eHealth Technology: What Do We Know and What do We Need to Learn

eHealth is the use of information and communication technologies for health. eHealth serves multiple utilization purposes for storage, exchange, and retrieval of digital data for administrative, clinical, educational and research purposes. The ultimate purpose of the eHealth use globally is to promote health for individuals efficiently and effectively [1].A growing body of evidence reveals potential benefits of eHealth on delivery of health care that are cost-effective and responsive to patient’s needs without compromising the quality of service [2]. Systematic reviews revealed promising results on improvement of patient outcomes with implementation of eHealth services [3] especially with challenging behavioral lifestyle modifications such as improving medication adherence [4] physical activity [5] and HIV prevention [6] as well as addressing mental health [7].With the continuous evolution of eHealth services, challenges to its application and utilization are on rise. Systematic review identified multiple challenges. First, stakeholders and systems users need to have enough training to use the eHealth technology effectively and optimally. Second, the robustness of the technology and its interoperability such as integrity of data and security concerns [8]. Third, capital and startup costs and maintenance can be too costly. Fourth, legal clarity and legal framework challenge relates to legal issues such as privacy [9]. Fifth, organizational context pertains to the environment where eHealth technology is utilized [10]. A critical focus on the emerging technologies currently provides the next context for the integration of eHealth data in every aspect of human activity. The Internet of Things will cause an unpredicted explosion in the delivery of directed custom-made eHealth services to human beings; and thus, the new generation of eHealth data movement will exploit this feature to its full potential. In a way the matching of eHealth services to human needs will incorporate the identification, distribution, and management of many machine-generated eHealth data.With the most documented challenges reported, what are the lessons learnt and how do we tackle them? Opportunities for confronting such challenges are proposed. Investment in advancing competencies of human resources in relation to information systems design and implementation as deemed crucial for optimal utilization of eHealth technology. Governments and national bodies should support eHealth technology to achieve its optimal purposes and become incorporated in health organizations. Researchers and clinicians also need to learn how to apply eHealth technology fully to extend their ability to study and influence health behavior as well as engage patients.</p

Results from the first culturally tailored, multidisciplinary diabetes education in Lebanese adults with type 2 diabetes: effects on self-care and metabolic outcomes

Objective: Diabetes self-management education (DSME) is an essential component of lifestyle management needed for diabetes care. This pilot-study tested the effect of culturally-tailored education targeting diabetes selfcare on glycemia and cardiovascular risk factors of Lebanese with type 2 diabetes mellitus (T2DM) (n = 27; Age: 61 ± 10 yrs, 59% males, HbA1c: 8.98 ± 1.38%). Results: Diabetes self-care (Diet, Self-Monitoring Blood Glucose and foot care) improved after 6 months, which was reflected in a significant drop in glycemic levels (HbA1c:-0.5%; FPG: − 38 mg/dl), and cholesterol/HDL ratio (4.45 ± 1.39 vs. 4.06 ± 1.29). Waist circumference decreased at 6 months compared to 3 months (p < 0.05). This is the first effective culturally-tailored intervention that improved self-care, glycemic control, body adiposity and lipid profile of Lebanese with T2DM. Larger scale implementation with representative sample is warranted.This work was supported by the Lebanese American University-intramural fund granted to the first author. Roche Diagnostics Middle East provided screening tools for HbA1c levels and LifeScan MEA donated the glucometers and test strips. The companies did not have any role in the design of the study, data analyses, or decision to publish

Descriptive statistics of the diabetes fatalism scale^*.

<p>Descriptive statistics of the diabetes fatalism scale^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190719#t002fn001" target="_blank">*</a>}.</p

Psychometric properties of the Arabic version of the 12-item diabetes fatalism scale

<div><p>Background</p><p>There are widespread fatalistic beliefs in Arab countries, especially among individuals with diabetes. However, there is no tool to assess diabetes fatalism in this population. This study describes the processes used to create an Arabic version of the Diabetes Fatalism Scale (DFS) and examine its psychometric properties.</p><p>Methods</p><p>A descriptive correlational design was used with a convenience sample of Lebanese adults (N = 274) with type 2 diabetes recruited from a major hospital in Beirut, Lebanon and by snowball sampling. The 12- item Diabetes Fatalism Scale- Arabic (12-item DFS-Ar) was back-translated from the original version, pilot tested on 22 adults with type 2 diabetes and then administered to 274 patients to assess the validity and reliability of the scale. Confirmatory factor analysis (CFA) was used to test the hypothesized factor structure. Cronbach’s alpha was used to test for reliability.</p><p>Results</p><p>CFA supported the existence of the three factor hypothesis of the original DFS scale. The five items measuring “emotional distress” loaded under Factor 1, the four items measuring “spiritual coping” loaded under factor 2 and the last three items measuring “perceived self-efficacy” of the original scale loaded under Factor 3 (p <0.001 for all three subscales). Goodness of fit indices confirmed adequateness of the CFA model (CFI = 0.97, TLI = 0.96, RMSEA = 0.067 and pclose = 0.05). The 12-item DFS-Ar showed good reliability (Cronbach’s alpha of 0.86) and significantly predicted HbA1c (β = 0.20, p < 0.01). After adjusting for the demographic characteristics and the number of diabetes comorbid conditions, the 12-item DFS-Ar score was independently associated with HbA1c in a multivariable model (β = 0.16, p < 0.05).</p><p>Conclusions</p><p>The 12-item DFS-Ar demonstrated good psychometric properties that are comparable to the original scale. It is a valid and reliable measure of diabetes fatalism. Further testing with larger and non-Lebanese Arabic population is needed.</p></div

Demographic characteristics of study participants^*.

<p>Demographic characteristics of study participants^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190719#t001fn001" target="_blank">*</a>}.</p

Structural equation model with standardized path coefficients of Diabetes Fatalism Scale dimensions.

<p>The fit of the final model was as follows: R2 = 99, root mean square error of approximation = 0.067, pclose = 0.05, comparative fit index = 0.97. *p < 0.05. **p < 0.01.</p

Independent relationship between 12-item DFS and demographic characteristics and hemoglobin A1c.

<p>Independent relationship between 12-item DFS and demographic characteristics and hemoglobin A1c.</p