There is an app for that!:Active2Gether – Smart coaching strategies to promote physical activity

Brug, J. [Promotor]Velde, S.J. te [Copromotor]Klein, M.C.A. [Copromotor

Incentivizing appropriate prescribing in primary care:Development and first results of an electronic health record-based pay-for-performance scheme

Objective Part of the funding of Dutch General Practitioners (GPs) care is based on pay-for-performance, including an incentive for appropriate prescribing according to guidelines in national formularies. Aim of this paper is to describe the development of an indicator and an infrastructure based on prescription data from GP Electronic Health Records (EHR), to assess the level of adherence to formularies and the effects of the pay-for-performance scheme, thereby assessing the usefulness of the infrastructure and the indicator. Methods Adherence to formularies was calculated as the percentage of first prescriptions by the GP for medications that were included in one of the national formularies used by the GP, based on prescription data from EHRs. Adherence scores were collected quarterly for 2018 and 2019 and subsequently sent to health insurance companies for the pay-for-performance scheme. Adherence scores were used to monitor the effect of the pay-for-performance scheme. Results 75% (2018) and 83% (2019) of all GP practicesparticipated. Adherence to formularies was around 85% or 95%, depending on the formulary used. Adherence improved significantly, especially for practices that scored lowest in 2018. Discussion We found high levels of adherence to national formularies, with small improvements after one year. The infrastructure will be used to further stimulate formulary-based prescribing by implementing more actionable and relevant indicators on adherence scores for GPs

Взаимосвязь ожирения и нарушений углеводного обмена с синдромом обструктивного апноэ во сне

Представлены литературные данные клинических исследований, в которых синдром обструктивного апноэ во сне (СОАС) рассматривается как фактор риска развития нарушений углеводного обмена, в том числе сахарного диабета 2−го типа. Анализируется взаимосвязь наиболее значимых факторов, влияющих на прогрессирование нарушений углеводного обмена у пациентов с СОАС. Приведен анализ данных о связи СОАС с диабетической автономной нейропатией и инсулинорезистентностью. Рассматривается возможность применения СРАР−терапии для коррекции метаболических нарушений у пациентов с сахарным диабетом.Представлено літературні дані клінічних досліджень, у яких синдром обструктивного апное під час сну (СОАС) розглянуто як фактор ризику розвитку порушень вуглеводного обміну, у тому числі цукрового діабету 2−го типу. Аналізується взаємозв'язок найбільш значущих факторів, що впливають на прогресування порушень вуглеводного обміну у пацієнтів із СОАС. Наведено аналіз даних про зв'язок СОАС із діабетичною автономною нейропатією та інсулінорезистентністю. Розглянуто можливість використання СРАР−терапії для корекції метаболічних порушень у пацієнтів із цукровим діабетом.Literature data about clinical trials, in which sleep apnea syndrome (SAS) is featured as a risk factor of carbohydrate metabolism disorders, including type 2 diabetes mellitus, are presented. Association of the most significant factors influencing the progress carbohydrate metabolism disorders in patients with SAS is analyzed. The data about the association of SAS and diabetic autonomous neuropathy and insulin resistance are featured. Possibility to use CPAP therapy for correction of metabolic disorders in patients with diabetes mellitus is discussed

Using text messages to support recovering substance misusers

YesBackground: The use of digital technology in health and social care is developing rapidly. It is promoted in UK policy and research which suggests varied results surrounding its implementation and outcomes. Introduction: This article aimed to test the implementation and outcomes of a short messaging service sent to a dedicated phone. The target cohort were drug treatment clients in two sites in Northern England. Materials and methods: Through staff focus groups and interviews with a small cohort of clients, the implementation and perceptions of the system were examined. Results: Nineteen participants were recruited to site 1 (15 male, 4 female, average age=37.7 years) and 12 participants were recruited to site 2 (9 male, 3 female, average age=40.3 years). One outcome that was of interest was well-being in treatment which, in this study, was described as an overall sense of feeling better rather than just focusing on the rehabilitation aspect of the programme. Other outcomes included: the successful completion of treatment and any relapse or associated reported drug use. Discussion: The system shows some evidence of its ‘social actor’ role; however, its implementation was hindered by staff citing that it called for increased resources. For future implementation the use of client’s own phones may be considered which may help to embed the system more fully in recovery planning and targeting clients at a different treatment stage. Conclusions: Despite some indications of positive results for clients and a perception that the system may have value as an addition to existing clinical interventions, more evaluation is required to determine whether this system can be implemented in a drug treatment setting

What technological features are used in smartphone apps that promote physical activity? A review and content analysis

Despite the well-known health benefits of physical activity, a large proportion of the population does not meet the guidelines. Hence, effective and widely accessible interventions to increase levels of physical activity are needed. Over the recent years, the number of health and fitness apps has grown rapidly, and they might form part of the solution to the widespread physical inactivity. However, it remains unclear to which extent they make use of the possibilities of mobile technology and form real e-coaching systems. This study aims to investigate the current landscape of smartphone apps that promote physical activity for healthy adults. Therefore, we present a framework to rate the extent to which such apps incorporate technological features. And, we show that the physical activity promotion apps included in the review implemented an average of approximately eight techniques and functions. The features that were implemented most often were user input, textual/numerical overviews of the user’s behavior and progress, sharing achievements or workouts in social networks, and general advice on physical activity. The features that were present least often were adaptation, integration with external sources, and encouragement through gamification, some form of punishment or the possibility to contact an expert. Overall, the results indicate that apps can be improved substantially in terms of their utilization of the possibilities that current mobile technology offers

Dutch Young Adults Ratings of Behavior Change Techniques Applied in Mobile Phone Apps to Promote Physical Activity: A Cross-Sectional Survey

BACKGROUND: Interventions delivered through new device technology, including mobile phone apps, appear to be an effective method to reach young adults. Previous research indicates that self-efficacy and social support for physical activity and self-regulation behavior change techniques (BCT), such as goal setting, feedback, and self-monitoring, are important for promoting physical activity; however, little is known about evaluations by the target population of BCTs applied to physical activity apps and whether these preferences are associated with individual personality characteristics. OBJECTIVE: This study aimed to explore young adults’ opinions regarding BCTs (including self-regulation techniques) applied in mobile phone physical activity apps, and to examine associations between personality characteristics and ratings of BCTs applied in physical activity apps. METHODS: We conducted a cross-sectional online survey among healthy 18 to 30-year-old adults (N=179). Data on participants’ gender, age, height, weight, current education level, living situation, mobile phone use, personality traits, exercise self-efficacy, exercise self-identity, total physical activity level, and whether participants met Dutch physical activity guidelines were collected. Items for rating BCTs applied in physical activity apps were selected from a hierarchical taxonomy for BCTs, and were clustered into three BCT categories according to factor analysis: “goal setting and goal reviewing,” “feedback and self-monitoring,” and “social support and social comparison.” RESULTS: Most participants were female (n=146), highly educated (n=169), physically active, and had high levels of self-efficacy. In general, we observed high ratings of BCTs aimed to increase “goal setting and goal reviewing” and “feedback and self-monitoring,” but not for BCTs addressing “social support and social comparison.” Only 3 (out of 16 tested) significant associations between personality characteristics and BCTs were observed: “agreeableness” was related to more positive ratings of BCTs addressing “goal setting and goal reviewing” (OR 1.61, 95% CI 1.06-2.41), “neuroticism” was related to BCTs addressing “feedback and self-monitoring” (OR 0.76, 95% CI 0.58-1.00), and “exercise self-efficacy” was related to a high rating of BCTs addressing “feedback and self-monitoring” (OR 1.06, 95% CI 1.02-1.11). No associations were observed between personality characteristics (ie, personality, exercise self-efficacy, exercise self-identity) and participants’ ratings of BCTs addressing “social support and social comparison.” CONCLUSIONS: Young Dutch physically active adults rate self-regulation techniques as most positive and techniques addressing social support as less positive among mobile phone apps that aim to promote physical activity. Such ratings of BCTs differ according to personality traits and exercise self-efficacy. Future research should focus on which behavior change techniques in app-based interventions are most effective to increase physical activity

Encouraging physical activity via a personalized mobile system

Engaging in sufficient physical activity has several beneficial effects on physical and mental health. Still, a large proportion of the Western population doesn't meet the guidelines of being moderately to vigorously active for a minimum of 30 minutes on at least five days a week. Mobile technology provides possibilities for personalized coaching systems, and the results of existing mobile interventions look promising. Here, the authors present a mobile system that goes beyond existing (mobile) physical activity interventions. The approach combines theory and evidence-based behavior-change techniques with a model-based reasoning system, to provide the right support and strategies at the right time for obtaining a physically active lifestyle

Active2Gether: Innovative and smart coaching strategies to promote physical activity: A research protocol

Background: Because 67.5 % of Dutch adults is not sufficiently physically active, effective innovative and smart physical activity (PA) promotion is warranted. The ubiquitous nature of smartphones offer new possibilities in PA promotion. Because of their built-in sensors and their ability to connect with the internet or third-party sensors, smartphones offer the possibility to monitor the behavior continuously, provide personalized, real-time and context specific feedback. Active2Gether (A2G) makes use of these possibilities and uniquely combines behavior change techniques with a model-based reasoning system in order to provide context specific coaching messages via smartphones to empower young adults to be and remain more physically active. Intervention: A2G is an app-based intervention that focuses on PA. The overall aim is to increase levels of weekly moderate-vigorous PA (MVPA). It surpasses existing apps as the coaching messages are theory- and literature- based and tailored to psychological and social concepts (e.g. outcome expectations, self-efficacy, social support) as well as to the physical and social context. The messages are based on proven behavior change techniques (e.g., self-monitoring, goal-setting, social-comparison) and are framed in an autonomy-supportive style. Furthermore, social network techniques will be used to influence beliefs and subsequent behavior. The intervention will start with an intake and an initial monitoring phase followed by 6 months of coaching. Methods: A 3-arm randomized trial with a baseline and three followup assessments will be carried out with approximately 150 young adults (18-30 years) in April 2015. PA and determinants of PA will be measured for two purposes: for the evaluation of the intervention and to serve as input for the personalized coaching messages. Actigraph accelerometers will measure weekly PA at baseline and at 3- and 6-months during and at 6 month after the intervention. Additionally, PA will continuously be assessed with a Fitbit One activity tracker. An extensive survey at baseline and the follow-ups will assess demographic characteristics and determinants of PA, while these determinants will repeatedly be measured during the intervention by means of short versions of existing questionnaires. Analysis: An outcome and a process evaluation will be conducted to estimate the effect of the A2G intervention on levels of weekly MVPA and to evaluate the underlying processes and mechanisms with mixed models and latent class growth analysis to explain the (lack of) effect