Are processes in acceptance & commitment therapy (Act) related to chronic pain outcomes within individuals over time? : an exploratory study using n-of-1 designs

Acknowledgements The authors would like to thank the European Health Psychology Society for providing a grant that enabled the collaboration of the co-authors for this article. Author contributions HT designed the study, organized the data collection, carried out the statistical analyses and drafted the first version of the manuscript. DJ and MJ supervised the statistical analyses and were actively involved in writing and revising the manuscript. MVH and KS designed the study and were actively involved in writing and revising the manuscript. All authors read and approved the final manuscript.Peer reviewedPublisher PD

Glucose regulation beyond hba_1c in type 2 diabetes treated with insulin:Real-world evidence from the dialect-2 cohort

OBJECTIVE: To investigate glucose variations associated with glycated hemoglobin (HbA(1c)) in insulin-treated patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: Patients included in Diabetes and Lifestyle Cohort Twente (DIALECT)-2 (n = 79) were grouped into three HbA(1c) categories: low, intermediate, and high (≤53, 54–62, and ≥63 mmol/mol or ≤7, 7.1–7.8, and ≥7.9%, respectively). Blood glucose time in range (TIR), time below range (TBR), time above range (TAR), glucose variability parameters, day and night duration, and frequency of TBR and TAR episodes were determined by continuous glucose monitoring (CGM) using the FreeStyle Libre sensor and compared between HbA(1c) categories. RESULTS: CGM was performed for a median (interquartile range) of 10 (7–12) days/patient. TIR was not different for low and intermediate HbA(1c) categories (76.8% [68.3–88.2] vs. 76.0% [72.5.0–80.1]), whereas in the low category, TBR was higher and TAR lower (7.7% [2.4–19.1] vs. 0.7% [0.3–6.1] and 8.2% [5.7–17.6] vs. 20.4% [11.6–27.0], respectively; P < 0.05). Patients in the highest HbA(1c) category had lower TIR (52.7% [40.9–67.3]) and higher TAR (44.1% [27.8–57.0]) than the other HbA(1c) categories (P < 0.05), but did not have less TBR during the night. All patients had more (0.06 ± 0.06/h vs. 0.03 ± 0.03/h; P = 0.002) and longer (88.0 [45.0–195.5] vs. 53.4 [34.4–82.8] minutes; P < 0.001) TBR episodes during the night than during the day. CONCLUSIONS: In this study, a high HbA(1c) did not reduce the occurrence of nocturnal hypoglycemia, and low HbA(1c) was not associated with the highest TIR. Optimal personalization of glycemic control requires the use of newer tools, including CGM-derived parameters

Development of machine learning models to predict cancer-related fatigue in Dutch breast cancer survivors up to 15 years after diagnosis

Purpose: To prevent (chronic) cancer-related fatigue (CRF) after breast cancer, it is important to identify survivors at risk on time. In literature, factors related to CRF are identified, but not often linked to individual risks. Therefore, our aim was to predict individual risks for developing CRF.Methods: Two pre-existing datasets were used. The Nivel-Primary Care Database and the Netherlands Cancer Registry (NCR) formed the Primary Secondary Cancer Care Registry (PSCCR). NCR data with Patient Reported Outcomes Following Initial treatment and Long-term Evaluation of Survivorship (PROFILES) data resulted in the PSCCR-PROFILES dataset. Predictors were patient, tumor and treatment characteristics, and pre-diagnosis health. Fatigue was GP-reported (PSCCR) or patient-reported (PSCCR-PROFILES). Machine learning models were developed, and performances compared using the C-statistic.Results: In PSCCR, 2224/12813 (17%) experienced fatigue up to 7.6 ± 4.4 years after diagnosis. In PSCCR-PROFILES, 254 (65%) of 390 patients reported fatigue 3.4 ± 1.4 years after diagnosis. For both, models predicted fatigue poorly with best C-statistics of 0.561 ± 0.006 (PSCCR) and 0.669 ± 0.040 (PSCCR-PROFILES).Conclusion: Fatigue (GP-reported or patient-reported) could not be predicted accurately using available data of the PSCCR and PSCCR-PROFILES datasets.Implications for Cancer Survivors: CRF is a common but underreported problem after breast cancer. We aimed to develop a model that could identify individuals with a high risk of developing CRF, ideally to help them prevent (chronic) CRF. As our models had poor predictive abilities, they cannot be used for this purpose yet. Adding patient-reported data as predictor could lead to improved results. Until then, awareness for CRF stays crucial

Co-creation of an ICT-supported cancer rehabilitation application for resected lung cancer survivors: design and evaluation

Background Lung cancer (LC) patients experience high symptom burden and significant decline of physical fitness and quality of life following lung resection. Good quality of survivorship care post-surgery is essential to optimize recovery and prevent unscheduled healthcare use. The use of Information and Communication Technology (ICT) can improve post-surgery care, as it enables frequent monitoring of health status in daily life, provides timely and personalized feedback to patients and professionals, and improves accessibility to rehabilitation programs. Despite its promises, implementation of telehealthcare applications is challenging, often hampered by non-acceptance of the developed service by its end-users. A promising approach is to involve the end-users early and continuously during the developmental process through a so-called user-centred design approach. The aim of this article is to report on this process of co-creation and evaluation of a multimodal ICT-supported cancer rehabilitation program with and for lung cancer patients treated with lung resection and their healthcare professionals (HCPs). Methods A user-centered design approach was used. Through semi-structured interviews (n = 10 LC patients and 6 HCPs), focus groups (n = 5 HCPs), and scenarios (n = 5 HCPs), user needs and requirements were elicited. Semi-structured interviews and the System Usability Scale (SUS) were used to evaluate usability of the telehealthcare application with 7 LC patients and 10 HCPs. Results The developed application consists of: 1) self-monitoring of symptoms and physical activity using on-body sensors and a smartphone, and 2) a web based physical exercise program. 71 % of LC patients and 78 % of HCPs were willing to use the application as part of lung cancer treatment. Accessibility of data via electronic patient records was essential for HCPs. LC patients regarded a positive attitude of the HCP towards the application essential. Overall, the usability (SUS median score = 70, range 35–95) was rated acceptable. Conclusions A telehealthcare application that facilitates symptom monitoring and physical fitness training is considered a useful tool to further improve recovery following surgery of resected lung cancer (LC) patients. Involvement of end users in the design process appears to be necessary to optimize chances of adoption, compliance and implementation of telemedicine

Low Physical Activity in Patients with Complicated Type 2 Diabetes Mellitus Is Associated with Low Muscle Mass and Low Protein Intake

Objective: In order to promote physical activity (PA) in patients with complicated type 2 diabetes, a better understanding of daily movement is required. We (1) objectively assessed PA in patients with type 2 diabetes, and (2) studied the association between muscle mass, dietary protein intake, and PA. Methods: We performed cross-sectional analyses in all patients included in the Diabetes and Lifestyle Cohort Twente (DIALECT) between November 2016 and November 2018. Patients were divided into four groups: = 10,000 steps/day. We studied the association between muscle mass (24 h urinary creatinine excretion rate, CER) and protein intake (by Maroni formula), and the main outcome variable PA (steps/day, Fitbit Flex device) using multivariate linear regression analyses. Results: In the 217 included patients, the median steps/day were 6118 (4115-8638). Of these patients, 48 patients (22%) took 7000-9999 steps/day, 37 patients (17%) took >= 10,000 steps/day, and 78 patients (36%) took = 10,000 steps/day, a higher body mass index (BMI) (33 +/- 6 vs. 30 +/- 5 kg/m(2), p = 0.009), lower CER (11.7 +/- 4.8 vs. 14.8 +/- 3.8 mmol/24 h, p = 0.001), and lower protein intake (0.84 +/- 0.29 vs. 1.08 +/- 0.22 g/kg/day, p < 0.001). Both creatinine excretion (beta = 0.26, p < 0.001) and dietary protein intake (beta = 0.31, p < 0.001) were strongly associated with PA, which remained unchanged after adjustment for potential confounders. Conclusions: Prevalent insufficient protein intake and low muscle mass co-exist in obese patients with low physical activity. Dedicated intervention studies are needed to study the role of sufficient protein intake and physical activity in increasing or maintaining muscle mass in patients with type 2 diabetes

Telemonitoring of Daily Activity and Symptom Behavior in Patients with COPD

Objectives. This study investigated the activity behavior of patients with COPD in detail compared to asymptomatic controls, and the relationship between subjective and objective activities (awareness), and readiness to change activity behavior. Methods. Thirtynine patients with COPD (66.0 years; FEV 1 % predicted: 44.9%) and 21 healthy controls (57.0 years) participated. Objective daily activity was assessed by accelerometry and expressed as amount of activity in counts per minute (cpm). Patients&apos; baseline subjective activity and stage of change were assessed prior to measurements. Results. Mean daily activity in COPD patients was significantly lower compared to the healthy controls (864 ± 277 cpm versus 1162 ± 282 cpm, P &lt; 0.001). COPD patients showed a temporary decrease in objective activities in the early afternoon. Objective and subjective activities were significantly moderately related and most patients (55.3%) were in the maintenance phase of the stages of change. Conclusions. COPD patients show a distinctive activity decrease in the early afternoon. COPD patients are moderately aware of their daily activity but regard themselves as physically active. Therefore, future telemedicine interventions might consider creating awareness of an active lifestyle and provide feedback that aims to increase and balance activity levels

Interpreting streaming biosignals:in search of best approaches to augmenting mobile health monitoring with machine learning for adaptive clinical decision support

We investigate Body Area Networks for ambulant patient monitoring. As well as sensing physiological parameters, BAN applications may provide feedback to patients. Automating formulation of feedback requires realtime analysis and interpretation of streaming biosignals and other context and knowledge sources. We illustrate with two prototype applications: the first is designed to detect epileptic seizures and support appropriate intervention. The second is a decision support application aiding weight management; the goal is to promote health and prevent chronic illnesses associated with overweight/obesity. We begin to explore extending these and other m-health applications with generic AI-based decision support and machine learning. Monitoring success of different behavioural change strategies could provide a basis for machine learning, enabling adaptive clinical decision support by personalising and adapting strategies to individuals and their changing needs. Data mining applied to BAN data aggregated from large numbers of patients opens up possibilities for discovery of new clinical knowledge

Rest rust! physical active for active and healthy ageing

The aim of this paper is to give an insight on how physical activity can be defined, parameterized and measured in older adults and on different options to deal with citizen physical activity promotion at European level. Three relevant aspects are highlighted: 1. When talking about physical activity, two different aspects are often unfairly mixed up: “physical activity” and “physical capacity”. • Physical activity, is referred to as the level of physical activity someone is actually performing in daily life. • Physical capacity is referred to as the maximum physical activity a person can perform. 2. Both physical activity and physical capacity can be expressed in different dimensions such as time, frequency, or type of activity with the consequence that there are many tools and techniques available. In order to support people to choose an appropriate instrument in their everyday practice a list of 9 criteria that are considered important is defined. 3. Older adults score differently across the various physical dimensions, so strategies to promote physical activity should consider individual differences, in order to adapt for these variations

Design decisions for a real time, alcohol craving study using physio- and psychological measures

The current study was a pilot for an alcohol craving monitoring study with a biosensor (E4 wristband) and ecological momentary assessment (EMA) smartphone app. The E4 wristband was evaluated on compliance rates, usability, comfort and stigmatization. Two EMA methodologies (signal- and interval-contingent design) were compared on data variability, compliance and perceived burden. Results show that both EMA methodologies captured variability of craving and compliance rates were between medium to low. The perceived burden of the designs was high, in particular for the signal-contingent design. Participants wore the wristband ranging from occasionally to often and the usability was rated good. Many participants reported frequent questioning about the bracelet, which they indicated as positive. However, addicted individuals are expected not to appreciate this attention, we therefore propose to provide them with coping strategies. Efforts should be made to increase compliance, we therefore propose the interval contingent design with micro incentives