@article{aguilar-fariasActiGraphGT3XCutpoints2014,
  title = {{{ActiGraph GT3X}}+ Cut-Points for Identifying Sedentary Behaviour in Older Adults in Free-Living Environments},
  author = {Aguilar-Farías, Nicolás and Brown, Wendy J. and Peeters, G.M.E.E. (Geeske)},
  date = {2014-05},
  journaltitle = {J Sci Med Sport},
  volume = {17},
  number = {3},
  pages = {293--299},
  issn = {14402440},
  doi = {10.1016/j.jsams.2013.07.002},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S1440244013001485},
  urldate = {2021-08-27},
  langid = {english}
}

@article{barreiraComparisonOlderAdults2013,
  title = {Comparison of Older Adults’ Steps per Day Using an {{NL-1000}} Pedometer and Two {{GT3X}}+ Accelerometer Filters},
  author = {Barreira, Tiago V. and Brouillette, Robert M. and Foil, Heather C. and Keller, Jeffrey N. and Tudor-Locke, Catrine},
  date = {2013-10},
  journaltitle = {J Aging Phys Act},
  volume = {21},
  number = {4},
  pages = {402--416},
  issn = {1063-8652, 1543-267X},
  doi = {10.1123/japa.21.4.402},
  url = {https://journals.humankinetics.com/view/journals/japa/21/4/article-p402.xml},
  urldate = {2022-01-28},
  abstract = {The purpose of this study was to compare the steps/d derived from the ActiGraph GT3X+ using the manufacturer’s default filter (DF) and low-frequency-extension filter (LFX) with those from the NL-1000 pedometer in an older adult sample. Fifteen older adults (61–82 yr) wore a GT3X+ (24 hr/day) and an NL-1000 (waking hours) for 7 d. Day was the unit of analysis (               n               = 86 valid days) comparing (a) GT3X+ DF and NL-1000 steps/d and (b) GT3X+ LFX and NL-1000 steps/d. DF was highly correlated with NL-1000 (               r               = .80), but there was a significant mean difference (–769 steps/d). LFX and NL-1000 were highly correlated (               r               = .90), but there also was a significant mean difference (8,140 steps/d). Percent difference and absolute percent difference between DF and NL-1000 were –7.4\% and 16.0\%, respectively, and for LFX and NL-1000 both were 121.9\%. Regardless of filter used, GT3X+ did not provide comparable pedometer estimates of steps/d in this older adult sample.}
}

@article{bowlerRealworldUseRescue2019,
  title = {Real-World Use of Rescue Inhaler Sensors, Electronic Symptom Questionnaires and Physical Activity Monitors in {{COPD}}},
  author = {Bowler, Russell and Allinder, Matthew and Jacobson, Sean and Miller, Andrew and Miller, Bruce and Tal-Singer, Ruth and Locantore, Nicholas},
  date = {2019-02},
  journaltitle = {BMJ Open Resp Res},
  volume = {6},
  number = {1},
  pages = {e000350},
  issn = {2052-4439},
  doi = {10.1136/bmjresp-2018-000350},
  url = {https://bmjopenrespres.bmj.com/lookup/doi/10.1136/bmjresp-2018-000350},
  urldate = {2022-06-15},
  abstract = {Background               Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characterised by airflow obstruction and other morbidities such as respiratory symptoms, reduced physical activity and frequent bronchodilator use. Recent advances in personal digital monitoring devices can permit continuous collection of these data in COPD patients, but the relationships among them are not well understood.                                         Methods               184 individuals from a single centre of the COPDGene cohort agreed to participate in this 3-week observational study. Each participant used a smartphone to complete a daily symptom diary (EXAcerbations of Chronic pulmonary disease Tool, EXACT), wore a wrist-worn accelerometer to record continuously physical activity and completed the Clinical Visit PROactive Physical Activity in COPD questionnaire. 58 users of metered dose inhalers for rescue (albuterol) were provided with an inhaler sensor, which time stamped each inhaler actuation.                                         Results               Rescue inhaler use was strongly correlated with E-RS:COPD score, while step counts were correlated with neither rescue use nor E-RS:COPD score. Frequent, unpatterned inhaler use pattern was associated with worse respiratory symptoms and less physical activity compared with frequent inhaler use with a regular daily pattern. There was a strong week-by-week correlation among measurements, suggesting that 1\,week of monitoring is sufficient to characterise stable patients with COPD.                                         Discussion               The study highlights the interaction and relevance of personal real-time monitoring of respiratory symptoms, physical activity and rescue medication in patients with COPD. Additionally, visual displays of longitudinal data may be helpful for disease management to help drive conversations between patients and caregivers and for risk-based monitoring in clinical trials.},
  langid = {english},
  file = {C\:\\Users\\pydemullenheim\\Zotero\\storage\\TG2WYHD8\\Bowler et al. - 2019 - Real-world use of rescue inhaler sensors, electron.pdf}
}

@article{butteAssessingPhysicalActivity2012,
  title = {Assessing Physical Activity Using Wearable Monitors: Measures of Physical Activity},
  shorttitle = {Assessing {{Physical Activity Using Wearable Monitors}}},
  author = {Butte, Nancy F. and Ekelund, Ulf and Westerterp, Klaas R.},
  date = {2012-01},
  journaltitle = {Med Sci Sports Exerc},
  volume = {44},
  pages = {S5-S12},
  issn = {0195-9131},
  doi = {10.1249/MSS.0b013e3182399c0e},
  url = {https://journals.lww.com/00005768-201201001-00002},
  urldate = {2022-01-28},
  issue = {1S},
  langid = {english},
  file = {C\:\\Users\\pydemullenheim\\Zotero\\storage\\XLDUBR98\\Butte et al. - 2012 - Assessing Physical Activity Using Wearable Monitor.pdf}
}

@article{chastinMethodsObjectiveMeasure2010,
  title = {Methods for Objective Measure, Quantification and Analysis of Sedentary Behaviour and Inactivity},
  author = {Chastin, S.F.M. and Granat, M.H.},
  date = {2010-01},
  journaltitle = {Gait Posture},
  volume = {31},
  number = {1},
  pages = {82--86},
  issn = {09666362},
  doi = {10.1016/j.gaitpost.2009.09.002},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S096663620900602X},
  urldate = {2022-12-26},
  langid = {english}
}

@article{chastinStrikingRightBalance2021,
  title = {Striking the Right Balance: Evidence to Inform Combined Physical Activity and Sedentary Behavior Recommendations},
  shorttitle = {Striking the {{Right Balance}}},
  author = {Chastin, Sebastien F.M. and McGregor, Duncan E. and Biddle, Stuart J.H. and Cardon, Greet and Chaput, Jean-Philippe and Dall, Philippa M. and Dempsey, Paddy C. and DiPietro, Loretta and Ekelund, Ulf and Katzmarzyk, Peter T. and Leitzmann, Michael and Stamatakis, Emmanuel and Van der Ploeg, Hidde P.},
  date = {2021-06-01},
  journaltitle = {J Phys Act Health},
  volume = {18},
  number = {6},
  pages = {631--637},
  issn = {1543-3080, 1543-5474},
  doi = {10.1123/jpah.2020-0635},
  url = {https://journals.humankinetics.com/view/journals/jpah/18/6/article-p631.xml},
  urldate = {2021-10-01},
  abstract = {Background               : Crucial evidence gaps regarding: (1)~the joint association of physical activity and sedentary time with health outcomes and (2)~the benefits of light-intensity physical activity were identified during the development of recommendations for the World Health Organization Guidelines on physical activity and sedentary behavior (SB). The authors present alternative ways to evidence the relationship between health outcomes and time spent in physical activity and SB and examine how this could be translated into a combined recommendation in future guidelines.               Methods               : We used compositional data analysis to quantify the dose–response associations between the balance of time spent in physical activity and SB with all-cause mortality. The authors applied this approach using 2005–2006 National Health and Nutrition Examination Survey accelerometer data.               Results               : Different combinations of time spent in moderate- to vigorous-intensity physical activity, light-intensity physical activity, and SB are associated with similar all-cause mortality risk level. A balance of more than 2.5 minutes of moderate- to vigorous-intensity physical activity per hour of daily sedentary time is associated with the same magnitude of risk reduction for all-cause mortality as obtained by being physically active according to the current recommendations.               Conclusion               : This method could be applied to provide evidence for more flexible recommendations in the future with options to act on different behaviors depending on individuals’ circumstances and capacity.},
  file = {C\:\\Users\\pydemullenheim\\Zotero\\storage\\YRAVQW4Z\\Chastin et al. - 2021 - Striking the Right Balance Evidence to Inform Com.pdf}
}

@article{choiAssessmentWearNonwear2012,
  title = {Assessment of Wear/Nonwear Time Classification Algorithms for Triaxial Accelerometer},
  author = {Choi, Leena and Ward, Suzanne Capen and Schnelle, John F. and Buchowski, Maciej S.},
  date = {2012-10},
  journaltitle = {Med Sci Sports Exerc},
  volume = {44},
  number = {10},
  pages = {2009--2016},
  issn = {0195-9131},
  doi = {10.1249/MSS.0b013e318258cb36},
  url = {https://journals.lww.com/acsm-msse/Fulltext/2012/10000/Assessment_of_Wear_Nonwear_Time_Classification.23.aspx},
  urldate = {2021-08-25},
  abstract = {Purpose~         The objective of this study is to assess the performance of existing wear/nonwear time classification algorithms for accelerometry data collected in the free-living environment using a wrist-worn triaxial accelerometer and a waist-worn uniaxial accelerometer in older adults.         Methods~         Twenty-nine adults age 76 to 96 yr wore wrist accelerometers for approximately 24 h per day and waist accelerometers during waking for approximately 7 d of free living. Wear and nonwear times were classified by existing algorithms (Alg[ActiLife], Alg[Troiano], and Alg[Choi]) and compared with wear and nonwear times identified by data plots and diary records. With the use of bias and probability of correct classification, the performance of the algorithms, two time windows (60 and 90 min), and vector magnitude (VM) versus vertical axis (V) counts from a triaxial accelerometer were compared.         Results~         Automated algorithms (Alg[Choi] and Alg[Troiano]) classified wear/nonwear time intervals more accurately from VM than V counts. The use of the 90-min time window improved wear/nonwear classification accuracy when compared with the 60-min window. The Alg[Choi] and Alg[Troiano] performed better than the manufacturer-provided algorithm (Alg[ActiLife]), and the Alg[Choi] performed better than the Alg[Troiano] for wear/nonwear time classification using the data collected by both accelerometers.         Conclusions~         Triaxial wrist-worn accelerometer can be used for an accurate wear/nonwear time classification in free-living older adults. The use of the 90-min window and the VM counts improves the performance of commonly used algorithms for wear/nonwear classification for both uniaxial and triaxial accelerometers.},
  langid = {american},
  file = {C\:\\Users\\pydemullenheim\\Zotero\\storage\\C923VDI5\\Choi et al. - 2012 - Assessment of WearNonwear Time Classification Alg.pdf;C\:\\Users\\pydemullenheim\\Zotero\\storage\\G7CX29HD\\Assessment_of_Wear_Nonwear_Time_Classification.23.html}
}

@software{choiPhysicalActivityProcessAccelerometer2021,
  title = {{{PhysicalActivity}}: Process Accelerometer Data for Physical Activity Measurement},
  author = {Choi, Leena and Beck, Cole and Liu, Zhouwen and Moore, Ryan and Matthews, Charles E. and Buchowski, Maciej S.},
  date = {2021},
  url = {https://CRAN.R-project.org/package=PhysicalActivity},
  version = {0.2-4}
}

@article{choiValidationAccelerometerWear2011,
  title = {Validation of Accelerometer Wear and Nonwear Time Classification Algorithm},
  author = {Choi, Leena and Liu, Zhouwen and Matthews, Charles E. and Buchowski, Maciej S.},
  date = {2011-02},
  journaltitle = {Med Sci Sports Exerc},
  volume = {43},
  number = {2},
  pages = {357--364},
  issn = {0195-9131},
  doi = {10.1249/MSS.0b013e3181ed61a3},
  url = {https://journals.lww.com/acsm-msse/pages/articleviewer.aspx?year=2011&issue=02000&article=00022&type=Fulltext},
  urldate = {2021-08-25},
  abstract = {Introduction:~         The use of movement monitors (accelerometers) for measuring physical activity (PA) in intervention and population-based studies is becoming a standard methodology for the objective measurement of sedentary and active behaviors and for the validation of subjective PA self-reports. A vital step in PA measurement is the classification of daily time into accelerometer wear and nonwear intervals using its recordings (counts) and an accelerometer-specific algorithm.         Purpose:~         The purpose of this study was to validate and improve a commonly used algorithm for classifying accelerometer wear and nonwear time intervals using objective movement data obtained in the whole-room indirect calorimeter.         Methods:~         We conducted a validation study of a wear or nonwear automatic algorithm using data obtained from 49 adults and 76 youth wearing accelerometers during a strictly monitored 24-h stay in a room calorimeter. The accelerometer wear and nonwear time classified by the algorithm was compared with actual wearing time. Potential improvements to the algorithm were examined using the minimum classification error as an optimization target.         Results:~         The recommended elements in the new algorithm are as follows: 1) zero-count threshold during a nonwear time interval, 2) 90-min time window for consecutive zero or nonzero counts, and 3) allowance of 2-min interval of nonzero counts with the upstream or downstream 30-min consecutive zero-count window for detection of artifactual movements. Compared with the true wearing status, improvements to the algorithm decreased nonwear time misclassification during the waking and the 24-h periods (all P values {$<$} 0.001).         Conclusions:~         The accelerometer wear or nonwear time algorithm improvements may lead to more accurate estimation of time spent in sedentary and active behaviors.},
  langid = {american},
  file = {C\:\\Users\\pydemullenheim\\Zotero\\storage\\97HPIPXP\\Choi et al. - 2011 - Validation of Accelerometer Wear and Nonwear Time .pdf;C\:\\Users\\pydemullenheim\\Zotero\\storage\\WVP3PZGA\\articleviewer.html}
}

@article{demeyerStandardizingAnalysisPhysical2014,
  title = {Standardizing the {{Analysis}} of Physical Activity in Patients with {{COPD}} Following a Pulmonary Rehabilitation Program},
  author = {Demeyer, Heleen and Burtin, Chris and Van Remoortel, Hans and Hornikx, Miek and Langer, Daniel and Decramer, Marc and Gosselink, Rik and Janssens, Wim and Troosters, Thierry},
  date = {2014-08},
  journaltitle = {Chest},
  volume = {146},
  number = {2},
  pages = {318--327},
  issn = {00123692},
  doi = {10.1378/chest.13-1968},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S001236921548821X},
  urldate = {2021-12-27},
  langid = {english},
  file = {C\:\\Users\\pydemullenheim\\Zotero\\storage\\59PSY2CB\\Demeyer et al. - 2014 - Standardizing the Analysis of Physical Activity in.pdf}
}

@article{ekelundDoseresponseAssociationsAccelerometry2019,
  title = {Dose-Response Associations between Accelerometry Measured Physical Activity and Sedentary Time and All Cause Mortality: Systematic Review and Harmonised Meta-Analysis},
  shorttitle = {Dose-Response Associations between Accelerometry Measured Physical Activity and Sedentary Time and All Cause Mortality},
  author = {Ekelund, Ulf and Tarp, Jakob and Steene-Johannessen, Jostein and Hansen, Bjørge H and Jefferis, Barbara and Fagerland, Morten W and Whincup, Peter and Diaz, Keith M and Hooker, Steven P and Chernofsky, Ariel and Larson, Martin G and Spartano, Nicole and Vasan, Ramachandran S and Dohrn, Ing-Mari and Hagströmer, Maria and Edwardson, Charlotte and Yates, Thomas and Shiroma, Eric and Anderssen, Sigmund A and Lee, I-Min},
  date = {2019-08-21},
  journaltitle = {BMJ},
  pages = {l4570},
  issn = {0959-8138, 1756-1833},
  doi = {10.1136/bmj.l4570},
  url = {https://www.bmj.com/lookup/doi/10.1136/bmj.l4570},
  urldate = {2021-10-01},
  abstract = {Abstract                            Objective               To examine the dose-response associations between accelerometer assessed total physical activity, different intensities of physical activity, and sedentary time and all cause mortality.                                         Design               Systematic review and harmonised meta-analysis.                                         Data sources               PubMed, PsycINFO, Embase, Web of Science, Sport Discus from inception to 31 July 2018.                                         Eligibility criteria               Prospective cohort studies assessing physical activity and sedentary time by accelerometry and associations with all cause mortality and reported effect estimates as hazard ratios, odds ratios, or relative risks with 95\% confidence intervals.                                         Data extraction and analysis               Guidelines for meta-analyses and systematic reviews for observational studies and PRISMA guidelines were followed. Two authors independently screened the titles and abstracts. One author performed a full text review and another extracted the data. Two authors independently assessed the risk of bias. Individual level participant data were harmonised and analysed at study level. Data on physical activity were categorised by quarters at study level, and study specific associations with all cause mortality were analysed using Cox proportional hazards regression analyses. Study specific results were summarised using random effects meta-analysis.                                         Main outcome measure               All cause mortality.                                         Results               39 studies were retrieved for full text review; 10 were eligible for inclusion, three were excluded owing to harmonisation challenges (eg, wrist placement of the accelerometer), and one study did not participate. Two additional studies with unpublished mortality data were also included. Thus, individual level data from eight studies (n=36\,383; mean age 62.6 years; 72.8\% women), with median follow-up of 5.8 years (range 3.0-14.5 years) and 2149 (5.9\%) deaths were analysed. Any physical activity, regardless of intensity, was associated with lower risk of mortality, with a non-linear dose-response. Hazards ratios for mortality were 1.00 (referent) in the first quarter (least active), 0.48 (95\% confidence interval 0.43 to 0.54) in the second quarter, 0.34 (0.26 to 0.45) in the third quarter, and 0.27 (0.23 to 0.32) in the fourth quarter (most active). Corresponding hazards ratios for light physical activity were 1.00, 0.60 (0.54 to 0.68), 0.44 (0.38 to 0.51), and 0.38 (0.28 to 0.51), and for moderate-to-vigorous physical activity were 1.00, 0.64 (0.55 to 0.74), 0.55 (0.40 to 0.74), and 0.52 (0.43 to 0.61). For sedentary time, hazards ratios were 1.00 (referent; least sedentary), 1.28 (1.09 to 1.51), 1.71 (1.36 to 2.15), and 2.63 (1.94 to 3.56).                                         Conclusion               Higher levels of total physical activity, at any intensity, and less time spent sedentary, are associated with substantially reduced risk for premature mortality, with evidence of a non-linear dose-response pattern in middle aged and older adults.                                         Systematic review registration               PROSPERO CRD42018091808.},
  langid = {english},
  file = {C\:\\Users\\pydemullenheim\\Zotero\\storage\\Q8H3HBXG\\Ekelund et al. - 2019 - Dose-response associations between accelerometry m.pdf}
}

@article{freedsonCalibrationComputerScience1998,
  title = {Calibration of the {{Computer Science}} and {{Applications}}, {{Inc}}. Accelerometer:},
  shorttitle = {Calibration of the {{Computer Science}} and {{Applications}}, {{Inc}}. Accelerometer},
  author = {Freedson, Patty S. and Melanson, Edward and Sirard, John},
  date = {1998-05},
  journaltitle = {Med Sci Sports Exerc},
  volume = {30},
  number = {5},
  pages = {777--781},
  issn = {0195-9131},
  doi = {10.1097/00005768-199805000-00021},
  url = {http://journals.lww.com/00005768-199805000-00021},
  urldate = {2021-08-27},
  langid = {english}
}

@article{garcia-aymerichValidityResponsivenessDaily2021,
  title = {Validity and Responsiveness of the {{Daily-}} and {{Clinical}} Visit-{{PROactive Physical Activity}} in {{COPD}} ({{D-PPAC}} and {{C-PPAC}}) Instruments},
  author = {Garcia-Aymerich, Judith and Puhan, Milo A and Corriol-Rohou, Solange and de Jong, Corina and Demeyer, Heleen and Dobbels, Fabienne and Erzen, Damijan and Frei, Anja and Gimeno-Santos, Elena and Hopkinson, Nicholas S and Ivanoff, Nathalie and Karlsson, Niklas and Louvaris, Zafeiris and Polkey, Michael I and Rabinovich, Roberto A and Scuri, Mario and Tabberer, Maggie and Vogiatzis, Ioannis and Troosters, Thierry},
  options = {useprefix=true},
  date = {2021-03},
  journaltitle = {Thorax},
  volume = {76},
  number = {3},
  pages = {228--238},
  issn = {0040-6376, 1468-3296},
  doi = {10.1136/thoraxjnl-2020-214554},
  url = {https://thorax.bmj.com/lookup/doi/10.1136/thoraxjnl-2020-214554},
  urldate = {2022-03-20},
  abstract = {Background               The Daily-PROactive and Clinical visit-PROactive Physical Activity (D-PPAC and C-PPAC) instruments in chronic obstructive pulmonary disease (COPD) combines questionnaire with activity monitor data to measure patients’ experience of physical activity. Their amount, difficulty and total scores range from 0 (worst) to 100 (best) but require further psychometric evaluation.                                         Objective               To test reliability, validity and responsiveness, and to define minimal important difference (MID), of the D-PPAC and C-PPAC instruments, in a large population of patients with stable COPD from diverse severities, settings and countries.                                         Methods               We used data from seven randomised controlled trials to evaluate D-PPAC and C-PPAC internal consistency and construct validity by sex, age groups, COPD severity, country and language as well as responsiveness to interventions, ability to detect change and MID.                                         Results               We included 1324 patients (mean (SD) age 66 (8) years, forced expiratory volume in 1 s 55 (17)\% predicted). Scores covered almost the full range from 0 to 100, showed strong internal consistency after stratification and correlated as a priori hypothesised with dyspnoea, health-related quality of life and exercise capacity. Difficulty scores improved after pharmacological treatment and pulmonary rehabilitation, while amount scores improved after behavioural physical activity interventions. All scores were responsive to changes in self-reported physical activity experience (both worsening and improvement) and to the occurrence of COPD exacerbations during follow-up. The MID was estimated to 6 for amount and difficulty scores and 4 for total score.                                         Conclusions               The D-PPAC and C-PPAC instruments are reliable and valid across diverse COPD populations and responsive to pharmacological and non-pharmacological interventions and changes in clinically relevant variables.},
  langid = {english},
  file = {C\:\\Users\\pydemullenheim\\Zotero\\storage\\NESJLIQK\\Garcia-Aymerich et al. - 2021 - Validity and responsiveness of the Daily- and Clin.pdf}
}

@article{gimeno-santosPROactiveInstrumentsMeasure2015,
  title = {The {{PROactive}} Instruments to Measure Physical Activity in Patients with Chronic Obstructive Pulmonary Disease},
  author = {Gimeno-Santos, Elena and Raste, Yogini and Demeyer, Heleen and Louvaris, Zafeiris and de Jong, Corina and Rabinovich, Roberto A. and Hopkinson, Nicholas S. and Polkey, Michael I. and Vogiatzis, Ioannis and Tabberer, Maggie and Dobbels, Fabienne and Ivanoff, Nathalie and de Boer, Willem I. and van der Molen, Thys and Kulich, Karoly and Serra, Ignasi and Basagaña, Xavier and Troosters, Thierry and Puhan, Milo A. and Karlsson, Niklas and Garcia-Aymerich, Judith},
  options = {useprefix=true},
  date = {2015-10},
  journaltitle = {Eur Respir J},
  volume = {46},
  number = {4},
  pages = {988--1000},
  issn = {0903-1936, 1399-3003},
  doi = {10.1183/09031936.00183014},
  url = {http://erj.ersjournals.com/lookup/doi/10.1183/09031936.00183014},
  urldate = {2022-01-08},
  abstract = {No current patient-centred instrument captures all dimensions of physical activity in chronic obstructive pulmonary disease (COPD). Our objective was item reduction and initial validation of two instruments to measure physical activity in COPD.             Physical activity was assessed in a 6-week, randomised, two-way cross-over, multicentre study using PROactive draft questionnaires (daily and clinical visit versions) and two activity monitors. Item reduction followed an iterative process including classical and Rasch model analyses, and input from patients and clinical experts.             236 COPD patients from five European centres were included. Results indicated the concept of physical activity in COPD had two domains, labelled “amount” and “difficulty”. After item reduction, the daily PROactive instrument comprised nine items and the clinical visit contained 14. Both demonstrated good model fit (person separation index {$>$}0.7). Confirmatory factor analysis supported the bidimensional structure. Both instruments had good internal consistency (Cronbach's α{$>$}0.8), test–retest reliability (intraclass correlation coefficient ≥0.9) and exhibited moderate-to-high correlations (r{$>$}0.6) with related constructs and very low correlations (r{$<$}0.3) with unrelated constructs, providing evidence for construct validity.             Daily and clinical visit “PROactive physical activity in COPD” instruments are hybrid tools combining a short patient-reported outcome questionnaire and two activity monitor variables which provide simple, valid and reliable measures of physical activity in COPD patients.},
  langid = {english},
  file = {C\:\\Users\\pydemullenheim\\Zotero\\storage\\NYJKUZB2\\Gimeno-Santos et al. - 2015 - The PROactive instruments to measure physical acti.pdf}
}

@article{henryBasalMetabolicRate2005,
  title = {Basal Metabolic Rate Studies in Humans: Measurement and Development of New Equations},
  shorttitle = {Basal Metabolic Rate Studies in Humans},
  author = {Henry, Cjk},
  date = {2005-10},
  journaltitle = {Public Health Nutr},
  volume = {8},
  pages = {1133--1152},
  issn = {1368-9800, 1475-2727},
  doi = {10.1079/PHN2005801},
  url = {https://www.cambridge.org/core/product/identifier/S1368980005001394/type/journal_article},
  urldate = {2021-08-27},
  abstract = {Abstract                            Objective                                To facilitate the Food and Agriculture Organization/World Health Organization/United Nations University Joint (FAO/WHO/UNU) Expert Consultation on Energy and Protein Requirements which met in Rome in 1981, Schofield                 et al.                 reviewed the literature and produced predictive equations for both sexes for the following ages: 0–3, 3–10, 10–18, 18–30, 30–60 and {$>$}60 years. These formed the basis for the equations used in 1985 FAO/WHO/UNU document,                 Energy and Protein Requirements                 .                              While Schofield's analysis has served a significant role in re-establishing the importance of using basal metabolic rate (BMR) to predict human energy requirements, recent workers have subsequently queried the universal validity and application of these equations. A survey of the most recent studies (1980–2000) in BMR suggests that in most cases the current FAO/WHO/UNU predictive equations overestimate BMR in many communities. The FAO/WHO/UNU equations to predict BMR were developed using a database that contained a disproportionate number – 3388 out of 7173 (47\%) – of Italian subjects. The Schofield database contained relatively few subjects from the tropical region.                                The objective here is to review the historical development in the measurement and application of BMR and to critically review the Schofield                 et al.                 BMR database presenting a series of new equations to predict BMR.                                                        Design               This division, while arbitrary, will enable readers who wish to omit the historical review of BMR to concentrate on the evolution of the new BMR equations.                                         Setting               BMR data collected from published and measured values.                                         Subjects               A series of new equations (Oxford equations) have been developed using a data set of 10 552 BMR values that (1) excluded all the Italian subjects and (2) included a much larger number (4018) of people from the tropics.                                         Results               In general, the Oxford equations tend to produce lower BMR values than the current FAO/WHO/UNU equations in 18–30 and 30–60 year old males and in all females over 18 years of age.                                         Conclusions               This is an opportune moment to re-examine the role and place of BMR measurements in estimating total energy requirements today. The Oxford equations' future use and application will surely depend on their ability to predict more accurately the BMR in contemporary populations.},
  issue = {7a},
  langid = {english},
  file = {C\:\\Users\\pydemullenheim\\Zotero\\storage\\TU4QIRM4\\Henry - 2005 - Basal metabolic rate studies in humans measuremen.pdf}
}

@article{korenyPatternsPhysicalActivity2021,
  title = {Patterns of Physical Activity Progression in Patients with {{COPD}}},
  author = {Koreny, Maria and Demeyer, Heleen and Benet, Marta and Arbillaga-Etxarri, Ane and Balcells, Eva and Barberan-Garcia, Anael and Gimeno-Santos, Elena and Hopkinson, Nicholas S. and De Jong, Corina and Karlsson, Niklas and Louvaris, Zafeiris and Polkey, Michael I. and Puhan, Milo A. and Rabinovich, Roberto A. and Rodríguez-Roisin, Robert and Vall-Casas, Pere and Vogiatzis, Ioannis and Troosters, Thierry and Garcia-Aymerich, Judith and Arbillaga-Etxarri, Ane and Benet, Marta and Delgado, Anna and Garcia-Aymerich, Judith and Gimeno-Santos, Elena and Torrent-Pallicer, Jaume and Vilaró, Jordi and Barberan-Garcia, Anael and Rodriguez-Roisín, Robert and Balcells, Eva and Chiaradía, Diego A Rodríguez and Marín, Alicia and Ortega, Pilar and Celorrio, Nuria and Monteagudo, Mónica and Montellà, Nuria and Muñoz, Laura and Toran, Pere and Simonet, Pere and Jané, Carme and Martín-Cantera, Carlos and Borrell, Eulàlia and Vall-Casas, Pere and Ivanoff, Nathalie and Karlsson, Niklas and Corriol-Rohou, Solange and Jarrod, Ian and Erzen, Damijen and Brindicci, Caterina and Higenbottam, Tim and Scuri, Mario and McBride, Paul and Kamel, Nadia and Tabberer, Margaret and Troosters, Thierry and Dobbels, Fabienne and Garcia-Aymerich, Judith and de Boer, Pim and Kulich, Karoly and Glendenning, Alastair and Rudell, Katja and Wilson, Frederick J. and Polkey, Michael I. and Hopkinson, Nick S. and Vogiatzis, Ioannis and Nikai, Enkeleida and van der Molen, Thys and De Jong, Corina and Rabinovich, Roberto A. and MacNee, Bill and Puhan, Milo A. and Frei, Anja},
  options = {useprefix=true},
  date = {2021-03},
  journaltitle = {Arch Bronconeumol},
  volume = {57},
  number = {3},
  pages = {214--223},
  issn = {03002896},
  doi = {10.1016/j.arbres.2020.08.001},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S0300289620302805},
  urldate = {2022-06-10},
  langid = {english},
  file = {C\:\\Users\\pydemullenheim\\Zotero\\storage\\6VWW3DUK\\Koreny et al. - 2021 - Patterns of Physical Activity Progression in Patie.pdf}
}

@article{miguelesAccelerometerDataCollection2017,
  title = {Accelerometer {{Data Collection}} and {{Processing Criteria}} to {{Assess Physical Activity}} and Other Outcomes: A Systematic Review and Practical Considerations},
  shorttitle = {Accelerometer {{Data Collection}} and {{Processing Criteria}} to {{Assess Physical Activity}} and {{Other Outcomes}}},
  author = {Migueles, Jairo H. and Cadenas-Sanchez, Cristina and Ekelund, Ulf and Delisle Nyström, Christine and Mora-Gonzalez, Jose and Löf, Marie and Labayen, Idoia and Ruiz, Jonatan R. and Ortega, Francisco B.},
  date = {2017-09},
  journaltitle = {Sports Med},
  volume = {47},
  number = {9},
  pages = {1821--1845},
  issn = {0112-1642, 1179-2035},
  doi = {10.1007/s40279-017-0716-0},
  url = {http://link.springer.com/10.1007/s40279-017-0716-0},
  urldate = {2021-08-27},
  langid = {english},
  file = {C\:\\Users\\pydemullenheim\\Zotero\\storage\\A67ZGVUW\\Migueles et al. - 2017 - Accelerometer Data Collection and Processing Crite.pdf}
}

@software{petkovaActigraphSleeprDetect2021,
  title = {Actigraph.Sleepr: Detect Periods of Sleep and Non-Wear from '{{ActiGraph}}' Data.},
  author = {Petkova, Desislava},
  date = {2021},
  url = {http://github.com/dipetkov/actigraph.sleepr},
  version = {0.2.0}
}

@article{rowlandsCutPointsAccelerometer2018,
  title = {Beyond Cut Points: Accelerometer Metrics That Capture the Physical Activity Profile},
  shorttitle = {Beyond {{Cut Points}}},
  author = {Rowlands, Alex V. and Edwardson, Charlotte L. and Davies, Melanie J. and Khunti, Kamlesh and Harrington, Deirdre M. and Yates, Tom},
  date = {2018-06},
  journaltitle = {Med Sci Sports Exerc},
  volume = {50},
  number = {6},
  pages = {1323--1332},
  issn = {1530-0315, 0195-9131},
  doi = {10.1249/MSS.0000000000001561},
  url = {https://journals.lww.com/00005768-201806000-00025},
  urldate = {2022-12-26},
  langid = {english},
  file = {C\:\\Users\\pydemullenheim\\Zotero\\storage\\P2A2PKU8\\Rowlands et al. - 2018 - Beyond Cut Points Accelerometer Metrics that Capt.pdf}
}

@article{santos-lozanoActigraphGT3XValidation2013,
  title = {Actigraph {{GT3X}}: Validation and Determination of Physical Activity Intensity Cut Points},
  shorttitle = {Actigraph {{GT3X}}},
  author = {Santos-Lozano, A. and Santín-Medeiros, F. and Cardon, G. and Torres-Luque, G. and Bailón, R. and Bergmeir, C. and Ruiz, J. and Lucia, A. and Garatachea, N.},
  date = {2013-05-22},
  journaltitle = {Int J Sports Med},
  volume = {34},
  number = {11},
  pages = {975--982},
  issn = {0172-4622, 1439-3964},
  doi = {10.1055/s-0033-1337945},
  url = {http://www.thieme-connect.de/DOI/DOI?10.1055/s-0033-1337945},
  urldate = {2021-08-27},
  langid = {english}
}

@article{sasakiValidationComparisonActiGraph2011,
  title = {Validation and Comparison of {{ActiGraph}} Activity Monitors},
  author = {Sasaki, Jeffer E. and John, Dinesh and Freedson, Patty S.},
  date = {2011-09},
  journaltitle = {J Sci Med Sport},
  volume = {14},
  number = {5},
  pages = {411--416},
  issn = {14402440},
  doi = {10.1016/j.jsams.2011.04.003},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S1440244011000788},
  urldate = {2021-08-27},
  langid = {english}
}

@article{tudor-lockeHowManySteps2011,
  title = {How Many Steps/Day Are Enough? For Adults},
  shorttitle = {How Many Steps/Day Are Enough?},
  author = {Tudor-Locke, Catrine and Craig, Cora L and Brown, Wendy J and Clemes, Stacy A and De Cocker, Katrien and Giles-Corti, Billie and Hatano, Yoshiro and Inoue, Shigeru and Matsudo, Sandra M and Mutrie, Nanette and Oppert, Jean-Michel and Rowe, David A and Schmidt, Michael D and Schofield, Grant M and Spence, John C and Teixeira, Pedro J and Tully, Mark A and Blair, Steven N},
  date = {2011},
  journaltitle = {Int J Behav Nutr Phys Act},
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  number = {1},
  pages = {79},
  issn = {1479-5868},
  doi = {10.1186/1479-5868-8-79},
  url = {http://ijbnpa.biomedcentral.com/articles/10.1186/1479-5868-8-79},
  urldate = {2022-01-06},
  langid = {english},
  file = {C\:\\Users\\pydemullenheim\\Zotero\\storage\\ZZ75V4RD\\Tudor-Locke et al. - 2011 - How many stepsday are enough for adults.pdf}
}
