Characterization and clustering of intra-day physical activity patterns using accelerometry among sexual and gender minority adults | BMC Public Health
Strain T, Flaxman S, Guthold R, Semenova E, Cowan M, Riley LM, et al. National, regional, and global trends in insufficient physical activity among adults from 2000 to 2022: A pooled analysis of 507 population-based surveys with 5·7 million participants. The Lancet Global health. 2024;S2214–109X(24)00150–5:1–12.
Testa RJ, Habarth J, Peta J, Balsam K, Bockting W. Development of the gender minority stress and resilience measure. Psychol Sex Orientat Gend Divers. 2015;2(1):65–77.
Google Scholar
Meyer IH. Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: conceptual issues and research evidence. Psychol Bull. 2003;129(5):674–97.
Google Scholar
Fredriksen-Goldsen KI, Romanelli M, Jung HH, Kim HJ. Health, economic, and social disparities among lesbian, gay, bisexual, and sexually diverse adults: results from a population-based study. Behav Med. 2023;50(2):141–52.
Google Scholar
Wittgens C, Fischer MM, Buspavanich P, Theobald S, Schweizer K, Trautmann S. Mental health in people with minority sexual orientations: a meta-analysis of population-based studies. Acta Psychiatr Scand. 2022;145(4):357–72.
Google Scholar
Hanna B, Desai R, Parekh T, Guirguis E, Kumar G, Sachdeva R. Psychiatric disorders in the U.S. transgender population. Ann Epidemiol. 2019;39:1–7.
Google Scholar
Pinna F, Paribello P, Somaini G, Corona A, Ventriglio A, Corrias C, Frau I, Murgia R, El Kacemi S, Galeazzi GM, Mirandola M, Amaddeo F, Crapanzano A, Converti M, Piras P, Suprani F, Manchia M, Fiorillo A, Carpiniello B. Mental health in transgender individuals: a systematic review. Int Rev Psychiatry. 2022;34(3–4):292–359.
Google Scholar
Lampe NM, Barbee H, Tran NM, Bastow S, McKay T. Health disparities among lesbian, gay, bisexual, transgender, and queer older adults: A structural competency approach. Int J Aging Hum Dev. 2024;98(1):39–55.
Google Scholar
Caceres BA, Streed CGJ, Corliss HL, Lloyd-Jones DM, Matthews PA, Mukherjee M, et al. Assessing and addressing cardiovascular health in LGBTQ adults: a scientific statement from the American Heart Association. Circulation. 2020;142(19):e321–32.
Google Scholar
Abichahine H, Veenstra G. Inter-categorical intersectionality and leisure-based physical activity in Canada. Health Promot Int. 2017;32(4):691–701.
Google Scholar
Boehmer U, Miao X, Linkletter C, Clark MA. Adult health behaviors over the life course by sexual orientation. Am J Public Health. 2012;102(2):292–300.
Google Scholar
Fricke J, Gordon N, Downing J. Sexual orientation disparities in physical activity. Med Care. 2019;57(2):138–44.
Google Scholar
Brittain DR, Dinger MK. An examination of health inequities among college students by sexual orientation identity and sex. J Public Health Res. 2015;4(1):1–6.
Google Scholar
Dinger MK, Brittain DR, Patten L, Hall KC, Burton S, Hydock DS, et al. Gender identity and health-related outcomes in a national sample of college students. Am J Health Educ. 2020;51(6):383–94.
Google Scholar
Downing JM, Przedworski JM. Health of transgender adults in the U.S., 2014–2016. Am J Prev Med. 2018;55(3):336–44.
Google Scholar
Laska MN, VanKim NA, Erickson DJ, Lust K, Eisenberg ME, Rosser BR. Disparities in weight and weight behaviors by sexual orientation in college students. Am J Public Health. 2015;105(1):111–21.
Google Scholar
Wilson OWA, Jones BA, Bopp M. College student aerobic and muscle-strengthening activity: disparities between cis-gender and transgender students in the United States. J Am Coll Health. 2023;71(2):507–12.
Google Scholar
Meredith OS, Litchfield C, Dionigi RA, Olsen M, Osborne J, Crawford R, et al. Experiences of belonging and exclusion in sport and physical activity for individuals of diverse sexual orientation and gender identity (SOGI) in rural Australia. Sport Soc. 2023;27(7):1022–36.
Google Scholar
Frederick GM, Castillo-Hernández IM, Williams ER, Singh AA, Evans EM. Differences in physical activity and perceived benefits and barriers to physical activity between LGBTQ + and non-LGBTQ + college students. J Am Coll Health. 2022;70(7):2085–90.
Google Scholar
Caudwell J. Transgender and non-binary swimming in the UK: indoor public pool spaces and un/safety. Front Sociol. 2020;5(64):1–12.
Teti M, Bauerband LA, Rolbiecki A, Young C. Physical activity and body image: intertwined health priorities identified by transmasculine young people in a non-metropolitan area. Int J Transgend Health. 2020;21(2):209–19.
Google Scholar
Ferrari GLM, Kovalskys I, Fisberg M, Gómez G, Rigotti A, Sanabria LYC, et al. Comparison of self-report versus accelerometer – measured physical activity and sedentary behaviors and their association with body composition in Latin American countries. PLoS ONE. 2020;15(4):e0232420.
Google Scholar
Colley RC, Butler G, Garriguet D, Prince SA, Roberts KC. Comparison of self-reported and accelerometer-measured physical activity among Canadian youth. Health Rep. 2019;30(7):3–12.
Google Scholar
Vetter VM, Özince DD, Kiselev J, Düzel S, Demuth I. Self-reported and accelerometer-based assessment of physical activity in older adults: results from the Berlin aging study II. Sci Rep. 2023;13(1):10047.
Google Scholar
Brown SG, Morrison LA, Calibuso MJ, Christiansen TM. The menstrual cycle and sexual behavior: relationship to eating, exercise, sleep, and health patterns. Women Health. 2008;48(4):429–44.
Google Scholar
Tomisek A, Flinn B, Balsky T, Gruman C, Rizer AM. Strong, healthy, energized: striving for a healthy weight in an older lesbian population. J Women Aging. 2017;29(3):230–42.
Google Scholar
Sardinha LB, Santos DA, Silva AM, Baptista F, Owen N. Breaking-up sedentary time is associated with physical function in older adults. J Gerontol A Biol Sci Med Sci. 2015;70(1):119–24.
Google Scholar
Owen N. Too much sitting and too little exercise: sedentary behavior and health. Revista Brasileira de Atividade Física & Saúde. 2018;23:1–4.
Google Scholar
Healy GN, Dunstan DW, Salmon J, Cerin E, Shaw JE, Zimmet PZ, et al. Breaks in sedentary time: beneficial associations with metabolic risk. Diabetes Care. 2008;31(4):661–6.
Google Scholar
Bouveyron C, Bozzi L, Jacques J, Jollois F-X. The functional latent block model for the co-clustering of electricity consumption curves. J R Stat Soc: Ser C: Appl Stat. 2018;67(4):897–915.
Google Scholar
Callahan CM, Unverzagt FW, Hui SL, Perkins AJ, Hendrie HC. Six-item screener to identify cognitive impairment among potential subjects for clinical research. Med Care. 2002;40(9):771–81.
Google Scholar
Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O’Neal L, et al. The REDCap consortium: building an international community of software platform partners. J Biomed Inform. 2019;95:103208.
Google Scholar
Researchmatch. Make a positive impact by volunteering for research n.d. [Available from: https://www.researchmatch.org/.
Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–81.
Google Scholar
Belloir JA, Ensari I, Jackman K, Shechter A, Bhargava A, Bockting WO, et al. Day-to-day associations of intersectional minority stressors with sleep health in sexual and gender minority people of color. Health Psychol. 2024;43(8):591.
Google Scholar
Collins JE, Yang HY, Trentadue TP, Gong Y, Losina E. Validation of the Fitbit Charge 2 compared to the ActiGraph GT3X+ in older adults with knee osteoarthritis in free-living conditions. PLoS ONE. 2019;14(1):e0211231.
Google Scholar
Tudor-Locke C, Johnson WD, Katzmarzyk PT. Accelerometer-determined steps per day in US adults. Med Sci Sports Exerc. 2009;41(7):1384–91.
Google Scholar
Ensari I, Caceres BA, Jackman KB, Goldsmith J, Suero-Tejeda NM, Odlum ML, et al. Characterizing daily physical activity patterns with unsupervised learning via functional mixture models. J Behav Med. 2024;48:149. https://doi.org/10.1007/s10865-024-00519-w.
Google Scholar
Airlie J, Forster A, Birch KM. An investigation into the optimal wear time criteria necessary to reliably estimate physical activity and sedentary behaviour from ActiGraph wGT3X+ accelerometer data in older care home residents. BMC Geriatr. 2022;22(1):136.
Google Scholar
Prescott S, Traynor JP, Shilliday I, Zanotto T, Rush R, Mercer TH. Minimum accelerometer wear-time for reliable estimates of physical activity and sedentary behaviour of people receiving haemodialysis. BMC Nephrol. 2020;21(1):230.
Google Scholar
Young DS, Roemmele ES, Yeh P. Zero-inflated modeling part I: traditional zero-inflated count regression models, their applications, and computational tools. WIREs Comput Stat. 2022;14(1):e1541.
Google Scholar
Saint-Maurice PF, Troiano RP, Bassett DR Jr, Graubard BI, Carlson SA, Shiroma EJ, et al. Association of daily step count and step intensity with mortality among US adults. JAMA. 2020;323(12):1151–60.
Google Scholar
U.S. Department of Health and Human Services. Physical activity guidelines for Americans. 2nd ed. Washington, DC: U.S. Department of Health and Human Services; 2018.
Tudor-Locke C, Craig CL, Thyfault JP, Spence JC. A step-defined sedentary lifestyle index: <5000 steps/day. Appl Physiol Nutr Metab. 2013;38(2):100–14.
Google Scholar
Tudor-Locke C, Hatano Y, Pangrazi RP, Kang M. Revisiting” how many steps are enough?”. Med Sci Sports Exerc. 2008;40(7):S537–43.
Google Scholar
van Buuren S, Groothuis-Oudshoorn K. mice: multivariate imputation by chained equations in R. J Stat Softw. 2011;45(3):1–67.
Google Scholar
Robitzsch A, Grund S. miceadds: Some additional multiple imputation functions, especially for ‘mice’. R package version 3.17‑44. 2024. Available from: https://CRAN.R-project.org/package=miceadds.
Ensari I, Lipsky-Gorman S, Horan EN, Bakken S, Elhadad N. Associations between physical exercise patterns and pain symptoms in individuals with endometriosis: a cross-sectional mHealth-based investigation. BMJ Open. 2022;12(7):1–12.
Google Scholar
Kleinke K. Multiple imputation under violated distributional assumptions: A systematic evaluation of the assumed robustness of predictive mean matching. J Educ Behav Stat. 2017;42(4):371–404.
Google Scholar
Marshall A, Altman DG, Holder RL. Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study. BMC Med Res Methodol. 2010;10:112.
Google Scholar
Van Buuren S. Flexible imputation of missing data. 2nd ed. Boca Raton, FL: Chapman & Hall/CRC; 2018.
Google Scholar
De Nicola G, Sischka B, Kauermann G. Mixture models and networks: The stochastic blockmode. Stat Model. 2022;22(1–2):67–94.
Google Scholar
Patton AJ, Weller BM. Testing for unobserved heterogeneity via k-means clustering. J Bus Econ Stat. 2023;41(3):737–51.
Google Scholar
Lam J, Garcia J. Contour of the day: social patterning of time in later life and variation in reported well-being in activities. J Aging Health. 2021;33(9):751–6.
Google Scholar
Ullah S, Finch CF. Applications of functional data analysis: a systematic review. BMC Med Res Methodol. 2013;13(1):43.
Google Scholar
Biernacki C, Celeux G, Govaer G. Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Trans Pattern Anal Mach Intell. 2000;22(7):719–25.
Google Scholar
Bouveyron C, Côme E, Jacques J. The discriminative functional mixture model for a comparative analysis of bike sharing systems. Ann Appl Stat. 2015;9(4):1726–60.
Google Scholar
Bertoletti M, Friel N, Rastelli R. Choosing the number of clusters in a finite mixture model using an exact integrated completed likelihood criterion. METRON. 2015;73(2):177–99.
Google Scholar
Yu L, Buysse DJ, Germain A, Moul DE, Stover A, Dodds NE, et al. Development of short forms from the PROMIS™ sleep disturbance and Sleep-Related Impairment item banks. Behav Sleep Med. 2011;10(1):6–24.
Google Scholar
Morin CM, Belleville G, Bélanger L, Ivers H. The Insomnia Severity Index: psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep. 2011;34(5):601–8.
Google Scholar
Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213.
Google Scholar
Casa A, Bouveyron C, Erosheva E, Menardi G. Co-clustering of time-dependent data via the shape invariant model. J Classif. 2021;38(3):626–49.
Google Scholar
Evenson KR, Wen F, Metzger JS, Herring AH. Physical activity and sedentary behavior patterns using accelerometry from a national sample of United States adults. Int J Behav Nutr Phys Act. 2015;12(1):20.
Google Scholar
Santos DA, Júdice PB, Magalhães JP, Correia IR, Silva AM, Baptista F, et al. Patterns of accelerometer-derived sedentary time across the lifespan. J Sports Sci. 2018;36(24):2809–17.
Google Scholar
Lindstrom M, Rosvall M. Sexual identity and low leisure-time physical activity: a population-based study. Public Health. 2020;182:77–9.
Google Scholar
VanKim NA, Erickson DJ, Eisenberg ME, Lust K, Rosser BR, Laska MN. College women’s weight-related behavior profiles differ by sexual identity. Am J Health Behav. 2015;39(4):461–70.
Google Scholar
Caceres BA, Makarem N, Hickey KT, Hughes TL. Cardiovascular disease disparities in sexual minority adults: an examination of the behavioral risk factor surveillance system (2014–2016). Am J Health Promot. 2019;33(4):576–85.
Google Scholar
Cunningham TJ, Xu F, Town M. Prevalence of five health-related behaviors for chronic disease prevention among sexual and gender minority adults — 25 U.S. states and Guam. Morbid Mortal Weekly Rep. 2018;67(32):888–93.
Google Scholar
Ferrero-Hernández P, Farías-Valenzuela C, Jofré-Saldía E, Marques A, Kovalskys I, Gómez G, et al. Physical activity and daily steps cut offs points for overweight/obesity prevention among eight Latin American countries. Sci Rep. 2022;12(1):18827.
Google Scholar
Paluch AE, Bajpai S, Bassett DR, Carnethon MR, Ekelund U, Evenson KR, et al. Daily steps and all-cause mortality: a meta-analysis of 15 international cohorts. Lancet Public Health. 2022;7(3):e219–28.
Google Scholar
Master H, Annis J, Huang S, Beckman JA, Ratsimbazafy F, Marginean K, et al. Association of step counts over time with the risk of chronic disease in the all of us research program. Nat Med. 2022;28(11):2301–8.
Google Scholar
Held NJ, Perrotta AS, Mueller T, Pfoh-MacDonald SJ. Agreement of the Apple Watch® and Fitbit Charge® for recording step count and heart rate when exercising in water. Med Biol Eng Compu. 2022;60(5):1323–31.
Google Scholar
Feehan LM, Geldman J, Sayre EC, Park C, Ezzat AM, Yoo JY, et al. Accuracy of Fitbit devices: systematic review and narrative syntheses of quantitative data. JMIR Mhealth Uhealth. 2018;6(8):e10527.
Google Scholar
Bai Y, Hibbing P, Mantis C, Welk GJ. Comparative evaluation of heart rate-based monitors: Apple Watch vs Fitbit Charge HR. J Sports Sci. 2018;36(15):1734–41.
Google Scholar
Imboden MT, Nelson MB, Kaminsky LA, Montoye AHK. Comparison of four Fitbit and Jawbone activity monitors with a research-grade ActiGraph accelerometer for estimating physical activity and energy expenditure. Br J Sports Med. 2018;52(13):844–50.
Google Scholar
Adam Noah J, Spierer DK, Gu J, Bronner S. Comparison of steps and energy expenditure assessment in adults of Fitbit Tracker and Ultra to the Actical and indirect calorimetry. J Med Eng Technol. 2013;37(7):456–62.
Google Scholar
Middelweerd A, Van der Ploeg HP, Van Halteren A, Twisk JWR, Brug J, Te Velde SJ. A validation study of the Fitbit one in daily life using different time intervals. Med Sci Sports Exerc. 2017;49(6):1270–9.
Google Scholar
Nazari G, MacDermid JC, Sinden KE, Richardson J, Tang A. Reliability of zephyr bioharness and fitbit charge measures of heart rate and activity at rest, during the modified Canadian aerobic fitness test, and recovery. J Strength Condition Res. 2019;33(2):559–71.
Google Scholar
Bai Y, Tompkins C, Gell N, Dione D, Zhang T, Byun W. Comprehensive comparison of Apple Watch and Fitbit monitors in a free-living setting. PLoS ONE. 2021;16(5):1–12.
Google Scholar
Diaz KM, Krupka DJ, Chang MJ, Peacock J, Ma Y, Goldsmith J, Schwartz JE, Davidson KW. Fitbit®: an accurate and reliable device for wireless physical activity tracking. Int J Cardiol. 2015;185:138–40.
Google Scholar
Dontje ML, de Groot M, Lengton RR, van der Schans CP, Krijnen WP. Measuring steps with the Fitbit activity tracker: an inter-device reliability study. J Med Eng Technol. 2015;39(5):286–90.
Google Scholar
link
