Systematic Review of Physical Activity, Sedentary Behaviour and Sleep Among Adults Living with Chronic Respiratory Disease in Low- and Middle-Income Countries

Akila R Jayamaha; Amy V Jones; Winceslaus Katagira; Bhushan Girase; Zainab K Yusuf; Ilaria Pina; Laura J Wilde; Azamat Akylbekov; Pip Divall; Sally J Singh; Mark W Orme

doi:10.2147/COPD.S345034

. 2022 Apr 18;17:821–854. doi: 10.2147/COPD.S345034

Systematic Review of Physical Activity, Sedentary Behaviour and Sleep Among Adults Living with Chronic Respiratory Disease in Low- and Middle-Income Countries

Akila R Jayamaha ^1,^2,^✉, Amy V Jones ^1,³, Winceslaus Katagira ⁴, Bhushan Girase ⁵, Zainab K Yusuf ^1,³, Ilaria Pina ^1,³, Laura J Wilde ^1,³, Azamat Akylbekov ⁶, Pip Divall ⁷, Sally J Singh ^1,³, Mark W Orme ^1,³

PMCID: PMC9033501 PMID: 35469273

Abstract

Physical activity (PA), sedentary behaviour (SB) and sleep are important lifestyle behaviours associated with chronic respiratory disease (CRD) morbidity and mortality. These behaviours need to be understood in low- and middle-income countries (LMIC) to develop appropriate interventions.

Purpose

Where and how have free-living PA, SB and sleep data been collected for adults living with CRD in LMIC? What are the free-living PA, SB and sleep levels of adults living with CRD?

Patients and Methods

The literature on free-living PA, SB and sleep of people living with CRD in LMIC was systematically reviewed in five relevant scientific databases. The review included empirical studies conducted in LMIC, reported in any language. Reviewers screened the articles and extracted data on prevalence, levels and measurement approach of PA, SB and sleep using a standardised form. Quality of reporting was assessed using bespoke criteria.

Results

Of 89 articles, most were conducted in Brazil (n=43). PA was the commonest behaviour measured (n=66). Questionnaires (n=52) were more commonly used to measure physical behaviours than device-based (n=37) methods. International Physical Activity Questionnaire was the commonest for measuring PA/SB (n=11). For sleep, most studies used Pittsburgh Sleep Quality Index (n=18). The most common ways of reporting were steps per day (n=21), energy expenditure (n=21), sedentary time (n=16), standing time (n=13), sitting time (n=11), lying time (n=10) and overall sleep quality (n=32). Studies revealed low PA levels [steps per day (range 2669–7490steps/day)], sedentary lifestyles [sitting time (range 283–418min/day); standing time (range 139–270min/day); lying time (range 76–119min/day)] and poor sleep quality (range 33–100%) among adults with CRD in LMIC.

Conclusion

Data support low PA levels, sedentary lifestyles and poor sleep among people in LMIC living with CRDs. More studies are needed in more diverse populations and would benefit from a harmonised approach to data collection for international comparisons.

Keywords: chronic respiratory disease, low- and middle-income countries, physical activity, sedentary behaviour, sleep

Introduction

Chronic respiratory disease (CRD) (chronic diseases of the airways and the other structures of the lungs) is associated with cigarette smoking, fumes from cooking on open stoves, air pollution,¹ and pulmonary tuberculosis,² and is highly prevalent across low- and middle-income countries (LMIC).³ Physical behaviours include all activities over a 24-h period across the movement spectrum, from no/little movement (sleep, sedentary behaviour) to movement of greater intensities (light and moderate-to-vigorous physical activity), which are important modifiable risk factors for CRD morbidity and mortality. Physical activity (PA) is defined as any bodily movement produced by skeletal muscles that results in energy expenditure. Optimising the 24-hour behavioural profile of people remains a global challenge.⁴

Physical inactivity is as a key risk factor for non-communicable disease, has been estimated to cause 9% of premature deaths worldwide⁵ and global trends are not on track to meet the global physical activity target, which is 10% relative reduction in the prevalence of insufficient physical activity by 2025.⁶^,⁷ People living with CRD are not only less physically active than healthy adults,⁸ but also those living with other chronic diseases.⁹ Symptoms of CRD, including breathlessness, deconditioning and declining lung function, contribute to a physically inactive lifestyle; linked to an increased risk of hospitalisation¹⁰ and premature death.¹¹ Sedentary behaviour (SB) (any waking behaviour characterized by an energy expenditure ≤1.5 metabolic equivalents, while in a sitting, reclining or lying posture)¹² comprises the majority of people’s waking day. People living with CRD spend more time sedentary¹³ which has been associated with premature mortality.¹⁴ Poor sleep quality,¹⁵ sleep dissatisfaction,¹⁶ and inadequate sleep¹⁷ contributes significantly to societies’ health burden.¹⁸ Sleep disturbances are a common feature of CRD, leading to more severe respiratory symptoms and putting people at a greater risk of an acute exacerbation.¹⁹

Whilst distinct behaviours, PA, SB and sleep are intrinsically interrelated components of the 24-hour day, demonstrating the need to examine them in the context of each other rather than in isolation. Recent WHO guidelines for PA and SB²⁰ and Canadian 24-hour movement guidelines²¹ have started to reflect this notion, but research is almost exclusively from high-income countries (HIC).²² Not least, PA in LMIC is mainly accumulated through transportation and occupation-related activities, contrasting HIC where most PA is undertaken recreationally.²³ Economical, societal and cultural diversities in LMIC must be considered in the evaluation and monitoring of physical behaviours.²⁴ A range of measurement options are available to quantify free-living PA, SB and sleep.

By compiling the current evidence on free-living PA, SB and sleep of adults living with CRD in LMIC, this review seeks to highlight research priorities, enabling researchers and clinicians to address important lifestyle health challenges in a more time- and cost-effective manner. Our scoping exercise identified no similar published reviews in LMIC. Similar reviews have been conducted in HIC²⁵^,²⁶ which have aided comparisons between our findings from LMIC with findings from HIC. This systematic review addressed the following questions: Where have free-living PA, SB and sleep data been collected for adults living with CRD in LMIC? How have the data been collected? What are the free-living PA, SB and sleep levels of adults living with CRD?

Materials and Methods

This systematic review is reported in accordance with the Preferred Reporting Items for Systematic Review and Meta‐Analysis (PRISMA) statement.²⁷ The protocol was prospectively registered on the International Prospective Register of Systematic Reviews (PROSPERO; CRD42020176196).

The search strategy was developed by an experienced clinical librarian [PD] using appropriate subject headings for each database. Search terms included chronic respiratory disease, “physical* activ*”, “sedent*” and sleep*alongside filters on LMIC (a full search strategy is provided in Appendix A). The strategy was adapted for each database.

Relevant articles were identified through systematic searches in five databases (Ovid-MEDLINE, Cochrane library, Ovid-EMBASE, Ovid-EMCARE and CINAHL). Searches for articles published in peer-reviewed journals were from the inception of each database until 30th January 2020. Global Index Medicus was searched, which included African Index Medicus, Index Medicus for Eastern Mediterranean Region, Index Medicus for South-East Asia Region, Latin Am. and Carib. Center on Health Sci Info, and Western Pacific Region Index Medicus. Reference lists of included articles were searched by hand for additional eligible articles. Studies on adults living with CRD in LMIC (population) which related to PA, SB and sleep (intervention), irrespective of the presence or absence of comparator (control group) which report what, where and how PA, SB and sleep data have been collected (outcome) were included in the review. Eligible studies included cross-sectional, longitudinal and case-control studies. Intervention studies were included if baseline data were presented. No restrictions were placed on sample size or language. The full text of the conference abstracts were requested from the corresponding author and if the authors did not respond, conference abstracts were also considered due to the paucity of the empirical evidence in LMICs.

Search results were screened using Rayyan software.²⁸ Two reviewers independently screened for eligibility by title and abstract [MO, AVJ]. Interrater agreement was 99.6%. Discrepancies between the two reviewers were resolved through discussion and with final decision provided by ZY. The full texts of these potentially eligible articles were then retrieved and independently assessed by two reviewers [MO, ZY]. Discrepancies between the two reviewers were resolved through discussion and final decision provided by SS in the event of no consensus. Interrater agreement was 88.1%. Study authors were asked for full text copies if these could not be retrieved.

Data from included studies were extracted using a bespoke Microsoft Excel spreadsheet which included details on study location,²⁹ study design, participant characteristics, free-living 24-hour physical behaviour measurements and methods, income classification,³⁰ climate classification,³¹ air quality³² (see Appendix B for full data extraction form/table). Authors of included studies were not contacted in the event of missing data. Data extraction was performed by five authors [AA, ARJ, BG, MO, WK], with double-data extraction for each study. Any conflicts in data extraction were resolved through discussion with final decision provided by MO/SS.

The main summary measures were objectively measured overall free-living PA, SB and sleep. Given the nature of the review, no formal risk of bias assessment was conducted. Instead, indicators of quality of reporting and questionnaire/device deployment were examined. Reporting quality of the included studies was conducted by considering whether key information was provided. The presence or absence of information relating to each of the above items was used to describe the quality of reporting. Reporting quality of the included studies was presented through narrative and table summaries. In accordance with our PROSPERO registration, a meta-analysis was not planned due to anticipated heterogeneity in measurement approaches (Table 1).

Table 1.

Summary of the Variables Used to Describe Physical Activity, Sedentary Behaviour and Sleep

Physical Behaviour Category of Variables	Frequency
Physical activity (PA)
Steps per day	21
Energy expenditure (e.g. kcal/day, EE during activity)	21
Active/inactive binary groups (e.g. ≥80 min MVPA, ≥30 min above 3METs)	19
Time spent walking	16
A physical activity score or index (e.g. IPAQ score, YPAS summary index)	16
Time in moderate-to-vigorous PA (MVPA) (e.g. time in >3METs, time in ≥3METs)	11
Movement intensity (e.g. movement intensity during walking)	9
Physical inactivity (e.g. severe inactivity <4580 steps)	8
Active/inactive 3+ groups (e.g. Inactive/Moderately inactive/Moderately active/Active)	7
Time in PA (e.g. total active time)	6
Leisure activities	4
Metabolic equivalents (MET)-min/week (e.g. MET-min/week walking)	4
Sport activities	4
Time in vigorous PA	4
Time in moderate PA	3
Household PA	3
Standing and walking time	2
Time in light PA	2
Distance walked	1
Impaired daily functioning	1
Ratio between time in moderate PA & walking	1
Time in other behaviours	1
Sedentary behaviour
Sedentary time (e.g. time in <1.5METs, time in <2METs)	16
Standing time	13
Sitting time	11
Lying time	10
Sedentary (yes/no) (e.g. >=8hr/day, >=8.5hr/day)	4
Sitting index	1
Standing index	1
Time television watching/video games	1
Sleep
Overall sleep quality (e.g. Global PSQI score, poor sleep quality [yes/no])	32
Sleep duration (e.g. total sleep time, short/medium/long sleep)	14
Use of sleep medication	11
Sleep disturbances (e.g. wake up several times per night)	11
Sleep efficiency	9
Sleep latency (e.g. time to fall asleep, trouble falling asleep)	8
Daytime sleep dysfunction	7
Appropriate sleep	1
Sleep alterations	1
Fixed sleep schedule	1

Open in a new tab

Extracted data and summary measures were stratified into subgroups: Region²⁹ (African, Region of the Americas, South-East Asia, European, Eastern Mediterranean, Western Pacific); income classification (Countries with low-income economies (Gross national income per capita $1045 or less in 2020); lower middle-income economies (GNI per capita between $1046 and $4095) and upper middle-income economies (GNI per capita between $4096 and $12,695) constitute low-and middle-income countries (LMIC)),³³ Köppen-Geiger climate classification (Equatorial, Arid, Warm temperature, Snow, Polar),³¹ air quality levels (PM10: above or below 20µg/m³ annual mean and PM2.5: above or below 10µg/m³ annual mean),³⁴ and measurement approach.

Results

Study Selection and Characteristics

Figure 1 shows the selection process according to the PRISMA statement.²⁷ The initial search retrieved 18,121 records, 153 full texts were screened for eligibility and 89 articles (0.6%) met the criteria for inclusion (Appendix C). Of the 89 articles identified, 78 were articles and 11 were conference abstracts. The full text of the selected conference abstracts were requested from the corresponding authors but none responded. Most studies recruited exclusively COPD (65 studies [73%]) and asthma patients (13 studies [15%]). Four studies (4%) recruited a combined sample of COPD and asthma and two studies (2%) recruited bronchiectasis patients. Excluding studies covering all regions, sample sizes totalled 8986 participants. Most studies were cross-sectional in design (65 studies [73%]), comprised male majority samples (55 studies [62%]) and included data collected in warm temperature climates (45 studies [51%]) (Table 2). Fifteen studies reported having a control group and eight studies compare the patients with healthy control (Appendix D).

Table 2.

Characteristics of Individual Studies Included in the Review

Reference	Study Location: Country	Income Classification	Climate Classification	Air Quality PM2.5	Air Quality PM10	Study Design	CRD Population	Sample Size	Age (Years)	Gender Mix (Majority %)	Behaviour Measured
African region
Adetiloye (2018)⁴²	Nigeria (Ile-Ife)	Lower-Middle	Equatorial	Not available	Not available	Cross-sectional	COPD	60	70 (8)	53% male	Sleep
Adewolea (2017)⁴³	Nigeria (Ile-Ife)	Lower-Middle	Equatorial	Not available	Not available	Cross-sectional	Chronic pulmonary parenchyma	410	48.5 (16.5)	69.3% female	Sleep
Desalu (2019)⁴⁴	Nigeria (Northwest, Southeast, Northcentral geopolitcal zones)	Lower-Middle	Not available	Not available	Not available	Cross-sectional	Asthma	172	37 (15)	67.4% female	Sleep
Eastern Mediterranean Region
Ali Zohal (2013)⁴⁵	Iran (Qazvin city)	Upper-Middle	Warm temperate	Above	Below	Case-control	COPD	120	66.4 (10.8)	66% male	Sleep
Borji (2018)⁴⁶	Iran (Ilam)	Upper-Middle	Arid	Above	Above	RCT	Asthma	120	Not stated	Not stated	Sleep
Chegeni (2018)⁴⁷	Iran (Khorramabad)	Upper-Middle	Arid	Above	Above	RCT	COPD	91	57.14 (11.8)	59% male	Sleep
Eslaminejad (2017)⁴⁸	Iran (Tehran)	Upper-Middle	Arid	Above	Above	Cross-sectional	COPD	850	53.88 (16.5)	53.6% female	Sleep
Mahmood (2019)⁴⁹	Iraq (Basra, Baghdad)	Upper-Middle	Arid	Above	Above	Cross-sectional	Asthma	56	40–49years: 50%; 50–59years: 12.4%; 60+ years: 28.6%	62.5% female	Physical activity
Moussa (2014)⁵⁰	Tunisia (Sousse)	Lower-Middle	Arid	Above	Above	Case-control	COPD	16	48.99 (5.33)	100% male	Physical activity
Moussa (2016)⁵¹	Tunisia (Sousse)	Lower-Middle	Arid	Above	Above	Case-control	COPD	16	48.99 (5.33)	100% male	Physical activity
European Region
Mihaltan (2019)⁵²	Bulgaria, Serbia (Unknown)	Upper-Middle	Not available	Not available	Not available	Cross-sectional	COPD	Not stated	Not stated	Not stated	Physical activity
Mihaltan (2019)⁵³	Bulgaria; Serbia (Unknown)	Upper-Middle	Not available	Not available	Not available	Cross-sectional	COPD	Bulgaria: 210; Serbia: 250	B: 66.7; S: 68.0	Not stated	Physical activity
Vukoja (2018)⁵⁴	Serbia (Sremska Kamenica)	Upper-Middle	Snow	Not available	Not available	Cross-sectional	Asthma, COPD	Asthma: 117, COPD: 100	Asthma: 39 (14); COPD: 63 (10)	Asthma 48% male; COPD 61% male	Sleep
Akinci (2013)⁵⁵	Turkey (Kirklareli)	Upper-Middle	Warm temperate	Above	Above	Cross-sectional	COPD	102	66.8 (10.0)	86.1% male	Sleep
Genc (2012)⁵⁶	Turkey (Afyonkarahisar)	Upper-Middle	Warm temperate	Above	Above	Pre-post trial	COPD	30	57.9 (10.2)	100% male	Physical activity
Genc (2014)⁵⁷	Turkey (Afyonkarahisar)	Upper-Middle	Warm temperate	Above	Above	Cross-sectional	COPD	30	54.1 (8.1)	100% male	Physical activity
Pehlivan (2019)⁵⁸	Turkey (Istanbul)	Upper-Middle	Warm temperate	Above	Below	Single-arm intervention study	Non-cystic fibrosis bronchiectasis	19	Median [IQR] 48.36 [34–73]	63% female	Physical activity
Sahin (2015)⁵⁹	Turkey (Kafkas)	Upper-Middle	Snow	Not available	Not available	Single-group pretest/posttestpretrial model	COPD	45	58.36 (10.12)	50.3% male	Sleep
Saglam (2013)⁶⁰	Turkey (Ankara)	Upper-Middle	Arid	Above	Below	Cross-sectional	COPD	28	NW: 63.07 (6.32); OW/OB: 63.79 (6.87)	100% male	Physical activity
Turan (2016)⁶⁰	Turkey (Afyonkarahisar)	Upper-Middle	Warm temperate	Above	Above	Cross-sectional	COPD	93	61.4 (9.8)	100% male	Physical activity
Yilmaz (2016)⁶²	Turkey (Sivas)	Upper-Middle	Snow	Above	Above	Experimental design	COPD	50	Median [IQR] NW: 61[56–65]; OW: 65[62–69]; OB: 66[61–69]	90% male	Physical activity
Region of the Americas
Lopez Jove (2013)⁶³	Argentina (Buenos Aires)	Upper-Middle	Warm temperate	Above	Below	Observational prospective study	COPD	113	63.1 (8.3)	71.1% male	Physical activity
Molinas (2007)⁶⁴	Argentina (Córdoba)	Upper-Middle	Warm temperate	Not available	Not available	Cross-sectional	Asthma	151	24.24 (8.86)	81.5% female	Physical activity
Amorim (2014)⁶⁵	Brazil (São Paulo)	Upper-Middle	Warm temperate	Above	Below	Cross-sectional	COPD	40	64.4 (7.7)	55% male	Physical activity
Athayde (2014)⁶⁶	Brazil (Belo Horizonte)	Upper-Middle	Equatorial	Above	Below	Cross-sectional	COPD	72	65.86 (9.25)	65.3% male	Physical activity
Borges (2012)⁶⁷	Brazil (São Paulo)	Upper-Middle	Warm temperate	Above	Below	Longitudinal	COPD	20	68.6 (10.7)	70% male	Physical activity; Sedentary behaviour
Campos (2017)⁶⁸	Brazil (Fortaleza)	Upper-Middle	Equatorial	Not available	Not available	Cross-sectional	Asthma	123	50.8 (12.2)	100% female	Sleep
Cani (2019)⁶⁹	Brazil (Florianópolis, São Jose ´)	Upper-Middle	Warm temperate	Not available	Not available	Cross-sectional	COPD	59	DOT: 68.3 (7.72); CG: 67.5 (7.61)	DOT: 72% male; CG: Not stated	Physical activity; Sedentary behaviour
Cavalcante (2012)⁷⁰	Brazil (Fortaleza)	Upper-Middle	Equatorial	Not available	Not available	Cross-sectional	COPD	104	69.1 (8.0)	Not stated	Sleep
Cetlin (2012)⁷¹	Brazil (Ribeirão Preto)	Upper-Middle	Warm temperate	Above	Below	Cross-sectional	Asthma	200	Not stated	67% female	Physical activity
Coelho (2018)⁷²	Brazil (Minas Gerais)	Upper-Middle	Equatorial	Not available	Not available	Cross-sectional	Asthma	36	45.8 (12.3)	100% female	Physical activity
Coelho (2018)⁷³	Brazil (Minas Gerais)	Upper-Middle	Equatorial	Not available	Not available	Randomized parallel-group controlled trial	Asthma	37	Median [IQR] Ped: 45.0 [19.0]; Con: 47.0 [14.0]	86% female	Physical activity
da Costa (2012)⁷⁴	Brazil (Vale Dos Sinos)	Upper-Middle	Warm temperate	Not available	Not available	Prospective comparative study	COPD	7	64 (8.12)	100% female	Physical activity
Cukier (20 20)⁷⁵	Brazil (Blumenau, Botucatu, Campinas, Porto Alegre, Rio de Janeiro, Sao Bernardo do Campo, São Paulo)	Upper-Middle	Warm temperate, Equatorial	Above	Below	Cross-sectional	COPD	593	67.7 (9.0)	52.1% male	Physical activity
Felcar (2018)⁷⁶	Brazil (Londrina)	Upper-Middle	Warm temperate	Not available	Not available	Randomised trial	COPD	36	LG: 68 (8); WG: 69 (9)	64% male	Physical activity
Filippin (2011)⁷⁷	Brazil (Cascavel)	Upper-Middle	Warm temperate	Not available	Not available	Cohort retrospective cross-sectional study	COPD	55	Physically active: 67.12 (5.72); physically inactive: 71 (6.18)	Not stated	Physical activity
Fonesca (2016)⁷⁸	Brazil (Florianópolis)	Upper-Middle	Warm temperate	Not available	Not available	Cross-sectional	COPD	17	67 (8)	82% male	Physical activity; Sedentary behaviour
Freitas (2018)⁷⁹	Brazil (São Paulo)	Upper-Middle	Warm temperate	Above	Below	Randomised trial	Asthma	51	WL+S: 48.5 (9.8); WL+E: 45.9 (7.7)	98% female	Physical activity; Sedentary behaviour; Sleep
Furlanetto (2016)⁸⁰	Brazil (Londrina)	Upper-Middle	Warm temperate	Not available	Not available	Cross-sectional	COPD	104	66 (8)	63% male	Physical activity
Furlanetto (2017)¹⁴	Brazil (Londrina)	Upper-Middle	Warm temperate	Not available	Not available	Cohort retrospective cross-sectional study	COPD	101	Median [IQR] 66 [60–72]	57% male	Physical activity; Sedentary behaviour
Furlanetto (2017)⁸¹	Brazil (Londrina)	Upper-Middle	Warm temperate	Not available	Not available	Cross-sectional	COPD	19	69 (7)	53% male	Physical activity
Gulart (2019)⁸²	Brazil (Florianópolis)	Upper-Middle	Warm temperate	Not available	Not available	Cross-sectional	COPD	61	65.5 (8.76)	77% male	Physical activity; Sedentary behaviour
Hernandes (2009)⁸³	Brazil (Northern Paraná)	Upper-Middle	Warm temperate	Not available	Not available	Cross-sectional	COPD	40	66 (8)	55% female	Physical activity; Sedentary behaviour
Jardim (2011)⁸⁴	Brazil (São Paulo)	Upper-Middle	Warm temperate	Above	Above	Cross-sectional	COPD	49	68.9 (8.5)	59% male	Physical activity
Karloh (2016)⁸⁵	Brazil (Florianópolis)	Upper-Middle	Warm temperate	Not available	Not available	Cross-sectional	COPD	38	Median [IQR] 65 [63–68]	58% male	Physical activity; Sedentary behaviour
Landal (2014)⁸⁶	Brazil (Northern Paraná)	Upper-Middle	Warm temperate	Not available	Not available	Longitudinal	COPD	30	66 (7)	51% female	Physical activity; Sedentary behaviour
Lanza (2018)⁸⁷	Brazil (São Paulo)	Upper-Middle	Warm temperate	Above	Below	Cross-sectional	Bronchiectasis	100	48 (14)	59% female	Physical activity
Lopes (2019)⁸⁸	Brazil (São Paulo)	Upper-Middle	Warm temperate	Above	Below	Cross-sectional	COPD	150	68 (8.3)	67% male	Physical activity
Mantoani (2011)⁸⁹	Brazil (Londrina)	Upper-Middle	Warm temperate	Not available	Not available	Cross-sectional	COPD	67	Median [IQR] 66 [61–72]	54% male	Physical activity; Sedentary behaviour
Monteiro (2012)⁹⁰	Brazil (Londrina)	Upper-Middle	Warm temperate	Not available	Not available	Cross-sectional	COPD	74	65 (9)	61% male	Physical activity
Morita (2018)⁹¹	Brazil (Londrina)	Upper-Middle	Warm temperate	Not available	Not available	Retrospective cross-sectional	COPD	145	Median [IQR] 65 [60–73]	54% male	Physical activity; Sedentary behaviour
Munari (2018)⁹²	Brazil (Florianópolis)	Upper-Middle	Warm temperate	Not available	Not available	Cross-sectional	COPD	110	66 (8)	68.2% male	Physical activity; Sedentary behaviour
Nobeschi (2020)⁹³	Brazil (Santo André)	Upper-Middle	Warm temperate	Above	Below	Single-arm intervention study	COPD	30	68.17 (6.11)	56.7% male	Sleep
Nunes (2008)⁹⁴	Brazil (Ceará)	Upper-Middle	Equatorial	Not available	Not available	Randomized, double-blind, parallel-group, placebo-controlled study	COPD	25	Melatonin: 64.17 (9.9); Placebo: 67.38 (8.1)	56% male	Sleep
Nyssen (2013)⁹⁵	Brazil (São Carlos)	Upper-Middle	Warm temperate	Not available	Not available	Cross-sectional	COPD	30	68 (10)	77% male	Physical activity
Pitta (2009)⁹⁶	Brazil (Londrina)	Upper-Middle	Warm temperate	Not available	Not available	Cross-sectional	COPD	40	66 (8)	55% female	Physical activity; Sedentary behaviour
Probst (2011)⁹⁷	Brazil (Londrina)	Upper-Middle	Warm temperate	Not available	Not available	Prospective randomized trial	COPD	40	Calisthenics and Breathing Exercises Group: 65 (10); Endurance and Strength Training Group: 67 (7)	53% male	Physical activity; Sedentary behaviour
Reboredo (2017)⁹⁸	Brazil (São Pedro)	Upper-Middle	Warm temperate	Not available	Not available	Cross-sectional	COPD	166	64.94 (9.9)	59% male	Physical activity
Rocha (2017)⁹⁹	Brazil (Florianópolis)	Upper-Middle	Warm temperate	Not available	Not available	Cross-sectional	COPD	25	67.56 (9.21)	56% male	Physical activity; Sedentary behaviour
Rodrigues (2014)¹⁰⁰	Brazil (Gurupi)	Upper-Middle	Equatorial	Not available	Not available	Cross-sectional	Lung diseases	Not stated	Not stated	Not stated	Physical activity
Schneider (2018)¹⁰¹	Brazil (Londrina)	Upper-Middle	Warm temperate	Not available	Not available	Cross-sectional	COPD	45	66 (8)	56% male	Physical activity; Sedentary behaviour
Silva (2011)¹⁰²	Brazil (Porto Alegre)	Upper-Middle	Warm temperate	Not available	Not available	Cross-sectional	COPD	95	67.3 (8.1)	65% male	Physical activity
Simon (2009)¹⁰³	Brazil (Curitiba)	Upper-Middle	Warm temperate	Not available	Not available	Cross-sectional	COPD	38	Physically active: 67 (6); Phiysically inactive: 65 (9)	71% male	Physical activity
Tavares (2017)¹⁰⁴	Brazil (Recife)	Upper-Middle	Equatorial	Not available	Not available	Cross-sectional	COPD	54	≥60 years: 68.5%	68.5% male	Physical activity
Vitorasso (2012)¹⁰⁵	Brazil (Londrina)	Upper-Middle	Warm temperate	Not available	Not available	Cross-sectional	COPD	73	65 (9)	63% male	Physical activity; Sedentary behaviour
Xavier (2016)¹⁰⁶	Brazil (Unknown)	Upper-Middle	Not available	Not available	Not available	Cross-sectional	COPD	153	68 (8)	68% male	Physical activity
South-East Asian Region
Yunus (2018)¹⁰⁷	Bangladesh (Dhaka)	Lower-Middle	Equatorial	Above	Above	Cross-sectional	COPD	116	Not stated	Not stated	Sleep
Bhuker (2012)¹⁰⁸	India (New Delhi)	Lower-Middle	Arid	Above	Above	Cross-sectional	Asthma	50	Range: 18–50 years	Not stated	Physical activity
De (2012)¹⁰⁹	India (Maharashtra)	Lower-Middle	Arid	Not available	Not available	Cross-sectional	COPD	40	62.2 (9.2)	Not stated	Sleep
Gupta (2018)¹¹⁰	India (Doiwala, Chakrata, Joshimath)	Lower-Middle	Warm temperate	Not available	Not available	Cross-sectional	COPD	130	Not stated	Not stated	Sleep, physical activity
Panigrahi (2018)¹¹¹	India (Bhubneshwar)	Lower-Middle	Equatorial	Above	Above	Cross-sectional	Asthma	101	38 (16.2)	44% male	Sleep
Poongadan (2016)¹¹²	India (New Delhi)	Lower-Middle	Arid	Above	Above	Cross-sectional	Asthma	125	Not stated	56% male	Physical activity; Sedentary behaviour; Sleep
Sharma (2019)¹¹³	India (Nalhar)	Lower-Middle	Arid	Not available	Not available	Cross-sectional	COPD	120	Dep: 58.1 (6.43); No Dep: 57.72 (7.95)	59% male	Physical activity
Ulfathinah (2019)¹¹⁴	Indonesia (DKI Jakarta Province)	Upper-Middle	Equatorial	Above	Above	Cross-sectional	COPD	200	61.76 (10.089)	67.5% male	Sleep
Widyastuti (2017)¹¹⁵	Indonesia (Central Java)	Upper-Middle	Equatorial	Not available	Not available	RCT	COPD	36	Not stated	Not stated	Physical activity
Chanthitivech (2017)¹¹⁶	Thailand (Bangkok)	Upper-Middle	Equatorial	Above	Above	Cross-sectional	COPD	26	67.73 (7.56)	88.5% male	Physical activity
Western Pacific Region
Chunrong (2014)¹¹⁷	China (Guangzhou)	Upper-Middle	Warm temperate	Above	Above	Cross-sectional	COPD	71	65.17 (6.80)	76% male	Physical activity
Ding (2017)¹¹⁸	China (Beijing, Chengdu, Guangzhou, Jinan, Nanjing, Shanghai, Shenyang, Wuhan, Xi’an)	Upper-Middle	Snow climate, Warm temperate	Above	Above	Cross-sectional	COPD and Asthma-COPD Overlap	675	62.0 (11.4)	60.7% male	Sleep
Tao (2016)¹¹⁹	China (Tianjin)	Upper-Middle	Snow	Above	Above	Cross-sectional	COPD	35 b	69.1 (6.9)	64.1% male	Physical activity
Wang (2016)¹²⁰	China (Tianjin)	Upper-Middle	Snow	Above	Above	Cross-sectional	COPD	154	72.8 (9.6)	59.7% male	Physical activity
Wang (2016)¹²¹	China (Tianjin)	Upper-Middle	Snow	Above	Above	Cross-sectional	COPD	100	71.23 (9.58)	71% male	Physical activity
Wang (2017)¹²²	China (Jilin)	Upper-Middle	Snow	Above	Above	Cross-sectional	COPD	334	Not stated	Not stated	Sleep
Zheng (2019)¹²³	China (Qingdao city, Shandong province)	Upper-Middle	Snow	Not available	Not available	Cohort retrospective study	COPD	72	66.00 (5.59)	76% male	Sleep
All Regions
Vancampfort (2017)¹²⁴	See footnote c	All	Not available	Not available	Not available	Cross-sectional	Asthma	11,857	Not stated	Not stated	Physical activity
Koyanagi (2017)¹²⁵	China, Ghana, India, Mexico, Russia, South Africa	Lower-Middle; Upper-Middle	Not available	Not available	Not available	Cross-sectional	Asthma, COPD	Asthma: 2696; COPD: 5392 d	Not stated	Not stated	Physical activity
Vancampfort (2017)¹²⁶	China, Ghana, India, Mexico, Russia, South Africa	Lower-Middle; Upper-Middle	Not available	Not available	Not available	Cross-sectional	Chronic lung disease	4477	Not stated	Not stated	Sedentary behaviour
Vancampfort (2018)¹²⁷	China, Ghana, India, Mexico, Russia, South Africa	Lower-Middle; Upper-Middle	Not available	Not available	Not available	Cross-sectional	Asthma; Chronic lung disease	Not stated	Not stated	Not stated	Physical activity
Vancampfort (2017)¹²⁸	China, Ghana, India, Mexico, Russia, South Africa	Lower-Middle; Upper-Middle	Not available	Not available	Not available	Cross-sectional	Asthma, COPD	Not stated	Not stated	Not stated	Physical activity
Vancampfort (2018)¹²⁹	China, Ghana, India, Mexico, Russia, South Africa	Lower-Middle; Upper-Middle	Not available	Not available	Not available	Cross-sectional	Asthma, COPD	Not stated	Not stated	Not stated	Sedentary behaviour

Open in a new tab

Income Classification

Of the 89 articles identified, none were conducted in low-income countries, 12 (13%) were conducted in lower-middle-income countries, 71 (80%) were conducted in upper-middle-income countries (UMIC) and six (7%) spanned income classifications.

WHO Regions

The most prevalent region for included studies was the Region of the Americas (45 studies [51%]; 43 in Brazil) followed by European (11 studies [12%], eight in Turkey) and South-East Asian (10 studies [11%], six in India). Three studies were conducted solely in Africa (all in Nigeria).

The Methods of Physical Behaviour Data Collection

PA (66 studies) was the most commonly measured behaviour; measured solely in 47 studies, in combination with SB in 16 studies, with sleep in one study and with SB and sleep in two studies. Sleep (24 studies) was measured exclusively in 21 studies and SB (20 studies) was measured solely in two studies.

Majority of studies assessed physical behaviours using questionnaires only (52 studies [58%]). Fourteen different questionnaires were used across included studies, with an additional 12 studies reporting specific questions. For sleep, the Pittsburgh Sleep Quality Index (PSQI) was the most common questionnaire (18 studies). For PA/SB, the International Physical Activity Questionnaire (IPAQ) was most common (11 studies). Questions or questionnaires were not provided in four studies. Most studies (58%) did not report the recall period of the questionnaires used and only one study (2%) reported how missing data were handled (Appendix E).

Of the 36 studies using device-based measurement of physical behaviours, twenty-six studies (72%) used accelerometers and 10 studies (28%) used pedometers. Dynaport activity monitors were used in 15 studies (58% of accelerometer studies) and the SenseWear Armband in seven studies. Five studies (19%) deployed a Dynaport and SenseWear simultaneously. Four studies (15%) did not provide the model of activity monitor used. Most studies (32, 89%) did not report whether participants had access to feedback on their behaviour during the measurement period. Four studies (11%) did not report the number of days participants were asked to wear the device. Most studies (18, 50%) monitored behaviours for two days and six studies (17%) had a seven-day wear protocol. Fifteen studies (42%) did not report how long each day participants were asked to wear a device. Of the 21 studies reporting this information, the majority (18 studies, 86%) asked for 12 hours of wear and during a fixed time period (19 studies, 90%) to be included in analysis, 14 studies (39%) did not report the minimum number of hours each day and minimum number of days the device needed to be worn. Most studies (35, 97%) did not report average wear-time and 34 studies (94%) did not report if or how periods of non-wear were calculated (Appendix F).

Variables to Assess 24-Hour Physical Behaviours

A summary of the variables used to describe physical behaviours is given in Table 1 (full extraction of the variables in Appendix B). The most common PA categories of variables used were steps per day (n=21), energy expenditure (n=21), binary classification of active/inactive (n=19), time spent walking (n=16) and classification of active/inactive using at least three groups (n=16). For SB, the common categories of variables used were sedentary time (n=16), standing time (n=13), sitting time (n=11) and lying time (n=10). Overall sleep quality (n=32), sleep duration (n=14), use of sleep medication (n=11) and sleep disturbances (n=11) were the most commonly used categories of sleep variables.

Levels of Free-Living PA, SB and Sleep

Time spent walking (12 studies; range 36–91min/day) and movement intensity (eight studies; range 0.18–1.9m/s²) measured by Dynaport activity monitor were most common, followed by the number of steps per day measured by Dynaport (six studies; range 2669–6557steps/day) and Yamax (five studies; range 4227–7490 steps/day) and MVPA measured by SenseWear Armband (five studies; range 29–89min/day). Three SB variables, all measured using Dynaport, were directly comparable between studies; comprising sitting time (12 studies; range 283–418min/day); standing time (12 studies; range 139–270min/day); lying time (11 studies; range 76–119min/day). For sleep, PSQI score was provided in 12 studies (range 4–11) and PSQI classification of poor sleep quality in 11 studies (range 33–100%) (Table 3).

Table 3.

Summary of the Studies with Comparable Measurement of Physical Activity, Sedentary Behaviour and Sleep

Reference	Value for Physical Behaviour	Study Location(s): Country	Study Location(s): Region	Income Classification(s)	Climate Classification(s)	PM2.5 Air Quality (Year Taken)	PM10 Air Quality (Year Taken)
Step count (Dynaport, steps/day)
Borges (2012)⁶⁷	3575±2799	Brazil	Region of the Americas	Upper-Middle	Warm temperate	35 (2012)	18 (2012)
Cani (2019)⁶⁹	DOT: 2669±1883; CG: 4599±2308	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Gulart (2019)⁸²	5273±2726	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Karloh (2016)⁸⁵	6557[5496–7619]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Munari (2018)⁹²	5292±3200	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Rocha (2017)⁹⁹	6388.12 ± 3671.66	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Walking time (Dynaport, min/day)
Borges (2012)⁶⁷	6.1±4.4%	Brazil	Region of the Americas	Upper-Middle	Warm temperate	35 (2012)	18 (2012)
Cani (2019)⁶⁹	DOT: 36.1±23.7; CG: 90.7±39.6	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Fonesca (2016)⁷⁸	68.0[57.5–87.0]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Gulart (2019)⁸²	68.5±38.2	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Hernandes (2009)⁸³	55 ± 33	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Karloh (2016)⁸⁵	81.1[68.1–94.0]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Mantoani (2011)⁸⁹	51[34–73]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Morita (2018)⁹¹	55[37–81]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Munari (2018)⁹²	67.4±31.5	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Pitta (2009)⁹⁶	56±32	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Probst (2011)⁹⁷	CBEG 54±28; ESTG 57±32	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Vitorasso (2012)¹⁰⁵	53[36–80]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Movement Intensity (Dynaport, m/s²)
Cani (2019)⁶⁹	DOT: 1.53±0.22; CG: 1.88±1.01	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Fonesca (2016)⁷⁸	1.81±0.30	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Hernandes (2009)⁸³	1.9 ± 0.4	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Karloh (2016)⁸⁵	1.78[1.70–1.97]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Mantoani (2011)⁸⁹	1.9[1.6–2.1]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Munari (2018)⁹²	1.72±0.27	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Pitta (2009)⁹⁶	1.9±0.4	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Rocha (2017)⁹⁹	0.18 ± 0.03	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
MVPA time (SenseWear, min/day)
Furlanetto (2017)¹⁴	0.48 [0.14–1.24] hours/day	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Furlanetto (2017)⁸¹	89±15 vs 84±15 during summer and winter	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Mantoani (2011)⁸⁹	29 [10–86]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Probst (2011)⁹⁷	CBEG 4533±5968; ESTG 4539±5314	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Schneider (2018)¹⁰¹	49.3 [25.7–100]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Step count (Yamax, steps/day)
Amorim (2014)⁶⁵	6251.0 ± 2422.8	Brazil	Region of the Americas	Upper-Middle	Warm temperate	35 (2014)	19 (2014)
Coelho (2018)⁷²	7490.3±3330.2	Brazil	Region of the Americas	Upper-Middle	Equatorial	Not available	Not available
Coelho (2018)⁷³	PED: 7295±3241; CON: 6998±3490	Brazil	Region of the Americas	Upper-Middle	Equatorial	Not available	Not available
Felcar (2018)⁷⁶	LG: 5891±3054; WG: 6101±3591	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Nyssen (2013)⁹⁵	4227±2075	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Sitting time (Dynaport, min/day)
Borges (2012)⁶⁷	38.9±17.6%	Brazil	Region of the Americas	Upper-Middle	Warm temperate	35 (2012)	18 (2012)
Cani (2019)⁶⁹	DOT: 418±104; CG: 390±111	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Fonesca (2016)⁷⁸	394.9±93.6	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Furlanetto (2017)¹⁴	5.28 [3.95–6.52] hr/day	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Gulart (2019)⁸²	383±104	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Hernandes (2009)⁸³	294 ± 114/41%	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Karloh (2016)⁸⁵	381[351–412]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Morita (2018)⁹¹	317[248–386]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Munari (2018)⁹²	378±105	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Pitta (2009)⁹⁶	296±109	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Probst (2011)⁹⁷	CBEG 283±121; ESTG 296±91	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Vitorasso (2012)¹⁰⁵	307[228–376]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Standing time (Dynaport, min/day)
Borges (2012)⁶⁷	22.7±14.0%	Brazil	Region of the Americas	Upper-Middle	Warm temperate	35 (2012)	18 (2012)
Cani (2019)⁶⁹	DOT:139±64.3; CG:149±60.9	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Fonesca (2016)⁷⁸	130.0[112.0–163.5]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Gulart (2019)⁸²	140±54.9	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Hernandes (2009)⁸³	36%	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Karloh (2016)⁸⁵	155[140–171]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Mantoani (2011)⁸⁹	237[161–345]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Morita (2018)⁹¹	209[147–276]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Munari (2018)⁹²	149±62.4	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Pitta (2009)⁹⁶	246±122	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Probst (2011)⁹⁷	CBEG 270±139; ESTG 248±95	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Vitorasso (2012)¹⁰⁵	196[137–289]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Lying time (Dynaport, min/day)
Borges (2012)⁶⁷	30.7±20.2%	Brazil	Region of the Americas	Upper-Middle	Warm temperate	35 (2012)	18 (2012)
Cani (2019)⁶⁹	DOT: 114±81.6; CG: 103±85.4	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Fonesca (2016)⁷⁸	76[0–143.5]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Furlanetto (2017)¹⁴	1.72 [0.56–2.97] hr/day	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Gulart (2019)⁸²	105±105	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Hernandes (2009)⁸³	15%	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Karloh (2016)⁸⁵	77.1[53.3–101]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Morita (2018)⁹¹	107[25–183]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Munari (2018)⁹²	101±87.2	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Pitta (2009)⁹⁶	119±110	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Probst (2011)⁹⁷	CBEG 108±100; ESTG 113±101	Brazil	Region of the Americas	Upper-Middle	Warm temperate	Not available	Not available
Sleep quality (PSQI Score)
Adetiloye (2018)⁴²	9.93±4.19	Nigeria	African Region	Lower-Middle	Equatorial	Not available	Not available
Akinci (2013)⁵⁵	7.1±3.9	Turkey	European Region	Upper-Middle	Warm temperate	70 (2016)	47 (2016, converted)
Ali Zohal (2013)⁴⁵	8.03±3.66	Iran	Eastern Mediterranean Region	Upper-Middle	Warm temperate	44 (2013)	18 (2013, converted)
Campos (2017)⁶⁸	9.9±4.6	Brazil	Region of the Americas	Upper-Middle	Equatorial	Not available	Not available
Chegeni (2018)⁴⁷	TG: 6.33±3.41, CG: 6.41±3.12	Iran	Eastern Mediterranean Region	Upper-Middle	Arid	75 (2016, converted)	33 (2016)
De (2012)¹⁰⁹	Median 11	India	South-East Asian Region	Lower-Middle	Arid	Not available	Not available
Nobeschi (2020)⁹³	6.5[4.75–8.75]	Brazil	Region of the Americas	Upper-Middle	Warm temperate	27 (2016)	12 (2016, converted)
Panigrahi (2018)¹¹¹	4 [3–7]	India	South-East Asian Region	Lower-Middle	Equatorial	81 (2012)	43 (2012, converted)
Sahin (2015)⁵⁹	9.52±0.22	Turkey	European Region	Upper-Middle	Snow	Not available	Not available
Ulfathinah (2019)¹¹⁴	Median 6	Indonesia	South-East Asian Region	Upper-Middle	Equatorial	82 (2016, converted)	45 (2016)
Vukoja (2018)⁵⁴	Asthma 4.9±3.9; COPD 5.8±4.3	Serbia	European Region	Upper-Middle	Snow	Not available	Not available
Zheng (2019)¹²³	Intervention 9.50±2.87; Control 9.31±2.98	China	Western Pacific Region	Upper-Middle	Snow	Not available	Not available
Poor sleep quality (PSQI, %)
Adetiloye (2018)⁴²	81.7%	Nigeria	African Region	Lower-Middle	Equatorial	Not available	Not available
Adewolea (2017)⁴³	37.8%	Nigeria	African Region	Lower-Middle	Equatorial	Not available	Not available
Ali Zohal (2013)⁴⁵	67.9%	Iran	Eastern Mediterranean Region	Upper-Middle	Warm temperate	44 (2013)	18 (2013, converted)
Campos (2017)⁶⁸	80.4%	Brazil	Region of the Americas	Upper-Middle	Equatorial	Not available	Not available
Cavalcante (2012)⁷⁰	59.6%	Brazil	Region of the Americas	Upper-Middle	Equatorial	Not available	Not available
De (2012)¹⁰⁹	100%	India	South-East Asian Region	Lower-Middle	Arid	Not available	Not available
Gupta (2018)¹¹⁰	33.3%	India	South-East Asian Region	Lower-Middle	Warm temperate	Not available	Not available
Nobeschi (2020)⁹³	73%	Brazil	Region of the Americas	Upper-Middle	Warm temperate	27 (2016)	12 (2016, converted)
Panigrahi (2018)¹¹¹	48.5%	India	South-East Asian Region	Lower-Middle	Equatorial	81 (2012)	43 (2012, converted)
Ulfathinah (2019)¹¹⁴	66%	Indonesia	South-East Asian Region	Upper-Middle	Equatorial	82 (2016, converted)	45 (2016)
Vukoja (2018)⁵⁴	Asthma 40.4%; COPD 38%	Serbia	European Region	Upper-Middle	Snow	Not available	Not available

Open in a new tab

Sub-Group Analysis of 24-Hour Physical Behaviours

Four studies measuring PA and two studies measuring SB spanned all regions. The Region of the Americas accounted for the majority of studies measuring PA (45 studies [68%]) (Figure 2A) and SB (19 studies [95%]) (Figure 2B). Studies measuring sleep were distributed across regions in small numbers with South-East Asia as the most common (six studies [25%]) (Figure 2C). One study measuring PA included data from all low- and middle- income classifications. No studies solely took place in low-income countries. Most took place in upper-middle income countries (60 [91%] measured PA, 19 [95%] measured SB and 16 [67%] measured sleep). We were unable to classify climate for seven studies measuring PA, two studies measuring SB and one study measuring sleep. Most studies were conducted in warm temperature climates (41 studies [69%] of PA, 17 studies [94%] of SB and six studies of sleep [26%]). There were 52 studies where we were unable to classify air quality (61% measuring PA, 85% measuring SB and 46% measuring sleep). All other studies exceeded the annual 10µg/m³ PM2.5 threshold. Using the annual 20µg/m³ PM10 threshold, 15 studies (58%) measured PA, one study (33%) measured SB and ten studies (77%) measured sleep in areas exceeding the threshold (Table 4).

(A) Location of studies measuring physical activity. (B) Location of studies measuring sedentary behaviour. (C) Locations of studies measuring sleep.

Table 4.

Summary of the Number of Studies Measuring Each Physical Behaviour, Stratified by Region, Income Classification, Measurement Approach, Climate Classification and Air Quality

Sub-Grouping	Physical Activity	Sedentary Behaviour	Sleep
Region
African Region	4	2	3
European Region	12	2	3
Eastern Mediterranean Region	7	2	4
Region of the Americas	45	19	5
South-East Asia Region	10	3	6
Western Pacific Region	8	2	3
Income classification
Low	1	0	0
Lower-middle	10	3	8
Upper-middle	60	19	16
Measurement
Device	36	17	1
Questionnaire	32	3	23
Not provided	1	0	0
Köppen-Geiger climate classification
Arid	7	1	5
Equatorial	8	0	8
Polar	0	0	0
Snow	4	0	5
Warm temperature	41	17	6
Unable to classify	7	2	1
Air quality classification
Above 10µg/m³ PM2.5	26	3	13
Below 10µg/m³PM2.5	0	0	0
Above 20µg/m³ PM10	15	1	10
Below 20µg/m³ PM10	11	2	3
Unable to classify	40	17	11

Open in a new tab

Discussion

This systematic review provided a comprehensive assessment of the prevalence, levels and measurement approaches of studies assessing 24-hour physical behaviours in people living with CRD in LMIC. The majority of studies examining PA and SB in CRD in LMIC are limited to Brazil (UMIC), whilst studies measuring sleep were more evenly distributed across regions. Questionnaires were more commonly used to measure physical behaviours than device-based methods, with data supporting low physical activity levels, sedentary lifestyles and poor sleep quality for people in LMIC living with CRDs.

Most studies were conducted in upper-middle-income countries with Brazil accounting for almost half. In other regions, Turkey (European), India (South-East Asian) and Nigeria (Africa) had the monopoly of identified articles within their respective regions. No studies were conducted exclusively in low-income countries. Therefore, more studies are needed to compare physical behaviours within and between regions and income classifications. For large cohort studies of general adult populations, such as those included in The Lancet physical inactivity series,⁵ data were from predominantly HIC in Europe and North America; with data from Africa and Central Asia particularly scarce.³⁵ Efforts to increase the available data on PA, SB and sleep in other LMIC are needed before any reliable prevalence estimates and worthwhile comparisons can be made.

PA was most commonly investigated. There was a relatively equal split of device- and questionnaire-based assessment of PA, with almost exclusively device-based assessment of SB and questionnaire-based assessment of sleep. Compared with a previous systematic review of 76 studies measuring free-living PA in COPD (almost exclusively studies in HIC), the Dynaport activity monitor was more commonly used (42% versus 14%). The disproportionate use of the Dynaport in the included studies from Brazil explains this disparity, whereas the previous review contained a large proportion of studies from HIC. For questionnaire-based PA and SB, we found the IPAQ to be most commonly used; consistent with the evidence base for large epidemiological studies of healthy adult populations.⁵ IPAQ is commonly used in LMIC for population surveillance, with more than 50 counties adopting it as of 2016,⁷ but it was developed for individuals aged 15–69 years of age which may not be appropriate for many CRD populations.³⁶ The PSQI was the most common questionnaire in our review with relatively consistent use of the overall PSQI score and classification of poor sleep quality. The PSQI was also more consistently used across regions and currently offers the greatest opportunity for pooled analysis and standardisation. The popularity of the PSQI is supported by a previous review of sleep disorders in COPD.²⁶ Although it is also possible to assess free-living sleep in CRD populations using devices,³⁷^,³⁸ the PSQI offers a simple tool to examine sleep quality and duration in LMIC.

Articles included in our review relied predominantly on summary variables of physical behaviours, such as steps per day, sedentary time and overall sleep quality. The high prevalence of summary variables supports previous work exploring device-based PA, which found 70% of studies used a marker for total activity, with half using step count. Postural assessments such as sitting, standing or lying down were more commonly reported in the included studies using more device-based assessments than previously found (44% versus 20%); due to the exclusive measurement of postures in Brazilian studies.

The commonly reported variables of PA were mostly related to walking (time spent walking and step count) which is easily interpretable and at the heart of clinical practice guidelines for COPD.³⁹ It is estimated that 7000–10,000 steps per day is approximately equivalent to taking part in 30 minutes of daily MVPA⁹ and data from the included articles support people living with CRD in LMIC to be physically inactive. Indicating a global nature of the problem, prevalence of physical inactivity was found more than twice as high in high-income countries compared to low-income countries.⁶ However, the considerable variation between studies and locations, such as their measurement and data processing protocols, make it difficult to generalise.

Only the range of PA, SB and sleep levels were presented in the study. The reported study variables were not consistent across the included articles which caused considerable heterogeneity, and it may lead to false conclusions. Pooling the data on various study designs increase the methodological heterogeneity. Studies included in our review suggest people living with CRD in LMIC have worse sleep quality than healthy counterparts across income classifications. We found PSQI classification of poor sleep quality ranged 33–100%; higher than the estimated prevalence of 32.8% (95% confidence interval: 25.9–39.7%) based on a meta-analysis of 19 studies of working-age adults in LMIC⁴⁰ and from adults residing in high-income European countries.⁴¹

The strengths of our systematic review include a pre-defined rigorous search methodology, registered on PROSPERO before study screening, bias assessment, including the use of validated search terms, the comprehensive array of databases searched, the absence of language restrictions and screening included reference lists to reduce selection bias. The data extraction phase was also duplicated to minimise assessor bias and error. However, there may be databases unknown to the authors where additional studies may be published in this area. We were unable to translate two non-English publications (Persian). The climate and air quality classifications were not always possible to identify from information reported in the included studies, limiting subgroup analyses.

Conclusion

Based on 89 articles included in our systematic review of the PA, SB and sleep of people living with CRD in LMIC, we showed considerable inequity between behaviours, geographical locations and income classifications. The majority of studies examining PA and SB in CRD in LMIC were limited to Brazil, whilst studies measuring sleep were more limited and spread across regions. Questionnaires were more commonly used to measure physical behaviours than device-based methods, with summary data supporting low physical activity levels, sedentary lifestyles and poor sleep quality for people in LMIC living with CRDs. There was large variation in measurement approaches and poor quality methodological reporting for all physical behaviours which limited direct comparisons. The lack of standardisation and poor reporting reduces the strength of evidence needed to strengthen the scientific integrity of data and resulting clinical and policy implications to support patients to lead healthier lifestyles. More studies of PA, SB and sleep are needed in a broader range of LMICs. Future work would benefit from a harmonised approach to data collection to ensure international comparisons.

Acknowledgments

The authors thank the University Hospitals of Leicester NHS Trust Libraries, British Library and authors of included studies for providing full-text articles. The authors send a special thank you to Dr Xiaorong Ding (Institute of Biomedical Engineering, University of Oxford, UK), Carlos Morgado Areia (Nuffield Department of Clinical Neurosciences, University of Oxford, UK), Daniel Filipe Cunha Taveira (NIHR Leicester Biomedical Research Centre-Respiratory, University of Leicester, UK) and Joao Sousa (NIHR Leicester Biomedical Research Centre-Respiratory, University of Leicester, UK) for extracting data from non-English full-text publications.

Funding Statement

This research was funded by the National Institute for Health Research (NIHR) (17/63/20) using UK aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Department of Health and Social Care.

Abbreviations

CRD, Chronic respiratory disease; HIC, High-income countries; LMIC, Low- and middle-income; countries; PA, Physical activity; PRISMA, Preferred reporting items for systematic review and meta‐analysis; PROSPERO, International prospective register of systematic reviews; SB, Sedentary behaviour; UMIC, Upper-middle-income countries; WHO, World health organisation.

Disclosure

The authors report no conflicts of interest in this work.

References

1.Hu G, Zhou Y, Tian J, et al. Risk of COPD from exposure to biomass smoke: a metaanalysis. Chest. 2010;138(1):20–31. doi: 10.1378/chest.08-2114 [DOI] [PubMed] [Google Scholar]
2.Amaral AFS, Coton S, Kato B, et al. Tuberculosis associates with both airflow obstruction and low lung function: BOLD results. Eur Respir J. 2015;46(4):1104–1112. doi: 10.1183/13993003.02325-2014 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Salvi SS, Manap R, Beasley R. Understanding the true burden of COPD: the epidemiological challenges. Prim Care Respir J. 2012;21(3):249–251. doi: 10.4104/pcrj.2012.00082 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Rollo S, Antsygina O, Tremblay MS. The whole day matters: understanding 24-hour movement guideline adherence and relationships with health indicators across the lifespan. J Sport Health Sci. 2020;9:493–510. doi: 10.1016/j.jshs.2020.07.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Impact of physical inactivity on the world’s major non- communicable diseases. Lancet. 2012;380(9838):219–229. doi: 10.1016/S0140-6736(12)61031-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.World Health Organization. Global action plan for the prevention and control of noncommunicable diseases 2013–2020. World Health Organization; 2013. Available from: https://apps.who.int/iris/bitstream/handle/10665/94384/?sequence=1. Accessed March 7, 2022. [Google Scholar]
7.Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Glob Health. 2018;6(10):e1077–e1086. doi: 10.1016/S2214-109X(18)30357-7 [DOI] [PubMed] [Google Scholar]
8.Vorrink SNW, Kort HSM, Troosters T, Lammers J-WJ. Level of daily physical activity in individuals with COPD compared with healthy controls. Respir Res. 2011;12(1):1–8. doi: 10.1186/1465-9921-12-33 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Tudor-Locke C, Craig CL, Aoyagi Y, et al. How many steps/day are enough? For older adults and special populations. Int J Behav Nutr Phys Act. 2011;8(1):1–19. doi: 10.1186/1479-5868-8-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Pitta F, Troosters T, Probst VS, Spruit MA, Decramer M, Gosselink R. Physical activity and hospitalization for exacerbation of COPD. Chest. 2006;129(3):536–544. doi: 10.1378/chest.129.3.536 [DOI] [PubMed] [Google Scholar]
11.Waschki B, Kirsten A, Holz O, et al. Physical activity is the strongest predictor of all-cause mortality in patients with COPD: a prospective cohort study. Chest. 2011;140(2):331–342. doi: 10.1378/chest.10-2521 [DOI] [PubMed] [Google Scholar]
12.Tremblay MS, Aubert S, Barnes JD, et al. Sedentary Behavior Research Network (SBRN) - Terminology Consensus Project process and outcome. Int J Behav Nutr Phys Act. 2017;14(1):1–17. doi: 10.1186/s12966-017-0525-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Pitta F, Troosters T, Spruit MA, Probst VS, Decramer M, Gosselink R. Characteristics of physical activities in daily life in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2005;171(9):972–977. doi: 10.1164/rccm.200407-855OC [DOI] [PubMed] [Google Scholar]
14.Furlanetto KC, Donária L, Schneider LP, et al. Sedentary behavior is an independent predictor of mortality in subjects with COPD. Respir Care. 2017;62(5):579–587. doi: 10.4187/respcare.05306 [DOI] [PubMed] [Google Scholar]
15.Ohayon M, Wickwire EM, Hirshkowitz M, et al. National Sleep Foundation’s sleep quality recommendations: first report. Sleep Health. 2017;3(1):6–19. doi: 10.1016/j.sleh.2016.11.006 [DOI] [PubMed] [Google Scholar]
16.Ohayon MM, Paskow M, Roach A, et al. The National Sleep Foundation’s Sleep Satisfaction Tool. Sleep Health. 2019;5(1):5–11. doi: 10.1016/j.sleh.2018.10.003 [DOI] [PubMed] [Google Scholar]
17.Hirshkowitz M, Whiton K, Albert SM, et al. National Sleep Foundation’s updated sleep duration recommendations: final report. Sleep Health. 2015;1(4):233–243. doi: 10.1016/j.sleh.2015.10.004 [DOI] [PubMed] [Google Scholar]
18.Chattu V, Manzar MD, Kumary S, Burman D, Spence DW, Pandi-Perumal SR. The global problem of insufficient sleep and its serious public health implications. Healthcare. 2019;7(1):1–16. doi: 10.3390/healthcare7010001 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Shorofsky M, Bourbeau J, Kimoff J, et al. Impaired sleep quality in COPD is associated with exacerbations: the CanCOLD cohort study. Chest. 2019;156(5):852–863. doi: 10.1016/j.chest.2019.04.132 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Bull FC, Al-Ansari SS, Biddle S, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451–1462. doi: 10.1136/bjsports-2020-102955 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Ross R, Chaput JP, Giangregorio LM, et al. Canadian 24-Hour Movement Guidelines for Adults aged 18–64 years and Adults aged 65 years or older: an integration of physical activity, sedentary behaviour, and sleep. Appl Physiol Nutr Metab. 2020;45(10):S57–S102. doi: 10.1139/apnm-2020-0467 [DOI] [PubMed] [Google Scholar]
22.Sallis JF, Bull F, Guthold R, et al. Progress in physical activity over the Olympic quadrennium. Lancet. 2016;388(10051):1325–1336. doi: 10.1016/S0140-6736(16)30581-5 [DOI] [PubMed] [Google Scholar]
23.Lambert EV, Kolbe-Alexander T, Adlakha D, et al. Making the case for ‘physical activity security’: the 2020 WHO guidelines on physical activity and sedentary behaviour from a Global South perspective. Br J Sports Med. 2020;54(24):1447–1448. doi: 10.1136/bjsports-2020-103524 [DOI] [PubMed] [Google Scholar]
24.Strain T, Wijndaele K, Garcia L, et al. Levels of domain-specific physical activity at work, in the household, for travel and for leisure among 327 789 adults from 104 countries. Br J Sports Med. 2020;54(24):1488–1497. doi: 10.1136/bjsports-2020-102601 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Byrom B, Rowe DA. Measuring free-living physical activity in COPD patients: deriving methodology standards for clinical trials through a review of research studies. Contemp Clin Trials. 2016;47:172–184. doi: 10.1016/j.cct.2016.01.006 [DOI] [PubMed] [Google Scholar]
26.Garrow AP, Yorke J, Khan N, Vestbo J, Singh D, Tyson S. Systematic literature review of patient-reported outcome measures used in assessment and measurement of sleep disorders in chronic obstructive pulmonary disease. Int J COPD. 2015;10:293–307. doi: 10.2147/COPD.S68093 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;88:105906. doi: 10.1136/bmj.n71 [DOI] [PubMed] [Google Scholar]
28.Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):1–10. doi: 10.1186/s13643-016-0384-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.World Health Organisation. Definition of regional groupings. Available from: http://www.who.int/healthinfo/global_burden_disease/definition_regions/en/. Accessed March 7, 2022.
30.The World Bank. World Bank Country and Lending Groups. Country Classification. Available from: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups. Accessed March 7, 2022.
31.Kottek M, Grieser J, Beck C, Rudolf B, Rubel F. World map of the Köppen-Geiger climate classification updated. Meteorol Zeitschrift. 2006;15(3):259–263. doi: 10.1127/0941-2948/2006/0130 [DOI] [Google Scholar]
32.Peel MC, Finlayson BL, McMahon TA. Updated world map of the Koppen-Geiger climate classification. Hydrol Earth Syst Sci. 2007;11(5):1633–1644. doi: 10.1002/ppp.421 [DOI] [Google Scholar]
33.World Bank. New World Bank Country Classifications by Income Level: 2020–2021; Washington, DC, USA: World Bank; 2020. Available from: https://blogs.worldbank.org/opendata/new-world-bank-country-classifications-income-level-2020-2021. Accessed March 7, 2022. [Google Scholar]
34.World Health Organisation. WHO Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide. Global update 2005. Summary of risk assessment; 2005. Available from: https://apps.who.int/iris/handle/10665/69477. Accessed March 7, 2022.
35.Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U. Physical activity levels of the world’s population Surveillance progress, gaps and prospects. Lancet. 2012;380:247–257. doi: 10.1016/S0140-6736(12)60646-1 [DOI] [PubMed] [Google Scholar]
36.Ainsworth BE, Pratt M, Ekelund U, Yngve A, Sallis JF, Oja P. International physical activity questionnaire: 12-Country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–1395. doi: 10.1249/01.MSS.0000078924.61453.FB [DOI] [PubMed] [Google Scholar]
37.Orme MW, Steiner MC, Morgan MD, et al. 24-hour accelerometry in COPD: exploring physical activity, sedentary behavior, sleep and clinical characteristics. Int J COPD. 2019;14:419–430. doi: 10.2147/COPD.S183029 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Spina G, Spruit MA, Alison J, et al. Analysis of nocturnal actigraphic sleep measures in patients with COPD and their association with daytime physical activity. Thorax. 2017;72(8):694–701. doi: 10.1136/thoraxjnl-2016-208900 [DOI] [PubMed] [Google Scholar]
39.Lewthwaite H, Effing TW, Olds T, Williams MT. Physical activity, sedentary behaviour and sleep in COPD guidelines: a systematic review. Chron Respir Dis. 2017;14(3):231–244. doi: 10.1177/1479972316687224 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Simonelli G, Marshall NS, Grillakis A, Miller CB, Hoyos CM, Glozier N. Sleep health epidemiology in low and middle-income countries: a systematic review and meta-analysis of the prevalence of poor sleep quality and sleep duration. Sleep Health. 2018;4(3):239–250. doi: 10.1016/j.sleh.2018.03.001 [DOI] [PubMed] [Google Scholar]
41.Ohayon MM, Reynolds III CF. Epidemiological and clinical relevance of insomnia diagnosis algorithms according to the DSM-IV and the International Classification of Sleep Disorders (ICSD). Sleep Med. 2009;10(9):952–960. doi: 10.1016/j.sleep.2009.07.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Adetiloye AO, Erhabor GE, Adewole OO, Awopeju O. The determinants of sleep quality and its impact on exercise capacity of patients with COPD. Afr J Respir Med. 2018;13(2):10–14. [Google Scholar]
43.Adewole O, Erhabor G, Oguntola O. Sleep pattern, quality and sleepiness among patients with chronic pulmonary parenchyma disease in Nigeria. Sleep Med. 2011;12:S47. doi: 10.1016/S1389-9457(11)70173-3 [DOI] [Google Scholar]
44.Desalu OO, Onyedum CC, Makusidi MA, et al. Physical and socioeconomic impact of asthma in Nigeria: experience of patients attending three tertiary hospitals. Niger J Clin Pract. 2019;22(6):855–861. doi: 10.4103/njcp.njcp_294_18 [DOI] [PubMed] [Google Scholar]
45.Ali Zohal M, Yazdi Z, Kazemifar AM. Daytime sleepiness and quality of sleep in patients with COPD compared to control group. Glob J Health Sci. 2013;5(3):150–155. doi: 10.5539/gjhs.v5n3p150 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Borji M, Salimi E. Comparing the effect of home care nursing and health promotion model on quality of life, sleep quality and life style of patients with asthma: a randomized clinical trial. Iran J Allergy Asthma Immunol. 2018;17(1):150. [Google Scholar]
47.Chegeni SP, Gholami M, Azargoon A, Hossein Pour AH, Birjandi M, Norollahi H. The effect of progressive muscle relaxation on the management of fatigue and quality of sleep in patients with chronic obstructive pulmonary disease: a randomized controlled clinical trial. Complement Ther Clin Pract. 2018;31:64–70. doi: 10.1016/j.ctcp.2018.01.010 [DOI] [PubMed] [Google Scholar]
48.Eslaminejad A, Safa M, Ghassem BF, Hajizadeh F, Pashm FM. Relationship between sleep quality and mental health according to demographics of 850 patients with chronic obstructive pulmonary disease. J Health Psychol. 2017;22(12):1603–1613. doi: 10.1177/1359105316684937 [DOI] [PubMed] [Google Scholar]
49.Mahmood ZA, Malghooth ZT. Relationship of hips and knees osteoarthritis with bronchial asthma. Res J Pharm Biol Chem Sci. 2019;10(2):64–70. [Google Scholar]
50.Moussa SB, Sfaxi I, Tabka Z, Saad HB, Rouatbi S. Oxidative stress and lung function profiles of male smokers free from COPD compared to those with COPD: a case-control study. Libyan J Med. 2014;9(1):23873. doi: 10.3402/ljm.v9.23873 [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Moussa SB, Rouatbi S, Saad HB. Incapacity, handicap, and oxidative stress markers of male smokers with and without COPD. Respir Care. 2016;61(5):668–679. doi: 10.4187/respcare.04420 [DOI] [PubMed] [Google Scholar]
52.Mihaltan F, Adir Y, Antczak A, et al. Importance of the relationship between symptoms and self-reported physical activity level in stable COPD based on the results from the SPACE study. Respir Res. 2019;20(1). doi: 10.1186/s12931-019-1053-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Mihaltan F, Antczak A, Radulovic V, Chen Y, Alecu S. Physical activity level in stable copd patients from central eastern europe: results from a large observational study. Chest. 2019;156(4):A903. doi: 10.1016/j.chest.2019.08.848 [DOI] [Google Scholar]
54.Vukoja M, Kopitovic I, Milicic D, Maksimovic O, Pavlovic-Popovic Z, Ilic M. Sleep quality and daytime sleepiness in patients with COPD and asthma. Clin Respir J. 2018;12(2):398–403. doi: 10.1111/crj.12528 [DOI] [PubMed] [Google Scholar]
55.Akinci AC, Yildirim E. Factors affecting health status in patients with chronic obstructive pulmonary disease. Int J Nurs Pract. 2013;19(1):31–38. doi: 10.1111/ijn.12034 [DOI] [PubMed] [Google Scholar]
56.Genc A, Ucok K, Gunay E, et al. Effects of long-acting beta-2 agonist treatment on daily energy balance and body composition in patients with chronic obstructive pulmonary disease. Turk J Med Sci. 2012;42:1414–1422. [Google Scholar]
57.Genc A, Ucok K, Sener U, et al. Association analyses of oxidative stress, aerobic capacity, daily physical activity, and body composition parameters in patients with mild to moderate COPD. Turk J Med Sci. 2014;44(6):972–979. doi: 10.3906/sag-1308-65 [DOI] [PubMed] [Google Scholar]
58.Pehlivan E, Niksarlioglu EY, Balci A, Kilic L. The effect of pulmonary rehabilitation on the physical activity level and general clinical status of patients with bronchiectasis. Turk Thorac J. 2019;20(1):30–35. doi: 10.5152/TurkThoracJ.2018.18093 [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Şahin AZ, Dayapoğlu N. Effect of progressive relaxation exercises on fatigue and sleep quality in patients with chronic obstructive lung disease (COPD). Complement Ther Clin Pract. 2015;21(4):277–281. doi: 10.1016/j.ctcp.2015.10.002 [DOI] [PubMed] [Google Scholar]
60.Saglam M, Savci S, Vardar Yagli N, et al. Relationship between obesity and respiratory muscle strength, functional capacity, and physical activity level in patients with chronic obstructive pulmonary disease. Fiz Rehabil. 2013;24(3):157–162. [Google Scholar]
61.Turan O, Ure I, Turan PA. Erectile dysfunction in COPD patients. Chronic Respir Dis. 2016;13(1):5–12. doi: 10.1177/1479972315619382 [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Yilmaz FT, Aydin HT. The effect of a regular walking program on dyspnoea severity and quality of life in normal weight, overweight, and obese patients with chronic obstructive pulmonary disease. Int J Nurs Pract. 2018;24(3):1. doi: 10.1111/ijn.12636 [DOI] [PubMed] [Google Scholar]
63.Lopez Jove OR, Galdames A, Barrionuevo V, et al. Depression in patients with chronic obstructive pulmonary disease (COPD): relationship to dyspnea degrees and impact on quality of life (QOL). Am J Respir Crit Care Med. 2013;187:A2505. [Google Scholar]
64.Molinas JA, Torrent C, Ardusso LR, Crisci CD, Barayazarra S, Crisci S. Relationship between body mass index and severity of bronchial asthma in adults. Arch Allergy Immunol Clin. 2007;38(1):19–28. [Google Scholar]
65.Amorim PB, Stelmach R, Carvalho CRF, Fernandes FLA, Carvalho-Pinto R, Cukier A. Barriers associated with reduced physical activity in COPD patients. J Bras Pneumol. 2014;40(5):504–512. doi: 10.1590/S1806-37132014000500006 [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Athayde FTS, Vieira DSR, Britto RR, Parreira VF. Functional outcomes in patients with chronic obstructive pulmonary disease: a multivariate analysis. Rev Brasil Fisioter. 2014;18(1):63–71. doi: 10.1590/S1413-35552012005000142 [DOI] [PMC free article] [PubMed] [Google Scholar]
67.Borges RC, Carvalho CRF. Physical activity in daily life in Brazilian COPD patients during and after exacerbation. COPD. 2012;9(6):596–602. doi: 10.3109/15412555.2012.705364 [DOI] [PubMed] [Google Scholar]
68.Campos FL, de Bruin PFC, Pinto TF, da Silva FGC, Pereira EDB, de Bruin VMS. Depressive symptoms, quality of sleep, and disease control in women with asthma. Sleep Breathing. 2017;21(2):361–367. doi: 10.1007/s11325-016-1422-0 [DOI] [PubMed] [Google Scholar]
69.Cani KC, Matte DL, Silva IJCS, Gulart AA, Karloh M, Mayer AF. Impact of home oxygen therapy on the level of physical activities in daily life in subjects with COPD. Respir Care. 2019;64(11):1392–1400. doi: 10.4187/respcare.06206 [DOI] [PubMed] [Google Scholar]
70.Cavalcante AM, De Bruin VMS, Pereira EDB, et al. Restless leg syndrome, fatigue and quality of sleep in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2012;185:A4533. [Google Scholar]
71.Cetlin AA, Gutierrez MR, Bettiol H, Barbieri MA, Vianna EO. Influence of asthma definition on the asthma-obesity relationship. BMC Public Health. 2012;12:844. doi: 10.1186/1471-2458-12-844 [DOI] [PMC free article] [PubMed] [Google Scholar]
72.Coelho CM, Campos LA, Pereira FO, et al. Objectively measured daily-life physical activity of moderate-to-severe Brazilian asthmatic women in comparison to healthy controls: a cross-sectional study. J Asthma. 2018;55(1):73–78. doi: 10.1080/02770903.2017.1306547 [DOI] [PubMed] [Google Scholar]
73.Coelho CM, Reboredo MM, Valle FM, et al. Effects of an unsupervised pedometer-based physical activity program on daily steps of adults with moderate to severe asthma: a randomized controlled trial. J Sports Sci. 2018;36(10):1186–1193. doi: 10.1080/02640414.2017.1364402 [DOI] [PubMed] [Google Scholar]
74.Da Costa CC, Bordin CD, Roberto CJP, Lermen C, Colombo C, Machado DSR. The repercussions of a pulmonary rehabilitation program on the level of physical activity in patients with chronic obstructive pulmonary disease. Rev Inspirar Movimento Saude. 2012;4(5):34–37. [Google Scholar]
75.Cukier A, Godoy I, Costa CHD, et al. Symptom variability over the course of the day in patients with stable COPD in Brazil: a real-world observational study. J Bras Pneumol. 2020;46(3):e20190223. doi: 10.36416/1806-3756/e20190223 [DOI] [PMC free article] [PubMed] [Google Scholar]
76.Felcar JM, Probst VS, De Carvalho DR, et al. Effects of exercise training in water and on land in patients with COPD: a randomised clinical trial. Physiotherapy. 2018;104(4):408–416. doi: 10.1016/j.physio.2017.10.009 [DOI] [PubMed] [Google Scholar]
77.Filippin TR, Ariza D, Beloto A-CNDO. Correlation of the bod mortality predictor index in physically active and sedentary chronic obstructive pulmonary diseased patients. Rev Inspirar Movimento Saude. 2011;3(4):30–35. [Google Scholar]
78.Fonseca FR, Karloh M, De araujo CLP, Dos santos K, Mayer AF. Nutritional status and its relationship with different dimensions of functional status in patients with chronic obstructive pulmonary disease. Rev Nutr. 2016;29(5):635–644. doi: 10.1590/1678-98652016000500002 [DOI] [Google Scholar]
79.Freitas PD, Silva AG, Ferreira PG, et al. Exercise improves physical activity and comorbidities in obese adults with asthma. Med Sci Sports Exerc. 2018;50(7):1367–1376. doi: 10.1249/MSS.0000000000001574 [DOI] [PubMed] [Google Scholar]
80.Furlanetto KC, Pinto IFS, Sant’Anna T, Hernandes NA, Pitta F. Profile of patients with chronic obstructive pulmonary disease classified as physically active and inactive according to different thresholds of physical activity in daily life. Braz J Phys Ther. 2016;20(6):517–524. doi: 10.1590/bjpt-rbf.2014.0185 [DOI] [PMC free article] [PubMed] [Google Scholar]
81.Furlanetto KC, Demeyer H, Sant’anna T, et al. Physical activity of patients with COPD from regions with different climatic variations. COPD. 2017;14(3):276–283. doi: 10.1080/15412555.2017.1303039 [DOI] [PubMed] [Google Scholar]
82.Gulart AA, Munari AB, Klein SR, Venancio RS, Alexandre HF, Mayer AF. The London Chest Activity of Daily Living scale cut-off point to discriminate functional status in patients with chronic obstructive pulmonary disease. Braz J Phys Ther. 2019;24(3):264–272. doi: 10.1016/j.bjpt.2019.03.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
83.Hernandes NA, Teixeira DC, Probst VS, Brunetto AF, Ramos EMC, Pitta F. Profile of the level of physical activity in the daily lives of patients with COPD in Brazil. J Bras Pneumol. 2009;35(10):949–956. doi: 10.1590/S1806-37132009001000002 [DOI] [PubMed] [Google Scholar]
84.Jardim JR, Piazza M, Carvalho AK, Ivanaga IT, Nascimento OA. Assessment of physical activity by pedometer in patients with chronic obstructive pulmonary disease (COPD) In an emerging country. Am J Respir Crit Care Med. 2011;183(1):A2034. [Google Scholar]
85.Karloh M, Araujo CLP, Gulart AA, Reis CM, Steidle LJM, Mayer AF. The Glittre-ADL test reflects functional performance measured by physical activities of daily living in patients with chronic obstructive pulmonary disease. Braz J Phys Ther. 2016;20(3):223–230. doi: 10.1590/bjpt-rbf.2014.0155 [DOI] [PMC free article] [PubMed] [Google Scholar]
86.Landal AC, Monteiro F, Hevely BC, Kanesawa LM, Hernandes N, Pitta F. Factors associated with improved body composition in individuals with COPD after physical training. Fisioter mov. 2014;27(4):633–641. doi: 10.1590/0103-5150.027.004.AO15 [DOI] [Google Scholar]
87.Lanza FC, Castro RAS, de Camargo A, et al. COPD assessment test (CAT) is a valid and simple tool to measure the impact of Bronchiectasis on affected patients. COPD. 2018;15(5):512–519. doi: 10.1080/15412555.2018.1540034 [DOI] [PubMed] [Google Scholar]
88.Lopes AC, Xavier RF, Pereira AC AC, et al. Identifying COPD patients at risk for worse symptoms, HRQoL, and self-efficacy: a cluster analysis. Chronic Illn. 2019;15(2):138–148. doi: 10.1177/1742395317753883 [DOI] [PubMed] [Google Scholar]
89.Mantoani LC, Hernandes N, Guimarães M, Vitorasso RL, Probst VS, Pitta F. Does the BODE index reflect the level of physical activity in daily life in patients with COPD? Braz j Phys Ther (Impr). 2011;15(2):131–137. doi: 10.1590/S1413-35552011000200008 [DOI] [PubMed] [Google Scholar]
90.Monteiro F, Camillo CA, Vitorasso R, et al. Obesity and physical activity in the daily life of patients with COPD. Lung. 2012;190(4):403–410. doi: 10.1007/s00408-012-9381-0 [DOI] [PubMed] [Google Scholar]
91.Morita AA, Silva LKO, Bisca GW, et al. Heart rate recovery, physical activity level, and functional status in subjects with COPD. Respir Care. 2018;63(8):1002–1008. doi: 10.4187/respcare.05918 [DOI] [PubMed] [Google Scholar]
92.Munari AB, Gulart AA, Dos Santos K, Venâncio RS, Karloh M, Mayer AF. Modified Medical Research Council Dyspnea Scale in GOLD classification better reflects physical activities of daily living. Respir Care. 2018;63(1):77–85. doi: 10.4187/respcare.05636 [DOI] [PubMed] [Google Scholar]
93.Nobeschi L, Zangirolami-Raimundo J, Cordoni PK, et al. Evaluation of sleep quality and daytime somnolence in patients with chronic obstructive pulmonary disease in pulmonary rehabilitation. BMC Pulm Med. 2020;20(1):1–7. doi: 10.1186/s12890-020-1046-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
94.Nunes DM, Mota RMS, Machado MO, Pereira EDB, de Bruin VMS, de Bruin PFC. Effect of melatonin administration on subjective sleep quality in chronic obstructive pulmonary disease. Braz J Med Biol Res. 2008;41(10):926–931. doi: 10.1590/S0100-879X2008001000016 [DOI] [PubMed] [Google Scholar]
95.Nyssen SM, Santos J, Barusso MS, Delfino de OA Jr, Lorenzo VA, Jamami M. Levels of physical activity and predictors of mortality in COPD. J Bras Pneumol. 2013;39(6):659–666. doi: 10.1590/S1806-37132013000600004 [DOI] [PMC free article] [PubMed] [Google Scholar]
96.Pitta F, Breyer M, Hernandes NA, et al. Comparison of daily physical activity between COPD patients from Central Europe and South America. Respir Med. 2009;103(3):421–426. doi: 10.1016/j.rmed.2008.09.019 [DOI] [PubMed] [Google Scholar]
97.Probst VS, Kovelis D, Hernandes N, Camillo CA, Cavalheri V, Pitta F. Effects of 2 exercise training programs on physical activity in daily life in patients with COPD. Respir Care. 2011;56(11):1799–1807. doi: 10.4187/respcare.01110 [DOI] [PubMed] [Google Scholar]
98.Reboredo MDM, Lucinda LM, Yecker GD, et al. Barriers to physical activity in patients with chronic respiratory diseases. Am J Respir Crit Care Med. 2018;197:A2174. [Google Scholar]
99.Rocha FR, Brüggemann A, Karla V, et al. Diaphragmatic mobility: relationship with lung function, respiratory muscle strength, dyspnea, and physical activity in daily life in patients with COPD. J Bras Pneumol. 2017;43(1):32–37. doi: 10.1590/s1806-37562016000000097 [DOI] [PMC free article] [PubMed] [Google Scholar]
100.Rodrigues ESR, Moreira RDF, Rezende AAB, Costa LD. Sedentarism and smoking in patients with cardiovascular, respiratory and orthopedic diseases. Rev Enfermagem Ufpe. 2014;8(3):591–599. [Google Scholar]
101.Schneider PL, Couto FK, Aparecida HN, Pitta F. Does the wearing time of motion sensor interfere with the choice of physical activity in daily life outcomes of COPD patients? Fisioter Pesquisa. 2018;25(1):43–48. doi: 10.1590/1809-2950/16768425012018 [DOI] [Google Scholar]
102.Silva DR, Coelho AC, Dumke A, et al. Osteoporosis prevalence and associated factors in patients with COPD: a cross-sectional study. Respir Care. 2011;56(7):961–968. doi: 10.4187/respcare.01056 [DOI] [PubMed] [Google Scholar]
103.Simon KM, Hass AP, Zimmermman JL, Carpes MF. BODE mortality prognostic index and physical activity in chronic obstructive pulmonary patients. Rev bras med esporte. 2009;15(1):19–22. doi: 10.1590/S1517-86922009000100004 [DOI] [Google Scholar]
104.Tavares MG, Nascimento ACSD, Ferraz MCCN, Medeiros RABD, Cabral PC, Burgos MGPDA. Overweight and obesity in patients with chronic obstructive pulmonary disease. Braspen J. 2017;32(1):58–62. [Google Scholar]
105.Vitorasso R, Camillo CA, Cavalheri V, et al. Is walking in daily life a moderate intensity activity in patients with chronic obstructive pulmonary disease? Eur J Phys Rehabil Med. 2012;48(4):587–592. [PubMed] [Google Scholar]
106.Xavier R, Lopes AC, Pereira ACAC, et al. Factors associated with daily life physical activity in Brazilian COPD patients. Eur Respir J. 2016;48:PA1889. [Google Scholar]
107.Yunus FM, Khan S, Mitra DK, Mistry SK, Afsana K, Rahman M. Relationship of sleep pattern and snoring with chronic disease: findings from a nationwide population-based survey. Sleep Health. 2018;4(1):40–48. doi: 10.1016/j.sleh.2017.10.003 [DOI] [PubMed] [Google Scholar]
108.Bhuker M, Kapoor S. Environmental and occupational respiratory diseases-1043. Effects of socioeconomic status, life style patterns and demographic factors. World Allergy Organ J. 2013;6:P42. doi: 10.1186/1939-4551-6-S1-P42 [DOI] [Google Scholar]
109.De S. Subjective assessment of quality of sleep in chronic obstructive pulmonary disease patient and its relationship with associated depression. Lung India. 2012;29(4):332–335. doi: 10.4103/0970-2113.102808 [DOI] [PMC free article] [PubMed] [Google Scholar]
110.Gupta R, Ulfberg J, Allen RP, Goel D. Comparison of subjective sleep quality of long-term residents at low and high altitudes: SARAHA study. J Clin Sleep Med. 2018;14(1):15–21. doi: 10.5664/jcsm.6870 [DOI] [PMC free article] [PubMed] [Google Scholar]
111.Panigrahi MK, Padhan M, Mohapatra PR. Relationship between sleep quality, asthma control and quality of life in adults with asthma. Am J Respir Crit Care Med. 2018;197:A1089. doi: 10.1164/rccm.201708-1728OC [DOI] [Google Scholar]
112.Poongadan MN, Gupta N, Kumar R. Lifestyle factors and asthma in India - a case-control study. Pneumonol Alergol Pol. 2016;84(2):104–108. doi: 10.5603/PiAP.2016.0008 [DOI] [PubMed] [Google Scholar]
113.Sharma K, Jain A, Goyal V, et al. Depression and physical activity impairment in COPD subjects. J Clin Diagn Res. 2019;13(7):OC16–22. [Google Scholar]
114.Ulfathinah A, Rachmi SF, Indracahyani A. Characteristics affecting sleep quality of COPD patients. Enferm Clin. 2019;29:30–35. doi: 10.1016/j.enfcli.2019.04.005 [DOI] [Google Scholar]
115.Widyastuti K, Makhabah DN, Rima A, Sutanto YS, Suradi S, Ambrosino N. Home based pulmonary rehabilitation with pedometers in Indonesian COPD patients. Eur Respir J. 2017;50:PA777. [Google Scholar]
116.Chanthitivech N, Sirichana W. Cardiopulmonary exercise testing parameters and level of daily physical activity correlation in stable COPD patients. Eur Respir J. 2017;50:PA2250. [Google Scholar]
117.Chunrong J, Rongchang C. Factors associated with impairment of quadriceps muscle function in Chinese patients with chronic obstructive pulmonary disease. PLoS One. 2014;9(2):e84167. doi: 10.1371/journal.pone.0084167 [DOI] [PMC free article] [PubMed] [Google Scholar]
118.Ding B, Small M, Bergstrom G, Holmgren U. A cross-sectional survey of night-time symptoms and impact of sleep disturbance on symptoms and health status in patients with COPD. Int J COPD. 2017;12:589–599. doi: 10.2147/COPD.S122485 [DOI] [PMC free article] [PubMed] [Google Scholar]
119.Tao Y, Wang L, Dong X, et al. Psychometric properties of the physical activity scale for the elderly in Chinese patients with COPD. Int J COPD. 2017;12:105–114. doi: 10.2147/COPD.S120700 [DOI] [PMC free article] [PubMed] [Google Scholar]
120.Wang L, Nygardh A, Zhao Y, Martensson J. Self-management among patients with chronic obstructive pulmonary disease in China and its association with sociodemographic and clinical variables. Appl Nurs Res. 2016;32:61–66. doi: 10.1016/j.apnr.2016.05.001 [DOI] [PubMed] [Google Scholar]
121.Wang L, Tao Y, Dong X, et al. Demographic, health behavioral, and selfmanagement abilities associated with disease severity among patients with chronic obstructive pulmonary disease: an exploratory study. Int J Nurs Pract. 2017;23(1):e12509. doi: 10.1111/ijn.12509 [DOI] [PubMed] [Google Scholar]
122.Wang S, Wu Y, Ungvari GS, et al. Sleep duration and its association with demographics, lifestyle factors, poor mental health and chronic diseases in older Chinese adults. Psychiatry Res. 2017;257:212–218. doi: 10.1016/j.psychres.2017.07.036 [DOI] [PubMed] [Google Scholar]
123.Zheng L, Wang A. Application effect of rational emotional behavior therapy for home chronic obstructive pulmonary disease patients with depression. Chin Nurs J. 2019;33:1135–1140. [Google Scholar]
124.Vancampfort D, Koyanagi A, Ward PB, et al. Chronic physical conditions, multimorbidity and physical activity across 46 low and middle income countries. Int J Behav Nutr Phys Act. 2017;14(1):6. doi: 10.1186/s12966-017-0463-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
125.Koyanagi A, Stubbs B, Smith L, Gardner B, Vancampfort D. Correlates of physical activity among community-dwelling adults aged 50 or over in six low- and middle-income countries. PLoS One. 2017;12(10):e0186992. doi: 10.1371/journal.pone.0186992 [DOI] [PMC free article] [PubMed] [Google Scholar]
126.Vancampfort D, Stubbs B, Koyanagi A. Physical chronic conditions, multimorbidity and sedentary behavior amongst middle-aged and older adults in six low- and middle-income countries. Int J Behav Nutr Phys Act. 2017;14(1):147. doi: 10.1186/s12966-017-0602-z [DOI] [PMC free article] [PubMed] [Google Scholar]
127.Vancampfort D, Lara E, Stubbs B, Swinnen N, Probst M, Koyanagi A. Physical activity correlates in people with mild cognitive impairment: findings from six low- and middle-income countries. Public Health. 2018;156:15–25. doi: 10.1016/j.puhe.2017.12.002 [DOI] [PubMed] [Google Scholar]
128.Vancampfort D, Stubbs B, Veronese N, Mugisha J, Swinnen N, Koyanagi A. Correlates of physical activity among depressed older people in six low-income and middle-income countries: a community-based cross-sectional study. Int J Geriatr Psychiatry. 2018;33(2):e314–22. doi: 10.1002/gps.4796 [DOI] [PubMed] [Google Scholar]
129.Vancampfort D, Stubbs B, Mugisha J, Firth J, Schuch FB, Koyanagi A. Correlates of sedentary behavior in 2375 people with depression from 6 low- and middle-income countries. J Affect Disord. 2018;234:97–104. doi: 10.1016/j.jad.2018.02.088 [DOI] [PubMed] [Google Scholar]

[cit0001] 1.Hu G, Zhou Y, Tian J, et al. Risk of COPD from exposure to biomass smoke: a metaanalysis. Chest. 2010;138(1):20–31. doi: 10.1378/chest.08-2114 [DOI] [PubMed] [Google Scholar]

[cit0002] 2.Amaral AFS, Coton S, Kato B, et al. Tuberculosis associates with both airflow obstruction and low lung function: BOLD results. Eur Respir J. 2015;46(4):1104–1112. doi: 10.1183/13993003.02325-2014 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0003] 3.Salvi SS, Manap R, Beasley R. Understanding the true burden of COPD: the epidemiological challenges. Prim Care Respir J. 2012;21(3):249–251. doi: 10.4104/pcrj.2012.00082 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0004] 4.Rollo S, Antsygina O, Tremblay MS. The whole day matters: understanding 24-hour movement guideline adherence and relationships with health indicators across the lifespan. J Sport Health Sci. 2020;9:493–510. doi: 10.1016/j.jshs.2020.07.004 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0005] 5.Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Impact of physical inactivity on the world’s major non- communicable diseases. Lancet. 2012;380(9838):219–229. doi: 10.1016/S0140-6736(12)61031-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0006] 6.World Health Organization. Global action plan for the prevention and control of noncommunicable diseases 2013–2020. World Health Organization; 2013. Available from: https://apps.who.int/iris/bitstream/handle/10665/94384/?sequence=1. Accessed March 7, 2022. [Google Scholar]

[cit0007] 7.Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Glob Health. 2018;6(10):e1077–e1086. doi: 10.1016/S2214-109X(18)30357-7 [DOI] [PubMed] [Google Scholar]

[cit0008] 8.Vorrink SNW, Kort HSM, Troosters T, Lammers J-WJ. Level of daily physical activity in individuals with COPD compared with healthy controls. Respir Res. 2011;12(1):1–8. doi: 10.1186/1465-9921-12-33 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0009] 9.Tudor-Locke C, Craig CL, Aoyagi Y, et al. How many steps/day are enough? For older adults and special populations. Int J Behav Nutr Phys Act. 2011;8(1):1–19. doi: 10.1186/1479-5868-8-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0010] 10.Pitta F, Troosters T, Probst VS, Spruit MA, Decramer M, Gosselink R. Physical activity and hospitalization for exacerbation of COPD. Chest. 2006;129(3):536–544. doi: 10.1378/chest.129.3.536 [DOI] [PubMed] [Google Scholar]

[cit0011] 11.Waschki B, Kirsten A, Holz O, et al. Physical activity is the strongest predictor of all-cause mortality in patients with COPD: a prospective cohort study. Chest. 2011;140(2):331–342. doi: 10.1378/chest.10-2521 [DOI] [PubMed] [Google Scholar]

[cit0012] 12.Tremblay MS, Aubert S, Barnes JD, et al. Sedentary Behavior Research Network (SBRN) - Terminology Consensus Project process and outcome. Int J Behav Nutr Phys Act. 2017;14(1):1–17. doi: 10.1186/s12966-017-0525-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0013] 13.Pitta F, Troosters T, Spruit MA, Probst VS, Decramer M, Gosselink R. Characteristics of physical activities in daily life in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2005;171(9):972–977. doi: 10.1164/rccm.200407-855OC [DOI] [PubMed] [Google Scholar]

[cit0014] 14.Furlanetto KC, Donária L, Schneider LP, et al. Sedentary behavior is an independent predictor of mortality in subjects with COPD. Respir Care. 2017;62(5):579–587. doi: 10.4187/respcare.05306 [DOI] [PubMed] [Google Scholar]

[cit0015] 15.Ohayon M, Wickwire EM, Hirshkowitz M, et al. National Sleep Foundation’s sleep quality recommendations: first report. Sleep Health. 2017;3(1):6–19. doi: 10.1016/j.sleh.2016.11.006 [DOI] [PubMed] [Google Scholar]

[cit0016] 16.Ohayon MM, Paskow M, Roach A, et al. The National Sleep Foundation’s Sleep Satisfaction Tool. Sleep Health. 2019;5(1):5–11. doi: 10.1016/j.sleh.2018.10.003 [DOI] [PubMed] [Google Scholar]

[cit0017] 17.Hirshkowitz M, Whiton K, Albert SM, et al. National Sleep Foundation’s updated sleep duration recommendations: final report. Sleep Health. 2015;1(4):233–243. doi: 10.1016/j.sleh.2015.10.004 [DOI] [PubMed] [Google Scholar]

[cit0018] 18.Chattu V, Manzar MD, Kumary S, Burman D, Spence DW, Pandi-Perumal SR. The global problem of insufficient sleep and its serious public health implications. Healthcare. 2019;7(1):1–16. doi: 10.3390/healthcare7010001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0019] 19.Shorofsky M, Bourbeau J, Kimoff J, et al. Impaired sleep quality in COPD is associated with exacerbations: the CanCOLD cohort study. Chest. 2019;156(5):852–863. doi: 10.1016/j.chest.2019.04.132 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0020] 20.Bull FC, Al-Ansari SS, Biddle S, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451–1462. doi: 10.1136/bjsports-2020-102955 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0021] 21.Ross R, Chaput JP, Giangregorio LM, et al. Canadian 24-Hour Movement Guidelines for Adults aged 18–64 years and Adults aged 65 years or older: an integration of physical activity, sedentary behaviour, and sleep. Appl Physiol Nutr Metab. 2020;45(10):S57–S102. doi: 10.1139/apnm-2020-0467 [DOI] [PubMed] [Google Scholar]

[cit0022] 22.Sallis JF, Bull F, Guthold R, et al. Progress in physical activity over the Olympic quadrennium. Lancet. 2016;388(10051):1325–1336. doi: 10.1016/S0140-6736(16)30581-5 [DOI] [PubMed] [Google Scholar]

[cit0023] 23.Lambert EV, Kolbe-Alexander T, Adlakha D, et al. Making the case for ‘physical activity security’: the 2020 WHO guidelines on physical activity and sedentary behaviour from a Global South perspective. Br J Sports Med. 2020;54(24):1447–1448. doi: 10.1136/bjsports-2020-103524 [DOI] [PubMed] [Google Scholar]

[cit0024] 24.Strain T, Wijndaele K, Garcia L, et al. Levels of domain-specific physical activity at work, in the household, for travel and for leisure among 327 789 adults from 104 countries. Br J Sports Med. 2020;54(24):1488–1497. doi: 10.1136/bjsports-2020-102601 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0025] 25.Byrom B, Rowe DA. Measuring free-living physical activity in COPD patients: deriving methodology standards for clinical trials through a review of research studies. Contemp Clin Trials. 2016;47:172–184. doi: 10.1016/j.cct.2016.01.006 [DOI] [PubMed] [Google Scholar]

[cit0026] 26.Garrow AP, Yorke J, Khan N, Vestbo J, Singh D, Tyson S. Systematic literature review of patient-reported outcome measures used in assessment and measurement of sleep disorders in chronic obstructive pulmonary disease. Int J COPD. 2015;10:293–307. doi: 10.2147/COPD.S68093 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0027] 27.Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;88:105906. doi: 10.1136/bmj.n71 [DOI] [PubMed] [Google Scholar]

[cit0028] 28.Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):1–10. doi: 10.1186/s13643-016-0384-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0029] 29.World Health Organisation. Definition of regional groupings. Available from: http://www.who.int/healthinfo/global_burden_disease/definition_regions/en/. Accessed March 7, 2022.

[cit0030] 30.The World Bank. World Bank Country and Lending Groups. Country Classification. Available from: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups. Accessed March 7, 2022.

[cit0031] 31.Kottek M, Grieser J, Beck C, Rudolf B, Rubel F. World map of the Köppen-Geiger climate classification updated. Meteorol Zeitschrift. 2006;15(3):259–263. doi: 10.1127/0941-2948/2006/0130 [DOI] [Google Scholar]

[cit0032] 32.Peel MC, Finlayson BL, McMahon TA. Updated world map of the Koppen-Geiger climate classification. Hydrol Earth Syst Sci. 2007;11(5):1633–1644. doi: 10.1002/ppp.421 [DOI] [Google Scholar]

[cit0033] 33.World Bank. New World Bank Country Classifications by Income Level: 2020–2021; Washington, DC, USA: World Bank; 2020. Available from: https://blogs.worldbank.org/opendata/new-world-bank-country-classifications-income-level-2020-2021. Accessed March 7, 2022. [Google Scholar]

[cit0034] 34.World Health Organisation. WHO Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide. Global update 2005. Summary of risk assessment; 2005. Available from: https://apps.who.int/iris/handle/10665/69477. Accessed March 7, 2022.

[cit0035] 35.Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U. Physical activity levels of the world’s population Surveillance progress, gaps and prospects. Lancet. 2012;380:247–257. doi: 10.1016/S0140-6736(12)60646-1 [DOI] [PubMed] [Google Scholar]

[cit0036] 36.Ainsworth BE, Pratt M, Ekelund U, Yngve A, Sallis JF, Oja P. International physical activity questionnaire: 12-Country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–1395. doi: 10.1249/01.MSS.0000078924.61453.FB [DOI] [PubMed] [Google Scholar]

[cit0037] 37.Orme MW, Steiner MC, Morgan MD, et al. 24-hour accelerometry in COPD: exploring physical activity, sedentary behavior, sleep and clinical characteristics. Int J COPD. 2019;14:419–430. doi: 10.2147/COPD.S183029 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0038] 38.Spina G, Spruit MA, Alison J, et al. Analysis of nocturnal actigraphic sleep measures in patients with COPD and their association with daytime physical activity. Thorax. 2017;72(8):694–701. doi: 10.1136/thoraxjnl-2016-208900 [DOI] [PubMed] [Google Scholar]

[cit0039] 39.Lewthwaite H, Effing TW, Olds T, Williams MT. Physical activity, sedentary behaviour and sleep in COPD guidelines: a systematic review. Chron Respir Dis. 2017;14(3):231–244. doi: 10.1177/1479972316687224 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0040] 40.Simonelli G, Marshall NS, Grillakis A, Miller CB, Hoyos CM, Glozier N. Sleep health epidemiology in low and middle-income countries: a systematic review and meta-analysis of the prevalence of poor sleep quality and sleep duration. Sleep Health. 2018;4(3):239–250. doi: 10.1016/j.sleh.2018.03.001 [DOI] [PubMed] [Google Scholar]

[cit0041] 41.Ohayon MM, Reynolds III CF. Epidemiological and clinical relevance of insomnia diagnosis algorithms according to the DSM-IV and the International Classification of Sleep Disorders (ICSD). Sleep Med. 2009;10(9):952–960. doi: 10.1016/j.sleep.2009.07.008 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0042] 42.Adetiloye AO, Erhabor GE, Adewole OO, Awopeju O. The determinants of sleep quality and its impact on exercise capacity of patients with COPD. Afr J Respir Med. 2018;13(2):10–14. [Google Scholar]

[cit0043] 43.Adewole O, Erhabor G, Oguntola O. Sleep pattern, quality and sleepiness among patients with chronic pulmonary parenchyma disease in Nigeria. Sleep Med. 2011;12:S47. doi: 10.1016/S1389-9457(11)70173-3 [DOI] [Google Scholar]

[cit0044] 44.Desalu OO, Onyedum CC, Makusidi MA, et al. Physical and socioeconomic impact of asthma in Nigeria: experience of patients attending three tertiary hospitals. Niger J Clin Pract. 2019;22(6):855–861. doi: 10.4103/njcp.njcp_294_18 [DOI] [PubMed] [Google Scholar]

[cit0045] 45.Ali Zohal M, Yazdi Z, Kazemifar AM. Daytime sleepiness and quality of sleep in patients with COPD compared to control group. Glob J Health Sci. 2013;5(3):150–155. doi: 10.5539/gjhs.v5n3p150 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0046] 46.Borji M, Salimi E. Comparing the effect of home care nursing and health promotion model on quality of life, sleep quality and life style of patients with asthma: a randomized clinical trial. Iran J Allergy Asthma Immunol. 2018;17(1):150. [Google Scholar]

[cit0047] 47.Chegeni SP, Gholami M, Azargoon A, Hossein Pour AH, Birjandi M, Norollahi H. The effect of progressive muscle relaxation on the management of fatigue and quality of sleep in patients with chronic obstructive pulmonary disease: a randomized controlled clinical trial. Complement Ther Clin Pract. 2018;31:64–70. doi: 10.1016/j.ctcp.2018.01.010 [DOI] [PubMed] [Google Scholar]

[cit0048] 48.Eslaminejad A, Safa M, Ghassem BF, Hajizadeh F, Pashm FM. Relationship between sleep quality and mental health according to demographics of 850 patients with chronic obstructive pulmonary disease. J Health Psychol. 2017;22(12):1603–1613. doi: 10.1177/1359105316684937 [DOI] [PubMed] [Google Scholar]

[cit0049] 49.Mahmood ZA, Malghooth ZT. Relationship of hips and knees osteoarthritis with bronchial asthma. Res J Pharm Biol Chem Sci. 2019;10(2):64–70. [Google Scholar]

[cit0050] 50.Moussa SB, Sfaxi I, Tabka Z, Saad HB, Rouatbi S. Oxidative stress and lung function profiles of male smokers free from COPD compared to those with COPD: a case-control study. Libyan J Med. 2014;9(1):23873. doi: 10.3402/ljm.v9.23873 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0051] 51.Moussa SB, Rouatbi S, Saad HB. Incapacity, handicap, and oxidative stress markers of male smokers with and without COPD. Respir Care. 2016;61(5):668–679. doi: 10.4187/respcare.04420 [DOI] [PubMed] [Google Scholar]

[cit0052] 52.Mihaltan F, Adir Y, Antczak A, et al. Importance of the relationship between symptoms and self-reported physical activity level in stable COPD based on the results from the SPACE study. Respir Res. 2019;20(1). doi: 10.1186/s12931-019-1053-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0053] 53.Mihaltan F, Antczak A, Radulovic V, Chen Y, Alecu S. Physical activity level in stable copd patients from central eastern europe: results from a large observational study. Chest. 2019;156(4):A903. doi: 10.1016/j.chest.2019.08.848 [DOI] [Google Scholar]

[cit0054] 54.Vukoja M, Kopitovic I, Milicic D, Maksimovic O, Pavlovic-Popovic Z, Ilic M. Sleep quality and daytime sleepiness in patients with COPD and asthma. Clin Respir J. 2018;12(2):398–403. doi: 10.1111/crj.12528 [DOI] [PubMed] [Google Scholar]

[cit0055] 55.Akinci AC, Yildirim E. Factors affecting health status in patients with chronic obstructive pulmonary disease. Int J Nurs Pract. 2013;19(1):31–38. doi: 10.1111/ijn.12034 [DOI] [PubMed] [Google Scholar]

[cit0056] 56.Genc A, Ucok K, Gunay E, et al. Effects of long-acting beta-2 agonist treatment on daily energy balance and body composition in patients with chronic obstructive pulmonary disease. Turk J Med Sci. 2012;42:1414–1422. [Google Scholar]

[cit0057] 57.Genc A, Ucok K, Sener U, et al. Association analyses of oxidative stress, aerobic capacity, daily physical activity, and body composition parameters in patients with mild to moderate COPD. Turk J Med Sci. 2014;44(6):972–979. doi: 10.3906/sag-1308-65 [DOI] [PubMed] [Google Scholar]

[cit0058] 58.Pehlivan E, Niksarlioglu EY, Balci A, Kilic L. The effect of pulmonary rehabilitation on the physical activity level and general clinical status of patients with bronchiectasis. Turk Thorac J. 2019;20(1):30–35. doi: 10.5152/TurkThoracJ.2018.18093 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0059] 59.Şahin AZ, Dayapoğlu N. Effect of progressive relaxation exercises on fatigue and sleep quality in patients with chronic obstructive lung disease (COPD). Complement Ther Clin Pract. 2015;21(4):277–281. doi: 10.1016/j.ctcp.2015.10.002 [DOI] [PubMed] [Google Scholar]

[cit0060] 60.Saglam M, Savci S, Vardar Yagli N, et al. Relationship between obesity and respiratory muscle strength, functional capacity, and physical activity level in patients with chronic obstructive pulmonary disease. Fiz Rehabil. 2013;24(3):157–162. [Google Scholar]

[cit0061] 61.Turan O, Ure I, Turan PA. Erectile dysfunction in COPD patients. Chronic Respir Dis. 2016;13(1):5–12. doi: 10.1177/1479972315619382 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0062] 62.Yilmaz FT, Aydin HT. The effect of a regular walking program on dyspnoea severity and quality of life in normal weight, overweight, and obese patients with chronic obstructive pulmonary disease. Int J Nurs Pract. 2018;24(3):1. doi: 10.1111/ijn.12636 [DOI] [PubMed] [Google Scholar]

[cit0063] 63.Lopez Jove OR, Galdames A, Barrionuevo V, et al. Depression in patients with chronic obstructive pulmonary disease (COPD): relationship to dyspnea degrees and impact on quality of life (QOL). Am J Respir Crit Care Med. 2013;187:A2505. [Google Scholar]

[cit0064] 64.Molinas JA, Torrent C, Ardusso LR, Crisci CD, Barayazarra S, Crisci S. Relationship between body mass index and severity of bronchial asthma in adults. Arch Allergy Immunol Clin. 2007;38(1):19–28. [Google Scholar]

[cit0065] 65.Amorim PB, Stelmach R, Carvalho CRF, Fernandes FLA, Carvalho-Pinto R, Cukier A. Barriers associated with reduced physical activity in COPD patients. J Bras Pneumol. 2014;40(5):504–512. doi: 10.1590/S1806-37132014000500006 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0066] 66.Athayde FTS, Vieira DSR, Britto RR, Parreira VF. Functional outcomes in patients with chronic obstructive pulmonary disease: a multivariate analysis. Rev Brasil Fisioter. 2014;18(1):63–71. doi: 10.1590/S1413-35552012005000142 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0067] 67.Borges RC, Carvalho CRF. Physical activity in daily life in Brazilian COPD patients during and after exacerbation. COPD. 2012;9(6):596–602. doi: 10.3109/15412555.2012.705364 [DOI] [PubMed] [Google Scholar]

[cit0068] 68.Campos FL, de Bruin PFC, Pinto TF, da Silva FGC, Pereira EDB, de Bruin VMS. Depressive symptoms, quality of sleep, and disease control in women with asthma. Sleep Breathing. 2017;21(2):361–367. doi: 10.1007/s11325-016-1422-0 [DOI] [PubMed] [Google Scholar]

[cit0069] 69.Cani KC, Matte DL, Silva IJCS, Gulart AA, Karloh M, Mayer AF. Impact of home oxygen therapy on the level of physical activities in daily life in subjects with COPD. Respir Care. 2019;64(11):1392–1400. doi: 10.4187/respcare.06206 [DOI] [PubMed] [Google Scholar]

[cit0070] 70.Cavalcante AM, De Bruin VMS, Pereira EDB, et al. Restless leg syndrome, fatigue and quality of sleep in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2012;185:A4533. [Google Scholar]

[cit0071] 71.Cetlin AA, Gutierrez MR, Bettiol H, Barbieri MA, Vianna EO. Influence of asthma definition on the asthma-obesity relationship. BMC Public Health. 2012;12:844. doi: 10.1186/1471-2458-12-844 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0072] 72.Coelho CM, Campos LA, Pereira FO, et al. Objectively measured daily-life physical activity of moderate-to-severe Brazilian asthmatic women in comparison to healthy controls: a cross-sectional study. J Asthma. 2018;55(1):73–78. doi: 10.1080/02770903.2017.1306547 [DOI] [PubMed] [Google Scholar]

[cit0073] 73.Coelho CM, Reboredo MM, Valle FM, et al. Effects of an unsupervised pedometer-based physical activity program on daily steps of adults with moderate to severe asthma: a randomized controlled trial. J Sports Sci. 2018;36(10):1186–1193. doi: 10.1080/02640414.2017.1364402 [DOI] [PubMed] [Google Scholar]

[cit0074] 74.Da Costa CC, Bordin CD, Roberto CJP, Lermen C, Colombo C, Machado DSR. The repercussions of a pulmonary rehabilitation program on the level of physical activity in patients with chronic obstructive pulmonary disease. Rev Inspirar Movimento Saude. 2012;4(5):34–37. [Google Scholar]

[cit0075] 75.Cukier A, Godoy I, Costa CHD, et al. Symptom variability over the course of the day in patients with stable COPD in Brazil: a real-world observational study. J Bras Pneumol. 2020;46(3):e20190223. doi: 10.36416/1806-3756/e20190223 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0076] 76.Felcar JM, Probst VS, De Carvalho DR, et al. Effects of exercise training in water and on land in patients with COPD: a randomised clinical trial. Physiotherapy. 2018;104(4):408–416. doi: 10.1016/j.physio.2017.10.009 [DOI] [PubMed] [Google Scholar]

[cit0077] 77.Filippin TR, Ariza D, Beloto A-CNDO. Correlation of the bod mortality predictor index in physically active and sedentary chronic obstructive pulmonary diseased patients. Rev Inspirar Movimento Saude. 2011;3(4):30–35. [Google Scholar]

[cit0078] 78.Fonseca FR, Karloh M, De araujo CLP, Dos santos K, Mayer AF. Nutritional status and its relationship with different dimensions of functional status in patients with chronic obstructive pulmonary disease. Rev Nutr. 2016;29(5):635–644. doi: 10.1590/1678-98652016000500002 [DOI] [Google Scholar]

[cit0079] 79.Freitas PD, Silva AG, Ferreira PG, et al. Exercise improves physical activity and comorbidities in obese adults with asthma. Med Sci Sports Exerc. 2018;50(7):1367–1376. doi: 10.1249/MSS.0000000000001574 [DOI] [PubMed] [Google Scholar]

[cit0080] 80.Furlanetto KC, Pinto IFS, Sant’Anna T, Hernandes NA, Pitta F. Profile of patients with chronic obstructive pulmonary disease classified as physically active and inactive according to different thresholds of physical activity in daily life. Braz J Phys Ther. 2016;20(6):517–524. doi: 10.1590/bjpt-rbf.2014.0185 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0081] 81.Furlanetto KC, Demeyer H, Sant’anna T, et al. Physical activity of patients with COPD from regions with different climatic variations. COPD. 2017;14(3):276–283. doi: 10.1080/15412555.2017.1303039 [DOI] [PubMed] [Google Scholar]

[cit0082] 82.Gulart AA, Munari AB, Klein SR, Venancio RS, Alexandre HF, Mayer AF. The London Chest Activity of Daily Living scale cut-off point to discriminate functional status in patients with chronic obstructive pulmonary disease. Braz J Phys Ther. 2019;24(3):264–272. doi: 10.1016/j.bjpt.2019.03.002 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0083] 83.Hernandes NA, Teixeira DC, Probst VS, Brunetto AF, Ramos EMC, Pitta F. Profile of the level of physical activity in the daily lives of patients with COPD in Brazil. J Bras Pneumol. 2009;35(10):949–956. doi: 10.1590/S1806-37132009001000002 [DOI] [PubMed] [Google Scholar]

[cit0084] 84.Jardim JR, Piazza M, Carvalho AK, Ivanaga IT, Nascimento OA. Assessment of physical activity by pedometer in patients with chronic obstructive pulmonary disease (COPD) In an emerging country. Am J Respir Crit Care Med. 2011;183(1):A2034. [Google Scholar]

[cit0085] 85.Karloh M, Araujo CLP, Gulart AA, Reis CM, Steidle LJM, Mayer AF. The Glittre-ADL test reflects functional performance measured by physical activities of daily living in patients with chronic obstructive pulmonary disease. Braz J Phys Ther. 2016;20(3):223–230. doi: 10.1590/bjpt-rbf.2014.0155 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0086] 86.Landal AC, Monteiro F, Hevely BC, Kanesawa LM, Hernandes N, Pitta F. Factors associated with improved body composition in individuals with COPD after physical training. Fisioter mov. 2014;27(4):633–641. doi: 10.1590/0103-5150.027.004.AO15 [DOI] [Google Scholar]

[cit0087] 87.Lanza FC, Castro RAS, de Camargo A, et al. COPD assessment test (CAT) is a valid and simple tool to measure the impact of Bronchiectasis on affected patients. COPD. 2018;15(5):512–519. doi: 10.1080/15412555.2018.1540034 [DOI] [PubMed] [Google Scholar]

[cit0088] 88.Lopes AC, Xavier RF, Pereira AC AC, et al. Identifying COPD patients at risk for worse symptoms, HRQoL, and self-efficacy: a cluster analysis. Chronic Illn. 2019;15(2):138–148. doi: 10.1177/1742395317753883 [DOI] [PubMed] [Google Scholar]

[cit0089] 89.Mantoani LC, Hernandes N, Guimarães M, Vitorasso RL, Probst VS, Pitta F. Does the BODE index reflect the level of physical activity in daily life in patients with COPD? Braz j Phys Ther (Impr). 2011;15(2):131–137. doi: 10.1590/S1413-35552011000200008 [DOI] [PubMed] [Google Scholar]

[cit0090] 90.Monteiro F, Camillo CA, Vitorasso R, et al. Obesity and physical activity in the daily life of patients with COPD. Lung. 2012;190(4):403–410. doi: 10.1007/s00408-012-9381-0 [DOI] [PubMed] [Google Scholar]

[cit0091] 91.Morita AA, Silva LKO, Bisca GW, et al. Heart rate recovery, physical activity level, and functional status in subjects with COPD. Respir Care. 2018;63(8):1002–1008. doi: 10.4187/respcare.05918 [DOI] [PubMed] [Google Scholar]

[cit0092] 92.Munari AB, Gulart AA, Dos Santos K, Venâncio RS, Karloh M, Mayer AF. Modified Medical Research Council Dyspnea Scale in GOLD classification better reflects physical activities of daily living. Respir Care. 2018;63(1):77–85. doi: 10.4187/respcare.05636 [DOI] [PubMed] [Google Scholar]

[cit0093] 93.Nobeschi L, Zangirolami-Raimundo J, Cordoni PK, et al. Evaluation of sleep quality and daytime somnolence in patients with chronic obstructive pulmonary disease in pulmonary rehabilitation. BMC Pulm Med. 2020;20(1):1–7. doi: 10.1186/s12890-020-1046-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0094] 94.Nunes DM, Mota RMS, Machado MO, Pereira EDB, de Bruin VMS, de Bruin PFC. Effect of melatonin administration on subjective sleep quality in chronic obstructive pulmonary disease. Braz J Med Biol Res. 2008;41(10):926–931. doi: 10.1590/S0100-879X2008001000016 [DOI] [PubMed] [Google Scholar]

[cit0095] 95.Nyssen SM, Santos J, Barusso MS, Delfino de OA Jr, Lorenzo VA, Jamami M. Levels of physical activity and predictors of mortality in COPD. J Bras Pneumol. 2013;39(6):659–666. doi: 10.1590/S1806-37132013000600004 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0096] 96.Pitta F, Breyer M, Hernandes NA, et al. Comparison of daily physical activity between COPD patients from Central Europe and South America. Respir Med. 2009;103(3):421–426. doi: 10.1016/j.rmed.2008.09.019 [DOI] [PubMed] [Google Scholar]

[cit0097] 97.Probst VS, Kovelis D, Hernandes N, Camillo CA, Cavalheri V, Pitta F. Effects of 2 exercise training programs on physical activity in daily life in patients with COPD. Respir Care. 2011;56(11):1799–1807. doi: 10.4187/respcare.01110 [DOI] [PubMed] [Google Scholar]

[cit0098] 98.Reboredo MDM, Lucinda LM, Yecker GD, et al. Barriers to physical activity in patients with chronic respiratory diseases. Am J Respir Crit Care Med. 2018;197:A2174. [Google Scholar]

[cit0099] 99.Rocha FR, Brüggemann A, Karla V, et al. Diaphragmatic mobility: relationship with lung function, respiratory muscle strength, dyspnea, and physical activity in daily life in patients with COPD. J Bras Pneumol. 2017;43(1):32–37. doi: 10.1590/s1806-37562016000000097 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0100] 100.Rodrigues ESR, Moreira RDF, Rezende AAB, Costa LD. Sedentarism and smoking in patients with cardiovascular, respiratory and orthopedic diseases. Rev Enfermagem Ufpe. 2014;8(3):591–599. [Google Scholar]

[cit0101] 101.Schneider PL, Couto FK, Aparecida HN, Pitta F. Does the wearing time of motion sensor interfere with the choice of physical activity in daily life outcomes of COPD patients? Fisioter Pesquisa. 2018;25(1):43–48. doi: 10.1590/1809-2950/16768425012018 [DOI] [Google Scholar]

[cit0102] 102.Silva DR, Coelho AC, Dumke A, et al. Osteoporosis prevalence and associated factors in patients with COPD: a cross-sectional study. Respir Care. 2011;56(7):961–968. doi: 10.4187/respcare.01056 [DOI] [PubMed] [Google Scholar]

[cit0103] 103.Simon KM, Hass AP, Zimmermman JL, Carpes MF. BODE mortality prognostic index and physical activity in chronic obstructive pulmonary patients. Rev bras med esporte. 2009;15(1):19–22. doi: 10.1590/S1517-86922009000100004 [DOI] [Google Scholar]

[cit0104] 104.Tavares MG, Nascimento ACSD, Ferraz MCCN, Medeiros RABD, Cabral PC, Burgos MGPDA. Overweight and obesity in patients with chronic obstructive pulmonary disease. Braspen J. 2017;32(1):58–62. [Google Scholar]

[cit0105] 105.Vitorasso R, Camillo CA, Cavalheri V, et al. Is walking in daily life a moderate intensity activity in patients with chronic obstructive pulmonary disease? Eur J Phys Rehabil Med. 2012;48(4):587–592. [PubMed] [Google Scholar]

[cit0106] 106.Xavier R, Lopes AC, Pereira ACAC, et al. Factors associated with daily life physical activity in Brazilian COPD patients. Eur Respir J. 2016;48:PA1889. [Google Scholar]

[cit0107] 107.Yunus FM, Khan S, Mitra DK, Mistry SK, Afsana K, Rahman M. Relationship of sleep pattern and snoring with chronic disease: findings from a nationwide population-based survey. Sleep Health. 2018;4(1):40–48. doi: 10.1016/j.sleh.2017.10.003 [DOI] [PubMed] [Google Scholar]

[cit0108] 108.Bhuker M, Kapoor S. Environmental and occupational respiratory diseases-1043. Effects of socioeconomic status, life style patterns and demographic factors. World Allergy Organ J. 2013;6:P42. doi: 10.1186/1939-4551-6-S1-P42 [DOI] [Google Scholar]

[cit0109] 109.De S. Subjective assessment of quality of sleep in chronic obstructive pulmonary disease patient and its relationship with associated depression. Lung India. 2012;29(4):332–335. doi: 10.4103/0970-2113.102808 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0110] 110.Gupta R, Ulfberg J, Allen RP, Goel D. Comparison of subjective sleep quality of long-term residents at low and high altitudes: SARAHA study. J Clin Sleep Med. 2018;14(1):15–21. doi: 10.5664/jcsm.6870 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0111] 111.Panigrahi MK, Padhan M, Mohapatra PR. Relationship between sleep quality, asthma control and quality of life in adults with asthma. Am J Respir Crit Care Med. 2018;197:A1089. doi: 10.1164/rccm.201708-1728OC [DOI] [Google Scholar]

[cit0112] 112.Poongadan MN, Gupta N, Kumar R. Lifestyle factors and asthma in India - a case-control study. Pneumonol Alergol Pol. 2016;84(2):104–108. doi: 10.5603/PiAP.2016.0008 [DOI] [PubMed] [Google Scholar]

[cit0113] 113.Sharma K, Jain A, Goyal V, et al. Depression and physical activity impairment in COPD subjects. J Clin Diagn Res. 2019;13(7):OC16–22. [Google Scholar]

[cit0114] 114.Ulfathinah A, Rachmi SF, Indracahyani A. Characteristics affecting sleep quality of COPD patients. Enferm Clin. 2019;29:30–35. doi: 10.1016/j.enfcli.2019.04.005 [DOI] [Google Scholar]

[cit0115] 115.Widyastuti K, Makhabah DN, Rima A, Sutanto YS, Suradi S, Ambrosino N. Home based pulmonary rehabilitation with pedometers in Indonesian COPD patients. Eur Respir J. 2017;50:PA777. [Google Scholar]

[cit0116] 116.Chanthitivech N, Sirichana W. Cardiopulmonary exercise testing parameters and level of daily physical activity correlation in stable COPD patients. Eur Respir J. 2017;50:PA2250. [Google Scholar]

[cit0117] 117.Chunrong J, Rongchang C. Factors associated with impairment of quadriceps muscle function in Chinese patients with chronic obstructive pulmonary disease. PLoS One. 2014;9(2):e84167. doi: 10.1371/journal.pone.0084167 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0118] 118.Ding B, Small M, Bergstrom G, Holmgren U. A cross-sectional survey of night-time symptoms and impact of sleep disturbance on symptoms and health status in patients with COPD. Int J COPD. 2017;12:589–599. doi: 10.2147/COPD.S122485 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0119] 119.Tao Y, Wang L, Dong X, et al. Psychometric properties of the physical activity scale for the elderly in Chinese patients with COPD. Int J COPD. 2017;12:105–114. doi: 10.2147/COPD.S120700 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0120] 120.Wang L, Nygardh A, Zhao Y, Martensson J. Self-management among patients with chronic obstructive pulmonary disease in China and its association with sociodemographic and clinical variables. Appl Nurs Res. 2016;32:61–66. doi: 10.1016/j.apnr.2016.05.001 [DOI] [PubMed] [Google Scholar]

[cit0121] 121.Wang L, Tao Y, Dong X, et al. Demographic, health behavioral, and selfmanagement abilities associated with disease severity among patients with chronic obstructive pulmonary disease: an exploratory study. Int J Nurs Pract. 2017;23(1):e12509. doi: 10.1111/ijn.12509 [DOI] [PubMed] [Google Scholar]

[cit0122] 122.Wang S, Wu Y, Ungvari GS, et al. Sleep duration and its association with demographics, lifestyle factors, poor mental health and chronic diseases in older Chinese adults. Psychiatry Res. 2017;257:212–218. doi: 10.1016/j.psychres.2017.07.036 [DOI] [PubMed] [Google Scholar]

[cit0123] 123.Zheng L, Wang A. Application effect of rational emotional behavior therapy for home chronic obstructive pulmonary disease patients with depression. Chin Nurs J. 2019;33:1135–1140. [Google Scholar]

[cit0124] 124.Vancampfort D, Koyanagi A, Ward PB, et al. Chronic physical conditions, multimorbidity and physical activity across 46 low and middle income countries. Int J Behav Nutr Phys Act. 2017;14(1):6. doi: 10.1186/s12966-017-0463-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0125] 125.Koyanagi A, Stubbs B, Smith L, Gardner B, Vancampfort D. Correlates of physical activity among community-dwelling adults aged 50 or over in six low- and middle-income countries. PLoS One. 2017;12(10):e0186992. doi: 10.1371/journal.pone.0186992 [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0126] 126.Vancampfort D, Stubbs B, Koyanagi A. Physical chronic conditions, multimorbidity and sedentary behavior amongst middle-aged and older adults in six low- and middle-income countries. Int J Behav Nutr Phys Act. 2017;14(1):147. doi: 10.1186/s12966-017-0602-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0127] 127.Vancampfort D, Lara E, Stubbs B, Swinnen N, Probst M, Koyanagi A. Physical activity correlates in people with mild cognitive impairment: findings from six low- and middle-income countries. Public Health. 2018;156:15–25. doi: 10.1016/j.puhe.2017.12.002 [DOI] [PubMed] [Google Scholar]

[cit0128] 128.Vancampfort D, Stubbs B, Veronese N, Mugisha J, Swinnen N, Koyanagi A. Correlates of physical activity among depressed older people in six low-income and middle-income countries: a community-based cross-sectional study. Int J Geriatr Psychiatry. 2018;33(2):e314–22. doi: 10.1002/gps.4796 [DOI] [PubMed] [Google Scholar]

[cit0129] 129.Vancampfort D, Stubbs B, Mugisha J, Firth J, Schuch FB, Koyanagi A. Correlates of sedentary behavior in 2375 people with depression from 6 low- and middle-income countries. J Affect Disord. 2018;234:97–104. doi: 10.1016/j.jad.2018.02.088 [DOI] [PubMed] [Google Scholar]

PERMALINK

Systematic Review of Physical Activity, Sedentary Behaviour and Sleep Among Adults Living with Chronic Respiratory Disease in Low- and Middle-Income Countries

Akila R Jayamaha

Amy V Jones

Winceslaus Katagira

Bhushan Girase

Zainab K Yusuf

Ilaria Pina

Laura J Wilde

Azamat Akylbekov

Pip Divall

Sally J Singh

Mark W Orme

Abstract

Abstract

Purpose

Patients and Methods

Results

Conclusion

Introduction

Materials and Methods

Table 1.

Results

Study Selection and Characteristics

Figure 1.

Table 2.

Income Classification

WHO Regions

The Methods of Physical Behaviour Data Collection

Variables to Assess 24-Hour Physical Behaviours

Levels of Free-Living PA, SB and Sleep

Table 3.

Sub-Group Analysis of 24-Hour Physical Behaviours

Figure 2.

Figure 2.

Figure 2.

Table 4.

Discussion

Conclusion

Acknowledgments

Funding Statement

Abbreviations

Disclosure

References

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases