mHealth technology for ecological momentary assessment in physical activity research: a systematic review Rafael Zapata-Lamana1,*, Jaume F. Lalanza2, Josep-Maria Losilla3,4, Eva Parrado2,4 and Lluis Capdevila2,4,* 1 Escuela de Educación, Universidad de Concepción, Los Ángeles, Chile 2 Department of Basic Psychology, Universitat Autónoma de Barcelona, Bellaterra, Spain 3 Department of Psychobiology and Methodology of Health Science, Universitat Autónoma de Barcelona, Bellaterra, Spain 4 Sport Research Institute UAB, Universitat Autònoma de Barcelona, Bellaterra, Spain * These authors contributed equally to this work. ABSTRACT Objective: To systematically review the publications on ecological momentary assessment (EMA) relating to physical activity (PA) behavior in order to classify the methodologies, and to identify the main mHealth technology-based tools and procedures that have been applied during the first 10 years since the emergence of smartphones. As a result of this review, we want to ask if there is enough evidence to propose the use of the term “mEMA” (mobile-based EMA). Design: A systematic review according to PRISMA Statement (PROSPERO registration: CRD42018088136). Method: Four databases (PsycINFO, CINALH, Medline and Web of Science Core Collection) were searched electronically from 2008 to February 2018. Results: A total of 76 studies from 297 potential articles on the use of EMA and PA were included in this review. It was found that 71% of studies specifically used “EMA” for assessing PA behaviors but the rest used other terminology that also adjusted to the inclusion criteria. Just over half (51.3%) of studies (39) used mHealth technology, mainly smartphones, for collecting EMA data. The majority (79.5%) of Submitted 16 October 2019 Accepted 3 March 2020 these studies (31 out of 39) were published during the last 4 years. On the other Published 26 March 2020 hand, 58.8% of studies that only used paper-and-pencil were published during the Corresponding author first 3 years of the 10-year period analyzed. An accelerometer was the main built-in Lluis Capdevila, sensor used for collecting PA behavior by means of mHealth (69%). Most of the lluis.capdevila@uab.cat studies were carried out on young-adult samples, with only three studies in older Academic editor adults. Women were included in 60% of studies, and healthy people in 82%. Amy Morgan The studies lasted between 1 and 7 days in 57.9%, and between three and seven Additional Information and assessments per day were carried out in 37%. The most popular topics evaluated Declarations can be found on together with PA were psychological state and social and environmental context. page 20 Conclusions: We have classified the EMA methodologies used for assessing PA DOI 10.7717/peerj.8848 behaviors. A total of 71% of studies used the term “EMA” and 51.3% used mHealth Copyright technology. Accelerometers have been the main built-in sensor used for collecting 2020 Zapata-Lamana et al. PA. The change of trend in the use of tools for EMA in PA coincides with the Distributed under technological advances of the last decade due to the emergence of smartphones and Creative Commons CC-BY 4.0 mHealth technology. There is enough evidence to use the term mEMA when mHealth technology is being used for monitoring real-time lifestyle behaviors in How to cite this article Zapata-Lamana R, Lalanza JF, Losilla J-M, Parrado E, Capdevila L. 2020. mHealth technology for ecological momentary assessment in physical activity research: a systematic review. PeerJ 8:e8848 DOI 10.7717/peerj.8848 natural situations. We define mEMA as the use of mobile computing and communication technologies for the EMA of health and lifestyle behaviors. It is clear that the use of mHealth is increasing, but there is still a lot to be gained from taking advantage of all the capabilities of this technology in order to apply EMA to PA behavior. Thus, mEMA methodology can help in the monitoring of healthy lifestyles under both subjective and objective perspectives. The tendency for future research should be the automatic recognition of the PA of the user without interrupting their behavior. The ecological information could be completed with voice messages, image captures or brief text selections on the touch screen made in real time, all managed through smartphone apps. This methodology could be extended when EMA combined with mHealth are used to evaluate other lifestyle behaviors. Subjects Kinesiology, Psychiatry and Psychology, Human-Computer Interaction Keywords mHealth, EMA, Ecological momentary assessment, Physical activity, Health, Lifestyle, Smartphones, Mobile devices, Systematic review, eHealth INTRODUCTION Physical inactivity is a leading cause of death worldwide. It is considered a pandemic in the 21st Century (Kohl et al., 2012) and is more prevalent in developed countries (Dumith et al., 2011). Unfortunately, despite the consensus on the benefits of physical activity (PA), the levels of sedentary lifestyle have increased worldwide (Booth et al., 2017). Regular PA of moderate intensity should be promoted for the entire population in order to reduce the risk of suffering many health disorders, such as cardiovascular disease, type 2 diabetes, Alzheimer’s disease or depression (Fiuza-Luces et al., 2013), and to achieve substantial health benefits (Physical Activity Guidelines Advisory Committee, 2018). In this context, an important problem is to unify the research methodology so that scientific evidence can be recognized. On the one hand, PA is often assessed by weekly self-reports (Prince et al., 2008). The main limitation of these studies is that they are based on retrospective assessments which can lead to a recall bias (Shiffman, Stone & Hufford, 2008), and thereby errors could occur due to overestimation and underestimation of PA (Shephard, 2003). In addition, these assessments are subjective estimations by the participants, thus reliability and validity could be highly affected (Marszalek et al., 2014). On the other hand, there are objective methods available with greater reliability, such as accelerometry, GPS positioning, heart rate monitoring and movement sensors. But studies using these methods generally report PA values without examining contextual co-variables (Ainsworth et al., 2015). In fact, some studies tend to not taken in account the temporal influences (Bauman et al., 2012) nor the individual characteristics (Dunton, 2017). In order to solve these problems, the objective measures could be matched with self-reports in real time regarding the context of PA. In this sense, Ecological Momentary Assessment (EMA) (Stone & Shiffman, 1994) is a suitable methodology which enables studies to be conducted in order to analyse lifestyle experiences in real-time, in real-world settings, over time and across contexts. EMA is based on monitoring or sampling strategies to assess phenomena at the moment they occur Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 2/29 in natural settings. Thus, from the original proposal of this methodology, “Ecological” means that the data are captured in the natural environment of the subjects; “Momentary” means that assessments focus on current feelings and behaviors, rather than concentrating on recall or summary over long periods of time; and “Assessment” means that multiple assessments are collected over time to provide a profile for behavior throughout time (Shiffman, Stone & Hufford, 2008). A good contribution to EMA methodology has been a checklist for reporting EMA studies (CREMAS) in nutrition and PA among young people (Liao et al., 2016). However, this checklist becomes very demanding for describing published studies that, in general terms, comply with the basic features of the EMA methodology. For instance, in a recent systematic review using CREMAS to assess sedentary behavior in articles published between 2007 and 2017, only 21 of 50 potential studies were included. It is surprising that only four of these works were combining EMA with objective measurement like accelerometry (Romanzini et al., 2019), since those 10 years is when the massive appearance of smartphones took place worldwide. Since the emergence of the first mobile phone in the 1970s, and from the emergence of the first applications (apps) for iOS and Android operating systems around 2008, smartphones have rapidly evolved. The improvement in fast processors, small and long-lasting batteries, large memory capacity and very precise built-in sensors has paved the way for apps that are now affecting our lifestyle (Ozdalga, Ozdalga & Ahuja, 2012). Thus, in the last years, we are talking about mobile health (mHealth) when we are using some of the smartphone capabilities for assessing or monitoring health or lifestyle (Fiordelli, Diviani & Schulz, 2013). More specifically, the term mHealth has been defined as “the use of mobile computing and communication technologies in health care and public health” (Free et al., 2010). For example, you can use the camera of a smartphone to capture images for assessing dietary intake (Daugherty et al., 2012); or mobile phone’s built-in motion sensors and self-report through the touch screen for measuring PA (Dunton et al., 2014a). Existing systematic reviews of EMA interventions focus on specific age groups such as young people (Marszalek et al., 2014; Liao et al., 2016); or about aspects of the lifestyle other than the PA, such as emphasizing sedentary behavior (Romanzini et al., 2019); or are based on an excessively strict list of criteria that does not include all EMA studies according to their original definition (Liao et al., 2016; Romanzini et al., 2019). Our objective is to systematically review the scientific publications on EMA relating to PA behavior in order to classify the methodologies and to identify the main mHealth technology-based tools and procedures that have been applied during the first 10 years since the emergence of smartphones. As a result of this review we want to ask if there is enough evidence to propose the use of the term “mEMA” (mobile-based EMA) when mHealth technology is being used together with the EMA methodology for monitoring lifestyle behaviors such as PA, in real time and in natural environments. METHODOLOGY The study was undertaken in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (Liberati et al., 2009). Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 3/29 A systematic review protocol had previously been registered in the PROSPERO repository with the code: CRD42018088136 (Zapata-Lamana et al., 2018). Search strategy A systematic review of the literature was performed using the following databases and portals according to the order indicated: PsycINFO by PsycNET, CINAHL by EBSCOhost, MEDLINE by PubMed, and Core Collection of Web of Science by Web of Science. Our aim was to identify studies that used EMA methodologies to measure PA in participants of all ages. The search comprised the period between 2008 and 2018 because we were interested in analyzing the use of mHealth technology to evaluate PA behavior. This is because mHealth solutions are based on applications for smartphones (apps) and we have already commented that the first apps appeared around 2008. According to Liao et al. (2016), an increasing number of studies on PA have adopted EMA due to methodological advances in mobile technologies in recent years. Thus, we consider that before 2008 smartphones or tablets still did not have enough capable built-in sensors for allowing EMA. When possible, keywords and terms were obtained from Thesaurus. The search strategy followed the guidelines from Peer Review of Electronic Search Strategies (PRESS) (McGowan et al., 2016). The searching general syntax was: (“EMA” OR “experience sampling” OR “experience samplings”) AND (Exercise OR Exercises OR “PA” OR “physical activities”), and it was adapted to each database (the specific search syntaxes are provided in a Supplemental File). Study selection and inclusion criteria No exclusion criteria were applied for gender, age or clinical condition, but as regards the language, only full-text articles in English or Spanish were reviewed. Reviews, editorials, protocols and theses were not included. The articles selected by title and abstract met the conditions indicated in Table 1. EMA could be applied to PA or to other variables, but then PA has to be assessed by other methodologies and compared to the main EMA variable. Data extraction In a first step, duplicate articles from the four databases were deleted using Mendeley. One reviewer (Rafael Zapata-Lamana) applied the inclusion/exclusion criteria to all titles and abstracts. Articles meeting the inclusion criteria were selected and when decisions could not be made from the title and abstract alone, the full article was also retrieved. The selected papers were checked independently by two review authors (Rafael Zapata- Lamana, Jaume F. Lalanza). Discrepancies were resolved through discussion (with a third author where necessary, Lluis Capdevila) until reaching consensus. A standardized, pre-piloted form was used to extract data from the included articles in order to assess the study quality and for the synthesis of the evidence. Extracted information included: general information (author, year, country); sample (size, population, age, etc.); details of tools used (measure, purpose/use, type of tool, etc.); methodological protocol (experimental design, response rate, time interval required to completion, and mode of administration, Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 4/29 Table 1 Inclusion criteria and description for this review. Criteria Description (1) EMA has to accomplish the (a) Instruments that collect data in real time. Participants reported the activities and/or, moods, etc. at the following points moment they are experiencing them or up to 24 h, after the activity was carried out (b) In a natural environment (c) Repeated measures (2 or more measures) (d) Self-reports and/or automatic recordings (e) Using both electronic devices and/or paper and pencil format (2) PA is considered PA has to be spontaneous or planned activity, carried out individually or collectively and that incorporates a physical effort component of any intensity (3) Type of article All articles that provide original data on the use of EMA in PA (criteria 1 and 2) and are published in a scientific journal without taking into account the type and number of sample as well as the experimental design Note: EMA, Ecological Momentary Assessment; PA, Physical Activity. sensor use, etc.) as well as assessing the main variables. Furthermore, the data extraction was carried out by two reviewers (Rafael Zapata-Lamana, Jaume F. Lalanza). Risk of bias assessment tools The tool proposed by National Institute for Health and Care Excellence (NICE) for prognostic studies (National Institute for Health and Care Excellence, 2012) was applied by two reviews (Rafael Zapata-Lamana, Jaume F. Lalanza) to assess the risk of bias (RoB) of the selected non-experimental studies. Following the same procedure, to assess the RoB of the selected experimental and quasi-experimental studies we applied the “Cochrane Risk of Bias Tool for Randomized Trials” (Higgins et al., 2011) and the “Risk Of Bias In Non- randomized Studies—of Interventions” (ROBINS-I) (Sterne et al., 2016), respectively. Since the aim of the systematic review was not to analyze the obtained results, the following items were removed: from NICE 1.3 (“the prognostic factor of interest is adequately measured in study participants, sufficient to limit potential bias”) and 1.6 (“the statistical analysis is appropriately accounted for, limiting potential bias with respect to the prognostic factor of interest”); from Cochrane “Selective Reporting” and “Other Bias”; and from ROBINS-I “Bias in measurement of outcomes” and “Bias in selection of the reported result”. Strategy for data synthesis We provide a narrative synthesis of the findings from the included studies structuring it around the methods and the procedure related to EMA in PA research. The main information is also showed in the Tables. In the Discussion, an analysis is made on the combination of EMA and mHealth technology and some suggestions are given for future researchers in order to standardize the application of this mobile-based methodology for monitoring PA behaviors. RESULTS Literature search Figure 1 shows the flow diagram of systematic reviews for analysis of studies proposed by PRISMA. After the duplicate records in databases were excluded, a total of 297 potential Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 5/29 Figure 1 PRISMA flow diagram with the different previous phases to the qualitative synthesis. Full-size  DOI: 10.7717/peerj.8848/fig-1 studies as regards EMA in PA were identified. The eligibility criteria were applied and the analysis was made by reading the title and the abstracts. During the selection phase, all discrepancies between the two reviewers were resolved by consensus without requiring a third reviewer. Finally, 74 articles were considered for this review for the qualitative synthesis of the data. In fact, a total of 76 studies were analyzed because two of those 74 articles contained 2 studies with each one with different samples. Studies characteristics with EMA in PA Table 1 described the inclusion criteria for this review. In our systematic review, we identified 54 of 76 studies (71%) that specifically used “EMA” for assessing PA behaviors. But there were 29% studies that used other terminology that also adjusted to the inclusion criteria. Thus, we identified studies that used “Experienced Sampling Method” (ESM) (Bohnert et al., 2013; Cabrita et al., 2017; Fuller-Tyszkiewicz, Skouteris & Mccabe, 2013; Heininga et al., 2017; McCormick et al., 2008, 2009; Snippe et al., 2016; Wichers et al., 2015; Salvy et al., 2008) for referring to a similar methodology. We also found other terms used in a similar way, like “Ambulatory Assessment” was used in 6 studies (Bossmann et al., 2013; Kanning, Ebner-Priemer & Schlicht, 2015; Kanning & Schoebi, 2016; Kanning & Hansen, 2017; Timmerman et al., 2015; Von Haaren et al., 2013), with “Naturalistic Study”, only used in 1 study (Bonham, Pepper & Nettle, 2018). Finally, we found 5 studies that applied a similar methodology without specifically naming it (Aggio et al., 2017; Hyde et al., 2011; LePage & Crowther, 2010; Mata et al., 2012; Sternfeld et al., 2012). The publication date of articles ranged between 2008 and 2018. With 19.7% of articles being published between 2008 and 2011, 39.5% between 2012 and 2015, and 38.2% Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 6/29 between 2016 and 2018. When clustering the studies geographically, 63.2% of these were performed in North America (including the USA), 34.2% in Europe, 5.3% in Asia and 1.3% in Oceania, with the USA being the country with the highest publication rate (56.6%). The majority of studies included both males and females, with a total number of 13.663 participants (60.2% females). As regards the characteristics of the participants (Table 2), 25% of studies were carried out with children and adolescents (9–17 years), with a total number of 8,543 participants (55.7% females). On the one hand, 26.3% of studies were carried out with university students, with a total number of 1388 participants (67.4% females), and an age range between 18 and 28 years. On the other hand, 44.7% of studies were carried out with 3,585 adult participants (69.8% females), an age range between 18 and 59 years, with heterogeneous characteristics (patients, healthy subjects, active, inactive, recreational runners, etc.). Only 3 studies (3.9%) were carried out with 147 older adult participants (49.7% females), with an age range between 60 and 73 years. As regards the designs of the studies, 92.1% used a longitudinal prospective design. Only 5 studies (6.6%) used a randomized-experimental design and only 1 study used a quasi-experimental design. Characteristics of the main methodological aspects about EMA The detailed characteristics of the main methodological aspects about EMA in PA are presented in Table 3. As regards the study duration, 57.9% of studies were performed between 1 and 7 days, 25% of studies were performed for a duration of 1–2 weeks. Six studies (7.9%) were performed for a duration of 3–4 weeks, and another 7 studies (9.2%) took longer than 1 month. In relation to the daily period, 46.1% of studies performed measurements all day (46.1%) and 10.5% of studies took measurement for only half a day. An individualized time of collection was used in 32.9% of studies, taking into account the timetable of each participant (e.g., studies with children sample avoided the school time). Only 10.5% of studies did not report any daily measurement. As regards the frequency of the measurements during a day, 36.8% of studies used 3 to 7 prompts per day, 22.4 of studies used 8 to 12 prompts per day, 21.1% used more than 13 prompts per day, 15.8% used 1 to 2 prompt per day. Only in 3 studies (3.9%) participants were asked to complete the EMA immediately after a target event (based on event). According to column “Use the name EMA” in Table 3, 71% of studies used the EMA expression for naming the main methodology, with 13.2% of studies using “Experienced Sampling Method” (ESM), and 7,9% used “Ambulatory Assessment”. Only 1 study used the expression “Naturalistic Study”, and the rest (6.6%) did not use any specific nomenclature to name the methodology of the study. As regards the recording system for data collection, 39 studies (51.3%) used mHealth technology (mainly smartphones), 18 studies (23.7%) used PDAs (personal digital assistant, also known as a handheld PC), 18 studies (23.7%) used the most traditional system of paper and pencil, and only 2 studies (2.6%) used an online survey system. A large majority (79.5%) of the studies (31 of 39) were published during the last 4 years of the 10-year period analyzed between 2014 and February 2018. On the other hand, 76.5% of the studies that only used paper and pencil to collect EMA data were published before 2014, Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 7/29 Table 2 General characteristics of reviewed studies about ecological momentary assessment (EMA) in physical activity (PA). Reference, year, country Study population n (males) Age Design Aggio et al. (2017), England US 51 (26) 24.0 ± 4.7 L, P, NI Basen-Engquist et al. (2013), USA Women Endometrial Cancer Survivor* 79 (0) 57.0 ± 11.01 L, P, NI Biddle et al. (2009c), Hun, Slo, Ro Adolescents 623 (247) 15.5 ± 0.9 L, P, NI Biddle, Gorely & Marshall (2009), UK Adolescents 1,484 (561) 14.67 ± 0.92 L, P, NI Biddle et al. (2009b), UK Adolescents 1,493 (566) 14.8 L, P, NI Biddle et al. (2009a), Scotland Adolescents 991 (385) 14.1 L, P, NI Bohnert et al. (2013), USA Adolescents 25 (9) 12.6 ± 1.0 L, P, NI Bond et al. (2013), USA Bariatric Surgery Patients* 21 (4) 48.5 ± 2.8 L, P, NI Bonham, Pepper & Nettle (2018), England Adults (Recreational Runners) 38 (20) 18–49 L, P, NI Bond et al. (2013), Germany US 62 (53) 21.4 ± 1.8 L, P, NI Brannon et al. (2016), USA Adolescents 20 (12) 15.67 ± 1.75 L, P, NI Burg et al. (2017), USA Adults (eager to became active) 63 (27) 31.9 ± 9.5 L, E, CG, R Brannon et al. (2016), Netherlands Older adults 10 (4) 68.7 ± 5.5 L, P, NI Conroy et al. (2013), USA US 128 (53) 21.3 ± 1.1 L, P, NI Cabrita et al. (2017), USA Children 121 (62) 9–13 L, P, NI Conroy et al. (2013), USA Children 108 (59) 11 L, P, NI Dunton et al. (2011), USA Children 94 (49) 9–13 L, QE, CG Dunton et al. (2012a), USA Adults 110 (30) 40.4 ± 9.74 L, P, NI Dunton et al. (2012b), USA Children 114 (56) 9–13 L, P, NI Dunton et al. (2012c), USA Adults 116 (32) 40.5 ± 9.5 L, P, NI Dunton et al. (2014b), USA Adolescents 39 (18) 15.9 ± 1.2 L, P, NI Elavsky, Kishida & Mogle (2016), USA Women (Peri-post menopausal) 121 (0) 51.5 L, P, NI Emerson, Dunsiger & Williams (2018), USA Adults (Low-Active and Obese)* 59 (7) 47.71 ± 11.06 L, E, CG, R Fanning et al. (2016), USA US 33 (9) 20.5 ± 1.5 L, P, NI Fortier et al. (2015), Canada Women (Physically active Mothers) 63 (0) 42.6 ± 5.61 L, P, NI Fuller-Tyszkiewicz, Skouteris & Mccabe (2013), Australia Adult Women 37 (0) 34.05 ± 9.73 L, P, NI Gorely et al. (2009a), UK Adolescents 1,171 (477) 14.8 ± 0.86 L, P, NI Gorely et al. (2009b), UK Adolescent boys 561 (561) 14.6 ± 0.89 L, P, NI Hager et al. (2017), USA Mother-Toddler Dyads 160 (0) 26.6 L, P, NI Hausenblas et al. (2008) US 40 (14) 20.5 ± 2.5 L, E, R Heininga et al. (2017), Netherlands Adults (with or without anhedonia)* 138 (28) 21.48 L, P, NI Hekler et al. (2012), USA Adults 14 (7) 59.4 ± 6.4 L, P, NI Hyde et al. (2011), USA US 190 (65) 19.3 ± 2.8 L, P, NI Jones et al. (2017), USA Adults (stressed)* 105 (29) 40.3 ± 9.8 L, P, NI Kanning, Ebner-Priemer & Schlicht (2015), Germany Older adults 69 (35) 60.1 ± 7.1 L, P, NI Kanning & Schoebi (2016), Germany US 65 (28) 24.6 ± 3.2 L, P, NI Kanning & Hansen (2017), Germany Older adults 68 (35) 60.1 ± 7.1 L, P, NI Kim et al. (2014), Japan Depressive patients and healthy adults* 57 (55) 37.35 L, P, NI, CG Knell et al. (2017), USA Adults 238 (78) 43.3 L, P, NI LePage et al. (2012), USA – – – – Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 8/29 Table 2 (continued ). Reference, year, country Study population n (males) Age Design Study 1 US 51 (0) 19.06 ± 3.10 L, P, NI Study 2 US with eating disorders* 76 (0) 19.08 ± 2.86 L, P, NI, CG LePage & Crowther (2010), USA US (with body dissatisfaction) 54 (0) 19.1 ± 2.88 L, P, NI, CG Liao et al. (2014), USA Children and Adolescents 120 (62) 9–13 L, P, NI Liao, Intille & Dunton (2015), USA Adults 114 (30) 27–73 L, P, NI Liao et al. (2017a), USA Adults 82 (22) 39.8 L, P, NI Liao et al. (2017b), USA Adults (dog owners) 71 (17) 40.2 ± 8.6 L, P, NI Liao, Solomon & Dunton (2017), USA Adults 110 (30) 40.4 ± 9.74 L, P, NI Maher et al. (2014), USA US 128 (53) 21.3 ± 1.1 L, P, NI Maher et al. (2016), USA Adults 90 (30) 40.3 ± 9.6 L, P, NI Marquet et al. (2017), USA US (players of Pokemon Go) 47 (24) 19.45 L, P, NI Marquet, Alberico & Hipp (2018), USA US (players of Pokemon Go) 74 (37) 19.6 L, P, NI Mata et al. (2012), USA Adults with depression and controls* 106 (32) 26.8 L, P, NI, CG Marquet et al. (2017), USA and Ser Adults with severe mental illness* 22 (12) 38.95 L, P, NI Marquet, Alberico & Hipp (2018), USA and Ser Adults with severe mental illness* 22 (Not inf) Not inf L, P, NI Nadell et al. (2015), USA Adults (Tabacoo smokers) 188 (88) 21.32 ± 0.77 L, P, NI O’Connor et al. (2017), USA Mother Children Dyads 175 (84) 41.1 L, P, NI Pickering et al. (2016), USA Adults 103 (30) 40.3 ± 9.6 L, P, NI Nadell et al. (2015), England US 84 (46) 19.85 L, P, NI Rusby et al. (2014), USA Adolescents 82 (40) Not inf L, P, NI Sala et al. (2017), USA US with eating disorders* 129 (0) 19.19 ± 1.40 L, P, NI Salvy et al. (2008), USA Adolescents (overweight) 20 (10) 13.52 L, P, NI, CG Schöndube, Kanning & Fuchs (2016), Germany US 60 (20) 23.5 ± 2.8 L, P, NI Schöndube et al. (2017), Germany US 63 (21) 23.5 ± 2.8 L, P, NI Seto et al. (2014), China US and their friends 12 (Not inf) 18–31 L, P, NI Seto et al. (2016), China US 12 (4) 24.6 ± 3.06 L, P, NI Snippe et al. (2016), Netherlands Depressive patients* 85 (47) 48 ± 10.2 L, E, CG, R Soos et al. (2012), Ro and Slo Adolescents 635 (251) 16 ± 1.0 L, P, NI Soos et al. (2014), UK, Hu, Ro, Slo, Chi Adolescents 812 (348) 15.6 ± 1.0 L, P, NI Spook et al. (2013), Netherlands Adolescents and Adults 30 (13) 16–21 L, P, NI Sternfeld et al. (2012), USA – – – – Pilot Adults 33 (23) 55.6 ± 8.8 L, P, NI Validation Adults 345 (296) 56.9 ± 5.7 L, P, NI Thomas et al. (2011), USA Gastric patients* 21 (4) 48.5 ± 12.6 L, P, NI Timmerman et al. (2015), Netherlands Cancer Survivors* 36 (12) 55.95 L, P, NI, CG Von Haaren et al. (2013), Germany US 29 (Not inf) 21.3 ± 1.7 L, P, NI Wichers et al. (2015), Belgium Women (Twins) 504 (0) 27 ± 7.6 L, P, NI Williams et al. (2016), USA Sedentary and obese adults 59 (7) 47.7 ± 11.1 L, E, CG, R Notes: * With physical diseases or psychological disorders. Hu, Hungary; UK, United Kingdom; Slo, Slovenia; Ro, Romania; Ser, Serbia, Chi, China; US, University students; L, Longitudinal; P, Prognostic; NI, No intervention; CG, Control group; QE, Quasi Experimental; R, Randomization. Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 9/29 Table 3 Characteristics of the main methodological aspects of EMA. Reference Study Day period Prompts EMA EMA EMA Topics evaluated duration per day named instruments sensors in addition to PA (d) Aggio et al. (2017) 7 Each evening Each 24 h No OSS Acc PS, OV Basen-Engquist et al. (2013) 5 Morning and evening Each 12 h Yes PDA Acc BMI, BPA, OV Biddle et al. (2009c) 4 All day 15 min Yes P&P No GA, ISD, OV Biddle, Gorely & Marshall 4 All day 15 min Yes P&P No GA (2009) Biddle et al. (2009b) 4 All day 15 min Yes P&P No EB, SLP Biddle et al. (2009a) 4 All day 15 min Yes P&P No GA Bohnert et al. (2013) 14 During all day Ran (8 × d) No, P&P Acc BMI, EB, GA, SC ESM Bond et al. (2013) 6 At evening Each 24 h Yes PDA No BPA, EB, ISD, OV Bonham, Pepper & Nettle 42 During all day Ran (1 a × d) No, NS mHealth Gps PS, OV (2018) Bossmann et al. (2013) 7 During all day 1h No, AA mHealth Acc PS Brannon et al. (2016) 20 Morning and evening Depending on Yes mHealth Acc, HR EB, ISD, PS, SLP, SC, participants CH-B OV Burg et al. (2017) 365 During all day 3a×d Yes mHealth Acc ISD, PS, SC Cabrita et al. (2017) 38 All day 1h No, ES mHealth Acc BMI, GA, EC, GA, SC Conroy et al. (2013) 14 Evening 24 h Yes OSS Acc EB, GA, SLP, SC Dunton et al. (2011) 4 Afternoon and Ran (20 a × d) Yes mHealth Acc BMI, EC, GA, ISD, PS, evening (Fri/Mon) SC, OV Dunton et al. (2012a) 8 Afternoon and Ran (20 a × d) Yes mHealth Acc BMI, EC, GA, ISD, OV evening (Fri/Mon) Dunton et al. (2012b) 8 Afternoon and Ran (20 a × d) Yes mHealth Acc BMI, EC, GA, ISD, SC, evening (Fri/Mon) OV Dunton et al. (2012c) 4 All day (Sat/Tu) Ran (8 a × d) Yes mHealth Acc BMI, GA, ISD, OV Dunton et al. (2014a) 8 Afternoon and Ran (20 a × d) Yes mHealth Acc BMI, GA, ISD, PS evening (Fri/Mon) Dunton et al. (2015) 12 All day (Sat/Tu) Ran (8 a × d) Yes mHealth Acc BMI, EC, ISD, GA, PS, SC, OV Dunton, Dzubur & Intille 14 All day Ran (3 or 7 a × d) No, CS- mHealth Acc BMI, GA, SC, OV (2016) EMA Elavsky, Kishida & Mogle 15 All day Ran (4 a × d) Yes PDA Acc BMI, GA, ISD, PS (2016) Emerson, Dunsiger & 62 Individual Schedule 24 h Yes PDA No BMI, ISD, PS Williams (2018) Fanning et al. (2016) 7 All day 16 hourly prompts Yes mHealth Acc ISD, LEP, PS (1 h) Fortier et al. (2015) 14 At evening 24 h No, P&P No ISD, PS, SLP DDM/ ES Fuller-Tyszkiewicz, Skouteris 7 All day Ran (6 a × d) No, PDA No BMI, OV & Mccabe (2013) ESM Fortier et al. (2015) 4 All day 15 min Yes P&P No EC, GA, ISD, SC Gorely et al. (2009b) 4 All day 15 min Yes P&P No EC, GA, ISD, SC Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 10/29 Table 3 (continued ). Reference Study Day period Prompts EMA EMA EMA Topics evaluated duration per day named instruments sensors in addition to PA (d) Hager et al. (2017) 8 All day Ran (max 8 a × d) Yes PDA Acc EC, ISD, GA, OV, SC, SLP, Hausenblas et al. (2008) 6 During all day Ran (3 a × d) Yes P&P No PS Heininga et al. (2017) 30 During all day Ran (3 a × d) No, mHealth No ISD, SC ESM Hekler et al. (2012) 56 Afternoon and 2a×d Yes PDA No BPA, EC, ISD, PS, OV, evening SC Hyde et al. (2011) 7 Evening 24 h No P&P No GA, PS, SLP Jones et al. (2017) 4 Not Informed Ran (8 a × d) Yes mHealth Acc BMI, GA, ISD, OV, PS Kanning, Ebner-Priemer & 3 Not Informed Accelerometer was No, AA mHealth Acc ISD, PS Schlicht (2015) active Kanning & Schoebi (2016) 1 All day 45 min No, AA PDA Acc PS Kanning & Hansen (2017) 3 All day 40–100 min No, AA mHealth Acc BMI, ISD, OV, PS, SC Kim et al. (2014) 37 During all day Semi Ran (4 × d) Yes PDA Acc PS Knell et al. (2017) 7 Morning 24 h Yes mHealth Acc BMI, GA, ISD LePage et al. (2012) – – – – – – – Study 1 10 During all day Ran (4 a × d) Yes PDA No BMI, EB, OV, PS Study 2 7 Evening 24 h Yes PDA No EB, OV, PS LePage & Crowther (2010) 10 During all day Ran (4 a × d) Yes PDA No BMI, PS, OV Liao et al. (2014) 4 Morning and Ran (3 week, 7 Yes mHealth No BMI, GA, ISD, SC afternoon weekend) Liao, Intille & Dunton (2015) 4 Pre-programed Ran (8 a × d) Yes mHealth Acc BMI, EC, GA, ISD, OV, intervals SC Liao et al. (2017a) 4 Not informed Ran (8 a × d) Yes mHealth Acc BMI, ISD, OV, PS Liao et al. (2014) 12 All day Ran (8 a × d) Yes mHealth No ISD, OV, PS Liao, Intille & Dunton (2015) 4 All day Ran (8 a × d) Yes mHealth Acc BMI, ISD, OV, PS Maher et al. (2014) 14 Evening 24 h Yes P&P Acc BMI, OV Maher et al. (2016) 12 All day Ran (8 a × d) Yes mHealth Acc BMI, BPA, ISD, OV Marquet et al. (2017) 7 During all day 3a×d Yes mHealth Acc EC, ISD, OV, SC Marquet, Alberico & Hipp 7 During all day 3a×d Yes mHealth Acc EC, ISD, OV, SC (2018) Mata et al. (2012) 7 All day Ran (8 a × d) No PDA No OV, PS Marquet et al. (2017) 7 All day Ran (7 a × d) No, P&P Acc EC, GA, ISD, PS, SC ESM Marquet, Alberico & Hipp 7 Not informed Ran (7 a × d) No, P&P Acc EC, SC (2018) ESM Nadell et al. (2015) 7 Not informed Ran (5–7 a × d) Yes PDA No BMI, ISD, OV PS O’Connor et al. (2017) 8 During all day Ran (week 3/4, Yes mHealth No BMI, EB, ISD, OV weekend 7/8) Pickering et al. (2016) 4 All day Ran (8 a × d) Yes mHealth Acc BMI, BPA, EC, ISD, OV Rouse & Biddle (2010) 2 Not informed 15 min Yes P&P No GA, ISD (Continued ) Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 11/29 Table 3 (continued ). Reference Study Day period Prompts EMA EMA EMA Topics evaluated duration per day named instruments sensors in addition to PA (d) Rusby et al. (2014) 28 During all day Ran (3–6 a × d) Yes mHealth No GA, ISD, PS, SC Sala et al. (2017) 7 Personalized for Ran (4 a × d) Yes mHealth No EB, OV, PS participants Salvy et al. (2008) 7 All day 2h No, PDA No BMI, SC ESM Schöndube, Kanning & Fuchs 20 During all day Ran (4 a × d) Yes mHealth No ISD, PS (2016) Schöndube et al. (2017) 20 During all day Ran (4 a × d) Yes mHealth No ISD, OV Seto et al. (2014) 7 Not informed Each meal Yes mHealth GPS, Acc EB, EC, PS Seto et al. (2016) 14 Not informed Ran (5 a × d) Yes mHealth GPS, Acc BMI, EB, EC, ISD, PS, OV, SC, SLP Snippe et al. (2016) 12 All day Semi Ran (10 a × No, mHealth No GA, ISD, PS, SC d) ESM Soos et al. (2012) 4 All day 15 min (the end of Yes P&P No EC, GA, ISD, SC day) Soos et al. (2014) 3 All day 15 min (the end of Yes P&P No EC, GA, ISD, SC day) Spook et al. (2013) 7 During all day Ran (5 a × d) Yes mHealth No BPA, EC, GA, OV, PS, SC Sternfeld et al. (2012) – – – – – – – Pilot 12 During all day 3a×d No mHealth, No BMI, GA, ISD P&P Validation 14 During all day 3a×d No mHealth Acc BMI, GA, ISD Thomas et al. (2011) 6 All day Semi Ran (6 a × d) Yes PDA No BMI, EB, GA, ISD, Timmerman et al. (2015) 5 During all day 3 (13:00, 17:00, No, AA mHealth Acc BMI, ISD, OV 20:00) Von Haaren et al. (2013) 2 During all day 2 h a little bit Ran No, AA PDA Acc BMI, PS Wichers et al. (2015) 5 All day Semi Ran (10 a × No, P&P No GA, ISD, PS, OV d) ESM Williams et al. (2016) 42 All day 12 × d Yes PDA No BMI, ISD, PS Note: Study duration column: d: days. Day period column: Sat: Saturday, Tu: Tuesday, Mo: Monday, Fri: Friday. Prompts per day column: h: hours, m: minutes, a × d: assessment per day, Ran: Random. EMA named column: ESM: Experienced Sampling Method, NS: Naturalistic Study, AA: Ambulatory Assessment, ES: Experience Sampling, CS-EMA: Context-Sensitive EMA, DDM/ES: Daily Diary Method or Experience Sampling. EMA instruments and sensors columns: mHealth (includes: Smartphone; iPod, Video Recording; Automatized call survey; SMS), PDA: Personal Digital Assistant or similar apparatus like watch-type computer, OSS: Online Survey System, P&P, Paper and Pencil; ACC, Accelerometers; GPS, Global Positioning System; HRCH-B, Heart rate Chest Band. Topic column, BMI, Body Mass Index, ISD: Income and Socio Demographics; GA, General Activity (included: sedentary behavior, time with the smartphone, time reading, eating, active traveling), PS: Psychological State, BPA: Barriers to Physical Activity, SLP: Sleep, EB: Eating Behavior, SC: Social Context, EC: Environmental Context, OV: Other Variables (included: self-efficacy, type of country, arousal, acceptability, safety, traffic, use of smartphone during exercise, toddler with mother, waist circumference, autonomic, competencies, vegetation, cultural variables and body perceptions, amount of garbage, energy and fatigue). and 58.8% during the three 1st years of the 10-year period analyzed between 2008 and February 2010 (Biddle, Gorely & Marshall, 2009; Biddle et al., 2009a, 2009b, 2009c; Gorely et al., 2009a, 2009b; Hausenblas et al., 2008; McCormick et al., 2008, 2009; Rouse & Biddle, 2010). For its part, 11 of 18 studies (61.1%) that only used PDAs were published during the middle years of the 10-year period analyzed between 2011 and 2015 (Basen- Engquist et al., 2013; Bond et al., 2013; Fuller-Tyszkiewicz, Skouteris & Mccabe, 2013; Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 12/29 Figure 2 Other topics or target variables reported in the reviewed studies, different from the behavior of PA. Psychological state includes: affection, emotions, depression, stress and anxiety. Gen- eral activity includes: sedentary behavior, time with the smartphone, time reading, eating, active traveling, etc. Full-size  DOI: 10.7717/peerj.8848/fig-2 Hekler et al., 2012; Kim et al., 2014; Mata et al., 2012; Nadell et al., 2015; Thomas et al., 2011; Von Haaren et al., 2013; LePage & Crowther, 2010). As regards the sensors for collecting data (see column “EMA sensors” in Table 3), the accelerometer was used in 39 studies (51.3%), and 27 of these studies used the accelerometer built-in to the smartphone as an own sensor (mHealth technology). Two of them used the accelerometer combined with GPS using mHealth technology. Only 1 study used GPS only (with mHealth), and the rest of studies did not use any sensor to collect data. 81.5% of studies using mHealth with accelerometers were published between 2014 and 2018. The rest of the studies that recorded PA with accelerometry but not with mHealth technology (12 studies) used conventional accelerometers for the study combined with PDA, paper and pencil, and online survey recordings. Only 1 study used a cardiac chest band for recording heart rate with mHealth. Finally, it was found 25 studies did not use mHealth nor accelerometry, and 64% of them were published between 2008 and 2013. Target variables analyzed in the studies Some studies reported other target variables in addition to PA, even if they were not registered under EMA protocols. Figure 2 shows these complementary target variables, which included, among others, psychological state, body mass index (BMI), or contextual, social and dietary variables. Risk of bias assessment NICE tool was applied to assess the RoB of 92.1% of studies (70 longitudinal prospective studies), the Cochrane tool was applied to 5 experimental studies (6.6%), and ROBINS-I Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 13/29 Figure 3 Risk of bias (RoB) of the reviewed studies expressed by the number of studies. (A) Risk of bias of prognostic studies (NICE tool). Representativeness: the study sample represents the population of interest with regard to key characteristics; Loss to follow-up is unrelated to key characteristics; Outcome is adequately measured in study participants; Confounding: important potential confounders are appropriately accounted for limiting potential bias with respect to the prognostic factor of interest. (B) Risk of bias of experimental and quasi-experimental studies (Cochrane tools). Random allocation: random sequence generation; Allocation concealment; Blinding of participants and personnel; Outcome: blinding of outcomes assessment; Missing data: incomplete outcome data. Full-size  DOI: 10.7717/peerj.8848/fig-3 tool was applied to 1 quasi-experimental study. Figure 3 shows a summary of the results of RoB assessment. About half of the longitudinal prospective studies were evaluated as high RoB in three NICE criteria (representativeness, outcome adequately measured and potential confounders appropriately controlled for), and 29% of studies did not provide enough information about loss to follow-up of participants and/or the amount of missing data. Half of the experimental and quasi-experimental studies showed a high RoB regarding the allocation concealment of participants and the amount of missing data in the outcome measure, and half of the studies did not provide enough information about this last RoB. Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 14/29 DISCUSSION The aim of this study was to systematically review the scientific publications on EMA relating to PA behavior in order to classify the methodologies and to identify the main mHealth technology-based tools and procedures that have been applied during the first 10 years since the emergence of smartphones. We have found enough evidence to use the term mEMA (mobile-based EMA) when mHealth technology is being used together with the EMA methodology for monitoring lifestyle behaviors such as PA, in real time and in natural environments. A total of 76 studies (in 74 articles) on the use of EMA and PA were included for the synthesis. The majority of studies were carried out on healthy adults, lasted around 1 week and applied mHealth technology (39 studies, 51.3%), mainly using smartphones and accelerometers. EMA, mHealth and mEMA The term EMA means ecological momentary assessment and was proposed before 2000 (Stone & Shiffman, 1994). We identified 71% of the studies that specifically used “EMA” for assessing PA behaviors. This results indicate that the term EMA is mostly accepted by the scientific community in the field of PA, but that there are some authors who do not use EMA to refer to the same concept. In this sense, the recent proposal of a checklist for reporting EMA studies (CREMAS) in nutrition and PA (Liao et al., 2016) has become very interesting. However, as we have already commented, this checklist becomes very demanding when you want to describe some published studies that, in general terms, comply with the basic features of the EMA methodology. We did not use this checklist because we were interested in identifying studies that have used EMA or similar basic procedures since the emergence of smartphones on the market. In this sense, we identified half of the reviewed studies (51.3%, 39 studies) using mHealth technology; that is, they mainly used smartphones for collecting EMA data. This coincides with the technological advances of the last decade due to the emergence of smartphones and mHealth technology. Thus, in the 1st years, the studies that did not use any technology to collect data predominated and most were based on paper and pencil (P&P) self-reports. When smartphones were in full swing but were not yet powerful enough to collect and process multiple data in real time, PDAs predominated. In fact, PDAs were also known as handheld PCs and were small computers that made the collection of user self-reports easier than P&P. The improvement was that it allowed EMA records to be more environmentally friendly than the P&P system. In fact, if more objective data were to be collected, accelerometers were also used (6 of 18 studies using PDAs). Finally, in the most recent years the emergence of mHealth technology in the field of research on PA and health is most noticeable. This has been possible thanks to the rapid evolution and expansion of both smartphones and applications (apps). Current smartphones have much faster processors than PCs 10 years ago and are capable of monitoring lifestyle behaviors using multi-tasking tools. This improvement, along with small and long-lasting batteries has enabled the recordings to have multiple and very precise built-in sensors (Ozdalga, Ozdalga & Ahuja, 2012) and use the smartphone Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 15/29 capabilities to be synchronized for assessing or monitoring health or lifestyle (Fiordelli, Diviani & Schulz, 2013; Free et al., 2010). In other systematic reviews on the application of EMA in PA behavior, only one study used a combination of technology for the EMAs in nutrition and PA among young people (Liao et al., 2016), and only 3 studies with adults combined EMA with mobile phones and accelerometers (Romanzini et al., 2019). In our systematic review, much less restrictive with the concept of EMA, the main built-in sensor used for collecting PA behavior with smartphones was the accelerometer (27 of 39 studies using mHealth). Only 3 studies used GPS (35,89,90) and only 1 study used a cardiac chest band for recording heart rate to collect objective data related to PA (Brannon et al., 2016). Only 3 of these studies used the combination of 2 sensors (Brannon et al., 2016; Seto et al., 2014, 2016). In short, it is clear that the use of mHealth is increasing, but there is still a long way to make the most of the capabilities of this technology for applying EMA to PA behavior. Thus, based on our systematic review, we have found enough evidence to firmly propose the use of the term mEMA (mobile-based EMA) when mHealth technology is being used together with the EMA methodology, although this term had already been used previously (Spook et al., 2013). More specifically, we agree with defining mEMA as “the use of mobile computing and communication technologies for the EMA of health and lifestyle behaviors”. As an example, some researchers have combined mHealth and EMA methodologies to study alcohol, tobacco and drugs consumption (Serre et al., 2015), depression and anxiety (Schueller, Aguilera & Mohr, 2017), eating disorders and obesity (Engel et al., 2016), or nutrition and PA behaviors (Bruening et al., 2016). Thus, mEMA methodology can facilitate monitoring of healthy lifestyles under both subjective and objective perspectives, using tools such as written diaries or self-reports on a touch screen, messages on social networks, and motion or physiological sensors, all managed through smartphone apps (Shiffman, Stone & Hufford, 2008; Van Os et al., 2017). Profile of the populations studied with EMA and PA The selected studies included heterogeneous samples from children to older adults, men and women, healthy and clinical participants. Regarding the age of the participants, the studies were carried out on young-adult samples, including children and adolescents (25%), university students (26%) and adults (45%). In general, this variety of population age is receptive to the use of new technologies like smartphones as well as using this technological advance daily (Bagot et al., 2018; Poscia et al., 2015). This fact could explain the high rate of technology used for EMA assessments in our review (76.3%). Conversely, only 3 studies were found using EMA and PA in older adults (Cabrita et al., 2017; Kanning, Ebner-Priemer & Schlicht, 2015; Kanning & Hansen, 2017). Studies in the elderly are an important challenge for science, due to the increase in life expectancy (World Health Organization, 2018). Thus, we encourage scientists to incorporate older adults and elderly samples in the future EMA projects. It has been pointed out that older people are reluctant to participate in studies that promote electronic forms of data collection (Maher, Rebar & Dunton, 2018). However, technology is becoming an increasingly used strategy in health research (Patrick et al., 2008), because, among other reasons, it may offer Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 16/29 a great level of accuracy in PA research using EMA (Myin-Germeys et al., 2018). Thereby, mHealth research has the capability to adapt the advances in technologies to elderly and overcome the initial rejection. In fact, the 3 studies found with older adults used smartphones and accelerometers (Kanning, Ebner-Priemer & Schlicht, 2015; Kanning & Hansen, 2017), and only one of them reported difficulties with the battery management and the size of the response scales on the screen (Cabrita et al., 2017). It is noteworthy that the average age of the participants in the two studies without technological issues was 60.1 ± 7.1, which is not even considered old population in most countries; whereas the average age was 68.7 ± 5.5 years in the only study with few technological difficulties, which could be considered pre-old age (Ouchi et al., 2017). This fact could explain the positive acceptance of technology by the older adult samples analyzed in the 3 selected studies. As regards gender, we found more studies carried out with women (60%) rather than men. Previous literature has indicated that women could be more adherent than men to participate in research projects and this could be a possible explanation for the gender differences found (Fouad et al., 2014). Interestingly, in general women were less likely to participate in both PA and exercise behaviors (World Health Organization, 2018). Therefore, future EMA and PA studies should take into account possible gender differences and it should continue working along this line, seeking gender equality in scientific studies, as part of the RRI framework (Responsible, Research, Innovation) (Pacifico Silva et al., 2018). As regards health status, most participants were considered healthy and only 14 studies (18%) included samples with both a physiological or psychological clinical condition. For the latter, the most common were mood disorders. In this sense, current studies show possible relationships between mood disorders and PA. In detail, PA has been presented as a good complemental therapeutic strategy to reduce stress, alleviate depression symptoms and enhance psychological states (Mikkelsen et al., 2017). Thus, EMA methodology could contribute to further explain the relationship between psychological states and PA engagement. In addition, the main topic that accompanied PA in healthy samples was “affect and emotions” (Fig. 2), highlighting the influence of PA on psychological states. Study designs used in EMA and PA, and methodological aspects An interesting result from the reviewed studies corresponds to the research designs. This systematic review shows that 92% of the studies followed a longitudinal prospective design, while only 7% of them followed an experimental design, and 1% a quasi-experimental design (Table 2). Hence, increasing the amount of experimental designs could help in determining cause-effect relationships as regards PA and other variables like mental states in future studies. As regards the duration of the EMA assessments, the range was from 1 day (Kanning & Schoebi, 2016) up to 12 months (Burg et al., 2017). The majority of the studies lasted between 1 and 7 days (58%). From this result, it is suggested to increase the duration of the future studies using EMA in order to determine long-term habits. However, a limitation of extended longitudinal studies could be an increase in the number of dropouts (Gustavson et al., 2012). This is known as Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 17/29 attrition concern can lead to the subsequent biases of auto-selection and experimental survival (Carmona-Bayonas et al., 2018). In other words, participants who reported all EMA assessments throughout a very long study could have different individual characteristics from those who not complete all the study. It was then checked whether studies showing a high Risk of Bias related to loss to follow-up were those with longer duration, without finding significant results. Therefore, we suggest a duration between 1 and 4 weeks as an optimal balance between habits information and low levels of dropout for future EMA studies. In relation to the within-day intervals for EMA assessments, 35% of the studies established EMA assessments throughout the whole day, whereas 32% scheduled the assessments according to the availability of the participants; for example, studies with children samples avoided school hours (O’Connor et al., 2017). Interestingly, only 26% of studies were to found directly differentiate between working days and non-working days. The comparison of working days and non-working days enables possible patterns of PA depending on the type of day to be studied (Parrado et al., 2009). Finally, in our systematic review there is a great variety in the range of the number of assessments per day. Between 3 and 7 assessments per day were carried out in 37% of studies, followed by 8 to 12 per day (22%) and 1 to 2 per day (16%). We recommend between 3 and 7 assessments per day. In this context, it has been published that excessive prompts or requests for EMA surveys could increase the number of lost responses, or it could cause participants to respond randomly (Dunton, 2017). Topics associated with the research in EMA and PA The main topic (omitting demographic data) that accompanied PA was psychological state (Fig. 2), which is a global variable that includes: affection, emotions, depression, stress or anxiety. This is interesting due to the high rates of mental disorders in the general population worldwide (World Health Organization, 2017) and the positive relation between PA practice and mental health. General activity (as well as BMI) was the second topic studied along with PA. It is related to everyday behaviors like active transport, watching TV or eating. These behaviors are important to understand the lifestyle of the participants and their possible motives and barriers for PA practice (Parrado et al., 2009). Finally, some stable anthropometric variables like BMI have been assessed in 35 studies (46.1%), although they have not been evaluated by EMA, demonstrating the importance of PA behavior in relation to overweight and obesity (Chin, Kahathuduwa & Binks, 2016). Limitations Our systematic review is pioneer in examining mHealth application for EMA studies in the field of PA, though there are some limitations. First, it was not possible to report on the adherence levels to EMA in the participants of most of the studies, because the methodological strategies for reporting EMA data collection were diverse. Similar information has been reported by Liao et al. (2016) in a systematic review on the use of EMA on diet and PA in young population, highlighting the heterogeneity in the EMA data collection methods. We encourage researchers to incorporate results on adherence to EMA Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 18/29 interventions as well as the number of dropouts. Although mHealth technologies can help to provide objective EMA recordings, they also have some difficulties. For instance, the high cost of development and maintenance for mHealth apps, the lack of standardization, data management, technical problems, slow connections, and so on are possible problems that should be borne in mind (Kip et al., 2018). In addition, mEMA devices could be expensive if they are only used for research purposes. Future research directions mHealth technology could be of great help to apply EMA strategies in developing countries (Hurt et al., 2016), since it reduces the costs compared to a traditional intervention (Chung et al., 2015). A few years ago, specific and expensive sensors like isolated accelerometers were used to investigate PA behaviors. We encourage researchers to require participants to use their own devices instead of providing specific research instruments to take advantage of the increasing ability of smartphones for synchronously monitoring different objective parameters (Ozdalga, Ozdalga & Ahuja, 2012; Fiordelli, Diviani & Schulz, 2013; Free et al., 2010). This should be the current trend in research on healthy lifestyles like PA behavior. Smartphone apps and sensors (mHealth technology) allow the concept of mEMA to be accepted, especially in relation to PA. It is ideal to use built-in sensors or other sensors easily connected by Bluetooth for monitoring the duration, frequency and intensity of PA. Thus, built-in sensors like accelerometer, GPS, altimeter or gyroscope can provide continuous information about PA running in background while the user performs their daily activities. Or you can also add simultaneous parameters from other external sensors connected via Bluetooth, such as cardiorespiratory information using thoracic bands or other wearables. The tendency should be the automatic recognition of the user activity without interrupting their behavior, based on machine learning algorithms. The ecological information could be completed with voice messages, image captures or brief text selections on the touch screen made in real time. Current mobile devices already have the ability to process all this information, but it will be necessary to persuade the user to carry their smartphone during the whole day. CONCLUSIONS In our review we have classified the EMA methodologies used for assessing PA behaviors and found that 71% of studies specifically used the term “EMA”. Just over half (51.3%) of studies used mHealth technology, mainly smartphones, for collecting EMA data. An accelerometer was the main built-in sensor used for collecting PA behavior by means of mHealth (69%). The change of trend in the use of tools for EMA in PA coincides with the technological advances of the last decade due to the emergence of smartphones and mHealth technology. There is enough evidence to use the term mEMA (mobile-based EMA) when mHealth technology is being used together with the EMA methodology for monitoring lifestyle behaviors such as PA, in real time and in natural environments. We define mEMA as the use of mobile computing and communication technologies for the EMA of health and Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 19/29 lifestyle behaviors. It is clear that the use of mHealth is increasing, but there is still a long way to make the most of this technology in order to apply EMA to PA behavior. Thus, mEMA methodology can help in the monitoring of healthy lifestyles under both subjective and objective perspectives. The tendency for future research should be the automatic recognition of the user’s PA without interrupting their behavior. From our review, we suggest the use of mEMA methodology with experimental designs, a duration between 1 and 4 weeks as an optimal balance between habits information and low levels of dropout, a number of assessments per day between three and seven, differentiating between working days and non-working days. The ecological information could be completed with synchronized information from other sensors or wearables, all managed through smartphone apps. This methodology could be extended when EMA combined with mHealth are used to evaluate other lifestyle behaviors. ADDITIONAL INFORMATION AND DECLARATIONS Funding This study was supported by the grants DEP2015-68538-C2-1-R and PGC2018-100675- B-I00 from The Spanish Government. Rafael Zapata-Lamana is supported by a scholarship from the Universidad de Concepción (Chile). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Grant Disclosures The following grant information was disclosed by the authors: Spanish Government: DEP2015-68538-C2-1-R and PGC2018-100675-B-I00. Universidad de Concepción (Chile). Competing Interests The authors declare that they have no competing interests. Author Contributions  Rafael Zapata-Lamana conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft.  Jaume F. Lalanza performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft.  Josep-Maria Losilla conceived and designed the experiments, authored or reviewed drafts of the paper, and approved the final draft.  Eva Parrado analyzed the data, authored or reviewed drafts of the paper, and approved the final draft.  Lluis Capdevila conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft. Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 20/29 Data Availability The following information was supplied regarding data availability: The references for this systematic review were obtained from the following databases and portals: PsycINFO by PsycNET, CINAHL by EBSCOhost, MEDLINE by PubMed, and Core Collection of Web of Science by Web of Science. The data is available in the tables. Supplemental Information Supplemental information for this article can be found online at http://dx.doi.org/10.7717/ peerj.8848#supplemental-information. REFERENCES Aggio D, Wallace K, Boreham N, Shankar A, Steptoe A, Hamer M. 2017. Objectively measured daily physical activity and postural changes as related to positive and negative affect using ambulatory monitoring assessments. Psychosomatic Medicine 79(7):792–797 DOI 10.1097/PSY.0000000000000485. Ainsworth B, Cahalin L, Buman M, Ross R. 2015. The current state of physical activity assessment tools. Progress in Cardiovascular Diseases 57(4):387–395 DOI 10.1016/j.pcad.2014.10.005. Bagot KS, Matthews SA, Mason M, Squeglia LM, Fowler J, Gray K, Herting M, May A, Colrain I, Godino J, Tapert S, Brown S, Patrick K. 2018. Current, future and potential use of mobile and wearable technologies and social media data in the ABCD study to increase understanding of contributors to child health. Developmental Cognitive Neuroscience 32:121–129 DOI 10.1016/j.dcn.2018.03.008. Basen-Engquist K, Carmack CL, Li Y, Brown J, Jhingran A, Hughes DC, Perkins HY, Scruggs S, Harrison C, Baum G, Bodurka DC, Waters A. 2013. Social-cognitive theory predictors of exercise behavior in endometrial cancer survivors. Health Psychology 32(11):1137–1148 DOI 10.1037/a0031712. Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RJF, Martin BW, Lancet Physical Activity Series Working Group. 2012. Correlates of physical activity: why are some people physically active and others not? Lancet 380(9838):258–271 DOI 10.1016/S0140-6736(12)60735-1. Biddle SJH, Gorely T, Marshall SJ. 2009. Is television viewing a suitable marker of sedentary behavior in young people? Annals of Behavioral Medicine 38(2):147–153. Biddle SJH, Gorely T, Marshall SJ, Cameron N. 2009a. The prevalence of sedentary behavior and physical activity in leisure time: a study of Scottish adolescents using ecological momentary assessment. Preventive Medicine 48(2):151–155 DOI 10.1016/j.ypmed.2008.10.025. Biddle SJH, Marshall SJ, Gorely T, Cameron N. 2009b. Temporal and environmental patterns of sedentary and active behaviors during adolescents’ leisure time. International Journal of Behavioral Medicine 16(3):278–286 DOI 10.1007/s12529-008-9028-y. Biddle SJH, Soos I, Hamar P, Sandor I, Simonek J, Karsai I. 2009c. Physical activity and sedentary behaviours in youth: data from three Central-Eastern European countries. European Journal of Sport Science 9(5):295–301 DOI 10.1080/17461390902829234. Bohnert A, Burdette K, Dugas L, Travers L, Randall E, Richards M, Luke A. 2013. Multimethod analyses of discretionary time use and health behaviors among urban low-income African–American adolescents: a pilot study. Journal of Developmental & Behavioral Pediatrics 34(8):589–598 DOI 10.1097/DBP.0b013e3182a5d297. Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 21/29 Bond DS, Thomas JG, Ryder BA, Vithiananthan S, Pohl D, Wing RR. 2013. Ecological momentary assessment of the relationship between intention and physical activity behavior in bariatric surgery patients. International Journal of Behavioral Medicine 20(1):82–87 DOI 10.1007/s12529-011-9214-1. Bonham T, Pepper GV, Nettle D. 2018. The relationships between exercise and affective states: a naturalistic, longitudinal study of recreational runners. PeerJ 6:e4257 DOI 10.7717/peerj.4257. Booth FW, Roberts CK, Thyfault JP, Ruegsegger GN, Toedebusch RG. 2017. Role of inactivity in chronic diseases: evolutionary insight and pathophysiological mechanisms. Physiological Reviews 97(4):1351–1402 DOI 10.1152/physrev.00019.2016. Bossmann T, Kanning M, Koudela-Hamila S, Hey S, Ebner-Priemer U. 2013. The association between short periods of everyday life activities and affective states: a replication study using ambulatory assessment. Frontiers in Psychology 4:102 DOI 10.3389/fpsyg.2013.00102. Brannon EE, Cushing CC, Crick CJ, Mitchell TB. 2016. The promise of wearable sensors and ecological momentary assessment measures for dynamical systems modeling in adolescents: a feasibility and acceptability study. Translational Behavioral Medicine 6(4):558–565 DOI 10.1007/s13142-016-0442-4. Bruening M, Van Woerden I, Todd M, Brennhofer S, Laska MN, Dunton G. 2016. A mobile ecological momentary assessment tool (devilSPARC) for nutrition and physical activity behaviors in college students: a validation study. Journal of Medical Internet Research 18(7):e209 DOI 10.2196/jmir.5969. Burg MM, Schwartz JE, Kronish IM, Diaz KM, Alcantara C, Duer-Hefele J, Davidson KW. 2017. Does stress result in you exercising less? or Does exercising result in you being less stressed? or Is it both? Testing the bi-directional stress-exercise association at the group and person (N of 1) level. Annals of Behavioral Medicine 51(6):799–809 DOI 10.1007/s12160-017-9902-4. Cabrita M, Lousberg R, Tabak M, Hermens HJ, Vollenbroek-Hutten MMR. 2017. An exploratory study on the impact of daily activities on the pleasure and physical activity of older adults. European Review of Aging and Physical Activity 14(1):870 DOI 10.1186/s11556-016-0170-2. Carmona-Bayonas A, Jimenez-Fonseca P, Fernández-Somoano A, Álvarez-Manceñido F, Castañón E, Custodio A, De la Peña FA, Payo RM, Valiente LP. 2018. Top ten errors of statistical analysis in observational studies for cancer research. Clinical and Translational Oncology 20(8):954–965 DOI 10.1007/s12094-017-1817-9. Chin S-H, Kahathuduwa CN, Binks M. 2016. Physical activity and obesity: what we know and what we need to know. Obesity Reviews 17(12):1226–1244 DOI 10.1111/obr.12460. Chung LMY, Law QPS, Fong SSM, Chung JWY, Yuen PP. 2015. A cost-effectiveness analysis of teledietetics in short-, intermediate-, and long-term weight reduction. Journal of Telemedicine and Telecare 21(5):268–275 DOI 10.1177/1357633X15572200. Conroy DE, Maher JP, Elavsky S, Hyde AL, Doerksen SE. 2013. Sedentary behavior as a daily process regulated by habits and intentions. Health Psychology 32(11):1149–1157 DOI 10.1037/a0031629. Daugherty BL, Schap TE, Ettienne-Gittens R, Zhu FM, Bosch M, Delp EJ, Ebert DS, Kerr DA, Boushey CJ. 2012. Novel technologies for assessing dietary intake: evaluating the usability of a mobile telephone food record among adults and adolescents. Journal of Medical Internet Research 14(2):e58 DOI 10.2196/jmir.1967. Dumith SC, Hallal PC, Reis RS, Kohl HW III. 2011. Worldwide prevalence of physical inactivity and its association with human development index in 76 countries. Preventive Medicine 53(1–2):24–28 DOI 10.1016/j.ypmed.2011.02.017. Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 22/29 Dunton GF. 2017. Ecological momentary assessment in physical activity research. Exercise and Sport Sciences Reviews 45(1):48–54 DOI 10.1249/JES.0000000000000092. Dunton GF, Dzubur E, Intille S. 2016. Feasibility and performance test of a real-time sensor- informed context-sensitive ecological momentary assessment to capture physical activity. Journal of Medical Internet Research 18(6):e106 DOI 10.2196/jmir.5398. Dunton GF, Dzubur E, Kawabata K, Yanez B, Bo B, Intille S. 2014a. Development of a smartphone application to measure physical activity using sensor-assisted self-report. Frontiers in Public Health 2:12. Dunton GF, Huh J, Leventhal AM, Riggs N, Hedeker D, Spruijt-Metz D, Pentz MA. 2014b. Momentary assessment of affect, physical feeling states, and physical activity in children. Health Psychology 33(3):255–263 DOI 10.1037/a0032640. Dunton GF, Intille SS, Wolch J, Pentz MA. 2012a. Children’s perceptions of physical activity environments captured through ecological momentary assessment: a validation study. Preventive Medicine 55(2):119–121 DOI 10.1016/j.ypmed.2012.05.015. Dunton GF, Intille SS, Wolch J, Pentz MA. 2012b. Investigating the impact of a smart growth community on the contexts of children’s physical activity using ecological momentary assessment. Health & Place 18(1):76–84 DOI 10.1016/j.healthplace.2011.07.007. Dunton GF, Liao Y, Intille S, Huh J, Leventhal A. 2015. Momentary assessment of contextual influences on affective response during physical activity. Health Psychology 34(12):1145–1153 DOI 10.1037/hea0000223. Dunton GF, Liao Y, Intille SS, Spruijt-Metz D, Pentz M. 2011. Investigating children’s physical activity and sedentary behavior using ecological momentary assessment with mobile phones. Obesity 19(6):1205–1212 DOI 10.1038/oby.2010.302. Dunton GF, Liao Y, Kawabata K, Intille S. 2012c. Momentary assessment of adults’ physical activity and sedentary behavior: feasibility and validity. Frontiers in Psychology 3:260 DOI 10.3389/fpsyg.2012.00260. Elavsky S, Kishida M, Mogle JA. 2016. Concurrent and lagged relations between momentary affect and sedentary behavior in middle-aged women. Menopause 23(8):919–923 DOI 10.1097/GME.0000000000000645. Emerson JA, Dunsiger S, Williams DM. 2018. Reciprocal within-day associations between incidental affect and exercise: an EMA study. Psychology & Health 33(1):130–143 DOI 10.1080/08870446.2017.1341515. Engel SG, Crosby RD, Thomas G, Bond D, Lavender JM, Mason T, Steffen KJ, Green DD, Wonderlich SA. 2016. Ecological momentary assessment in eating disorder and obesity research: a review of the recent literature. Current Psychiatry Reports 18(4):37 DOI 10.1007/s11920-016-0672-7. Fanning J, Mackenzie M, Roberts S, Crato I, Ehlers D, McAuley E. 2016. Physical activity, mind wandering, affect, and sleep: an ecological momentary assessment. JMIR mHealth and uHealth 4(3):e104 DOI 10.2196/mhealth.5855. Fiordelli M, Diviani N, Schulz PJ. 2013. Mapping mHealth research: a decade of evolution. Journal of Medical Internet Research 15(5):e95 DOI 10.2196/jmir.2430. Fiuza-Luces C, Garatachea N, Berger NA, Lucia A. 2013. Exercise is the real polypill. Physiology 28(5):330–358 DOI 10.1152/physiol.00019.2013. Fortier MS, Guerin E, Williams T, Strachan S. 2015. Should I exercise or sleep to feel better? A daily analysis with physically active working mothers. Mental Health and Physical Activity 8:56–61 DOI 10.1016/j.mhpa.2015.03.001. Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 23/29 Fouad MN, Johnson RE, Nagy MC, Person SD, Partridge EE. 2014. Adherence and retention in clinical trials: a community-based approach. Cancer 120(Suppl. 7):1106–1112 DOI 10.1002/cncr.28572. Free C, Phillips G, Felix L, Galli L, Patel V, Edwards P. 2010. The effectiveness of M-health technologies for improving health and health services: a systematic review protocol. BMC Research Notes 3(1):250 DOI 10.1186/1756-0500-3-250. Fuller-Tyszkiewicz M, Skouteris H, Mccabe M. 2013. A re-examination of the benefits of exercise for state body satisfaction: consideration of individual difference factors. Journal of Sports Sciences 31(7):706–713 DOI 10.1080/02640414.2012.746723. Gorely T, Atkin AJ, Biddle SJH, Marshall SJ. 2009a. Family circumstance, sedentary behaviour and physical activity in adolescents living in England: project STIL. International Journal of Behavioral Nutrition and Physical Activity 6(1):33 DOI 10.1186/1479-5868-6-33. Gorely T, Biddle SJH, Marshall SJ, Cameron N. 2009b. The prevalence of leisure time sedentary behaviour and physical activity in adolescent boys: an ecological momentary assessment approach. International Journal of Pediatric Obesity 4(4):289–298 DOI 10.3109/17477160902811181. Gustavson K, Von Soest T, Karevold E, Røysamb E. 2012. Attrition and generalizability in longitudinal studies: findings from a 15-year population-based study and a Monte Carlo simulation study. BMC Public Health 12(1):918 DOI 10.1186/1471-2458-12-918. Hager ER, Tilton NA, Wang Y, Kapur NC, Arbaiza R, Merry BC, Black MM. 2017. The home environment and toddler physical activity: an ecological momentary assessment study. Pediatric Obesity 12(1):1–9 DOI 10.1111/ijpo.12098. Hausenblas HA, Gauvin L, Symons Downs D, Duley AR. 2008. Effects of abstinence from habitual involvement in regular exercise on feeling states: an ecological momentary assessment study. British Journal of Health Psychology 13(Pt 2):237–255 DOI 10.1348/135910707X180378. Heininga VE, Van Roekel E, Wichers M, Oldehinkel AJ. 2017. Reward and punishment learning in daily life: a replication study. PLOS ONE 12:e0180753 DOI 10.1371/journal.pone.0180753. Hekler EB, Buman MP, Ahn D, Dunton G, Atienza AA, King AC. 2012. Are daily fluctuations in perceived environment associated with walking? Psychology & Health 27(9):1009–1020 DOI 10.1080/08870446.2011.645213. Higgins JPT, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD, Savovic J, Schulz KF, Weeks L, Sterne JA, Cochrane Bias Methods Group, Cochrane Statistical Methods Group. 2011. The cochrane collaboration’s tool for assessing risk of bias in randomised trials. BMJ 343:d5928. Hurt K, Walker RJ, Campbell JA, Egede LE. 2016. mHealth Interventions in low and middle-income countries: a systematic review. Global Journal of Health Science 8(9):54429 DOI 10.5539/gjhs.v8n9p183. Hyde AL, Conroy DE, Pincus AL, Ram N. 2011. Unpacking the feel-good effect of free-time physical activity: between- and within-person associations with pleasant-activated feeling states. Journal of Sport and Exercise Psychology 33(6):884–902 DOI 10.1123/jsep.33.6.884. Jones M, Taylor A, Liao Y, Intille SS, Dunton GF. 2017. Real-time subjective assessment of psychological stress: associations with objectively-measured physical activity levels. Psychology of Sport and Exercise 31:79–87. Kanning M, Ebner-Priemer U, Schlicht W. 2015. Using activity triggered e-diaries to reveal the associations between physical activity and affective states in older adult’s daily living. International Journal of Behavioral Nutrition and Physical Activity 12:111 DOI 10.1186/s12966-015-0272-7. Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 24/29 Kanning M, Hansen S. 2017. Need satisfaction moderates the association between physical activity and affective states in adults aged 50+: an activity-triggered ambulatory assessment. Annals of Behavioral Medicine 51(1):18–29 DOI 10.1007/s12160-016-9824-6. Kanning MK, Schoebi D. 2016. Momentary affective states are associated with momentary volume, prospective trends, and fluctuation of daily physical activity. Frontiers in Psychology 7:744 DOI 10.3389/fpsyg.2016.00744. Kim J, Nakamura T, Kikuchi H, Yoshiuchi K, Yamamoto Y. 2014. Co-variation of depressive mood and spontaneous physical activity evaluated by ecological momentary assessment in major depressive disorder. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago. 6635–6638. Kip H, Bouman YHA, Kelders SM, Van Gemert-Pijnen LJEWC. 2018. eHealth in treatment of offenders in forensic mental health: a review of the current state. Frontiers in Psychiatry 9:42 DOI 10.3389/fpsyt.2018.00042. Knell G, Gabriel KP, Businelle MS, Shuval K, Wetter DW, Kendzor DE. 2017. Ecological momentary assessment of physical activity: validation study. Journal of Medical Internet Research 19(7):e253 DOI 10.2196/jmir.7602. Kohl HW, Craig CL, Lambert EV, Inoue S, Alkandari JR, Leetongin G, Kalmeier S. 2012. The pandemic of physical inactivity: global action for public health. Lancet 380(9838):294–305 DOI 10.1016/S0140-6736(12)60898-8. LePage ML, Crowther JH. 2010. The effects of exercise on body satisfaction and affect. Body Image 7(2):124–130 DOI 10.1016/j.bodyim.2009.12.002. LePage ML, Price M, O’Neil P, Crowther JH. 2012. The effect of exercise absence on affect and body dissatisfaction as moderated by obligatory exercise beliefs and eating disordered beliefs and behaviors. Psychology of Sport and Exercise 13(4):500–508. Liao Y, Chou C-P, Huh J, Leventhal A, Dunton G. 2017a. Associations of affective responses during free-living physical activity and future physical activity levels: an ecological momentary assessment study. International Journal of Behavioral Medicine 24(4):513–519 DOI 10.1007/s12529-016-9626-z. Liao Y, Chou C-P, Huh J, Leventhal A, Dunton G. 2017b. Examining acute bi-directional relationships between affect, physical feeling states, and physical activity in free-living situations using electronic ecological momentary assessment. Journal of Behavioral Medicine 40(3):445–457 DOI 10.1007/s10865-016-9808-9. Liao Y, Intille SS, Dunton GF. 2015. Using ecological momentary assessment to understand where and with whom adults’ physical and sedentary activity occur. International Journal of Behavioral Medicine 22(1):51–61 DOI 10.1007/s12529-014-9400-z. Liao Y, Intille S, Wolch J, Pentz MA, Dunton GF. 2014. Understanding the physical and social contexts of children’s non-school sedentary behavior: an ecological momentary assessment study. Journal of Physical Activity & Health 11:588–613. Liao Y, Skelton K, Dunton G, Bruening M. 2016. A systematic review of methods and procedures used in ecological momentary assessments of diet and physical activity research in youth: an adapted STROBE checklist for reporting EMA studies (CREMAS). Journal of Medical Internet Research 18(6):e151 DOI 10.2196/jmir.4954. Liao Y, Solomon O, Dunton GF. 2017. Does the company of a dog influence affective response to exercise? using ecological momentary assessment to study dog-accompanied physical activity. American Journal of Health Promotion 31(5):388–390 DOI 10.1177/0890117116666947. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA, Clarke M, Deveraux PJ, Kleijnen J, Moher D. 2009. The PRISMA statement for reporting systematic Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 25/29 reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ 339:b2700 DOI 10.1136/bmj.b2700. Maher JP, Doerksen SE, Elavsky S, Conroy DE. 2014. Daily satisfaction with life is regulated by both physical activity and sedentary behavior. Journal of Sport and Exercise Psychology 36(2):166–178 DOI 10.1123/jsep.2013-0185. Maher JP, Dzubur E, Huh J, Intille S, Dunton GF. 2016. Within-day time-varying associations between behavioral cognitions and physical activity in adults. Journal of Sport and Exercise Psychology 38(4):423–434 DOI 10.1123/jsep.2016-0058. Maher JP, Rebar AL, Dunton GF. 2018. Ecological momentary assessment is a feasible and valid methodological tool to measure older adults’ physical activity and sedentary behavior. Frontiers in Psychology 9:1485. Marquet O, Alberico C, Adlakha D, Hipp JA. 2017. Examining motivations to play pokemon GO and their influence on perceived outcomes and physical activity. JMIR Serious Games 5(4):e21 DOI 10.2196/games.8048. Marquet O, Alberico C, Hipp AJ. 2018. Pokémon GO and physical activity among college students: a study using ecological momentary assessment. Computers in Human Behavior 81:215–222 DOI 10.1016/j.chb.2017.12.028. Marszalek J, Morgulec-Adamowicz N, Rutkowska I, Kosmol A. 2014. Using ecological momentary assessment to evaluate current physical activity. BioMed Research International 2014(2):1–9 DOI 10.1155/2014/915172. Mata J, Thompson RJ, Jaeggi SM, Buschkuehl M, Jonides J, Gotlib IH. 2012. Walk on the bright side: physical activity and affect in major depressive disorder. Journal of Abnormal Psychology 121(2):297–308 DOI 10.1037/a0023533. McCormick BP, Frey G, Lee C-T, Chun S, Sibthorp J, Gajic T, Stamatovic-Gajic B, Maksimovich M. 2008. Predicting transitory mood from physical activity level among people with severe mental illness in two cultures. International Journal of Social Psychiatry 54(6):527–538 DOI 10.1177/0020764008091423. McCormick BP, Frey GC, Lee C-T, Gajic T, Stamatovic-Gajic B, Maksimovic M. 2009. A pilot examination of social context and everyday physical activity among adults receiving Community Mental Health Services. Acta Psychiatrica Scandinavica 119(3):243–247 DOI 10.1111/j.1600-0447.2008.01331.x. McGowan J, Sampson M, Salzwedel DM, Cogo E, Foerster V, Lefebvre C. 2016. PRESS peer review of electronic search strategies: 2015 guideline statement. Journal of Clinical Epidemiology 75:40–46 DOI 10.1016/j.jclinepi.2016.01.021. Mikkelsen K, Stojanovska L, Polenakovic M, Bosevski M, Apostolopoulos V. 2017. Exercise and mental health. Maturitas 106:48–56 DOI 10.1016/j.maturitas.2017.09.003. Myin-Germeys I, Kasanova Z, Vaessen T, Vachon H, Kirtley O, Viechtbauer W, Reininghaus U. 2018. Experience sampling methodology in mental health research: new insights and technical developments. World Psychiatry 17(2):123–132 DOI 10.1002/wps.20513. Nadell MJ, Mermelstein RJ, Hedeker D, Marquez DX. 2015. Work and non-work physical activity predict real-time smoking level and urges in young adults. Nicotine & Tobacco Research 17(7):803–809 DOI 10.1093/ntr/ntu244. National Institute for Health and Care Excellence. 2012. The guidelines manual: appendices B-I— Tailored service improvement support. London: NICE. Ouchi Y, Rakugi H, Arai H, Akishita M, Ito H, Toba K, Kai I, Joint Committee of Japan Gerontological Society (JGLS) and Japan Geriatrics Society (JGS) on the definition and classification of the elderly. 2017. Redefining the elderly as aged 75 years and older: proposal Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 26/29 from the joint committee of Japan Gerontological Society and the Japan Geriatrics Society. Geriatrics & Gerontology International 17(7):1045–1047 DOI 10.1111/ggi.13118. Ozdalga E, Ozdalga A, Ahuja N. 2012. The smartphone in medicine: a review of current and potential use among physicians and students. Journal of Medical Internet Research 14(5):e128 DOI 10.2196/jmir.1994. O’Connor SG, Koprowski C, Dzubur E, Leventhal AM, Huh J, Dunton GF. 2017. Differences in mothers’ and children’s dietary intake during physical and sedentary activities: an ecological momentary assessment study. Journal of the Academy of Nutrition and Dietetics 117(8):1265–1271. Pacifico Silva H, Lehoux P, Miller FA, Denis J-L. 2018. Introducing responsible innovation in health: a policy-oriented framework. Health Research Policy and Systems 16(1):90 DOI 10.1186/s12961-018-0362-5. Parrado E, Cervantes JC, Ocaña M, Pintanel M, Valero M, Capdevila L. 2009. Evaluación de la conducta activa: El registro semanal de actividad física (RSAF). Revista de Psicología del Deporte 18(2):197–216. Patrick K, Griswold WG, Raab F, Intille SS. 2008. Health and the mobile phone. American Journal of Preventive Medicine 35(2):177–181 DOI 10.1016/j.amepre.2008.05.001. Physical Activity Guidelines Advisory Committee. 2018. 2018 Physical Activity Guidelines Advisory Committee Scientific Report. Washington, D.C.: U.S. Department of Health and Human Services. Pickering TA, Huh J, Intille S, Liao Y, Pentz MA, Dunton GF. 2016. Physical activity and variation in momentary behavioral cognitions: an ecological momentary assessment study. Journal of Physical Activity and Health 13(3):344–351 DOI 10.1123/jpah.2014-0547. Poscia A, Frisicale EM, Parente P, De Waure C, La Milia DI, Di Pietro ML. 2015. Study habits and technology use in Italian university students. Annali dell’Istituto superiore di sanita 51(2):126–130. Prince SA, Adamo KB, Hamel ME, Hardt J, Connor Gorber S, Tremblay M. 2008. A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. International Journal of Behavioral Nutrition and Physical Activity 5(1):56 DOI 10.1186/1479-5868-5-56. Romanzini CLP, Romanzini M, Batista MB, Barbosa CCL, Shigaki GB, Dunton G, Mason T, Ronque ERV. 2019. Methodology used in ecological momentary assessment studies about sedentary behavior in children, adolescents, and adults: systematic review using the checklist for reporting ecological momentary assessment studies. Journal of Medical Internet Research 21(5):e11967 DOI 10.2196/11967. Rouse PC, Biddle SJH. 2010. An ecological momentary assessment of the physical activity and sedentary behaviour patterns of university students. Health Education Journal 69(1):116–125 DOI 10.1177/0017896910363145. Rusby JC, Westling E, Crowley R, Light JM. 2014. Psychosocial correlates of physical and sedentary activities of early adolescent youth. Health Education & Behavior 41(1):42–51 DOI 10.1177/1090198113485753. Sala M, Brosof LC, Rosenfield D, Fernandez KC, Levinson CA. 2017. Stress is associated with exercise differently among individuals with higher and lower eating disorder symptoms: an ecological momentary assessment study. International Journal of Eating Disorders 50(12):1413–1420 DOI 10.1002/eat.22799. Salvy S-J, Bowker JW, Roemmich JN, Romero N, Kieffer E, Paluch R, Epstein LH. 2008. Peer influence on children’s physical activity: an experience sampling study. Journal of Pediatric Psychology 33(1):39–49 DOI 10.1093/jpepsy/jsm039. Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 27/29 Schueller SM, Aguilera A, Mohr DC. 2017. Ecological momentary interventions for depression and anxiety. Depression and Anxiety 34(6):540–545 DOI 10.1002/da.22649. Schöndube A, Bertrams A, Sudeck G, Fuchs R. 2017. Self-control strength and physical exercise: an ecological momentary assessment study. Psychology of Sport and Exercise 29:19–26. Schöndube A, Kanning M, Fuchs R. 2016. The bidirectional effect between momentary affective states and exercise duration on a day level. Frontiers in Psychology 7:1414. Serre F, Fatseas M, Swendsen J, Auriacombe M. 2015. Ecological momentary assessment in the investigation of craving and substance use in daily life: a systematic review. Drug and Alcohol Depend 148:1–20 DOI 10.1016/j.drugalcdep.2014.12.024. Seto E, Hua J, Wu L, Bestick A, Shia V, Eom S, Han J, Wang M, Li Y. 2014. The Kunming CalFit study: modeling dietary behavioral patterns using smartphone data. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago. 6884–6887. Seto E, Hua J, Wu L, Shia V, Eom S, Wang M, Li Y. 2016. Models of individual dietary behavior based on smartphone data: the influence of routine, physical activity, emotion, and food environment. PLOS ONE 11(4):e0153085. Shephard RJ. 2003. Limits to the measurement of habitual physical activity by questionnaires. British Journal of Sports Medicine 37(3):197–206 DOI 10.1136/bjsm.37.3.197. Shiffman S, Stone AA, Hufford MR. 2008. Ecological momentary assessment. Annual Review of Clinical Psychology 4(1):1–32 DOI 10.1146/annurev.clinpsy.3.022806.091415. Snippe E, Simons CJP, Hartmann JA, Menne-Lothmann C, Kramer I, Booij SH, Viechtbauer W, Delespaul P, Myin-Germeys I, Wichers M. 2016. Change in daily life behaviors and depression: within-person and between-person associations. Health Psychology 35(5):433–441 DOI 10.1037/hea0000312. Soos I, Biddle S, Boros-Balint I, Sandor I, Szabo P, Hamar P, Simonek J. 2012. Prevalence of sedentary behaviour in young people in Romania and Slovakia. European Physical Education Review 18(1):19–46 DOI 10.1177/1356336X11430659. Soos I, Biddle SJH, Ling J, Hamar P, Sandor I, Boros-Balint I, Szabo P, Simonek J. 2014. Physical activity, sedentary behaviour, use of electronic media, and snacking among youth: an international study. Kinesiology 46(2):155–163. Spook JE, Paulussen T, Kok G, Van Empelen P. 2013. Monitoring dietary intake and physical activity electronically: feasibility, usability, and ecological validity of a mobile-based ecological momentary assessment tool. Journal of Medical Internet Research 15(9):e214 DOI 10.2196/jmir.2617. Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, Henry D, Altman DG, Ansari MT, Boutron I, Carpenter JR, Chan A-W, Churchill R, Deeks JJ, Hróbjartsson A, Kirkham J, Jüni P, Loke YK, Pigott TD, Ramsay CR, Regidor D, Rothstein HR, Sandhu L, Santaguida PL, Schünemann HJ, Shea B, Shrier I, Tugwell P, Turner L, Valentine JC, Waddington H, Waters E, Wells GA, Whiting PF, Higgins JPT. 2016. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ 355:i4919 DOI 10.1136/bmj.i4919. Sternfeld B, Jiang S-F, Picchi T, Chasan-Taber L, Ainsworth B, Quesenberry CP Jr. 2012. Evaluation of a cell phone–based physical activity diary. Medicine & Science in Sports & Exercise 44(3):487–495 DOI 10.1249/MSS.0b013e3182325f45. Stone AA, Shiffman S. 1994. Ecological momentary assessment (EMA) in behavorial medicine. Annals of Behavioral Medicine 16(3):199–202 DOI 10.1093/abm/16.3.199. Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 28/29 Thomas JG, Bond DS, Ryder BA, Leahey TM, Vithiananthan S, Roye GD, Wing RR. 2011. Ecological momentary assessment of recommended postoperative eating and activity behaviors. Surgery for Obesity and Related Diseases 7(2):206–212 DOI 10.1016/j.soard.2010.10.007. Timmerman JG, Van Weering MGHD, Toenis TM, Hermens HJ, Vollenbroek-Hutten MMR. 2015. Relationship between patterns of daily physical activity and fatigue in cancer survivors. European Journal of Oncology Nursing 19(2):162–168 DOI 10.1016/j.ejon.2014.09.005. Van Os J, Verhagen S, Marsman A, Peeters F, Bak M, Marcelis M, Drukker M1, Reininghaus U, Jacobs N, Lataster T, Simons C, Lousberg R, Gülöksüz S1, Leue C, Groot PC, Viechtbauer W, Delespaul P, ESM-MERGE Investigators. 2017. The experience sampling method as an mHealth tool to support self-monitoring, self-insight, and personalized health care in clinical practice. Depression and Anxiety 34(6):481–493 DOI 10.1002/da.22647. Von Haaren B, Loeffler SN, Haertel S, Anastasopoulou P, Stumpp J, Hey S, Boes K. 2013. Characteristics of the activity-affect association in inactive people: an ambulatory assessment study in daily life. Frontiers in Psychology 4:163 DOI 10.3389/fpsyg.2013.00163. Wichers M, Kasanova Z, Bakker J, Thiery E, Derom C, Jacobs N, Van Os J. 2015. From affective experience to motivated action: tracking reward-seeking and punishment-avoidant behaviour in real-life. PLOS ONE 10:e0129722. Williams DM, Dunsiger S, Emerson JA, Gwaltney CJ, Monti PM, Miranda R. 2016. Self-paced exercise, affective response, and exercise adherence: a preliminary investigation using ecological momentary assessment. Journal of Sport and Exercise Psychology 38(3):282–291 DOI 10.1123/jsep.2015-0232. World Health Organization. 2017. Depression and other common mental disorders: global health estimates. Available at https://apps.who.int/iris/bitstream/handle/10665/254610/WHO-MSD- MER-2017.2-eng.pdf. World Health Organization. 2018. Global action plan on physical activity 2018–2030: more active people for a healthier world. Available at https://apps.who.int/iris/bitstream/handle/10665/ 272722/9789241514187-eng.pdf. Zapata-Lamana R, Lalanza JF, Losilla JM, Capdevila L. 2018. Methodology of ecological momentary assessment (EMA) in physical activity research: a systematic review. PROSPERO. CRD42018088136. Available at https://www.crd.york.ac.uk/prospero/display_record.php? ID=CRD42018088136. Zapata-Lamana et al. (2020), PeerJ, DOI 10.7717/peerj.8848 29/29