All four video gaming indicators—days/week played, school/work day play, nonschool/work day play, and problem play—had significant curvilinear relationships with age. The “shape” of video gaming's relationship with age is, therefore, similar to that of substance use, but video gaming appears to peak earlier in life than substance use, that is, in late adolescence rather than emerging adulthood. Of the four video gaming indicators, role incompatibility only significantly affected school/work day play, the dimension with the clearest potential to interfere with life obligations.This study explored the relationship between video gaming and age during childhood, adolescence, and emerging adulthood. It also examined whether “role incompatibility,” the theory that normative levels of substance use decrease through young adulthood as newly acquired adult roles create competing demands, generalizes to video gaming. Emerging adult video gamers ( Video games are an indelible part of the modern American early life course. A 2008 Pew Research Center survey found that 99% of males and 94% of females ages 12–17 play video games . Video gBecause substance and behavioral addictions have similar biological mechanisms –30, it mFor the average youth, substance use decreases through emerging and young adulthood as, according to role incompatibility theory, competing demands of school, work, and relationships make previous substance use levels untenable –38. HoweIf video gaming, or at least certain patterns of video gaming, fills a similar role in the life course to that of substance use, then average levels of duration/frequency of video gaming and PVGP should follow the same trends across the early life course as substance use, that is, rising through childhood and adolescence to an inflection point and then falling through emerging adulthood as competing demands of adult roles make previous levels of play untenable , 33, 40.Participants were emerging/young adults ages 18–29 recruited in and around 52 different video game stores, arcades, internet/cyber cafes, game-themed convention booths, and retail stores with large video game departments in New York City. Time and day of data collection were varied to obtain a diverse sample. Quota sampling was used to obtain at least 10 participants for every “cell” that would be created by cross-tabulating gender, race, and illicit substance user status, with some participants screened out if they were not needed to meet a quota. Initial contact was made with 1090 potential participants. Of these, 150 were ineligible or screened out, and 238 declined to participate. If participants were interested but could not complete the interview at that time, interviewers set appointments. Ten cases were invalid because of incomplete responses. Our total valid n was 692, for a response rate of 692/940 = 74%. Field interviewers took participants to a mutually agreeable public location for the interview, often a park, fast-food restaurant, or coffee shop. Measures included a computer-assisted personal interview (CAPI) for time-invariant indicators, including demographics and personality variables. Time-varying variables including life-course indicators, caffeine and sugar consumption, and video game playing were measured via a quantitative life-history calendar LHC; –52). The. The52])The LHC included three questions about duration/frequency of video gaming. Days/week of play was measured by “In a typical week, on how many days did you play games?” Separate questions were included for school/work day play: “On weekdays (or days that you had to go to school or work), how many hours per day did you play video games?” and nonschool/work day play: “On weekends , how many hours per day did you play video games?” because the former seemed more likely to interfere with life obligations . ResponsThis was the average of responses to one question about sugar drinks and another about high-sugar food, each on a 6-point Likert scale ranging from 0 = “never or less than once a week” to 5 = “exclusively—it was almost all I [ate/drank].”Caffeinated drinks/day was the number of caffeinated drinks or caffeine pills the participant reported on days of caffeine use. Degree of problem use was a single-item measure with the same 5-point Likert scale as problem video game play. Zeroes were imputed for nonusers.For each year, each of these factors was coded 1 if they applied to the participant for more than half of the year and 0 if not. They included whether the participant attended a 2-year or 4-year school, whether they held a full-time job, whether they lived in student housing, and whether they lived away from home .α = .64, shyness, seven items, α = .73, and sociability, three items, α = .69. These modest α's may have made these variables less competitive for variance in multivariate models.Personality measures were adapted from the National Longitudinal Survey of Adolescent Health . They iThese included race, gender, and age at the time of the interview. Our analyses built upon an existing example of multilevel modeling (MLM) for LHC data , treatinM = 2.3, SD = 0.7; for sensation-seeking, M = 3.3, SD = 0.6; for sociability, M = 3.5, SD = 0.8.Participants were 22% white, 24% African-American/Black, 20% Latino, 20% Asian, and 14% other/mixed. About two-thirds (66%) were male. Mean age at the time of the interview was 21.2, SD = 3.1. Most (58%) had spent at least half a year as a 2-year or 4-year college student, and 42% reported at least half a year of full-time work. At least half a year of living in student housing was reported by 20%. Only 1% of participants had never lived away from home at the time of the interview; 5% were living away from home at age 17, 14% at 18, 49% at 19, 63% at 20, 76% at 21, 84% at 22, 88% at 23, and above 90% at later ages. Personality variables were roughly normally distributed and their means were close to their scales' middle ranges : For shyness, y-axis range as the frequency/duration indicators. All four basic relationships depicted in R2's between .02 and .09 and corresponding P's ≤ .001) predicting all four video gaming indicators from linear and quadratic terms for age and controlling only for age at time of interview. In spite of the second inflection point around ages 21-22 that appears to emerge in Other findings in the final model were that caffeine use reliably predicted video game play, problem caffeine use predicted PVGP, and sugar/junk food consumption was associated with all indicators of video game play and PVGP. Black, Latino, and other/multiracial respondents played video games more often and longer than whites, and Latino and Asian respondents reported more PVGP than white respondents. Personality was generally unrelated to video gaming except for a significant association between shyness and PVGP; these tests may have been underpowered due to the marginal reliability of the measures.  Results confirmed hypotheses that all four video gaming indicators studied—days/week of play, school/work day play, non-school/work day play, and problem play (PVGP)—had curvilinear relationships with age, rising through childhood and adolescence to a peak and then leveling off or decreasing in emerging adulthood. Other than that the inflection point was apparently earlier in the life course for video games than it is for substances (a common sense explanation for which would be that adolescents have easier access to video games than to substances) these trends were congruent with those which earlier research found between age and substance use , 40. We This study's primary strength was its developmental/life-course perspective. Although video gaming studies usually focus on children and adolescents , 56, theAlthough conclusions about development based on retrospective data are admittedly tentative, our findings may be used to support a case for gathering true longitudinal data, perhaps through including additional questions about video games and other media use in large-scale survey studies of adolescents like Monitoring the Future  or Add H


Fitness video games are popular, but little is known about their content. Because many contain interactive tools that mimic behavioral strategies from weight loss intervention programs, it is possible that differences in content could affect player physical activity and/or weight outcomes. There is a need for a better understanding of what behavioral strategies are currently available in fitness games and how they are implemented.The purpose of this study was to investigate the prevalence of evidence-based behavioral strategies across fitness video games available for home use. Games available for consoles that used camera-based controllers were also contrasted with games available for a console that used handheld motion controllers.Fitness games (N=18) available for three home consoles were systematically identified and play-tested by 2 trained coders for at least 3 hours each. In cases of multiple games from one series, only the most recently released game was included. The Sony PlayStation 3 and Microsoft Xbox360 were the two camera-based consoles, and the Nintendo Wii was the handheld motion controller console. A coding list based on a taxonomy of behavioral strategies was used to begin coding. Codes were refined in an iterative process based on data found during play-testing.P=.04).The most prevalent behavioral strategies were modeling (17/18), specific performance feedback (17/18), reinforcement (16/18), caloric expenditure feedback (15/18), and guided practice (15/18). All games included some kind of feedback on performance accuracy, exercise frequency, and/or fitness progress. Action planning (scheduling future workouts) was the least prevalent of the included strategies (4/18). Twelve games included some kind of social integration, with nine of them providing options for real-time multiplayer sessions. Only two games did not feature any kind of reward. Games for the camera-based consoles  included a greater number of strategies than those for the handheld motion controller console (mean 10.00, SD 2.74, Behavioral strategies for increasing self-efficacy and self-regulation are common in home console fitness video games. Social support and reinforcement occurred in approximately half of the studied games. Strategy prevalence varies by console type, partially due to greater feedback afforded by camera-based controllers. Experimental studies are required to test the effects of these strategies when delivered as interactive tools, as this medium may represent an innovative platform for disseminating evidence-based behavioral weight loss intervention components. Insufficient physical activity and poor dietary habits contribute to obesity -3, whichA potential platform for those solutions is the video game console. Due to their popularity and potential for influencing health, fitness-themed video games have recently attracted substantial research attention -13. FitnLittle is known about their content and how differences in content may affect player behavior. Specifically, many fitness games include interactive behavioral tools. These tools typically use strategies that are hallmarks of successful weight loss interventions such as modeling and goal-setting -26. TherStandard behavioral weight loss interventions typically include a large number of strategies aimed at behavior change. Recent meta-analyses and meta-regressions have identified several strategies that appear to be particularly effective for promoting weight loss and physical activity ,31. ManyA strategy from face-to-face interventions used commonly in video games is modeling, also called observational learning or vicarious experience. In this strategy, an individual is observed while performing targeted activities; the player learns how to perform the activities by watching the model. Models can be computer-generated agents such as virtual trainers, or they can be representations of the player him/herself in video or computer-generated avatar form. Virtual self-modeling using video or avatars may be particularly powerful. Several studies have found that watching a virtual self eat or exercise  can affect later real-world eating and exercise ,33.Another very common strategy with proven effectiveness is provision of feedback on performance . One of Both modeling and feedback can be integrated with guided practice, also known as guided mastery. Guided practice provides a chance for an individual to practice enacting a skill while receiving instruction. In fitness video games, guided practice may consist of players simultaneously receiving instruction, watching a model perform a set of exercises, enacting the movements themselves, and receiving feedback on the accuracy of their practice movements. Verbal persuasion as to the player’s competence  may also be integrated with these strategies. Verbal persuasion can occur during exercise (“You can do it!”) or afterward as part of evaluative feedback .Self-regulation is often used synonymously with self-control and encompasses self-corrective adjustments towards some purpose . Self-reMany current fitness video games track physical activity frequency, duration, and caloric expenditure. Some also track weight  and fitness (via heart rate and/or number of repetitions during periodic fitness tests). This information is typically provided as graphs, progress bars, and leaderboards. Players may also earn achievements or trophies  for reaching caloric expenditure or frequency cut-points. These tracking systems are more sophisticated than physical activity self-monitoring in typical weight loss interventions, which is often self-reported. Automated measurement and feedback from monitoring systems similar to these have been found to be very effective in weight loss trials -37.Goal-setting provides a framework for self-monitoring and feedback, which are specific to a particular goal . Goals can also influence performance in multiple ways, including via motivating greater effort and by directing effort towards task-relevant activities . Goals cComparison is also an important component of feedback that affects self-monitoring. By viewing workout calendars and graphs of progress, individuals can compare their current performance to past performance. Inclusion of multiplayer graphs and leaderboards allow individuals to compare their performance to that of other individuals. Monitoring one’s progress and comparing it to benchmarks allows individuals to quickly adapt their behavior . Comparison is also very effective in increasing self-efficacy for exercise .Several additional behavioral strategies used in weight loss studies may be implemented in fitness video games, such as reinforcement and social integration. Reinforcement is a key feature of video games that is also used in fitness video games. These games may include in-game and/or console-based rewards. In-game rewards typically are virtual badges of some kind that are achieved by reaching preset goals. These badges may also be tied to unlocking new parts of the game, such as new environments, music, or exercises. Console-based rewards are also virtual badges, but these are tied to an individual’s online account and are visible to others on online game services. In addition to serving as rewards and indicators of progress, these virtual badges  also indicate group affiliations and individual status by displaying to others what games individuals play and how they play them. It has been hypothesized that virtual badges may be more motivating when they serve these additional social purposes ,40, and Other methods of integrating social support and influence may also affect physical activity and weight loss behaviors. Social support from family, friends, and other participants is often a feature of in-person and Internet-mediated programming. Social play is also one of the major uses and gratifications of video game play . GeneralBodyMedia, FitBit). Several studies have found that technology-enhanced self-monitoring produces greater adherence and thus greater intervention effects when compared to traditional paper self-monitoring [Though we are unaware of any studies of behavioral strategies in commercially available video games, these strategies have been used in technology-enhanced interventions using activity monitors, PDAs, and mobile phones. Some activity monitoring systems provide detailed graphs and leaderboards to show progress over time and in comparison to others  are the Microsoft Xbox 360, Nintendo Wii, and Sony PlayStation 3. Each console uses a different method of evaluating player movement during play of fitness video games.The Xbox 360 uses its Kinect peripheral, which is a camera that analyzes body movement with no need for handheld controllers. Because the Kinect is camera-based, it allows for still pictures and videos to be used by game software. Real-time video of player movement can be shown on the screen and integrated into game scenes.The Wii uses two handheld controllers (Wiimote and Nunchuk) and a balance board to evaluate player movement. Different games use different combinations and configurations of these three controllers to measure arm, leg, and body movement. The balance board can detect shifts in weight, allowing it to be used for weigh-ins as well.The PlayStation 3 uses the PlayStation Eye and Move peripheral devices. The Eye is a camera, and the Move controllers are similar to Wiimotes. Thus, this system is a hybrid of the two mentioned above. Still pictures and videos can appear on screen, and the player also holds a tangible controller.Jillian Michaels Fitness Adventure is the 2012 entry into the Jillian Michaels Fitness franchise), and they must have primarily a fitness focus rather than a party game or minigame collection focus . After excluding multiple games in a franchise, 23 games remained; six were eliminated due to a primary focus as a party/minigame game. To this total of 17 games, an additional game was added. It was deemed that a downloadable fitness pack created sufficient fitness content that The Fight: Lights Out could be included. Thus, the final total of games was 18. The preliminary search was conducted in November 2011. A follow-up search was conducted in June 2012 to update franchises to their latest iterations (Jillian Michaels Fitness Adventure and Zumba Fitness Rush replaced earlier versions of both games). We chose to include My Fitness Coach despite the existence of My Fitness Coach 2 because it was an updated re-release of the Xbox/PC game Yourself Fitness, whereas the US release of My Fitness Coach 2 was a re-branded version of the New U series of games.A systematic search was conducted to find available fitness games for home consoles. Two of the largest sellers of video games, Amazon.com and Gamestop.com, were searched using four search terms: “fit,” “fitness,” “active,” and “workout” . After eliminating duplicate entries, a total of 40 unique titles were found. To be included in the analysis, games must have been the most recent game available within a franchise . These games were typically identical across consoles except for slight changes made possible by different controller systems, which allowed inclusion of several behavioral tools .In cases in which a game was ported to multiple consoles, each version of the game was play-tested. The version that included the highest number of behavioral tools was included in this analysis . Games played on a Nintendo Wii console were: 10 Minute Solution, Daisy Fuentes Pilates, Exerbeat, Gold’s Gym Cardio Workout, Gold’s Gym Dance Workout, My Fitness Coach, New U Fitness First Yoga and Pilates, Walk It Out!, and Wii Fit Plus.Included games played on an Xbox 360 console were: Coding procedures for this study followed both theory-based and grounded theory strategies. The 2 coders began with a coding list based on Abraham and Michie’s taxonomy of behavior change strategies  as well Your Shape: Fitness Evolved 2012 in A final list of 17 codes is displayed in Each game was play-tested for at least 3 hours by each independent coder. Interrater agreement between the 2 coders was high (0.90). Cohen’s kappa was also calculated to compare coder agreement to chance (κ=.80). The 2 coders met to discuss all disagreements and decide upon appropriate final codes.t tests were used.Simple descriptive statistics were calculated to investigate the prevalence of strategies in the games. To compare consoles, Student’s Behavioral strategies found in each game are shown in P=.04).The number of strategies used in a game were higher in the camera-based games  than in controller-based games  contained more strategies than games that used non–camera-based controllers (Wii).Reviews and secondary analyses of behavioral weight loss trials suggest that inclusion of self-monitoring ,26, goalThe public health impact of an intervention can be conceptualized by the RE-AIM framework  . IncreasYour Shape: Fitness Evolved 2012 superimposes lines on video of the player’s arms, trunk, and legs during yoga moves. As the player’s body aligns properly for the move, the lines change color to indicate successful performance. Non–camera-based games can also provide in-depth feedback. They typically use the placement of the handheld controller to sense whether actions are being performed precisely and provide numerical (number of repetitions) or qualitative  feedback. By immediately evaluating the precision of the player’s movements and encouraging correct form, these games may promote safe and effective home-based exercise.Weight loss trials that adhere to theory have been found to be more effective than those that do not ,53. ThusWii Fit U, will also be able to track lifestyle activity by incorporating pedometers.Behavioral weight loss interventions often include assistive devices that help participants self-monitor progress towards their goals. These devices can include calorie books, diaries, activity monitors, scales, and online monitoring programs . Many ofThe games studied here included a wide variety of evidence-based interactive tools. However, some effective strategies were underrepresented. Action planning, operationalized here as scheduling workouts, is associated with increased efficacy  but was Your Shape and Wii Fit, will be available for this console. Follow-ups to the PlayStation 3 and/or Xbox 360 may have more technologically sophisticated camera controllers, which could impact the types of motions that can be interpreted by games. In addition, camera-based game apps for mobile devices have also begun to appear. For example, Bit Breaker for iOS uses a mobile device’s camera to sense movement and map that movement onto a simple, Pong-like game. Many mobile devices include cameras, accelerometers, and GPS, making them an attractive option for future fitness game development. For example, the smartphone game Zombies, Run! provides extensive performance feedback on distance and speed for overall workouts and even during specific songs the player listened to during the workout. GPS capabilities of smartphones provide more accurate and sophisticated data than are possible from current consoles. Thus, mobile games may also be a fruitful area for future research into behavioral strategies, particularly feedback.Games for the camera-based Kinect and Move controllers (for the Microsoft Xbox 360 and Sony PlayStation 3) provided more tools for promoting self-efficacy, self-regulation, and weight loss than games for a console with no camera (Nintendo Wii). By its nature, a camera-based controller allows for more extensive feedback than a handheld controller that uses an accelerometer. These console differences may change with the upcoming next generation of the three major home consoles, however. The Nintendo Wii U will use a tablet-based controller that houses a camera in addition to a touch screen and motion control. Also, two of the game franchises that included the highest number of behavioral strategies, This study represents a first step in identifying and measuring interactive tools in video games that may hold potential for weight loss, but its preliminary nature prevents the drawing of strong conclusions. Because this was a content analysis, we cannot determine from these data whether or not the existence of more behavioral tools in a game is associated with greater effectiveness in promoting weight loss or physical activity. The comparison across console types is also limited by our inclusion criteria, as we specifically included only the version of some games that had the most behavioral tools so as not to compare different console versions of the same game .XaviX). Further, many nongame applications for smartphones and tablets have been “gamified” and thus, though they are not true video games, they include many game-like aspects . Some nonfitness games also include some integration of fitness content, such as calorie tracking in dance simulation games. These other games and applications and their incorporation of behavioral strategies are worth research attention in the future.Commercial  exergames and mobile exergames were not included in this analysis, nor were active games that did not focus on fitness. Some motion-controlled games are now available for tablets, smartphones, and other consoles (eg, A broad range of fitness video games are available for home use. All of the studied games incorporated at least five behavioral strategies similar to those used in clinical weight loss interventions. It is premature to draw conclusions as to whether games with more behavioral strategies may produce greater physical activity or weight loss; the potential of specific strategies or groups of strategies for increasing the effectiveness of active video games deserves study in both cross-sectional laboratory studies and in randomized controlled trials. There is a need for careful attention to game content prior to the implementation of active games in health promotion programs. Future studies should closely investigate how behavioral tools in fitness video games are used by participants and measure intermediate process and mediating variables to better understand the possible impacts of interactive behavioral tools.


Maternal mortality ratio in Nigeria is one of the highest in the world. Near misses occur in larger numbers than maternal deaths hence they allow for a more comprehensive analysis of risk factors and determinants as well as outcomes of life-threatening complications in pregnancy. The study determined the incidence, characteristics, determinants and perinatal outcomes of near misses in a tertiary hospital in South-west Nigeria.A prospective case control study was conducted at the maternity units of the Obafemi Awolowo University Teaching Hospitals Complex, Ile-Ife Nigeria between July 2006 and July 2007. Near miss cases were defined based on validated disease-specific criteria which included severe haemorrhage, hypertensive disorders in pregnancy, prolonged obstructed labour, infection and severe anemia. Four unmatched controls of pregnant women were selected for every near miss case. Three categories of risk factors  which derived from a conceptual framework were examined. The perinatal outcomes were also assessed. Bi-variate logistic regressions were used for multivariate analysis of determinants and perinatal outcomes of near miss.The incidence of near miss was 12%. Severe haemorrhage (41.3%), hypertensive disorders in pregnancy (37.3%), prolonged obstructed labour (23%), septicaemia (18.6%) and severe anaemia (14.6%) were the direct causes of near miss. The significant risk factors with their odds ratio and 95% confidence intervals were: chronic hypertension  having experienced a phase one delay , Emergency caesarian section , assisted vaginal delivery . The protective factors included antenatal care attendance at tertiary facility , knowledge of pregnancy complications . Stillbirth  was the most significant adverse perinatal outcomes associated with near miss event.The analysis of near misses has evolved as a useful tool in the investigation of maternal health especially in life-threatening situations. The significant risk factors identified in this study are amenable to appropriate public health and medical interventions. Adverse perinatal outcomes are clearly attributable to near miss events. Therefore the findings should contribute to Nigeria’s effort to achieving MDG 4 and 5. Worldwide, more than half a million women between the ages of 15 and 49 years die each year from the complications of pregnancy and childbirth. DevelopThe predictors of maternal morbidity have been categorized into three groups,15: thosUsually the health of mothers and their newborn are inseparable. Perinatal outcomes refer to life events that occur to a newborn infant between the age of viability (i.e. after 28 weeks of gestation) and the first week of life. Studies have found that maternal complications have higher risk of adverse perinatal outcomes like stillbirth, birth asphyxia and neonatal deaths-18. GeneStudies on near misses have been scarce in Nigeria, despite her high maternal death burden. With a maternal mortality ratio of 840 per 1000000 live births, NigeriaThe study, a prospective case control study, was carried out at the Obafemi Awolowo University Teaching Hospitals Complex (OAUTHC), Ile-Ife, South-Western Nigeria from July 2006 to June 2007. OAUTHC is a multi-center facility that serves as the lead referral center in Osun State and neighbouring Ondo and Ekiti States with a combined 2006 population of over ten million. The hosThe study population consisted of pregnant women who sought care at the hospitals during antenatal (third trimester), intrapartum or within 42 days after delivery. A maternal near miss was defined as any woman who experienced a life-threatening complication and who nearly died but for the hospital care she received. The operational definitions for the near miss were based on the disease-specific criteria described by Filippi et al. which waNear misses events were identified by resident doctors in labour ward according to the above-mentioned criteria. The women who survived were interviewed using structured pre-tested questionnaires which were administered by trained research assistants who were all medical personnel , power ofStatistical analysis was performed using STATA version 8. Univariate analysis was carried out to characterize the near miss cases. The differences in the proportion of the characteristics of near misses and controls were compared using chi-square test. Risk factors were assessed using bivariate logistic regression and the Odds Ratio and 95% confidence interval are reported. For the multivariate analysis; the dependent variable was near miss and the independent factors derived from the conceptual framework – background, proximate and clinical factors which were fitted into the models one after the other. Factors included in the model were those found significant at the bivariate level. The three types of models in the multivariate analysis: Model A had only the background characteristics of the respondents as independent variable, while Model B included proximate determinants in addition, while Model C further added clinical factors as part of the independent variables. Perinatal outcomes were also examined for significant association using both chi-square test and bivariate logistic regression. The dependent variable was still near miss and the independent factors were the different perinatal outcomes.The mean age was 28.6 years (6) and 29.8 years (5) among near misses and control respectively (p=0.032). As shown in Table In terms of reproductive health characteristics, the booking status was significantly different between the two groups of mothers, with a majority of the near misses (70.7%) not obtaining antenatal care at the tertiary facility compared with the controls (27.0%). There was no significant difference in the parity (p=0.887), contraceptive use prior to conception (p=0.419) and referral status (p=0.342)The incidence rate of near miss over an uninterrupted six month period was 12% . Majority of the near miss morbidities resulted from severe haemorrhage (41.3%) and hypertensive disorders in pregnancy (37.3%) ] and having experienced a phase one delay  increased the odds of experiencing a near miss event. Antenatal care attendance at a tertiary facility  was protective of a near miss event, reducing the risk by 5 times. Knowledge of pregnancy complications also had a borderline significant relationship with near miss, reducing the risk by half .In Model C, containing socio-demographic factors, proximate determinants and clinical variables, the results in Model B were sustained. In addition, while emergency caesarian section had borderline statistical significance , assisted vaginal delivery increased the odds of a near miss event significantly .The findings regarding perinatal outcomes among the near misses and controls are presented in Table A sustained commitment to maternal health issues in Nigeria is vital to the attainment of Millennium Development Goal 5 globally. This is because Nigeria, with an estimated current population of over 160 million, is the most populous country in Africa as well as the second largest contributor of maternal deaths globally. This prospective case control study on the determinants and perinatal outcomes of near miss maternal morbidity was conducted in South Western Nigeria. A prospective approach has an advantage over a retrospective study in investigating etiologic relationships as it deals with incident rather than prevalent cases. The incHaemorrhage and hypertensive disorders in pregnancy were the two leading causes of near misses in this study; this is consistent with earlier studies in most part of the world,20,27,31In this study, chronic hypertension has the strongest association as a risk factor for near misses with a seven fold increase in risk. Hypertension and diabetes have been predictors of near misses in the United Kingdom. ChronicPhase one delay, which is the delay in making the decision to seek care, was also an important risk factor for near miss in this study. Delays in accessing obstetric care during life-threatening complications is a major reason for poor maternal health outcomes in developing countries. In our The determinants most amenable to change are those linked to obstetric interventions for instance, emergency caesarean section (odds ratio 3.72) and assisted vaginal delivery (odd ratio 2.55). Waterstone et al. also found a strong association between emergency caesarean section and near misses in the United Kingdom. The increased odds of near miss may be associated with the outcome or survival of a near miss rather than being a risk factor due to the temporal sequence of the events. This is because such treatment modalities are employed after the occurrence of a complication and not vice-versa. This notwithstanding, this increased risk associated with emergency caesarean section may be related to the aversion of women and their family members towards caesarian delivery in developing countries such that even in event of a complication women are reluctant to access care until their conditions become life threatening.When socio-demographic factors alone were considered as a group, being unmarried was the only significant determinant among the socio-demographic characteristics (odd ratio 3.09). It however, became insignificant after adjustment for the proximate risk factors. Marital status, although not amenable to change, may bring to light the issue of male involvement in obstetric care. Adewuyi et al. in an interventional study in south western Nigeria demonstrated that women who lacked male support were more likely to require emergency obstetric care.Perinatal outcomes are important indicators of maternal and newborn health care. In this study, stillbirth (odds ratio 7.15) low birth weight (odds ratio 3.38), and post mature pregnancy (odds ratio 3.24), were strongly associated with near misses. Although several studies have reported the link between maternal morbidity and adverse perinatal outcome very few have described their relationship to near miss maternal morbidity. An example of the latter was the study of Fillipi and her colleagues in Burkina Faso where they also demonstrated a significant association between near misses and stillbirth. ConsideA challenge in a study of this nature is the number of ‘cases’ as near miss is a rare event. However, this was addressed by using a high case to control ratio (1:4) thus increasing the statistical power of the study. In addition, attempts were made to minimize some of the problems associated with a case control design. For instance, recall and misclassification biases were lessened by using incident rather than prevalent cases as well as employing validated operational definitions in the selection of cases. Lastly, although the study was conducted over a one year period there were times the study was discontinued due to circumstances beyond the control of the researchers particularly industrial action by health workers; which interrupted the study for a period of time. Hence the near miss rate in this study was limited to an uninterrupted six-month period. Studies on determinants and perinatal outcomes of near miss that address these limitations need to be performed in future, preferably prospective multicenter study carried out over a period of two years or more to generate more stable estimates. Also, near-miss studies need to be conducted in other parts of the country to produce a more comprehensive national picture of the near miss morbidity for Nigeria. Finally, it is imperative that findings from this study be used to inform interventions as Nigeria continues to strive towards achieving the fourth and fifth Millennium Development Goal.In summary, the analysis of near misses has evolved as a useful tool in the investigation of maternal ill health especially in life-threatening situations. The key determinants of near miss in this study were: phase one delay, history of chronic hypertension, emergency caesarean section and assisted vaginal delivery. Quality antenatal care and the knowledge of complications were found protective of a near miss event. Many of these are amenable by appropriate public and medical interventions. Importantly, chronic hypertension was the strongest determinant of near miss in this study. Therefore with the increasing burden chronic diseases globally, their interaction with pregnancy and its outcome requires careful research in the future. In addition, near misses are strongly associated with adverse perinatal outcomes which imply that efforts directed at maternal health will inadvertently lead to reduction of child mortality.The authors declare that they have no completing interest.IAA and AAO designed the study. IAA conducted the study under the supervision of AAO and AOF. IAA analyzed the data and wrote the initial draft of the manuscript. AAO and AOF reviewed and critically revised the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2393/13/93/prepubDeterminants and Outcome of Near Miss Maternal Morbidity in a Tertiary Hospital in South West, Nigeria: Data Collection Instrument.Click here for file


Audio visual media (AVM) have disseminated abundant information on Swine flu (H1N1) during the 2009 pandemic. Hence it was decided to find out the Knowledge, Attitude, Behavior and Practices regarding Swine flu and to elicit the influence of AVM on the general public regarding preventive measures.Population sampling was carried out taking into account that sizeable numbers represented different population groups. An anonymous pre tested questionnaire  was given to 2193 participants. Voluntary participation was encouraged. Data were analyzed using SPSS17.0 software.Response rate was 98%. The Urban to Rural Population ratio in the survey was 1:1.2. Television (86%) was the main source of information. Knowledge on pandemic nature, symptoms, personal protective measures, treatment and preventive strategies were acceptable among 90.2, 96, 36, 12 & 31% respectively. None of them used face mask or hand wash, though known to 78 & 20% respectively. Literacy level was directly proportional to knowledge gained and retained. However, there was no statistical difference among gender or domicile status.AVM have sensitized the public on symptomatology and enhanced health seeking behavior but not stressed effectively on preventive measures. AVM disseminated information during an outbreak, but lacked reinforcement during quiescent period. So it is suggested that health authorities should constantly provide information choosing the right media to the right population group towards preparing them for similar outbreaks.None declared.


Androgen-dependent signaling regulates the growth of the fingers on the human hand during embryogenesis. A higher androgen load results in lower 2D:4D (second digit to fourth digit) ratio values. Prenatal androgen exposure also impacts brain development. 2D:4D values are usually lower in males and are viewed as a proxy of male brain organization. Here, we quantified video gaming behavior in young males. We found lower mean 2D:4D values in subjects who were classified according to the CSAS-II as having at-risk/addicted behavior (n = 27) compared with individuals with unproblematic video gaming behavior (n = 27). Thus, prenatal androgen exposure and a hyper-male brain organization, as represented by low 2D:4D values, are associated with problematic video gaming behavior. These results may be used to improve the diagnosis, prediction, and prevention of video game addiction. A high prenatal androgen load, induced either by enhanced hormone levels or more sensitive androgen signal transduction pathways, results in a longer fourth digit (4D) relative to the second digit (2D) in the adult human hand We have previously shown lower mean 2D:4D values in patients with alcohol dependence This study is part of the Finger-Length in Psychiatry (FLIP) project of the Erlangen Department of Psychiatry and Psychotherapy as well as the longitudinal interview study module of the project entitled “Internet and Video Game Addiction – diagnostics, epidemiology, etiopathogenesis, treatment and prevention” of the Criminology Research Institute of Lower Saxony. The FLIP-Project was realized as an add-on at the second measurement occasion (t2) of the longitudinal interview study. This investigation has been conducted according to the principles expressed in the Declaration of Helsinki. The study was approved by the local ethics committee . Written informed consent was obtained after providing a complete description of the study to all of the subjects.SD = 1.1). 24 of the participants had a higher educational level (Abitur or higher), another 24 had secondary schooling , 5 reported lower secondary schooling (Hauptschule) and one no graduation.Between February and December 2011, 70 subjects participated at the first measurement occasion (t1) of the longitudinal interview study . Prerequisites for study participation at t1: male, 18-21 years old, habitual video gamers with either more than 2.5 hours of gaming a day or a Video Game Addition Scale (CSAS-II) score > 41  =  incorrect to 4  =  absolutely true) and covers the dimensions preoccupation/salience (4 items), conflict (4 items), loss of control (2 items), withdrawal symptoms (2 items), and tolerance (2 items). The items of the CSAS-II show high face validity, and the instrument demonstrates good convergent validity for subjective self-evaluation measures of video game addiction Video game addiction was assessed using the CSAS II According to CSAS-II classification, which is going beyond mere gaming times, 27 participants were classified as unproblematic video gamers, 17 as at risk of becoming addicted and 10 as addicted. Because of the small number of subjects investigated, the two groups “at risk of becoming addicted” and “addicted” were joined for analyses. Thus, two CSAS-II categories (unproblematic vs. at risk/addicted) with each 27 subjects were investigated in this study.T = 52.26, SD = 11.81), depression , anxiety , and hostility  were used as control variables in the multivariate analyses. In addition, ADHD symptomatology, which was also used as control variable, was assessed using the ADHD-Screening for adults  Psychological problems and symptoms of psychopathology were assessed at t1 using the Brief Symptom Inventory (BSI) www.gimp.org) to measure the lengths of the index (2D) and ring (4D) fingers from the hand scans. This technique provides good reliability An Avision IS1000 flatbed scanner  was used to scan the participants’ hands at t2. To increase accuracy, small marks were drawn on the basal creases of each of the participants’ index and ring fingers before scanning. Both hands were scanned at the same time, with palms down, in black-white mode. We used the GNU Image Manipulation Program  and the R software.t = 1.544, p = 0.129) and educational level  Z = 0.931, p = 0.351, left hand: Z = 0.550, p = 0.923, right hand: Z = 0.913, p = 0.375) and Dr–l  values did not deviate from a normal distribution. The mean 2D:4D and Dr–l values are presented in Deviation from normal distribution was tested by the Kolmogorov-Smirnov test. The 2D:4D (arithmetic mean: p < 0.01). 2D:4D and Dr–l values did not differ significantly depending on educational level within the unproblematic   = 1.831, p = 0.400, left hand: 2χ  = 2.247, p = 0.325, right hand:  2χ  = 2.005, p = 0.367, Dr–1: 2χ  = 0.637, p = 0.747) and at risk/addicted group   = 3.363, p = 0.339, left hand: 2χ  = 2.139, p = 0.544, right hand: 2χ  = 3.348, p = 0.341, Dr–1: 2χ = 0.460, p = 0.928).Differences in 2D:4D and Dr–1 values depending on educational level were tested for the unproblematic and at risk/addicted group by the Kruskal Wallis test. Pearson correlation coefficients were calculated. The correlation between 2D:4D values for the right vs. left hand was 0.788  and video game addiction (unproblematic vs. at risk/addicted group) were tested by a non-parametric multivariate approach based on the principle of recursive partitioning, i.e. conditional inference trees C-Tree; . Controlp = 0.027, d = 0.71). 2. For the left hand, study participants with a 2D:4D ratio lower than 0.982 showed a significantly higher risk of being video game addicted . 3. For the right hand study participants with a 2D:4D ratio lower than 0.979 showed a significantly higher risk of being video game addicted on the level of p < 0.10 . Moreover, study participants who additionally scored higher than 60 (T-score) on the ADHS-E were particularly at risk . No significant association was found for Dr–1 (p = 0.127). In the multivariate non-parametric analyses, measures of 2D:4D  were associated with video game addiction (unproblematic vs. at risk/addicted group) when controlling for interpersonal sensitivity, depression, anxiety, hostility and ADHD: 1. Study participants with a mean 2D:4D ratio lower than 0.966 showed a significantly higher risk of being video game addicted .To estimate the value of the 2D:4D ratio as a diagnostic test for the discrimination of video game-addicted/at risk individuals vs. controls with unproblematic gaming behavior, we used a ROC analysis to calculate AUC values, as well as sensitivity and specificity at the Youden point This is the first investigation linking prenatal androgen exposure with addictive video gaming behavior. In this study, we found low mean 2D:4D values in subjects with at risk and addicted video gaming behavior. Effect sizes larger than d = 0.66 point to a moderate to strong effect Understanding the pathways leading from enhanced prenatal testosterone to game addiction will be crucial for defining potential policies targeting video game addiction. Prenatal testosterone may induce addictive behavior through several channels including the following: (1) Prenatal testosterone abundance modulates the mesolimbic reward system Previously, we found low mean 2D:4D values in individuals with alcohol addiction Many phenomena have been linked to low 2D:4D values, most of which are compatible with the hyper-male brain hypothesis. Thus, low 2D:4D values may be regarded as a proxy of the endophenotype “hyper-male brain organization”. However, the precise effect of a high prenatal androgen load on the life of an individual and on that individual’s future adult behavior must also depend on additional variables and influences. The specific behavioral phenotype evolving as a result of the hyper-male brain organization most likely depends on a myriad of genetic and environmental factors that are experienced over an individual’s lifetime. Therefore, the presence of low 2D:4D values does not suggest a specific diagnosis or prognosis for any single individual. However, knowledge of 2D:4D values may aid in improving an individual’s diagnosis and prognosis associated with different problematic behaviors and disorders when used in combination with other markers.These results may have important implications for the diagnosis, prevention, and consequences of addictive gaming. A low 2D:4D value alone is not diagnostic of addictive gaming, but this factor may facilitate the diagnosis when used in conjunction with other markers. A low 2D:4D value may help to identify individuals who are at risk for future development of addictive gaming and, thus, may facilitate prevention. Several attempts have been made to predict the development of internet gaming addiction in individuals We investigated individuals in a narrow age range; furthermore, the mean age did not differ between the two groups. In previous studies, age was, if at all, only marginally associated with 2D:4D values In additional analyses we also checked for a possible non-monotonic relationship between measures of 2D:4D and video game addiction using the CSAS-II sum score, as this has been reported for example for measures of 2D:4D and altruism The time spent with video gaming alone does not define addiction. For the diagnosis “video game addiction” further criteria have to be met: preoccupation, withdrawal, tolerance, loss of control, and continued use despite negative consequences. A strength of this study is the composition of the participants. All of the participants spent some time each day with video gaming, but only half of the participants had additional criteria defining them being at risk/addicted (as assessed by CSAS-II). Our results thus define 2D:4D as a risk factor specifically related to video game addiction, not just to video game playing per se.Several study limitations should be noted. We used a mono-centric, cross-sectional, case-control design, which allows the detection of associations only, without causal relationships. Additionally, we investigated only males, and the sample group was relatively small. The strong effect size of 2D:4D on video gaming addiction probably enabled the detection of group differences despite the relatively low number of subjects. In our previous study, we also found a strong effect size relating 2D:4D to alcohol addiction


England’s extensive NHS patient survey programme has not fulfilled government promises of widespread improvements in patients’ experiences, and media reports of poor nursing care in NHS hospitals are increasingly common. Impediments to the surveys’ impact on the quality of nursing care may include: the fact that they are not ward-specific, so nurses claim “that doesn’t happen on my ward”; nurses’ scepticism about the relevance of patient feedback to their practice; and lack of prompt communication of results. The surveys’ impact could be increased by: conducting ward-specific surveys; returning results to ward staff more quickly; including patients’ written comments in reports; and offering nurses an opportunity to discuss the feedback. Very few randomised trials have been conducted to test the effectiveness of patient feedback on quality improvement and there have been few, if any, published trials of ward-specific patient surveys.Basic Feedback (ward-specific printed patient survey results including patients’ written comments sent to nurses by letter); Feedback Plus  or Control (no active feedback of survey results). Patient survey responses to questions about nursing care were used to compute wards’ average Nursing Care Scores at each interval. Nurses’ reactions to the patient feedback were recorded.Over two years, postal surveys of recent inpatients were conducted at four-monthly intervals in 18 wards in two NHS Trusts in England. Wards were randomly allocated to Nursing Care Scores improved more for Feedback Plus than Basic Feedback or Control (difference between Control and Feedback Plus = 8.28 ± 7.2 (p = 0.02)).Conducting ward-level surveys and delivering ward-specific results was feasible. Ward meetings were effective for engaging nurses and challenging scepticism and patients’ written comments stimulated interest. 4,236 (47%) patients returned questionnaires. This study provides preliminary evidence that facilitated patient feedback can improve patients’ experiences such that a full trial is justified. These findings suggest that merely informing nurses of patient survey results in writing does not stimulate improvements, even if results are disaggregated by ward, but the addition of ward meetings had an important and significant impact. NHS Plan pledged that a patient survey would “secure year-on-year improvements in patient satisfaction”. Since 2002, the Inpatient Survey for Acute NHS Trusts has been conducted in all acute NHS hospitals in England, sampling approximately 135,000 adult patients each year. The questionnaires, sent by post, cover waiting times, doctors’ and nurses’ attitudes, staff responsiveness, hospital food and cleanliness, patient information, co-ordination of care and patients’ dignity [One of the assumptions underlying England’s National Health Service (NHS) policy is that giving feedback about patients’ experiences to healthcare organisations will drive improvements -5. Speci dignity . To dateOver the last 10 years, the surveys have detected improvements in the national results, but the aspects of care that have improved are those that can be linked to national targets or high-profile media campaigns (such as waiting times for inpatient admissions and ward cleanliness). The results for most questions have stayed the same or, in some cases, have deteriorated. For example, in 2011, only 53% of patients said their call bells were usually answered within two minutes (10% worse than 2004) and 21% said nurses “talked in front of them as if they were not there” (3% worse than 2002) ,8. Theredirectly to those who are towards the bottom of hospitals’ hierarchies, even though they are the staff members who are disproportionately responsible for making day-to-day decisions about the way care is delivered [Qualitative research suggests that NHS staff recognise the national surveys’ methodological robustness ,10 but telivered . The extelivered . Other belivered ,11-13. Celivered ,15,16.per se, rather than using results to improve the quality of care. The recent promotion of “real-time” feedback will increase the volume of survey data collected by NHS trusts while reducing the rigour of survey methods [Arguably, the surveys’ lack of impact could also be attributed to an over-emphasis on data collection  methods . Uniform methods .Very few randomised controlled trials have been conducted to test the effects of quality improvement programmes . The ovewhat happened to them regarding specific aspects of their care episode, rather than asking them to rate their satisfaction more generally [To enhance the intervention’s acceptability to policy makers and NHS managers, and to maximise its chances of being adopted throughout England’s NHS hospitals, the Care Quality Commission’s (CQC’s) standard questionnaire and survey method currently used for inpatient national survey programme were used to obtain patient feedback . The preenerally . The senNursing Care Scores were computed at each interval using responses to nursing questions from different groups of patients who were recently discharged at each survey interval.This was a pilot study, designed to guide the planning for a large-scale trial corresponding to phase III of the Medical Research Council (MRC) guidelines on developing and evaluating complex interventions -27. The Ethical approval for the study was obtained from Bexley & Greenwich Research Ethics Committee (reference 08/H0809/55).The study was conducted in two single-hospital-site Acute Hospital NHS Trusts in London (Trust A and Trust B). The data collection took two years from 2009 to 2010 in a total of 18 wards.Basic Feedback and Feedback Plus. The participating wards were randomly allocated in equal numbers to Control, Basic Feedback (on which nurses would receive written feedback only), and Feedback Plus . Separately for each trust, all eligible wards were listed in one column of an Excel spreadsheet, then using the “Rand” function, a random number was generated alongside each ward. The ward list was then sorted by the random number and the first three wards were assigned to the Basic Feedback condition, the next three to Feedback Plus and the next three to Control. Once nine wards were selected for each trust, the remaining wards were not included in the study. All ward managers, matrons and Directors of Nursing were aware throughout the study of the condition to which wards had been randomised.Adult non-maternity wards that treated inpatients wards eligible for inclusion. There were 18 eligible wards in Trust A and 14 in Trust B. Two levels of the intervention were tested: Each ward’s sample comprised de-duplicated consecutively-discharged patients from the previous two months, up to a maximum of 160 patients per ward. All adult patients were eligible for inclusion in the sample. At the end of each four-month survey interval, NHS trust record-keeping departments were asked to collate the sample of patients using sampling instructions adapted from those used for the national Inpatient Survey to allow the addition of ward information. The information about each sampled patient included admission and discharge dates, year of birth, sex, and ward on which patients had spent the longest time. Patients who had spent time on more than one ward were assigned to the sample for the ward on which they had spent the most time. If a ward’s sample included more than 160 patients, the most recent 160 were included and the remainder were removed from the sample.At four-monthly intervals, patient feedback was gathered using the CQC’s Inpatient Questionnaire (2009/2010 version) and postal survey method. Prior to mailings, patients known to have died were removed from the sample. Researchers did not have access to patients’ name and address information. The CQC-approved survey company carried out the mailings under a similar contractual arrangement with the NHS trust as used for the national patient surveys. For the purposes of carrying out mailings, a separate file included the sampled patients’ names and addresses and code numbers corresponding to the anonymous patient sample file. The contractors sent out code-numbered postal surveys to patients, monitored whether or not they had responded and sent out reminders accordingly.Questionnaires were sent out by post to patients’ home addresses. Letters accompanying questionnaires corresponded to the standard covering letter used for the CQC Inpatient Survey, with the added request that patients should relate their responses to the ward named in the covering letter. Consistent with the CQC survey protocol, the covering letter included details of a free telephone number, by which patients or their relatives could speak to a member of the survey contractor’s staff if they had any questions about the survey. Included with the questionnaire was a free postage envelope in which to return the questionnaire. At two-weekly intervals, two postal reminders were sent to non-responders. The second reminder included a duplicate questionnaire.Basic Feedback and Feedback Plus wards and their matrons included detailed ward-level patient survey results. The printed survey results comprised: (1) a bar chart for each of 10 to 15 questions about nursing care comparing the six feedback wards in one hospital (with the target’s own ward highlighted in a different colour) and the NHS average. The bars showed the percentage of positive responses to a question; (2) as the study progressed and historical data were available, 10 to 15 line graphs, each showing the ward’s changes over time on one question about nursing care; and (3) a transcription of the comments patients had written in the spaces at the end of the questionnaire, under the headings “Was there anything particularly good about your care?”; “Was there anything that could be improved?” and “Any other comments?”. A covering letter to the nurse explained the purpose of the study and included contact details for the lead researcher.Individual letters sent to nurses employed on Feedback Plus wards, the printed survey results were supplemented with ward meetings with the researchers, during which the results discussed and, if necessary, explained, and there was an opportunity to ask questions about survey methods and to plan improvements in practice. Ward managers were responsible for inviting as many or few other members of their nursing team as they chose. Meetings took place in ward offices within normal working hours. At the beginning of each meeting, nurses’ consent to take part in the study was obtained with the understanding that any comments they might make would not be attributed to individual nurses, and any details identifying individuals or their ward affiliation would be removed before they were shared with nurse managers or anyone else. Following meetings, the written survey results were sent individually to all of the ward’s nurses (regardless of whether they had attended the meeting) in the same format as used for Basic Feedback.On Control wards closely matched the usual condition for the annual national surveys. Their data were included in the set of results given to the Director of Nursing but no special efforts were made to communicate them at ward level. However, results would be made available to ward nurses if they requested them.The intervention’s feasibility was assessed according to the following criteria:•Could NHS trust record-keeping staff collate accurate ward-specific samples of patients?•Could ward-level postal surveys be conducted successfully?•Could researchers use the data from the returned questionnaires to produce ward-specific reports on the survey results?Feedback Plus wards and would nurses attend them?•Would the researchers successfully arrange and conduct meetings on •Would ward nursing staff understand the ward-specific survey reports and show an interest in them?•Would nurses engage with the patient feedback and accept it as valid information about the quality of care they had provided?Basic Feedback wards, asking them what, if any, actions they had implemented as a result of the patient feedback. The qualitative data collected in meetings and telephone calls was examined for common themes.The data used to assess feasibility were collected during the course of the study. The survey contractor was asked to compare their experience of carrying out the national inpatient surveys and study’s surveys, and to report any differences. They were also asked to note the volume of calls to the telephone helpline, and to record the subjects of the telephone calls. The researchers made notes about nurses’ comments in ward meetings and conducted a telephone survey of ward managers on Nursing Care Score. The selected questions, listed below, were those which were most closely associated with the quality of ward nursing. Each question was scored between 0 and 100, where higher scores indicate better care. For the responders in this study, Nursing Care Scores ranged between 5 and 100 and the mean score was 75.4.The aim of the intervention was to improve nursing care in general, rather than to focus on specific aspects of nursing care. Therefore, the mean of a subset of 20 questions was used to derive a composite Survey questions included in the Nursing Care Score1. Were you ever bothered by noise at night from other patients?2. Were you ever bothered by noise at night from staff?3. In your opinion, how clean was the room or ward that you were in?4. How clean were the toilets and bathrooms that you used in hospital?5. Did you get enough help from staff to eat your meals?6. When you had important questions to ask a nurse, did you get answers that you could understand?7. Did you have confidence and trust in the nurses treating you?8. Did nurses talk in front of you as if you weren’t there?9. As far as you know, did nurses wash or clean their hands between touching patients?10. Did you find someone on the hospital staff to talk to about your worries and fears?11. Do you think the hospital staff did everything they could to help control your pain?12. How many minutes after you used the call button did it usually take before you got the help you needed?13. Was your discharge delayed by waiting for doctors or medicines and if so how long was the extra wait?14. Before you left hospital, were you given any written or printed information about what you should or should not do after leaving hospital?15. Were you told how to take your medication in a way you could understand?16. Did a member of staff tell you about any danger signals you should watch for after you went home?17. Did hospital staff tell you who to contact if you were worried about your condition or treatment after you left hospital?18. Overall, did you feel you were treated with respect and dignity while you were in the hospital?19. Overall, how would you rate the care you received?20. Did you want to complain about the care you received in hospital?The outcome measure described above was designed to be analysed using a standard model that allows examination of its variation over the six time periods using a form of multilevel regression. The statistical model to be estimated is shown in equation (1):Ywt is the mean Nursing Care Score in ward w at time t, β0w is a random intercept defined by the second equation, Nw is a dummy variable indicating that ward w is in Trust B, Gbw is a dummy variable indicating the ward was in the Basic Feedback group, Gpw is a dummy variable indicating the ward was in the Feedback Plus group, t is the time in months since the baseline survey was conducted, and ϵwt and δw are random variables with zero mean and standard deviations σϵ and σδ respectively. The model implies that there is a fixed effect of Trust (β1), main effects of treatment group, which allows for there to be different mean Nursing Care Scores at baseline observation in the different groups (β2 and β3), a main effect of time (β4), and interaction effects of treatment group and time, which allows us to see whether the direction and rate of change in nursing score varies across treatment groups (β5 and β6).where β5 and β6. If Basic Feedback and Feedback Plus are effective, we would expect that β5 > 0 and β6 >0 and if Feedback Plus is better than Basic Feedback, we would expect β6 > β5.The hypotheses to be tested involve tests of the statistical significance of Surveys were conducted at six intervals in Trust A, and three in Trust B, where it was curtailed due to administrative difficulties following a change of Director of Nursing. In 18 wards, a total of 9,565 patients were surveyed and 4,236 returned usable questionnaires, representing a response rate of 47% once deceased patients and questionnaires that were returned undelivered had been accounted for.The survey contractor reported that the number and type of calls to the free telephone helpline about the study’s surveys were consistent with their usual experience of carrying out national inpatient surveys. The contractors reported that no new difficulties had arisen as a result of carrying out ward-based surveys. The resources required to implement the ward-level surveys were similar (per patient surveyed) to those needed to conduct the national inpatient surveys.Basic Feedback ward managers identified specific actions resulting from the printed results, and none of them initiated contact with the researchers.Printed feedback alone did not seem to stimulate interest in survey results. When asked, none of the The meetings varied in the number of attendees: some were attended only by the ward manager, while others included most of the ward’s nursing staff. Matrons attended some meetings, and they usually had a positive influence: offering suggestions for improvement, encouraging ward nurses to take responsibility for results or supporting efforts to implement changes.Feedback Plus ward meetings seemed to facilitate nurses’ engagement. Patients’ written comments were particularly useful in illustrating quantitative results and stimulating their interest. However, there remained many challenges to engaging nurses and maintaining their interest. During each meeting, researchers needed to prompt them several times to return their focus to understanding the patient feedback and planning strategies for improvement. Without these prompts, nurses were inclined to discuss other more general matters, such as the many difficulties they experienced in fulfilling their duties; staffing shortages; NHS policy or their perceptions of hospital managers. The meetings were usually interrupted to allow nurses to attend to patients’ immediate needs. Several nurses said that they felt under greater pressure to focus on formally-monitored ward performance criteria, such as incidences of falls, pressure ulcers, clostridium difficile and methicillin-resistant staphylococcus aureus (MRSA), rather than patient survey results, which were not part of formal ward performance measures. Nurses were often reluctant to acknowledge negative feedback and many of them questioned the validity of results or argued that they could not do anything to improve the care they offered. For example:The “Patients think we are talking in front of them as if they are not there because we have to talk quietly to maintain patient confidentiality.”“Some patients use call buttons for trivial reasons so it would not be a good use of our time to answer them all immediately.”“Ward/toilet areas are often dirty because our patients are particularly untidy.”After the first meeting on each ward, at subsequent meetings the researchers asked nurses what actions they had taken to improve survey results, but it was difficult to ascertain clear examples of innovations in patient care. Their most common response was that nurse managers had raised the issue at daily handover meetings or in ward meetings and had reminded nurses of the importance of fulfilling their duties relating to ensuring patients’ experiences were positive.Nursing Care Scores for each participating ward.Table β1) indicates that the average Nursing Care Score of the wards in Trust B was 9.27 lower than that of the wards in Trust A at the time of the baseline assessment. Similarly, the main effects of treatment group (β2 and β3) account for differences in the baseline scores of the wards in these groups. These are not statistically significant, as would be expected given the random assignment of wards to treatment group.Estimates of the regression parameters in this model are shown in Table β4) shows that the average score among the wards involved in the study was declining by 0.41 each month. The two terms that are interactions of group and time show the extent to which the scores change over the period of the study in the different groups. The Control group is the excluded category, so the average monthly change in scores in these wards was just the main effect of month discussed above. In the case of the Basic Feedback group, the estimate of the interaction effect (β5) is small and not statistically significant, so we can conclude that scores change in a way that it essentially identical to those of wards in the Control group. However, the estimated interaction effect in the Feedback Plus group (β6) is 0.46 and is statistically significant. We can also reject the null hypothesis that β5 = β6 . This implies that the Nursing Care Scores in the Feedback Plus group change at a different rate to those in either the Control or Basic Feedback groups. The fact that β6 is positive implies that patients in Feedback Plus wards experienced an improvement in scores over the study relative to patients in the other wards. Over 18 months the difference in Nursing Care Score between Control and Feedback Plus wards is 8.28 ± 7.2 (p = 0.02).The main effect of months since the baseline survey  was 45 at each time point. Using the estimates of the standard deviations of the random effects in Table ρ) of 0.02. The observed standard deviation of the outcome variable (σ) is 6.5, so an effect size of 0.2 would correspond to a change in the outcome measure of 1.3. Taking these as the values required to calculate the sample size, and assuming one-tailed tests, an α level of 0.05 and power of 0.8, gives a sample size of 15 wards in the Feedback Plus group and 15 wards in the Control group. This calculation uses the formula given below:Using the figures in Table This pilot provides evidence that conducting ward-level surveys, and providing nurses with ward-specific results in printed form by letter and in ward meetings are feasible. Ward managers co-operated by attending ward meetings, and allowing other nursing staff to attend them.Feedback Plus were effective in encouraging nurses to pay attention to their survey results, facilitating ownership of results and planning changes in practice. The meetings provided vital opportunities for challenging nurses’ scepticism about the relevance of the results, correcting misunderstandings about the survey method and explaining its strengths compared to other less rigorous methods. There is no evidence that the weaker intervention (Basic Feedback) led to improved patients’ experiences, or that nurses pay attention to patient survey results when they are only in printed form. Patient survey results in Feedback Plus were significantly more improved than in Basic Feedback or Control.The structured ward meetings of The randomisation of wards to conditions made it possible to separate the effects of the intervention from extraneous factors in a way that a pre- and post-test within a single setting could not. With more than 4,000 patient responders, this was a fairly large-scale study, but the clustering of patients in wards for the purposes of computing wards’ average scores meant that the number of measurement units was only 18 wards in two hospital trusts, so its generalisability and statistical power were limited.The qualitative data collected by taking notes in ward meetings and conducting telephone interviews with ward managers provided useful insights into the mechanisms by which patient feedback is understood by nurses. However, the methods by which information about nurses’ reactions to the feedback were collected could have been specified in greater detail in the research design, and given a more formal structure.Nursing Care Score, based on questions that cover a wide range of aspects of nursing care, reduced the risk that any changes detected were isolated examples that masked deteriorations in other aspects of care.The standard 90-question CQC Inpatient Questionnaire was used but it covers a wider range of inpatients’ experiences than was needed for this study. A shorter instrument focusing on ward nursing care would have been more efficient and might have improved the response rate. These findings also suggest that the standard Inpatient Questionnaire comprises more than the optimal number of questions, and clinicians’ focus could improve if a shorter questionnaire were used for the National Inpatient Surveys. The use of a composite Nursing Care Scores in Feedback Plus wards compared to both Basic Feedback and Control wards was statistically significant, the change in scores in the Feedback Plus wards was small. Larger improvement may be achieved when there is not the downward trend on scores that we observed in other wards in this study, but this cannot be known without further trials.Although the difference in Feedback Plus: a validated survey instrument; robust survey methods; ward-specific surveys; facilitated ward meetings; the inclusion of patients’ written comments alongside numerical survey results; and the involvement of matrons in communicating and discussing patient feedback have been shown to be effective in engaging clinicians and thereby strengthening the impact of patient surveys.This study offers some explanations for the hitherto low impact of England’s patient survey programme. Elements of Basic Feedback condition would not be necessary, since the findings of this study provide good evidence that it is not sufficient to engage nurses’ interest. This would allow more wards to be allocated to a modified Feedback Plus condition and would further increase its power. A more detailed process evaluation plan should be included, along with a clearer structure for gathering information about nurses’ reactions to the feedback.In future studies, a This study shows that merely informing nurses of patient survey results in writing is not sufficient to stimulate their interest, even when results are disaggregated at ward level. However, the addition of facilitated feedback in the form of ward meetings can have an impact. To date, few studies have demonstrated that an intervention can improve patients’ experiences. This study provides preliminary evidence that patient feedback can improve patient survey results.NHS: National Health Service; CQC: Care quality commission; MRSA: Methicillin-resistant staphylococcus aureus.The authors declare that they have no competing financial interests. Rachel Reeves was employed in the development and management of the national inpatient survey at Picker Institute Europe from 2001 to 2005.RR: Original idea for the study; study design; literature search and review; descriptive statistics and preliminary data analysis. Main author of abstract, introduction, methods, results and discussion. EW: Contributed to study design, literature review, data interpretation, reviewed all sections of the manuscript. DB: Designed and conducted multi-level data analysis and wrote sections of results and discussion relating to multi-level analysis. Reviewed all sections of the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6963/13/259/prepub