Using Health Information Technology to Prevent and Treat Diabetes
2015; Mary Ann Liebert, Inc.; Volume: 17; Issue: S1 Linguagem: Inglês
10.1089/dia.2015.1507
ISSN1557-8593
Autores Tópico(s)Medication Adherence and Compliance
ResumoDiabetes Technology & TherapeuticsVol. 17, No. S1 Original ArticlesFree AccessUsing Health Information Technology to Prevent and Treat DiabetesNeal Kaufman and Rachel R. BianNeal KaufmanUCLA Schools of Medicine and Public Health, Los Angeles, CA.Search for more papers by this author and Rachel R. BianUniversity of Michigan Medical School, Ann Arbor, MI.Search for more papers by this authorPublished Online:13 Feb 2015https://doi.org/10.1089/dia.2015.1507AboutSectionsPDF/EPUB ToolsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail IntroductionFor this year's article on information technology, we selected articles that will give the reader a sense of the current state of the art of the field and a hint of where it is going. To help organize thinking about how technology can improve outcomes for people with or at risk for diabetes, it is helpful to frame technology's roles in the range of approaches that are being used to improve outcomes.A new equilibrium is being forged among independent, but overlapping, health-promoting paradigms for which information technology often is at the core of any approach. There are four tried and true approaches that complement each other but also compete for resources as well as attention.Personal health approaches: Those services and supports that are provided by an organized healthcare system primarily for the benefit of the individual receiving the service.Public health approaches: Activities aiming to provide conditions in which people can be healthy that focus on entire populations rather than on individual patients or diseases. Public health is concerned with the total system and not only the eradication of a particular disease.Direct-to-consumer approaches: Goods and services marketed and sold directly to end users and that are usually paid for by the consumer.Population health approaches: Improving health outcomes of one or more subpopulations out of the entire population is the area of focus. It is understood that such population health outcomes are the product of multiple determinants of health. The outcomes are influenced by social, economic, and physical environments; personal health and lifestyle practices; individual capacity and coping skills; human biology; prenatal and early childhood development; and healthcare services.These subpopulations can be geographic regions, such as nations or communities, but they can also be other groups, such as employees, ethnic groups, disabled persons, members of a health plan, patients being served by a healthcare system, or individuals with specific clinical conditions or life circumstances. Often groups are segmented by risk or health status (e.g., stable and well, moderate risk, high risk/high cost). Most of the articles chosen for this chapter would fall into population health approaches that are linked to, or integrated with, personal health approaches.As we think about ways to improve outcomes, there are six key questions that must be answered in order for the chosen approach to have a chance of being successful.1. How is the target population defined and segmented from the larger population?2. What are the characteristics of the target population?3. What are the key modifiable determinants of the health status and healthcare utilization patterns of the target population?4. What are the evidence-based approaches that have been shown to modify the identified determinants in the target population?5. Can these approaches be cost-effectively and efficiently provided at a large enough scale to improve the entire population's outcomes?6. Is there capacity to successfully deploy the chosen approaches?Fundamentally, to improve outcomes for people with, or at risk for, diabetes, we must focus on two independent but interrelated areas: 1. Helping individuals improve their lifestyle behaviors (e.g., eating better, being more active, sleeping enough, not smoking, practicing safe sex, maintaining friendships)2. Helping individuals better self-manage their chronic conditionsThere are many programs addressing these areas with effectiveness backed by decades of accumulated evidence. The challenge is getting them to the right person, at the right time, with the right degree of intensity. It is hoped that this review will supply the reader with information about a number of successful and scalable approaches.Effects of a web-based tailored multiple-lifestyle intervention for adults: a two-year randomized controlled trial comparing sequential and simultaneous delivery modesSchulz DN1, Kremers SPJ2, Vandelanotte C3, van Adrichem MJG1, Schneider F1, Candel MJJM4, de Vries H11Department of Health Promotion, CAPHRI School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands; 2Department of Health Promotion, Nutrition and Toxicology Research Institute Maastricht (NUTRIM), Maastricht University, Maastricht, The Netherlands; 3Institute for Health and Social Science Research, Centre for Physical Activity Studies, Central Queensland University, Rockhampton, Australia; and 4Department of Methodology and Statistics, CAPHRI School for Public Health and Primary Care, Maastricht University, Maastricht, NetherlandsJ Med Internet Res 2014;16: e26Note from author: Since these two articles are from the same study, the abstract and comments have been combined into one piece which can be found below.Economic evaluation of a web-based tailored lifestyle intervention for adults: findings regarding cost-effectiveness and cost-utility from a randomized controlled trialSchulz DN1, Smit ES1,2, Stanczyk NE1, Kremers SPJ3, de Vries H1, Evers SMAA41Department of Health Promotion, CAPHRI School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands; 2Department of Communication Science, Amsterdam School of Communication Research/ASCoR, University of Amsterdam, Amsterdam, The Netherlands; 3Department of Health Promotion, Nutrition and Toxicology Research Institute Maastricht (NUTRIM), Maastricht University, Maastricht, The Netherlands; and 4Department of Health Services Research, CAPHRI School for Public Health and Primary Care, Maastricht University, Maastricht, The NetherlandsJ Med Internet Res 2014;16: e91BackgroundDifferent studies have reported the effectiveness of web-based computer-tailored lifestyle interventions, but combined impact and economic evaluations of these interventions are rare. Doing such evaluations are critical to the ability of effective programs to go to scale. Computer-tailored interventions are an interesting and promising option to make the healthcare system more sustainable. The benefits of such approaches derive from proven clinical effectiveness. Also important is their potential cost-effectiveness and cost-savings due to relatively low intervention costs and wide reach. Information regarding the impact and cost-effectiveness of web-based computer-tailored intervention programs is crucial for healthcare decision makers and governments seeking to make evidence-based decisions regarding large-scale implementation of such programs.MethodsThe aim of the study was to assess the impact on individuals, and from a societal perspective the cost-effectiveness and cost-utility of two different versions of a web-based computer-tailored lifestyle intervention for adults compared to a control group that received only a minimal intervention. The economic evaluation was part of a 2-year randomized controlled trial including 3 study groups (control, sequential conditions, and simultaneous conditions). All groups received personalized health risk appraisals based on the guidelines for physical activity, fruit intake, vegetable intake, alcohol consumption, and smoking. Additionally, participants in the sequential condition group (N=1736) received personal advice about one lifestyle behavior in the first year and a second behavior in the second year; participants in the simultaneous condition group (N=1638) received personal advice about all unhealthy behaviors in both years. During a period of 24 months, healthcare utilization, medication use, absenteeism from work, and quality of life (EQ-5D-3L) were assessed every 3 months using web-based questionnaires. Demographics were assessed at baseline, and lifestyle behaviors were assessed at both baseline and after 24 months. Cost-effectiveness and cost-utility analyses were performed based on the outcome measures lifestyle factor (the number of guidelines respondents adhered to) and quality of life, respectively. Incremental costs (in Euros) and effects were calculated for all 3 study groups. A net monetary benefit was also calculated by valuing the effectiveness and utility outcomes in monetary values using a threshold for society's willingness to pay (WTP) per gain in lifestyle factors (i.e., per additional guideline met) and per QALY gained.ResultsBoth tailoring strategies were associated with small self-reported behavioral changes. The sequential condition had the most significant effects compared to the control condition after 12 months (effect size=0.28). After 24 months, the simultaneous condition was most effective (effect size=0.18). All 5 individual lifestyle behaviors changed over time, but few effects differed significantly between the conditions. At both follow-ups, the sequential condition had significant changes in smoking abstinence compared to the simultaneous condition (12-month effect size=0.31; 24 month effect size=0.41). The sequential condition was more effective in decreasing alcohol consumption than the control condition at 24 months (effect size=0.27). Change was predicted by the amount of exposure to the intervention (total visiting time: beta=−.06; p=.01; total number of visits: beta=−.11; p<.001). Both interventions were appreciated well by respondents without significant differences between conditions.A total of 1733 participants were included in the economic analyses. From a society's willingness to pay of €4594 per additional guideline met, the sequential intervention (n=552) was likely to be the most cost-effective, whereas from a society's willingness to pay of €10,850, the simultaneous intervention (n=517) was likely to be most cost-effective. The control condition (n=664) appeared to be preferred with regard to quality of life.ConclusionsAlthough both programs proved to be effective, no definite finding could be drawn as their effectiveness. The authors suggest that the best mode of intervention may depend on the behavior that is targeted or on personal preferences and motivation. Further research is needed to identify moderators of intervention effectiveness. The results need to be interpreted in view of the high and selective dropout rates, multiple comparisons, and modest effect sizes. However, a large number of people were reached at low cost and behavioral change was achieved after 2 years.Both the sequential and the simultaneous lifestyle interventions were quite cost-effective when it related to the lifestyle factor, whereas the control condition was cost-effective when it concerned quality of life. However, since there is no accepted cutoff point for the willingness to pay per gain in lifestyle behaviors, it is impossible to draw strong conclusions. There is a need for more economic evaluations of lifestyle intervention.CommentIt is encouraging to see that web-based programs can demonstrate positive behavior change and economic benefits. While it was somewhat discouraging to not see a greater overall effect, this study once again demonstrates how hard it is to show results that matter. Perhaps the wrong question was asked. Maybe the difference between sequential and simultaneous delivery isn't that important. Perhaps the quality and intensity of the intervention is more important. I would guess that approaches that provide more long-term engagement would be more successful.Attempting to prove the cost-effectiveness and cost-benefit of web-based interventions is critical to the field. Without these types of analysis, effective programs will not be able to go to scale. While the ability to improve health and save money is the hallmark of a commercially successful approach, there are many other outcomes that need to be assessed and valued depending on the exact circumstances of the program's deployment. Other key areas include improving the quality of care provided; enhancing patient/health plan member satisfaction; driving uptake of other important services and supports; improving the communication and relationships with providers in a provider network; and responding to the expectation of the entity's customer (i.e., payer or employer). It is critical that these and other outcomes are assessed and reached if there are to be widespread adoption of educational services and supports by healthcare providers, payers, or employers.Telemedicine application in the care of diabetes patients: systematic review and meta-analysisMarcolino MS1,2, Maia JX2, Alkmim MBM2, Boersma E3, Ribeiro A1,21Medical School, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil; 2University Hospital, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil; and 3Erasmus MC, Rotterdam, The NetherlandsPLoS One 2013;8: e79246BackgroundTelemedicine programs for patients with diabetes have increased in number, but insufficient research has been done to demonstrate if these approaches improve care, improve health outcomes, and/or decrease cost. The objective of this study is to conduct a systematic review and meta-analysis to document the impact of telemedicine on patients with diabetes.MethodsA comprehensive literature search identified randomized controlled trials (RCTs) assessing the impact of telemedicine interventions on change in hemoglobin A1c (HbA1c), blood pressure, LDL cholesterol (LDL-c), and body mass index (BMI) in diabetes patients. The strategies of telemedicine application included in this review were computerized systems for information exchange, video conferencing, and exchange of information via telephone or other mobile devices, short message service, or through the Internet. Reported outcomes include HbA1c, blood pressure, BMI, and LDL-c. Studies utilized personalized feedback from healthcare providers to the patient or other healthcare provider.ResultsFifteen articles with 13 studies were included, evaluating a total of 4207 outpatient participants. Telemedicine was associated with −0.44% reduction (95% CI −0.61 to −0.26%; p<0.001) in HbA1c (−4.8 mmol/mol) compared to usual care, and this effect is more pronounced in type 1 diabetes patients. Among studies evaluating effect of telemedicine on LDL-c, there was a reduction of 6.6 mg/dL (95% CI −8.3 to −4.9 mg/dL; p<0.001). Among those evaluating effect on blood pressure, there was no significant effect due to telemedicine. Only 2 studies evaluated effect on BMI and they did not show any significant decrease.ConclusionsThis meta-analysis revealed that use of telemedicine within diabetes care is associated with reductions in HbA1c levels, but is not associated with clinically significant reductions in blood pressure, LDL-c, or BMI. Further studies are needed to explore the possibility of using telemedicine to improve diabetes care outcomes.CommentThis study attempts to summarize the literature on the impact of "Internet" or "telemedicine" interventions on patients with diabetes. It may still be too early to tell if telemedicine approaches are able to change outcomes and/or lower costs. We fully expect that they will, but the challenge will be to understand which patients, with which behaviors and which complications, will most likely benefit from which intervention. As more is known, we have no doubt that interventions tailored to the unique characteristics of each patient will have a significant effect. The bigger question is whether creators of these approaches will develop sustainable business models that allow successful programs to go to scale so that we can make a real impact at a population health level. While this study can help point us in the right direction, it suffers from the limitations of this type of review and the limitations of meta-analysis. We may never have indisputable proof of efficacy but that shouldn't surprise us or stop us. Just as beauty is in the eyes of the beholder, so too is effectiveness. Each of us sees different outcomes as being more relevant or, perhaps better said, more important. The key is to continue to innovate, evaluate, and modify based on the results if one really wants to see impact.Diabetes self-management smartphone application for adults with type 1 diabetes: randomized controlled trialKirwan M1, Vandelanotte C1, Fenning A1, Duncan MJ11Institute for Health and Social Science Research, Central Queensland University, North Rockhampton, AustraliaJ Med Internet Res 2013;15: e235BackgroundPersistently poor glycemic control in adult type 1 diabetes patients is a common, complex, and serious problem initiating significant damage to the cardiovascular, renal, neural, and visual systems. Currently, there are numerous low-cost and free diabetes self-management smartphone applications available in online stores. Their effectiveness is unproven. The aim of this study was to examine the effectiveness of a freely available smartphone application combined with text-message feedback from a certified diabetes educator to improve glycemic control and other diabetes-related outcomes in adult patients with type 1 diabetes.MethodsThe authors performed a two-group randomized controlled trial. Patients were recruited through an online type 1 diabetes support group and letters mailed to adults with type 1 diabetes throughout Australia. In a 6-month intervention, followed by a 3-month follow-up, patients (n=72) were randomized to usual care (control group) or usual care and the use of a smartphone application (Glucose Buddy) with weekly text-message feedback from a Certified Diabetes Educator (intervention group). All outcome measures were collected at baseline and every 3 months over the study period. Patients' glycosylated hemoglobin levels (HbA1c) were measured with a blood test, and diabetes-related self-efficacy, self-care activities, and quality of life were measured with online questionnaires.ResultsThe mean age of patients was 35.2 years (SD 10.43) (28 male, 44 female). Patients had been diagnosed with type 1 diabetes for a mean of 18.94 years (SD 9.66). Of the initial 72 patients, 53 completed the study (25 intervention, 28 control group). The intervention group significantly improved glycemic control (HbA1c) from baseline (mean 9.08%, SD 1.18) to 9-month follow-up (mean 7.80%, SD 0.75), compared to the control group (baseline: mean 8.47%, SD 0.86, follow-up: mean 8.58%, SD 1.16). No significant change over time was found in either group in relation to self-efficacy, self-care activities, and quality of life.ConclusionsIn addition to the usual care, the use of a diabetes-related smartphone application together with weekly text-message support from professional health caregivers can significantly improve glycemic control in adults with type 1 diabetes.CommentThis small study is encouraging regarding changes in HbA1c though discouraging in terms of improving some patient attitudes and behaviors. A larger study would be necessary to document the generalizability of any of the results. We are not surprised that the more difficult to improve self-efficacy, self-care activities, and quality-of-life changes weren't demonstrated. Making a lasting impact on these and other psychosocial outcomes would probably require a more intensive and robust intervention. I am sure that those approaches are being studied and expect/hope that they will demonstrate results soon.Mobile applications for diabetics: a systematic review and expert-based usability evaluation considering the special requirements of diabetes patients age 50 years or olderArnhold M, Quade M, Kirch WResearch Association Public Health Saxony and Saxony-Anhalt, Medizinische Fakultät Carl Gustav Carus, Technische Universität Dresden, Dresden, GermanyJ Med Internet Res 2014;16: e104BackgroundMany mobile health (mhealth) applications (apps) have been developed in recent years to support effective self-management of patients with diabetes mellitus type 1 or 2. Their effectiveness and appropriateness for patient use has not adequately been evaluated. Given the extraordinary low barrier to entry (cost and complexity of developing app is quite low), it is even more important to get a solid understanding of the benefits and liabilities for patients who use apps.MethodsThe authors carried out a systematic review of all currently available diabetes apps for the operating systems iOS and Android. They considered the number of newly released diabetes apps, range of functions, target user groups, languages, acquisition costs, user ratings, available interfaces, and the connection between acquisition costs and user ratings. They also focused on whether the available applications serve the special needs of diabetes patients aged 50 or older by performing an expert-based usability evaluation based on a representative 10% sample of diabetes apps.ResultsIn total, the authors analyzed 656 apps and found that 355 (54.1%) offered just one function and 348 (53.0%) provided a documentation function. The dominating app language was English (85.4%, 560/656), patients represented the main user group (96.0%, 630/656), and the analysis of the costs revealed a trend toward free apps (53.7%, 352/656). The median price of paid apps was €1.90. The average user rating was 3.6 stars (maximum 5). The analyses indicated no clear differences in the user rating between free and paid apps. Only 30 (4.6%) of the 656 available diabetes apps offered an interface to a measurement device. The authors evaluated 66 apps within the usability evaluation. On average, apps were rated best regarding the criterion "comprehensibility" (4.0 out of 5.0), while showing a lack of "fault tolerance" (2.8 out of 5.0). Of the 66 apps, 48 (72.7%) offered the ability to read the screen content aloud. The number of functions was significantly negative correlated with usability. The presence of documentation and analysis functions reduced the usability score significantly by 0.36 and 0.21 points respectively.ConclusionsA great number of diabetes apps already exist, but the majority offer similar functionalities and combine only one to two functions in one app. Patients and physicians alike should be more involved in the app development process. The authors expect that the data transmission of health parameters to physicians will gain more importance in future applications. The usability of diabetes apps for patients aged 50 or older was moderate to good. But this result applied mainly to apps offering a small range of functions. Multifunctional apps were used in much smaller scale. Moreover, the presence of a documentation or analysis function resulted in significantly lower usability scores. The operability of accessibility features for diabetes apps was quite limited, except for the feature "screen reader."CommentOne thing for sure is that there are a plethora of applications claiming to provide education and support to individuals with diabetes. The overwhelming challenge is that very few of them actually have been demonstrated to help. We must be patient. After all, these applications have mostly been built with a single function in mind (e.g., track steps or blood glucose, communicate with a peer, receive reminders, ask an expert a question). It will only be when these apps are integrated into well-conceived and integrated approaches can we expect to see demonstrable and significant changes in outcomes.Web 2.0 chronic disease self-management for older adults: a systematic reviewStellefson M1, Chaney B1, Barry AE1, Chavarria E1, Tennant B1, Walsh-Childers K2, Sriram PS3, Zagora J41Department of Health Education and Behavior, Center for Digital Health and Wellness; 2Department of Journalism; 3Department of Medicine; and 4Department of Health Education and Behavior, University of Florida, Gainesville, FLJ Med Internet Res 2013;15: e35BackgroundParticipatory Web 2.0 interventions promote collaboration to support chronic disease self-management. (Note that Web 1.0 interventions are those that primarily make written and audio materials available online; Web 2.0 interventions utilize a fuller range of Internet services such as contributing to or consuming user-generated health content including blog posts, hospital or doctor reviews, and podcasts; interacting with digital coaches; participating in online discussion boards or online self-help groups; using multimedia-sharing software to share disease management videos, wikis, and podcasts; and using teleconferencing tools such as Skype provide intimate, two-way communication channels). Growth in Web 2.0 interventions has led to the emergence of e-patient communication tools that enable older adults to locate and share disease management information and receive interactive healthcare advice. To date, there are no review articles investigating the planning, implementation, and evaluation of Web 2.0 chronic disease self-management interventions for older adults (mean age ≥50) with one or more chronic disease(s).MethodsA systematic literature search was conducted using six popular health science databases. The RE-AIM (Reach, Efficacy, Adoption, Implementation, and Maintenance) model was used to organize findings and compute a study quality score (SQS) for 15 reviewed articles.ResultsMost interventions were adopted for delivery by multidisciplinary healthcare teams and tested among small samples of white females with diabetes. Studies indicated that Web 2.0 participants felt greater self-efficacy for managing their disease(s) and benefited from communicating with healthcare providers and/or website moderators to receive feedback and social support. Participants noted asynchronous communication tools (e.g., e-mail, discussion boards) and progress tracking features (e.g., graphical displays of uploaded personal data) as being particularly useful for self-management support. Despite high attrition being noted as problematic, this review suggests that greater Web 2.0 engagement may be associated with improvements in health behaviors (e.g., physical activity) and health status (e.g., HRQoL). However, few studies indicated statistically significant improvements in medication adherence, biological outcomes, or healthcare utilization. Mean SQS scores were notably low (mean=63%, SD 18%). Studies were judged to be weakest on the maintenance dimension of RE-AIM; 13 reviewed studies (87%) did not describe any measures taken to sustain Web 2.0 effects past designated study time periods. Detailed process and impact evaluation frameworks were also missing in almost half (n=7) of the reviewed interventions.ConclusionsThere is need for a greater understanding of the costs and benefits associated with using patient-centered Web 2.0 technologies for chronic disease self-management. More research is needed to determine whether the long-term effectiveness of these programs is sustainable among larger, more diverse samples of chronically ill patients. The effective translation of new knowledge, social technologies, and engagement techniques will likely result in novel approaches for empowering, engaging, and educating older adults with chronic disease.CommentThis study quite effectively demonstrates some of the benefits from advanced Internet functionalities on patient outcomes and satisfaction. The effectiveness of any of these interventions, and the components they contain, is based on a variety of elements. These include the appropriateness of foundational theories on which the program is based for the specific target population; the trustworthiness and reputation of the "sponsor" of the website; the usability of the user interfaces; the degree to which individuals are engaged and touch the website over time; the specifics of the content presented and its ability to influence the participant; the support the participant can receive beyond the website; the contextual influences on a person's ability to adopt and sustain new behaviors over time; and more. Despite all the complexity of these efforts, they are quite promising and give us hope for a future in which more individuals will be able to get the education and support they need, when and where they need it.Computer-based interventions to improve self-management in adults with type 2 diabetes: a systematic review and meta-analysisPal K1, Eastwood SV2, Michie S3, Farmer A4, Barnard ML5, Peacock R6, Wood B7, Edwards P8, Murray E11UCL Research Department of Primary Care and Population Health, University College London, London, UK; 2International Centre for Circulatory Health, Imperial College, London, UK; 3Department of Clinical, Educational and Health Psychology, University College, London, UK; 4Department of Primary Care Health Sciences, University of Oxford, Oxford, UK; 5Department of Diabetes, The Whittington Hospital NHS Trust, London, UK; 6Archway Healthcare Library, London, UK; 7Diabetes Self-Management Program (DSMP), Co-creating Health, London, UK; and 8Department of Population Health, London School of Hygiene & Tropical Medicine, London, UKDiabetes Care 2014;37: 1759–66BackgroundStructured patient education programs can reduce the risk of diabetes-related complications. However, people appear to have difficulties attending face-to-face education. Other delivery channels are needed but research supporting their use is limited.MethodsThis review looked at the impact of computer-based diabetes self-management interventions on health status, cardiovascular risk factors, and quality of life of
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