***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: OBJECTIVES Many individuals are advised to adhere to specific diet plans for their personal health ; hence , it is important that tools are available to support these behaviors . Smartphone applications ( apps ) may assist health care professionals in educating their clients on specific dietary modifications . This pilot study focused on a single dietary modification , reducing sodium intake , to determine whether a commercial health app is useful for promoting dietary change . METHODS Thirty healthy adults ( age 34.4 ± 15.7 y ; body mass index 25.6 ± 4.3 kg/m2 ) were recruited from a university community and completed this 4-wk r and omized parallel trial . Participants were instructed to reduce their sodium intake to ≤2300 mg/d by using the MyFitnessPal app to receive feedback on sodium content of foods or by paper tallying of estimated sodium intake .  The predicted 24-h sodium excretion , estimated using the ratio of sodium to creatinine from the first morning urine void , and participant satisfaction were the main outcomes measured . RESULTS The change in the predicted 24-h sodium excretion differed between groups : -838 ± 1093 and + 236 ± 1333 mg/24 h predicted for the app and journal groups , respectively ( P = 0.010 ) . Moreover , participants in the app group reported significantly greater satisfaction with their method of diet tracking than the journal group ( P = 0.001 ) . CONCLUSIONS These data suggest that smartphone apps have the potential to facilitate the implementation of dietary advice . This was a small pilot study with limited scope , and more research is necessary to determine the value of smartphone apps for facilitating dietary change Objectives Rates of youth suicide in Australian Indigenous communities are 4 times the national youth average and dem and innovative interventions . Historical and persistent disadvantage is coupled with multiple barriers to help seeking . Mobile phone applications offer the opportunity to deliver therapeutic interventions directly to individuals in remote communities . The pilot study aim ed to evaluate the effectiveness of a self-help mobile app ( ibobbly ) targeting suicidal ideation , depression , psychological distress and impulsivity among Indigenous youth in remote Australia . Setting Remote and very remote communities in the Kimberley region of North Western Australia . Participants Indigenous Australians aged 18–35 years . Interventions 61 participants were recruited and r and omised to receive either an app ( ibobbly ) which delivered acceptance-based therapy over 6 weeks or were waitlisted for 6 weeks and then received the app for the following 6 weeks . Primary and secondary outcome measures The primary outcome was the Depressive Symptom Inventory — Suicidality Subscale ( DSI-SS ) to identify the frequency and intensity of suicidal ideation in the previous weeks . Secondary outcomes were the Patient Health Question naire 9 ( PHQ-9 ) , The Kessler Psychological Distress Scale ( K10 ) and the Barratt Impulsivity Scale ( BIS-11 ) . Results Although preintervention and postintervention changes on the ( DSI-SS ) were significant in the ibobbly arm ( t=2.40 ; df=58.1 ; p=0.0195 ) , these differences were not significant compared with the waitlist arm ( t=1.05 ; df=57.8 ; p=0.2962 ) . However , participants in the ibobbly group showed substantial and statistically significant reductions in PHQ-9 and K10 scores compared with waitlist . No differences were observed in impulsivity . Waitlist participants improved after 6 weeks of app use . Conclusions Apps for suicide prevention reduce distress and depression but do not show significant reductions on suicide ideation or impulsivity . A feasible and acceptable means of lowering symptoms for mental health disorders in remote communities is via appropriately design ed self-help apps . Trial registration number ACTRN12613000104752 Objective : Posttraumatic stress disorder ( PTSD ) is highly prevalent in the population , but relatively few affected individuals receive treatment for it . Smartphone applications ( apps ) could help address this unmet need by offering sound psychoeducational information and evidence -based cognitive behavioral coping tools . We conducted a r and omized controlled trial to assess the efficacy of a free , publicly available smartphone app ( PTSD Coach ) for self-management of PTSD symptoms . Method : One hundred 20 participants who were an average of 39 years old , mostly women ( 69.2 % ) and White ( 66.7 % ) , recruited primarily through online advertisements , were r and omized to either a PTSD Coach ( n = 62 ) or a waitlist condition ( n = 58 ) for 3 months . Web-administered self-report measures of PTSD , PTSD symptom coping self-efficacy , depression , and psychosocial functioning were conducted at baseline , posttreatment , and 3 months following treatment . Results : Following the intent-to-treat principle , repeated- measures analyses of variance ( ANOVAs ) revealed that at posttreatment , PTSD Coach participants had significantly greater improvements in PTSD symptoms ( p = .035 ) , depression symptoms ( p = .005 ) , and psychosocial functioning ( p = .007 ) than did waitlist participants ; however , at posttreatment , there were no significant mean differences in outcomes between conditions . A greater proportion of PTSD Coach participants achieved clinical ly significant PTSD symptom improvement ( p = .018 ) than waitlist participants . Conclusion : PTSD Coach use result ed in significantly greater improvements in PTSD symptoms and other outcomes relative to a waitlist condition . Given the ubiquity of smartphones , PTSD Coach may provide a wide-reaching , convenient public health intervention for individuals with PTSD symptoms who are not receiving care It is well-known that the United States is facing an obesity epidemic , and the long-term sequelae are costly ( 1 , 2 ) . Research ers continue to search for effective  weight-loss interventions that can be applied in outpatient setting s , but these are often time-consuming and re source -intensive , requiring repeated counseling ( 3 ) . It is no surprise that primary care providers often omit discussing weight loss with obese patients and rarely spend adequate time on counseling ( 4 , 5 ) . Smartphone applications ( apps ) may provide an alternative to re source -intensive weight-loss programs . In December 2013 , a survey by the Pew Research Center found that 58 % of Americans own smartphones and ownership is increasing among every demographic group , including low-income population s ( 6 ) . The nascent field of mobile health is rapidly exp and ing ; experts estimate that as many as 40000 health-related apps were available in 2012 , comprising a $ 718 million industry ( 7 ) . Many of these apps aim to help persons change behaviors to improve health , including weight loss , yet few have been rigorously evaluated . An effective app for reducing body weight could produce tremendous cost savings by preventing long-term complications , such as diabetes and cardiovascular events . To our knowledge , however , no studies have examined the effectiveness of delivering or prescribing an app for weight loss to patients in a clinical setting . We evaluated one of the most popular publicly available apps for  weight loss : MyFitnessPal ( MFP ) ( MyFitnessPal ) . MFP has received the highest possible rating , 5 out of 5 stars , from thous and s of review ers on the Apple and And roid ( Google ) app store Web sites . It has nearly 1 million likes on Facebook , and the company reports more than 50 million registered users . MFP incorporates elements of social cognitive theory , including self-monitoring , goal setting , and feedback . We sought to test the effect of providing this free , widely used smartphone app for weight loss to patients in their primary care clinic . Methods Design Overview mFit ( The Mobile Fitness Project ) was a r and omized , controlled trial in which participants were r and omly assigned to receive  usual primary care ( n= 107 ) or usual primary care plus the MFP app ( n= 105 ) ( Figure 1 ) . Assessment s were completed at baseline , 3 months , and 6 months between August 2012 and May 2013 . The institutional review board of the University of California , Los Angeles ( UCLA ) , approved the study , and all participants provided written informed consent . Figure 1 . Study flow diagram . * Among the 6 intervention group participants who withdrew , 3 reported not having enough time to use the app , 2 did not have time to return for follow-up , and 1 was no longer interested in participating in the study . Among the 8 control group participants who withdrew , 6 reported not having time to return for follow-up and 2 were no longer interested in participating in the study . Study data were collected on Apple iPads using Research Electronic Data Capture ( REDCap ) tools hosted at UCLA . REDCap is a secure , Web-based application design ed to support data capture for research studies , providing an intuitive interface , audit trails , and automated export ( 8) . Setting and Participants Participants were recruited from 2 UCLA primary care clinics that serve ethnically and socioeconomically diverse patient population s. Eligibility criteria included age 18 years or older , body mass index ( BMI ) of 25 kg/m2 or greater , and smartphone ownership . Participants also had to answer yes when asked , Are you interested in losing weight ? Exclusion criteria were current , planned , or previous pregnancy within 6 months ; receipt of hemodialysis ; life expectancy less than 6 months ; lack of interest in weight loss ; or current use of a smartphone app for weight loss . Screening and R and omization Patients were recruited during routine primary care visits at their respective clinics . The research team provided a script to medical assistants to use with any patients with BMI greater than 25 kg/m2 . Patients interested in enrollment were referred to the on-site research assistant , who screened , received consent from , and completed surveys with each patient . Participants were r and omly assigned in blocks by BMI of 25 to 30 kg/m2 and BMI greater than 30 kg/m2 to ensure roughly equal distribution of overweight and obese patients between the intervention and control groups . Our statistician used R ( R Foundation for Statistical Computing ) to generate the permuted block sequence . We printed the sequence and placed it in opaque envelopes . Research assistants helped intervention group participants download the MFP app onto their smartphone and showed them an instructional video developed by MFP . These participants also received a telephone call from the same research assistant 1 week after enrollment to assist with any technical problems with the app . Research assistants told control group patients to choose any activities you 'd like to lose weight , without specifying any particular interventions . Control group participants were aware that they were participating in a study of a weight-loss app but were blinded to the name of the app . To minimize contamination of the control group , providers and clinic staff were also blinded to the name of the app and to group assignment . At the 3-month follow-up visit , all participants received a 1-page educational h and out on healthy eating from www.myplate.gov . Participants received a $ 20 gift card for attending each follow-up visit . Each participant 's primary care provider was notified of their enrollment in the study . Blood pressure was measured at baseline , 3 months , and 6 months by trained research assistants using an automated monitor ( Dinamap , GE Medical Systems ) . Intervention We selected MFP as our intervention on the basis of 2 focus groups held with overweight primary care patients . Patients were asked about their impressions of various text messagebased programs and smartphone apps . Overall , there was much more interest in smartphone apps than text-based programs . A few participants stated they enjoyed using MFP , and a majority expressed great interest in trying it . Although we selected MFP as our intervention , there are many similar , publicly available apps that may be as popular as MFP . Some of these apps have been assessed in prior studies , but to our knowledge , none have been evaluated in a r and omized trial ( 9 ) . MFP was design ed by software engineers in collaboration with dietitians to create an app for calorie counting . The app provides a data base of more than 3 million foods and an easy-to-use interface for logging food and exercise . Users enter their current weight , goal weight , and goal rate of weight loss ( limited to 0.23 to 0.90 kg/wk ) . The MFP app then shows the user their daily , individualized calorie goal . Each day , the app displays the user 's calorie goal relative to their recorded caloric intake .  MFP also generates real-time reports showing users their weight trend , caloric intake in the past week , and nutritional summaries of their diet ( for example , grams of fat , carbohydrates , and protein and milligrams of sodium ) . The app also includes a bar code scanner for store-bought foods and a social networking feature that enables users to find friends and share their progress . Study participants were encouraged to use the social networking feature with friends and to set reminders to log their food . MFP incorporates an evidence -based and theory-based approach to weight loss . Setting a realistic weight-loss goal of 0.23 to 0.90 kg/wk is supported in self-regulation theory and is a st and ard setting of the MFP app ( 10 ) . The social networking feature of MFP may be important , given prior studies demonstrating the benefits of social support on weight loss ( 11 ) . Self-monitoring , consisting of recording dietary intake , physical activity , and weight , is also strongly associated with weight loss ( 12 ) . One pilot trial recently found that adherence to diet self-monitoring is higher among patients using a smartphone app than among those using a paper diary ( 13 ) . Outcomes and Follow-up The primary outcome was change in weight at 6 months in the  intervention group compared with the usual primary care group .  Weight was measured at baseline , 3 months , and 6 months . Secondary outcomes were systolic blood pressure and 3 self-reported behavioral mediators of weight loss : exercise , dieting , and self-efficacy in weight loss . Data on these outcomes were also collected at baseline 
    Output:
    
Conclusions Despite the high risk of bias , such as selection , performance , and detection , this systematic review found that the use of mobile health applications has a positive impact on health-related behaviors and clinical health outcomes .
Application users were more satisfied with using mobile health applications to manage their health in comparison to users of conventional care