The effects of aerobic exercise and transcranial direct current stimulation on cognitive function in older adults with and without cognitive impairment:A systematic review and meta-analysis

Background: Aerobic exercise (AE) may slow age-related cognitive decline. However, such cognition-sparing effects are not uniform across cognitive domains and studies. Transcranial direct current stimulation (tDCS) is a form of non-invasive brain stimulation and is also emerging as a potential alternative to pharmaceutical therapies. Like AE, the effectiveness of tDCS is also inconsistent for reducing cognitive impairment in ageing. The unexplored possibility exists that pairing AE and tDCS could produce synergistic effects and reciprocally augment cognition-improving effects in older individuals with and without cognitive impairments. Previous research found such synergistic effects on cognition when cognitive training is paired with tDCS in older individuals with and without mild cognitive impairment (MCI) or dementia. Aim: The purpose of this systematic review with meta-analysis was to explore if pairing AE with tDCS could augment singular effects of AE and tDCS on global cognition (GC), working memory (WM) and executive function (EF) in older individuals with or without MCI and dementia. Methods: Using a PRISMA-based systematic review, we compiled studies that examined the effects of AE alone, tDCS alone, and AE and tDCS combined on cognitive function in older individuals with and without mild cognitive impairment (MCI) or dementia. Using a PICOS approach, we systematically searched PubMed, Scopus and Web of Science searches up to December 2021, we focused on ‘MoCA’, ‘MMSE’, ‘Mini-Cog’ (measures) and ‘cognition’, ‘cognitive function’, ‘cognitive’, ‘cognitive performance’, ‘executive function’, ‘executive process’, ‘attention’, ‘memory’, ‘memory performance’ (outcome terms). We included only randomized controlled trials (RTC) in humans if available in English full text over the past 20 years, with participants’ age over 60. We assessed the methodological quality of the included studies (RTC) by the Physiotherapy Evidence Database (PEDro) scale. Results: Overall, 68 studies were included in the meta-analyses. AE (ES = 0.56 [95% CI: 0.28–0.83], p = 0.01) and tDCS (ES = 0.69 [95% CI: 0.12–1.26], p = 0.02) improved GC in all three groups of older adults combined (healthy, MCI, demented). In healthy population, AE improved GC (ES = 0.46 [95% CI: 0.22–0.69], p = 0.01) and EF (ES = 0.27 [95% CI: 0.05–0.49], p = 0.02). AE improved GC in older adults with MCI (ES = 0.76 [95% CI: 0.21–1.32], p = 0.01). tDCS improved GC (ES = 0.69 [90% CI: 0.12–1.26], p = 0.02), all three cognitive function (GC, WM and EF) combined in older adults with dementia (ES = 1.12 [95% CI: 0.04–2.19], p = 0.04) and improved cognitive function in older adults overall (ES = 0.69 [95% CI: 0.20–1,18], p = 0.01). Conclusion: Our systematic review with meta-analysis provided evidence that beyond the cardiovascular and fitness benefits of AE, pairing AE with tDCS may have the potential to slow symptom progression of cognitive decline in MCI and dementia. Future studies will examine the hypothesis of this present review that a potentiating effect would incrementally improve cognition with increasing severity of cognitive impairment

Do physical activity interventions combining self-monitoring with other components provide an additional benefit compared with self-monitoring alone? A systematic review and meta-analysis

Objective To determine the net effect of different physical activity intervention components on step counts in addition to self-monitoring. Design A systematic review with meta-analysis and meta-regression. Data sources Five databases (PubMed, Scopus, Web of Science, ProQuest and Discus) were searched from inception to May 2022. The database search was complemented with backward and forward citation searches and search of the references from relevant systematic reviews. Eligibility criteria Randomised controlled trials comparing an intervention using self-monitoring (active control arm) with an intervention comprising the same treatment PLUS any additional component (intervention arm). Data extraction and synthesis The effect measures were mean differences in daily step count. Meta-analyses were performed using random-effects models, and effect moderators were explored using univariate and multivariate meta-regression models. Results Eighty-five studies with 12 057 participants were identified, with 75 studies included in the meta-analysis at postintervention and 24 at follow-up. At postintervention, the mean difference between the intervention and active control arms was 926 steps/day (95% CI 651 to 1201). At a follow-up, the mean difference was 413 steps/day (95% CI 210 to 615). Interventions with a prescribed goal and involving human counselling, particularly via phone/video calls, were associated with a greater mean difference in the daily step count than interventions with added print materials, websites, smartphone apps or incentives. Conclusion Physical activity interventions that combine self-monitoring with other components provide an additional modest yet sustained increase in step count compared with self-monitoring alone. Some forms of counselling, particularly remote phone/video counselling, outperformed other intervention components, such as websites and smartphone apps

mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED): rationale and study protocol for a pragmatic randomised controlled trial

Background The growing number of patients with type 2 diabetes and prediabetes is a major public health concern. Physical activity is a cornerstone of diabetes management and may prevent its onset in prediabetes patients. Despite this, many patients with (pre)diabetes remain physically inactive. Primary care physicians are well-situated to deliver interventions to increase their patients' physical activity levels. However, effective and sustainable physical activity interventions for (pre)diabetes patients that can be translated into routine primary care are lacking. Methods We describe the rationale and protocol for a 12-month pragmatic, multicentre, randomised, controlled trial assessing the effectiveness of an mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED). Twenty-one general practices will recruit 340 patients with (pre)diabetes during routine health check-ups. Patients allocated to the active control arm will receive a Fitbit activity tracker to self-monitor their daily steps and try to achieve the recommended step goal. Patients allocated to the intervention arm will additionally receive the mHealth intervention, including the delivery of several text messages per week, with some of them delivered just in time, based on data continuously collected by the Fitbit tracker. The trial consists of two phases, each lasting six months: the lead-in phase, when the mHealth intervention will be supported with human phone counselling, and the maintenance phase, when the intervention will be fully automated. The primary outcome, average ambulatory activity (steps/day) measured by a wrist-worn accelerometer, will be assessed at the end of the maintenance phase at 12 months. Discussion The trial has several strengths, such as the choice of active control to isolate the net effect of the intervention beyond simple self-monitoring with an activity tracker, broad eligibility criteria allowing for the inclusion of patients without a smartphone, procedures to minimise selection bias, and involvement of a relatively large number of general practices. These design choices contribute to the trial’s pragmatic character and ensure that the intervention, if effective, can be translated into routine primary care practice, allowing important public health benefits

The commensal infant gut meta-mobilome as a potential reservoir for persistent multidrug resistance integrons

Despite the accumulating knowledge on the development and establishment of the gut microbiota, its role as a reservoir for multidrug resistance is not well understood. This study investigated the prevalence and persistence patterns of an integrase gene (int1), used as a proxy for integrons (which often carry multiple antimicrobial resistance genes), in the fecal microbiota of 147 mothers and their children sampled longitudinally from birth to 2 years. The study showed the int1 gene was detected in 15% of the study population, and apparently more persistent than the microbial community structure itself. We found int1 to be persistent throughout the first two years of life, as well as between mothers and their 2-year-old children. Metagenome sequencing revealed integrons in the gut meta-mobilome that were associated with plasmids and multidrug resistance. In conclusion, the persistent nature of integrons in the infant gut microbiota makes it a potential reservoir of mobile multidrug resistance

Participatory development of an mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED)

Background The escalating global prevalence of type 2 diabetes and prediabetes presents a major public health challenge. Physical activity plays a critical role in managing (pre)diabetes; however, adherence to physical activity recommendations remains low. The ENERGISED trial was designed to address these challenges by integrating mHealth tools into the routine practice of general practitioners, aiming for a significant, scalable impact in (pre)diabetes patient care through increased physical activity and reduced sedentary behaviour. Methods The mHealth intervention for the ENERGISED trial was developed according to the mHealth development and evaluation framework, which includes the active participation of (pre)diabetes patients. This iterative process encompasses four sequential phases: (a) conceptualisation to identify key aspects of the intervention; (b) formative research including two focus groups with (pre)diabetes patients (n = 14) to tailor the intervention to the needs and preferences of the target population; (c) pre-testing using think-aloud patient interviews (n = 7) to optimise the intervention components; and (d) piloting (n = 10) to refine the intervention to its final form. Results The final intervention comprises six types of text messages, each embodying different behaviour change techniques. Some of the messages, such as those providing interim reviews of the patients’ weekly step goal or feedback on their weekly performance, are delivered at fixed times of the week. Others are triggered just in time by specific physical behaviour events as detected by the Fitbit activity tracker: for example, prompts to increase walking pace are triggered after 5 min of continuous walking; and prompts to interrupt sitting following 30 min of uninterrupted sitting. For patients without a smartphone or reliable internet connection, the intervention is adapted to ensure inclusivity. Patients receive on average three to six messages per week for 12 months. During the first six months, the text messaging is supplemented with monthly phone counselling to enable personalisation of the intervention, assistance with technical issues, and enhancement of adherence. Conclusions The participatory development of the ENERGISED mHealth intervention, incorporating just-in-time prompts, has the potential to significantly enhance the capacity of general practitioners for personalised behavioural counselling on physical activity in (pre)diabetes patients, with implications for broader applications in primary care

High-level classification of the Fungi and a tool for evolutionary ecological analyses

High-throughput sequencing studies generate vast amounts of taxonomic data. Evolutionary ecological hypotheses of the recovered taxa and Species Hypotheses are difficult to test due to problems with alignments and the lack of a phylogenetic backbone. We propose an updated phylum-and class-level fungal classification accounting for monophyly and divergence time so that the main taxonomic ranks are more informative. Based on phylogenies and divergence time estimates, we adopt phylum rank to Aphelidiomycota, Basidiobolomycota, Calcarisporiellomycota, Glomeromycota, Entomophthoromycota, Entorrhizomycota, Kickxellomycota, Monoblepharomycota, Mortierellomycota and Olpidiomycota. We accept nine subkingdoms to accommodate these 18 phyla. We consider the kingdom Nucleariae (phyla Nuclearida and Fonticulida) as a sister group to the Fungi. We also introduce a perl script and a newick-formatted classification backbone for assigning Species Hypotheses into a hierarchical taxonomic framework, using this or any other classification system. We provide an example of testing evolutionary ecological hypotheses based on a global soil fungal data set.Peer reviewe