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Meeting the 50th Percentile for 24-hour Movement Helps Cognitive Function

by Greg Margason | Aug 02, 2022

Alzheimer’s disease and related dementias are debilitating conditions leading to impaired cognitive processes and worsening physical functioning. They place significant burden on caregivers and health care systems. Roughly 15 million cases of cognitive impairment and Alzheimer’s disease in the U.S. are projected by 2060. While there are no medical treatments to delay Alzheimer’s disease progression, physical activity is one behavior that we can change that may help to delay or prevent cognitive decline. In fact, it is estimated that 1 in 5 Alzheimer’s disease cases in the U.S. are attributed to inadequate physical activity.

The Physical Activity Guidelines for Americans recommends that adults obtain 150 minutes or more of moderate- to vigorous-intensity physical activity. However, there are some promising findings for the protective health benefits of light-intensity physical activities such as yoga and housework. Thus, all movement throughout the day may be needed for good brain and body health. Advances in accelerometers and wearable devices have allowed for 24-hour continuous monitoring protocols to be implemented in large-scale studies, such as the nationally representative National Health and Nutrition Examination Survey (NHANES). From 2011 to 2014, NHANES implemented wrist-worn accelerometer devices into their health assessment of the American population. The device data are presented in Monitor-Independent Movement Summary units (MIMS-units). This is a measure of total continuous movement, with larger MIMS-units indicating higher amounts of movement performed throughout a 24-hour period.

Our study, published in Medicine & Science in Sports & Exercise®, examined the cross-sectional association of movement performed throughout a 24-hour period and cognitive function and subjective cognitive complaints. We used 2011-14 NHANES device-measured activity and cognitive tests and self-reported cognitive problems. We examined this in 2,708 adults over the age of 60 years.

We used the age- and sex-standardized MIMS-units percentiles previously developed to provide relative information about how a person’s movement behaviors across the day are associated with cognitive health compared to others of the same sex and age. Higher movement throughout the day was associated with higher scores in memory, language and verbal fluency, as well as executive function and processing speed tasks. The most important message is that cognitive functioning benefits appears to be among participants achieving at or above the 50th movement (MIMS-units) percentile for their age and sex. Similarly, those in the upper half of movement percentiles were half as likely to self-report difficulties in thinking or remembering, or experiencing any confusion or memory loss, compared to people in the lowest 25th percentile of movement.

This study further highlights the need to support total daily movement across the 24-hour period for healthy aging, as recently recommended in Canadian guidelines. Ultimately, using the standardized MIMS percentile can provide a reference for clinicians and researchers using wearable data to help patients achieve overall daily movement goals that are needed for brain and body health benefits.

Erin Dooley, Ph.D., is a postdoctoral researcher in the Department of Epidemiology at the University of Alabama at Birmingham. With a background in exercise and behavioral sciences, Dr. Dooley’s research uses wearable devices to measure sleep-wake time-use data with the aim to understand the interaction of physical activity and sleep cycles with health to inform sustainable public health recommendations and support healthy aging across the lifespan. She has been an ACSM member since 2016.

Viewpoints presented in SMB commentaries reflect opinions of the authors and do not necessarily represent ACSM positions or policies. Active Voice authors who have received financial or other considerations from a commercial entity associated with their topic must disclose such relationships at the time they accept an invitation to write for SMB.
Music that Moves: Building a Playlist that Hits

by Greg Margason | Jul 25, 2022
Anyone who has taken or taught a group exercise class knows the important role that music plays. The playlist sets the tone and tempo for the class, and it can greatly impact how much students enjoy the experience. Studies have shown that music can impact how hard students work during a class (intensity) and how long they will work (exercise fatigue perception). The right playlist can inspire your students to give their all during class and can increase the chances that they leave with a smile on their face, having enjoyed the burn.

There are two basic approaches to building a playlist for your class, dictated by the type of class you are teaching. The first type is class formats in which the movement dictates the music choice, and the second is formats in which the music dictates the movement. Let’s look at each independently.

Format A: Movement dictates music choice

Classes in format A demand that the movement be planned first and then music selected to enhance the class experience. These classes typically use movements that are done in a specific order, to a set number of reps or adhere to strict time intervals. Examples for classes in format A include barre, Pilates, yoga, strength, HIIT/Tabata and circuit. Creating a playlist for these classes should focus on two key elements: tempo and theme.

Tempo
The speed of the movement in your planned class will dictate the tempo range of the music you should play. Music is measured in beats per minute (BPM). There are standard BPM ranges for class formats.

It’s important to note that the BPM ranges in the graphic below reflect the “work” stages of the class. As you move through a warm-up, you should progressively build to that tempo range, while doing the reverse for the cooldown.

To manually determine the BPM for a song, you’ll want to determine the song’s time signature and count the measures in the first minute. Total measures multiplied by beats per measure will give you the BPM. For those who have a background in dance or know how to play an instrument, this will come fairly naturally. For others, there are many tools or apps you can use until you develop a natural sense of BPM. SONGBPM is a great, free online tool that will show the BPM for any song entered in the search bar. There are also a lot of searchable playlists on Spotify that are put together by BPM range, like this one.

Theme
Once you’ve determined your BPM range, it’s time to pick the specific songs for your playlist. To keep your students engaged (and to narrow your focus from the millions of songs out there), selecting themes for your playlists is a great way to go! Some themes I’ve successfully used:
- Decades
- Genres (e.g., country, punk pop, classic rock)
- Soundtracks
- A single artist (Folks like Britney Spears, Lady Gaga, Michael Jackson and Jason Derulo have plenty of music to fill a class on their own.)
- Holidays
- “Vibes” (Think anthems by women, club hits, chill beats, boy bands.)
Format B: Music dictates the movement

Classes in format B require the playlist to be created first, and then the movement is planned to match the music. These classes are typically choreography based or depend on significant tempo/intensity intervals. Examples of format B classes are: dance fitness, cycling, interval training and step classes. The key for these classes is developing a class map.

Mapping
A class map is a timeline for where tempo and intensity changes will occur throughout the class. You’ll need to consider the lengths of warm-up, intervals, active recoveries and cooldown. Once you’ve built your timeline, you’ll need to consider both BPM and length when selecting songs that can drive the movement you want. You can also apply themes to format B classes, but you’ll likely want to consider broader ones like decades or genres to allow you the flexibility you’ll need, particularly for dance-based fitness classes that require choreography.
General tips & taking it to the next level
Consider the age range of your students and the culture/policies of the facility in which you’re teaching. This particularly applies to explicit lyrics and mature themes. If you’re teaching to young people or in a church recreation facility for example, you’ll need to be particularly thoughtful. Additionally, some facilities may require clean music per company policy. Be sure to check if there are requirements.

Use your playlist to drive a theme “party” with your class. Share your playlist theme ahead of time and encourage your students to dress accordingly. This works particularly well with decade- or holiday-themed classes.

Look for remixes. Have a song you really want to use, but it just doesn’t fit the BPM range you need? Search for remixed versions or covers by other artists. These alternative versions often have BPMs that differ significantly from the original. You can also often find “fitness” versions of Top 40 hits that have been covered/altered to fit a specific BPM range.

Give your class control. This tactic isn’t for beginner instructors, but once you’ve become comfortable in front of a class, it’s a great engagement opportunity to prepare two different playlists and give them the option to vote on what vibe they want for the day. For example, greet your class by asking “Are we feeling ’80s rock or 2000s pop punk today?” This typically works more seamlessly in format A classes, but instructors with more experience can incorporate this option into format B classes if they are mentally prepared with two full movement plans/class maps.

Overall, playlists should be the most fun part of your class plan. Create different options, ask your students for their input on styles they like and keep innovating!

Additional Resources:
Infographic | Music Tempo Guidelines for Exercise
Blog | A Day in the Life of a Group Exercise Instructor
Certification | Become and ACSM Certified Group Exercise Instructor^®

Caitlin Kinser, M.S., has been teaching group exercise classes since 2010. She’s taught in a variety of settings including fitness studios, large gyms, college campuses, youth/community centers and virtually. She has taught multiple formats, but her heart belongs to dance fitness. Caitlin owned and operated a boutique fitness studio for two years prior to joining the professional staff at the American College of Sports Medicine^®, where she serves as the director of digital strategy.
Cancer Survivors with Chronic Fatigue Display Rapid Impairments in Muscle Function During Exercise

by Greg Margason | Jul 25, 2022

Cancer-related fatigue (C-rF) is a debilitating symptom that affects around one-third of people for months or years after cancer treatment. While the etiology of C-rF remains uncertain, it has been demonstrated that cancer survivors with C-rF display impaired exercise tolerance compared with those without C-rF, with the degree of exercise intolerance associated with chronic fatigue severity. Exercise intolerance in cancer survivors with C-rF likely contributes to reported difficulties in performing activities of daily living, as well as impaired quality of life. Thus, understanding the physiological (e.g., cardiopulmonary, metabolic and neuromuscular) alterations contributing to impaired exercise tolerance in people with C-rF is of importance, particularly given that these impairments could be reversible through exercise training.

One physiological alteration that could hinder the ability to perform activities of daily living is neuromuscular fatigability, defined as the reduction in neuromuscular function in response to exercise. To gain a better understanding of the etiology of neuromuscular fatigability in cancer survivors with C-rF, our study, published in Medicine and Science in Sports and Exercise_®, assessed the mechanisms of neuromuscular fatigability in cancer survivors with and without C-rF. We recruited 96 cancer survivors and separated the participants into two groups (fatigued and non-fatigued) based on a clinical cut-point for the diagnosis of chronic fatigue. In response to incremental cycling stages interspersed with measures of neuromuscular function, neuromuscular fatigability was assessed through changes in maximal voluntary contraction force of the knee extensors. The mechanisms of reduced maximal contraction force were determined through electrical stimulations either superimposed to voluntary contractions (capacity of the nervous system to activate muscle) or evoked on relaxed muscles (their contractility). Moreover, power outputs during cycling were expressed relative to gas exchange thresholds to determine the relative intensity of exercise.

Our results demonstrated that the magnitude and rate of decrements in neuromuscular function was substantially greater in the fatigued versus non-fatigued group. For example, following just 3 minutes of exercise at a low absolute (~20-25 W) and relative power output (47% of the gas exchange threshold), the fatigued group demonstrated decrements to maximal muscle force generating capacity that were five times greater than the non-fatigued group (−10% vs −2% on average, respectively). The responses to electrically evoked contractions revealed that mechanisms residing in the muscle were primarily responsible for the greater fatigability in the fatigued group.

The rapid and profound decrements in neuromuscular function we found in response to low-intensity exercise in those with C-rF was striking. Such a rapid decline in neuromuscular function in response to exercise of low power output has potentially important implications for the physiological and perceptual impact of typical activities of daily living. For example, the low intensity during the initial stages of cycling is likely to correspond with activities such as walking, housework, gardening or slowly climbing stairs. Accordingly, impaired neuromuscular fatigability, owing to rapid perturbations in the muscle, might represent an important contributor to the regularly reported difficulties in performing physical tasks of daily living in those with C-rF.

Dr. Callum Brownstein (@CGBrownstein) is a postdoctoral researcher at University Jean Monnet, Saint Etienne, France. He completed his Ph.D. at Northumbria University, Newcastle, U.K., where he assessed recovery of neuromuscular function following high-intensity intermittent exercise. His current research focuses on the acute integrative response to whole-body exercise in athletes, healthy active and clinical populations, with a focus on neuromuscular perturbations.

Dr. Guillaume Millet (@kinesiologui) is a professor of exercise physiology at University Jean Monnet, Saint-Etienne, France. After graduating from the University of Franche-Comte in 1997, he held various positions in Dijon, Saint-Etienne and Grenoble. Between 2013 and 2018, he was professor in the Human Performance Laboratory and the Faculty of Kinesiology at the University of Calgary (Canada), where he led a research group on neuromuscular fatigue. In 2019, he was named at the Institut Universitaire de France as a senior member. His general research area investigates the physiological, neurophysiological and biomechanical factors associated with fatigue, both in extreme exercise (ultra-endurance) and patients (neuromuscular diseases, cancer, multiple sclerosis, ICU).

Viewpoints presented in SMB commentaries reflect opinions of the authors and do not necessarily represent ACSM positions or policies. Active Voice authors who have received financial or other considerations from a commercial entity associated with their topic must disclose such relationships at the time they accept an invitation to write for SMB.
Cardiac Adaptation to Endurance and Resistance Training

by Greg Margason | Jul 18, 2022

In our Studies of Twin Responses to Understand Exercise THerapy (STRUETH) trial, we recruited monozygotic and dizygotic twin pairs and supervised them through three months of endurance training and three months of resistance exercise training. Each twin pair exercised together, at matched exercise prescriptions, and all subjects undertook both forms of exercise. We measured many outcomes, including body composition, fitness and strength, risk factors and vascular measures, but one of the most interesting outcomes involved cardiac magnetic resonance (CMR) assessments of heart chamber mass and volumes.

Not everyone responds to exercise in the same way, even when identical programs are administered. Thus, we were curious about the variability between individuals (and twin pairs) in heart muscle adaptation to training, and whether the modality of training affected this. An early study by Morganroth and colleagues in 1975 assessed different types of athletes, using the rudimentary echocardiography of that time, and reported distinct cardiac morphological findings between groups. From this grew the notion that endurance training induces eccentric hypertrophy (where ventricular volumes and mass increase), whereas resistance training leads to concentric hypertrophy (where mass and wall thicknesses increase, but volumes do not). But the original paper, and some that followed and supported the hypothesis, did not actually exercise-train the subjects. Rather, comparisons were cross-sectional and assumed that group differences were due to lifelong exercise exposure. However, athletes differ greatly in many ways, including body size and shape.

Our study, published in the July 2022 issue of Medicine & Science in Sports & Exercise_®, provides further insight. Using CMR, we found evidence for increased left ventricular mass and volume in response to endurance training, supporting the idea of eccentric hypertrophy. Somewhat in contrast, resistance training in the same individuals did not modify left ventricular dimensions or mass. This brings into question the proposition that resistance training induces concentric hypertrophy. By looking at the individual responses, we were able to quantify the proportion of the sample with large, modest and nonresponse to each modality of training. These data showed that low responders to one mode of exercise were often high responders to the alternative!

The modes of exercise we used were very different, targeting cardiovascular function versus skeletal muscle hypertrophy. They might therefore be expected to induce changes in distinct gene subsets. Perhaps individuals are genetically predisposed to respond to one or other forms of training? However, our estimates based on actual changes induced by training indicated limited genetic impact on cardiac adaptation to either mode of training.

Like all studies, ours has some limitations. The training period was relatively short, different exercise interventions may induce other outcomes, and the subjects were young and healthy (though initially inactive). Our power for twin analysis was somewhat limited, especially in the dizygotic paired groups. Interestingly, 10 pairs of twins who were recruited to the dizygotic group based on their lifelong assumptions turned out to be monozygotic when we genotyped them. Few previous twin studies have genotyped their sample to verify zygosity — our data suggest that this is necessary in future.

Our findings provide further insight into the ongoing debate regarding the utility of searching for genetic determinants of trainability in humans. At the end of the day, most people respond to some form of exercise — the challenge for exercise physiologists remains to fit the best approach to each individual.

Daniel J. Green, Ph.D., is a cardiovascular physiologist who studies exercise, inactivity, and cardiovascular function and adaptation in humans. His research has encompassed the development of new imaging platforms to study the impact of lifestyle factors (particularly exercise) on human adaptation across the lifespan. Dr. Green is Winthrop Professor in Sport and Exercise Science at the University of Western Australia, and this work was completed during his tenure as a National Health and Medical Research Council of Australia (NHMRC) principal research fellow (APP1080914).

Louise Naylor, Ph.D., is an associate professor at the University of Western Australia and a senior accredited exercise physiologist who works in cardiac rehabilitation in the Cardiac Transplant Unit and Advanced Heart Failure Service at Fiona Stanley Hospital. As both a practitioner and researcher, Dr. Naylor aims to optimize the prescription of exercise for people both at risk of and with established cardiovascular diseases.

Viewpoints presented in SMB commentaries reflect opinions of the authors and do not necessarily represent ACSM positions or policies. Active Voice authors who have received financial or other considerations from a commercial entity associated with their topic must disclose such relationships at the time they accept an invitation to write for SMB.
The Influence of the Journal Impact Factor

by Caitlin Kinser | Jul 15, 2022
There may be no more divisive topic in scholarly publishing than the Journal Impact Factor (JIF) — a metric first circulated in 1975 with the goal of providing librarians a tool with which to make informed journal purchases. Currently owned by Clarivate Analytics, JIFs are released in late June each year as an element of the Journal Citation Reports, and regardless of the growing number of alternative metrics available and wide-ranging criticism of the JIF itself, it is likely the most anticipated day of the year for the thousands of journals that will receive an updated JIF.

Journal success is measured using many data points: subscriptions, advertising, profitability. From an editorial perspective, JIF exists as both a measure of journal success and a primary driver of success. Author surveys often reveal that an author’s main priority when selecting a journal is “prestige,” conventionally measured by JIF, and consequently an improving JIF drives submissions. Editors recognize that JIF is not only a measure of how well the journal has performed but also predictive of how the journal will perform in the upcoming year, given the correlation between JIF increases and submission increases.

For this reason, the acquisition of a JIF is considered essential to the long-term survival of any journal — and a growing concern as Clarivate Analytics effectively controls what journals are included in the indices that receive JIFs. Journals that are not in the Science Citation Index Expanded or Social Sciences Citation Index (two of the indices that make up Web of Science) do not receive JIFs, although they are listed in the Journal Citation Reports and do receive other journal metrics. The process of acquiring a JIF has changed considerably in the last four years and, as Clarivate would admit, has become more exclusionary as the number of journals has exploded in the open access era.

What is the JIF?

On its surface, the formula used to derive the JIF appears straightforward: First, Clarivate tallies how many times articles the journal published in the previous two years have been cited by researchers in the current JIF year. Then they divide this value by the number of source items the journal published in the same two previous years. The fact I said “source items” — and not “articles” — illustrates why there is so much concern over the level of subjectivity and manipulation that the JIF invites.

What constitutes a source item? Roughly, “source items” are original research and reviews. (Note: contrary to myth, case reports are considered source items and do count in the denominator of the equation — hence the number of journals that no longer publish case reports and instead launch spin-off case report journals to protect the JIF of the “flagship” journal.) What about an article whose primary content is a link to a surgical video? Or an article that is a brief description of a research paper but does not itself contain any original research? If an article has no abstract, can it be a source item? The answer to most of these queries would be “it depends,” and that ambiguity introduces the opportunity for manipulation.

JIF: Manipulation or editorial strategy?

There exists a very fuzzy line between legitimate editorial strategy and JIF tampering. The most obvious manipulations involve self-citations, and journals are still occasionally called out for egregious examples, sometimes explicitly requiring an author to cite the journal as a prerequisite for acceptance. “Citation farms,” wherein journals engage in a coordinated effort to cite each other’s work, are high-profile examples of JIF manipulation — to Clarivate’s credit, they have improved their ability to govern this sort of behavior.
Most JIF-driven strategies revolve around the denominator, which the journal can exert control over. Strategies commonly include:
- Improving article discoverability on index searches by simplifying titles and improving abstracts.
- Implementing the use of research reporting guidelines and emphasizing articles that are associated with higher levels of evidence.
- Identifying the commonalities of highly cited articles and adjusting the journal’s acceptances accordingly.
- Carefully managing journal selectivity to avoid unwanted growth in the number of annually published articles.
Would we consider these strategies unethical in that they are overtly designed to affect the JIF? It’s a dilemma that confronts almost every editor-in-chief at some point. The issue is not that a high JIF is necessarily hard to achieve — the strategies for doing so are obvious — it’s whether the journal is willing to make difficult compromises with the express goal of improving JIF. If a journal introduces a new article type specifically designed for young researchers to be able to get their foot in the door of scientific publishing — and the submissions indicate great enthusiasm and usage statistics reflect high interest amongst readers — is it consistent with the mission of the society to discontinue the article type solely because they underperform from a citation standpoint?

Reactions to the JIF

There exist dozens of alternatives to the JIF — too many to mention here. Often, they are designed to specifically address one of the JIF formula’s perceived weaknesses. CiteScore extends the time frame for article citation from two to three years. Eigenfactor Score considers the relative quality of the citing journal. Journal Citation Indicator normalizes its score based on subject area. One indicator of how JIF has come to dominate the discussion around journal metrics is that in my 25 years of journal management, not one editor has ever contacted me to inquire about a journal’s Source Normalized Impact per Paper, h-index or Article Influence Score.

There have also been recent attempts to counter this oversized influence — DORA is one such high-profile attempt to disconnect JIF from funding, appointment and promotion processes. However, despite over 21,000 signatures to the DORA declaration, I suspect next June 30 will still be the most anticipated day in 2023 for journal editors and owners.

Learn About the JIFs for ACSM's Journals

Duncan MacRae is the director of editorial strategy and publishing policy for Wolters Kluwer, one of the world’s foremost publishers of medical, nursing and allied health journals. In this role, Duncan oversees the development and implementation of editorial policies followed by journals in the Lippincott and Medknow imprints. In addition, he works with a portfolio of editorial service providers to assist WK’s society partners in achieving their strategic goals.

Wolters Kluwer is the publishing partner of ACSM.