Atlas of type 2 dopamine receptors in the human brain: Age and sex dependent variability in a large PET cohort.

BACKGROUND: The dopamine system contributes to a multitude of functions ranging from reward and motivation to learning and movement control, making it a key component in goal-directed behavior. Altered dopaminergic function is observed in neurological and psychiatric conditions. Numerous factors have been proposed to influence dopamine function, but due to small sample sizes and heterogeneous data analysis methods in previous studies their specific and joint contributions remain unresolved. METHODS: In this cross-sectional register-based study we investigated how age, sex, body mass index (BMI), as well as cerebral hemisphere and regional volume influence striatal type 2 dopamine receptor (D2R) availability in the human brain. We analyzed a large historical dataset (n=156, 120 males and 36 females) of [11C]raclopride PET scans performed between 2004 and 2018. RESULTS: Striatal D2R availability decreased through age for both sexes (2-5 % in striatal ROIs per 10 years) and was higher in females versus males throughout age (7-8% in putamen). BMI and striatal D2R availability were weakly associated. There was no consistent lateralization of striatal D2R. The observed effects were independent of regional volumes. These results were validated using two different spatial normalization methods, and the age and sex effects also replicated in an independent sample (n=135). CONCLUSIONS: D2R availability is dependent on age and sex, which may contribute to the vulnerability of neurological and psychiatric conditions involving altering D2R expression.

lgpr: an interpretable non-parametric method for inferring covariate effects from longitudinal data.

MOTIVATION: Longitudinal study designs are indispensable for studying disease progression. Inferring covariate effects from longitudinal data, however, requires interpretable methods that can model complicated covariance structures and detect non-linear effects of both categorical and continuous covariates, as well as their interactions. Detecting disease effects is hindered by the fact that they often occur rapidly near the disease initiation time, and this time point cannot be exactly observed. An additional challenge is that the effect magnitude can be heterogeneous over the subjects. RESULTS: We present lgpr, a widely applicable and interpretable method for non-parametric analysis of longitudinal data using additive Gaussian processes. We demonstrate that it outperforms previous approaches in identifying the relevant categorical and continuous covariates in various settings. Furthermore, it implements important novel features, including the ability to account for the heterogeneity of covariate effects, their temporal uncertainty, and appropriate observation models for different types of biomedical data. The lgpr tool is implemented as a comprehensive and user-friendly R-package. AVAILABILITY AND IMPLEMENTATION: lgpr is available at jtimonen.github.io/lgpr-usage with documentation, tutorials, test data and code for reproducing the experiments of this article. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Qualifying drug dosing regimens in pediatrics using Gaussian processes.

Drug development commonly studies an adult population first and then the pediatric population. The knowledge from the adult population is taken advantage of for the design of the pediatric trials. Adjusted drug doses for these are often derived from adult pharmacokinetic (PK) models which are extrapolated to patients with smaller body size. This extrapolation is based on scaling physiologic model parameters with a body size measure accounting for organ size differences. The inherent assumption is that children are merely small adults. However, children can be subject to additional effects such as organ maturation. These effects are not present in the adult population and are possibly overlooked at the design stage of the pediatric trials. It is thus crucial to qualify the extrapolation assumptions once the pediatric trial data are available. In this work, we propose a model based on a non-parametric regression method called Gaussian process (GP) to detect deviations from the made extrapolation assumptions. We introduce the theoretical background of this model and compare its performance to a parametric expansion of the adult model. The comparison includes simulations and a clinical study data example. The results show that the GP approach can reliably detect maturation trends from sparse pediatric data.

Interindividual variability and lateralization of µ-opioid receptors in the human brain.

Alterations in the brain's µ-opioid receptor (MOR) system have been associated with several neuropsychiatric disorders. Central MOR availability also varies considerably in healthy individuals. Multiple epidemiological factors have been proposed to influence the MOR system, but due to small sample sizes the magnitude of their influence remains inconclusive. We compiled [11C]carfentanil positron emission tomography scans from 204 individuals with no neurologic or psychiatric disorders, and estimated the effects of sex, age, body mass index (BMI) and smoking on [11C]carfentanil binding potential using between-subject regression analysis. We also examined hemispheric differences in MOR availability. Older age was associated with increase in MOR availability in frontotemporal areas but decrease in amygdala, thalamus, and nucleus accumbens. The age-dependent increase was stronger in males. MOR availability was globally lowered in smokers but independent of BMI. Finally, MOR availability was higher in the right versus the left hemisphere. The presently observed variation in MOR availability may explain why some individuals are prone to develop MOR-linked pathological states, such as chronic pain or psychiatric disorders. Lateralized MOR system may reflect hemispheric work specialization in central emotion and pain processes.

Distributed neural signatures of natural audiovisual speech and music in the human auditory cortex.

During a conversation or when listening to music, auditory and visual information are combined automatically into audiovisual objects. However, it is still poorly understood how specific type of visual information shapes neural processing of sounds in lifelike stimulus environments. Here we applied multi-voxel pattern analysis to investigate how naturally matching visual input modulates supratemporal cortex activity during processing of naturalistic acoustic speech, singing and instrumental music. Bayesian logistic regression classifiers with sparsity-promoting priors were trained to predict whether the stimulus was audiovisual or auditory, and whether it contained piano playing, speech, or singing. The predictive performances of the classifiers were tested by leaving one participant at a time for testing and training the model using the remaining 15 participants. The signature patterns associated with unimodal auditory stimuli encompassed distributed locations mostly in the middle and superior temporal gyrus (STG/MTG). A pattern regression analysis, based on a continuous acoustic model, revealed that activity in some of these MTG and STG areas were associated with acoustic features present in speech and music stimuli. Concurrent visual stimulus modulated activity in bilateral MTG (speech), lateral aspect of right anterior STG (singing), and bilateral parietal opercular cortex (piano). Our results suggest that specific supratemporal brain areas are involved in processing complex natural speech, singing, and piano playing, and other brain areas located in anterior (facial speech) and posterior (music-related hand actions) supratemporal cortex are influenced by related visual information. Those anterior and posterior supratemporal areas have been linked to stimulus identification and sensory-motor integration, respectively.

SLUG transcription factor: a pro-survival and prognostic factor in gastrointestinal stromal tumour.

BACKGROUND: The SLUG transcription factor has been linked with the KIT signalling pathway that is important for gastrointestinal stromal tumour (GIST) tumourigenesis. Its clinical significance in GIST is unknown. METHODS: Influence of SLUG expression on cell proliferation and viability were investigated in GIST48 and GIST882 cell lines. The association between tumour SLUG expression in immunohistochemistry and recurrence-free survival (RFS) was studied in two clinical GIST series, one with 187 patients treated with surgery alone, and another one with 313 patients treated with surgery and adjuvant imatinib. RESULTS: SLUG downregulation inhibited cell proliferation, induced cell death in both cell lines, and sensitised GIST882 cells to lower imatinib concentrations. SLUG was expressed in 125 (25.0%) of the 500 clinical GISTs evaluated, and expression was associated with several factors linked with unfavourable prognosis. SLUG expression was associated with unfavourable RFS both when patients were treated with surgery alone (HR=3.40, 95% CI=1.67-6.89, P=0.001) and when treated with surgery plus adjuvant imatinib (HR=1.83, 95% CI=1.29-2.60, P=0.001). CONCLUSIONS: GIST patients with high tumour SLUG expression have unfavourable RFS. SLUG may mediate pro-survival signalling in GISTs.

Nudged elastic band calculations accelerated with Gaussian process regression.

Minimum energy paths for transitions such as atomic and/or spin rearrangements in thermalized systems are the transition paths of largest statistical weight. Such paths are frequently calculated using the nudged elastic band method, where an initial path is iteratively shifted to the nearest minimum energy path. The computational effort can be large, especially when ab initio or electron density functional calculations are used to evaluate the energy and atomic forces. Here, we show how the number of such evaluations can be reduced by an order of magnitude using a Gaussian process regression approach where an approximate energy surface is generated and refined in each iteration. When the goal is to evaluate the transition rate within harmonic transition state theory, the evaluation of the Hessian matrix at the initial and final state minima can be carried out beforehand and used as input in the minimum energy path calculation, thereby improving stability and reducing the number of iterations needed for convergence. A Gaussian process model also provides an uncertainty estimate for the approximate energy surface, and this can be used to focus the calculations on the lesser-known part of the path, thereby reducing the number of needed energy and force evaluations to a half in the present calculations. The methodology is illustrated using the two-dimensional Müller-Brown potential surface and performance assessed on an established benchmark involving 13 rearrangement transitions of a heptamer island on a solid surface.

The current duration design for estimating the time to pregnancy distribution: a nonparametric Bayesian perspective.

This paper was inspired by the studies of Niels Keiding and co-authors on estimating the waiting time-to-pregnancy (TTP) distribution, and in particular on using the current duration design in that context. In this design, a cross-sectional sample of women is collected from those who are currently attempting to become pregnant, and then by recording from each the time she has been attempting. Our aim here is to study the identifiability and the estimation of the waiting time distribution on the basis of current duration data. The main difficulty in this stems from the fact that very short waiting times are only rarely selected into the sample of current durations, and this renders their estimation unstable. We introduce here a Bayesian method for this estimation problem, prove its asymptotic consistency, and compare the method to some variants of the non-parametric maximum likelihood estimators, which have been used previously in this context. The properties of the Bayesian estimation method are studied also empirically, using both simulated data and TTP data on current durations collected by Slama et al. (Hum Reprod 27(5):1489-1498, 2012).

Gastrointestinal stromal tumor: a method for optimizing the timing of CT scans in the follow-up of cancer patients.

PURPOSE: To develop a mathematical model to adjust the timing of computed tomography (CT) scans with the hazard of cancer recurrence in time to facilitate early detection of cancer recurrence. MATERIALS AND METHODS: The clinical data were extracted from the randomized Scandinavian Sarcoma Group (SSG) XVIII/Arbeitsgemeinschaft Internistische Onkologie (AIO) trial database. The SSG XVIII/AIO trial was registered (trial no. NCT00116935) and approved by the national or institutional review boards. In the trial, 1- and 3-year durations of adjuvant imatinib mesylate in the treatment of patients with gastrointestinal stromal tumor (GIST) were compared. A nonhomogeneous Poisson model with a piecewise log-constant hazard in time that accounts for the nonlinear pattern of GIST recurrence was applied to tumor site, mitotic count, and recurrence data. The optimal times to obtain follow-up CT scans were computed by modifying the frequency of CT scans with the hazard of tumor recurrence in time. The hazard-adjusted follow-up schedules were compared with the National Comprehensive Cancer Network (NCCN) guidelines of the United States, which suggest imaging with CT at intervals of 3-6 months for 3-5 years and then annually. RESULTS: Optimized timing of CT scans on the basis of hazard of recurrence resulted in follow-up schedule options where CT is performed more sparsely than in the NCCN guidelines during adjuvant imatinib administration and more frequently, at approximately 3-month intervals, during the first 2 years that follow imatinib discontinuation when the risk of recurrence was the greatest. The number of CT scans could be reduced by a median of 31% (from 13 to nine) compared with the standard schedules within the first 6 years of follow-up without increasing the delay in recurrence detection. CONCLUSION: Detection of GIST recurrence may be enhanced by adjusting the timing of the CT scans with the hazard of recurrence. The method may be applicable to other human tumor types. Online supplemental material is available for this article.

Risk of recurrence of gastrointestinal stromal tumour after surgery: an analysis of pooled population-based cohorts.

BACKGROUND: The risk of recurrence of gastrointestinal stromal tumour (GIST) after surgery needs to be estimated when considering adjuvant systemic therapy. We assessed prognostic factors of patients with operable GIST, to compare widely used risk-stratification schemes and to develop a new method for risk estimation. METHODS: Population-based cohorts of patients diagnosed with operable GIST, who were not given adjuvant therapy, were identified from the literature. Data from ten series and 2560 patients were pooled. Risk of tumour recurrence was stratified using the National Institute of Health (NIH) consensus criteria, the modified consensus criteria, and the Armed Forces Institute of Pathology (AFIP) criteria. Prognostic factors were examined using proportional hazards and non-linear models. The results were validated in an independent centre-based cohort consisting of 920 patients with GIST. FINDINGS: Estimated 15-year recurrence-free survival (RFS) after surgery was 59·9% (95% CI 56·2-63·6); few recurrences occurred after the first 10 years of follow-up. Large tumour size, high mitosis count, non-gastric location, presence of rupture, and male sex were independent adverse prognostic factors. In receiver operating characteristics curve analysis of 10-year RFS, the NIH consensus criteria, modified consensus criteria, and AFIP criteria resulted in an area under the curve (AUC) of 0·79 (95% CI 0·76-0·81), 0·78 (0·75-0·80), and 0·82 (0·80-0·85), respectively. The modified consensus criteria identified a single high-risk group. Since tumour size and mitosis count had a non-linear association with the risk of GIST recurrence, novel prognostic contour maps were generated using non-linear modelling of tumour size and mitosis count, and taking into account tumour site and rupture. The non-linear model accurately predicted the risk of recurrence (AUC 0·88, 0·86-0·90). INTERPRETATION: The risk-stratification schemes assessed identify patients who are likely to be cured by surgery alone. Although the modified NIH classification is the best criteria to identify a single high-risk group for consideration of adjuvant therapy, the prognostic contour maps resulting from non-linear modelling are appropriate for estimation of individualised outcomes. FUNDING: Academy of Finland, Cancer Society of Finland, Sigrid Juselius Foundation and Helsinki University Research Funds.

Bird tolerance to humans in open tropical ecosystems.

Animal tolerance towards humans can be a key factor facilitating wildlife-human coexistence, yet traits predicting its direction and magnitude across tropical animals are poorly known. Using 10,249 observations for 842 bird species inhabiting open tropical ecosystems in Africa, South America, and Australia, we find that avian tolerance towards humans was lower (i.e., escape distance was longer) in rural rather than urban populations and in populations exposed to lower human disturbance (measured as human footprint index). In addition, larger species and species with larger clutches and enhanced flight ability are less tolerant to human approaches and escape distances increase when birds were approached during the wet season compared to the dry season and from longer starting distances. Identification of key factors affecting animal tolerance towards humans across large spatial and taxonomic scales may help us to better understand and predict the patterns of species distributions in the Anthropocene.

Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER.

In this article we introduce the DRIFTER algorithm, which is a new model based Bayesian method for retrospective elimination of physiological noise from functional magnetic resonance imaging (fMRI) data. In the method, we first estimate the frequency trajectories of the physiological signals with the interacting multiple models (IMM) filter algorithm. The frequency trajectories can be estimated from external reference signals, or if the temporal resolution is high enough, from the fMRI data. The estimated frequency trajectories are then used in a state space model in combination of a Kalman filter (KF) and Rauch-Tung-Striebel (RTS) smoother, which separates the signal into an activation related cleaned signal, physiological noise, and white measurement noise components. Using experimental data, we show that the method outperforms the RETROICOR algorithm if the shape and amplitude of the physiological signals change over time.

The effectiveness of physical activity monitoring and distance counseling in an occupational setting - results from a randomized controlled trial (CoAct).

BACKGROUND: Lack of physical activity (PA) is a known risk factor for many health conditions. The workplace is a setting often used to promote activity and health. We investigated the effectiveness of an intervention on PA and productivity-related outcomes in an occupational setting. METHODS: We conducted a randomized controlled trial of 12 months duration with two 1:1 allocated parallel groups of insurance company employees. Eligibility criteria included permanent employment and absence of any condition that risked the participant's health during PA. Subjects in the intervention group monitored their daily PA with an accelerometer, set goals, had access to an online service to help them track their activity levels, and received counseling via telephone or web messages for 12 months. The control group received the results of a fitness test and an information leaflet on PA at the beginning of the study. The intervention's aim was to increase PA, improve work productivity, and decrease sickness absence. Primary outcomes were PA (measured as MET minutes per week), work productivity (quantity and quality of work; QQ index), and sickness absence (SA) days at 12 months. Participants were assigned to groups using block randomization with a computer-generated scheme. The study was not blinded. RESULTS: There were 544 randomized participants, of which 521 were included in the analysis (64% female, mean age 43 years). At 12 months, there was no significant difference in physical activity levels between the intervention group (n = 264) and the control group (n = 257). The adjusted mean difference was -206 MET min/week [95% Bayesian credible interval -540 to 128; negative values favor control group]. There was also no significant difference in the QQ index (-0.5 [-4.4 to 3.3]) or SA days (0.0 [-1.2 to 0.9]). Of secondary outcomes, body weight (0.5 kg [0.0 to 1.0]) and percentage of body fat (0.6% [0.2% to 1.1%]) were slightly higher in the intervention group. An exploratory subgroup analysis revealed no subgroups in which the intervention affected physical activity. No adverse events were reported. CONCLUSIONS: The intervention was not found effective, and this study does not provide support for the effectiveness of the workplace PA intervention used here. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00994565.

One vs three years of adjuvant imatinib for operable gastrointestinal stromal tumor: a randomized trial.

CONTEXT: Adjuvant imatinib administered for 12 months after surgery has improved recurrence-free survival (RFS) of patients with operable gastrointestinal stromal tumor (GIST) compared with placebo. OBJECTIVE: To investigate the role of imatinib administration duration as adjuvant treatment of patients who have a high estimated risk for GIST recurrence after surgery. DESIGN, SETTING, AND PATIENTS: Patients with KIT-positive GIST removed at surgery were entered between February 2004 and September 2008 to this randomized, open-label phase 3 study conducted in 24 hospitals in Finland, Germany, Norway, and Sweden. The risk of GIST recurrence was estimated using the modified National Institutes of Health Consensus Criteria. INTERVENTION: Imatinib, 400 mg per day, orally for either 12 months or 36 months, started within 12 weeks of surgery. MAIN OUTCOME MEASURES: The primary end point was RFS; the secondary end points included overall survival and treatment safety. RESULTS: Two hundred patients were allocated to each group. The median follow-up time after randomization was 54 months in December 2010. Diagnosis of GIST was confirmed in 382 of 397 patients (96%) in the intention-to-treat population at a central pathology review. KIT or PDGFRA mutation was detected in 333 of 366 tumors (91%) available for testing. Patients assigned for 36 months of imatinib had longer RFS compared with those assigned for 12 months (hazard ratio [HR], 0.46; 95% CI, 0.32-0.65; P < .001; 5-year RFS, 65.6% vs 47.9%, respectively) and longer overall survival (HR, 0.45; 95% CI, 0.22-0.89; P = .02; 5-year survival, 92.0% vs 81.7%). Imatinib was generally well tolerated, but 12.6% and 25.8% of patients assigned to the 12- and 36-month groups, respectively, discontinued imatinib for a reason other than GIST recurrence. CONCLUSION: Compared with 12 months of adjuvant imatinib, 36 months of imatinib improved RFS and overall survival of GIST patients with a high risk of GIST recurrence. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00116935.

Probabilistic outlier identification for RNA sequencing generalized linear models.

Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers.

Approximate inference for disease mapping with sparse Gaussian processes.

Gaussian process (GP) models are widely used in disease mapping as they provide a natural framework for modeling spatial correlations. Their challenges, however, lie in computational burden and memory requirements. In disease mapping models, the other difficulty is inference, which is analytically intractable due to the non-Gaussian observation model. In this paper, we address both these challenges. We show how to efficiently build fully and partially independent conditional (FIC/PIC) sparse approximations for the GP in two-dimensional surface, and how to conduct approximate inference using expectation propagation (EP) algorithm and Laplace approximation (LA). We also propose to combine FIC with a compactly supported covariance function to construct a computationally efficient additive model that can model long and short length-scale spatial correlations simultaneously. The benefit of these approximations is computational. The sparse GPs speed up the computations and reduce the memory requirements. The posterior inference via EP and Laplace approximation is much faster and is practically as accurate as via Markov chain Monte Carlo.

Fragmented QRS in prediction of cardiac deaths and heart failure hospitalizations after myocardial infarction.

BACKGROUND: Increased QRS fragmentation in visual inspection of 12-lead ECG has shown association with cardiac events in postmyocardial infarction (MI) patients. We investigated user-independent computerized intra-QRS fragmentation analysis in prediction of cardiac deaths and heart failure (HF) hospitalizations after MI. METHODS: Patients (n = 158) with recent MI and reduced left ventricular ejection fraction (LVEF) were studied. A 120-lead body surface potential mapping was performed at hospital discharge. Intra-QRS fragmentation was computed as the number of extrema (fragmentation index FI) in QRS. QRS duration (QRSd) was computed for comparison. RESULTS: During a mean follow-up of 50 months 15 patients suffered cardiac death and 23 were hospitalized for HF. Using the mean + 1 SD as cut-point both parameters were univariate predictors of both end-points. In multivariate analysis including age, gender, LVEF, previous MI, bundle branch block, atrial fibrillation, and diabetes FI was an independent predictor for cardiac deaths (HR 8.7, CI 3.0-25.6) and HF hospitalizations (HR 3.8, CI 1.6-9.3) whereas QRSd only predicted HF hospitalizations (HR 4.6, CI 2.0-10.7). In comparison to QRSd, FI showed better positive (PPA) and equal negative (NPA) predictive accuracy for both end-points, and PPA was further improved when combined to LVEF < 40%. Limiting fragmentation analysis to 12-lead ECG or a randomly selected 8-lead set instead of all 120 leads resulted in an almost similar prediction. CONCLUSIONS: Increased QRS fragmentation in post-MI patients predicts cardiac deaths and HF progression. A computer-based fragmentation analysis is a stronger predictor than QRSd.

Minimum Mode Saddle Point Searches Using Gaussian Process Regression with Inverse-Distance Covariance Function.

The minimum mode following method can be used to find saddle points on an energy surface by following a direction guided by the lowest curvature mode. Such calculations are often started close to a minimum on the energy surface to find out which transitions can occur from an initial state of the system, but it is also common to start from the vicinity of a first-order saddle point making use of an initial guess based on intuition or more approximate calculations. In systems where accurate evaluations of the energy and its gradient are computationally intensive, it is important to exploit the information of the previous evaluations to enhance the performance. Here, we show that the number of evaluations required for convergence to the saddle point can be significantly reduced by making use of an approximate energy surface obtained by a Gaussian process model based on inverse interatomic distances, evaluating accurate energy and gradient at the saddle point of the approximate surface and then correcting the model based on the new information. The performance of the method is tested with start points chosen randomly in the vicinity of saddle points for dissociative adsorption of an H2 molecule on the Cu(110) surface and three gas phase chemical reactions.

Automatic fMRI-guided MEG multidipole localization for visual responses.

Previously, we introduced the use of individual cortical location and orientation constraints in the spatiotemporal Bayesian dipole analysis setting proposed by Jun et al. ([2005]; Neuroimage 28:84-98). However, the model's performance was limited by slow convergence and multimodality of the numerically estimated posterior distribution. In this paper, we present an intuitive way to exploit functional magnetic resonance imaging (fMRI) data in the Markov chain Monte Carlo sampling -based inverse estimation of magnetoencephalographic (MEG) data. We used simulated MEG and fMRI data to show that the convergence and localization accuracy of the method is significantly improved with the help of fMRI-guided proposal distributions. We further demonstrate, using an identical visual stimulation paradigm in both fMRI and MEG, the usefulness of this type of automated approach when investigating activation patterns with several spatially close and temporally overlapping sources. Theoretically, the MEG inverse estimates are not biased and should yield the same results even without fMRI information, however, in practice the multimodality of the posterior distribution causes problems due to the limited mixing properties of the sampler. On this account, the algorithm acts perhaps more as a stochastic optimizer than enables a full Bayesian posterior analysis.

The effectiveness of physical activity monitoring and distance counselling in an occupational health setting--a research protocol for a randomised controlled trial (CoAct).

BACKGROUND: The CoAct (Cocreating Activity) study is investigating a novel lifestyle intervention, aimed at the working population, with daily activity monitoring and distance counselling via telephone and secure web messages. The main purpose of this study is to evaluate the effectiveness of lifestyle counselling on the level of physical activity in an occupational health setting. The purposes include also analysing the potential effects of changes in physical activity on productivity at work and sickness absence, and healthcare costs. This article describes the design of the study and the participant flow until and including randomization. METHODS/DESIGN: CoAct is a randomised controlled trial with two arms: a control group and intervention group with daily activity monitoring and distance counselling. The intervention focuses on lifestyle modification and takes 12 months. The study population consists of volunteers from 1100 eligible employees of a Finnish insurance company. The primary outcomes of this study are change in physical activity measured in MET minutes per week, work productivity and sickness absence, and healthcare utilisation. Secondary outcomes include various physiological measures. Cost-effectiveness analysis will also be performed. The outcomes will be measured by questionnaires at baseline, after 6, 12, and 24 months, and sickness absence will be obtained from the employer's registers. DISCUSSION: No trials are yet available that have evaluated the effectiveness of daily physical activity monitoring and distance counselling in an occupational health setting over a 12 month period and no data on cost-effectiveness of such intervention are available. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00994565.