BACKGROUND: The proportion of overweight or obese pregnant women is increasing in many countries and babies born to a mother who is overweight or obese are at higher risk for complications. Our primary objective was to describe sociodemographic and obstetric factors across Body Mass Index (BMI) classifications, with secondary objective to investigate stillbirth and other pregnancy outcomes in relation to BMI classifications and gestational week.

METHODS: This population-based cohort study with data partly based on a cluster-randomized controlled trial includes 64,632 women with singleton pregnancy, giving birth from 28 weeks' gestation. The time period was January 2016 to 30 June 2018 (2.5 years). Women were divided into five groups according to BMI: below 18.5 underweight, 18.5-24.9 normal weight, 25.0-29.9 overweight, 30.0-34.9 obesity, 35.0 and above, severe obesity.

RESULTS: Data was obtained for 61,800 women. Women who were overweight/obese/severely obese had lower educational levels, were to a lesser extent employed, were more often multiparas, tobacco users and had maternal diseases to a higher extent than women with normal weight. From 40 weeks' gestation, overweight women had a double risk of stillbirth compared to women of normal weight (RR 2.06, CI 1.01-4.21); the risk increased to almost four times higher for obese women (RR 3.97, CI 1.6-9.7). Women who were obese or severely obese had a higher risk of almost all pregnancy outcomes, compared to women of normal weight, such as Apgar score < 7 at 5 min (RR1.54, CI 1.24-1.90), stillbirth (RR 2.16, CI 1.31-3.55), transfer to neonatal care (RR 1.38, CI 1.26-1.50), and instrumental delivery (RR 1.26, CI 1.21-1.31).

CONCLUSIONS: Women who were obese or severely obese had a higher risk of almost all adverse pregnancy outcomes and from gestational week 40, the risk of stillbirth was doubled. The findings indicate a need for national guidelines and individualized care to prevent and reduce negative pregnancy outcomes in overweight/obese women. Preventive methods including preconception care and public health policies are needed to reduce the number of women being overweight/obese when entering pregnancy.

BACKGROUND: Physical activity and a diet that follows general recommendations can help to prevent noncommunicable diseases. However, most adults do not meet current recommended guidelines, and support for behavior change needs to be strengthened. There is growing evidence that shows the benefits of eHealth and mobile health (mHealth) services in promoting healthy habits; however, their long-term effectiveness is uncertain because of nonadherence.

OBJECTIVE: We aimed to explore users' perceptions of acceptability, engagement, and usability of eHealth and mHealth services that promote physical activity, healthy diets, or both in the primary or secondary prevention of noncommunicable diseases.

METHODS: We conducted a systematic review with a narrative synthesis. We performed the literature search in PubMed, PsycINFO, and CINAHL electronic databases in February 2021 and July 2021. The search was limited to papers published in English between 2016 and 2021. Papers on qualitative and mixed method studies that encompassed eHealth and mHealth services for adults with a focus on physical activity, healthy diet, or both in the primary or secondary prevention of noncommunicable diseases were included. Three authors screened the studies independently, and 2 of the authors separately performed thematic analysis of qualitative data.

RESULTS: With an initial finding of 6308 articles and the removal of 427 duplicates, 23 articles were deemed eligible for inclusion in the review. Based on users' preferences, an overarching theme-eHealth and mHealth services provide value but need to be tailored to individual needs-and 5 subthemes-interactive and integrated; varying and multifunctional; easy, pedagogic, and attractive; individualized and customizable; and reliable-emerged.

CONCLUSIONS: New evidence on the optimization of digital services that promote physical activity and healthy diets has been synthesized. The findings represent users' perceptions of acceptability, engagement, and usability of eHealth and mHealth services and show that services should be personalized, dynamic, easily manageable, and reliable. These findings can help improve adherence to digital health-promoting services.

PURPOSE: This study aimed to explore correlations between insulin-degrading enzyme (IDE) and markers of metabolic function in a group of patients diagnosed with type 2 diabetes mellitus (T2DM) or Alzheimer's disease (AD) and metabolically healthy volunteers.

METHOD: We included 120 individuals (47 with T2DM, 9 with AD, and 64 healthy controls). Serum levels of IDE were measured with commercial kits for ELISA. Differences in IDE levels between groups were analyzed with non-parametric ANCOVA, and correlations were analyzed with Spearman's rank correlations. We also investigated the influence of age, sex, and the use of insulin on the correlation using a non-parametric version of partial correlation.

RESULTS: Patients diagnosed with T2DM had higher IDE levels than patients diagnosed with AD and healthy controls after adjustment for age and sex. IDE was increasingly associated with body mass index (BMI), fasting blood glucose, C-peptide, hemoglobin A1c (HbA1c), insulin resistance, and triglycerides. In stratified analyses, we found a decreasing partial correlation between IDE and HbA1c in patients diagnosed with AD and a decreasing partial correlation between IDE and C-peptide in healthy controls. In patients diagnosed with T2DM, we found no partial correlations.

CONCLUSION: These results indicate that IDE is essential in metabolic function and might reflect metabolic status, although it is not yet a biomarker that can be utilized in clinical practice. Further research on IDE in human blood may provide crucial insights into the full function of the enzyme.

PURPOSE: Decreasing levels of serum insulin-degrading enzyme (IDE) have been associated with an increased risk for Alzheimer´s disease (AD) in patients with type 2 diabetes mellitus (T2DM). Research on serum IDE levels in patients with T2DM is sparse and the aim of this study was to explore serum levels of IDE in patients with T2DM.

METHOD: Blood serum samples were obtained from a biobank. Samples from subjects with T2DM and without metabolic disease were divided into subgroups; lifestyle treatment (n = 10), oral antidiabetic treatment (n = 17), insulin treatment (n = 20) and metabolically healthy controls (n = 18). Serum levels of IDE were analysed using specific ELISA assays.

RESULTS: Serum levels of IDE were elevated in subjects with T2DM compared to metabolically healthy individuals (p = 0.033). No significant differences were detected between treatment subgroups.

CONCLUSION: The present study indicates that patients with T2DM have increased serum IDE levels, compared to metabolically healthy individuals. However, for IDE to be clinically useful as a biomarker, its full function and possible use needs to be further elucidated in larger studies showing reproducible outcomes.

OBJECTIVE: To be regularly physically active is of major importance for the health of people with metabolic risk factors. Many of these persons are insufficiently active and in need of support. This study aimed to explore barriers and facilitators perceived by health care professionals' within Swedish primary care in their work to support persons with metabolic risk factors to increase their physical activity.

DESIGN: A qualitative design with focus group discussions was used. The data were analysed using qualitative content analysis with a manifest, inductive approach.

SETTING: Primary health care in five Swedish healthcare regions.

SUBJECTS: Nine physiotherapists, ten physicians and five nurses participated in six digital focus group discussions including two to six participants.

RESULTS: Barriers and facilitators to supporting persons with metabolic risk factors to increase their physical activity were found within four generic categories, where the barriers and facilitators related to each generic category: 'Patient readiness for change', 'Supporting the process of change', 'The professional role', and 'The organisation of primary care'.

CONCLUSION: The findings suggests that barriers and facilitators for supporting patients with metabolic risk factors can be found at several levels within primary care, from individual patient and the health care professionals to the organisational level. In the primary care setting, this should be highlighted when implementing support to increase physical activity in people with metabolic risk factors.KEY POINTSHealth care professionals within primary care are in a position to support people with metabolic risk factors to increase their physical activity.Barriers and facilitators to support the patients should be addressed at several levels within primary care.The study highlights factors on multiple levels such as professional responsibility, organisational prioritisation and resources, and the challenge to motivate behaviour change.

Background: This study aimed to explore predictors associated with intermediate (six months) and post-intervention (24 months) increases in daily steps among people with prediabetes or type 2 diabetes participating in a two-year pedometer intervention.

Methods: A secondary analysis was conducted based on data from people with prediabetes or type 2 diabetes from two intervention arms of the randomised controlled trial Sophia Step Study. Daily steps were measured with an ActiGraph GT1M accelerometer. Participants were divided into two groups based on their response to the intervention: Group 1) ≥ 500 increase in daily steps or Group 2) a decrease or < 500 increase in daily steps. Data from baseline and from six- and 24-month follow-ups were used for analysis. The response groups were used as outcomes in a multiple logistic regression together with baseline predictors including self-efficacy, social support, health-related variables, intervention group, demographics and steps at baseline. Predictors were included in the regression if they had a p-value < 0.2 from bivariate analyses.

Results: In total, 83 participants were included. The mean ± SD age was 65.2 ± 6.8 years and 33% were female. At six months, a lower number of steps at baseline was a significant predictor for increasing ≥ 500 steps per day (OR = 0.82, 95% CI 0.69-0.98). At 24 months, women had 79% lower odds of increasing ≥ 500 steps per day (OR = 0.21, 95% CI 0.05-0.88), compared to men. For every year of increase in age, the odds of increasing ≥ 500 steps per day decreased by 13% (OR = 0.87, 95% CI 0.78-0.97). Also, for every step increase in baseline self-efficacy, measured with the Self-Efficacy for Exercise Scale, the odds of increasing ≥ 500 steps per day increased by 14% (OR = 1.14, 95% CI 1.02-1.27).

Conclusions: In the Sophia Step Study pedometer intervention, participants with a lower number of steps at baseline, male gender, lower age or higher baseline self-efficacy were more likely to respond to the intervention with a step increase above 500 steps per day. More knowledge is needed about factors that influence response to pedometer interventions.

Background: People with prediabetes or type 2 diabetes (T2D) need to be physically active, including moderate-to-vigorous intensity physical activity (MVPA) and light-intensity physical activity (LIPA) and reduce time in sedentary behaviour (SB). Few studies have evaluated the effect of randomised controlled trials taking all movement behaviours into account. This study aimed to investigate the effects of a 2-year pedometer-based intervention in people with prediabetes or T2D on relative time in movement behaviours.

Methods: Secondary analysis of longitudinal data on individuals with prediabetes or T2D from a three-armed randomised controlled trial, the Sophia Step Study, was conducted. The three groups were (1) a multi‑component group (self‑monitoring of steps with a pedometer plus counselling), (2) a single‑component group (self‑monitoring of steps with a pedometer, without counselling), and (3) a standard care group (control). The three behaviours MVPA, LIPA and SB during waking hours were measured with an ActiGraph GT1M accelerometer at baseline, 6, 12, 18 and 24 months. Relative time in MVPA, LIPA and SB for each participant at each time point was calculated and used as outcome measures. Linear mixed models assessed the effect of the intervention over time.

Results: In total 184 participants with mean (SD) age 64.3 (7.6) years and 41% female was included. In the multi-component group, compared to the control group, a significant group-by-time interaction effect for relative time in all three behaviours was found at 6 and 18 months and for MVPA and SB at 24 months. In the single-component group, compared to the control group, an effect occurred in the MVPA and SB behaviours at 6 months and MVPA and LIPA at 24 months. The estimated marginal means ranged from 0.9 to 1.5% of more MVPA, 1.9–3.9% of less LIPA and from 0.5% of less SB to 1.7 more SB in the intervention groups compared to the control group.

Conclusions: The findings show a beneficial effect on all behaviours over time in the two intervention groups compared to the control group. A more pronounced effect occurred in the multi-component intervention compared to the single-component intervention, implicating the importance of counselling in pedometer-based interventions.

Trial registration ClinicalTrials.gov, NCT02374788

OBJECTIVE: Mobile health (mHealth) services suffer from high attrition rates yet represent a viable strategy for adults to improve their health. There is a need to develop evidence-based mHealth services and to constantly evaluate their feasibility. This study explored the acceptability, usability, engagement and optimisation of a co-developed mHealth service, aiming to promote healthy lifestyle behaviours.

METHODS: The service LongLife Active® (LLA) is a mobile app with coaching. Adults were recruited from the general population. Quantitative results and qualitative findings guided the reasoning for the acceptability, usability, engagement and optimisation of LLA. Data from: questionnaires, log data, eight semi-structured interviews with users, feedback comments from users and two focus groups with product developers and coaches were collected. Inductive content analysis was used to analyse the qualitative data. A mixed method approach was used to interpret the findings.

RESULTS: The final sample was 55 users (82% female), who signed up to use the service for 12 weeks. Engagement data was available for 43 (78%). The action plan was the most popular function engaged with by users. The mean scores for acceptability and usability were 3.3/5.0 and 50/100, respectively, rated by 15 users. Users expressed that the service's health focus was unique, and the service gave them a 'kickstart' in their behaviour change. Many ways to optimise the service were identified, including to increase personalisation, promote motivation and improve usability.

CONCLUSION: By incorporating suggestions for optimisation, this service has the potential to support peoples' healthy lifestyle behaviours.

A health promoting-care approach includes supporting the individual to increase control over and to improve his or her health to obtain the best possible wellbeing and quality of life. In prediabetes and type 2 diabetes regular physical activity is of pronounced importance for preventing complications and premature death as well as to enhance quality of life. Supporting the individual in self-management of physical activity and establishing new routines in daily life is a major challenge for the health-care system. There is a need for feasible, low-cost intervention programs to support physical activity in type 2 diabetes care.

The overall aim of this thesis was to design and evaluate the implementation of an intervention in primary care intended to support individuals with prediabetes or type 2 diabetes to become physically active on a regular basis. A secondary aim was to explore the theoretical associations of replacing prolonged sedentary time with time being sedentary but taking breaks, engaging in light intensity physical activity or moderate and vigorous physical activity with health parameters.

Study I was a study protocol describing the assessment protocol and the theoretical framework underlying the randomized controlled trial Sophia Step Study.

Study II was a process evaluation study describing the context of the Sophia Step Study and evaluating the implementation during the first 6 months of intervention. Intervention delivery and dose received were obtained through the process of continuous dialogue with the diabetes specialist nurses, attendance records and data on the number days for step registration and step goal. Changes in physical activity behavior were measured objectively for 159 participants by accelerometers and health outcomes by blood samples, anthropometry and blood pressure. Study III was a qualitative interview study reporting the experiences of 18 adhering participants' after attending 2 years of the Sophia Step Study. Study IV employed a cross-sectional design that included 124 individuals with prediabetes or type 2 diabetes. The study investigated the associations of reallocating sedentary time in bouts (>60 min) to sedentary time in non-bouts (<60 min), light intensity physical activity and to moderate and vigorous physical activity with cardiometabolic risk factors.

The participants in the Sophia Step Study were randomized into one of three parallel groups: a multicomponent intervention (A) entailing individual consultations with a diabetes specialist nurse based on motivational interviewing and physical activity on prescription, group meetings and self-monitoring of steps; a single component intervention (B) encompassing self-monitoring of steps; or a control group (C) entailing usual care except for the assessments that were included in the study (study I). Between April 2013 and October 2016 159 persons were recruited. The interventions were feasible to implement in primary care with a low dropout rate (3%) at 6 months and high fidelity to the study protocol, except for the physical activity on prescription component. There was a high grade of delivery and dose among those enrolled in the study. Group A increased mean daily steps with 1097 steps (CI: 232, 1962), group B increased mean daily steps with 1242 steps (CI: 313, 2171) and the control group (C) decreased mean daily steps with 457 step (CI:-1164, 250). Clinical improvements after 6 months were found in the two intervention groups, while the control group showed mixed results (study II). In total 18 participants with high adherence to the interventions and study assessments were interviewed in study III. The health check-ups were described as personalized, giving feedback on health outcomes and positive reinforcement. Overall, the participants felt that they received good care throughout the study. The self-monitoring of steps, the group sessions and the health check-ups were recalled as resources that increased motivation for physical activity, led to the establishment of new daily routines and in empowering them to take control over their own health. Study IV demonstrated that modeling reallocation of 30 min of objectively measured time from sedentary time in 60 min bouts to moderate and vigorous physical activity was beneficially associated with BMI (b = -1.46 95% CI: -2.60, -0.33 kg/m2), waist circumference (b = -4.30 95% CI: -7.23, -1.38 cm), and HDL cholesterol b = 0.11 95% CI: 0.02, 0.21 mmol/l). No associations of reallocating sedentary time from long bouts to shorter bouts or to light intensity physical activity were seen. Accumulating time in moderate and vigorous physical activity was beneficial, independent of the behavior it replaced.

In conclusion, this thesis shows that it is possible to implement self-monitoring of steps both with and without counseling support as a strategy to support individuals with prediabetes or type 2 diabetes in the primary care setting. The two interventions were effective in increasing physical activity after 6 months. Self-monitoring of steps, feedback on health outcomes and a personalized approach were highly valued by the individuals and should be considered in primary care. The thesis also confirmed that modeling replacement of sedentary time with time in moderate and vigorous physical activity showed beneficial associations with HDL cholesterol, waist circumference and BMI levels in individuals with prediabetes or type 2 diabetes.

AIM: The aim of this study was to investigate the potential associations of reallocating 30 minutes sedentary time in long bouts (>60 min) to sedentary time in non-bouts, light intensity physical activity (LPA) and moderate- to vigorous physical activity (MVPA) with cardiometabolic risk factors in a population diagnosed with prediabetes or type 2 diabetes.

METHODS: Participants diagnosed with prediabetes and type 2 diabetes (n = 124, 50% men, mean [SD] age = 63.8 [7.5] years) were recruited to the physical activity intervention Sophia Step Study. For this study baseline data was used with a cross-sectional design. Time spent in sedentary behaviors in bouts (>60 min) and non-bouts (accrued in <60 min bouts) and physical activity was measured using the ActiGraph GT1M. Associations of reallocating bouted sedentary time to non-bouted sedentary time, LPA and MVPA with cardiometabolic risk factors were examined using an isotemporal substitution framework with linear regression models.

RESULTS: Reallocating 30 minutes sedentary time in bouts to MVPA was associated with lower waist circumference (b = -4.30 95% CI:-7.23, -1.38 cm), lower BMI (b = -1.46 95% CI:-2.60, -0.33 kg/m2) and higher HDL cholesterol levels (b = 0.11 95% CI: 0.02, 0.21 kg/m2. Similar associations were seen for reallocation of sedentary time in non-bouts to MVPA. Reallocating sedentary time in bouts to LPA was associated only with lower waist circumference.

CONCLUSION: Reallocation of sedentary time in bouts as well as non-bouts to MVPA, but not to LPA, was beneficially associated with waist circumference, BMI and HDL cholesterol in individuals with prediabetes and type 2 diabetes. The results of this study confirm the importance of reallocation sedentary time to MVPA.

BACKGROUND: This study aimed to identify distinct profiles of physical activity (PA) patterns among individuals with prediabetes or type 2 diabetes participating in a two-year PA trial and to investigate predictors of the profiles.

METHODS: Data (n = 168, collected 2013-2020) from the cohort of a randomized trial aimed at increasing PA in individuals with prediabetes and type 2 diabetes were used. PA and sedentary behaviours were assessed by waist-worn ActiGraph GT1M accelerometers at baseline and at 6, 12, 18 and 24 months. Fifteen PA and sedentary variables were entered into a latent class mixed model for multivariate longitudinal outcomes. Multinominal regression analysis modelled profile membership based on baseline activity level, age, gender, BMI, disease status and group randomisation.

RESULTS: Two profiles of PA patterns were identified: "Increased activity" (n = 37, 22%) included participants increasing time in PA and decreasing sedentary time. "No change in activity" (n = 131, 78%) included participants with no or minor changes. "Increased activity" were younger (p = 0.003) and more active at baseline (p = 0.011), compared to "No change in activity". No other predictor was associated with profile membership.

CONCLUSIONS: A majority of participants maintained PA and sedentary patterns over two years despite being part of a PA intervention. Individuals improving PA patterns were younger and more active at baseline.

BACKGROUND: Describing implementation features of an intervention is required to compare interventions and to inform policy and best practice. The aim of this study was to conduct a process evaluation of the first 12 months of the Sophia Step Study: a primary care based RCT evaluating a multicomponent (self-monitoring of daily steps plus counseling) and a single component (self-monitoring of steps only) physical activity intervention to standard care on cardiometabolic health.

METHODS: The evaluation was guided by the Medical Research Council Guidance for complex interventions. To describe the implementation communication with the health professionals implementing the interventions, attendance records and tracking of days with self-monitored pedometer-determined steps were used. Change in physical activity behaviour was measured at baseline, 6 and 12 months as daily steps by accelerometry.

RESULTS: During April 2013 to January 2018 188 participants were randomized and intervened directly after inclusion. Response rate was 49% and drop out was 10%. A majority, 78%, had type 2 diabetes and 22% were diagnosed with prediabetes. Mean [Standard deviation (SD)] body mass index was 30.4 (4.4) kg/m² and steps per day was 6566 (3086). The interventions were delivered as intended with minor deviation from the protocol and dose received was satisfying for both the multicomponent and single component group. The mean [95% Confidence Interval (CI)] change in daily steps from baseline to 6 months was 941(227, 1655) steps/day for the multicomponent intervention group, 990 (145, 1836) step/day for the single component group and - 506 (- 1118, 107) for the control group. The mean (95% CI) change in daily steps from baseline to 12 months was 31(- 507, 570) steps/day for the multicomponent intervention group, 144 (- 566, 853) step/day for the single component group and - 890 (- 1485, - 294) for the control group. There was a large individual variation in daily steps at baseline as well as in step change in all three groups.

CONCLUSIONS: Applying self-monitoring of steps is a feasible method to implement as support for physical activity in the primary care setting both with and without counseling support.

TRIAL REGISTRATION: ClinicalTrials.gov , NCT02374788 . Registered 2 March 2015.

BACKGROUND: This aimed to evaluate the effects of self-monitoring of daily steps with or without counselling support on HbA1c, other cardiometabolic risk factors and objectively measured physical activity (PA) during a 2-year intervention in a population with prediabetes or type 2 diabetes.

METHODS: The Sophia Step Study was a three-armed parallel randomised controlled trial. Participants with prediabetes or type 2 diabetes were recruited in a primary care setting. Allocation (1:1:1) was made to a multi-component intervention (self-monitoring of steps with counselling support), a single-component intervention (self-monitoring of steps without counselling support) or standard care. Data were collected for primary outcome HbA1c at baseline and month 6, 12, 18 and 24. Physical activity was assessed as an intermediate outcome by accelerometer (ActiGraph GT1M) for 1 week at baseline and the 6-, 12-, 18- and 24-month follow-up visits. The intervention effects were evaluated by a robust linear mixed model.

RESULTS: In total, 188 subjects (64, 59, 65 in each group) were included. The mean (SD) age was 64 (7.7) years, BMI was 30.0 (4.4) kg/m² and HbA1c was 50 (11) mmol/mol, 21% had prediabetes and 40% were female. The dropout rate was 11% at 24 months. Effect size (CI) for the primary outcome (HbA1c) ranged from -1.3 (-4.8 to 2.2) to 1.1 (-2.4 to 4.6) mmol/mol for the multi-component vs control group and from 0.3 (-3.3 to 3.9) to 3.1 (-0.5 to 6.7) mmol/mol for the single-component vs control group. Effect size (CI) for moderate-to-vigorous physical activity ranged from 8.0 (0.4 to 15.7) to 11.1 (3.3 to 19.0) min/day for the multi-component vs control group and from 7.6 (-0.4 to 15.6) to 9.4 (1.4 to 17.4) min/day for the single-component group vs control group.

CONCLUSION: This 2-year intervention, including self-monitoring of steps with or without counselling, prevented a decrease in PA but did not provide evidence for improved metabolic control and cardiometabolic risk factors in a population with prediabetes or type 2 diabetes.

TRIAL REGISTRATION: ClinicalTrials.gov, NCT02374788 . Registered 2 March 2015-Retrospectively registered.

Introduction: Support for physical activity (PA) is central in diabetes care. The Sophia Step Study is a three-armed randomised controlled trial aiming to evaluate different levels of support for increased PA in prediabetes and Type 2 diabetes. With the purpose to reveal the programme components and the mediating factors from the participants’ perspective this paper aims to report a qualitative exploration of adhering participants’ experiences after two years’ study participation.

Methods: Semi-structured interviews were conducted with 18 participants (men, n = 11, women, n = 7, prediabetes, n = 5, Type 2 diabetes, n = 13, median age 68.5 years) who completed a two-year multi-component (n = 7), singlecomponent (n = 6) intervention or served as controls (n = 5) at a primary care center in Stockholm, Sweden. The interviews were analysed using content analysis with an inductive approach. Sophia Step Study is registered at ClinicalTrials.gov with Identifier: NCT02374788.

Results: The participants recalled the frequent study assessments as providing feedback of health outcomes; positive reinforcement; a sense of sentinel and a personalised approach. Group meetings, pedometers and health check-ups were valued as resources for increased awareness and motivation of PA; establishment of new routines and control over the own health. The long program duration allowed for maintenance of awareness and routines for PA

Conclusion: Adhering participants in theory-based interventions, but also in the control group, identified key mediators to support for PA. Feedback of results, personalised encouragement, emotional support and selfmonitoring should be regarded in self-management of PA to optimise patient motivation and outcomes.

Objectives: To investigate the associations between objectively measured sedentary behavior (SB), light-intensity physical activity (LIPA) and moderate-to-vigorous physical activity (MVPA) and cardiometabolic and endocrine biomarkers, and to estimate the associations of reallocating time from one behavior to another with cardiometabolic and endocrine biomarkers.

Methods: Baseline data from participants diagnosed with prediabetes or type 2 diabetes, n = 175, 58% men, mean (SD) age = 64.4 (7.7), recruited to a physical activity intervention was used. Time spent in SB, LIPA and MVPA was measured by accelerometer and transformed into isometric log-ratio coordinates. The associations between time spent in SB, LIPA and MVPA and biomarkers were examined by linear regression models. The change in each outcome of reallocating time between the three behaviors was estimated.

Results: The findings show strong positive associations of time spent in MVPA and negative associations of time spent in SB relative to time spent in the other behaviors with sagittal abdominal diameter (SAD) and homeostasis model assessment for insulin resistance (HOMA-IR) and negative associations of time spent in SB with high-density lipoprotein (HDL) cholesterol. Theoretically, reallocation of 19 minutes MVPA to SB or to LIPA was associated with a 17% and 17% larger SAD, 39% and 36% larger HOMA-IR values and 3.3% and 2.3% lower levels of HDL, respectively.

Conclusion: In conclusion, our analysis from a time-use perspective supports the current evidence that sedentary time is devastating for the cardiometabolic health. While LIPA probably requires more time, maintaining or increasing time in MVPA are the most important features of the time use behaviors when promoting a favorable cardiometabolic risk profile in adults with prediabetes and type 2 diabetes.

Trial registration: ClinicalTrials.gov, NCT02374788. Registered 2 March 2015 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT02374788.

BACKGROUND: Physical activity prevents or delays progression of impaired glucose tolerance in high-risk individuals. Physical activity promotion should serve as a basis in diabetes care. It is necessary to develop and evaluate health-promoting methods that are feasible as well as cost-effective within diabetes care. The aim of Sophia Step Study is to evaluate the impact of a multi-component and a single component physical activity intervention aiming at improving HbA1c (primary outcome) and other metabolic and cardiovascular risk factors, physical activity levels and overall health in patients with pre- and type 2 diabetes.

METHODS/DESIGN: Sophia Step Study is a randomized controlled trial and participants are randomly assigned to either a multi-component intervention group (A), a pedometer group (B) or a control group (C). In total, 310 patients will be included and followed for 24 months. Group A participants are offered pedometers and a website to register steps, physical activity on prescription with yearly follow-ups, motivational interviewing (10 occasions) and group consultations (including walks, 12 occasions). Group B participants are offered pedometers and a website to register steps. Group C are offered usual care. The theoretical framework underpinning the interventions is the Health Belief Model, the Stages of Change Model, and the Social Cognitive Theory. Both the multi-component intervention (group A) and the pedometer intervention (group B) are using several techniques for behavior change such as self-monitoring, goal setting, feedback and relapse prevention. Measurements are made at week 0, 8, 12, 16, month 6, 9, 12, 18 and 24, including metabolic and cardiovascular biomarkers (HbA1c as primary health outcome), accelerometry and daily steps. Furthermore, questionnaires were used to evaluate dietary intake, physical activity, perceived ability to perform physical activity, perceived support for being active, quality of life, anxiety, depression, well-being, perceived treatment, perceived stress and diabetes self- efficacy.

DISCUSSION: This study will show if a multi-component intervention using pedometers with group- and individual consultations is more effective than a single- component intervention using pedometers alone, in increasing physical activity and improving HbA1c, other metabolic and cardiovascular risk factors, physical activity levels and overall health in patients with pre- and type 2 diabetes.

TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02374788 . Registered 28 January 2015.

Device-measured physical activity and sedentary time are suggested to be more important determinants of all-cause mortality compared to body mass index (BMI) in mainly older adults. However, the joint associations of physical activity and sedentary time with BMI in relation to mortality risk in relatively healthy middle-aged individuals are unclear. We followed 770 adults (56 % women, mean age 55.6 years) from a population-based cohort study for up to 15.3 years. BMI categories were combined with tertiles of total, light, and moderate-to-vigorous physical activity and sedentary time. Cox proportional hazards models estimated hazard ratios (HR) of all-cause mortality with 95% confidence intervals (CI). High total and light intensity physical activity and low sedentary time were associated with a lower risk of mortality in normal weight individuals compared with low active overweight/obese; HR: 0.35 (CI: 0.14, 0.86), HR: 0.33 (CI 0.12, 0.89) and HR: 0.34 (CI: 0.13, 0.92). Among overweight/obese individuals, those who were medium active in light physical activity had a lower mortality risk, HR: 0.36 (CI: 0.15, 0.83), compared with low active. Medium sedentary individuals had a lower risk, HR: 0.43 (CI: 0.20, 0.94) compared with those who were most sedentary. Associations among the most active or least sedentary tertiles were similar irrespective of BMI category. In conclusion, higher physical activity and lower sedentary time were associated with lower mortality risk irrespective of BMI. Physical activity should be promoted and prescribed to individuals with low physical activity levels irrespective of weight status.

BACKGROUND: Many studies have identified key factors affecting the rates of engagement in physical activity in older adults with chronic disease. Environmental conditions, such as weather variations, can present challenges for individuals with chronic diseases, such as type 2 diabetes when engaging in physical activity. However, few studies have investigated the influence of weather on daily steps in people with chronic diseases, especially those with prediabetes and type 2 diabetes.

OBJECTIVE: This study investigated the association between weather variations and daily self-monitored step counts over two years among individuals with prediabetes and type 2 diabetes in Sweden.

METHODS: The study is a secondary analysis using data from the Sophia Step Study, aimed at promoting physical activity among people with prediabetes and type 2 diabetes, which recruited participants from two urban primary care centers in Stockholm and one rural primary care center in southern Sweden over eight rounds. This study measured physical activity using step counters (Yamax Digiwalker SW200) and collected self-reported daily steps. Environmental factors such as daily average temperature, precipitation, and hours of sunshine were obtained from the Swedish Meteorological and Hydrological Institute. A robust linear mixed-effects model was applied as the analysis method.

RESULTS: There was no association found between weather variations and the number of steps taken on a daily basis. The analysis indicated that only 10% of the variation in daily steps could be explained by the average temperature, precipitation, and sunshine hours after controlling for age, gender, and BMI. Conversely, individual factors explained approximately 38% of the variation in the observations.

CONCLUSION: This study revealed that there was no association between weather conditions and the number of daily steps reported by individuals with prediabetes and type 2 diabetes taking part in a physical activity intervention over two years. Despite the weather conditions, women and younger people reported more steps than their male and older counterparts.

BACKGROUND: The trend of an exponential increase in prediabetes and type 2 diabetes (T2D) is projected to continue rising worldwide. Physical activity could help prevent T2D and the progression and complications of the disease. Therefore, we need to create opportunities for individuals to acquire the necessary knowledge and skills to self-manage their chronic condition through physical activity. eHealth is a potential resource that could facilitate self-management and thus improve population health. However, there is limited research on users' perception of eHealth in promoting physical activity in primary care settings.

OBJECTIVE: This study aims to explore the perspectives of health care professionals and individuals with prediabetes and T2D on eHealth to promote physical activity in primary care.

METHODS: A qualitative approach was applied using focus group discussions among individuals with prediabetes or T2D (14 participants in four groups) and health care professionals (10 participants in two groups). The discussions were audio-recorded and transcribed verbatim. Qualitative content analysis was used inductively to code the data.

RESULTS: Three main categories emerged: utility, adoption process, and accountability. The utility of eHealth was described as a motivational, entertaining, and stimulating tool. Registration of daily medical measurements and lifestyle parameters in a cohesive digital platform was recognized as a potential resource for strengthening self-management skills. The adoption process includes eHealth to increase the accessibility of care and personalize the support of physical activity. However, participants stated that digital technology might only suit some and could increase health care providers' administrative burden. Accountability refers to the knowledge and skills to optimize eHealth and ensure data integrity and security.

CONCLUSIONS: People with prediabetes and T2D and health care professionals positively viewed an integration of eHealth technology in primary care to promote physical activity. A cohesive platform using personal metrics, goal-setting, and social support to promote physical activity was suggested. This study identified eHealth illiteracy, inequality, privacy, confidentiality, and an increased workload on health care professionals as factors of concern when integrating eHealth into primary care. Continuous development of eHealth competence was reported as necessary to optimize the implementation of eHealth technology in primary care.