted (Boston College 2012).

5. Mental Illness

Technology overuse is implicated as a causal factor in rising rates of child depression, anxiety, attachment disorder, attention deficit, autism, bipolar disorder, psychosis and problematic child behavior (Bristol University 2010, Mentzoni 2011, Shin 2011, Liberatore 2011, Robinson 2008). One in six Canadian children have a diagnosed mental illness, many of whom are on dangerous psychotropic medication (Waddell 2007).

6. Aggression

Violent media content can cause child aggression (Anderson, 2007). Young children are increasingly exposed to rising incidence of physical and sexual violence in today's media. "Grand Theft Auto V" portrays explicit sex, murder, rape, torture and mutilation, as do many movies and TV shows. The U.S. has categorized media violence as a Public Health Risk due to causal impact on child aggression (Huesmann 2007). Media reports increased use of restraints and seclusion rooms with children who exhibit uncontrolled aggression.

7. Digital dementia

High speed media content can contribute to attention deficit, as well as decreased concentration and memory, due to the brain pruning neuronal tracks to the frontal cortex (Christakis 2004, Small 2008). Children who can't pay attention can't learn.

8. Addictions

As parents attach more and more to technology, they are detaching from their children. In the absence of parental attachment, detached children can attach to devices, which can result in addiction (Rowan 2010). One in 11 children aged 8-18 years are addicted to technology (Gentile 2009).

9. Radiation emission

In May of 2011, the World Health Organization classified cell phones (and other wireless devices) as a category 2B risk (possible carcinogen) due to radiation emission (WHO 2011). James McNamee with Health Canada in October of 2011 issued a cautionary warning stating "Children are more sensitive to a variety of agents than adults as their brains and immune systems are still developing, so you can't say the risk would be equal for a small adult as for a child." (Globe and Mail 2011). In December, 2013 Dr. Anthony Miller from the University of Toronto's School of Public Health recommend that based on new research, radio frequency exposure should be reclassified as a 2A (probable carcinogen), not a 2B (possible carcinogen). American Academy of Pediatrics requested review of EMF radiation emissions from technology devices, citing three reasons regarding impact on children (AAP 2013).

10. Unsustainable

The ways in which children are raised and educated with technology are no longer sustainable (Rowan 2010). Children are our future, but there is no future for children who overuse technology. A team-based approach is necessary and urgent in order to reduce the use of technology by children. Please reference below slide shows on www.zonein.ca under "videos" to share with others who are concerned about technology overuse by children.

Problems - Suffer the Children - 4 minutes

Solutions - Balanced Technology Management - 7 minutes

The following Technology Use Guidelines for children and youth were developed by Cris Rowan, pediatric occupational therapist and author of Virtual Child; Dr. Andrew Doan, neuroscientist and author of Hooked on Games; and Dr. Hilarie Cash, Director of reSTART Internet Addiction Recovery Program and author of Video Games and Your Kids, with contribution from the American Academy of Pediatrics and the Canadian Pediatric Society in an effort to ensure sustainable futures for all children

Screen time tied to poor wellbeing among kids

BY ANDREW M. SEAMAN

NEW YORK Wed Mar 19, 2014 3:05am IST

(Reuters Health) - Spending too much time in front of a television, computer or other devices with screens may signal problems in a child's family and personal wellbeing, according to a new study.

Based on data for more than 3600 children in eight European countries, researchers found that family functioning and emotional wellbeing were especially linked to changes in the amount of time kids spent in front of screens.

The study's lead author said they can't say what factors may be behind the associations. "We really need to do a little bit more digging in this area before we can answer some of the basic questions," Trina Hinkley told Reuters Health.

Hinkley is a research fellow at the Center for Physical Activity and Nutrition Research at Deakin University in Melbourne.

Several recent studies have highlighted the possible negative effects of kids spending too much time watching televisions, playing video games and working on computers.

Specifically, screen time has been linked to differences among children in weight and sleep quality (see Reuters Health stories of March 17, 2014 here: reut.rs/1ifw3F2 and March 12, 2014 here: reut.rs/1ifw5wz.)

Late last year, the American Academy of Pediatrics (AAP) also urged parents to keep tabs on their children's media use and limit screen time to no more than one to two hours of high quality programming (see Reuters Health story of October 28, 2014 here: reut.rs/1f0lfYE.)

For the new study, researchers from the Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants Consortium analyzed data on kids who were between two and six years of age when they entered the study between September 2007 and June 2008.

At that time, the parents completed questionnaires about their children's media use and wellbeing - including the child's emotional and peer problems, self-esteem and family and social functioning. Parents answered another questionnaire two years later.

Overall, the researchers found that for social and peer-related measures, screen time had no effect. But for each additional hour or so of screen time parents reported, a child's risk of emotional and family problems rose up to two-fold.

"We found that family functioning and emotional problems did seem to have some association with electronic media, but the others didn't show any association at all," Hinkley said.

Linda Pagani, who was not involved in the new study but has researched screen time among children, cautioned that there may be other explanations behind some of the results. "It could be that families who used screen time more were families who weren't functioning that well to begin with," she said.

Pagani is psychologist and senior researcher at Saint-Justine's Hospital Research Center at the University of Montreal in Canada. She also cautioned that the results are based on the parents' reports, which are subject to inaccuracies.

Despite the study's limitations, however, Pagani said there are several drawbacks to letting children have a lot of screen time, including sleep disturbances and lost face-to-face communication time.

"My message is, the brain is very dependent on human social interaction and this excessive screen time on a computer or television may be at the detriment of time for other people," she said.

She also endorsed the two-hour rule set by the AAP, but cautioned that screen time shouldn't be right before bed. "As a clinician, as a parent and a psychologist, use that two-hour rule, but make sure those two hours don't occur right before bed, because they're losing precious sleep time," she said.

SOURCE: bit.ly/1opNKUF JAMA Pediatrics, online March 17, 2014.

Original Investigation | March 17, 2014

Parental Monitoring of Children’s Media Consumption

The Long-term Influences on Body Mass Index in Children ONLINE FIRST

Stacey S. Tiberio, PhD1; David C. R. Kerr, PhD1,2; Deborah M. Capaldi, PhD1; Katherine C. Pears, PhD1; Hyoun K. Kim, PhD1; Paulina Nowicka, PhD3

[+] Author Affiliations

JAMA Pediatr. Published online March 17, 2014. doi:10.1001/jamapediatrics.2013.5483 Text Size: A A A

Importance Although children’s media consumption has been one of the most robust risk factors for childhood obesity, effects of specific parenting influences, such as parental media monitoring, have not been effectively investigated.

Objectives To examine the potential influences of maternal and paternal monitoring of child media exposure and children’s general activities on body mass index (BMI) in middle childhood.

Design, Setting, and Participants A longitudinal study, taken from a subsample of the Three Generational Study, a predominantly white, Pacific Northwest community sample (overall participation rate, 89.6%), included assessments performed from June 1998 to September 2012. Analyses included 112 mothers, 103 fathers, and their 213 children (55.4% girls) at age 5, 7, and/or 9 years. Participation rates ranged from 66.7% to 72.0% of all eligible Three Generational Study children across the 3 assessments.

Exposures Parents reported on their general monitoring of their children (whereabouts and activities), specific monitoring of child media exposure, children’s participation in sports and recreational activities, children’s media time (hours per week), annual income, and educational level. Parental BMI was recorded.

Main Outcomes and Measures Predictions to level and change in child BMI z scores were tested.

Results Linear mixed-effects modeling indicated that more maternal, but not paternal, monitoring of child media exposure predicted lower child BMI z scores at age 7 years (95% CI, -0.39 to -0.07) and less steeply increasing child BMI z scores from 5 to 9 years (95% CI, -0.11 to -0.01). These effects held when more general parental monitoring, and parent BMI, annual income, and educational level were controlled for. The significant negative effect of maternal media monitoring on children’s BMI z scores at age 7 years was marginally accounted for by the effect of child media time. The maternal media monitoring effect on children’s BMI z score slopes remained significant after adjustment for children’s media time and sports and recreational activity.

Conclusions and Relevance These findings suggest that parental behaviors related to children’s media consumption may have long-term effects on children’s BMI in middle childhood. They underscore the importance of targeting parental media monitoring in efforts to prevent childhood obesity.

Parenting In The Age Of Apps: Is That iPad Help Or Harm?

by NPR STAFF

March 16, 2014 4:00 PM

Listen to the Story

With tablet technology still relatively new, pediatricians are trying to understand how interactive media affects children.

When it comes to media, parents all want to know: How much is too much for my child?

Dr. Dimitri Christakis, a pediatrician, professor and father of two, has spent a lot of time thinking about the effects of media on young children. Christakis tells NPR's Arun Rath that not all TV is bad.

Some children's programs are educational and engaging, he says. But if a TV show is overstimulating, it can lead to developmental problems.

"The medium can be too fast, it's surreally paced," he says, "and that can over-stimulate and ultimately damage young brains."

Some of the evidence for this over-stimulation effect comes from studying mice.

"It's funny, there's only so much you can do with live infants," Christakis says. "So we've developed a mouse model of over-stimulation."

Using a kind of 'mouse TV' in the lab, researchers have found that mice that watch a lot of television early in their development have problems later in life. They are hyperactive and take lots of risks that normal mice don't — for instance, sitting unprotected in an open field, which is a big mistake for a small animal with lots of natural predators.

Enter The iPad

The question of how much screen time is good for kids has only gotten more complicated with the arrival of interactive devices like smartphones and tablets, Christakis says.

"We have to take a step back and remind ourselves that iPads are only 4 years old. And most of us can't even conceive of a world that existed before iPads; they feel like they've been here forever."

Because tablet technology is so new, pediatric researchers don't have a lot of data on how touchscreen devices affect children.

"Unfortunately, the pace of research is much, much slower than the pace of technological advances," Christakis says.

TEDx Talks/YouTube

But pediatricians are looking closely at interactive media's effect on children. Relying on admittedly limited evidence and a strong theoretical framework, Christakis is comfortable making a recommendation to parents.

"Judicious use of these touchscreen technologies is fine and may even be beneficial," he says.

"One thing children of all ages never say or never even think when they interact with passive media is, 'I did it,'" he says. "Because of course, you don't do anything when you watch a screen. But you do do things when you interact with a touchscreen device."

This kind of interactive play is essential to learning and vital for brain development, he says.

All Things In Moderation

Of course all screen time, interactive or not, comes at the expense of some other activity, whether it's playing with other children or spending time with a parent.

With tablet technology, screen time doesn't necessarily mean time spent alone. Why not integrate the devices into family time?

"There's no reason whatsoever that a caregiver can't use an app with their child," he says. "It's a great opportunity for what we call 'joint attention' — the interactions between a child and a caregiver, the back-and-forth, which is critical not just to language development, but brain development."

Sound familiar? It should. This, says Christakis, isn't much different from sitting down and reading a book with your child.

Text-messaging program good option for keeping teen girls healthy

Friday 14 March 2014 - 1am PST

Megan Ranney, MD, MPH, an emergency medicine attending physician at Hasbro Children's Hospital, recently led a study that found a text-message program may be an effective violence prevention tool for at-risk teen girls. The study has been published online in the Journal of Adolescent Health.

"Mobile health, or 'mHealth,' is increasingly being used as a way to improve people's health, via text-messaging or phone-based applications," said Ranney. "However, few people have studied whether teens are interested in mHealth, especially for prevention-type messages, even though the vast majority of teens who come to the emergency department (ED) use mobile phones and more than 95 percent of those patients report that they use text messaging."

Ranney's team interviewed girls between the ages of 13 and 17 who reported past-year peer violence and depressive symptoms during emergency department visits for any medical issue. Overwhelmingly, the interviews showed that at-risk teen girls coming to the ED for care are very interested in receiving a text-message violence prevention intervention. The teens felt that a text-message program would enhance their existing coping strategies, and that they would not only use it themselves, but also refer their friends to it.

"The ED is the primary source of care for many teens with high risk behaviors, such as peer violence, and it provides an important opportunity to initiate preventive interventions. However, there can be many limitations to providing such interventions in real time, including lack of time and resources on the part of ED staff, poor accessibility and availability of community resources, and low rates of follow-through with treatment referrals, leaving this group of teens largely under-served," said Ranney. "For these high-risk populations, who have high rates of mobile phone ownership but low accessibility to traditional health care, mHealth may be a particularly promising format for delivering preventive care."

The research team also discovered some important guidelines about how a text-message preventive intervention should be structured. The intervention should be personalized, positively worded, and conversational, but also it should be clear that the messages are coming from an expert. The teens also expressed a need for the ability to request additional text messages as needed, in addition to receiving pre-scheduled text content.

"We know that a history of fights or violence increases girls' long-term risk of alcohol and drug use, dating violence and depression," said Ranney. "Sadly, high-risk teen girls have few options to help them prevent fights, and traditional ways of helping teens, such as parents, grandparents, and physicians, may not be available or accessible."

Ranney continued, "But almost every teen girl has a cell phone and uses text messaging. If we can develop a text-message program that works for these teens, we may be able to help them make it through their teen years with fewer problems. This study is an important first step in developing such a program."

In the future, Ranney hopes to also study teen boys and non-English speaking patients as possible participants in the delivery of counseling and behavioral skills text messaging. "By developing evidence-based text-message interventions, clinicians may be able to have a big influence on these teens' coping skills, involvement in fights and life choices," said Ranney.

Original Investigation | March 10, 2014

Relationship Between Peer Victimization, Cyberbullying, and Suicide in Children and Adolescents

A Meta-analysis ONLINE FIRST

Mitch van Geel, PhD1; Paul Vedder, PhD1; Jenny Tanilon, PhD2

[+] Author Affiliations

JAMA Pediatr. Published online March 10, 2014. doi:10.1001/jamapediatrics.2013.4143 Text Size: A A A

Importance Peer victimization is related to an increased chance of suicidal ideation and suicide attempts among children and adolescents.

Objective To examine the relationship between peer victimization and suicidal ideation or suicide attempts using meta-analysis.

Data Sources Ovid MEDLINE, PsycINFO, and Web of Science were searched for articles from 1910 to 2013. The search terms were bully*, teas*, victim*, mobbing, ragging, and harassment in combination with the term suic*. Of the 491 studies identified, 34 reported on the relationship between peer victimization and suicidal ideation, with a total of 284?375 participants. Nine studies reported on the relationship between peer victimization and suicide attempts, with a total of 70?102 participants.

Study Selection Studies were eligible for inclusion if they reported an effect size on the relationship between peer victimization and suicidal ideation or suicide attempt in children or adolescents.

Data Extraction and Synthesis Two observers independently coded the effect sizes from the articles. Data were pooled using a random effects model.

Main Outcomes and Measures This study focused on suicidal ideation and suicide attempts. Peer victimization was hypothesized to be related to suicidal ideation and suicide attempts.

Results Peer victimization was found to be related to both suicidal ideation (odds ratio, 2.23 [95% CI, 2.10-2.37]) and suicide attempts (2.55 [1.95 -3.34]) among children and adolescents. Analyses indicated that these results were not attributable to publication bias. Results were not moderated by sex, age, or study quality. Cyberbullying was more strongly related to suicidal ideation compared with traditional bullying.

Conclusions and Relevance Peer victimization is a risk factor for child and adolescent suicidal ideation and attempts. Schools should use evidence-based practices to reduce bullying.

Cyberbullying 'causes suicidal thoughts in kids more than traditional bullying

Tuesday 11 March 2014 - 12am PST

Cyberbullying is more strongly related to suicidal thoughts in children and adolescents than traditional bullying, according to a new analysis published in JAMA Pediatrics.

Some estimates suggest that - depending on the country of origin - between 5% and 20% of children are victims of physical, verbal or exclusion-based bullying. Previous studies have also confirmed that bullying is a strong risk factor for adolescent suicide.

Suicide is one of the biggest causes of death in adolescents worldwide. In the US, about 20% of adolescents seriously consider suicide and between 5% and 8% of adolescents attempt suicide each year.

The relationship between cyberbullying and suicide has only been explored in a few studies, but evidence has suggested that cyberbullying is as equal a risk factor for suicidal ideation - thoughts about suicide - as traditional bullying.

The new analysis, from researchers in the Netherlands, tests this evidence by reviewing all available medical literature on the subject. This "meta-analysis" looked at 34 studies focusing on the relationship between bullying and suicidal ideation, and nine studies looking at the relationship between bullying and suicide attempts.

The researchers limited their evidence to studies on "peer victimization." Other kinds of victimization, such as assault, sexual abuse or robbery, were not included.

They also excluded some studies looking at self-harm, because the reasons why someone may self-harm can be different to the reasons why someone may think about committing suicide.

Research looking at youth in hospitals or juvenile detention centers was also omitted, because the researchers wanted to make sure they could generalize their findings to the usual population.

Overall, the meta-analysis included 284,375 participants.

Large meta-analysis contradicts findings of some previous individual studies

The researchers found an association between cyberbullying and suicidal ideation in 70,102 of the participants. The meta-analysis did not find a difference between older and younger children or boys and girls in how likely they were to have suicidal thoughts.

This contradicts some individual studies that had suggested girl victims have immediate increased risk for suicidal ideation, while boys are likely to have suicidal thoughts only if they suffer prolonged episodes of bullying.

Another area where the findings of the meta-analysis differed from some individual studies was the extent of the association between cyberbullying and suicidal ideation.

Though previous evidence had indicated that cyberbullying has an equal association with suicidal thoughts as traditional bullying, the meta-analysis found that the association was stronger for cyberbullying.

The authors suggest a reason for this:

"Potentially, the effects of cyberbullying are more severe because wider audiences can be reached through the internet and material can be stored online, resulting in victims reliving denigrating experiences more often."

As the studies in the meta-analysis mainly looked at suicidal ideation, with some studies looking at non-successful suicide attempts, the analysis cannot explain precisely how cyberbullying might be associated with children who have committed suicide.

However, the researchers acknowledge that "suicidal ideation is thought to invariably precede suicide attempts, and suicide attempts are the strongest known risk factor for future actual suicide."

The authors conclude:

"This meta-analysis establishes that peer victimization is a risk factor of suicidal ideation and suicide attempts. Efforts should continue to identify and help victims of bullying, as well as to create bullying prevention and intervention programs that work."

Original Investigation | March 03, 2014

Bidirectional Relationships Between Sleep Duration and Screen Time in Early Childhood

Christopher A. Magee, PhD1; Jeong Kyu Lee, PhD1; Stewart A. Vella, PhD2

[+] Author Affiliations

JAMA Pediatr. Published online March 03, 2014. doi:10.1001/jamapediatrics.2013.4183 Text Size: A A A

Importance Sleep duration and media use (ie, computer use and television viewing) have important implications for the health and well-being of children. Population data suggest that shorter sleep duration and excessive screen time are growing problems among children and could be interacting issues.

Objective To examine whether bidirectional relationships exist between sleep duration and media use among children, and whether these associations are moderated by child- and household-related factors.

Design, Setting, and Participants Cohort study of a representative sample of 3427 Australian children (4-5 years of age at baseline [51.2% male children]), obtained from the Longitudinal Study of Australian Children. Data were available from 3 waves (2004, 2006, and 2008) when children were 4, 6, and 8 years of age, respectively.

Main Outcomes and Measures Sleep duration and media use.

Results Bidirectional relationships were observed between sleep duration and media use; for instance, total media use at 4 years of age was significantly associated with sleep duration at 6 years of age (ß?=?-0.06 [95% CI, -0.10 to -0.02]), with media use at 6 years of age predicting sleep duration at 8 years of age (ß?=?-0.06 [95% CI, -0.11 to -0.02]). Sleep duration at 4 years of age was associated with media use at 6 years of age (ß?=?-0.10 [95% CI, -0.14 to -0.05]), with sleep duration at 6 years of age predicting media use at 8 years of age (ß?=?-0.08 [95% CI, -0.13 to -0.03]). Several of these bidirectional relationships varied by socioeconomic status.

Conclusions and Relevance The results supported the hypotheses that bidirectional relationships exist between sleep duration and media use among children. These findings are important given recent population trends for increased media use and shorter sleep durations among children.

Original Investigation | March 03, 2014

Association of a Television in the Bedroom With Increased Adiposity Gain in a Nationally Representative Sample of Children and Adolescents

Diane Gilbert-Diamond, ScD1,2; Zhigang Li, PhD1,2; Anna M. Adachi-Mejia, PhD3,4,5; Auden C. McClure, MD3,4,5; James D. Sargent, MD3,4,5

[+] Author Affiliations

JAMA Pediatr. Published online March 03, 2014. doi:10.1001/jamapediatrics.2013.3921 Text Size: A A A

Importance Obesity affects health in children and adolescents. Television viewing is an established risk factor for obesity in youth. No prospective study has assessed whether a bedroom television confers an additional risk for obesity in youth.

Objective To assess the prospective association between the presence of a bedroom television and change in body mass index (BMI; calculated as weight in kilograms divided by height in meters squared), independent of television viewing, in a nationally representative sample of US children and adolescents.

Design, Setting, and Participants We conducted a random-digit prospective telephone survey that captured children and adolescents from across the United States. Participants included 6522 boys and girls aged 10 to 14 years at baseline who were surveyed via telephone about media risk factors for obesity. Weighted regressions assessed adiposity at 2- and 4-year follow-up, controlling for television and movie viewing, video-game playing, parenting, age, sex, race or ethnicity, household income, and parental educational level.

Exposure Report of having a television in the bedroom at baseline.

Main Outcomes and Measures Age- and sex-adjusted BMI based on self-report and parent report of weight and height at 2- and 4-year follow-up.

Results Distributions for age, sex, race or ethnicity, and socioeconomic status were similar to census estimates for the US population. Sample weighting methods accounted for higher dropout rates among ethnic minorities and those with lower socioeconomic status. Bedroom televisions were reported by 59.1% of participants at baseline, with boys, ethnic minorities, and those of lower socioeconomic status having significantly higher rates. In multivariate analyses, having a bedroom television was associated with an excess BMI of 0.57 (95% CI, 0.31-0.82) and 0.75 (0.38-1.12) at years 2 and 4, respectively, and a BMI gain of 0.24 (0.02-0.45) from years 2 to 4.

Conclusions and Relevance Having a bedroom television is associated with weight gain beyond the effect of television viewing time. This association could be the result of uncaptured effects of television viewing or of disrupted sleep patterns. With the high prevalence of bedroom televisions, the effect attributable to this risk factor among US children and adolescents is excess weight of 8.7 million kg/y.

Original Investigation | March 03, 2014

Effects of a Pediatric Weight Management Program With and Without Active Video Games -- A Randomized Trial

Stewart G. Trost, PhD1; Deborah Sundal, MA2; Gary D. Foster, PhD3; Michelle R. Lent, PhD3; Deneen Vojta, MD2

[+] Author Affiliations

JAMA Pediatr. Published online March 03, 2014. doi:10.1001/jamapediatrics.2013.3436 Text Size: A A A

ARTICOLO INTERO CON FIGURE:

https://archpedi.jamanetwork.com/article.aspx?articleid=1838346&utm_campaign=social_030414&utm_medium=twitter&utm_source=@jamapeds

Importance Active video games may offer an effective strategy to increase physical activity in overweight and obese children. However, the specific effects of active gaming when delivered within the context of a pediatric weight management program are unknown.

Objective To evaluate the effects of active video gaming on physical activity and weight loss in children participating in an evidence-based weight management program delivered in the community.

Design, Setting, and Participants Group-randomized clinical trial conducted during a 16-week period in YMCAs and schools located in Massachusetts, Rhode Island, and Texas. Seventy-five overweight or obese children (41 girls [55%], 34 whites [45%], 20 Hispanics [27%], and 17 blacks [23%]) enrolled in a community-based pediatric weight management program. Mean (SD) age of the participants was 10.0 (1.7) years; body mass index (BMI) z score, 2.15 (0.40); and percentage overweight from the median BMI for age and sex, 64.3% (19.9%).

Interventions All participants received a comprehensive family-based pediatric weight management program (JOIN for ME). Participants in the program and active gaming group received hardware consisting of a game console and motion capture device and 1 active game at their second treatment session and a second game in week 9 of the program. Participants in the program-only group were given the hardware and 2 games at the completion of the 16-week program.

Main Outcomes and Measures Objectively measured daily moderate-to-vigorous and vigorous physical activity, percentage overweight, and BMI z score.

Results Participants in the program and active gaming group exhibited significant increases in moderate-to-vigorous (mean [SD], 7.4 [2.7] min/d) and vigorous (2.8 [0.9] min/d) physical activity at week 16 (P?

Conclusions and Relevance Incorporating active video gaming into an evidence-based pediatric weight management program has positive effects on physical activity and relative weight.

Trial Registration clinicaltrials.gov Identifier: NCT01757925

During the past 3 decades, the prevalence of obesity has more than tripled among US children and adolescents and is now almost 17% of those aged 2 to 19 years.1 Obese youth are at increased risk for adult obesity2,3 and significant short-term health problems such as type 2 diabetes mellitus, hypertension, sleep apnea, and orthopedic complications.4,5 Moreover, the adverse social consequences of childhood obesity are significant.4,6

Although considerable attention has been given to dietary risk factors for childhood obesity,7,8 physical inactivity is also an important contributing factor to the development and maintenance of childhood obesity.9 Unfortunately, we have limited understanding of how to promote physical activity effectively in overweight and obese youth, especially outside clinical settings. Given the popularity and pervasiveness of video gaming in youth culture, active video games may be an effective strategy to increase physical activity in overweight and obese children.10,11

Although consistent evidence exists that active video game play can deliver a significant dose of health-enhancing physical activity,12,13 the extent to which habitual game play leads to measurable increases in daily physical activity remains an open question. Among the 6 published studies14- 19 that have evaluated the effects of regular active video game play in children and adolescents on overall activity levels, only 2 studies18,19 reported significant increases in daily physical activity.

Among overweight and obese children, only 2 studies14,18 have evaluated the effect of active video gaming on weight and activity. A 6-month randomized clinical trial14 found positive effects of active gaming on weight but not on objectively measured physical activity or aerobic fitness. More recently, a 10-week study18 reported significant reductions in body mass index (BMI) and significant increases in vigorous physical activity after incorporating active video gaming into a 10-week community-based pediatric weight management program. However, the study did not include a control group, and physical activity was measured using a single-item self-report. Moreover, because the multifaceted intervention included nutrition education, behavioral counseling, and group-based exercise sessions, the effects of the active video gaming are difficult to isolate. Thus, among overweight and obese children, the specific effects of active gaming when delivered within the context of a pediatric weight management program are unknown.

The purpose of this study was to evaluate the effects of incorporating active video gaming into an evidence-based pediatric weight management program using a randomized clinical trial design. We hypothesized that the addition of active video gaming to the program would result in significant increases in objectively measured daily moderate-to-vigorous physical activity (MVPA) and vigorous physical activity (VPA). We further hypothesized that adding active video gaming to the usual 16-week treatment program would lead to significantly greater reductions in relative weight.

METHODS

Design and Setting

The study design was a group-randomized clinical trial with assessment of outcome variables at baseline and at 8 and 16 weeks. Recruitment began in August 2011, and the last participant completed the final assessment in July 2012. The study was completed in YMCAs and schools located in Massachusetts, Rhode Island, and Texas. Study sites were randomly assigned within location to a program and active gaming (P?+?AG) condition or a program-only (PO) condition by a study coordinator via a random number generator. Treatment providers and outcome assessors were aware of randomization status, but those who analyzed the data were not. We included 11 study sites (7 YMCAs and 4 schools), 6 of which were randomized to the P?+?AG condition (4 YMCAs and 2 schools) and 5 of which were randomized to the PO condition (3 YMCAs and 2 schools). The complete study protocol can be obtained on request from one of us (D.V.).

Recruitment

Participants were recruited through local pediatric practices, employers of parents, e-mail announcements to YMCA members, flyers posted in YMCAs and schools, and referrals by school nurses. Inclusion criteria were (1) BMI greater than the 85th percentile for sex and age; (2) age ranging from 8 to 12 years; and (3) willingness of the parent or guardian to participate in weekly treatment sessions. Exclusion criteria were (1) use of medications that would affect weight or appetite; (2) physical conditions that would prevent physical activity or affect weight or appetite; and (3) unwillingness or unsuitability to participate in group treatment. Interested participants underwent screening by telephone or online using a standard script and enrollment criteria. Study eligibility was validated at the first program session. Among the 195 participants who expressed an interest in the program, 59 declined participation, 41 did not fully complete the enrollment process, and 20 failed to meet the inclusion criteria, leaving 75 participants enrolled in the study (Figure). A sample size of 30 to 42 participants per condition provided 80% power to detect net differences of 4.0 to 4.6 min/d in MVPA, assuming 6 sites and 5 to 7 participants per site, an SD of 10 min/d, and an intracluster correlation coefficient of 0.01. Before the first treatment session, informed written consent from the parent and child were obtained. The study was approved by the New England Institutional Review Board.

Figure.

CONSORT Flow Diagram

Diagram depicts the assessment and randomization procedures.

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Interventions

All randomized participants were enrolled in a comprehensive family-based pediatric weight management program (JOIN for ME). Detailed information about the program and its effectiveness on weight status and health-related quality of life has been described previously.20 In brief, the JOIN for ME treatment program is informed by the empirically validated principles of family-based treatment of childhood obesity.21 To reduce cost and increase scalability beyond a specialized clinic, some major modifications were made. First, rather than having separate treatment groups for children and parents, child-parent dyads were combined in a single treatment group. Second, group time was reduced from the recommended 90 minutes to 60 minutes. Third, the intervention sessions were delivered by trained facilitators without any prior experience in treating pediatric obesity. For the present study, a number of modifications were made to the previously evaluated program.20

Participants received family-based behavioral treatment in groups of 5 to 11 child-parent dyads. In total, 16 weekly sessions consisted of an individual weigh-in, an assessment of progress toward behavioral goals, an introduction to new content, and specification of new weekly goals. Session topics included self-monitoring, calorie range targets, “LESS” (ie, less nutritious with higher levels of calories, fat, and sugar) and “YES!” (ie, more nutritious with lower levels of calories, fat, and sugar) foods and drinks, reduction of screen time, goal setting, and an increase of physical activity.19,22 Although the guardians attended each session, the focus of treatment was the child. Treatment materials focused on the guardians’ roles in supporting the child’s weight management efforts (reinforcement, modeling, and changing the home environment) rather than parental weight outcomes.

In addition to completing the JOIN for ME program, participants in the P?+?AG group were provided a game console and motion capture device (Xbox and Kinect; Microsoft Corporation) and 1 active sports game (Kinect Adventures!; Good Science Studio, Microsoft Game Studios) at their second treatment session. A second active game (Kinect Sports; Rare, Microsoft Game Studios) was provided in week 9 of the program. No explicit advice or goals were given to any study participant regarding the use of their active gaming tool. At the completion of the 16-week program, participants in the PO group were given the hardware and 2 games.

Assessment of Physical Activity

Physical activity was measured using an accelerometer-based motion sensor (GT3X or GT3X+; ActiGraph). The motion sensor has been shown to be a valid and reliable instrument for assessing physical activity in children and adolescents.23 The GT3X model was initialized to collect data in 15-second epochs. The GT3X+ model was set to collect a raw triaxial acceleration signal at 30 Hz. These data were subsequently processed into 15-second epochs after download using proprietary software for the motion sensor. Recent work has shown the output from the GT3X and GT3X+ models to be identical.24 Participants were instructed to wear the motion sensor device during the waking hours for 7 consecutive days.

Stored accelerometer data were uploaded to a customized Visual Basic (Microsoft Corp) macro for determination of daily wear time and daily time spent in MVPA and VPA. Counts were classified into the physical activity intensity categories using the cut points developed by Evenson and colleagues,25 whose work has been shown to be the most accurate of all currently available cut points for youth.26 Nonwear time was defined as an interval with at least 60 consecutive minutes of zero counts, with allowance for as long as 2 minutes under the count threshold for sedentary activity.27 A day was considered valid if daily wear time exceeded 9 hours. Participants were included in the analyses if they had 3 or more valid monitoring days.23

Relative Weight and BMI z Score

Height and weight were measured at baseline and weeks 8 and 16 of the program. Weight was measured using calibrated electronic scales (Detecto model 6129; Cardinal Scale Manufacturing Company) while participants wore light clothing and no shoes. Height was measured by a mobile stadiometer (Seca 217; Seca GmbH). Body mass index was calculated as weight in kilograms divided by height in meters squared. Percentage overweight was calculated as the percentage greater than the median BMI for age and sex. The BMI scores were converted to percentiles using the 2000 BMI-for-age growth charts from the Centers for Disease Control and Prevention.28

Statistical Analysis

We used linear mixed models to evaluate between-condition differences with respect to changes in the physical activity and weight-related outcomes over time. For the physical activity outcomes, average daily wear time was included as a time-varying covariate in all models. To control for the clustering effects, each model included intervention delivery site as a random effect. To assess potential bias associated with loss to follow-up and/or missing accelerometer data, an intention-to-treat analysis (using the last observation carried forward) was conducted as a sensitivity analysis. All analyses were completed using commercially available software (SAS PROC Mixed; SAS Institute, Inc). Significance was set at an a level of .05. Unless otherwise indicated, data are expressed as mean (SD).

RESULTS

Participants

Baseline characteristics of the 75 children by treatment condition are listed in Table 1. No significant between-group differences were observed with respect to the demographic or baseline anthropometric measures.

Table 1. Descriptive Characteristics for the Total Sample and the Treatment Groups at Baselinea

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Attrition

The overall retention rate for the 16-week program was 80%. Retention rates at 8 and 16 weeks were 32 of 34 (94%) and 26 of 34 (76%), respectively, for the P?+?AG group, and 40 of 41 (98%) and 34 of 41 (83%), respectively, for the PO group. No statistically significant differences between completers (n?=?60) and noncompleters (n?=?15) for mean baseline MVPA (27.5 [1.9] vs 26.0 [3.9] min/d), VPA (4.8 [0.6] vs 3.9 [1.1] min/d), percentage overweight (63.8% [26.5%] vs 68.1% [28.8%]), and BMI z score (2.16 [0.41] vs 2.18 [0.42]) were found. Compliance with the accelerometer monitoring protocol (percentage of planned accelerometer data collections yielding =3 valid monitoring days) was similar for the P?+?AG and PO groups at 72.5% and 71.9%, respectively.

Physical Activity

Results for physical activity and relative weight outcomes are reported in Table 2. Relative to baseline, participants in the P?+?AG group exhibited a significant increase in MVPA at weeks 8 and 16. In the PO group, MVPA levels declined at weeks 8 and 16. The increase in MVPA from weeks 1 to 16 in the P?+?AG group was significantly greater than that observed in the PO group (net difference,?8.0 [3.8; 95% CI, 0.5-15.4] min/d; P?=?.04).

Table 2. Between-Group Differences in the Physical Activity and Relative Weight Outcomes

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Relative to baseline levels, participants in the P?+?AG group exhibited a significant increase in VPA at week 16. Among participants in the PO group, VPA levels remained relatively unchanged at 8 weeks and then declined at 16 weeks. The increase in VPA from weeks 1 to 16 among P?+?AG participants was significantly greater than that observed among PO participants (net difference, 3.1 [1.3; 95% CI, 0.6-5.8] min/d; P?=?.02).

Both groups exhibited significant reductions in percentage overweight and BMI z score at week 16. However, relative to the PO group, participants in the P?+?AG group exhibited significantly greater reductions in percentage overweight (net difference,?5.4% [2.2%; 95% CI,?1.1%- 9.9%]; P?=?.02) and BMI z score (net difference, 0.14 [0.04; 95% CI, 0.07-0.22]; P?

Table 3. Results of the Intention-to-Treat Sensitivity Analyses for the Physical Activity and Relative Weight Outcomes

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DISCUSSION

The study had two principal findings. First, the addition of active gaming to an established community-based pediatric weight management program resulted in significant increases in MVPA among overweight and obese children. The introduction of active gaming resulted in additional MVPA of 7.4 min/d, with approximately one-third of the increase coming from VPA. In contrast, participants randomized to the usual 16-week program without active gaming exhibited little or no change in physical activity.

Although the observed changes were small in magnitude, the between-group difference in MVPA of 8 min/d is not trivial. Assuming a mean intensity level of 5 metabolic equivalent for task (MET; 1 MET?=?1 kcal/kg/h) and a mean body mass of 60 kg, the difference of 8 minutes of MVPA equates to an energy expenditure of approximately 40 kcal/d. During a 1-year period, this level of energy expenditure equals just more than 14?500 kcal or the equivalent of 4 pounds of fat. Thus, even small changes in physical activity such as those observed in the current study, when combined with modest reductions in energy intake, have important implications for long-term energy balance. In support of this concept, Hill and colleagues29 calculated that the annual weight gain observed in 90% of the US population (0.8-0.9 kg) could be eliminated by some combination of increasing energy expenditure and reducing intake by 100 kcal/d. The mean weight reduction observed in the P?+?AG group (0.85 kg) is consistent with these calculations.

The second major finding was that the addition of active gaming to an established pediatric weight management program significantly enhanced weight loss. Consistent with the results of a previous evaluation of the JOIN for ME program,20 both groups, regardless of study allocation, exhibited significant and clinically meaningful reductions in relative weight. However, providing participants an active gaming console and a game resulted in a doubling of the reduction in relative weight and BMI z score. This finding suggests that the additional daily energy expenditure associated with active game play promoted a more favorable energy gap leading to greater reductions in relative weight. Alternatively, active game play may have helped participants adhere to the program’s dietary intake goals, resulting in greater reductions in energy intake. Irrespective of the underlying mechanism, the greater weight loss associated with the provision of active gaming resources represents an important new finding. That significant and positive changes in physical activity and weight were achieved in the absence of any specific instruction or goals related to active game play contradicts the results of earlier studies15,17 and suggests that active gaming may be an effective strategy to promote physical activity and healthy weight among overweight and obese youth. The results may be even more impressive if specific behavioral targets for active gaming are provided.

To date, only two previous studies have evaluated the effects of active gaming on habitual physical activity among overweight and obese youth. Our findings are consistent with those of Christison and Khan,18 who found significant increases in self-reported VPA after integrating active video games into a multifaceted community-based weight management program. However, our findings are in conflict with those of Maddison and colleagues,14 who reported no significant changes in objectively measured MVPA after a 6-month active gaming intervention, despite observing significant reductions in fat mass and BMI z scores. The discrepancy in findings may be attributable, at least in part, to differences in the accelerometer data reduction protocol. Maddison et al converted the accelerometer data into daily time spent in light, moderate, and vigorous physical activity by applying the age-specific cut points of Freedson et al.30 Trost et al26 have shown these cut points to significantly overestimate MVPA levels, particularly among younger children. Therefore, the extremely high levels of MVPA reported in that study (>90 min/d at baseline)14 may have created a ceiling effect, making detection of small changes in physical activity level difficult.

Our study had several strengths that warrant consideration. To our knowledge, this study is the first to use a randomized clinical trial design to delineate the effects of active gaming on habitual physical activity among overweight and obese children. We also examined the impact of active gaming in the context of a fully scalable, community-based pediatric weight management program administered in schools and YMCAs. Thus the potential for translating these findings to other settings and population groups is high. Finally, the study used a state-of-the-art objective measure of physical activity, thus eliminating the substantial recall bias and measurement error associated with self-report methods.

However, our study has a number of limitations. First, the weight management program and assessment were completed during a 16-week period. Thus, the observed changes in physical activity and relative weight should be viewed as relatively short-term effects. Whether participants in the P?+?AG group would have sustained their increases in physical activity and reductions in relative weight for longer periods remains a question for future research. Second, although compliance with the accelerometer protocol was similar for both treatment groups, a significant number of participants failed to provide 3 or more valid monitoring days. Although we cannot completely rule out the potential for bias, the results of the intention-to-treat analyses revealed quantitatively similar changes in the physical activity and weight-related outcomes during the 16-week program. Thus we believe that the results are robust and not a function of bias associated with missing data or withdrawal from the study. Third, although active gaming in the home is convenient, safe, and popular among young people, the costs associated with the purchase of game consoles and individual games (approximately US $350) may be a barrier for low-income families.

CONCLUSIONS

Incorporating active video gaming into an evidence-based pediatric weight management program had positive effects on physical activity and relative weight. Future studies should examine the effects of active gaming during longer follow-up periods, complete formal cost-effectiveness analyses, and examine whether the effects on weight loss and physical activity could be enhanced by incorporating goals specific to gaming into the program.

ARTICLE INFORMATION

Accepted for Publication: July 2, 2013.

Corresponding Author: Stewart G. Trost, PhD, Center for Research on Exercise, Physical Activity and Health, School of Human Movement Studies, The University of Queensland, Brisbane, QLD, Australia 4072 (s.trost@uq.edu.au).

Published Online: March 3, 2014. doi:10.1001/jamapediatrics.2013.3436.

Author Contributions: Drs Trost and Foster had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

REFERENCES

Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of obesity and trends in body mass index among US children and adolescents, 1999-2010. JAMA. 2012;307(5):483-490.

PubMed | Link to Article

Guo SS, Roche AF, Chumlea WC, Gardner JD, Siervogel RM. The predictive value of childhood body mass index values for overweight at age 35 y. Am J Clin Nutr. 1994;59(4):810-819.

PubMed

Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH. Predicting obesity in young adulthood from childhood and parental obesity. N Engl J Med. 1997;337(13):869-873.

PubMed | Link to Article

Dietz WH. Health consequences of obesity in youth: childhood predictors of adult disease. Pediatrics. 1998;101(3, pt 2):518-525.

PubMed

Must A, Strauss RS. Risks and consequences of childhood and adolescent obesity. Int J Obes Relat Metab Disord. 1999;23(suppl 2):S2-S11.

Link to Article

Puhl RM, Latner JD. Stigma, obesity, and the health of the nation’s children. Psychol Bull. 2007;133(4):557-580.

PubMed | Link to Article

Borradaile KE, Sherman S, Vander Veur SS, et al. Snacking in children: the role of urban corner stores. Pediatrics. 2009;124(5):1293-1298.

PubMed | Link to Article

Nielsen SJ, Popkin BM. Patterns and trends in food portion sizes, 1977-1998. JAMA. 2003;289(4):450-453.

PubMed | Link to Article

Steinbeck KS. The importance of physical activity in the prevention of overweight and obesity in childhood: a review and an opinion. Obes Rev. 2001;2(2):117-130.

PubMed | Link to Article

Read JL, Shortell SM. Interactive games to promote behavior change in prevention and treatment. JAMA. 2011;305(16):1704-1705.

PubMed | Link to Article

Barnett A, Cerin E, Baranowski T. Active video games for youth: a systematic review. J Phys Act Health. 2011;8(5):724-737.

PubMed

Bailey BW, McInnis K. Energy cost of exergaming: a comparison of the energy cost of 6 forms of exergaming. Arch Pediatr Adolesc Med. 2011;165(7):597-602.

PubMed | Link to Article

Foley L, Maddison R. Use of active video games to increase physical activity in children: a (virtual) reality? Pediatr Exerc Sci. 2010;22(1):7-20.

PubMed

Maddison R, Foley L, Ni Mhurchu C, et al. Effects of active video games on body composition: a randomized controlled trial. Am J Clin Nutr. 2011;94(1):156-163.

PubMed | Link to Article

Maloney AE, Bethea TC, Kelsey KS, et al. A pilot of a video game (DDR) to promote physical activity and decrease sedentary screen time. Obesity (Silver Spring). 2008;16(9):2074-2080.

PubMed | Link to Article

Madsen KA, Yen S, Wlasiuk L, Newman TB, Lustig R. Feasibility of a dance videogame to promote weight loss among overweight children and adolescents. Arch Pediatr Adolesc Med. 2007;161(1):105-107.

PubMed | Link to Article

Baranowski T, Abdelsamad D, Baranowski J, et al. Impact of an active video game on healthy children’s physical activity. Pediatrics. 2012;129(3):e636-e642. doi:10.1542/peds.2011-2050.

PubMed | Link to Article

Christison A, Khan HA. Exergaming for health: a community-based pediatric weight management program using active video gaming. Clin Pediatr (Phila). 2012;51(4):382-388.

PubMed | Link to Article

Ni Mhurchu C, Maddison R, Jiang Y, Jull A, Prapavessis H, Rodgers A. Couch potatoes to jumping beans: a pilot study of the effect of active video games on physical activity in children. Int J Behav Nutr Phys Act. 2008;5:8. doi:10.1186/1479-5868-5-8.

PubMed | Link to Article

Foster GD, Sundal D, McDermott C, Jelalian E, Lent MR, Vojta D. Feasibility and preliminary outcomes of a scalable, community-based treatment of childhood obesity. Pediatrics. 2012;130(4):652-659.

PubMed | Link to Article

Wilfley DE, Vanucci A, White EK. Family-based behavioral interventions. In: Freemark M, ed. Contemporary Endocrinology: Pediatric Obesity: Etiology, Pathogenesis and Treatment. New York, NY: Humana Press; 2010:281-301.

Kalarchian MA, Levine MD, Arslanian SA, et al. Family-based treatment of severe pediatric obesity: randomized, controlled trial. Pediatrics. 2009;124(4):1060-1068.

PubMed | Link to Article

Trost SG, McIver KL, Pate RR. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc. 2005;37(11)(suppl):S531-S543.

PubMed | Link to Article

Robusto KM, Trost SG. Comparison of three generations of ActiGraph activity monitors in children and adolescents. J Sports Sci. 2012;30(13):1429-1435.

PubMed | Link to Article

Evenson KR, Catellier DJ, Gill K, Ondrak KS, McMurray RG. Calibration of two objective measures of physical activity for children. J Sports Sci. 2008;26(14):1557-1565.

PubMed | Link to Article

Trost SG, Loprinzi PD, Moore R, Pfeiffer KA. Comparison of accelerometer cut points for predicting activity intensity in youth. Med Sci Sports Exerc. 2011;43(7):1360-1368.

PubMed | Link to Article

Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181-188.

PubMed | Link to Article

Kuczmarski RJ, Ogden CL, Grummer-Strawn LM, et al. CDC growth charts: United States. Adv Data. 2000;(314):1-27.

PubMed

Hill JO, Wyatt HR, Reed GW, Peters JC. Obesity and the environment: where do we go from here? Science. 2003;299(5608):853-855.

PubMed | Link to Article

Freedson P, Pober D, Janz KF. Calibration of accelerometer output for children. Med Sci Sports Exerc. 2005;37(11)(suppl):S523-S530.

PubMed | Link to Article

I pazienti che vanno su INTERNET: pericoli per diagnosi e terapia

Is the internet harming medical research?

It has never been easier for patients to support each other using social media, but might this undermine clinical trials?

While the movie Dallas Buyers Club might not have been completely factually accurate, it raises an interesting question about medical research. The movie depicts experimental trials for Aids treatments in the USA in the 1980s, and how a group of patients involved in the trials came together to share their experiences about treatments, side effects and the like, eventually smuggling the experimental drugs into the US and selling them to fellow patients.

Leap forward to the present, and in this online age it has never been easier for patients to seek out each other and discuss the minutiae of their symptoms and treatment.

An opinion piece in Nature recently called for researchers to consider participants’ use of social media, as it might compromise the integrity of clinical trials. However, it also pointed out the potential benefits of social media to researchers (and patients) as well.

If you find yourself diagnosed with an illness, it’s a natural reaction to want to seek out as much information as possible about it. And of course, we turn to the internet to find it. But beyond that, patient self-help groups can be incredibly valuable in preventing a person feeling like they’re on their own; support from someone who completely understands how you’re feeling can make a huge difference. The internet has made this sort of peer support easier, even for groups who can’t meet up in person (people with cystic fibrosis for example run the risk of cross-infections if they meet in person).

There are a number of reasons, though, that patients in clinical trials might risk undermining the trial if they discussed their experiences with each other. And what’s more, they might not even realise they’re doing so. Clinical trials are double-blinded wherever possible, meaning both the researcher and the participant don’t know whether the participant is in the experimental or control condition. The randomisation will be set up by someone other than the researcher who interacts with the patient, so they will give a pot of pills (or another treatment) to the participant, without knowing whether these are sugar pills, or the medicine being investigated.

There is plenty of evidence why this is important. If the people administering the trial know who is in what condition, they may, consciously or unconsciously, treat people differently. This could involve seeing improvements more in the group you’re expecting to improve more, or downplaying side effects.

But participants talking to each other about their experiences within a trial might accidentally unblind them as well. Hearing about other participants’ side effects might make a person more likely to report such things themselves. It may also become clear after talking to other participants in a study whether you are in the treatment or placebo group, which might affect how you report your symptoms next time you speak to the study organisers, without your even realising.

Before these digitally connected days, trials could operate out of a variety of different locations, minimising this bleed from participants. Nowadays, when patients can discuss their experiences digitally, physical distance is no longer a barrier.

It’s unlikely that participants are deliberately sabotaging the trials they are involved in. After all, they have signed up to be in the trial in the first place. The Nature editorial is clear in suggesting researchers need to actively consider this problem, and discuss with participants the potential harm to the study from sharing information about symptoms and side effects.

All this isn’t to say that the internet shouldn’t be used by those seeking comfort and solidarity from those in the same situation as them. In fact, there’s evidence to suggest that online support groups of this kind might provide health benefits as well as reassurance.

The internet isn’t going anywhere, and patients should do whatever they feel will help them come to terms with a diagnosis and understand their illness. But as the Nature piece concludes, it’s something researchers need to consider. What is the effect of social media on the biases in trials, and can patients in trials be encouraged to avoid accidentally un-blinding themselves without forcing them to give up these valuable support networks?

Indietro