Published on 19.02.18 in Vol 6, No 2 (2018): February
Preprints (earlier versions) of this paper are available at http://preprints.jmir.org/preprint/8123, first published May 31, 2017.
Mobile Phone Ownership Is Not a Serious Barrier to Participation in Studies: Descriptive Study
This is a corrected version. See correction statement: http://mhealth.jmir.org/2018/4/e10403/
Background: Rather than providing participants with study-specific data collection devices, their personal mobile phones are increasingly being used as a means for collecting geolocation and ecological momentary assessment (EMA) data in public health research.
Objective: The purpose of this study was to (1) describe the sociodemographic characteristics of respondents to an online survey screener assessing eligibility to participate in a mixed methods study collecting geolocation and EMA data via the participants’ personal mobile phones, and (2) examine how eligibility criteria requiring mobile phone ownership and an unlimited text messaging plan affected participant inclusion.
Methods: Adult (≥18 years) daily smokers were recruited via public advertisements, free weekly newspapers, printed flyers, and word of mouth. An online survey screener was used as the initial method of determining eligibility for study participation. The survey screened for twenty-eight inclusion criteria grouped into three categories, which included (1) cell phone use, (2) tobacco use, and (3) additional criteria
Results: A total of 1003 individuals completed the online screener. Respondents were predominantly African American (605/1003, 60.3%) (60.4%), male (514/1003, 51.3%), and had a median age of 35 years (IQR 26-50). Nearly 50% (496/1003, 49.5%) were unemployed. Most smoked menthol cigarettes (699/1003, 69.7%), and had a median smoking history of 11 years (IQR 5-21). The majority owned a mobile phone (739/1003, 73.7%), could install apps (86.8%), used their mobile phone daily (89.5%), and had an unlimited text messaging plan (871/1003, 86.8%). Of those who completed the online screener, 302 were eligible to participate in the study; 163 were eligible after rescreening, and 117 were enrolled in the study. Compared to employed individuals, a significantly greater proportion of those who were unemployed were ineligible for the study based on mobile phone inclusion criteria (P<.001); yet, 46.4% (333/717) of the individuals who were unemployed met all mobile phone inclusion criteria.
Conclusions: Inclusion criteria requiring participants to use their personal mobile phones for data collection was not a major barrier to study participation for most respondents who completed the online screener, including those who were unemployed.
Trial Registration: ClinicalTrials.gov NCT02261363; https://clinicaltrials.gov/ct2/show/NCT02261363 (Archived by WebCite at http://www.webcitation.org/6wOmDluSt)
JMIR Mhealth Uhealth 2018;6(2):e21
- smartphone ownership;
- online survey screener;
- ecological momentary assessment;
- tobacco products/utilization;
- electronic cigarettes;
- observational study;
- United States
Ecological momentary assessment (EMA), a real-time data capture method, has been increasingly used as a viable method to collect data from research participants over time and in various contexts outside of a research lab[- ]. EMA focuses on collecting data and recording subject experiences at a particular moment in the context of the research participants’ natural environment [ ]. The types of devices used in EMA data collection have changed with the development of, access to, and utilization of new technology [ ]. Whereas EMA once entailed that the participants keep written records, most real-time data is now collected via handheld electronic devices, such as tablets and cell phones[ - ]. Along with EMA, geotracking is another method that is being used more frequently to collect information regarding the participants’ real-time physical location or surroundings (eg, Global Positioning System [GPS] coordinates, home, office) by obtaining GPS data from their mobile phones or other GPS-enabled devices. Geotracking also allows the identification and analysis of those locations in relation to ones’ actions and/or experiences [ - ]. The technology used for both EMA and geotracking within public health research has developed alongside advancements in consumer electronics, and the methods can now be implemented through apps on the study participants’ personal mobile phones [ , ].
The use of mobile phones has become a method for EMA and geotracking data collection over the past ten years [, , ]. Such a preference may be attributed to the surge in mobile phone ownership among the general population. Between 2011 and 2016, there was a 42% increase in mobile phone ownership, and 77% Americans now own a mobile phone [ ]. The unique features of mobile phones include internet accessibility and the ability to download and install apps.
Instead of providing participants with study-specific handheld electronic devices, personal mobile phones are increasingly being used as a means for collecting EMA and geotracking data, providing a noncoercive (eg, no free phone for study participation), low-cost tool to collect data in real-time . While the majority of US adults own a mobile phone, it cannot be assumed that a potential study participant will have access to a mobile phone [ ]. Factors such as socioeconomic status (SES), which encompasses employment, income, and educational levels, may impact mobile phone ownership. Researchers must consider the potential participants’ mobile phone ownership and usage when conducting studies utilizing EMA and geotracking methods. Thus, understanding a potential participant’s access to a mobile phone is important for interpreting the generalizability of results when implementing these methods. The purpose of this study was to (1) describe the sociodemographic characteristics of respondents to an online survey screener assessing their eligibility to participate in the “Moment Study”: a mixed methods study that examined e-cigarette initiation among adult cigarette smokers using geolocation and text message-based EMA data collected via the participants’ personal mobile phones, and (2) examine how eligibility criteria requiring mobile phone ownership and an unlimited texting plan affected study eligibility.
Data come from a parent study called the “Moment Study”, a mixed method longitudinal study that examined factors influencing e-cigarette initiation using a convenience sample of adult daily smokers residing in Washington, DC . Briefly, the parent study involved data collected over three-weeks, and included: 1) geotracking; 2) EMA; 3) individual interviews; 4) biosamples; and 5) an online follow-up survey 30-days after the participant’s last study visit. Ethics approval for the study was obtained from the Chesapeake IRB (Pro00008526). Text message-based EMA data were collected via mobile phones. A secure, automated text message-based EMA system prompted participants to respond to 6 random text message surveys a day for 21 days. Participants also initiated text message surveys whenever they smoked a cigarette or used an e-cigarette. Geotracking data was collected via an app downloaded to participants’ mobile phones. The app collected one tracking point every five minutes [ ]. The analyses presented here include data from the online survey screener only.
Recruitment and Eligibility
We recruited a convenience sample of adult smokers via public advertisements, free weekly newspapers, printed flyers, and word of mouth. The advertisements included a link to an online survey, which was the initial screening tool. The online survey screened for twenty-eight inclusion criteria grouped into three categories, which included (1) mobile phone use, (2) tobacco use, and (3) additional inclusion criteria.
The inclusion criteria for mobile phone use derived from the technological needs for EMA and geotracking data collection. We required daily mobile phone use and a preexisting unlimited text messaging plan to ensure that participants were comfortable sending and receiving texts and the number of text messages sent and received over the course of three weeks did not pose a financial burden on the participants. We also required the participants to have an Android or iPhone mobile phone because the geotracking app was only available on these operating systems. Device requirements were not expected to be restrictive, as Android and iPhone are the leading mobile phone operating systems in the United States with Android holding 56.4% and iPhone accounting for 42% of the market as of January 2017 .
In addition to the mobile phone inclusion criteria, we also required that participants be adult (≥18 years) daily cigarette smokers with restricted past 30-day use of other tobacco products. Additional inclusion criteria included age, pregnancy status, and both physical and mental health status (). Prior to study enrollment, participants deemed eligible by the online screener were re-screened via telephone by study personnel to confirm that they met the inclusion criteria.
Descriptive statistics were used to summarize the sociodemographic characteristics of individuals who completed the online screener, overall and eligibility by virtue of mobile phone ownership, and to describe the frequency of respondents meeting the study criteria. Observations with missing data were listwise deleted. Chi square tests and Mood’s median tests (a special case of chi square tests) were used to test the equality of proportions or medians. Statistical significance was set to a P-value of 0.05. All analyses were conducted using Stata 14.2 (Stata Corp, College Station, TX, USA).
A total of 1003 individuals took the online screener. Most respondents were African American (605/1003, 60.3%), male (514/1003, 51.3%), and had a median age of 35 years (IQR 26-50). Nearly all the respondents lived in Washington, DC, Virginia or Maryland (976/1003, 97.3%), and approximately half (496/1003, 49.5%) were unemployed. Most smoked menthol cigarettes (699/1003, 69.7%) and had smoked for a median of 11 years (IQR 5-21) ().
Of the 1003 respondents to the online screener, 28.5% (286/1003) were ineligible because of mobile phone inclusion criteria. Differences by sociodemographic characteristics and mobile phone inclusion criteria were evident. African Americans (57.4% vs 70.5%, P<.001) and menthol smokers (72.0% vs 68.8%, P<.001) made up greater proportions of people who were ineligible owing to mobile phone inclusion criteria. Additionally, people who were ineligible owing to mobile phone inclusion criteria were older (median age of 47 vs 32, P<.001) and had smoked for more years (median years smoked being 20 vs 10, P<.001). Of those who met the mobile phone inclusion criteria, 53.6% (384/717) were employed; conversely, among those who did not meet mobile phone criteria, 68.9% (197/286) were unemployed.
Among the other inclusion criteria, 44.0% of the unemployed individuals met all tobacco use inclusion criteria, and 93.0% of unemployed individuals met all the additional inclusion criteria; there was no difference in eligibility by employment status for the tobacco use or all other inclusion criteria (results not shown).
|Characteristic||Mobile phone inclusion criteria|
|Overall (N=1003)||Met inclusion criteria (>N=717)||Did not meet inclusion criteria (N=286)||P value|
|Gender, n (%)a||<.001|
|Female||478 (47.7)||346 (48.3)||132 (46.2)|
|Male||514 (51.3)||371 (51.7)||143 (50.0)|
|Race, n (%)a||<.001|
|White||248 (24.7)||193 (27.0)||55 (20.0)|
|African American||605 (60.3)||411 (57.4)||194 (70.5)|
|Asian||39 (3.9)||35 (4.9)||4 (1.5)|
|Native Hawaiian||1 (0.1)||1 (0.1)||0 (0)|
|American Indian||2 (0.2)||1 (0.1)||1 (0.4)|
|Other||52 (5.2))||41 (5.7)||11 (4.0)|
|More than 1 race||44 (4.4)||34 (4.7)||11 (3.6)|
|Hispanic, n (%)||77 (7.7)||58 (8.1)||19 (6.6)||.530|
|Age, median (IQRb)c||35 (26-50)||32 (25-45)||47 (32-55)||<.001|
|Years smoking, median (IQR)c||11 (5-21)||10 (6-20)||20 (11-35)||<.001|
|Menthol smoker n (%)||699 (69.7)||493 (68.8)||206 (72.0)||<.001|
|Live in the DC metro area, n (%)||976 (97.3)||708 (98.9)||268 (93.7)||.201|
|Employment, n (%)a|
|Employed||473 (47.2)||384 (53.6)||89 (31.3)||<.001|
|Not employed||496 (49.5)||333 (46.4)||197 (68.9)|
aThe following variables had missing data: gender (1%), race (1.2%), employment (3.3%).
bIQR: interquartile range.
cMood’s median tests were used to test for differences in median age and years smoked.
|Inclusion criteria||n (%)|
|Mobile phone criteria|
|Have Android or iPhone||739 (73.7)|
|Mobile phone allows app installation||871 (86.8)|
|Use mobile phone daily||898 (89.5)|
|Unlimited text message plan||871 (86.8)|
|Meet all the mobile phone inclusion criteria||717 (71.5)|
|Tobacco use criteria|
|Daily smoker||890 (88.7)|
|≥5 years of smoking||772 (77.0)|
|≥8 cigarettes a day||689 (68.7)|
|Use of little cigars no more than 5 times in the last 30 days||814 (81.2)|
|Use of cigars no more than 5 times in last the 30 days||998 (99.5)|
|Use of hookah no more than 5 times in the last 30 days||996 (96.3)|
|No use of pipe (with tobacco, not including hookah) in the last 30 days||995 (95.2)|
|No use of chewing tobacco in the last 30 days||995 (99.2)|
|No use of dip/snuff in the last 30 days||997 (99.4)|
|No use of snus in the last 30 days||1000 (99.7)|
|No use of nicotine products (like gum, patches) in the last 30 days||961 (95.8)|
|No use of e-cigarettes in the last 30 days||833 (83.1)|
|Interested in trying an e-cigarette||689 (68.7)|
|Meet all tobacco use inclusion criteria||426 (42.5)|
|Additional inclusion criteria|
|Not on cessation medication||947 (94.4)|
|Not breastfeeding or planning to become pregnant||975 (97.2)|
|No heart disease/uncontrolled blood pressure||941 (93.8)|
|No psychosis||943 (94.0)|
|No suicidal thoughts||949 (94.6)|
|Not enrolled currently in an alcohol treatment program||940 (93.7)|
|Not out of town for more than 5 nights in the next 6 weeks||933 (93.0)|
|18 years or older||1002 (99.9)|
|Reside in the DC metro area||976 (97.3)|
|Willing to travel to data collection site four times in three weeks||949 (94.6)|
|Meet all additional inclusion criteria||916 (91.3)|
The majority of the respondents owned an iPhone or Android mobile phone (739/1003, 73.7%) and could install apps on their mobile phone (871/1003, 86.8%). Many used their mobile phones daily (898/1003, 89.5%) and had an unlimited text messaging plan (871/1003, 86.8%). Most (717/1003, 71.5%) met the mobile phone inclusion criteria, and 91.3% met all the additional inclusion criteria; however, only 42.5% (426/1003) met all the tobacco inclusion criteria. Overall, 302 individuals were eligible to participate in the study with 163 eligible after being rescreened; 117 were ultimately enrolled in the study ().
This study describes the sociodemographic characteristics of individuals responding to an online screener, and examines how eligibility criteria requiring mobile phone ownership affected study eligibility. A total of 1003 individuals completed the online screener of whom 73.7% (739/1003) owned a mobile phone; of the mobile phone owners, 86.8% (871/1003) could install apps, 89.5% (898/1003) used their mobile phones daily, and 86.8% (871/1003) had an unlimited text messaging plan. While our mobile phone inclusion criteria were strict, 46.4% (333/717) of the unemployed respondents still met the criteria, suggesting that mobile phone ownership is not a major barrier to study participation, even for unemployed individuals.
These findings also have broader relevance for the tobacco control field, which increasingly uses mobile phone technology to examine tobacco product use, surveil tobacco retail environments, and deliver smoking cessation interventions [, , ]. Smoking is concentrated among low-SES groups with sometimes unstable employment; provision of a free mobile phone as part of study participation may be coercive for members of such vulnerable groups. Examination of who has access to a mobile phone is essential to assessing how planning a protocol using methods such as EMA or geotracking might affect ethical considerations, study recruitment, and generalizability of the findings.
The study utilized an online screener, which may have been a barrier to individuals who lacked internet access. Had we initially screened potential participants via telephone, we may have found that a greater proportion of respondents were ineligible because of lack of mobile phone ownership. Additionally, given that this study was conducted in Washington, DC, our findings may not be generalizable to other settings. Moreover, it is important to consider that all data collected in this study were self-reported responses to the online screener questions. Therefore, the respondents’ mobile phone and tobacco use may have been under- or over-reported owing to social desirability and recall bias.
This research suggests that using the participants’ own mobile phones for data collection, including geotracking and text messaging, was not a barrier to study participation for the majority of the respondents who took the initial online screener. Employment was related to fulfilling the mobile phone inclusion criteria; however, nearly half of the eligible respondents were unemployed. As mobile phone ownership continues to grow, researchers should consider using the participants’ own mobile phones as feasible data collection devices given their study’s target population.
Research reported in this publication was supported by the National Institute on Drug Abuse of the National Institutes of Health and FDA Center for Tobacco Products under grant number 5R21DA036472. JP’s time was also supported by the National Institute on Drug Abuse of the National Institutes of Health (NIH) and FDA Center for Tobacco Products under grant K01DA037950. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the Food and Drug Administration. At the time of the study, SS was a scholar with the HIV/AIDS, Substance Abuse, and Trauma Training Program (HA-STTP) at the University of California, Los Angeles, supported by an award from the National Institute on Drug Abuse (R25DA035692).
EH conceived the study design and the overall manuscript, led the data collection and analysis team, wrote the first draft of the paper, and worked as a research associate in the parent study. HE and YZ conducted the data analyses. SS and LR contributed to the analysis, interpretation of results, and manuscript writing. LR was involved as a research assistant in the parent study; SS was project director in the parent study, and JP was principal investigator in the parent study.
Conflicts of Interest
- Shiffman S. Ecological momentary assessment (EMA) in studies of substance use. Psychol Assess 2009 Dec;21(4):486-497 [FREE Full text] [CrossRef] [Medline]
- Swendsen J. Contributions of mobile technologies to addiction research. Dialogues Clin Neurosci 2016 Jun;18(2):213-221 [FREE Full text] [Medline]
- Smyth JM, Stone AA. Ecological Momentary Assessment Research in Behavioral medicine. Journal of Happiness Studies 2003;4(1):35-52. [CrossRef]
- Hedeker D, Mermelstein RJ, Demirtas H. An application of a mixed-effects location scale model for analysis of Ecological Momentary Assessment (EMA) data. Biometrics 2008 Jun;64(2):627-634 [FREE Full text] [CrossRef] [Medline]
- Ravert RD, Calix SI, Sullivan MJ. Research In Brief: Using Mobile Phones to Collect Daily Experience Data From College Undergraduates. Journal of College Student Development 2010;51(3):343-352. [CrossRef]
- Litt MD, Cooney NL, Morse P. Ecological momentary assessment (EMA) with treated alcoholics: methodological problems and potential solutions. Health Psychol 1998 Jan;17(1):48-52. [Medline]
- Kuntsche E, Labhart F. Using Personal Cell Phones for Ecological Momentary Assessment. European Psychologist 2013 Jan;18(1):3-11. [CrossRef]
- Christian WJ. Using geospatial technologies to explore activity-based retail food environments. Spat Spatiotemporal Epidemiol 2012 Dec;3(4):287-295. [CrossRef] [Medline]
- Hurvitz PM, Moudon AV. Home versus nonhome neighborhood: quantifying differences in exposure to the built environment. Am J Prev Med 2012 Apr;42(4):411-417 [FREE Full text] [CrossRef] [Medline]
- Kirchner TR, Cantrell J, Anesetti-Rothermel A, Ganz O, Vallone DM, Abrams DB. Geospatial exposure to point-of-sale tobacco: real-time craving and smoking-cessation outcomes. Am J Prev Med 2013 Oct;45(4):379-385 [FREE Full text] [CrossRef] [Medline]
- Berkman ET, Dickenson J, Falk EB, Lieberman MD. Using SMS text messaging to assess moderators of smoking reduction: Validating a new tool for ecological measurement of health behaviors. Health Psychol 2011 Mar;30(2):186-194 [FREE Full text] [CrossRef] [Medline]
- Whittaker R, McRobbie H, Bullen C, Borland R, Rodgers A, Gu Y. Mobile phone-based interventions for smoking cessation. Cochrane Database Syst Rev 2012;11:CD006611. [CrossRef] [Medline]
- Pew Research Center. 2017 Jan 12. Mobile Fact Sheet URL: http://www.pewinternet.org/fact-sheet/mobile/ [accessed 2017-04-14] [WebCite Cache]
- Pearson JL, Smiley SL, Rubin LF, Anesetti-Rothermel A, Elmasry H, Davis M, et al. The Moment Study: protocol for a mixed method observational cohort study of the Alternative Nicotine Delivery Systems (ANDS) initiation process among adult cigarette smokers. BMJ Open 2016;6(4):e011717 [FREE Full text] [CrossRef] [Medline]
- Kantar World Panel. Smartphone OS sales market share URL: https://www.kantarworldpanel.com/global/smartphone-os-market-share/ [accessed 2017-04-14] [WebCite Cache]
- Businelle MS, Ma P, Kendzor DE, Frank SG, Vidrine DJ, Wetter DW. An Ecological Momentary Intervention for Smoking Cessation: Evaluation of Feasibility and Effectiveness. J Med Internet Res 2016 Dec 12;18(12):e321 [FREE Full text] [CrossRef] [Medline]
- Schuster RM, Mermelstein RJ, Hedeker D. Ecological momentary assessment of working memory under conditions of simultaneous marijuana and tobacco use. Addiction 2016 Aug;111(8):1466-1476. [CrossRef] [Medline]
|EMA: ecological momentary assessment|
|GPS: Global Positioning System|
|IQR: interquartile range|
Edited by M Bars; submitted 31.05.17; peer-reviewed by L Mudie, C Brady; comments to author 26.07.17; revised version received 13.11.17; accepted 22.11.17; published 19.02.18
©Emily J Harvey, Leslie F Rubin, Sabrina L Smiley, Yitong Zhou, Hoda Elmasry, Jennifer L Pearson. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 19.02.2018.
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