Background: This study compared levels of physical activity (PA) and sedentary behavior (SB) in adults with and without chronicdiseases or multimorbidity, acknowledging sociodemographic factors. Methods: A cross-sectional analysis was conducted with datafrom 27,890 participants (52% women), aged 50–64, from the Swedish CArdioPulmonary bioImage Study cohort. Over 1500chronic diseases were included and categorized into chronic disease and multimorbidity groups. Chronic diseases were retrieved fromnational registries, using International Classification of Disease codes. PA and SB were measured with a triaxial hip-wornaccelerometer, over 7 consecutive days. General linear models were used to calculate estimated means for daily time spent in light-intensity PA (LIPA), moderate- to vigorous-intensity PA and SB. Results: Time spent in LIPA, moderate- to vigorous-intensity PA,and SB did not differ between individuals in a chronic disease group and individuals without chronic diseases. Individuals living withany multimorbidity spent less time in moderate- to vigorous-intensity PA than participants without chronic diseases. Individualsliving with 4 or more chronic diseases spent more time sedentary than individuals without chronic diseases. Women spent more timein LIPA and less time in SB than men, regardless of chronic disease or multimorbidity. Individuals with less education spent moretime in LIPA and less time in SB than individuals with more education, regardless of chronic disease or multimorbidity. Conclusions:When using an extensive set of chronic diseases, marginal differences in PA and SB were seen between individuals living with andwithout chronic diseases. PA and SB seem to significantly differ by sex and education, rather than living with chronic disease.

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.

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.

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.

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.

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.

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: 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.

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: 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