Validation of Objectively and Subjectively Measured Physical Activity Against Energy Expenditure in Older Adults

no Condition, Community-dwelling 80+ Older Adults Clinical Trial

Official title:

ENerGetics in Old AGE (ENGAGE Study) - Validation of Multiple Accelerometers and Physical Activity Questionnaires Against Indirect Calorimetry Under Laboratory and Free-living Conditions in Very Old Adults

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04821713
• Other study ID #: 65838
• Secondary ID: 5R01CA183570-05
• Status: Recruiting
• Start date: March 15, 2019
• Est. completion date: March 2022

Study information

• Verified date: March 2021
• Source: University of Southern Denmark
• Contact: Li-Tang Tsai, PhD
• Phone: +45 6550 7572
• Email: ltsai[@]health.sdu.dk
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Physical inactivity is identified as one of the most important modifiable risk factors for chronic diseases, functional loss and disability and reliable assessment tools of physical activity are crucial in both research and clinical settings.

Traditionally, physical activity and sedentary behavior have been primarily assessed with questionnaires. Recently, accelerometers have been widely used to measure physical activity, sedentary behavior and sleep patterns in ageing. Still, the diversity of brands and models, various assessment protocols (e.g. anatomic locations, sampling frequency), data processing and outcome measures have posed challenges to the interpretation and comparability of results across studies. Therefore, despite some limitations, questionnaires are still considered an important assessment method, especially in large-scale studies. In order to bridge the differences in the interpretation of data from questionnaires to accelerometers among older adults, there is a need to validate existing physical activity and sedentary behavior questionnaires with energy expenditure in this population.

Energy expenditure has been used to "translate" accelerometer output into physiological outcomes. Nevertheless, several issues remain unresolved, including (1) limited calibration studies focusing on older adults; (2) resting metabolic rate and maximum physiological capacity typically decrease with aging, which makes daily activities "more intense" for an older person compared to a younger person; and (3) the same accelerometer metric measured at different body positions may be linked to completely different physiological outcomes.

Such diverse physiological impact according to the anatomical placement of accelerometers requires a rigorous harmonization of metrics from the accelerometers with energy expenditure during representative activities at different intensities.

The aims of this methodological study focusing on 80+ year-olds are to:

1. develop cut-points from accelerometers at different anatomical positions for different intensities of physical activity based on energy expenditure during semi-standardized daily tasks in the lab.

2. validate accelerometer at different anatomical positions against energy expenditure measured by double-labelled water (DLW) in free-living conditions.

3. validate existing physical activity and sedentary behavior questionnaires against DLW in free-living conditions.

Description:

Background

Physical inactivity has been estimated to cause 5.3 million deaths per year globally and is identified as one of the most important modifiable risk factors for chronic diseases, functional loss and disability. To correctly identify and study physical inactivity in old age, reliable assessment tools are crucial in both research and clinical settings.

Recent technological advancements in wearable activity monitors have led to a growing number of large, population-based studies using accelerometers to measure physical activity, sedentary behavior (in terms of overall volume, intensity and pattern), and sleep among older adults. However, the wide use of accelerometers also brings challenges when comparing the results across different studies that use different brands and models, various assessment protocols (e.g. anatomic locations, sampling frequency), data processing procedures and report different outcomes. Different accelerometer set-ups (i.e. brand, model, body position, initialization protocol, etc.) may challenge the interpretation of health-enhancing effect of physical activity and detrimental effect of sedentary behavior simply because of methodological issues.

Accelerometers measure acceleration, defined as the change in velocity over time (Δv/t), which quantifies the volume and intensity of movement. In addition, acceleration is a vector quantity with components of magnitude and direction, which can be used to identify the position of accelerometer in relation to gravity. Therefore, accelerometer could also be used to distinguish posture (e.g. sitting, standing, lying down) depending on the site of placement.

Since raw acceleration signals are difficult to interpret, they are typically "translated" by calibration studies to physiological outcomes (e.g. energy expenditure, oxygen consumption) or behavioral categories (e.g. sitting, walking, running). Through regression modelling, calibration studies often produce point estimates of energy expenditure from accelerometer counts or establish a range of accelerometer counts for different physical activity intensity levels. This range of accelerometer counts is referred to as "cut-points" for different intensities of physical activity.

Before the use of accelerometers, physical activity and sedentary behavior has been primarily assessed with questionnaires. Even as the scale of accelerometer studies are growing rapidly, questionnaires are still considered an important assessment method when assessing physical activity and sedentary behavior in large-scale studies. Some accelerometers and questionnaires have been validated by objectively measured energy expenditure. But for physical activity questionnaires, the validity against objective methods (e.g. accelerometry, heart rate monitor and double-labelled water) were found to be moderate at best and very few validation studies focused on older adults. In order to bridge and interpret data from questionnaires to accelerometers among older adults, there is a need to validate existing physical activity and sedentary behavior questionnaires with energy expenditure in this population.

Energy expenditure has earlier been used to validate accelerometers and physical activity questionnaires metrics. Nevertheless, several issues remain unsolved: (1) despite the correlation between these methods has been reported to be high in few studies, the agreement is generally low especially in populations with altered metabolic states such as obese and overweight individuals; (2) while the total volume of physical activity is a clear metric which is presumably positively correlated with total daily energy expenditure, intensity of physical activity in older adults may be affected by a number of physiological determinants such as cardiovascular and neuromuscular function, inflammation status and malnutrition. Resting metabolic rate (RMR) and maximum physiological capacity (e.g. maximum oxygen update (VO2 max) typically decrease with age, which may be particular important as the same daily activities would likely be "more intense" to an older person compared to a younger person; (3) despite that numerous activity monitors provide similar metrics (e.g. counts/minutes), the physiological implication can be highly different depending on what such metrics stand for. For example, despite the output from accelerometers placed on wrist and on the hip may be identical (counts/minutes), the different magnitude of acceleration may have a completely different physiological relationship when assessed by physiological metrics (e.g. energy, oxygen uptake). Such diverse physiological impact according to the anatomical placement of accelerometers requires a rigorous harmonization of metrics from the accelerometers coupled with energy expenditure in representative activities at different intensities.

To date, validation studies with accelerometers worn on multiple body positions are scarce and not all studies uses energy expenditure as the reference. One way to disentangle the physiological meaning of physical activity and sedentary behavior is to anchor accelerometer output to objective measurements of physiological capacity such as VO2 max, oxygen uptake during semi-standardized activities performed at self-selected pace and overall energy expenditure during free-living conditions collected over a representative period of time. This will contribute to a deeper understanding and accurate interpretation of the accelerometer data among community-dwelling older adults and to provide more specific accelerometry-based recommendations for physical activity, especially for the oldest population.

Objectives

The aims of this methodological study are to:

1. develop hip-, wrist-, thigh-, and low back- worn accelerometer cut-points for different intensities of physical activity based on energy expenditure during semi-standardized daily tasks for the 80+ year-olds.

2. validate accelerometry at the wrist, hip, thigh, and low back for estimating energy expenditure in free-living conditions, measured by the gold standard of energy expenditure, double-labelled water (DLW).

3. validate existing physical activity and sedentary behavior questionnaires against gold standard of energy expenditure (DLW) in free-living conditions in the 80+ year-olds.

Recruitment and data collection procedures

The investigators will conduct a cross-sectional study with two modes of data collection: 1) in the laboratory; and 2) in the field. Participants will be recruited through two pathways: 1)(Pathway A) invited personally during the routine preventive home-visits performed by the Municipality of Odense ; (Pathway B)or 2) invited through a letter to community-dwelling older citizens who lives in the Municipality of Odense. Those who show interest to participate will then receive detailed written information about the project and will be invited to a medical screening after signing an informed consent. The entire testing procedure will span across 14 days as urine samples for DLW method is needed on Day 1 and Day

14. In between, participants will be invited to attend one for lab test for measuring energy expenditure during semi-standardized functional tasks.

Statistical considerations and data management A detailed data management plan will be followed in the collection, entering, and analysis of data. In the laboratory examination day, all measurement devices will be synchronized before the measurement begins.

Sample size Assuming that different accelerometers can capture same physical activity behaviour, in a test for agreement between two raters using the Kappa statistic, a sample size of 95 subjects achieves 80% power to detect a true Kappa value of 0.70 when there are 3 categories with frequencies equal to 0.01, 0.20, and 0.79. (representing estimated percentage of time spent in moderate-to-vigorous activity, light activity, and sedentary behavior in this population). This power calculation is based on an alpha of 0.05.

Creation of a research biobank

A research biobank (blood, urine, and saliva samples) will be created for the described project. The blood samples will be destroyed no later than 5 years after the project is ending. Blood samples will be taking at the facilities of the University (SDU). An authorized nurse or biomedical laboratory scientist will be in charge of obtaining blood samples from the participants. The biobank will be stored at SDU. In total, 8 urine samples will be obtained from each participant during the entire project period. Urine samples will be frozen and sent to USA for analysis. The urine samples will be anonymized and stored in the research biobank until the end of the project, when the samples will be destroyed. A trained project personnel will be in charge of collecting urine samples from the subjects. In this protocol, University of Southern Denmark acts as the controller of the biological materials collected (urine samples). Data processing agreements will be made between SDU and the University of Wisconsin, USA; and between SDU and University of California, Berkeley. The US processor shall comply with Danish law governing protection of personal data.

Ethical considerations

The research activities of this study will be carried out in accordance with the Declaration of Helsinki. All subjects will receive the pamphlets from The National Committee on Health Research Ethics regarding personal rights when participating in a health research project and aspects to consider before deciding to participate. This study will involve older adults, who are considered vulnerable individuals. It is important to acknowledge and respect participants' autonomy, i.e., they have to be able to decide by themselves their participation in the study with the right to withdraw at any stage. Moreover, a communication strategy suitable for this population will be applied to prevent any risk of enhancing vulnerability and stigmatization of the older adults.

This project will generate valuable and novel knowledge about how do objectively measure physical activity measured by different accelerometers on the same individual correlate with energy expenditure among in +80-year-old community-dwelling citizens; and whether these patterns differ according to protein level. These knowledges that can be used to optimize and target the preventive and treatment strategies for malnourished older adults hence offer a benefit for the individual older adult and for the health care sector. Thus, the investigators believe that the benefits from the study is greater than the limited discomforts that may follow participation. Subjects are covered by the Danish Patient Insurance Association and can thus receive insurance of financial compensation in case any damage occurs while participating in the research project. This information will be given to participants both verbally and written (in the pamphlets from The National Committee on Health Research Ethics) during recruitment.

All personal data will become anonymized and handled with full confidentiality in compliance with the Personal Data Processing Act. The procedure of personal data processing will be reported to the Danish Data Protection Agency in University of Southern Denmark. In this study protocol, only the anonymized urine and saliva samples will be sent abroad for analysis (details described earlier) with no linkage to any other personal data (e.g. CPR number, address, telephone number etc..).

Participants will not receive any compensation for their participation in the study.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 100
• Est. completion date: March 2022
• Est. primary completion date: March 2022
• Gender: All
• Age group: 80 Years and older

Eligibility

Inclusion Criteria:

• Community-dwelling older adults aged 80+ years old.

• Have intact cognitive function, evaluated by a score = 3 in the short form of the Mini- mental state evaluation (MMSE)14.

• Signed an informed letter of consent.

Exclusion Criteria:

• Have severe organic or mental disease as determined by history

• Have terminal cancer.

• Are on chemotherapy.

• Have a drug and/or alcohol abuse (>50 g alcohol a day).

• Have been prescribed new medication within 2 months with potential to alter metabolism.

• Are hospitalized or bed-ridden because they will not be able to perform any of the tasks in the laboratory and movement during the free-living activity would be limited.

• Those who need assistive devices (including wheelchair, cane, walkers etc..) for lab testing.

• Those who are unable to speak and read Danish because they will not be able to follow instructions in the laboratory and during the free-living conditions.

Related Conditions & MeSH terms

• Disease
• no Condition, Community-dwelling 80+ Older Adults

Locations

Country	Name	City	State
Denmark	University of Southern Denmark	Odense

Sponsors (9)

Lead Sponsor	Collaborator
University of Southern Denmark	Maastricht University, National Cancer Institute (NCI), National Institutes of Health (NIH), Odense University Hospital, University of California, Berkeley, University of Ulster, University of Wisconsin, Madison, University Ramon Llull

Country where clinical trial is conducted

Denmark, 

References & Publications (17)

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Barnett A, van den Hoek D, Barnett D, Cerin E. Measuring moderate-intensity walking in older adults using the ActiGraph accelerometer. BMC Geriatr. 2016 Dec 8;16(1):211. — View Citation

Crouter SE, Churilla JR, Bassett DR Jr. Estimating energy expenditure using accelerometers. Eur J Appl Physiol. 2006 Dec;98(6):601-12. Epub 2006 Oct 21. — View Citation

Doherty A, Jackson D, Hammerla N, Plötz T, Olivier P, Granat MH, White T, van Hees VT, Trenell MI, Owen CG, Preece SJ, Gillions R, Sheard S, Peakman T, Brage S, Wareham NJ. Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study. PLoS One. 2017 Feb 1;12(2):e0169649. doi: 10.1371/journal.pone.0169649. eCollection 2017. — View Citation

Freedson P, Pober D, Janz KF. Calibration of accelerometer output for children. Med Sci Sports Exerc. 2005 Nov;37(11 Suppl):S523-30. Review. — View Citation

Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Glob Health. 2018 Oct;6(10):e1077-e1086. doi: 10.1016/S2214-109X(18)30357-7. Epub 2018 Sep 4. Erratum in: Lancet Glob Health. 2019 Jan;7(1):e36. — View Citation

Helmerhorst HJ, Brage S, Warren J, Besson H, Ekelund U. A systematic review of reliability and objective criterion-related validity of physical activity questionnaires. Int J Behav Nutr Phys Act. 2012 Aug 31;9:103. doi: 10.1186/1479-5868-9-103. Review. — View Citation

Hoogendijk EO, Deeg DJ, Poppelaars J, van der Horst M, Broese van Groenou MI, Comijs HC, Pasman HR, van Schoor NM, Suanet B, Thomése F, van Tilburg TG, Visser M, Huisman M. The Longitudinal Aging Study Amsterdam: cohort update 2016 and major findings. Eur J Epidemiol. 2016 Sep;31(9):927-45. doi: 10.1007/s10654-016-0192-0. Epub 2016 Aug 20. — View Citation

Koster A, Shiroma EJ, Caserotti P, Matthews CE, Chen KY, Glynn NW, Harris TB. Comparison of Sedentary Estimates between activPAL and Hip- and Wrist-Worn ActiGraph. Med Sci Sports Exerc. 2016 Aug;48(8):1514-1522. doi: 10.1249/MSS.0000000000000924. — View Citation

Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT; Lancet Physical Activity Series Working Group. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012 Jul 21;380(9838):219-29. doi: 10.1016/S0140-6736(12)61031-9. — View Citation

Lewis LS, Hernon J, Clark A, Saxton JM. Validation of the IPAQ Against Different Accelerometer Cut-Points in Older Cancer Survivors and Adults at Risk of Cancer. J Aging Phys Act. 2018 Jan 1;26(1):34-40. doi: 10.1123/japa.2016-0207. Epub 2017 Dec 7. — View Citation

Matthew CE. Calibration of accelerometer output for adults. Med Sci Sports Exerc. 2005 Nov;37(11 Suppl):S512-22. Review. — View Citation

Nishida Y, Tanaka S, Nakae S, Yamada Y, Morino K, Kondo K, Nishida K, Ohi A, Kurihara M, Sasaki M, Ugi S, Maegawa H, Ebine N, Sasaki S, Katsukawa F. Validity of the Use of a Triaxial Accelerometer and a Physical Activity Questionnaire for Estimating Total Energy Expenditure and Physical Activity Level among Elderly Patients with Type 2 Diabetes Mellitus: CLEVER-DM Study. Ann Nutr Metab. 2020;76(1):62-72. doi: 10.1159/000506223. Epub 2020 Mar 13. — View Citation

Pisanu S, Deledda A, Loviselli A, Huybrechts I, Velluzzi F. Validity of Accelerometers for the Evaluation of Energy Expenditure in Obese and Overweight Individuals: A Systematic Review. J Nutr Metab. 2020 Aug 4;2020:2327017. doi: 10.1155/2020/2327017. eCollection 2020. Review. — View Citation

Santos-Lozano A, Santín-Medeiros F, Cardon G, Torres-Luque G, Bailón R, Bergmeir C, Ruiz JR, Lucia A, Garatachea N. Actigraph GT3X: validation and determination of physical activity intensity cut points. Int J Sports Med. 2013 Nov;34(11):975-82. doi: 10.1055/s-0033-1337945. Epub 2013 May 22. — View Citation

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* Note: There are 17 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Accelerometer-assessed physical activity in free-living condition	Physical activity and sedentary behavior measured from accelerometers at multiple body locations in free-living condition. Raw accelerometer data will be processed in standard protocol and summarized in counts per minutes in vector magnitude.	Through study completion, an average of 14 days
Primary	Energy expenditure in free-living condition	Oxygen uptake measured by doubly labelled water under free-living condition	Through study completion, an average of 14 days
Primary	Accelerometer-assessed physical activity in the lab	Physical activity and sedentary behavior measured from accelerometers at multiple body locations in the lab. Raw accelerometer data will be processed in standard protocol and summarized in counts per minutes in vector magnitude.	During lab measurement days up to 3 days
Primary	Energy expenditure in the lab	Oxygen uptake measured by indirect calorimetry in the lab	During lab measurement days up to 3 days
Secondary	Self-reported physical activity	Measured by existing and ad hoc physical activity questionnaires: International Physical Activity Questionnaire--short form (IPAQ--short form), Physical Activity Scale for the Elderly (PASE).; IPAQ-SF: IPAQ-SF is measuring PA across three domains: walking, moderate PA and vigorous PA. The total amount of PA is the sum of the three domains.; Minimum 0 MET-minutes/week; Maximum 19278 MET-minutes/week (truncated); PASE: The instrument is suitable for over 65 years old and comprised of self-reported occupational, household and leisure activities items over a one week period. PASE scores are calculated from weights and frequency values for each of 12 types of activity.; Minimum 0 PASE score; Maximum 400 (or more) PASE score; Physical Activity Scale: The physical activity scale is a 24-hours measure and encompasses sleep, sedentary behavior, work, leisure time, and sports activity in one measure.; Minimum 25 MET-time/day; Maximum 150 MET-time/day	During lab measurement days up to 1 day
Secondary	Self-reported sedentary behavior	Measured by the Sedentary Behaviour Questionnaire (SBQ).; SBQ: The SBQ measures time spent in sedentary behavior across different domains during weekdays and weekend days.; Minimum 0 hours; Maximum 24 hours	During lab measurement days up to 1 day
Secondary	Protein level	Measured by 4-day food diary, protein screener, biomarkers from blood	Up to 4 days
Secondary	Muscle mass with DEXA scan	Measured by DEXA scan	During lab measurement days up to 1 day
Secondary	Muscle mass	D3-creatine method	Up to 4 days
Secondary	Handgrip Strength	Handgrip strength measured by dynamometer in kg.	During lab measurement days up to 1 day
Secondary	Lower extremity function	Short Physical Performance Battery (SPPB), scores from 0 to 12, higher score indicating better function	During lab measurement days up to 1 day
Secondary	Functional walking test	Walking speed from 6-minutes walking test	During lab measurement days up to 1 day
Secondary	Cardiovascular function	VO2 max test	During lab measurement days up to 1 day