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Background As the number of cancer survivors is increasing, it is important to be able to offer exercise and physical activity (PA)–promoting interventions that are both effective and reasonably accessible. Internet-based interventions are typically less expensive and more accessible alternatives to on-site supervised interventions. Currently, little is known about the characteristics of nonparticipants in PA promotion trials in the cancer survivorship setting, both in general and specifically in trials using internet-supported interventions. Objective This study aims to gain insight into the characteristics associated with nonparticipation in a blended internet-based supported intervention trial to promote PA. Methods Breast and prostate cancer survivors, 3-36 months after primary curative treatment, were invited to participate in the PABLO trial; this trial compared an internet-based intervention to enhance PA levels, with or without additional support from a physical therapist, to usual care. Participants and nonparticipants were asked to complete a comprehensive questionnaire assessing sociodemographics, fatigue, and health-related quality of life. Baseline data for participants and nonparticipants were compared using the independent Student t test and chi-square test. Results The inclusion rate in the trial was 11.03% (137/1242). Of the nonparticipants, 13.95% (154/1104) completed the questionnaire. Participants were more highly educated (P=.04), had a paid job less often (P=.03), and were on sick leave more often (P=.03). They reported less PA per week, both moderate (P=.03) and vigorous (P Conclusions The participants who self-selected for trial participation reported lower PA levels before diagnosis and a stronger need for support compared with nonparticipants. The trial thus included those patients who might benefit the most from internet-based supportive PA interventions. Trial Registration Netherlands trial register NTR6911; https://www.trialregister.nl/trial/6733

OBJECTIVE To forecast dementia prevalence with a dynamic modelling approach that integrates calendar trends in dementia incidence with those for mortality and cardiovascular disease. DESIGN Modelling study. SETTING General adult population of England and Wales. PARTICIPANTS The English Longitudinal Study of Ageing (ELSA) is a representative panel study with six waves of data across 2002-13. Men and women aged 50 or more years, selected randomly, and their cohabiting partners were recruited to the first wave of ELSA (2002-03). 11392 adults participated (response rate 67%). To maintain representativeness, refreshment participants were recruited to the study at subsequent waves. The total analytical sample constituted 17 906 people. Constant objective criteria based on cognitive and functional impairment were used to ascertain dementia cases at each wave. MAIN OUTCOME MEASURES To estimate calendar trends in dementia incidence, correcting for bias due to loss to follow-up of study participants, a joint model of longitudinal and time-to-event data was fitted to ELSA data. To forecast future dementia prevalence, the probabilistic Markov model IMPACT-BAM (IMPACT-Better Ageing Model) was developed. IMPACT-BAM models transitions of the population aged 35 or more years through states of cardiovascular disease, cognitive and functional impairment, and dementia, to death. It enables prediction of dementia prevalence while accounting for the growing pool of susceptible people as a result of increased life expectancy and the competing effects due to changes in mortality, and incidence of cardiovascular disease. RESULTS In ELSA, dementia incidence was estimated at 14.3 per 1000 person years in men and 17.0/1000 person years in women aged 50 or more in 2010. Dementia incidence declined at a relative rate of 2.7% (95% confidence interval 2.4% to 2.9%) for each year during 2002-13. Using IMPACT-BAM, we estimated there were approximately 767 000 (95% uncertainty interval 735 000 to 797 000) people with dementia in England and Wales in 2016. Despite the decrease in incidence and age specific prevalence, the number of people with dementia is projected to increase to 872 000, 1 092 000, and 1 205 000 in 2020, 2030, and 2040, respectively. A sensitivity analysis without the incidence decline gave a much larger projected growth, of more than 1.9 million people with dementia in 2040. CONCLUSIONS Age specific dementia incidence is declining. The number of people with dementia in England and Wales is likely to increase by 57% from 2016 to 2040. This increase is mainly driven by improved life expectancy.

Background Participation in epidemiological studies has strongly declined in recent years. We examined the reasons for (non)participation in population-based health studies among participants and nonparticipants of a prospective study on influenza vaccination among the elderly. Methods Males and females between 65 and 80 years of age (N = 5582) were randomly selected from the residents’ registration office in Hannover, Germany, and were invited to participate in a study featuring vaccination with a seasonal adjuvanted influenza vaccine (Fluad™, Novartis) including five follow-up visits (day 0, 1/3, 7, 21, 70 with respect to vaccination). A 24-item nonresponder questionnaire, including 10 items on reasons for participating in a hypothetical health study, was mailed to 1500 randomly selected nonparticipants. The same 10 items were included in the end-of-study questionnaire administered to the participants in the vaccination study (n = 200). Logistic regression analysis with backward elimination was used to identify the reasons most strongly associated with nonparticipation. Results Five hundred thirty-one (35%) nonparticipants and 200 participants (100%) returned the respective questionnaires. Nonparticipation was associated with a lower interest in obtaining personal health information (OR = 3.32) and a preference for less invasive (OR = 3.01) and less time-demanding (OR = 2.19) studies. Responses to other items, e.g. regarding altruistic motives, monetary compensation, general interest of the study, or study approval through ethics committee and data security authority, did not differ between participants and nonparticipants. Conclusions Participation rates in health studies among elderly individuals could potentially be improved by reducing interventions and time demand, for instance by implementing methods of self-sampling and remote data collection. Trial registration No. 1100359 (ClinicalTrials.gov, date of registration: 09.02.2015). Electronic supplementary material The online version of this article (doi:10.1186/s12874-017-0302-z) contains supplementary material, which is available to authorized users.

Karin Biering,1 Niels Henrik Hjollund,2,3 Morten Frydenberg4 1Danish Ramazzini Centre, Department of Occupational Medicine – University Research Clinic, Hospital West Jutland, Herning, Denmark; 2WestChronic, Regional Hospital West Jutland, Herning, Denmark; 3Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; 4Section of Biostatistics, Department of Public Health, Aarhus University, Aarhus, Denmark Objective: Missing data is a ubiquitous problem in studies using patient-reported measures, decreasing sample sizes and causing possible bias. In longitudinal studies, special problems relate to attrition and death during follow-up. We describe a methodological approach for the use of multiple imputation (MI) to meet these challenges. Methods: In a cohort of patients treated with percutaneous coronary intervention followed with use of repetitive questionnaires and information from national registers over 3 years, only 417 out of 1,726 patients had complete data on all measure points and covariates. We suggest strategies for use of MI and different methods for dealing with death along with sensitivity analysis of deviations from the assumption of missing at random, all with the use of standard statistical software. The Mental Component Summary from Short Form 12-item survey was used as an example. Conclusion: Ignoring missing data may cause bias of unknown size and direction in longitudinal studies. We have illustrated that MI is a feasible method to try to deal with bias due to missing data in longitudinal studies, including attrition and nonresponse, and should be considered in combination with analysis of sensitivity in longitudinal studies. How to handle dropout due to death is still open for debate. Keywords: PCI, SF-12, nonparticipants, nonrespondents