Posts Tagged physical activity

[Abstract] Sleep Duration, Sedentary Behavior, Physical Activity, and Quality of Life after Inpatient Stroke Rehabilitation 

Objective

The aim of this study was to describe accelerometer-derived sleep duration, sedentary behavior, physical activity, and quality of life and their association with demographic and clinical factors within the first month after inpatient stroke rehabilitation.

Materials and Methods

Thirty people with stroke (mean ± standard deviation, age: 63.8 ± 12.3 years, time since stroke: 3.6 ± 1.1 months) wore an activPAL3 Micro accelerometer (PAL Technologies, Glasgow, Scotland) continuously for 7 days to measure whole-day activity behavior. The Stroke Impact Scale and the Functional Independence Measure were used to assess quality of life and function, respectively.

Results

Sleep duration ranged from 6.6 to 11.6 hours/day. Fifteen participants engaged in long sleep greater than 9 hours/day. Participants spent 74.8% of waking hours in sedentary behavior, 17.9% standing, and 7.3% stepping. Of stepping time, only a median of 1.1 (interquartile range: .3-5.8) minutes were spent walking at a moderate-to-vigorous intensity (≥100 steps/minute). The time spent sedentary, the stepping time, and the number of steps differed significantly by the hemiparetic side (P < .05), but not by sex or the type of stroke. There were moderate to strong correlations between the stepping time and the number of steps with gait speed (Spearman r = .49 and .61 respectively, P < .01). Correlations between accelerometer-derived variables and age, time since stroke, and cognition were not significant.

Conclusions

People with stroke sleep for longer than the normal duration, spend about three quarters of their waking hours in sedentary behaviors, and engage in minimal walking following stroke rehabilitation. Our findings provide a rationale for the development of behavior change strategies after stroke.

Source: Sleep Duration, Sedentary Behavior, Physical Activity, and Quality of Life after Inpatient Stroke Rehabilitation – Journal of Stroke and Cerebrovascular Diseases

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[BOOK] Technology in Physical Activity and Health Promotion – Google Books

Front CoverAs technology becomes an ever more prevalent part of everyday life and population-based physical activity programmes seek new ways to increase lifelong engagement with physical activity, so the two have become increasingly linked. This book offers a thorough, critical examination of emerging technologies in physical activity and health, considering technological interventions within the dominant theoretical frameworks, exploring the challenges of integrating technology into physical activity promotion and offering solutions for its implementation.

Technology in Physical Activity and Health Promotion occupies a broadly positive stance toward interactive technology initiatives and, while discussing some negative implications of an increased use of technology, offers practical recommendations for promoting physical activity through a range of media, including:

  • social media
  • mobile apps
  • global positioning and geographic information systems
  • wearables
  • active videogames (exergaming)
  • virtual reality settings.

Offering a logical and clear critique of technology in physical activity and health promotion, this book will serve as an essential reference for upper-level undergraduates, postgraduate students and scholars working in public health, physical activity and health and kinesiology, and healthcare professionals.

Preview this book »

 

Source: Technology in Physical Activity and Health Promotion – Google Books

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[Abstract] Fatigue and its’ relationship to physical activity in adolescents and young adults with traumatic brain injury: a cross-sectional study

Physical activity (PA) in patients with traumatic brain injury (TBI) may be impaired leading to secondary health issues and limitations in participation.This study aims to determine the level of PA and its determinants in adolescents and young adults with TBI.Cross-sectional survey study.Outpatient clinic of a rehabilitation centre.Discharged patients aged 12-39 years with a diagnosis of TBI >6 months treated in the rehabilitation centre between 2009-2012.The Activity Questionnaire for Adults and Adolescents (AQuAA) measuring PA, with results dichotomized for meeting or not meeting Dutch recommendations for health enhancing physical activity (D-HEPA) and the Checklist Individual Strength (CIS; range 20- 140, higher scores represent higher levels of fatigue), measuring fatigue, were administered.Fifty (47%) of the 107 invited patients completed the questionnaire. Mean age was 25.0 years (SD 7.2)) and 22 (44%) were male. Eighteen (36%) had a mild injury, 13 (26%) a moderate injury and 19 (38%) a severe injury. Median time spent on moderate-vigorous physical activity was 518 minutes/week (IQR 236-1725) (males performing significantly more minutes on moderate-vigorous activity than women) and on sedentary activity 2728 minutes/week (IQR 1637-3994). Thirty-two (64%) participants met the D-HEPA. According to the CIS, 19 participants (38%) were severely fatigued. Both the CIS total score and the subscales motivation and physical activitywere associated with meeting the D-HEPA.The proportion of individuals with TBI meeting D-HEPA was similar to the general population, with the PA level being associated with self-reported fatigue.Physical activity programmes are continuously being developed to increase the percentage of individuals meeting public health recommendations for PA; when developing programmes for individuals with TBI extra consideration should be taken for the presence of fatigue. As in the general population, females with TBI are less active, PA programmes should probably consider gender differences in their development.

Source: Fatigue and its’ relationship to physical activity in adolescents and young adults with… – Abstract – Europe PMC

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[ARTICLE] The effect of active video games on cognitive functioning in clinical and non-clinical populations: A meta-analysis of randomized controlled trials – Full Text

Abstract

Physically-active video games (‘exergames’) have recently gained popularity for leisure and entertainment purposes. Using exergames to combine physical activity and cognitively-demanding tasks may offer a novel strategy to improve cognitive functioning. Therefore, this systematic review and meta-analysis was performed to establish effects of exergames on overall cognition and specific cognitive domains in clinical and non-clinical populations. We identified 17 eligible RCTs with cognitive outcome data for 926 participants. Random-effects meta-analyses found exergames significantly improved global cognition (g = 0.436, 95% CI = 0.18–0.69, p = 0.001). Significant effects still existed when excluding waitlist-only controlled studies, and when comparing to physical activity interventions. Furthermore, benefits of exergames where observed for both healthy older adults and clinical populations with conditions associated with neurocognitive impairments (all p < 0.05). Domain-specific analyses found exergames improved executive functions, attentional processing and visuospatial skills. The findings present the first meta-analytic evidence for effects of exergames on cognition. Future research must establish which patient/treatment factors influence efficacy of exergames, and explore neurobiological mechanisms of action.

1. Introduction

Cognition can be broadly defined as the actions of the brain involved in understanding and functioning in our external environment (Hirschfeld and Gelman, 1994). As it is generally accepted that cognition requires multiple mental processes, this broader concept has been theoretically separated into multiple ‘cognitive domains’ (Hirschfeld and Gelman, 1994). Although definitions vary, and the boundaries between domains often overlap, examples of distinct areas of cognitive functioning include the processes for learning and remembering verbal and spatial information, attentional capacities, response speed, problem-solving and planning (Strauss et al., 2006).

Various neuropsychological tests have been developed as tools for assessing and quantifying an individual’s overall cognitive functioning (or ‘global cognition’) along with their performance within the separable domains of cognition (Strauss et al., 2006). Performance in these various cognitive tests has been found to be relatively stable over time in healthy adults, and moderately accurate predictors of real-world functioning and occupational performance (Chaytor and Schmitter-Edgecombe, 2003 ;  Hunter, 1986). Furthermore, neuropsychological tests can detect the deficits in cognitive functioning which arise as a consequence of various psychiatric and neurological diseases (Mathuranath et al., 2000 ;  Nuechterlein et al., 2004). For example, people with Parkinson’s disease show marked impairments in planning and memory tasks (Dubois and Pillon, 1996), whereas those with schizophrenia have cognitive pervasive deficits, 1–2 standard deviations below population norms, which also predict the severity of disability in this population (Green et al., 2000). Additionally, cognitive abilities decline naturally in almost all people during healthy ageing (Van Hooren et al., 2007). In an ageing population, the functional consequences of cognitive decline may ultimately have a severe social and economic impact. Thus, interventions which improve cognition hold promise for the treatment of psychiatric and neurological diseases, an have positive implications for population health.

Fortunately, interventions which stimulate the brain and/or body can improve cognition, or attenuate decline. For instance, physical exercise has been shown to significantly improve global cognition, along with working memory and attentional processes, in both clinical and healthy populations (Firth et al., 2016Smith et al., 2010 ;  Zheng et al., 2016). Interventions can also be designed to target cognition directly, as computerized training programs for memory and other functions have been found to provide significant cognitive benefits, at least in the short term (Hill et al., 2017 ;  Melby-Lervåg and Hulme, 2013). Furthermore, ‘gamification’ of cognitive training programs can maximize their clinical effectiveness, as more complex and interesting programs are capable of better engaging patients in cognitively-demanding tasks while also training multiple cognitive processes simultaneously (Anguera et al., 2013).

Previous studies have found that providing both aerobic exercise and cognitive training together may have additive effects, preventing ageing-related cognitive decline more effectively (Shatil, 2013). This may be due to aerobic and cognitive activity stimulating neurogenesis through independent but complementary pathways; as animal studies show that while exercise stimulates cell proliferation, learning tasks support the survival of these new cells (Kempermann et al., 2010), such that combining these two types of training results in 30% more new neurons than either task alone (Fabel et al., 2009).

Rather than delivering aerobic and cognitive training in separate training sessions, recent advances in technology has presented an opportunity for combining physical activity with cognitively-challenging tasks in a single session through ‘exergames’. Exergames are considered as interactive video-games which require the player to produce physical body movements in order to complete set tasks or actions, in response to visual cues (Oh and Yang, 2010). Common examples include the ‘Nintendo Wii’ (along with ‘Wii Fit’ or ‘Wii Sports software’) or the ‘Microsoft Xbox Kinect’. Additionally, virtual reality systems which use exercise bikes and/or treadmills as a medium for players to interact with three-dimensional worlds have also been developed to provide immersive training experiences (Sinclair et al., 2007).

Along with their popular usage for leisure and entertainment, there is growing interest in the application of exergame systems to improve clinical outcomes. Recent systematic reviews and meta-analyses of this growing literature have provided preliminary evidence that exergames can improve various health-related outcomes, including reducing childhood obesity, improving balance and falls risk factors in elderly adults, facilitating functional rehabilitation in people with parkinson’s disease, and even reduce depression (Barry et al., 2014Li et al., 2016 ;  van’t Riet et al., 2014). However, the effects of exergames on cognitive functioning have not been systematically reviewed, despite many individual studies in this area.

Therefore, the aim of this study was to systematically review all existing trials of exergames for cognition, and apply meta-analytic techniques to establish the effects of exergames on global cognition along with individual cognitive domains. We also sought to (i) examine the effects of exergames on cognition in healthy and clinically-impaired populations, and (ii) investigate if the effects of exergames differed from those of aerobic exercise alone, by comparing exergames to traditional physical activity control conditions.

Fig. 1

Fig. 1. PRISMA flow diagram of systematic search and study selection.

Continue —> The effect of active video games on cognitive functioning in clinical and non-clinical populations: A meta-analysis of randomized controlled trials

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[ARTICLE] mHealth or eHealth? Efficacy, Use, and Appreciation of a Web-Based Computer-Tailored Physical Activity Intervention for Dutch Adults: A Randomized Controlled Trial  – Full Text

ABSTRACT

Background: Until a few years ago, Web-based computer-tailored interventions were almost exclusively delivered via computer (eHealth). However, nowadays, interventions delivered via mobile phones (mHealth) are an interesting alternative for health promotion, as they may more easily reach people 24/7.

Objective: The first aim of this study was to compare the efficacy of an mHealth and an eHealth version of a Web-based computer-tailored physical activity intervention with a control group. The second aim was to assess potential differences in use and appreciation between the 2 versions.

Methods: We collected data among 373 Dutch adults at 5 points in time (baseline, after 1 week, after 2 weeks, after 3 weeks, and after 6 months). We recruited participants from a Dutch online research panel and randomly assigned them to 1 of 3 conditions: eHealth (n=138), mHealth (n=108), or control condition (n=127). All participants were asked to complete questionnaires at the 5 points in time. Participants in the eHealth and mHealth group received fully automated tailored feedback messages about their current level of physical activity. Furthermore, they received personal feedback aimed at increasing their amount of physical activity when needed. We used analysis of variance and linear regression analyses to examine differences between the 2 study groups and the control group with regard to efficacy, use, and appreciation.

Results: Participants receiving feedback messages (eHealth and mHealth together) were significantly more physically active after 6 months than participants in the control group (B=8.48, df=2, P=.03, Cohen d=0.27). We found a small effect size favoring the eHealth condition over the control group (B=6.13, df=2, P=.09, Cohen d=0.21). The eHealth condition had lower dropout rates (117/138, 84.8%) than the mHealth condition (81/108, 75.0%) and the control group (91/127, 71.7%). Furthermore, in terms of usability and appreciation, the eHealth condition outperformed the mHealth condition with regard to participants receiving (t182=3.07, P=.002) and reading the feedback messages (t181=2.34, P=.02), as well as the clarity of the messages (t181=1.99, P=.049).

Conclusions: We tested 2 Web-based computer-tailored physical activity intervention versions (mHealth and eHealth) against a control condition with regard to efficacy, use, usability, and appreciation. The overall effect was mainly caused by the more effective eHealth intervention. The mHealth app was rated inferior to the eHealth version with regard to usability and appreciation. More research is needed to assess how both methods can complement each other.

Trial Registration: Netherlands Trial Register: NTR4503; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4503 (Archived by WebCite at http://www.webcitation.org/6lEi1x40s)

Introduction

Insufficient physical activity is considered to be a major public health issue worldwide [1,2]. The Dutch public health guidelines recommend adults to engage in moderate- to vigorous-intensity physical activity for at least 30 minutes on at least 5 days per week [3,4]. Studies suggest that sufficient physical activity can effectively prevent numerous chronic diseases and mental health issues [2,46]. Lee et al [7] argued that 6% to 10% of worldwide deaths caused by noncommunicable diseases, such as cancer, cardiovascular diseases, and diabetes, can be attributed to physical inactivity. Therefore, there is a need for interventions that increase the level of physical activity and can reach a broad population cost effectively [1].

Empirical research suggests that Web-based computer-tailored interventions are a promising solution [8]. These interventions provide tailored information and feedback via the Internet and therefore have some important advantages. First, Web-based computer-tailored interventions can adapt intervention materials according to the specific situation, characteristics, and needs of an individual and accordingly make information more personally relevant for the individual [911]. Second, research has shown that tailored messages are more likely to be read, understood, discussed with others, and remembered by the receiver [1214]. Third, due to the fact that more and more people are using the Internet to search for health-related information and health advice [1517], Web-based computer-tailored health interventions offer an effective method to reach a broad population cost effectively [1822]. Fourth, even though a broad population is targeted simultaneously, each individual can make use of the intervention privately at any given point in time or place [18,23].

Until a few years ago, Web-based computer-tailored interventions were almost exclusively delivered via computer. This medium of delivery has formed the term eHealth (electronic Health). The concept of eHealth has been described as the use of the Internet and related technologies to deliver health-related information and interventions [23]. Even though eHealth has been shown to be an efficient strategy to lower costs and deliver health messages more interactively, it also has several disadvantages. One of the major problems with eHealth interventions is the high percentage of dropout [24,25].

To make interventions even more accessible, and thereby decrease chances of dropout, health promotion professionals are increasingly interested in the use of mHealth (mobile Health). mHealth refers to the delivery of health messages and interventions via mobile phones or tablets by making use of telecommunication and multimedia technologies [2631]. In the Netherlands, almost 70% of Dutch households use the Internet via mobile phones and approximately 45% use tablets [32]. Based on the increasing usage of mobile phones as a lifestyle device, it has been argued that mHealth might increase the use of interventions and thereby also their efficacy [28,29]. Whereas computers and laptops are relatively stationary, mobile phones and tablets can be carried and used everywhere [33]. People are able to use mHealth independent of time or space, which could improve the usage and evaluation of interventions compared with eHealth [28,31,33].

Most people already use their phones for a variety of personal and work-related matters, such as social networking, calendaring, financial tracking, or emailing [33]. This leads to the assumption that the inclusion of health-related information would be advisable. However, previous research shows some pitfalls of mHealth. First, mobile phone technology is a rapidly changing field that introduces new apps, communication possibilities, and additional gadgets nearly by the day. This makes it difficult for intervention developers to keep up with the newest technologies and interests of their users [34,35]. Second, although using text messaging can be a very effective way of communicating, some intervention messages might be too long or difficult to be presented in such a short manner. This restricted communication can lead to more misunderstandings between the participant and health professional, which in turn can influence the effectiveness of the intervention [36]. And third, both participants and health professionals claim to feel unsure about the safety of private and sensitive information. Although this concern can also arise in the eHealth sector, the inferior but rapidly growing mHealth sector evokes skepticism on both sides [37].

To examine whether mHealth can improve the use and efficacy and reduce dropout rates of Web-based computer-tailored interventions, this study examined the effects of an mHealth and eHealth intervention on physical activity compared with a control group. Both interventions were identical with regard to content but differed in the medium of delivery. The main aim of the study was to examine the efficacy of the 2 versions on physical activity and to compare them with a control group. A secondary aim was to study potential differences in dropout and appreciation of the mHealth and eHealth intervention.

Figure 1. Flowchart of the participation of respondents.

Continue —> JMIR-mHealth or eHealth? Efficacy, Use, and Appreciation of a Web-Based Computer-Tailored Physical Activity Intervention for Dutch Adults: A Randomized Controlled Trial | Gomez Quiñonez | Journal of Medical Internet Research

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[Cochrane Review] Activity monitors for increasing physical activity in adult stroke survivors – Full Text

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Abstract

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:

To summarise the available evidence regarding the effectiveness of commercially available wearable devices and smart phone applications for increasing physical activity levels for people with stroke.

Background

Description of the condition

Between 1990 and 2010 absolute numbers of people living with stroke increased by 84% worldwide, and stroke is now the third leading cause of disability globally (Feigin 2014). As such, the disease burden of stroke is substantial. It has been estimated that 91% of the burden of stroke is attributable to modifiable risk factors such as smoking, poor diet, and low levels of physical activity (Feigin 2016). A low level of physical activity (less than four hours per week) is the second highest population-attributable risk factor for stroke, second only to hypertension (O’Donnell 2016). The promotion of physical activity, which has been defined as body movement produced by skeletal muscles resulting in energy expenditure (Caspersen 1985), is therefore an important health intervention for people with stroke.

The association between health and physical activity is well established. Prolonged, unbroken bouts of sitting is a distinct health risk independent of time engaged in regular exercise (Healy 2008). There is evidence from cross-sectional and longitudinal studies that high sitting time and low levels of physical activity contribute to poor glycaemic control (Owen 2010). Three systematic reviews and meta-analyses of observational studies have confirmed that, after adjusting for other demographic and behavioural risk factors, physical activity is inversely associated with all-cause mortality in men and women (Nocon 2008; Löllgen 2009; Woodcock 2011). Yet despite this knowledge, populations worldwide are becoming more sedentary, and physical inactivity has been labelled a global pandemic (Kohl 2012).

In addition to overcoming the sedentary lifestyles and habits prevalent in many modern societies, people with stroke have additional barriers to physical activity such as weakness, sensory dysfunction, reduced balance, and fatigue (Billinger 2014). Directly after a stroke, people should be admitted to hospital for co-ordinated care and commencement of rehabilitation (SUTC 2013). Early rehabilitation after stroke is frequently focused on the recovery of physical independence (Pollock 2014). Recovery after stroke is enhanced by active practice of specific tasks, and greater improvements are seen when people with stroke spend more time in active practice (Veerbeek 2014). Yet findings from research conducted around the world indicate that people in the first few weeks and months after stroke are physically inactive in hospital settings with around 80% of the day spent inactive (sitting or lying) (West 2012). These high levels of inactivity are concerning because recovering the ability to walk independently is an important goal of people with stroke. The reported paucity of standing and walking practice in the early phase after stroke potentially limits the opportunities of people with stroke to optimise functional recovery, particularly for standing and walking goals. Further, physical inactivity may lead to an increased risk of hospital-acquired complications, such as pressure ulcers, pneumonia, and cardiac compromise (Lindgren 2004).

Physical activity levels of people with stroke remain lower than their age-matched counterparts even when they return to living in the community (English 2016). Community-dwelling stroke survivors spend the vast majority of their waking time sitting down (English 2014). Promisingly, early research suggests that increasing physical activity in people with stroke is feasible, and that an increase in physical activity levels after stroke may have a positive impact on fatigue, mood, community participation, and quality of life (QoL) (Graven 2011; Duncan 2015).

Continue —> Activity monitors for increasing physical activity in adult stroke survivors – Lynch – 2017 – The Cochrane Library – Wiley Online Library

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[WEB SITE] Unmotivated to exercise? Dopamine could be to blame

 

Perhaps you have told yourself many times that, as of next week, you will start exercising more. Perhaps next month. Maybe even next year. For many of us, however, sticking to a disciplined program of physical exercise is one of the hardest New Year’s resolutions. New research offers clues as to why finding the motivation to exercise can be so difficult.

New research links a deficit in dopamine to the lack of physical activity in mice.

The benefits of physical activity are well known. The Centers for Disease Control and Prevention (CDC) report that regular physical activity can reduce the risk of severe illnesses, such as type 2 diabetes, cancer, and cardiovascular disease.

Exercise can also improve one’s overall physical and mental health, as well as increase longevity.

If you are looking to control your weight, the advantages of exercise are numerous. Not only has physical activity been shown to reduce metabolic syndrome – which means that it is good for regulating one’s metabolism – but it also burns calories, and in combination with a healthful diet, exercise can help to maintain weight over a long period of time.

While many people are aware of the benefits of physical activity in theory, many of us find it particularly hard in practice to stay physically active. New research may help to explain why this is so.

Can dopamine explain lack of physical activity?

Lead researcher Alexxai V. Kravitz – of the Diabetes, Endocrinology, and Obesity Branch at the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) – wondered why it is that obese animals have such a hard time doing physical activity.

The common perception is that animals, or people that are obese, are less physically active because they have to carry much more body weight. However, because Kravitz has a background in Parkinson’s disease, he noticed similarities between obese mice and Parkinsonian mice while he was studying.

This triggered his hypothesis that perhaps something else could contribute to physical inactivity.

“We know that physical activity is linked to overall good health, but not much is known about why people or animals with obesity are less active. There is a common belief that obese animals don’t move as much because carrying extra body weight is physically disabling. But our findings suggest that assumption does not explain the whole story.”

Alexxai V. Kravitz

Kravitz hypothesized that a dysfunction in rodents’ dopamine system might help to explain their lack of physical activity.

“Other studies have connected dopamine signaling defects to obesity, but most of them have looked at reward processing – how animals feel when they eat different foods. We looked at something simpler: dopamine is critical for movement, and obesity is associated with a lack of movement. Can problems with dopamine signaling alone explain the inactivity?”

Examining dopamine receptors in mice

Researchers set out to examine dopamine signaling in lean and obese mice, and the findings were published in the journal Cell Metabolism.

To do this, they fed a group of eight mice a normal diet, and they fed another group a high-fat diet for 18 weeks.

Starting from week 2, the mice on a high-fat diet started gaining significantly more weight than the lean ones. By week 4, obese mice spent less time moving, had fewer movements, and were slower when they did move, compared with lean mice.

Scientists examined whether changes in movement correlated with body weight gain, and they found that it did not. Interestingly, the mice on a high-fat diet moved less before they gained the majority of the weight, which suggests that the extra weight could not have been responsible for the reduced movement.

To identify the mechanisms behind physical inactivity, Kravitz and team quantified several aspects of dopamine signaling.

They found that the D-2 type receptor (D2R) binding, found in the striatum, was reduced in obese mice. This was consistent with previous research in rodents.

Then, scientists genetically removed D2Rs from the striatum of lean mice to determine if there was a causal link between D2Rs and inactivity. Researchers then placed the lean mice on a high-fat diet.

Surprisingly, they found that these mice did not gain more weight, despite their physical inactivity.

This suggests that although deficits in striatal D2R contribute to physical inactivity in obesity, such inactivity is more “a consequence than a cause of obesity,” as the authors put it.

Dopamine deficit may explain physical inactivity, reducing stigma

Although “there are probably other factors involved as well, the deficit in D2 is sufficient to explain the lack of activity,” says Danielle Friend, first author of the study and former NIDDK postdoctoral fellow.

Kravitz mentions that his future research will examine the connection between diet and dopamine signaling. Kravitz and team will investigate whether unhealthful eating affects dopamine signaling, and how quickly mice recover to normal activity levels once they start eating healthfully and losing weight.

Finally, Kravitz hopes that his research will help to relieve some of the stigma faced by people with obesity.

“In many cases, willpower is invoked as a way to modify behavior. But if we don’t understand the underlying physical basis for that behavior, it is difficult to say that willpower alone can solve it.”

Alexxai V. Kravitz

Learn how the brain thinks yo-yo dieting is a famine and how this causes weight gain.

Source: Unmotivated to exercise? Dopamine could be to blame – Medical News Today

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[Abstract] Evaluation of a Physical Activity Behavior Change Program for Individuals With a Brain Injury.

Abstract

Objective

To investigate the effectiveness of a physical activity intervention for use within a comprehensive outpatient rehabilitation program for individuals with brain injury.

Design

Quasi-experimental comparison group design with 3-month follow-up.

Setting

Comprehensive outpatient rehabilitation clinic that is a transitional setting between acute inpatient rehabilitation and community dwelling.

Participants

Individuals (N=47) with a brain injury were enrolled into either the intervention (n=22; 8 women, 14 men; mean age, 48.68y) or control group (n=25; 9 women, 16 men; mean age, 46.23y).

Intervention

Consisted of an 8-week informational and social/behavioral program that focused on enabling individuals to become independently active. The control group completed the standard of care typically available to patients in comprehensive outpatient rehabilitation.

Main Outcome Measures

Behavioral Risk Factor Surveillance Survey self-report physical activity items, Exercise Self-Efficacy Scale, and Mayo-Portland Adaptability Inventory-4.

Results

The intervention group reported significantly (P<.001) greater weekly activity, self-efficacy, and rehabilitation outcomes at the completion of the program as well as at the 3-month follow-up when compared with the control group. Significantly, individuals in the experimental group reported increasing their weekly activity from 45 minutes preprogram to 72 minutes postprogram (d=2.12; 95% confidence interval, 1.78–2.52), and 67 minutes at 3-month follow-up.

Conclusions

Findings suggest that the intervention may be effective in increasing the physical activity behaviors of individuals engaged in a comprehensive outpatient rehabilitation program after brain injury.

Source: Evaluation of a Physical Activity Behavior Change Program for Individuals With a Brain Injury – Archives of Physical Medicine and Rehabilitation

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[ARTICLE] Remote Physical Activity Monitoring in Neurological Disease: A Systematic Review – Full Text HTML

Abstract

Objective

To perform a systematic review of studies using remote physical activity monitoring in neurological diseases, highlighting advances and determining gaps.

Methods

Studies were systematically identified in PubMed/MEDLINE, CINAHL and SCOPUS from January 2004 to December 2014 that monitored physical activity for ≥24 hours in adults with neurological diseases. Studies that measured only involuntary motor activity (tremor, seizures), energy expenditure or sleep were excluded. Feasibility, findings, and protocols were examined.

Results

137 studies met inclusion criteria in multiple sclerosis (MS) (61 studies); stroke (41); Parkinson’s Disease (PD) (20); dementia (11); traumatic brain injury (2) and ataxia (1). Physical activity levels measured by remote monitoring are consistently low in people with MS, stroke and dementia, and patterns of physical activity are altered in PD. In MS, decreased ambulatory activity assessed via remote monitoring is associated with greater disability and lower quality of life. In stroke, remote measures of upper limb function and ambulation are associated with functional recovery following rehabilitation and goal-directed interventions. In PD, remote monitoring may help to predict falls. In dementia, remote physical activity measures correlate with disease severity and can detect wandering.

Conclusions

These studies show that remote physical activity monitoring is feasible in neurological diseases, including in people with moderate to severe neurological disability. Remote monitoring can be a psychometrically sound and responsive way to assess physical activity in neurological disease. Further research is needed to ensure these tools provide meaningful information in the context of specific neurological disorders and patterns of neurological disability.

Continue —> PLOS ONE: Remote Physical Activity Monitoring in Neurological Disease: A Systematic Review

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[Abstract] Interventions to improve real-world walking after stroke: A systematic review and meta-analysis

Abstract

Objective: This study aimed to determine the effectiveness of current interventions to improve real-world walking for people with stroke and specifically whether benefits are sustained.

Data sources: EBSCO Megafile, AMED, Cochrane, Scopus, PEDRO, OTSeeker and Psychbite databases were searched to identify relevant studies.

Review methods: Proximity searching with keywords such as ambulat*, walk*, gait, mobility*, activit* was used. Randomized controlled trials that used measures of real-world walking were included. Two reviewers independently assessed methodological quality using the Cochrane Risk of Bias Tool and extracted the data.

Results: Nine studies fitting the inclusion criteria were identified, most of high quality. A positive effect overall was found indicating a small effect of interventions on real-world walking (SMD 0.29 (0.17, 0.41)). Five studies provided follow-up data at >3–6 months, which demonstrated sustained benefits (SMD 0.32 (0.16, 0.48)). Subgroup analysis revealed studies using exercise alone were not effective (SMD 0.19 (–0.11, 0.49)), but those incorporating behavioural change techniques (SMD 0.27 (0.12, 0.41)) were.

Conclusions: A small but significant effect was found for current interventions and benefits can be sustained. Interventions that include behaviour change techniques appear more effective at improving real-world walking habits than exercise alone.

Source: Interventions to improve real-world walking after stroke: A systematic review and meta-analysis

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