Project description:BackgroundThere is robust evidence that falls in old age can be prevented by exercise programs that include balance training, functional exercises, and strength training. For the interventions to have a population health impact, outreach to the population of focus with suitable interventions is needed. While digital interventions are promising there is limited knowledge on the characteristics of who is reached. The aim of this study was to describe the recruitment process, estimate reach rate at the population level and to describe participants characteristics and representativeness in a digital fall prevention intervention study.MethodsIn a municipality-based observational study, reach of a digital fall prevention intervention was evaluated. The intervention included a digital exercise programme (Safe Step) and optional supportive strategies, complemented with a range of recruitment strategies to optimize reach. Recruitment during a period of 6 months was open to people 70 years or older who had experienced a fall or a decline in balance the past year. Reach was based on data from the baseline questionnaire including health and demographic characteristics of participants. Representativeness was estimated by comparing participants to a sample of older people from the Swedish National Public Health Survey.ResultsThe recruitment rate was 4.7% (n = 173) in relation to the estimated population of focus (n = 3,706). Most participants signed up within the first month of the intervention (n = 131). The intervention attracted primarily women, older people with high education, individuals who used the internet or digital applications almost every day and those perceiving their balance as fair or poor. Safe step participants lived more commonly alone and had higher education and better walking ability in comparison to the Swedish National Public Health Survey.ConclusionsWith a range of recruitment strategies most participants were recruited to a digital fall intervention during the first month. The intervention attracted primarily highly educated women who frequently used the internet or smart technologies. In addition to digital fall prevention interventions, a higher diversity of intervention types (digital and non-digital) is more likely to reach a larger group of older people with different needs.Clinical trial registrationClinicalTrials.gov, NCT04161625 (Retrospectively registered), https://clinicaltrials.gov/ct2/show/NCT04161625.

Project description:The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The study aimed to enroll 400 subjects with early mild cognitive impairment (MCI), 200 subjects with early AD, and 200 normal control subjects; $67 million funding was provided by both the public and private sectors, including the National Institute on Aging, 13 pharmaceutical companies, and 2 foundations that provided support through the Foundation for the National Institutes of Health. This article reviews all papers published since the inception of the initiative and summarizes the results as of February 2011. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimers Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, CSF biomarkers, and clinical tests; (4) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects, and are leading candidates for the detection of AD in its preclinical stages; (5) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Baseline cognitive and/or MRI measures generally predicted future decline better than other modalities, whereas MRI measures of change were shown to be the most efficient outcome measures; (6) the confirmation of the AD risk loci CLU, CR1, and PICALM and the identification of novel candidate risk loci; (7) worldwide impact through the establishment of ADNI-like programs in Europe, Asia, and Australia; (8) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker data with clinical data from ADNI to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (9) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world. The ADNI study was extended by a 2-year Grand Opportunities grant in 2009 and a renewal of ADNI (ADNI-2) in October 2010 through to 2016, with enrollment of an additional 550 participants.

Project description:BackgroundEarly conduct problems and school failure are prominent risk factors for several adverse outcomes in later life. With the potential of reaching many children at early stages of their life, school-based interventions constitute a valuable approach to universal prevention. Good behavior game (GBG) is a promising school-based behavior management program, having shown immediate reductions in conduct problems along with several long-term positive effects. Adapting interventions to new contexts may however affect their effectiveness. The current study aims to evaluate the effectiveness of a Swedish adaption of GBG under pragmatic conditions. The intervention is hypothesized to reduce conduct problems in the classroom (primary outcome). Secondary analyses will investigate changes in conduct problems in common school areas, classroom climate, teacher collective efficacy, on-task behavior, as well as investigating behavioral management practices, implementation, and barriers to implementation.MethodsThis is a cluster-randomized trial with two parallel groups. Schools will be randomized (1,1, stratified by their areas sociodemographic index score) to be provided training in GBG or perform business-as-usual. The intervention and data collection lasts for a school year. Data will be collected at three time points: at baseline in the beginning of the school year (prior to training in GBG), after three months, and after nine months (at the end of the school year; primary endpoint). Data consists of teacher-rated measures of conduct problems, classroom climate, teacher collective efficacy, behavior management practices, and implementation factors, along with demographic factors. In addition, data will be collected by independent and blinded observers using corresponding measures in a subset of randomly chosen classrooms. Procedural fidelity will be rated and collected by GBG-trainers during nine observations throughout the school year. Statistical analysis will include frequentist intention-to-treat analysis, and comparisons of estimates with a corresponding Bayesian model using weakly informative priors. The study has currently completed data collection.DiscussionThis study will provide knowledge in universal prevention and school-based interventions with high reach, as well as specific knowledge concerning the effectiveness of an adapted version of GBG under real-world conditions, along with factors affecting its implementation and effects.Clinical trial registrationClinicalTrials.gov, identifier NCT05794893.

Project description:The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.

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Health-promoting and preventive interventions for community-dwelling older people published from inception to 2019: a scoping review to guide decision making in a Swedish municipality context.

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