Mobile communication using a mobile phone with a glucometer for glucose control in Type 2 patients with diabetes: as effective as an Internet-based glucose monitoring system

Cho, Jae‐Hyoung; Lee, Hye-Chung; Lim, Dong-Jun; Kwon, Hyuk Sang; Yoon, Kun‐Ho

doi:10.1258/jtt.2008.080412

Cited by 148 publications

(183 citation statements)

References 18 publications

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“…For instance, personalized messages have shown to enable behavioral change in areas such as smoking cessation [21,22], diabetes management [23,24] and fitness activity [25]. Contrary to the self-exclusion oriented detection approach, we conclude that personalized feedback aims to prevent and minimize harm in the first place and is a much better approach to the prevention of problem gambling than using data from those that self-exclude from gambling.…”

Section: Personalization and Dynamic Feedbackmentioning

confidence: 85%

Should Voluntary "Self-Exclusion" by Gamblers be used as a Proxy Measure for Problem Gambling?

Griffiths¹,

Auer²

2016

MOJAMT

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Self-exclusion practices typically refer to the possibility for gamblers to voluntarily ban themselves from playing all (a selection of) games over a predetermined period. The period of exclusion can typically be chosen by the gambler although some operators have non-negotiable self-exclusion periods. Selfexclusion in both online sites and offline venues has become an important responsible gambling practice that is widely used by socially responsible operators. Auer, Littler and Griffiths. [1] briefly overviewed self-exclusion practices in both online and land-based environments. They argued that empirical research on the effectiveness of VSE in online gambling is rare. Hayer and Mayer [2] investigated a sample of 256 online gamblers who selfexcluded. who were then surveyed six & twelve months later. They found that VSE can have favourable psycho-social effects for the gambler. For instance, players showed a marked decrease in the willingness to gamble online shortly after they self-excluded. Haefeli et al. [3] tried to predict future self-exclusion by analyzing written player correspondence with a gaming company from 150 self-excluders (compared to 150 controls). They were able to correctly predict 76.6% of future self-exclusions based on written communications. Important indicators extracted from customer emails were increasing amount of interaction with customer services over a six-month period prior to self-exclusion, doubts about the results of games, and issues concerning account administration and financial transactions. Self-excluders were also different from controls with respect to the tonality of the email (i.e., they used threats and were more abusive in written communications). Based in previous empirical research, Gainsbury [4] claimed that VSE programs are under-utilized by problem gamblers. In general, it is known that individuals do not seek help for problem gambling until they reach serious crisis [5].Why do Players "Self-Exclude" and who are they?Reasons for players to self-exclude are manifold. In a study by Hayer and Meyer, [2] players frequently reported excluding as a preventive measure and annoyance with the operator as reasons for VSE. Furthermore, about one-fifth of self-excluders reported to be problem. gamblers (21.2%). Using the DSM-IV criteria for pathological gambling [6], Lischer [7] reported that in a study of three Swiss Casinos, 29% of self-excluders were pathological gamblers, 33% were problem gamblers, and 38% were recreational gamblers. Given that many voluntary self-excluders do not exclude themselves for gambling-related problems, Lischer [7] concluded that self-exclusion is not a good indicator of gambling-related problems. In line with these results Dragecevic and colleagues [8] compared self-excluders with other online players and reported no differences in the (i) mean number of gambling hours per month or (ii) minutes per gambling session. Similar to Hayer and Meyer [2], who report self-exclusion to be rather spontaneous, Dragicevic and colleagues [8] reported that 2...

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Section: Personalization and Dynamic Feedbackmentioning

confidence: 85%

Should Voluntary "Self-Exclusion" by Gamblers be used as a Proxy Measure for Problem Gambling?

Griffiths¹,

Auer²

2016

MOJAMT

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“…One example of a mHealth intervention with poor adaptability is the ‘Diabetes phone’, a device created to remotely measure and record a patient’s blood sugar. The device showed promise in reducing HbA1c levels in Korea, but was not adaptable to other cell phones, forcing patients to carry multiple devices in many cases, which decreased use of the device [31]. …”

Section: User Considerationsmentioning

confidence: 99%

Integrating mHealth at point of care in low- and middle-income settings: the system perspective

Wallis

Blessing

Dalwai

et al. 2017

Global Health Action

124

121

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While the field represents a wide spectrum of products and services, many aspects of mHealth have great promise within resource-poor settings: there is an extensive range of cheap, widely available tools which can be used at the point of care delivery. However, there are a number of conditions which need to be met if such solutions are to be adequately integrated into existing health systems; we consider these from regulatory, technological and user perspectives. We explore the need for an appropriate legislative and regulatory framework, to avoid ‘work around’ solutions, which threaten patient confidentiality (such as the extensive use of instant messaging services to deliver sensitive clinical information and seek diagnostic and management advice). In addition, we will look at other confidentiality issues such as the need for applications to remove identifiable information (such as photos) from users’ devices. Integration is dependent upon multiple technological factors, and we illustrate these using examples such as products made available specifically for adoption in low- and middle-income countries. Issues such as usability of the application, signal loss, data volume utilization, need to enter passwords, and the availability of automated or in-app context-relevant clinical advice will be discussed. From a user perspective, there are three groups to consider: experts, front-line clinicians, and patients. Each will accept, to different degrees, the use of technology in care – often with cultural or regional variation – and this is central to integration and uptake. For clinicians, ease of integration into daily work flow is critical, as are familiarity and acceptability of other technology in the workplace. Front-line staff tend to work in areas with more challenges around cell phone signal coverage and data availability than ‘back-end’ experts, and the effect of this is discussed.

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“…37 One study advised participants to measure their blood glucose level at least 8 times a week 43 while 4 studies gave recommendations on frequency per day but did not specify a pattern to follow (eg, before and after specific meals). [33][34][35]46 Frequency of SMBG was either based on primary care provider (PCP) order, not changed for the study, 36 or stated as individualized for the participants. 41,42,44 The most complex system analyzed data, including medications and time of poor SMBG control, to generate a patient specific multipoint profile to enhance data analysis and selfmanagement support.…”

Section: Smbg Profilesmentioning

confidence: 99%

Telehealth Remote Monitoring Systematic Review

Greenwood

Young

Quinn

2014

J Diabetes Sci Technol

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Approximately 26 million Americans have diabetes. When uncontrolled, diabetes is the seventh leading cause of death and the leading cause of kidney failure, blindness, and nontraumatic amputations in the United States. 1 Researchers have found a decrease in complications and improved quality of life when persons with type 2 diabetes achieve national diabetes outcomes targets for blood glucose, blood pressure, and blood fats. 2 Identifying evidence-based solutions to improve outcomes in this population is critical. Patient self-management is an integral component of effective diabetes care and improves diabetes short-term outcomes. 2-4 The American Association of Diabetes Educators (AADE) identifies healthy eating, being active, monitoring, taking medications, problem solving, healthy coping, and reducing risks as 7 essential self-management skills, the AADE7, 5 required for successful behavior change to promote health for persons with diabetes. 6 Clinical studies have demonstrated the importance of self-monitoring of blood glucose (SMBG) in improving hemoglobin A1C (A1C) for persons with type 2 diabetes who use insulin. 7 However, critics cite meta-analyses that demonstrate small improvements in A1C in studies of type 2 diabetes not managed with insulin that, although significant, may not be clinically meaningful. 8 Because the majority of individuals with type 2 diabetes are not treated with insulin, studying and understanding this population independently is important. 9,10 In 2009 the International Diabetes Federation (IDF) published guidelines that focus on structured SMBG protocols to 519311D STXXX10.

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Mobile communication using a mobile phone with a glucometer for glucose control in Type 2 patients with diabetes: as effective as an Internet-based glucose monitoring system

Cited by 148 publications

References 18 publications

Should Voluntary "Self-Exclusion" by Gamblers be used as a Proxy Measure for Problem Gambling?

Should Voluntary "Self-Exclusion" by Gamblers be used as a Proxy Measure for Problem Gambling?

Integrating mHealth at point of care in low- and middle-income settings: the system perspective

Telehealth Remote Monitoring Systematic Review

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