Associations Between Physical Activity and Gastrointestinal Transit Times in People with Normal Weight, Overweight, and Obesity.

BACKGROUND: Rapid gastric emptying is associated with obesity and overeating, whereas delayed gastric emptying is associated with anorexia. Acute effects of exercise on gastric emptying have been investigated extensively, but the influence of habitual physical activity on gastric emptying and transit time in other regions of the gastrointestinal tract is poorly understood. OBJECTIVE: The objective was to investigate associations between objectively measured habitual physical activity and gastrointestinal transit times in adults with varying degrees of adiposity. METHODS: 50 adults (58% women) were included in this cross-sectional study. Physical activity was measured by an accelerometer placed on the lower back for 7 d. Gastric emptying time, small bowel transit time, colonic transit time, and whole gut transit time were simultaneously evaluated by a wireless motility capsule, which was ingested together with a standardized mixed meal. Linear regression models were applied to assess the associations of total activity counts and time spent at different intensities-sedentary activity (0-100 counts/min), low light activity (101-759 counts/min), high light activity (760-1951 counts/min); moderate and vigorous activity (≥1952 counts/min)) with gastrointestinal transit times. RESULTS: Median [Q1; Q3] age was 56.5 [46.6-65.5] y, and body mass index (BMI) was 32.1 [28.5-35.1] kg/m2. For every additional hour spent performing high light intensity physical activity, colonic transit time was 25.5 % [95% CI: 3.10, 42.7] more rapid (P = 0.028), and whole gut transit time was 16.2 % [95% CI: 1.84, 28.4] more rapid (P = 0.028) when adjusted for sex, age, and body fat. No other associations were observed. CONCLUSIONS: More time spent on physical activity at high light intensity was associated with more rapid colonic and whole gut transit time, independent of age, sex, and body fat, whereas other intensities of physical activity and gastrointestinal transit times were not associated. TRIAL REGISTRATION: Clinicaltrials.gov IDs (NCT03894670, NCT03854656).

Impact of a 12-week high-intensity interval training intervention on cardiac structure and function after COVID-19 at 12-month follow-up.

In patients previously hospitalised for COVID-19, a 12-week high-intensity interval training (HIIT) intervention has previously been shown to increase left ventricular mass (LVM) immediately after the intervention. In the present study, we examined the effects of the same HIIT scheme on LVM, pulmonary diffusing capacity, symptom severity and functional capacity at 12-month follow-up. In this investigator-blinded, randomised controlled trial, 12 weeks of a supervised HIIT scheme (4 × 4 min, three times a week) was compared to standard care (control) in patients recently discharged from hospital due to COVID-19. At inclusion and at 12-month follow-up, LVM was assessed by cardiac magnetic resonance imaging (cMRI, primary outcome), while pulmonary diffusing capacity for carbon monoxide (DLCOc, secondary outcome) was examined by the single-breath method. Symptom severity and functional status were examined by the Post-COVID-19 Functional Scale (PCFS) and King's Brief Interstitial Lung Disease (KBILD) questionnaire score. Of the 28 patients assessed at baseline, 22 completed cMRI at 12-month follow-up (12.4 ± 0.6 months after inclusion). LVM was maintained in the HIIT but not the standard care group, with a mean between-group difference of 9.68 [95% CI: 1.72, 17.64] g (P = 0.0182). There was no differences in change from baseline to 12-month follow-up between groups in DLCOc % predicted (-2.45 [-11.25, 6.34]%; P = 0.578). PCFS and KBILD improved similarly in the two groups. In individuals previously hospitalised for COVID-19, a 12-week supervised HIIT scheme resulted in a preserved LVM at 12-month follow-up but did not affect pulmonary diffusing capacity or symptom severity.

Benefits and Harms of Digital Health Interventions Promoting Physical Activity in People With Chronic Conditions: Systematic Review and Meta-Analysis.

BACKGROUND: Digital health interventions for managing chronic conditions have great potential. However, the benefits and harms are still unclear. OBJECTIVE: This systematic review and meta-analysis aimed to investigate the benefits and harms of digital health interventions in promoting physical activity in people with chronic conditions. METHODS: We searched the MEDLINE, Embase, CINAHL, and Cochrane Central Register of Controlled Trials databases from inception to October 2022. Eligible randomized controlled trials were included if they used a digital component in physical activity promotion in adults with ≥1 of the following conditions: depression or anxiety, ischemic heart disease or heart failure, chronic obstructive pulmonary disease, knee or hip osteoarthritis, hypertension, or type 2 diabetes. The primary outcomes were objectively measured physical activity and physical function (eg, walk or step tests). We used a random effects model (restricted maximum likelihood) for meta-analyses and meta-regression analyses to assess the impact of study-level covariates. The risk of bias was assessed using the Cochrane Risk of Bias 2 tool, and the certainty of the evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation. RESULTS: Of 14,078 hits, 130 randomized controlled trials were included. Compared with usual care or minimal intervention, digital health interventions increased objectively measured physical activity (end of intervention: standardized mean difference [SMD] 0.29, 95% CI 0.21-0.37; follow-up: SMD 0.17, 95% CI 0.04-0.31) and physical function (end of intervention: SMD 0.36, 95% CI 0.12-0.59; follow-up: SMD 0.29, 95% CI 0.01-0.57). The secondary outcomes also favored the digital health interventions for subjectively measured physical activity and physical function, depression, anxiety, and health-related quality of life at the end of the intervention but only subjectively measured physical activity at follow-up. The risk of nonserious adverse events, but not serious adverse events, was higher in the digital health interventions at the end of the intervention, but no difference was seen at follow-up. CONCLUSIONS: Digital health interventions improved physical activity and physical function across various chronic conditions. Effects on depression, anxiety, and health-related quality of life were only observed at the end of the intervention. The risk of nonserious adverse events is present during the intervention, which should be addressed. Future studies should focus on better reporting, comparing the effects of different digital health solutions, and investigating how intervention effects are sustained beyond the end of the intervention. TRIAL REGISTRATION: PROSPERO CRD42020189028; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=189028.

The effect of metformin treatment on volumes of free-living physical activity and sedentary behaviour: A post-hoc analysis of the PRE-D trial.

Perceived physical exertion is increased when exercise is performed on metformin treatment, but the clinical relevance of this is unknown. In this post hoc analysis of a randomized, controlled trial, we investigated whether metformin treatment was associated with lower levels of free-living physical activity. Ninety individuals with overweight/obesity (BMI>25 m2/kg) and HbA1c-defined prediabetes (39-47 mmol/mol) were randomized to treatment with dapagliflozin (SGLT2-inhibitor; 10 mg once daily, n=30), metformin (850 mg twice daily, n=30) or no treatment (control, n=30) for 13 weeks in a parallel-group, open-label trial. Before (baseline), during (6 weeks) and immediately after (13 weeks) cessation of treatment, a 6-day assessment of physical activity and sedentary behaviour was performed using accelerometer-based physical activity monitors. Intention-to-treat analyses revealed no within-group changes or differences in change between the groups for any measures of physical activity or sedentary behaviour at neither 6 nor 13 weeks. Short-term metformin treatment does not reduce free-living physical activity level in individuals with overweight/obesity and HbA1c-defined prediabetes.

"I Tried Forcing Myself to do It, but Then It Becomes a Boring Chore": Understanding (dis)engagement in Physical Activity Among Individuals With Type 2 Diabetes Using a Practice Theory Approach.

Lack of physical activity (PA) is common among individuals with type 2 diabetes (T2D). We apply a practice theory approach to investigate PA engagement in the context of T2D. Data were collected through semi-structured individual interviews (n = 23) and focus groups (n = 3x6) and analyzed by deductive-inductive reflexive thematic analysis using a practice theory framework. Forty-one purposefully selected individuals with T2D (29 men) between the ages of 54 and 77 years were included. The analysis resulted in three main themes informed by five subthemes, reflecting the key elements of practice theory (i.e., meanings, materialities, and competencies). One overarching theme identified PA engagement as an unsustainable and insurmountable project in constant and unequal competition with the practice of physical inactivity. To increase PA among individuals with T2D, future PA interventions and strategies should aim to establish a stronger link between PA and everyday life practices.

The interaction between metformin and physical activity on postprandial glucose and glucose kinetics: a randomised, clinical trial.

AIMS/HYPOTHESIS: The aim of this parallel-group, double-blinded (study personnel and participants), randomised clinical trial was to assess the interaction between metformin and exercise training on postprandial glucose in glucose-intolerant individuals. METHODS: Glucose-intolerant (2 h OGTT glucose of 7.8-11.0 mmol/l and/or HbA1c of 39-47 mmol/mol [5.7-6.5%] or glucose-lowering-medication naive type 2 diabetes), overweight/obese (BMI 25-42 kg/m2) individuals were randomly allocated to a placebo study group (PLA, n = 15) or a metformin study group (MET, n = 14), and underwent 3 experimental days: BASELINE (before randomisation), MEDICATION (after 3 weeks of metformin [2 g/day] or placebo treatment) and TRAINING (after 12 weeks of exercise training in combination with metformin/placebo treatment). Training consisted of supervised bicycle interval sessions with a mean intensity of 64% of Wattmax for 45 min, 4 times/week. The primary outcome was postprandial glucose (mean glucose concentration) during a mixed meal tolerance test (MMTT), which was assessed on each experimental day. For within-group differences, a group × time interaction was assessed using two-way repeated measures ANOVA. Between-group changes of the outcomes at different timepoints were compared using unpaired two-tailed Student's t tests. RESULTS: Postprandial glucose improved from BASELINE to TRAINING in both the PLA group and the MET group (∆PLA: -0.7 [95% CI -1.4, 0.0] mmol/l, p = 0.05 and ∆MET: -0.7 [-1.5, -0.0] mmol/l, p = 0.03), with no between-group difference (p = 0.92). In PLA, the entire reduction was seen from MEDICATION to TRAINING (-0.8 [-1.3, -0.1] mmol/l, p = 0.01). Conversely, in MET, the entire reduction was observed from BASELINE to MEDICATION (-0.9 [-1.6, -0.2] mmol/l, p = 0.01). The reductions in mean glucose concentration during the MMTT from BASELINE to TRAINING were dependent on differential time effects: in the PLA group, a decrease was observed at timepoint (t) = 120 min (p = 0.009), whereas in the MET group, a reduction occurred at t = 30 min (p < 0.001). V̇O2peak increased 15% (4.6 [3.3, 5.9] ml kg-1 min-1, p < 0.0001) from MEDICATION to TRAINING and body weight decreased (-4.0 [-5.2, -2.7] kg, p < 0.0001) from BASELINE to TRAINING, with no between-group differences (p = 0.7 and p = 0.5, respectively). CONCLUSIONS/INTERPRETATION: Metformin plus exercise training was not superior to exercise training alone in improving postprandial glucose. The differential time effects during the MMTT suggest an interaction between the two modalities. FUNDING: The Beckett foundation, A.P Møller Foundation, DDA, the Research Foundation of Rigshospitalet and Trygfonden. TRIAL REGISTRATION: ClinicalTrials.gov (NCT03316690). Graphical abstract.

The effects of dapagliflozin, metformin or exercise on glycaemic variability in overweight or obese individuals with prediabetes (the PRE-D Trial): a multi-arm, randomised, controlled trial.

AIMS/HYPOTHESIS: We aimed to investigate the short-term efficacy and safety of three glucose-lowering interventions in overweight or obese individuals with prediabetes defined by HbA1c. METHODS: The PRE-D Trial was a randomised, controlled, parallel, multi-arm, open-label, non-blinded trial performed at Steno Diabetes Center Copenhagen, Gentofte, Denmark. One hundred and twenty participants with BMI ≥25 kg/m2, 30-70 years of age, and prediabetes (HbA1c 39-47 mmol/mol [5.7-6.4%]) were randomised 1:1:1:1 to dapagliflozin (10 mg once daily), metformin (1700 mg daily), interval-based exercise (5 days/week, 30 min/session) or control (habitual lifestyle). Participants were examined at baseline and at 6, 13 and 26 weeks after randomisation. The primary outcome was the 13 week change in glycaemic variability (calculated as mean amplitude of glycaemic excursions [MAGE]) determined using a continuous glucose monitoring system (pre-specified minimal clinically important difference in MAGE ∼30%). RESULTS: One hundred and twelve participants attended the examination at 13 weeks and 111 attended the follow-up visit at 26 weeks. Compared with the control group, there was a small decrease in MAGE in the dapagliflozin group (17.1% [95% CI 0.7, 30.8], p = 0.042) and a small, non-significant, reduction in the exercise group (15.3% [95% CI -1.2, 29.1], p = 0.067), whereas MAGE was unchanged in the metformin group (0.1% [95% CI -16.1, 19.4], p = 0.991)). Compared with the metformin group, MAGE was 17.2% (95% CI 0.8, 30.9; p = 0.041) lower in the dapagliflozin group and 15.4% (95% CI -1.1, 29.1; p = 0.065) lower in the exercise group after 13 weeks, with no difference between exercise and dapagliflozin (2.2% [95% CI -14.8, 22.5], p = 0.815). One serious adverse event occurred in the control group (lung cancer). CONCLUSIONS/INTERPRETATION: Treatment with dapagliflozin and interval-based exercise lead to similar but small improvements in glycaemic variability compared with control and metformin therapy. The clinical importance of these findings in prediabetes is uncertain. TRIAL REGISTRATION: ClinicalTrials.gov NCT02695810 FUNDING: The study was funded by the Novo Nordisk Foundation, AstraZeneca AB, the Danish Innovation Foundation, the University of Copenhagen and Ascensia Diabetes Care Denmark ApS Graphical abstract.

A systematic review of adherence to physical activity interventions in individuals with type 2 diabetes.

Lifestyle interventions are pivotal for successful management of type 2 diabetes (T2D), however, the proportion of people with T2D adhering to physical activity advice has not been thoroughly studied. The purpose of this systematic review was to summarise the evidence on adherence to exercise or physical activity components in lifestyle interventions in those with T2D. We searched MEDLINE EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews and Scopus on 12 November 2019. Eligible studies enrolled adults with T2D and reported the proportion of adherence to lifestyle interventions as a primary or secondary outcome. We included 11 studies (nine randomised controlled trials (RCTs) enrolling 1717 patients and two nonrandomised studies enrolling 62 patients). Only one of the studies had low risk of bias. The proportion of participants adhering to physical activity varied from 32% to 100% with a median of 58%. Adherence was higher in interventions using supervised training and lowest in interventions using remote coaching and the adherence rate in observational studies was higher compared to RCTs (92% vs. 55%; p < 0.01). Study duration, risk of bias, or participants' sex, were not associated with adherence to physical activity. The proportion of those with T2D adhering to physical activity interventions for T2D varies widely and most of the included studies had a high risk of bias. These findings have important implications for planning and power analysis of future trials and when counselling patients about lifestyle interventions including physical activity or exercise components.

No effects of dapagliflozin, metformin or exercise on plasma glucagon concentrations in individuals with prediabetes: A post hoc analysis from the randomized controlled PRE-D trial.

AIM: To assess the effects of dapagliflozin, metformin and exercise treatment on changes in plasma glucagon concentrations in individuals with overweight and HbA1c-defined prediabetes. MATERIALS AND METHODS: One-hundred and twenty individuals with overweight (body mass index ≥ 25 kg/m2 ) and prediabetes (HbA1c of 39-47 mmol/mol) were randomized to a 13-week intervention with dapagliflozin (10 mg once daily), metformin (850 mg twice daily), exercise (30 minutes of interval training 5 days per week) or control (habitual living). A 75-g oral glucose tolerance test (OGTT) (0, 30, 60 and 120 minutes) was administered at baseline, at 13 weeks (end of intervention) and at 26 weeks (end of follow-up). Linear mixed effects models with participant-specific random intercepts were used to investigate associations of the interventions with fasting plasma glucagon concentration, insulin/glucagon ratio and glucagon suppression during the OGTT. RESULTS: At baseline, the median (Q1; Q3) age was 62 (54; 68) years, median fasting plasma glucagon concentration was 11 (7; 15) pmol/L, mean (SD) HbA1c was 40.9 (2.3) mmol/mol and 56% were women. Compared with the control group, fasting glucagon did not change in any of the groups from baseline to the end of the intervention (dapagliflozin group: -5% [95% CI: -29; 26]; exercise group: -8% [95% CI: -31; 24]; metformin group: -2% [95% CI: -27; 30]). Likewise, there were no differences in insulin/glucagon ratio and glucagon suppression during the OGTT between the groups. CONCLUSIONS: In individuals with prediabetes, 13 weeks of treatment with dapagliflozin, metformin or exercise was not associated with changes in fasting or post-OGTT glucagon concentrations.

Effects of an intensive lifestyle intervention on the underlying mechanisms of improved glycaemic control in individuals with type 2 diabetes: a secondary analysis of a randomised clinical trial.

AIMS/HYPOTHESIS: The aim was to investigate whether an intensive lifestyle intervention, with high volumes of exercise, improves beta cell function and to explore the role of low-grade inflammation and body weight. METHODS: This was a randomised, assessor-blinded, controlled trial. Ninety-eight individuals with type 2 diabetes (duration <10 years), BMI of 25-40 kg/m2, no use of insulin and taking fewer than three glucose-lowering medications were randomised (2:1) to either the standard care plus intensive lifestyle group or the standard care alone group. Standard care consisted of individual guidance on disease management, lifestyle advice and blinded regulation of medication following a pre-specified algorithm. The intensive lifestyle intervention consisted of aerobic exercise sessions that took place 5-6 times per week, combined with resistance exercise sessions 2-3 times per week, with a concomitant dietary intervention aiming for a BMI of 25 kg/m2. In this secondary analysis beta cell function was assessed from the 2 h OGTT-derived disposition index, which is defined as the product of the Matsuda and the insulinogenic indices. RESULTS: At baseline, individuals were 54.8 years (SD 8.9), 47% women, type 2 diabetes duration 5 years (IQR 3-8) and HbA1c was 49.3 mmol/mol (SD 9.2); 6.7% (SD 0.8). The intensive lifestyle group showed 40% greater improvement in the disposition index compared with the standard care group (ratio of geometric mean change [RGM] 1.40 [95% CI 1.01, 1.94]) from baseline to 12 months' follow-up. Plasma concentration of IL-1 receptor antagonist (IL-1ra) decreased 30% more in the intensive lifestyle group compared with the standard care group (RGM 0.70 [95% CI 0.58, 0.85]). Statistical single mediation analysis estimated that the intervention effect on the change in IL-1ra and the change in body weight explained to a similar extent (59%) the variance in the intervention effect on the disposition index. CONCLUSIONS/INTERPRETATION: Our findings show that incorporating an intensive lifestyle intervention, with high volumes of exercise, in individuals with type 2 diabetes has the potential to improve beta cell function, associated with a decrease in low-grade inflammation and/or body weight. TRIAL REGISTRATION: ClinicalTrials.gov NCT02417012 Graphical abstract.

Birth weight, cardiometabolic risk factors and effect modification of physical activity in children and adolescents: pooled data from 12 international studies.

OBJECTIVES: Low and high birth weight is associated with higher levels of cardiometabolic risk factors and adiposity in children and adolescents, and increases the risk of cardiovascular diseases, obesity, and early mortality later in life. Moderate-to-vigorous physical activity (MVPA) is associated with lower cardiometabolic risk factors and may mitigate the detrimental consequences of high or low birth weight. Thus, we examined whether MVPA modified the associations between birth weight and cardiometabolic risk factors in children and adolescents. METHODS: We used pooled individual data from 12 cohort- or cross-sectional studies including 9,100 children and adolescents. Birth weight was measured at birth or maternally reported retrospectively. Device-measured physical activity (PA) and cardiometabolic risk factors were measured in childhood or adolescence. We tested for associations between birth weight, MVPA, and cardiometabolic risk factors using multilevel linear regression, including study as a random factor. We tested for interaction between birth weight and MVPA by introducing the interaction term in the models (birth weight x MVPA). RESULTS: Most of the associations between birth weight (kg) and cardiometabolic risk factors were not modified by MVPA (min/day), except between birth weight and waist circumference (cm) in children (p = 0.005) and HDL-cholesterol (mmol/l) in adolescents (p = 0.040). Sensitivity analyses suggested that some of the associations were modified by VPA, i.e., the associations between birth weight and diastolic blood pressure (mmHg) in children (p = 0.009) and LDL- cholesterol (mmol/l) (p = 0.009) and triglycerides (mmol/l) in adolescents (p = 0.028). CONCLUSION: MVPA appears not to consistently modify the associations between low birth weight and cardiometabolic risk. In contrast, MVPA may mitigate the association between higher birth weight and higher waist circumference in children. MVPA is consistently associated with a lower cardiometabolic risk across the birth weight spectrum. Optimal prenatal growth and subsequent PA are both important in relation to cardiometabolic health in children and adolescents.

The multivariate physical activity signature associated with metabolic health in children and youth: An International Children's Accelerometry Database (ICAD) analysis.

There is solid evidence for an association between physical activity and metabolic health outcomes in children and youth, but for methodological reasons most studies describe the intensity spectrum using only a few summary measures. We aimed to determine the multivariate physical activity intensity signature associated with metabolic health in a large and diverse sample of children and youth, by investigating the association pattern for the entire physical intensity spectrum. We used pooled data from 11 studies and 11,853 participants aged 5.8-18.4 years included in the International Children's Accelerometry Database. We derived 14 accelerometry-derived (ActiGraph) physical activity variables covering the intensity spectrum (from 0-99 to ≥8000 counts per minute). To handle the multicollinearity among these variables, we used multivariate pattern analysis to establish the associations with indices of metabolic health (abdominal fatness, insulin sensitivity, lipid metabolism, blood pressure). A composite metabolic health score was used as the main outcome variable. Associations with the composite metabolic health score were weak for sedentary time and light physical activity, but gradually strengthened with increasing time spent in moderate and vigorous intensities (up to 4000-5000 counts per minute). Association patterns were fairly consistent across sex and age groups, but varied across different metabolic health outcomes. This novel analytic approach suggests that vigorous intensity, rather than less intense activities or sedentary behavior, are related to metabolic health in children and youth.

Association of high amounts of physical activity with mortality risk: a systematic review and meta-analysis.

OBJECTIVES: To systematically review and analyse studies of high amounts of physical activity and mortality risk in the general population. ELIGIBILITY CRITERIA: Inclusion criteria related to follow-up (minimum 2 years), outcome (mortality from all causes, cancer, cardiovascular disease (CVD) or coronary heart disease), exposure (eg, a category of >1000 metabolic equivalent of task (MET) min/week), study design (prospective cohort, nested case control or case-cohort) and reports of cases and person years of exposure categories. INFORMATION SOURCES: Systematic searches were conducted in Embase and Pubmed from database inception to 2 March 2019. RISK OF BIAS: The quality of the studies was assessed with the Newcastle-Ottawa scale. INCLUDED STUDIES: From 31 368 studies identified, 48 were included. Two authors independently extracted outcome estimates and assessed study quality. SYNTHESIS OF RESULTS: We estimated hazard ratios (HRs) using random effect restricted cubic spline dose-response meta-analyses. Compared with the recommended level of physical activity (750 MET min/week), mortality risk was lower at physical activity levels exceeding the recommendations, at least until 5000 MET min/week for all cause mortality (HR=0.86, 95% CI 0.78 to 0.94) and for CVD mortality (HR=0.73, 95% CI 0.56 to 0.95). STRENGTHS AND LIMITATIONS OF EVIDENCE: The strengths of this study include the detailed dose-response analyses, inclusion of 48 studies and examination of sources of heterogeneity. The limitations include the observational nature of the included studies and the inaccurate estimations of amount of physical activity. INTERPRETATION: Compared with the recommended level, mortality risk was lower at physical activity levels well above the recommended target range. Further, there was no threshold beyond which lifespan was compromised. REGISTRATION: PROSPERO CRD42017055727.

Health Technology Readiness Profiles Among Danish Individuals With Type 2 Diabetes: Cross-Sectional Study.

BACKGROUND: Information technologies (IT) are increasingly implemented in type 2 diabetes (T2D) treatment as a resource for remotely supported health care. However, possible pitfalls of introducing IT in health care are generally overlooked. Specifically, the effectiveness of IT to improve health care may depend on the user's readiness for health technology. OBJECTIVE: We aim to investigate readiness for health technology in relation to mental well-being, sociodemographic, and disease-related characteristics among individuals with T2D. METHODS: Individuals with T2D (aged ≥18 years) who had been referred to self-management education, exercise, diet counseling, smoking cessation, or alcohol counseling completed a questionnaire survey covering (1) background information, (2) the 5-item World Health Organization Well-Being Index (WHO-5), (3) receptiveness to IT use in physical activity, and (4) the Readiness and Enablement Index for Health Technology (READHY), constituted by dimensions related to self-management, social support, and eHealth literacy. Individuals were divided into profiles using cluster analysis based on their READHY scores. Outcomes included differences across profiles in mental well-being, sociodemographic, and disease-related characteristics. RESULTS: Participants in the study were 155 individuals with T2D with a mean age of 60.2 (SD 10.7) years, 55.5% (86/155) of which were men and 44.5% (69/155) of which were women. Participants were stratified into 5 health technology readiness profiles based on the cluster analysis: Profile 1, high health technology readiness; Profile 2, medium health technology readiness; Profile 3, medium health technology readiness and high level of emotional distress; Profile 4, medium health technology readiness and low-to-medium eHealth literacy; Profile 5, low health technology readiness. No differences in sociodemographic and disease-related characteristics were observed across profiles; however, we identified 3 vulnerable subgroups of individuals: Profile 3 (21/155, 13.5%), younger individuals (mean age of 53.4 years, SD 8.9 years) with low mental well-being (mean 42.7, SD 14.7) and emotional distress (mean 1.69, SD 0.38); Profile 4 (20/155, 12.9%), older individuals (mean age 66.3 years, SD 9.0 years) with less IT use (50.0% used IT for communication) and low-to-medium eHealth literacy; and Profile 5 (36/155, 23.2%) with low mental well-being (mean 43.4, SD 20.1) and low readiness for health technology. CONCLUSIONS: Implementation of IT in health care of individuals with T2D should be based on comprehensive consideration of mental well-being, emotional distress, and readiness for health technology rather than sociodemographic and disease-related characteristics to identify the individuals in need of social support, self-management education, and extensive IT support. A one-size-fits-all approach to IT implementation in health care will potentially increase the risk of treatment failure among the most vulnerable individuals.

Cancer Survivors' Receptiveness to Digital Technology-Supported Physical Rehabilitation and the Implications for Design: Qualitative Study.

BACKGROUND: Physical activity is associated with a positive prognosis in cancer survivors and may decrease the risk of adverse effects of treatment. Accordingly, physical activity programs are recommended as a part of cancer rehabilitation services. Digital technology may support cancer survivors in increasing their level of physical activity and increase the reach or efficiency of cancer rehabilitation services, yet it also comes with a range of challenges. OBJECTIVE: The aim of this qualitative study was to explore cancer survivors' receptiveness to using digital technology as a mode of support to increase their physical activity in a municipality-based cancer rehabilitation setting. METHODS: Semistructured interviews were conducted with 11 cancer survivors (3 males, 8 females, age range 32-82 years) who were referred for cancer rehabilitation and had participated in a questionnaire survey using the Readiness and Enablement Index for Health Technology (READHY) questionnaire. Data analysis was based on the content analysis method. RESULTS: Two themes were identified as important for the interviewees' receptiveness to using digital technology services in connection with their physical activity during rehabilitation: their attitude toward physical activity and their attitude toward digital technology-assisted physical activity. Our results indicated that it is important to address the cancer survivors' motivation for using technology for physical activity and their individual preferences in terms of the following: (1) incidental or structured (eg, cardiovascular and strength exercises or disease-specific rehabilitative exercises) physical activity; (2) social or individual context; and (3) instruction (know-how) or information (know-why). CONCLUSIONS: The identified preferences provide new insight that complements the cancer survivors' readiness level and can likely help designers, service providers, and caregivers provide solutions that increase patient receptiveness toward technology-assisted physical activity. Combining digital technology informed by cancer survivors' needs, preferences, and readiness with the capacity building of the workforce can aid in tailoring digital solutions to suit not only individuals who are receptive to using such technologies but also those reluctant to do so.

Type 2 diabetes remission 1 year after an intensive lifestyle intervention: A secondary analysis of a randomized clinical trial.

AIM: To investigate whether an intensive lifestyle intervention induces partial or complete type 2 diabetes (T2D) remission. MATERIALS AND METHODS: In a secondary analysis of a randomized, assessor-blinded, single-centre trial, people with non-insulin-dependent T2D (duration <10 years), were randomly assigned (2:1, stratified by sex, from April 2015 to August 2016) to a lifestyle intervention group (n = 64) or a standard care group (n = 34). The primary outcome was partial or complete T2D remission, defined as non-diabetic glycaemia with no glucose-lowering medication at the outcome assessments at both 12 and 24 months from baseline. All participants received standard care, with standardized, blinded, target-driven medical therapy during the initial 12 months. The lifestyle intervention included 5- to 6-weekly aerobic and combined aerobic and strength training sessions (30-60 minutes) and individual dietary plans aiming for body mass index ≤25 kg/m2 . No intervention was provided during the 12-month follow-up period. RESULTS: Of the 98 randomized participants, 93 completed follow-up (mean [SD] age 54.6 [8.9] years; 46 women [43%], mean [SD] baseline glycated haemoglobin 49.3 [9.3] mmol/mol). At follow-up, 23% of participants (n = 14) in the intervention and 7% (n = 2) in the standard care group met the criteria for any T2D remission (odds ratio [OR] 4.4, 95% confidence interval [CI] 0.8-21.4]; P = 0.08). Assuming participants lost to follow-up (n = 5) had relapsed, the OR for T2D remission was 4.4 (95% CI 1.0-19.8; P = 0.048). CONCLUSIONS: The statistically nonsignificant threefold increased remission rate of T2D in the lifestyle intervention group calls for further large-scale studies to understand how to implement sustainable lifestyle interventions among people with T2D.

Substituting prolonged sedentary time and cardiovascular risk in children and youth: a meta-analysis within the International Children's Accelerometry database (ICAD).

BACKGROUND: Evidence on the association between sitting for extended periods (i.e. prolonged sedentary time (PST)) and cardio-metabolic health is inconsistent in children. We aimed to estimate the differences in cardio-metabolic health associated with substituting PST with non-prolonged sedentary time (non-PST), light (LIPA) or moderate-to-vigorous physical activity (MVPA) in children. METHODS: Cross-sectional data from 14 studies (7 countries) in the International Children's Accelerometry Database (ICAD, 1998-2009) was included. Accelerometry in 19,502 participants aged 3-18 years, together with covariate and outcome data, was pooled and harmonized. Iso-temporal substitution in linear regression models provided beta coefficients (95%CI) for substitution of 1 h/day PST (sedentary time accumulated in bouts > 15 min) with non-PST, LIPA or MVPA, for each study, which were meta-analysed. RESULTS: Modelling substitution of 1 h/day of PST with non-PST suggested reductions in standardized BMI, but estimates were > 7-fold greater for substitution with MVPA (- 0.44 (- 0.62; - 0.26) SD units). Only reallocation by MVPA was beneficial for waist circumference (- 3.07 (- 4.47; - 1.68) cm), systolic blood pressure (- 1.53 (- 2.42; - 0.65) mmHg) and clustered cardio-metabolic risk (- 0.18 (- 0.3; - 0.1) SD units). For HDL-cholesterol and diastolic blood pressure, substitution with LIPA was beneficial; however, substitution with MVPA showed 5-fold stronger effect estimates (HDL-cholesterol: 0.05 (0.01; 0.10) mmol/l); diastolic blood pressure: - 0.81 (- 1.38; - 0.24) mmHg). CONCLUSIONS: Replacement of PST with MVPA may be the preferred scenario for behaviour change, given beneficial associations with a wide range of cardio-metabolic risk factors (including adiposity, HDL-cholesterol, blood pressure and clustered cardio-metabolic risk). Effect estimates are clinically relevant (e.g. an estimated reduction in waist circumference of ≈1.5 cm for 30 min/day replacement). Replacement with LIPA could be beneficial for some of these risk factors, however with substantially lower effect estimates.

Technology in exercise-based cancer rehabilitation: a cross-sectional study of receptiveness and readiness for e-Health utilization in Danish cancer rehabilitation.

Background: During recent years, there has been an increased focus on technology within cancer survivorship and physical activity rehabilitation. Failure to acknowledge the individuals wishes and/or limitations in the use of technology introduces risk of neglecting or excluding low-resource individuals, thus adding to an already existing inequity within cancer management. It is important to identify vulnerable sub-populations with particular needs when introducing health technology to offer appropriate rehabilitation and support individuals in taking advantage of technology in their physical activity rehabilitation. We report how a population of cancer survivors referred to municipality-based rehabilitation can be segmented, according to their receptiveness and readiness for health technology utilization, to understand their potential barriers towards using technology. Methods: We used a cross-sectional design with convenience sampling among Danish cancer survivors (n = 305) referred to rehabilitation. Participants completed a questionnaire survey covering background information and a new tool to assess self-reported health technology readiness. Demographic, disease and behavioral factors were described in relation to the participants' receptiveness to use technology in physical activity rehabilitation. Participants were stratified according to their health technology readiness and the resulting groups described with regards to the above-mentioned factors. Results: Almost 30% of the participants were un-receptive to use technology in relation to physical activity rehabilitation and were characterized by being more vulnerable with regards to sociodemographic characteristics and scoring lower on dimensions related to eHealth literacy. Stratification of the participants according to their health technology readiness profile revealed four distinct profiles that were significantly different in sociodemographic, disease and behavioral factors. Conclusion: To reduce the risk of alienating low-resource individuals when introducing health technology, evaluation of the individuals' receptiveness to use technology in a rehabilitation context and their readiness for health technology may help tailor the extent to which technology should be offered to assist the cancer survivors.

Development of the Multidimensional Readiness and Enablement Index for Health Technology (READHY) Tool to Measure Individuals' Health Technology Readiness: Initial Testing in a Cancer Rehabilitation Setting.

BACKGROUND: The increasing digitization of health care services with enhanced access to fast internet connections, along with wide use of smartphones, offers the opportunity to get health advice or treatment remotely. For service providers, it is important to consider how consumers can take full advantage of available services and how this can create an enabling environment. However, it is important to consider the digital context and the attributes of current and future users, such as their readiness (ie, knowledge, skills, and attitudes, including trust and motivation). OBJECTIVE: The objective of this study was to evaluate how the eHealth Literacy Questionnaire (eHLQ) combined with selected dimensions from the Health Education Impact Questionnaire (heiQ) and the Health Literacy Questionnaire (HLQ) can be used together as an instrument to characterize an individual's level of health technology readiness and explore how the generated data can be used to create health technology readiness profiles of potential users of health technologies and digital health services. METHODS: We administered the instrument and sociodemographic questions to a population of 305 patients with a recent cancer diagnosis referred to rehabilitation in a setting that plans to introduce various technologies to assist the individuals. We evaluated properties of the Readiness and Enablement Index for Health Technology (READHY) instrument using confirmatory factor analysis, convergent and discriminant validity analysis, and exploratory factor analysis. To identify different health technology readiness profiles in the population, we further analyzed the data using hierarchical and k-means cluster analysis. RESULTS: The confirmatory factor analysis found a suitable fit for the 13 factors with only 1 cross-loading of 1 item between 2 dimensions. The convergent and discriminant validity analysis revealed many factor correlations, suggesting that, in this population, a more parsimonious model might be achieved. Exploratory factor analysis pointed to 5 to 6 constructs based on aggregates of the existing dimensions. The results were not satisfactory, so we performed an 8-factor confirmatory factor analysis, resulting in a good fit with only 1 item cross-loading between 2 dimensions. Cluster analysis showed that data from the READHY instrument can be clustered to create meaningful health technology readiness profiles of users. CONCLUSIONS: The 13 dimensions from heiQ, HLQ, and eHLQ can be used in combination to describe a user's health technology readiness level and degree of enablement. Further studies in other populations are needed to understand whether the associations between dimensions are consistent and the number of dimensions can be reduced.