This paper is in the following e-collection/theme issue:

Diabetes (150) Diabetes Reviews and Scoping Studies (29) Fitness Trackers and Smart Pedometers/Accelerometers (539) mHealth for Wellness, Behavior Change and Prevention (2729) Health Coaching for Diabetes Patients (142) Innovations and Technology for Physical Activity Education (695)

Published on in Vol 11, No 2 (2022): Jul-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34433, first published .
The Physical Activity Assessment of Adults With Type 2 Diabetes Using Accelerometer-Based Cut Points: Scoping Review

The Physical Activity Assessment of Adults With Type 2 Diabetes Using Accelerometer-Based Cut Points: Scoping Review

The Physical Activity Assessment of Adults With Type 2 Diabetes Using Accelerometer-Based Cut Points: Scoping Review

Authors of this article:

Ioana A Moldovan1 Author Orcid Image ;   Alexa Bragg2 Author Orcid Image ;   Anna S Nidhiry1 Author Orcid Image ;   Barbara A De La Cruz1 Author Orcid Image ;   Suzanne E Mitchell1, 2, 3 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

    Original Paper

    1Department of Family Medicine, Boston Medical Center, Boston, MA, United States

    2Department of Family Medicine, Boston University School of Medicine, Boston, MA, United States

    3Department of Family Medicine and Community Health, University of Massachusetts Chan Medical School, Worcester, MA, United States

    Corresponding Author:

    Suzanne E Mitchell, MS, MD

    Department of Family Medicine and Community Health

    University of Massachusetts Chan Medical School

    55 Lake Avenue

    Worcester, MA, 01655

    United States

    Phone: 1 9789856033

    Email: Suzanne.Mitchell2@umassmed.edu


    Background: Incorporating physical activity into lifestyle routines is recommended for individuals with type 2 diabetes. Accelerometers offer a promising method for objectively measuring physical activity and for assessing interventions. However, the existing literature for accelerometer-measured physical activity among middle-aged and older adults with type 2 diabetes is lacking.

    Objective: This study aims to identify research studies in which accelerometer-based cut points were used to classify the physical activity intensity of middle-aged to older adults with type 2 diabetes as sedentary, light, moderate, vigorous, and very vigorous, and to determine if validated accelerometer cut points specifically for this population exist.

    Methods: We followed the Joanna Briggs Institute methodology for scoping reviews. Between June 23 and July 12, 2020, two reviewers independently screened records from four databases (PubMed, Web of Science, Embase, Engineering Village) and the ActiGraph Corp web site for eligible studies that included patients with type 2 diabetes with a sample mean age ≥50 years, used research-grade accelerometers, applied cut points to categorize objectively measured physical activity, and were available in English. We excluded studies reporting exclusively steps or step counts measured by accelerometers or pedometers and conference abstracts or other sources that did not have a full text available. Data extraction was completed using Microsoft Excel. Data for the following variables were tabulated based on frequency distributions: study design, accelerometer type, device placement, epoch length, total wear time, and cut points used. Study aims and participant demographic data were summarized.

    Results: A total of 748 records were screened at the abstract level, and 88 full-text articles were assessed for eligibility. Ultimately, 46 articles were retained and analyzed. Participants’ mean ages ranged from 50 to 79.9 years. The ActiGraph accelerometer and the Freedson et al and Troiano et al counts-per-minute cut points were the most frequently used across the literature. Freedson et al and Troiano et al counts-per-minute cut points for light, moderate, and vigorous activity correspond to <1952, 1952-5724, and ≥5725, and 100-2019, 2020-5998, and ≥5999, respectively. The Lopes et al cut points were developed by calibrating the ActiGraph in middle-aged and older adults with overweight/obesity and type 2 diabetes. These counts-per-minute thresholds are ≥200 (light), ≥1240 (moderate), and ≥2400 (vigorous), and were applied in 1 interventional study.

    Conclusions: An assortment of accelerometer cut points have been used by researchers to categorize physical activity intensity for middle-aged and older adults with diabetes. Only one set of cut points was validated and calibrated in our population of interest. Additional research is warranted to address the need for diabetes-specific cut points to inform public health recommendations. This includes confirmation that the Lopes et al cut points reflect clinically meaningful changes in physical activity for adults with diabetes who have comorbidities other than overweight/obesity and the development of relative intensity cut points that may be more suitable for those with suboptimal physical functioning.

    Interact J Med Res 2022;11(2):e34433

    doi:10.2196/34433

    Keywords

    accelerometer; cut points; type 2 diabetes; physical activity 


    Background

    Approximately 462 million individuals are affected by type 2 diabetes (T2D) globally [1], and the vast majority of the estimated 37 million Americans with diabetes have T2D [2]. Physical inactivity is a known risk factor for diabetes complications, yet about 38% of adults with diabetes achieve less than 10 minutes per week of moderate or vigorous activity [3]. These individuals generally engage in lower levels of physical activity duration and intensity [4], and have lower physical functional capacity compared to adults without diabetes [5].

    Rationale

    While many interventions for individuals with diabetes promote reducing sedentary time by increasing physical activity, accurate assessment of physical activity remains a challenge. Self-report questionnaires have traditionally quantified physical activity in numerous research studies but are subject to recall bias and lack of standardization [6]. In the last decade, accelerometers have shown promise for their ability to objectively measure the body’s acceleration in at least one of three orthogonal planes (anteroposterior, mediolateral, and vertical) and convert it into activity counts, also reported as counts per minute. These counts, which are proportional to the amount of physical activity performed by an accelerometer wearer, can be further used to categorize exercise into intensity levels (light, moderate, vigorous) [7]. Intensity thresholds, or cut points, have commonly been determined from regression equations relating accelerometer-recorded counts per minute and simultaneous measurement of energy expenditure (as metabolic equivalents of task [METs]) during laboratory and free-living activities [8].

    For people with T2D, exercise can help lower blood glucose levels, lipid levels, and blood pressure, thus improving diabetes outcomes [9]. Several population-specific thresholds have been established to define physical activity intensity using accelerometers such as the ActiGraph monitors, which comprise ≥50% of activity monitors globally [10]. Existing reviews related to this topic are limited to only consumer-wearable activity trackers [11], randomized controlled trials (RCTs) [12,13], or walking measured as steps per day [14,15]. Determining the most appropriate protocols for capturing accelerometer data in this population to accurately inform disease-specific public health recommendations is critical. This scoping review is comprehensive and aims to describe the use of accelerometer-based cut points for assessing physical activity intensity of adults with T2D.

    Review Questions

    We address two questions: (1) which cut points, if any, have been used to objectively categorize the physical activity intensity of adults with T2D (mean age ≥50 years) and (2) do accelerometer thresholds specifically validated for this population exist in the literature?


    This scoping review was conducted following the Joanna Briggs Institute Manual for Evidence Synthesis [16] using the following protocol: a limited search of relevant databases with an analysis of title and abstract keywords, and of article index terms; a comprehensive search using identified keywords and index terms across all databases; and a search of the reference lists of all full-text articles included in the review.

    Inclusion Criteria for Sources of Evidence

    The criteria in Table 1 define eligibility of sources included in this scoping review. Publications were included if all, or a clear subset of, participants were selected for the study on the basis of a T2D diagnosis with an allowance for comorbidities. Abstracts that indicated use of research-grade activity monitors to objectively measure the physical activity of participants with T2D were included. Studies that exclusively reported steps or step counts measured by accelerometers or pedometers were excluded. Conference abstracts and other sources that did not have the full text available were also excluded. In accordance with the average age of diabetes diagnosis [17], a mean age threshold of ≥50 years was implemented. Full-text articles were included if authors used specific accelerometer thresholds to categorize physical activity by intensity.

    Table 1. Screening eligibility criteria.
    Screening stageInclusion criteria
    Abstracts
    • Participants selected for study on basis of type 2 diabetes diagnosis 
      • ‎
    • Research-grade (nonpedometer) accelerometers used to objectively measure physical activity
      • ‎
    • Source is not conference abstract and full text is available
      • ‎
    • Physical activity not tracked exclusively as steps or step counts
      • ‎
    • Study sample has mean age ≥50 years
      • ‎
    Full-text articles
    • Metabolic equivalents of task or counts-per-minute cut points reported/cited by authors and used to categorize physical activity intensity
      • ‎

    Search Strategy

    First, a limited pilot search of PubMed and Web of Science was conducted using the Medical Subject Headings terms “accelerometry” and “diabetes mellitus, type 2,” and all related entry terms from the database. Two reviewers individually selected and analyzed 10 abstracts at random to identify recurrent keywords. Subsequently, a comprehensive search was conducted across five sources including PubMed, Web of Science, Embase, Engineering Village, and the ActiGraph Corp website research database. Finally, the reference lists of all full-text articles eligible for the review were scanned for additional original sources of evidence. Full details regarding the search strategy for each database can be found in Multimedia Appendix 1.

    The PubMed search contained the major terms “accelerometry,” “diabetes mellitus, type 2,” and “exercise” as well as all related subentry terms. Results were controlled using the filter “Middle Aged + Aged: 45+” to maximize efficiency during the screening process given the age inclusion criterion stated in Table 1. All records published prior to June 23, 2020, were included, as this was the most recent date the database was accessed. The Embase search was conducted using the terms “accelerometer,” “diabetes mellitus type 2,” and “physical activity” as well as all of the related terms that automatically populate when selected. The filters “middle aged,” “aged,” and “very elderly” were used to maximize efficiency for the screening process given the age criterion in Table 1. All records published prior to July 6, 2020, were included in the screening and selection process. The Web of Science search was conducted using the complete list of terms searched in PubMed and included all articles published prior to July 12, 2020, when this search was concluded. In Engineering Village, a platform featuring multiple engineering databases such as Compendex and Inspec, records were searched using “diabetes,” “accelerometer or accelerometry or actigraph or actigraphy,” and “exercise or physical activity,” and were limited to journal articles published prior to July 12, 2020. ActiGraph, LLC produces several models of wearable activity and sleep monitors, which have been used in numerous clinical trials. The ActiGraph Corp website has a research database with various publications mentioning the use of these activity and sleep monitors. After completing the comprehensive search of the major databases mentioned hitherto, we conducted a final search for records in the ActiGraph Corp website’s research database page under the category “diabetes.” We screened all of the abstracts filed under this category with a publication date prior to July 12, 2020. Across all databases, only articles published in English were considered for inclusion.

    Source of Evidence Screening and Selection

    Two reviewers independently screened all abstracts and full texts for inclusion using the predefined criteria, seeking consensus or the opinion of a third reviewer in cases of disagreement. Initially, the title, name of first author, publication year, and database name of all identified records were collected using Excel (Microsoft Corporation). Abstracts were then screened against our criteria. In cases where abstracts met the first four inclusion criteria from Table 1 but did not clearly state the age of participants, reviewers consulted the full text to confirm full eligibility. Abstract information was organized in Excel to track their original source and avoid redundant screening of duplicate records. Afterward, full texts of available abstracts were accessed and assessed for eligibility. Finally, the reference lists of all eligible full-text articles were scanned to identify any additional studies that could be included in our final pool of eligible articles. The complete screening spreadsheet is in Multimedia Appendix 2.

    Data Extraction

    Two reviewers independently performed data extraction from full-text articles using a form created in Excel, with one reviewer extracting data and the other verifying it (Multimedia Appendix 3). We collected the following variables: author or authors, publication year, country, study aims and design, participant demographics, accelerometer type, placement, epoch length, total wear time, cut points, and physical activity outcomes/results. Information relevant to the accelerometer methodology that was presented in the discussion section of each study was also collected. Reviewers accessed cited articles or supplementary material if prompted by authors and necessary for data extraction.

    Synthesis and Presentation of Results

    Extracted data were tabulated based on frequency distributions for the following variables: study design, accelerometer type, device placement, epoch length, total wear time, and cut points used. Study aims were consolidated into broad themes and participant demographic data were summarized and presented as a narrative. A qualitative description follows all tabulated results to relate the findings to the objectives of this review.


    Search Results

    The scoping review search yielded 748 records across the five databases (158 in PubMed, 201 in Embase, 309 in Web of Science, 36 in Engineering Village, and 44 in the ActiGraph Corp website). After removal of duplicates and subsequent screening of the remaining 492 abstracts, 88 full-text articles were assessed for eligibility. Of those, 42 articles met our inclusion criteria. The reference lists of these articles were then searched for additional sources of evidence, yielding 4 new articles. Ultimately, 46 articles were retained and analyzed (Figure 1 [18]).

    ‎
    Figure 1. PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) flowchart of screening and selection process with reasons for elimination. MET: metabolic equivalent of task; T2D: type 2 diabetes.
    View this figure

    Source of Evidence Characteristics

    Complete data extraction details are available in Multimedia Appendix 4. Most sources were cross-sectional studies (n=24), followed by RCTs (n=13), calibration/validation studies (n=2), and prospective longitudinal cohort studies (n=2). The remaining studies were correlational, mixed methods, descriptive, pretest to posttest, and case-control (Table 2). Article publication years ranged from 2008 to 2020. Of the research published in the United States, all but 2 studies had an even distribution of male and female participants, exceptions included Vanden Bosch et al [19], which enrolled only middle-aged female participants, and Whipple et al [20] whose participants were mostly male participants (80%). Among the 11 of 13 American studies that reported race/ethnicity data, 1 was comprised of at least 50% African American or Black participants [21], while the remaining 10 featured a predominantly White study population. Our age criterion captured 26 study samples with a mean age of 50 to 59.9 years, 18 samples with a mean age of 60 to 69.9 years, and 2 samples with a mean age of 70 to 79.9 years.

    Table 2. Distribution of sources by study design (N=46).
    Study designStudy, n (%)
    Cross-sectional24 (52)
    Randomized controlled trial13 (28)
    Calibration/validation2 (4)
    Prospective longitudinal cohort2 (4)
    Mixed methods1 (2)
    Descriptive1 (2)
    Case control1 (2)
    Pre-to-post1 (2)
    Correlational1 (2)

    Accelerometer Parameters

    Selected RCTs examined the effect of various interventions on either physical activity (n=11) or on diabetes control (n=2), while non-RCTs similarly evaluated associations of physical activity (or lack thereof) on physical health; 2 studies conducted accelerometer calibration/validation. From an assortment of 16 different accelerometers identified across the literature, the ActiGraph was the most popular brand with its triaxial GT3X (n=10) and uniaxial GT1M (n=10) models being equally favored in 22% of studies (Table 3).

    Researcher-dependent parameters for accelerometer data collection and analysis are presented in Table 4. In most studies, the accelerometer device was secured on the participant’s waist (n=23, 50%) during active data collection. Alternative placement options included the hip, wrist, chest, and on a neck strap. A total of 29 (63%) studies collected movement data in 60-second epochs (data acquisition intervals), and 23 (50%) required participants to wear the accelerometer for 7 days during data collection. Corresponding to the observed preference for ActiGraphs, various versions of the ActiLife software were used to conduct data reduction and analysis.

    Table 3. Accelerometer devices used across research studies (N=46).
    AccelerometerNumber of axesStudy, n (%)Studies
    GT3X, ActiGraph, LLC; Pensacola, FLTriaxial10 (22)Poppe et al [22], Júdice et al [23], Mathe et al [24], Welch et al [25], Garcia et al [26], Castonguay and Miquelon [27], Britto et al [28], Do et al [29], Hamer et al [30], de Moura et al [31]
    GT1M, ActiGraph, LLC; Fort Walton Beach, FLUniaxial10 (22)Vanden Bosch et al [19], Winkler et al [32], Eakin et al [33], Eakin et al [34], Cooper et al [35], Falconer et al [36], Healy et al [37], Goode et al [38], Lee et al [39], Falconer et al [40]
    AM7164, ActiGraph, LLC; Pensacola, FLUniaxial6 (13)De Greef et al [41], Evenson et al [42], Lopes et al [43], De Greef et al [44], Loprinzi and Pariser [45], Loprinzi and Ramulu [46]
    MyWellnessKey, Technogym; Cesena, ItalyUniaxial4 (9)Balducci et al [47], Balducci et al [48], Balducci et al [49], McGinley et al [50]
    ActiGraph, Manufacturing Technology, Inc; Fort Walton Beach, FLUniaxial2 (4)Allen et al [51], Allen et al [52]
    HJA-350IT, Omron Healthcare; Kyoto, JapanTriaxial2 (4)Miyamoto et al [53], Miyamoto et al [54]
    RT3 Accelerometer, StayHealthy; Monrovia, CATriaxial2 (4)Unick et al [55], Jakicic et al [56]
    Actiheart, CamNtech; Cambridge, United KingdomTriaxial2 (4)Guo et al [57], Cichosz et al [58]
    Polar AW200 Activity Watch, Polar Electro Oy; Kempele, FinlandUniaxial1 (2)Karjalainen et al [59]
    MT-KT01, Terumo; Tokyo, JapanTriaxial1 (2)Miyauchi et al [60]
    GT9X Link, ActiGraph; Pensacola, FLTriaxial1 (2)Wooldridge et al [61]
    Actiwatch-Score, Philips Respironics; Bend, ORMultidimensional1 (2)Fritschi et al [21]
    GT3X+, ActiGraph, LLC; Fort Walton Beach, FLTriaxial1 (2)Sardinha et al [62]
    Fitbit Charge HR, Fitbit Inc; San Francisco, CATriaxial1 (2)An et al [63]
    GT3X-BT, ActiGraph, LLC; Pensacola, FLTriaxial1 (2)Whipple et al [20]
    Active Style Pro HJA-750C, Omron Healthcare; Kyoto, JapanTriaxial1 (2)Nishida et al [64]
    Table 4. Parameters for accelerometer data collection and analysis (N=46).
    Protocol variablesStudies, n (%)
    Device placement

    		Waist23 (50)

    		Hip14 (30)

    		Wrist4 (9)

    		Unknown3 (7)

    		Neck strap1 (2)

    		Chest1 (2)
    Epoch length (seconds)

    		152 (4)

    		302 (4)

    		6029 (63)

    		Not reported or not applicable to device13 (28)
    Total wear time (days)

    		<11 (2)

    		33 (7)

    		42 (4)

    		53 (7)

    		63 (7)

    		723 (50)

    		102 (4)

    		142 (4)

    		161 (2)

    		211 (2)

    		281 (2)

    		841 (2)

    		1271 (2)

    		1801 (2)

    		1831 (2)
    Analysis software

    		ActiLife (various versions)11 (24)

    		SAS (various versions)9 (20)

    		SPSS (various versions)9 (20)

    		STATA (various versions)4 (9)

    		KineSoft (Saskatoon, SK)3 (7)

    		MAHUffe Analyser (various versions)3 (7)

    		ActiGraph (DOS RIU256K.EXE)2 (4)

    		BI-LINK, Omron Healthcare, Kyoto, Japan1 (2)

    		JMP Ver. 11.0.0 (SAS Institute, Japan)1 (2)

    		MATLAB (MathWorks)1 (2)

    		MeterPlusTM (Santech, San Diego, CA)1 (2)

    		MyWellnessKey online portal1 (2)

    		SAS Programs for Analyzing NHANES 2003-2004 Accelerometer Data (National Cancer Institute)1 (2)

    		Respironics Actiware (Philips Respironics, Bend, OR)1 (2)

    		StayHealthy1 (2)

    		Not reported1 (2)

    Cut Points and Data Interpretation

    This scoping review included studies in which cut points for METs or counts per minute were applied to accelerometer-measured physical activity with the aim of categorizing movement as sedentary, light physical activity (LPA), moderate physical activity (MPA), vigorous physical activity (VPA), very vigorous physical activity (VVPA), moderate-to-vigorous physical activity (MVPA), and nonlocomotive physical activity. Table 5 contains all cited cut points; some authors referenced more than one set of cut points for their data analysis. A total of 9 articles used one or more thresholds derived from the 2011 Compendium of Physical Activity, a comprehensive codebook standardizing self-reported energy expenditure into MET intensity levels [65]. These absolute cut points quantify physical activity into sedentary behavior (1.0-1.5 METs), LPA (1.6-2.9 METs), MPA (3.0-5.9 METs), VPA (6.0-8.9 METs), and VVPA (≥9.0 METs) [66,67].

    The Freedson et al [67] cut points relate to the aforementioned MET intensity ranges by the regression equation shown in Multimedia Appendix 5. Per Freedson et al [67], the aforementioned MET values for LPA, MPA, and VPA correspond to <1952 counts per minute, 1952-5724 counts per minute, and ≥5725 counts per minute, respectively. Almost half (n=20) of the studies included in this scoping review used the Freedson et al [67] cut points. Of those using an ActiGraph device, 18 also used the study by Freedson et al [67] to categorize activity intensity. The Matthews et al [68] cut point for sedentary time (<100 counts per minute) compliments Freedson et al [67] but differentiates sedentary behavior from LPA when used concurrently.

    The Troiano et al [69] cut points, cited in 8 articles, categorize LPA as 100-2019 counts per minute, MPA as 2020-5998 counts per minute, and VPA as ≥5999 counts per minute. Of the remaining cut points identified through this scoping review, the ones calibrated by Lopes et al [43] are intended to reflect the expected metabolic capabilities of older adults with T2D with overweight or obesity. The thresholds for sedentary-light, light-moderate, and moderate-vigorous activity are 200, 1240, and 2400 counts per minute, respectively (see Multimedia Appendix 5 for the corresponding regression equation). The Lopes et al [43] cut points were applied in 1 additional study to determine the effects of an aerobic exercise intervention on physical activity levels in adults with T2D [31].

    Table 5. Frequency distribution for cited cut points (N=46).
    SourceCut pointsStudies, n (%)
    Counts per minute

    		Freedson et al [67]SEDa and LPAb: <195220 (43)


    		MPAc: 1952-5724


    		VPAd: ≥5725

    		Matthews et al [68]N/Ae11 (24)

    		Troiano et al [69]LPA: 100-20198 (17)


    		MPA: 2020‐5998


    		VPA: ≥5999

    		Matthew [8]LPA: 100-7592 (4)


    		MVPAf: ≥760

    		Lopes et al [43]SED: ≤2002 (4)


    		LPA: 201-1239


    		MPA:1240-2399


    		VPA: ≥2400

    		Unknown sourceSED: <2001 (2)


    		LPA: 202-1800


    		MVPA: >1800

    		Unknown sourceMVPA: ≥2296 1 (2)

    		Aguilar-Farías et al [70]SED: ≤2001 (2)


    		LPA: 201-2690


    		MPA: 2691-6166


    		VPA: 6167-9642


    		VVPAg: ≥9643

    		Spierer et al [71], Crouter et al [72], Harvey et al [73]SED: <201 (2)


    		MVPA: >20

    		Hamer et al [30]SED: ≤1991 (2)


    		LPA: 200-1998


    		MVPA: >1999
    Metabolic equivalent of task

    		Ainsworth et al (if used) [65]SED: <1.59 (20)


    		LPA: 1.5-2.9


    		MPA: 3-5.9


    		VPA: ≥6

    		Unknown sourceMPA: 2-51 (2)


    		VPA: >5

    		Oshima et al [74]N-LPAh: ≥1.5 2 (4)

    aSED: sedentary.

    bLPA: light physical activity.

    cMPA: moderate physical activity.

    dVPA: vigorous physical activity.

    eN/A: not applicable.

    fMVPA: moderate-to-vigorous physical activity.

    gVVPA: very vigorous physical activity.

    hN-LPA: nonlocomotive physical activity.


    Principal Findings

    Among 46 peer-reviewed publications that met our inclusion criteria, this scoping review revealed that an assortment of accelerometer cut points have been used by researchers to categorize physical activity intensity for middle-aged and older adults with T2D. We found that the ActiGraph models GT3x and GT1M were most frequently used for data collection, and the Freedson et al [67] cut points were most applied for analysis. Of the 2 validation/calibration studies identified, one [43] calibrated new ActiGraph cut points with our population of interest.

    Challenges in Accelerometer Use

    The current literature identifies certain limitations to accelerometer-based cut points that may lead to an underrepresentation of meaningful changes in physical activity. For example, the Freedson et al [67] thresholds were originally validated with a sample of healthy young adults (mean age 24.8, SD 4.2 years for male participants and 22.9, SD 3.8 years for female participants). In comparison, the mean age of study participants in this review ranged from 50 to 79.9 years. The Freedson et al [67] cut points are derived from the simultaneous measurement of activity counts with an accelerometer and metabolic cost with open circuit spirometry during graded treadmill exercises. Therefore, when these cut points are used to assess physical activity interventions among individuals with lower physical capacity, such as middle-aged or older adults with T2D, they potentially underestimate time spent performing MVPA. Miller et al [75] found that, when expressed relative to an individual’s maximal aerobic capacity, MPA intensity levels are not consistent across all ages of adulthood. Results revealed a substantial difference between the amount of MPA captured for older (60-69 years) and younger (20-29 years) age groups, 2847-5376 counts per minute versus 4573-6786 counts per minute, respectively. Thus, LPA intensity for a 20- or 40-year-old can be considered MPA intensity for a 60-year-old. With almost half of the studies in our scoping review favoring the application of the Freedson et al [67] cut points for data analysis, it is necessary to consider the implications of using these cut points to interpret physical activity data in populations with ages and abilities different than those of the original population used to validate them.

    Multiple authors in our review [21,24,27,42] also noted that data collection is limited by device location, and accelerometers are therefore unable to capture the entire spectrum of human movement, potentially causing a misrepresentation of physical activity quantity and intensity (eg, a waist-worn device may record upper body strength training as sedentary behavior). In addition, the lack of standardization in data collection protocols, as evidenced in our review by the variety of choices for device placement, epoch length, and total wear time, makes it difficult to compare physical activity data across studies. Furthermore, as previously exemplified with the Freedson et al [67] thresholds, the choice of cut points directs data interpretation. Some authors [24,25,55,59,76] recognize that absolute cut points are not sensitive to variations in individual fitness level, which is affected by age and health status. Physical activity data for adults who are older and have chronic diseases like T2D may be disproportionately affected by the use of absolute cut points originally validated with data from their younger and healthier counterparts.

    Limitations

    Our scoping review is subject to limitations. First, relevant articles may have been omitted due to our inclusion criteria and our specific research questions. For example, populations with lower physical fitness and chronic disease other than T2D were excluded. In addition, our studies were limited to those published in English and available at the time of screening. We eliminated abstracts exclusively reporting step counts; however, walking studies were acceptable if cut points were used to determine intensity. Second, we recognize an inherent potential for bias because we consulted the ActiGraph Corp website research database as the final step of our search strategy. A total of 25 unique articles were identified through this website but were subsequently excluded from analysis after failing to meet our screening criteria. Therefore, our final analysis was not impacted by the decision to search the website. Third, statistical analysis extends beyond the framework of scoping reviews, so we did not perform a quality assessment of the best cut points to use or methodology to follow for future physical activity interventions in our population of interest.

    Conclusion

    The use of appropriate accelerometer cut points for the measurement of physical activity in middle-aged and older adults with T2D is critical in guiding clinically meaningful public health recommendations for T2D management in these populations. While the Lopes et al [43] cut points have been documented and were used in 1 other study, more interventional research applying them is warranted to confirm if they reflect clinically significant changes in physical activity for adults with diabetes and comorbidities other than overweight/obesity. Alternatively, relative cut points, rather than absolute cut points, could provide a more appropriate determination of physical activity based on individual fitness. Ultimately, there remains a need to develop and further test diabetes-specific cut points that can precisely and accurately assess physical activity interventions and guide public health recommendations.

    Acknowledgments

    The authors would like to thank David Flynn for his contributions during the scoping review protocol development period and for his advice thereafter.

    Research reported in this publication was supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under award R21DK121092. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

    Conflicts of Interest

    Author SEM holds equity in an early-stage company, See Yourself Health Inc. She has presented sponsored lectures on relationship-centered care sponsored by Merck & Co, which are unrelated to the presented research. See Yourself Health Inc and Merck & Co have no relationship to the research presented in this manuscript.

    ‎
    Multimedia Appendix 1

    Narrative description of scoping review search strategy.

    DOCX File , 276 KB
    ‎
    Multimedia Appendix 2

    Scoping review screening results.

    XLSX File (Microsoft Excel File), 101 KB
    ‎
    Multimedia Appendix 3

    Data extraction form.

    XLSX File (Microsoft Excel File), 9 KB
    ‎
    Multimedia Appendix 4

    Scoping review data extraction.

    XLSX File (Microsoft Excel File), 44 KB
    ‎
    Multimedia Appendix 5

    Regression equations.

    DOCX File , 12 KB
    ‎
    Multimedia Appendix 6

    Completed PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review) checklist.

    PDF File (Adobe PDF File), 3278 KB
    1. Khan MAB, Hashim MJ, King JK, Govender RD, Mustafa H, Al Kaabi J. Epidemiology of type 2 diabetes - global burden of disease and forecasted trends. J Epidemiol Glob Health 2020 Mar;10(1):107-111 [FREE Full text] [CrossRef] [Medline]
    2. Type 2 diabetes. Centers for Disease Control and Prevention.   URL: https://www.cdc.gov/diabetes/basics/type2.html [accessed 2022-08-22]
    3. National Diabetes Statistics Report. Centers for Disease Control and Prevention.   URL: https://www.cdc.gov/diabetes/data/statistics-report/index.html [accessed 2022-04-25]
    4. Fagour C, Gonzalez C, Pezzino S, Florenty S, Rosette-Narece M, Gin H, et al. Low physical activity in patients with type 2 diabetes: the role of obesity. Diabetes Metab 2013 Feb;39(1):85-87. [CrossRef] [Medline]
    5. Ozdirenç M, Biberoğlu S, Ozcan A. Evaluation of physical fitness in patients with type 2 diabetes mellitus. Diabetes Res Clin Pract 2003 Jun;60(3):171-176. [CrossRef] [Medline]
    6. Celis-Morales CA, Perez-Bravo F, Ibañez L, Salas C, Bailey MES, Gill JMR. Objective vs. self-reported physical activity and sedentary time: effects of measurement method on relationships with risk biomarkers. PLoS One 2012;7(5):e36345 [FREE Full text] [CrossRef] [Medline]
    7. Chen K, Bassett J. The technology of accelerometry-based activity monitors: current and future. Med Sci Sports Exerc 2005 Nov;37(11 Suppl):S490-S500. [CrossRef] [Medline]
    8. Matthew C. Calibration of accelerometer output for adults. Med Sci Sports Exerc 2005 Nov;37(11 Suppl):S512-S522. [CrossRef] [Medline]
    9. Colberg SR, Sigal RJ, Yardley JE, Riddell MC, Dunstan DW, Dempsey PC, et al. Physical activity/exercise and diabetes: a position statement of the American Diabetes Association. Diabetes Care 2016 Nov;39(11):2065-2079 [FREE Full text] [CrossRef] [Medline]
    10. Wijndaele K, Westgate K, Stephens SK, Blair SN, Bull FC, Chastin SFM, et al. Utilization and harmonization of adult accelerometry data: review and expert consensus. Med Sci Sports Exerc 2015 Oct;47(10):2129-2139 [FREE Full text] [CrossRef] [Medline]
    11. Franssen WMA, Franssen GHLM, Spaas J, Solmi F, Eijnde BO. Can consumer wearable activity tracker-based interventions improve physical activity and cardiometabolic health in patients with chronic diseases? A systematic review and meta-analysis of randomised controlled trials. Int J Behav Nutr Phys Act 2020 May 11;17(1):57 [FREE Full text] [CrossRef] [Medline]
    12. Alothman S, Yahya A, Rucker J, Kluding PM. Effectiveness of interventions for promoting objectively measured physical activity of adults with type 2 diabetes: a systematic review. J Phys Act Health 2017 May;14(5):408-415. [CrossRef] [Medline]
    13. Baskerville R, Ricci-Cabello I, Roberts N, Farmer A. Impact of accelerometer and pedometer use on physical activity and glycaemic control in people with type 2 diabetes: a systematic review and meta-analysis. Diabet Med 2017 May;34(5):612-620. [CrossRef] [Medline]
    14. Tudor-Locke C, Washington T, Hart T. Expected values for steps/day in special populations. Prev Med 2009 Aug;49(1):3-11. [CrossRef] [Medline]
    15. Funk M, Taylor E. Pedometer-based walking interventions for free-living adults with type 2 diabetes: a systematic review. Curr Diabetes Rev 2013 Nov;9(6):462-471. [CrossRef] [Medline]
    16. Peters MDJ, Godfrey C, McInerney P, Munn Z, Trico AC, Khalil H. Chapter 11: scoping reviews (2020 version). In: Aromataris E, Munn Z, editors. JBI Manual for Evidence Synthesis. Adelaide, South Australia: JBI; 2020.
    17. Wang MC, Shah NS, Carnethon MR, O'Brien MJ, Khan SS. Age at diagnosis of diabetes by race and ethnicity in the United States from 2011 to 2018. JAMA Intern Med 2021 Nov 01;181(11):1537-1539. [CrossRef] [Medline]
    18. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med 2018 Oct 02;169(7):467-473 [FREE Full text] [CrossRef] [Medline]
    19. Vanden Bosch ML, Robbins LB, Anderson K. Correlates of physical activity in middle-aged women with and without diabetes. West J Nurs Res 2015 Dec;37(12):1581-1603. [CrossRef] [Medline]
    20. Whipple MO, Schorr EN, Talley KM, Lindquist R, Bronas UG, Treat-Jacobson D. A mixed methods study of perceived barriers to physical activity, geriatric syndromes, and physical activity levels among older adults with peripheral artery disease and diabetes. J Vasc Nurs 2019 Jun;37(2):91-105 [FREE Full text] [CrossRef] [Medline]
    21. Fritschi C, Park H, Richardson A, Park C, Collins EG, Mermelstein R, et al. Association between daily time spent in sedentary behavior and duration of hyperglycemia in type 2 diabetes. Biol Res Nurs 2016 Mar;18(2):160-166 [FREE Full text] [CrossRef] [Medline]
    22. Poppe L, De Bourdeaudhuij I, Verloigne M, Shadid S, Van Cauwenberg J, Compernolle S, et al. Efficacy of a self-regulation-based electronic and mobile health intervention targeting an active lifestyle in adults having type 2 diabetes and in adults aged 50 years or older: two randomized controlled trials. J Med Internet Res 2019 Aug 02;21(8):e13363 [FREE Full text] [CrossRef] [Medline]
    23. Júdice PB, Magalhães JP, Rosa GB, Correia IR, Ekelund U, Sardinha LB. Sedentary behavior compensation to 1‐year exercise RCT in patients with type 2 diabetes. Translational Sports Med 2019 Aug 09;3(2):154-163. [CrossRef]
    24. Mathe N, Boyle T, Al Sayah F, Mundt C, Vallance JK, Johnson JA, et al. Correlates of accelerometer-assessed physical activity and sedentary time among adults with type 2 diabetes. Can J Public Health 2017 Nov 09;108(4):e355-e361 [FREE Full text] [CrossRef] [Medline]
    25. Welch W, Alexander N, Swartz A, Miller N, Twardzik E, Strath S. Individualized estimation of physical activity in older adults with type 2 diabetes. Med Sci Sports Exerc 2017 Nov;49(11):2185-2190 [FREE Full text] [CrossRef] [Medline]
    26. Garcia JM, Cox D, Rice DJ. Association of physiological and psychological health outcomes with physical activity and sedentary behavior in adults with type 2 diabetes. BMJ Open Diabetes Res Care 2017;5(1):e000306. [CrossRef] [Medline]
    27. Castonguay A, Miquelon P. Motivational profiles, accelerometer-derived physical activity, and acute diabetes-related symptoms in adults with type 2 diabetes. BMC Public Health 2018 Apr 10;18(1):469 [FREE Full text] [CrossRef] [Medline]
    28. Britto A, Kumar S, Hazari A, Jadhav R, Babu AS, Bhat V, et al. Physical activity measurement using an accelerometer among Indians with type 2 diabetes mellitus: a cross-sectional study. Crit Rev Phys Rehabil Med 2018;30(2):141-150. [CrossRef]
    29. Do VV, Jancey J, Pham NM, Nguyen CT, Hoang MV, Lee AH. Objectively measured physical activity of Vietnamese adults with type 2 diabetes: opportunities to intervene. J Prev Med Public Health 2019 Mar;52(2):101-108. [CrossRef] [Medline]
    30. Hamer M, Bostock S, Hackett R, Steptoe A. Objectively assessed sedentary time and type 2 diabetes mellitus: a case–control study. Diabetologia 2013 Dec;56(12):2761-2762 [FREE Full text] [CrossRef] [Medline]
    31. de Moura BP, Marins JCB, Franceschini SDCC, Reis JS, Amorim PRDS. Aerobic exercise did not have compensatory effects on physical activity levels in type 2 diabetes patients. J Sports Sci 2015;33(6):545-551. [CrossRef] [Medline]
    32. Winkler E, Waters L, Eakin E, Fjeldsoe B, Owen N, Reeves M. Is measurement error altered by participation in a physical activity intervention? Med Sci Sports Exerc 2013 May;45(5):1004-1011. [CrossRef] [Medline]
    33. Eakin EG, Reeves MM, Winkler E, Healy GN, Dunstan DW, Owen N, et al. Six-month outcomes from living well with diabetes: a randomized trial of a telephone-delivered weight loss and physical activity intervention to improve glycemic control. Ann Behav Med 2013 Oct;46(2):193-203. [CrossRef] [Medline]
    34. Eakin EG, Winkler EA, Dunstan DW, Healy GN, Owen N, Marshall AM, et al. Living well with diabetes: 24-month outcomes from a randomized trial of telephone-delivered weight loss and physical activity intervention to improve glycemic control. Diabetes Care 2014 Aug;37(8):2177-2185. [CrossRef] [Medline]
    35. Cooper AR, Sebire S, Montgomery AA, Peters TJ, Sharp DJ, Jackson N, et al. Sedentary time, breaks in sedentary time and metabolic variables in people with newly diagnosed type 2 diabetes. Diabetologia 2012 Mar;55(3):589-599. [CrossRef] [Medline]
    36. Falconer C, Cooper A, Walhin J, Thompson D, Page A, Peters T, et al. Sedentary time and markers of inflammation in people with newly diagnosed type 2 diabetes. Nutr Metab Cardiovasc Dis 2014 Sep;24(9):956-962 [FREE Full text] [CrossRef] [Medline]
    37. Healy GN, Winkler EAH, Brakenridge CL, Reeves MM, Eakin EG. Accelerometer-derived sedentary and physical activity time in overweight/obese adults with type 2 diabetes: cross-sectional associations with cardiometabolic biomarkers. PLoS One 2015;10(3):e0119140 [FREE Full text] [CrossRef] [Medline]
    38. Goode AD, Winkler EAH, Reeves MM, Eakin EG. Relationship between intervention dose and outcomes in living well with diabetes--a randomized trial of a telephone-delivered lifestyle-based weight loss intervention. Am J Health Promot 2015;30(2):120-129. [CrossRef] [Medline]
    39. Lee W, Clark B, Winkler E, Eakin E, Reeves M. Responsiveness to change of self-report and device-based physical activity measures in the Living Well With Diabetes Trial. J Phys Act Health 2015 Aug;12(8):1082-1087. [CrossRef] [Medline]
    40. Falconer CL, Page AS, Andrews RC, Cooper AR. The potential impact of displacing sedentary time in adults with type 2 diabetes. Med Sci Sports Exerc 2015 Oct;47(10):2070-2075 [FREE Full text] [CrossRef] [Medline]
    41. De Greef KP, Deforche BI, Ruige JB, Bouckaert JJ, Tudor-Locke CE, Kaufman J, et al. The effects of a pedometer-based behavioral modification program with telephone support on physical activity and sedentary behavior in type 2 diabetes patients. Patient Educ Couns 2011 Aug;84(2):275-279. [CrossRef] [Medline]
    42. Evenson K, Wen F, Herring A. Associations of accelerometry-assessed and self-reported physical activity and sedentary behavior with all-cause and cardiovascular mortality among US adults. Am J Epidemiol 2016 Nov 01;184(9):621-632 [FREE Full text] [CrossRef] [Medline]
    43. Lopes VP, Magalhães P, Bragada J, Vasques C. Actigraph calibration in obese/overweight and type 2 diabetes mellitus middle-aged to old adult patients. J Phys Act Health 2009;6 Suppl 1:S133-S140. [CrossRef] [Medline]
    44. De Greef K, Van Dyck D, Deforche B, De Bourdeaudhuij I. Physical environmental correlates of self-reported and objectively assessed physical activity in Belgian type 2 diabetes patients. Health Soc Care Community 2011 Mar;19(2):178-188. [CrossRef] [Medline]
    45. Loprinzi PD, Pariser G. Physical activity intensity and biological markers among adults with diabetes: considerations by age and gender. J Diabetes Complications 2013;27(2):134-140. [CrossRef] [Medline]
    46. Loprinzi PD, Ramulu PY. Objectively measured physical activity and inflammatory markers among US adults with diabetes: implications for attenuating disease progression. Mayo Clin Proc 2013 Sep;88(9):942-951. [CrossRef] [Medline]
    47. Balducci S, D'Errico V, Haxhi J, Sacchetti M, Orlando G, Cardelli P, Italian Diabetes and Exercise Study 2 (IDES­_2) Investigators. Effect of a behavioral intervention strategy for adoption and maintenance of a physically active lifestyle: the Italian Diabetes and Exercise Study 2 (IDES_2): a randomized controlled trial. Diabetes Care 2017 Nov;40(11):1444-1452. [CrossRef] [Medline]
    48. Balducci S, D'Errico V, Haxhi J, Sacchetti M, Orlando G, Cardelli P, Italian Diabetes and Exercise Study 2 (IDES_2) Investigators. Level and correlates of physical activity and sedentary behavior in patients with type 2 diabetes: a cross-sectional analysis of the Italian Diabetes and Exercise Study_2. PLoS One 2017;12(3):e0173337 [FREE Full text] [CrossRef] [Medline]
    49. Balducci S, D'Errico V, Haxhi J, Sacchetti M, Orlando G, Cardelli P, Italian Diabetes and Exercise Study 2 (IDES_2) Investigators. Effect of a behavioral intervention strategy on sustained change in physical activity and sedentary behavior in patients with type 2 diabetes: the IDES_2 Randomized Clinical Trial. JAMA 2019 Mar 05;321(9):880-890. [CrossRef] [Medline]
    50. McGinley SK, Armstrong MJ, Khandwala F, Zanuso S, Sigal RJ. Assessment of the MyWellness Key accelerometer in people with type 2 diabetes. Appl Physiol Nutr Metab 2015 Nov;40(11):1193-1198. [CrossRef] [Medline]
    51. Allen NA, Fain JA, Braun B, Chipkin SR. Continuous glucose monitoring counseling improves physical activity behaviors of individuals with type 2 diabetes: a randomized clinical trial. Diabetes Res Clin Pract 2008 Jun;80(3):371-379 [FREE Full text] [CrossRef] [Medline]
    52. Allen N, Jacelon C, Chipkin S. Feasibility and acceptability of continuous glucose monitoring and accelerometer technology in exercising individuals with type 2 diabetes. J Clin Nurs 2009 Feb;18(3):373-383 [FREE Full text] [CrossRef] [Medline]
    53. Miyamoto T, Fukuda K, Oshima Y, Moritani T. Non-locomotive physical activity intervention using a tri-axial accelerometer reduces sedentary time in type 2 diabetes. Phys Sportsmed 2017 Sep;45(3):245-251. [CrossRef] [Medline]
    54. Miyamoto T, Fukuda K, Oshima Y, Moritani T. Effects of non-exercise activity and daily exercise activity upon glucose and fat metabolism in type 2 diabetes. J Sports Med Phys Fitness 2016 Oct;56(10):1214-1220. [Medline]
    55. Unick JL, Gaussoin SA, Hill JO, Jakicic JM, Bond DS, Hellgren M, et al. Four-year physical activity levels among intervention participants with type 2 diabetes. Med Sci Sports Exerc 2016 Dec;48(12):2437-2445 [FREE Full text] [CrossRef] [Medline]
    56. Jakicic J, Gregg E, Knowler W, Kelley DE, Lang E, Miller GD, et al. Activity patterns of obese adults with type 2 diabetes in the look AHEAD study. Med Sci Sports Exerc 2010 Nov;42(11):1995-2005 [FREE Full text] [CrossRef] [Medline]
    57. Guo VY, Brage S, Ekelund U, Griffin SJ, Simmons RK, ADDITION-Plus study team. Objectively measured sedentary time, physical activity and kidney function in people with recently diagnosed type 2 diabetes: a prospective cohort analysis. Diabet Med 2016 Sep;33(9):1222-1229 [FREE Full text] [CrossRef] [Medline]
    58. Cichosz SL, Fleischer J, Hoeyem P, Laugesen E, Poulsen PL, Christiansen JS, et al. Objective measurements of activity patterns in people with newly diagnosed type 2 diabetes demonstrate a sedentary lifestyle. Diabet Med 2013 Sep;30(9):1063-1066. [CrossRef] [Medline]
    59. Karjalainen J, Kiviniemi A, Hautala A, Niva J, Lepojärvi S, Mäkikallio TH, et al. Effects of exercise prescription on daily physical activity and maximal exercise capacity in coronary artery disease patients with and without type 2 diabetes. Clin Physiol Funct Imaging 2012 Nov;32(6):445-454. [CrossRef] [Medline]
    60. Miyauchi M, Toyoda M, Kaneyama N, Miyatake H, Tanaka E, Kimura M, et al. Exercise therapy for management of type 2 diabetes mellitus: superior efficacy of activity monitors over pedometers. J Diabetes Res 2016;2016:5043964. [CrossRef] [Medline]
    61. Wooldridge JS, Ranby KW, Roberts S, Huebschmann AG. A couples-based approach for increasing physical activity among adults with type 2 diabetes: a pilot feasibility randomized controlled trial. Diabetes Educ 2019 Dec;45(6):629-641. [CrossRef] [Medline]
    62. Sardinha LB, Magalhães JP, Santos DA, Júdice PB. Sedentary patterns, physical activity, and cardiorespiratory fitness in association to glycemic control in type 2 diabetes patients. Front Physiol 2017;8:262. [CrossRef] [Medline]
    63. An KH, Han KA, Sohn TS, Park IB, Kim HJ, Moon SD, et al. Body fat is related to sedentary behavior and light physical activity but not to moderate-vigorous physical activity in type 2 diabetes mellitus. Diabetes Metab J 2020 Apr;44(2):316-325 [FREE Full text] [CrossRef] [Medline]
    64. Nishida Y, Tanaka S, Nakae S, Yamada Y, Morino K, Kondo K, et al. 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. [CrossRef] [Medline]
    65. Ainsworth B, Haskell W, Herrmann S, Meckes N, Bassett DR, Tudor-Locke C, et al. 2011 Compendium of Physical Activities: a second update of codes and MET values. Med Sci Sports Exerc 2011 Aug;43(8):1575-1581. [CrossRef] [Medline]
    66. Physical activity guidelines for Americans, 2nd edition. Office of Disease Prevention and Health Promotion.   URL: https://health.gov/sites/default/files/2019-09/Physical_Activity_Guidelines_2nd_edition.pdf [accessed 2022-08-22]
    67. Freedson PS, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc 1998 May;30(5):777-781. [CrossRef] [Medline]
    68. Matthews CE, Chen KY, Freedson PS, Buchowski MS, Beech BM, Pate RR, et al. Amount of time spent in sedentary behaviors in the United States, 2003-2004. Am J Epidemiol 2008 Apr 01;167(7):875-881 [FREE Full text] [CrossRef] [Medline]
    69. Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 2008 Jan;40(1):181-188. [CrossRef] [Medline]
    70. Aguilar-Farías N, Brown WJ, Peeters GMEEG. ActiGraph GT3X+ cut-points for identifying sedentary behaviour in older adults in free-living environments. J Sci Med Sport 2014 May;17(3):293-299. [CrossRef] [Medline]
    71. Spierer DK, Hagins M, Rundle A, Pappas E. A comparison of energy expenditure estimates from the Actiheart and Actical physical activity monitors during low intensity activities, walking, and jogging. Eur J Appl Physiol 2011 Apr;111(4):659-667. [CrossRef] [Medline]
    72. Crouter SE, Churilla JR, Bassett DR. Accuracy of the Actiheart for the assessment of energy expenditure in adults. Eur J Clin Nutr 2008 Jun;62(6):704-711. [CrossRef] [Medline]
    73. Harvey NC, Cole ZA, Crozier SR, Kim M, Ntani G, Goodfellow L, SWS Study Group. Physical activity, calcium intake and childhood bone mineral: a population-based cross-sectional study. Osteoporos Int 2012 Jan;23(1):121-130 [FREE Full text] [CrossRef] [Medline]
    74. Oshima Y, Kawaguchi K, Tanaka S, Ohkawara K, Hikihara Y, Ishikawa-Takata K, et al. Classifying household and locomotive activities using a triaxial accelerometer. Gait Posture 2010 Mar;31(3):370-374. [CrossRef] [Medline]
    75. Miller N, Strath S, Swartz A, Cashin S. Estimating absolute and relative physical activity intensity across age via accelerometry in adults. J Aging Phys Act 2010 Apr;18(2):158-170 [FREE Full text] [CrossRef] [Medline]
    76. Gorman E, Hanson HM, Yang PH, Khan KM, Liu-Ambrose T, Ashe MC. Accelerometry analysis of physical activity and sedentary behavior in older adults: a systematic review and data analysis. Eur Rev Aging Phys Act 2014;11:35-49 [FREE Full text] [CrossRef] [Medline]

    ‎
    LPA: light physical activity
    MET: metabolic equivalent of task
    MPA: moderate physical activity
    MVPA: moderate-to-vigorous physical activity
    RCT: randomized controlled trial
    T2D: type 2 diabetes
    VPA: vigorous physical activity
    VVPA: very vigorous physical activity

    Edited by T Leung; submitted 25.10.21; peer-reviewed by M O'Brien, L Hu; comments to author 09.03.22; revised version received 25.04.22; accepted 11.07.22; published 06.09.22

    Copyright

    ©Ioana A Moldovan, Alexa Bragg, Anna S Nidhiry, Barbara A De La Cruz, Suzanne E Mitchell. Originally published in the Interactive Journal of Medical Research (https://www.i-jmr.org/), 06.09.2022.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Interactive Journal of Medical Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.i-jmr.org/, as well as this copyright and license information must be included.