Background and aims: Intensive insulin therapy relies on correct prandial
insulin dose adaptation dependent from current glucose level, amount of
planned carbohydrate intake and the consideration of other situational factors
like physical activity or circadian fluctuation of insulin sensitivity. People
with diabetes and intensive insulin therapy should be able to estimate carbohydrates
and calculate insulin bolus correctly, while regarding the factors
mentioned above. An assessment tool for the measurement of the ability of
carbohydrate estimation and bolus calculation is missing. The objective of
this study was the development and psychometric evaluation of an assessment
tool for carbohydrate estimation and bolus calculation (“aSsessMent of
the Ability of Bolus Calculation and CaRbohydrate esTimation” SMART). Of
special interest were the associations of both abilities with glycaemic control.
Materials and methods: The SMART consisted of one scale for the assessment
of bolus calculation (BOLUS) with 10 items and a scale for carbohydrate
estimation (CARB) with 12 items. People with type 1 or type 2 diabetes
on an intensive insulin regimen were invited to participate. HbA1c and stored
data of blood glucose meters were used to determine glycaemic control.
Results: 411 patients participated (age 42.9 ±15.7, 58% female, HbA1c 8.6
±1.8%, 28% with CSII-treatment) and approx. 56,000 blood glucose meter
readings could be obtained. The reliability of both scales was sufficient (Cron-bachs alpha for BOLUS r= 0.78 and the CARB r = 0.67). Better bolus calculation
was associated with a higher level of education (r = 0.24, p<.05), lower
HbA1c (r = -0.27, p<.05), lower mean blood glucose (r = -0.29, p<.05), and
a lower standard deviation of blood glucose values (r = -0.43, p<.05). Better
carbohydrate estimation was associated with a lower body mass index (r =
-0.2, p<.05), lower mean blood glucose (r = -0.3, p<.05), a lower frequency of
hyperglycaemia (r = -0.27, p<.05), and a higher frequency of euglycaemia (r
= 0.26, p<.05). Patients with an insulin pump were better on both scales than
patients with multiple daily insulin injections (BOLUS: 7.2 ± 2.4 vs. 6.4 ± 2.7,
p<.01; CARB: 7.8 ± 2.1 vs. 7.1 ± 2.6, p<.01). Patients with previous diabetes
education performed significantly better on both scales (BOLUS: 6.8 ± 2.5 vs.
5.7 ± 2.8, p<.01; CARB: 7.4 ± 2.4 vs. 6.5 ± 2.6, p<.01).
Conclusion: SMART provides a reliable and valid assessment of the ability to
estimate the correct amount of carbohydrates and to calculate the appropriate
prandial insulin dose. SMART is also sensitive to depict effects of diabetes
education and of CSII treatment in comparison to multiple daily insulin
injections. In summary SMART can assist the identification of people with
diabetes on an intensive insulin regimen, who are in need for improvements
in carbohydrate estimation and/or calculation of prandial insulin dose
Depressive disorders in diabetic patients are nearly twice as likely
compared to the non-diabetic population. There is evidence that patients’
perception of diabetes is a decisive aspect of this association. Therefore this
study prospectively investigates the role of patients’ perceived diabetesrelated
distress on incidence depression and recovery.
343 patients with type 1 diabetes completed the CES-D and the Problem
Areas in Diabetes Scale (PAID) at baseline and at the 6-month follow-up. A
CES-D score of ≥16 indicated elevated depressive symptoms. A PAID score
of ≥30 indicated elevated diabetes-related distress. Logistic regression
analyses were performed with recovery from and incidence of depressive
symptoms as dependent variables. Independent variable was diabetesrelated
distress adjusted for possible demographic (age, gender, BMI) and
medical confounders (diabetes duration, HbA1c, insulin pump therapy, and
At baseline 130 patients (37.9%) reported elevated depressive symptoms
and 40 of these patients (30.8%) recovered 6 months later. Of the 213 patients
without elevated depressive symptoms, 27 (12.7%) had elevated depressive
symptoms 6 months later. Diabetes-related distress at baseline diminished
the chance to recover from elevated depressive symptoms by 64% (OR =
0.36, p<.05). In addition, the chance for incident depressive symptoms if
diabetes-related distress was present at baseline is 2.5 times more likely in
contrast to no diabetes-related distress at baseline (OR=2.5; p<.05). In both
analyses, no other variables reached a signifi cant infl uence.
It could be demonstrated that preventing diabetes-related distress is a
protective factor regarding the incidence of elevated depressive symptoms.
Furthermore, preventing diabetes-related distress supports the recovery
from already elevated depressive symptoms. This supports the notion that
diabetes per se is not a risk factor for depressive disorders but the perceived
emotional distress is.
The increase of energy efficiency and reduction of greenhouse gas emissions have become important targets of EU initiatives (European Commission 2014). Emissions from personal vehicles are a key cause of worldwide greenhouse gas emissions with 90% of personal transport emissions being caused by private vehicles (Barkenbus 2010). Energy efficient driving techniques, or eco-driving, can realize energy savings up to 20% (Stillwater et al. 2012). Combined with behavioral approaches, which are increasingly being used by governments (Cabinet Office 2012), these techniques represent a promising way to increase energy efficiency in the transport sector. Yet, practical solutions which are based on scientific findings are scarce. Since mobile apps are versatile in their functionality, characterized by a short time to market and low costs, they can represent a solution. The research question therefore is: How can a mobile application contribute to energy-efficient driving?
The economic integration of markets induces a broad variety of new conditions and opportunities for economic units. Multinational enterprises are key players in this regard. The increasing liberalization of trade and capital flows facilitates entries into foreign markets and international fragmentation of production processes. The effects of foreign direct investments (FDI) have yet to be fully investigated. The empirical literature on this subject conducted so far refers to selective databases, which underrepresent small and medium-sized parent companies and/or affiliates. This doctoral thesis makes an important contribution to the closure of gaps in the literature on labor market effects of FDI. Part of this dissertation is the establishment of a unique database that covers the total population of German affiliates in the Czech Republic and the corresponding parent companies. By developing a new method for linking firm-level data with the plant-level data of the Institute for Employment Research (IAB), this database is linked with the data of the Federal Employment Agency. Therefore, it permits reliable conclusions to be drawn regarding the impact of FDI in this specific case. As the exact dates of the investments in the Czech Republic are known, the employment trend of German firms with Czech affiliates and firms without FDI is compared to evaluate the effects on the German workforce. Moreover, this thesis provides profound insights into the types of FDI and the location of German parent companies and their Czech affiliates, which reveals new findings on cross-border firm linkages and the role of border regions.
The water conservation campaigns in residential households are hindered by the poor understanding of residents of how much water they use. For the better designed interventions new tools are necessary to educate the consumers on the water usage of different consumption events. In this paper we use the fine grained (0.5 Hz) water consumption data that was collected non- intrusively in a household over the period of 21 days to develop such tools. We examine the collected data and disaggregate the consumption events into three different categories: short events (e.g., toilet flush), long regular events (e.g., washing machine) and long irregular events (e.g., showers). To achieve this, we use clustering methods, based on level set trees, to identify groups of events that are similar to each other.