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BackgroundThe One Drop | Mobile app supports manual and passive (via HealthKit and One Drop’s glucose meter) tracking of self-care and glycated hemoglobin A1c (HbA1c). ObjectiveWe assessed the HbA1c change of a sample of people with type 1 diabetes (T1D) or type 2 diabetes (T2D) using the One Drop | Mobile app on iPhone and Apple Watch, and tested relationships between self-care tracking with the app and HbA1c change. MethodsIn June 2017, we identified people with diabetes using the One Drop | Mobile app on iPhone and Apple Watch who entered two HbA1c measurements in the app 60 to 365 days apart. We assessed the relationship between using the app and HbA1c change. ResultsUsers had T1D (n=65) or T2D (n=191), were 22.7% (58/219) female, with diabetes for a mean 8.34 (SD 8.79) years, and tracked a mean 2176.35 (SD 3430.23) self-care activities between HbA1c entries. There was a significant 1.36% or 14.9 mmol/mol HbA1c reduction (F=62.60, P

The performance of multiple imputation in questionnaire data has been studied in various simulation studies. However, in practice, questionnaire data are usually more complex than simulated data. For example, items may be counterindicative or may have unacceptably low factor loadings on every subscale, or completely missing subscales may complicate computations. In this article, it was studied how well multiple imputation recovered the results of several psychometrically important statistics in a data set with such properties. Analysis of this data set revealed that multiple imputation was able to recover the results of these analyses well. Also, a simulation study showed that multiple imputation produced small bias in these statistics for simulated data sets with the same properties. (Contains 6 tables.)

The performance of five simple multiple imputation methods for dealing with missing data were compared. In addition, random imputation and multivariate normal imputation were used as lower and upper benchmark, respectively. Test data were simulated and item scores were deleted such that they were either missing completely at random, missing at random, or not missing at random. Cronbach's alpha, Loevinger's scalability coefficient H, and the item cluster solution from Mokken scale analysis of the complete data were compared with the corresponding results based on the data including imputed scores. The multiple-imputation methods, two-way with normally distributed errors, corrected item-mean substitution with normally distributed errors, and response function, produced discrepancies in Cronbach's coefficient alpha, Loevinger's coefficient H, and the cluster solution from Mokken scale analysis, that were smaller than the discrepancies in upper benchmark multivariate normal imputation.

Missing data is a problem that occurs frequently in many scientific areas. The most sophisticated method for dealing with this problem is multiple imputation. Contrary to other methods, like listwise deletion, this method does not throw away information, and partly repairs the problem of systematic dropout. Although from a theoretical point of view multiple imputation is considered to be the optimal method, many applied researchers are reluctant to use it because of persistent misconceptions about this method. Instead of providing an(other) overview of missing data methods, or extensively explaining how multiple imputation works, this article aims specifically at rebutting these misconceptions, and provides applied researchers with practical arguments supporting them in the use of multiple imputation. [ABSTRACT FROM AUTHOR]

A well-known problem in the analysis of test and questionnaire data is that some item scores may be missing. Advanced methods for the imputation of missing data are available, such as multiple imputation under the multivariate normal model and imputation under the saturated logistic model (Schafer, 1997). Accompanying software was made available by, for example, Schafer (1998a, 1998b) and in SOLAS (2001) and S-Plus 6 for Windows (2001). However, these methods and software may be too complicated for a typical psychological researcher, and for the imputation of his or her missing data, he or she depends on the help of a trained statistician. If available, this statistician may not always have enough time or may not be an experienced software user, so the researcher may decide to simply delete all incomplete observations. To help researchers impute scores using simple methods, two SPSS subroutines were written. The aim of these subroutines is that researchers can apply them easily within SPSS and without experienced help.

Whenever multiple regression is applied to a multiply imputed data set, several methods for combining significance tests for and the change in across imputed data sets may be used: the combination rules by Rubin, the Fisher z-test for by Harel, and F-tests for the change in by Chaurasia and Harel. For pooling itself, Harel proposed a method based on a Fisher z transformation. In the current article, it is argued that the pooled based on the Fisher z transformation, the Fisher z-test for , and the F-test for the change in have some theoretical flaws. An argument is made for using Rubin's method for pooling significance tests for instead, and alternative procedures for pooling are proposed: simple averaging and a pooled constructed from the pooled significance test by Rubin. Simulations show that the Fisher z-test and Chaurasia and Harel's F-tests generally give inflated type-I error rates, whereas the type-I error rates of Rubin's method are correct. Of the methods for pooling the point estimates of no method clearly performs best, but it is argued that the average of 's across imputed data set is preferred. [ABSTRACT FROM AUTHOR]

Unlabelled: Empirical evidence has shown that international adoptees present physical growth delays, precocious puberty, behavioral problems, and mental health referrals more often than non-adoptees. We hypothesized that the higher prevalence of (mental) health problems in adoptees is accompanied by elevated consumption of prescription drugs, including antidepressants, attention deficit hyperactivity disorder (ADHD) medication, and medication for growth inhibition/stimulation. In an archival, population-based Dutch cohort study, data on medication use were available from the Health Care Insurance Board by Statistics Netherlands from 2006 to 2011. The Dutch population born between 1994 and 2005 and alive during the period of measurement was included (2,360,450 including 10,602 international adoptees, of which 4447 from China). Their mean age was 6.5 years at start (range 1-12 years) and 11.5 years at the end of the measurement period (range 6-17 years). Chinese female adoptees used less medication for precocious puberty (as treatment for precocious puberty; odds ratio (OR) = 0.57, effect size Cohen's d = -0.31) and contraception (OR = 0.65, d = -0.24) than non-adoptees. For both males and females, non-Chinese adoptees used more medication for ADHD than non-adoptees (males: OR = 1.22, females: OR = 1.32), but the effect was small (males: d = 0.11, females: d = 0.15).Conclusions: Adoptees in the Netherlands generally do not use more medication than their non-adopted peers.What Is Known: • Meta-analytical evidence shows that international adoptees present physical growth delays and mental health referrals more often than non-adopted controls. • With the exception of one Swedish study on ADHD medication, there is no other systematic research on medication use of international adoptees. What is New: • All differences in medication use between international adoptees in the Netherlands and non-adopted controls were below the threshold of a small effect with the exception of medication for precocious puberty, but this effect was in the opposite direction with female adoptees using less medication for precocious puberty than non-adoptees. • International adoptees in the Netherlands do not use more medication despite experiences of preadoption adversity and higher rates of mental health referrals during childhood and adolescence. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) is a widely used statistical technique for determining subscales in questionnaire data. As in any other statistical technique, missing data may both complicate its execution and interpretation. In this study, six methods for dealing with missing data in the context of PCA are reviewed and compared: listwise deletion (LD), pairwise deletion, the missing data passive approach, regularized PCA, the expectation-maximization algorithm, and multiple imputation. Simulations show that except for LD, all methods give about equally good results for realistic percentages of missing data. Therefore, the choice of a procedure can be based on the ease of application or purely the convenience of availability of a technique. [ABSTRACT FROM AUTHOR]

We examined associations of disorganized attachment and maternal depressive symptoms with infant autonomic functioning in 450 infant-mother dyads enrolled in the Generation R study. Maternal depressive symptoms were measured 2 months postpartum with the Brief Symptom Inventory. At 14 months, we assessed infant attachment with a slightly shortened Strange Situation and measured infant resting heart rate. Respiratory sinus arrhythmia ( RSA) was calculated using spectral analysis. Higher levels of maternal postnatal depressive symptoms predicted lower resting RSA in disorganized infants ( B = −0.31, SE = 0.15, p = .04, R2 = .05) but not in nondisorganized infants ( B = 0.05, SE = 0.06, p = .36). This effect was buffered in disorganized infants with a secondary secure attachment classification. Disorganized infants were more vulnerable to the effect of maternal postnatal depressive symptoms on the physiological stress systems. [ABSTRACT FROM AUTHOR]

The article presents a study which aims to determine the extent of the relation of posttraumatic stress reactions, growth, and quality of life in the general population. It says that the study involved 1770 children from 36 randomly selected primary schools in Utrecht, Netherlands. The results show that posttraumatic stress reactions were strongly related to posttraumatic growth and quality of life where children experiencing more posttraumatic symptoms has a lower quality of life.