Aims/hypothesis There is a lack of e-health systems that integrate the complex variety of aspects relevant for diabetes self-management. We developed and field-tested an e-health system (POWER2DM)... Show moreAims/hypothesis There is a lack of e-health systems that integrate the complex variety of aspects relevant for diabetes self-management. We developed and field-tested an e-health system (POWER2DM) that integrates medical, psychological and behavioural aspects and connected wearables to support patients and healthcare professionals in shared decision making and diabetes self-management.Methods Participants with type 1 or type 2 diabetes (aged >18 years) from hospital outpatient diabetes clinics in the Netherlands and Spain were randomised using randomisation software to POWER2DM or usual care for 37 weeks. This RCT assessed the change in HbA(1c) between the POWER2DM and usual care groups at the end of the study (37 weeks) as a primary outcome measure. Participants and clinicians were not blinded to the intervention. Changes in quality of life (QoL) (WHO-5 Well-Being Index [WHO-5]), diabetes self-management (Diabetes Self-Management Questionnaire - Revised [DSMQ-R]), glycaemic profiles from continuous glucose monitoring devices, awareness of hypoglycaemia (Clarke hypoglycaemia unawareness instrument), incidence of hypoglycaemic episodes and technology acceptance were secondary outcome measures. Additionally, sub-analyses were performed for participants with type 1 and type 2 diabetes separately.Results A total of 226 participants participated in the trial (108 with type 1 diabetes; 118 with type 2 diabetes). In the POWER2DM group (n=111), HbA(1c) decreased from 60.6 +/- 14.7 mmol/mol (7.7 +/- 1.3%) to 56.7 +/- 12.1 mmol/mol (7.3 +/- 1.1%) (means +/- SD, p<0.001), compared with no change in the usual care group (n=115) (baseline: 61.7 +/- 13.7 mmol/mol, 7.8 +/- 1.3%; end of study: 61.0 +/- 12.4 mmol/mol, 7.7 +/- 1.1%; p=0.19) (between-group difference 0.24%, p=0.008). In the sub-analyses in the POWER2DM group, HbA(1c) in participants with type 2 diabetes decreased from 62.3 +/- 17.3 mmol/mol (7.9 +/- 1.6%) to 54.3 +/- 11.1 mmol/mol (7.1 +/- 1.0%) (p<0.001) compared with no change in HbA(1c) in participants with type 1 diabetes (baseline: 58.8 +/- 11.2 mmol/mol [7.5 +/- 1.0%]; end of study: 59.2 +/- 12.7 mmol/mol [7.6 +/- 1.2%]; p=0.84). There was an increase in the time during which interstitial glucose levels were between 3.0 and 3.9 mmol/l in the POWER2DM group, but no increase in clinically relevant hypoglycaemia (interstitial glucose level below 3.0 mmol/l). QoL improved in participants with type 1 diabetes in the POWER2DM group compared with the usual care group (baseline: 15.7 +/- 3.8; end of study: 16.3 +/- 3.5; p=0.047 for between-group difference). Diabetes self-management improved in both participants with type 1 diabetes (from 7.3 +/- 1.2 to 7.7 +/- 1.2; p=0.002) and those with type 2 diabetes (from 6.5 +/- 1.3 to 6.7 +/- 1.3; p=0.003) within the POWER2DM group. The POWER2DM integrated e-health support was well accepted in daily life and no important adverse (or unexpected) effects or side effects were observed.Conclusions/interpretation POWER2DM improves HbA(1c) levels compared with usual care in those with type 2 diabetes, improves QoL in those with type 1 diabetes, improves diabetes self-management in those with type 1 and type 2 diabetes, and is well accepted in daily life.Trial registration ClinicalTrials.gov NCT03588104.Funding This study was funded by the European Union's Horizon 2020 Research and Innovation Programme (grant agreement number 689444). Show less
Objective: We aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2)... Show moreObjective: We aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2) producing personalized intervention delivery strategies to support self-management by optimizing various intervention components tailored to people's individual needs, momentary contexts, and psychosocial variables.Materials and Methods: We propose a template-based digital intervention design mechanism enabling the configuration of evidence-based, just-in-time, adaptive intervention components. The design mechanism incorporates a rule definition language enabling experts to specify triggering conditions for interventions based on momentary and historical contextual/personal data. The framework continuously monitors and processes personal data space and evaluates intervention-triggering conditions. We benefit from reinforcement learning methods to develop personalized intervention delivery strategies with respect to timing, frequency, and type (content) of interventions. To validate the personalization algorithm, we lay out a simulation testbed with 2 personas, differing in their various simulated real-life conditions.Results: We evaluate the design mechanism by presenting example intervention definitions based on behavior change taxonomies and clinical guidelines. Furthermore, we provide intervention definitions for a real-world care program targeting diabetes patients. Finally, we validate the personalized delivery mechanism through a set of hypotheses, asserting certain ways of adaptation in the delivery strategy, according to the differences in simulation related to personal preferences, traits, and lifestyle patterns.Conclusion: While the design mechanism is sufficiently expandable to meet the theoretical and clinical intervention design requirements, the personalization algorithm is capable of adapting intervention delivery strategies for simulated real-life conditions. Show less
Osch, M. van; Rovekamp, A.J.M.; Bergman-Agteres, S.N.; Wijsman, L.W.; Ooms, S.J.; Mooijaart, S.P.; Vermeulen, J. 2015
