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

Study Overview
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Brief Summary
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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: 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. validate accelerometer at different anatomical positions against energy expenditure measured by double-labelled water (DLW) in free-living conditions. validate existing physical activity and sedentary behavior questionnaires against DLW in free-living conditions.

Detailed Description
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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: 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. 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). 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.

Official Title
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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

Conditions
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no Condition, Community-dwelling 80+ Older Adults

Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 80 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 
| 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 | 
| 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 | 
| Energy expenditure in the lab | Oxygen uptake measured by indirect calorimetry in the lab | During lab measurement days up to 3 days | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 
| 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 | 
| Protein level | Measured by 4-day food diary, protein screener, biomarkers from blood | Up to 4 days | 
| Muscle mass with DEXA scan | Measured by DEXA scan | During lab measurement days up to 1 day | 
| Muscle mass | D3-creatine method | Up to 4 days | 
| Handgrip Strength | Handgrip strength measured by dynamometer in kg. | During lab measurement days up to 1 day | 
| 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 | 
| Functional walking test | Walking speed from 6-minutes walking test | During lab measurement days up to 1 day | 
| Cardiovascular function | VO2 max test | During lab measurement days up to 1 day | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Accelerometer, Physical Activity, Sedentary behavior, Energy Expenditure, Older Adults