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Exercise as it relates to Disease/Technology and Cystic Fibrosis

< Exercise as it relates to Disease

This is a critique of the article by Daniela Savi, Luigi Graziano, Barbara Giordani, Stefano Schiavetto, Corrado De Vito, Giuseppe Migliara, Nicholas Simmonds, Paolo Palange & Stuart Elborn; New strategies of physical activity assessment in cystic fibrosis: a pilot study. Published by BMC Pulmonary Medicine in 2020[1].

What is the background to this research?

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Cystic fibrosis (CF) is a chronic inherited disorder that affects 3,538 Australians per year[2]. CF is caused by the malfunction of a gene which leads to abnormally thick, sticky mucus in the respiratory tract, digestive tract, and reproductive tract [3][4].There is no cure but through the use of medications and physical activity (PA) people with CF are living longer [3][5].

Throughout previous research, habitual physical activity and exercise have been shown in numerous studies to decrease pulmonary exacerbations and hospitalizations [6], and improve lung function, physical function, sputum clearance[2], energy level, and quality of life for both children and adults [1][4].

The use of modern day technology has changed the way clinicians collect data and monitor CF patients whilst allowing them to be independent[7]. Studies have validated the use of the accelerometer SenseWear Pro3 Armband (SWA) [8]. Research has shown that hyper salinity of sweat does not affect the accuracy of energy expenditure and that SWA provides a precise estimate of PA [8][9]. Variables measured by SWA were total energy expenditure (Kcal), active energy expenditure (Kcal), PA duration, number of steps and intensity of PA expressed as metabolic equivalents (METS) [1][9].

Savi et al [1] investigated the accuracy of new electronic devices (Smartwatch, Fitbit, Android phone and iOS phone) in measuring daily physical activity in comparison to SWA in the adult cystic fibrosis population.  

Where is the research from?

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The study was completed in Rome, Italy in the Policlincio Umberto I Hospital [1], where 7 of the authors conducted research.  

The lead author Savi has a broad background in researching CF, respiratory mechanics, pulmonology, asthma, and respiratory physiology. She has 47 publications and 465 citations by various researchers, indicating a excellent credibility.

The other 9 authors have prior research in CF, immunology, pulmonology, accelerometers, and physical activity, demonstrating a versatile pool.

What kind of research was this?

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The study was an observational, single centre, pilot study, involving 24 adults affected by CF, who volunteered and met the criteria for the 7 day trial [1]. The criteria for this trial were patients aged 18+ with a confirmed diagnosis of CF based on CF-mutations and/or a sweat chloride concentration test of >60 mmol/L, and access to the internet and a device [1].

A pilot study is a preliminary study conducted to evaluate feasibility, duration and cost, as well as improving the study design prior to a full scale study [10]. Through observation the results will depict whether this test is statistically sound before attempting an updated treatment.

What did the research involve?

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Over 7 days, physical activity, and habitual physical activity of the 24 participants were recorded via SenseWear Pro3 Armband and a personal device [1].

Participants were separated into 4 groups according to the device they routinely used (i.e Smartwatch, Fitbit, Android or iOS phone). Participants completed 4 supervised activities, recording the number of steps and duration of PA in an indoor environment over an hour. A static task of lying supine was the control method for baseline readings on the devices[1].

Active tasks included:

  • Stair climbing; indoor stairwell consisting of 24 steps.
  • Stationary cycling; 50% at their predicted maximum heart rate.
  • Walking; 6 minute walking test[1].

For the rest of the week patients used their personal devices and SWA to record their habitual physical activity.

What were the basic results?

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Results show significant differences (i.e p-vaule <0.05[1]) in daily physical activity data between SWA and Android in all 3 categories, meaning that the findings are reliable and not due to chance.

There was no statistical difference between SWA and Fitbit for active energy expenditure (p = 0.143) and number of steps (p = 0.605)[1]. Analysis of the Fitbit and iOS phone data provided daily PA measurements and monitoring steps similar to the SWA accelerometer.


Table 1: Differences between the SWA and the 4 devices on measuring daily physical activities[1].

Variables SenseWear Armband Personal Device P-value
SmartWatch
Active energy expenditure (Kcal) 1197.8± 719.0 2025.8±1337.7 <0.001
Steps (number/day) 9478.5±5251.1 14359.9± 8642.8 0.007
Duration Physical Activity (min/day) 237± 129 311.7±199.3 < 0.007
Fitbit
Active energy expenditure 1302.2±458.4 1076.6±1281.5 0,143
Steps 7008.7±3945.1 7461.1±3482.0 0,605
Duration Physical Activity 382 ±157 70.2±48.7 <0.001
Android
Active energy expenditure 1428.1±849.5 287.3±263.7 <0.001
Steps 9941.7±7056.2 8083.81±7683.5 <0.001
Duration Physical Activity 328±171 71.8±70.4 <0.001
iOS
Active energy expenditure 692.7±323.0 173.8±161.1 <0.001
Steps 4512.9±3683.9 4937.2±4057.4 0.911
Duration Physical Activity 159±70 54.2±50.8 <0.001

Data are presented as mean ± SD.

The researchers acknowledge the possibility of a type 2 error due to the small sample size[1]. There was poor correlation between Fitbit and SWA in the accuracy of measuring time whilst exercising. This could be due to the Fitbit only measuring activities of either moderate or high intensity, whilst SWA can record any intensity.

What conclusions can we take from this research?

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The study evaluated the accuracy of 4 electronic devices in comparison with the SenseWear Pro3 Armband in monitoring the PA of a CF patient in their daily life. Fitbit and iOS phones performed well when compared to the SWA. They are both able to accurately monitor step count and the Fitbit accurately assessed energy expenditure.

If confirmed by other larger studies, Fitbit and iOS phones might have an important role in future research studies, because step count and energy expenditure are the recommend minimum standard for reporting PA[1][5]. Fitbit is widely used in society and has the potential to be easily integrated into an exercise program for CF patients who could find this highly motivating[9].

This new technology can give instant feedback to illustrate how much work a person has done, thus positively reinforce exercising. It will help clinicians to easily collect data and continually monitor patients in the real world without limiting the participant. It is an easy and safe way to monitor and encourage PA to promote good habits i.e, goal setting[1] and gives a person accountability and independence. By having access to devices, the internet can grant easier access to online exercises programs and education sessions.

Practical advice

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The knowledge that PA is beneficial and electronic device can easily and effectively assist CF population to monitor their PA in both laboratory and daily life settings, will promote best health outcomes. The practicality of these devices demonstrates that they are user friendly for all ages thus overall improving adherence.

Further evidence from larger cohort studies in the CF population could result in Fitbit devices and iOS smartphones being introduced as a new strategy for assessing physical activity and lifestyle changes in the CF population.

Future research into these devices and cohort containing children, adolescences and adults will demonstrate the cost effective ways that these personal device can enhance health, education and exercise throughout the full disease spectrum.

Further information/resources

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References

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  1. ↑ a b c d e f g h i j k l m n o p Savi, D., Graziano, L., Giordani, B., Schiavetto, S., De Vito, C., Migliara, G., Simmonds, N., Palange, P., Elborn, JS. New strategies of physical activity assessment in cystic fibrosis: a pilot study. BMC Pulm Med. 2020; 20(1). Available from: https://bmcpulmmed.biomedcentral.com/articles/10.1186/s12890-020-01313-5
  2. ↑ a b Ahern, S., Salimi, F., Caruso, M., Ruseckaite, R., Bell, S., Burke, N. Australian Cystic Fibrosis Data Registry – Annual Report 2020. Monash University. 2021; 1(1). Available from: https://www.cysticfibrosis.org.au/getmedia/84536037-f04e-4cf3-b34c-f34031fbf995/ACFDR-2020-Annual-Report-V1-1.pdf.aspx
  3. ↑ a b Swisher, A., Hebestreit, H., Mejia-Downs, A., Lowman,  JD., Gruber, W., Nippins, M., Alison, J., Schneiderman, J. Exercise and Habitual Physical Activity for People With Cystic Fibrosis. Cardio Phy Ther J. 2015; 26(4): 85–98. Available from: https://journals.lww.com/cptj/Fulltext/2015/12000/Exercise_and_Habitual_Physical_Activity_for_People.2.aspx.
  4. ↑ a b Savi, D., Quattrucci, S., Internullo, M., De Biase, RV., Calverley, PM., Palange, P. Measuring habitual physical activity in adults with cystic fibrosis. Respir Med. 2013; 107(12): 1888–94. Available from:https://www.resmedjournal.com/article/S0954-6111(13)00377-6/fulltext#secsectitle0110
  5. ↑ a b Manini, TM., Everhart, JE., Patel, KV., Schoeller, DA., Colbert, LH., Visser, M. Daily activity energy expenditure and mortality among older adults. J Am Med Assoc. 2006; 296(2):171-179. Available from: https://pubmed.ncbi.nlm.nih.gov/16835422/
  6. ↑ van de Weert-van Leeuwen, PB., Arets, HGM., van der Ent, CK., Beekman, MJ. Chronic infection and inflammation affect exercise capacity in cystic fibrosis. Eur Respir J. 2012; 39(4):893-8. Available from: https://pubmed.ncbi.nlm.nih.gov/21885387/
  7. ↑ Höchsmann, C., Knaier, R., Eymann, J., Hintermann, J., Infanger, D., Schmidt-Trucksäss, A. Validity of activity trackers, smartphones, and phone applications to measure steps in various walking conditions. Scand J Med Sci Sports. 2018; 28(7):1818-1827. Available from: https://pubmed.ncbi.nlm.nih.gov/29460319/
  8. ↑ a b Dwyer, T., Alison, JA., McKeough, Z., Elkins, MR., Bye, PT. Evaluation of the SenseWear activity monitor during exercise in cystic fibrosis and in health. Respir Med. 2009; 103(2): 1511–7. Available from: https://pubmed.ncbi.nlm.nih.gov/19464863/
  9. ↑ a b c Cox, NS., Alison, JA., Button, BM., Wilson, JW., Morton, JM., Dowman, LM., Holland, AE. Validation of a multi-sensor armband during free-living activity in adults with cystic fibrosis. J Cyst Fibros. 2014;13(1): 347–50. Available from: https://pubmed.ncbi.nlm.nih.gov/24374296/
  10. ↑ Junyong, I. Introduction of a pilot study. Korean J Anesthesiol. 2017; 70(6): 601–605. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5716817/
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