Diabetes Technology and the Human Factor
2017; Mary Ann Liebert, Inc.; Volume: 19; Issue: S1 Linguagem: Inglês
10.1089/dia.2017.2511
ISSN1557-8593
AutoresAlon Liberman, Maja Drobnič Radobuljac,
Tópico(s)Diabetes Management and Research
ResumoDiabetes Technology & TherapeuticsVol. 19, No. S1 Original ArticlesFree AccessDiabetes Technology and the Human FactorAlon Liberman and Maja Drobnic̆ RadobuljacAlon LibermanJesse Z. and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel.Search for more papers by this author and Maja Drobnic̆ RadobuljacFaculty of Medicine, University of Ljubljana, Slovenia.Unit for Adolescent Psychiatry, Center for Mental Health, University Psychiatric Clinic, Ljubljana, Slovenia.Search for more papers by this authorPublished Online:1 Feb 2017https://doi.org/10.1089/dia.2017.2511AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail IntroductionDiabetes is one of the most prevalent chronic diseases in the world and the number of people with diabetes has steadily risen over the last decades. According to the 2014 World Health Organization (WHO) estimation, globally, 422 million adults aged over 18 years were living with diabetes of which type 1 diabetes (T1D) accounts for between 5% and 10% (1). Significant development achieved in the past few decades in diabetes technologies has made diabetes technological devices such as continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring (CGM) an important component of diabetes treatment.Unfortunately, there is a significant lack of congruence between the worldwide prevalence of diabetes and the prevalence of diabetes technologies utilization. Although insulin pumps have been used for more than 35 years, the actual number of patients using insulin pumps in the United States is difficult to ascertain, but has been reported to range from 350,000 to 515,000 in 2008 (2). The prevalence of CSII use in the SWEET (Better control in Paediatric and Adolescent diabeteS: Working to crEate CEnTers of Reference) registry, including 16,570 children with T1D, was recently reported to be 44.4% (3). The number of patients using CGM is even lower.The scientific field of “Human factor” utilizes information from a variety of disciplines including (for example) psychology, education, engineering, and design, focusing on the human being and their interaction with products, technology, and environments both at the workplace and during other daily activities, in order to better understand the interaction between human and technological products. This information can increase our knowledge and bridge the gap between the increasing number of patients with diabetes and the use of diabetes technologies.In the present article selected studies that were published between July 2015 and June 2016 concerning these issues are reviewed and discussed.Key Articles Reviewed for the ArticleA novel tool to predict youth who will show recommended usage of diabetes technologiesNeylon OM, Skinner TC, O'Connell MA, Cameron FJPediatr Diabetes 2016;17: 174–183Illness identity in adolescents and emerging adults with type 1 diabetes: introducing the illness identity questionnaireOris L, Rassart J, Prikken S, Verschueren M, Goubert L, Moons P, Berg CA, Weets I, Luyckx KDiabetes Care 2016;39: 757–763Development of a new measure for assessing glucose monitoring device-related treatment satisfaction and quality of lifePolonsky WH, Fisher L, Hessler D, Edelman SVDiabetes Technology & Therapeutics 2015;17: 657–663Development of a new measure for assessing insulin delivery device satisfaction in patients with type 1 and type 2 diabetesPolonsky WH, Fisher L, Hessler D, Edelman SVDiabetes Technology & Therapeutics 2015;17: 773–779Insulin pumps in type 1 diabetes with mental disorders: real-life clinical data indicate discrepancies to recommendationsPrinz N, Bächle C, Becker M, Berger G, Galler A, Haberland H, Meusers M, Mirza J, Plener PL, von Sengbusch S, Thienelt M, Holl RW; on behalf of the DPV InitiativeDiabetes Technol Ther 2016;18: 34–38Internet-based incentives increase blood glucose testing with a non-adherent, diverse sample of teens with type 1 diabetes mellitus: a randomized controlled trialRaiff BR, Barrry VB, Ridenour TA, Jitnarin NTransl Behav Med 2016;6: 179–88A novel tool to predict youth who will show recommended usage of diabetes technologiesNeylon OM1, Skinner TC2, O'Connell MA1, Cameron FJ11Department of Endocrinology, Murdoch Childrens Research Institute and The Royal Children's Hospital, Parkville, Australia.2School of Psychological and Clinical Sciences, Charles Darwin University, Darwin, Australia.Pediatr Diabetes 2016;17: 174–183Background and objectiveThere is no clear consensus or criteria on which patients will get the most from using diabetes technologies such as CSII or CGM. Higher usage of these technologies associates with glycated hemoglobin A1c (HbA1c) achieved. Therefore, the authors designed a questionnaire-based tool aiming to assess its ability to predict future technology usage in youth commencing CSII and ‘real-time’ CGM.DesignThis was a prospective cross-sectional study assessing the performance of the predictive tool in two groups of youth with T1D: group A (n=50; mean age 12±2.5 yr) starting with the use of CGM and Group B (n=47; mean age 13±3 y) switching from an injectable insulin regimen to CSII.MethodsEvaluated at three months for the CGM group, usage ≥5 days (70%) per week was considered high usage (HU) and when <70% was considered low usage (LU), In the CSII group, entering ≥5 blood sugars per day to the pump was considered HU and less than 5 was considered LU at six months from the start of the study.ResultsUsing gender, baseline HbA1c, and two subscales of the tool, in the CGM group, the authors generated a formula which predicted both HU (24% of patients) and LU (76%) at three months with 92% accuracy. In the CSII group, four tool items plus gender predicted HU (68%) or LU (32%) at six months with 95% accuracy.ConclusionsThe questionnaire in this pilot study proved successful in predicting the patients who will and will not be using the technologies as recommended.CommentPresented research (4) is aiming to select the patients who are more or less likely to cooperate with diabetes management recommendations and according to their results, the tool used is promising for the task. The tool itself needs to be applied to larger samples and thoroughly validated over various groups of patients, to be able to generalize its predictive value. Nevertheless, the pilot study opened a new and important area for identifying criteria which may help in future clinical decision making and patient management. Namely, if the diabetes teams were able to predict in which patients the risk for suboptimal adherence is higher, they could provide them with specific tailored support techniques that might help them to overcome the obstacles to successful treatment.Illness identity in adolescents and emerging adults with type 1 diabetes: introducing the illness identity questionnaireOris L1, Rassart J1, Prikken S1, Verschueren M1, Goubert L2, Moons P1,3, Berg CA4, Weets I5, Luyckx K11KU Leuven, Leuven, Belgium.2Ghent University, Ghent, Belgium.3University of Gothenburg, Gothenburg, Sweden.4The University of Utah, Salt Lake City, UT.5Free University of Brussels, Brussels, Belgium.Diabetes Care 2016;39: 757–763ObjectiveIdentity development constitutes an essential task during adolescence and emerging adulthood. Those with a chronic illness, such as T1D, may experience additional challenges with increased responsibility for diabetes management. Hence, integrating diabetes into one's sense of self is an important task during this period. In this study, the authors examined the utility of a new self-report questionnaire, the Illness Identity Questionnaire (IIQ), with four illness identity dimensions (engulfment, rejection, acceptance, and enrichment) for the evaluation of the illness identity concept in adolescents and young adults with T1D. They assessed its correlations with psychological and diabetes-specific functioning.Research design and methodsThe study included 575 participants (14–25 years of age) with T1D; 41.16% of the initially contacted 1450. Apart from the IIQ, they filled out the Center for Epidemiologic Studies Depression Scale (CESD), Satisfaction With Life Scale (SWLS), Self-Care Inventory (SCI), and Problem Areas in Diabetes Scale (PAID), which examine depressive symptoms, life-satisfaction, treatment adherence, and diabetes-correlated problems. HbA1c values were collected from the patients' medical records. The authors validated the IIQ using confirmatory factor analysis (CFA) and used path analysis with structural equation modeling to investigate correlations between illness identity and psychological and diabetes-specific functioning.ResultsThe CFA for IIQ showed a clear factor structure, which differentiated four illness identity dimensions: rejection was correlated to lower treatment adherence and worse metabolic control, engulfment to less adaptive psychological functioning and more diabetes-related problems, acceptance to more adaptive psychological functioning, fewer diabetes-related problems, and better treatment adherence, and enrichment to more adaptive psychological functioning. The patients administering injections scored lower on engulfment than patients using a pump.ConclusionsThe current findings emphasize the significance of the concept of illness identity, which may be one of the reasons why some individuals manage to adapt to the illness better than others. A valid and reliable measure, the IIQ was developed to measure four illness identity dimensions in individuals with type 1 diabetes. They were shown to uniquely correlate with psychological and diabetes-specific functioning.CommentIn 2005 the American Diabetes Association published a statement concerning the Care of Children and Adolescents with type 1 diabetes. They emphasized the relation between diabetes management and adherence to the tasks and challenges that patients face in their developmental stage. In adolescence, the most important challenge is to develop and establish a strong sense of self-identity. Adolescents with diabetes should be able to integrate diabetes into their sense of self or identity in order to accept their independent role in diabetes management (5).In the current study (6) the authors propose the concept of illness identity meaning “the degree to which diabetes becomes integrated into one's personal sense of self.” The four dimensions of diabetes-related illness identity represent differing attitudes toward diabetes management. Not surprisingly, it was found that those that accepted diabetes as a part of their self were more adherent to their diabetes regimen.Even though the study is cross-sectional (as opposed to a longitudinal follow up), used self-report questionnaires (as opposed to the interviews or clinical assessment), and had a relatively low response rate (41.16%), the importance of their findings for the clinical management may be high. Namely, when (adolescent, young adult or adult) patients reject their diabetes or feel overwhelmed by it, their motivation for adherence to treatment is lower. Challenging rejection and engulfment in a therapeutic relationship (within a psychotherapeutic or other relationship) could therefore increase acceptance of T1D and motivation for adherence to the treatment regimes.Development of a new measure for assessing glucose monitoring device-related treatment satisfaction and quality of lifePolonsky WH1,2, Fisher L3, Hessler D3, Edelman SV4,51University of California, San Diego, San Diego, CA.2Behavioral Diabetes Institute, San Diego, CA.3University of California, San Francisco, CA.4Division of Endocrinology and Metabolism, University of California, San Diego, CA.5Veterans Affairs Medical Center, San Diego, CA.Diabetes Technology & Therapeutics 2015;17: 657–663BackgroundThe authors developed the Glucose Monitoring System Satisfaction Survey (GMSS) as a response to the lack of validated measures for evaluating treatment satisfaction with glucose monitoring devices and the impact of the devices on quality of life and other patient-reported outcomes. The article describes the construction and validation of the GMSS and examines key patient factors' association with glucose device satisfaction.Materials and MethodsThey formed an initial pool of 42 items from interviews with 15 adult T1D or T2D patients and 10 diabetes health-care workers. They used these with 254 adults with T1D and 206 insulin-using adults with T2D and performed separate exploratory factor analyses. They established construct validity using the WHO-5 Well-being index which measures overall well-being, Diabetes Distress Scale (diabetes distress), Self-Monitoring of Blood Glucose Obstacles scale (attitudes toward glucose monitoring), and Blood Glucose Monitoring System Rating Questionnaire (multiple dimensions of treatment satisfaction with blood glucose monitoring systems).ResultsThe factor analyses provided four coherent and meaningful factors in each of the two patient samples. The items on Emotional Burden, Behavioral Burden, and Openness were the same for both patient groups; the fourth factor was Trust for the T1D group and Worthwhileness for the T2D group. They performed final factor analysis, which accounted for 66.5% of the variance in the T1D group and 67.0% in the T2D group.The correlations with criterion variables were significant, which established validity.ConclusionsThey concluded that GMSS proved to be a reliable, valid measure of glucose device satisfaction in both forms (the questions for T1D as well as insulin-using T2D) and recommended it for use as a measure of device satisfaction in clinical practice as well as research.Development of a new measure for assessing insulin delivery device satisfaction in patients with type 1 and type 2 diabetesPolonsky WH1,2, Fisher L3, Hessler D3, Edelman SV4,51University of California, San Diego, San Diego, CA.2Behavioral Diabetes Institute, San Diego, CA.3University of California, San Francisco, San Francisco, CA.4Division of Endocrinology and Metabolism, University of California, San Diego, CA.5Veterans Affairs Medical Center, San Diego, CA.Diabetes Technology & Therapeutics 2015;17: 773–779BackgroundThe authors developed the Insulin Device Satisfaction Survey (IDSS) aiming to build on the limitations of previously developed instruments for evaluation of patient satisfaction with insulin delivery devices and their impact on the quality of life and other patient-reported outcomes. The article describes the construction and validation of the IDSS and examines key patient factors' association with insulin delivery device satisfaction.Materials and MethodsStructured interviews were conducted with 10 T1D and 10 insulin-using T2D adults and with eight diabetes health-care professionals. Verbal descriptions of respondents' personal experiences with insulin delivery devices and the different features of each device were documented, focusing on both the positive and negative attributes of each device, how each device affected respondents' feelings about diabetes and their ability to manage diabetes, and the impact of the device on respondents' overall health and quality of life (QOL). Respondents' comments were reviewed for duplication and converted into 32 survey items. Patients and health-care professionals then reviewed the items for completeness and clarity. They used these with 279 adults with T1D (aged 47.1±15.1 years) and 209 insulin-using adults with T2D (aged 59.0±11.6 years) and performed separate exploratory factor analyses. They established construct validity using the WHO-5 Well-being index which measures overall well-being, Diabetes Distress Scale (diabetes distress), Self-Efficacy for Diabetes Management Scale (diabetes self-efficacy), and subscales from the Insulin Delivery System Rating Questionnaire (multiple dimensions of treatment satisfaction with insulin delivery systems).They used regression analyses to examine associations between total scale satisfaction and device type (pump vs. nonpump users), insulin adherence, clinical indicators, and demographics.ResultsThe factor analyses provided three coherent and meaningful factors in each of the two patient samples, accounting for 55.6% and 64.1% of the variance in the T1D and T2D samples, respectively. The correlations with all criterion variables were significant, which established validity.Higher scores on IDSS were significantly associated with more successful glycemic control and better insulin treatment adherence and insulin pump use for T1D and T2D patients. For T2D patients however, the scores on IDSS were significantly associated with older age, fewer low blood glucose readings, and fewer long-term complications.ConclusionsThey concluded that IDSS proved to be a reliable, valid measure of insulin device satisfaction in both forms (the questions for T1D as well as T2D) and recommended it for using as a measure of device satisfaction in clinical practice as well as research.Comment For the Previous Two ManuscriptsThe previous two manuscripts (7,8), published by the same authors, report on the development of tools for the assessment of patient satisfaction by the glucose monitoring and insulin delivery devices. Given the immense achievements in the field of diabetology and the escalating number of new diabetes management devices, it seems crucial to be able to evaluate the impact of the device use on the patients' satisfaction and Health-Related Quality of Life (“individual's perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns. It is a broad-ranging concept affected in a complex way by the individual's physical health, psychological state, and level of independence, social relationships, and their relationships to salient features of their environment” (WHO definition) (9,10).Especially when keeping in mind that it is not only diabetes-related quality of life which is influenced by the treatment regimen but that also the reverse is true – the better the treatment-related quality of life, the higher patient adherence and, consequently, chances for better metabolic control.The authors of the reviewed studies (7,8) presented two acceptably reliable and valid measures for the assessment of glucose monitoring and insulin delivery system satisfaction using real-user and health-care professional interview-based items confirmed by factor analyses. The presented measures use specific items for T1D and T2D patients, since their studies have shown that disease management satisfaction depends on different factors depending on diabetes type, which highlights the importance of differential measuring. Previous developed measures were either not validated, developed for only a specific type of device, or were not diabetes-type specific. The measures seem to be applicable to various devices and therefore able to compare treatment satisfaction across the devices. This will enable further improvements of the field of diabetes technology with the choice of devices offering the highest quality of life to the patients using them.An important finding of the devices satisfaction is that it seems relatively stable across demographic groups with an exception of younger patients. Therefore, while more data is needed from applications of the measures to larger and more diverse samples (race, education), it is important to pay attention to the specifics of the youth and to prepare adapted measures for the pediatric population.Insulin pumps in type 1 diabetes with mental disorders: real-life clinical data indicate discrepancies to recommendationsPrinz N1, Bächle C2, Becker M3, Berger G4, Galler A5, Haberland H6, Meusers M7, Mirza J8, Plener PL9, von Sengbusch S10, Thienelt M11, Holl RW1; on behalf of the DPV Initiative1Institute of Epidemiology and Medical Biometry, Central Institute for Biomedical Technology, University of Ulm, Ulm, Germany.2Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.3Clinic for Children and Adolescents, Dr. Horst-Schmidt-Kliniken GmbH, Wiesbaden, Germany.4Department of Pediatric and Adolescent Medicine, Medical University Vienna, Vienna, Austria.5Pediatric Endocrinology and Diabetology, University Hospital for Children and Adolescents, Campus Virchow, Charité–University Hospital Berlin, Berlin, Germany.6Hospital for Children and Adolescents, Sana Hospital Berlin Lindenhof, Berlin, Germany7Department of Child and Adolescent Psychiatry and Neurology, Community Hospital, Herdecke, Germany.8Children's Hospital, Hospitals of the City of Cologne, Cologne, Germany.9Department of Child and Adolescent Psychiatry and Psychotherapy, University of Ulm, Ulm, Germany.10Clinic for Child and Adolescent Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany.11Clinic for Children and Adolescents, St. Vincenz Hospital, Christophorus Clinics, Coesfeld, Germany.Diabetes Technol Ther 2016;18: 34–38BackgroundThe professional consensus (American Association of Clinical Endocrinologists/American College of Endocrinologists consensus statement 2014) does not recommend CSII use in patients with disorders of mental health. The presented research investigated the use and discontinuation of CSII in routine care of patients with T1D with or without comorbid mental disorders.Materials and MethodsBetween 2005 and 2013, a total of 48,700 insulin-treated T1D patients between 5 and 30 years of age (median age 15.6 years) were documented by 387 specialized diabetes clinics in the multicenter, standardized, prospective German/Austrian diabetes patient follow-up registry, DPV. To select patients with comorbid mental disorders, the registry was searched for a diagnosis and/or where applicable for specific pharmacological treatment of a mental disorder by using International Statistical Classification of Diseases and Related Health Problems-10 codes (ICD-10 codes) and Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) and Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5) criteria, as well as specific search terms (e.g., “attention deficit,” “rubifen”). Clinically recognized mental disorders were documented in 6.5% (n=3158) of the eligible T1D patients. Of these, 2.8% had attention-deficit hyperactivity disorder (ADHD), 1.4% depression, 0.8% eating disorders, 0.7% needle phobia, 0.5% anxiety/obsessive compulsive disorder (OCD), and 0.3% had psychosis and/or used neuroleptic medication. They compared groups using multivariable logistic regression with age, sex, diabetes duration, and migration background as independent variables.ResultsUse of CSII was more common in patients with needle phobia (75.8%), depression (41.5%), or anxiety/OCD (41.4%) as compared with patients without mental disorders (34.6%) after adjusting for confounders (P<0.05 for each). Psychotic patients (26.2%, P<0.05), however used CSII less often, and patients with ADHD (36.3%) or eating disorders (33.9%) used it with similar frequencies. The rates of CSII discontinuation were higher in patients with mental disorders (ADHD, 9.7%; depression, 8.2%; eating disorders; 10.0% (P<0.05, respectively)) compared with patients without mental disorders (5.1%), but similar in patients with needle phobia (5.3%), anxiety/OCD (6.0%), or psychosis (4.2%).ConclusionsThe authors conclude that there is wide variability in CSII use and discontinuation among T1D patients with mental disorders in routine diabetes care and indicate these are clear differences from the latest recommendations.CommentAccording to the recent edition of the Diagnostic and Statistical Manual of Mental Disorders “nonadherence to medical treatment” is diagnosed as nonadherent behavior to an important aspect of treatment for a mental disorder or another medical condition. One of the causes for this condition is the presence of a mental disorder (e.g., schizophrenia, personality disorder) (11). Previous studies have shown that adolescents and young adults with T1D who screen positive for anxiety, depression, and disordered eating behaviors are more likely to have poorer glycemic control than those without psychiatric comorbidities (12). According to their results (13), using a patient register on 48,700 insulin-treated T1D patients, the authors call for the re-evaluation of the recommendations. The first observation when reading the article was that the prevalence of the mental disorders they studied (6.5%) was much lower than in the general population where the estimations are that more than a third of population suffers from mental disorder each year which many go unrecognized or without treatment (14). Of course the bias in the present study was (at least in a large part) due to the selection criteria (only specific disorders were selected), but an unease still exists when thinking that this is a population with very frequent visits to a medical team and there is still a chance that many of them suffer from an unrecognized mental disorder. This may well explain at least a part of suboptimal compliance and also offer an opportunity for mental disorder screening on regular diabetes visits. Nevertheless, their data show that a mental disorder by itself should not be enough for the decision against the use of better technologies in higher-risk patients. As seen in the results, the recommendations are often not being followed (for the benefit of the patients) and also that patients with different mental disorders have different CSII discontinuation rates. In some mental disorders (e.g., anxiety and needle phobia) the use of CSII was more frequent than in the ones without mental disorders and the discontinuation rate was no higher. Therefore, the recommendations should include cooperation with mental health professionals, which may help with the clinical decisions on the use of specific technology – on a patient-to-patient basis.Internet-based incentives increase blood glucose testing with a non-adherent, diverse sample of teens with type 1 diabetes mellitus: a randomized controlled trialRaiff BR1, Barrry VB2,3, Ridenour TA4, Jitnarin N51Rowan University, Glassboro, NJ.2Center for Technology and Health, National Development and Research Institutes, Inc., New York, NY.3Department of Pediatrics/ Family Care Center, Harlem Hospital Center, New York, NY.4Research Triangle Institutes, Durham, NC.5Institute for Biobehavioral Health Research, National Development and Research Institutes, Inc., Leawood, KS.Transl Behav Med 2016;6: 179–188BackgroundThe presented study investigated the feasibility, acceptability, and preliminary efficacy of using Internet-based incentives for improving adherence with self-monitoring blood glucose (SMBG) in nonadherent adolescents.Materials and MethodsThe trial had a randomized controlled design (RCT). Patients with T1D, with SMBG less than four times daily were randomly assigned to contingent (CS; n=23), where they had to meet SMBG goals verified by web camera to earn monetary incentives, or noncontingent (NS) groups (n=18), where they earned incentives whether they adhered or not. The participants recorded videos where they were performing a complete blood glucose test and/or showing past meter result(s) to the camera from the meter log and posted it to the research website. Prior to the intervention, all participants received brief motivational interviewing (MI).ResultsAll participants showed an increase in the frequency of SMBG after receiving one brief MI session. The addition of incentives further increased frequency of testing, but more so for participants in the contingent group, older participants (16–18 years old) had significantly greater improvements in the frequency of SMBG than younger participants (13–15 years old), regardless of group assignment, and the increased benefits were more likely to be maintained during follow up. Participants and parents endorsed the intervention on all intervention dimensions. Although participants rated most elements of the intervention favorably, they rated convenience of the intervention lower, and would have preferred it be administered via mobile devices.ConclusionsThe authors conclude that Internet-based incentive interventions are feasible, acceptable, and show promise for improving adherence with SMBG.CommentWhat is presented is a small study (15), but nevertheless with a valuable and reliable design (RCT), that showed how positive incentives (in this case monetary) over an Internet-based program might improve adherence in less-cooperative adolescents with T1D. Although the authors noted, their design (computer Internet based) may be out of time (patients would have preferred mobile devices, or even direct communication through CGM devices), they have shown a useful way of motivating noncompliant adolescents toward adherence, as have some other Internet-based motivational regimes (16). Similar studies with larger samples and more innovative technological designs are needed and this type of technology use may well prove beneficial in improving adherence of less motivated patients.ConclusionsThe present article aims at critically reviewing selected recent publications on diabetes technology and the human factor. It focuses on research uncovering various patient (personality, psychopathology, illness acceptance, treatment acceptance) characteristics and their interaction with new and constantly emerging diabetes management technology.The emerging new and more efficient technology for the treatment of chronic diseases (such as diabetes) shifts the focus of profession from the treatment providers to the individuals accepting the health-care (the patient) and her/his compliance.Although the continuous debate concerning which patients will potentially benefit from the use of diabetes technologies has not yet been resolved, there is a broad agreement that a lot can be done in order to improve the adjustment of less motivated patients.One of the most important issues in this field is patient education. It is accepted that patients who have successfully undergone a structured process of diabetes technologies training (instruction to patients regarding the use of specific algorithms to adjust their insulin regimen based on glucose trends, right interpretation of trends, etc.) will be able to have a better understanding of the utilization of the device to its full potential and to have better adherence for longer periods of time (17,18).It turns out that, even if the education process is optimal, there will always be a fraction of patients with suboptimal adherence. Therefore, another promising research field is identifying various specific patient or parent characteristics, which may predict future adherence to the medical regimes (4,6–8,19). The question, however, remains whether to use more sophisticated technology with proven better results on metabolic control only for highly motivated individuals who are willing to actively collaborate with health-care providers and strictly follow their instructions, or to accept the patients with low motivation as well, aiming to enhance their motivation with methods such as psychosocial intervention and motivational interviewing (20,21).By early identification of patients/parents at risk for low adherence, hopefully, we will be able to deliver tailored psychological (or other types) of interventions that will improve the use of the advanced technology and the wellbeing of our patients. And needless to say, there is a large open field in applying interventions and the research of their efficacy.Author Disclosure StatementNo conflicts of interest are reported for A. L. and M. D. R.References1 NCD Risk Factor Collaboration (NCD-RisC). 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