Puigdomegravenech et al BMC Public Health 2015 152httpwwwbiomedcentralcom1471-2458152

RESEARCH ARTICLE Open Access

Information and communication technologies forapproaching smokers a descriptive study inprimary healthcareElisa Puigdomegravenech1 Jose-Manuel Trujillo-Goacutemez1 Carlos Martiacuten-Cantera123 Laura Diacuteaz-Gete4Moacutenica Manzano-Montero5 Jessica Saacutenchez-Fondevila1 Yolanda Gonzalez-Fernandez6 Beatriz Garcia-Rueda6Elena-Mercedes Briones-Carrioacute1 Mordf-Lourdes Clemente-Jimeacutenez7 Carmen Castantildeo8 Joan Biruleacutes-Muntaneacute1

and Grupo Estudio TABATIC1

Abstract

Background Common interventions for smoking cessation are based on medical advice and pharmacological aidInformation and communication technologies may be helpful as interventions by themselves or as complementarytools to quit smoking The objective of the study was to determine the use of information and communicationtechnologies (ICTs) in the smoking population attended in primary care and describe the major factors associatedwith its use

Methods Descriptive observational study in 84 health centres in Cataluntildea Aragon and Salamanca We included bysimple random sampling 1725 primary healthcare smokers (any amount of tobacco) aged 18ndash85 Through personalinterview professionals collected Socio-demographic data and variables related with tobacco consumption and ICTsuse were collected through face to face interviews Factors associated with the use of ICTs were analyzed by logisticregression

Results Users of at least one ICT were predominantly male young (18ndash45 years) from most favoured social classesand of higher education Compared with non-ICTs users users declared lower consumption of tobacco youngeronset age and lower nicotine dependence The percentages of use of email text messages and web pages were653 740 and 715 respectively Factors associated with the use of ICTs were age social class educationallevel and nicotine dependence level The factor most closely associated with the use of all three ICTs was agemainly individuals aged 18ndash24

Conclusions The use of ICTs to quit smoking is promising with the technology of mobile phones having a broaderpotential Younger and more educated subjects are good targets for ICTs interventions on smoking cessation

Keywords Smoking cessation Information and communication technologies Primary health care

BackgroundTobacco consumption is one of the leading preventablecauses of death worldwide [1] for instance respiratoryand cardiovascular diseases and cancer are three well-established health effects of tobacco consumptionamong both smokers and non-smokers [2] It has been

Correspondence cardiocatgmailcom1Unidad de Soporte a la Investigacioacuten Barcelona Ciudad InstitutoUniversitario de Investigacioacuten en Atencioacuten Primaria Jordi Gol (IDIAP JordiGol) C Sardenya 375 entresol 08025 Barcelona SpainFull list of author information is available at the end of the article

copy 2015 Puigdomegravenech et al licensee BioMedCreative Commons Attribution License (httpdistribution and reproduction in any mediumDomain Dedication waiver (httpcreativecomarticle unless otherwise stated

estimated that in Spain smoking is the health problemthat causes most mortality and morbidity Consequentlyit also originates higher health costs [3] The percentageof daily smokers aged 15 or older in Spain was 240(279 in men and 202 in women) according to thelast national survey conducted in 2011ndash12 [4] A largenumber of Spanish smokers declared their willingness toquit smoking (approximately 70) and 274 have triedit on the past year [3] but merely 3ndash5 of them accom-plished it [356]

Central This is an Open Access article distributed under the terms of thecreativecommonsorglicensesby20) which permits unrestricted use provided the original work is properly credited The Creative Commons Publicmonsorgpublicdomainzero10) applies to the data made available in this

Puigdomegravenech et al BMC Public Health 2015 152 Page 2 of 14httpwwwbiomedcentralcom1471-2458152

Interventions to quit smoking are one of the mostcost-effective methods to improve the health of thepopulation [35-8] It is well accepted that the more in-tensive the intervention the best cessation rates for in-stance whilst a 5 cessation per year is reached withminimum advice a 20 can be achieved with more in-tensive interventions [910] Common interventions tohelp smokers quit are based on medical advice andpharmacological assistance as nicotine replacement ther-apy and bupropion Alternative interventions such ashypnosis acupuncture exercise and opioid agonist haveassisted some people in smoking cessation but there isnot a clear consensus on its efficacy [811]The use of technologies that offers access to informa-

tion via telecommunications (Information and commu-nication technology (ICTs)) is augmenting progressivelymainly internet email and cell phone use in fact we livein a growingly electronic world [12-14] For instanceworldwide use of mobile phones increased by 155 in2010 reaching 78 telephone lines per 100 inhabitantswith a cumulative average growth between 2005 and2010 of 195 Likewise Internet use had a 13 ofgrowth in 2010 exceeding the number of 2044 millionsof users Europe and USA are the two geographical areaswith the largest number of internet users 67 and507 respectively [12] According to The National Ob-servatory for Telecommunications and the InformationSociety in Spain 2011 829 of people aged 15 or olderhad a mobile phone and 663 had accessed internet atleast once [15] If these data are analyzed as develop-ment indicators and especially in the case of internet aspotential tools to change behaviours these technologiescan pose a great influence on health policies (directly orindirectly) [1617]The use of ICTs has been growing in several fields in-

cluding medicine for instance appointments can bescheduled on-line and analytical results or health infor-mation can be consulted on internet ICTs technologyhas also been adopted on lifestyle interventions includingsmoking cessation [38] Recent reviews have analyzed theefficacy of web-based interventions on smoking cessation[1819] although results remain inconclusively [20] Ad-vantages of using ICTs on smoking cessation programs in-clude its wide use time and cost savings (they candiminish visits to the health centre and the possibility tocheck the information or messagesmails at patient orhealth professional convenience) and the possibility tosupply personalized support [21]Some recent systematic reviews that evaluates smok-

ing cesation programs that use computer internet mo-bile phone and other electronic aids conclude theireffectiveness altough small and mainly at long term onsmoking cessation compared to no intervention orstandard counseling [22-24]

Describing the use of ICTs among patients attendingprimary care could help us elucidate the viability of anICT intervention in smoking cessation in primary careFor instance our research group will compare brief ad-vice vs personalized E-mail tracking (TABATIC study)[25] Therefore the aim of the present study is to deter-mine the use of ICT in the smoking population attendedin primary healthcare and to describe the main factorsassociated with its use

MethodsStudy designWe conducted a cross-sectional study to describe the useof ICTs among smokers attended in primary care as wellas the main factors associated with that use The studywas multicentre 195 healthcare professionals (generalpractitioners or nurses) of 84 primary healthcare centresof the Spanish public health system in Cataluntildea Aragoacutenand Salamanca (Spain) participated in the recruitment ofpatients

SubjectsSample size was calculated according to the projectrsquosaim which was to estimate the use of ICTs in smokersattended in primary care Assuming an alpha risk of 005and a beta risk of 020 in a two-sided test and a no-response rate of 20 481 subjects were needed Weconsidered that at least half of the Spanish population in2011 had access to internet and mobile phones [15]From November 2011 to January 2012 individuals aged18ndash75 who answered positively to the question ldquoDo yousmokerdquo (independently of the amount) and signed theconsent form were recruited by random sampling Pa-tients were recruited as they visited the primary careteam and each day the first two subjects that fulfilled theinclusion criteria were invited to participate We askedthe health professionals to recruit participants at leasttwo days per week In case the patient declined to par-ticipate in the study the health professional gatheredage and sex and the reason of the refusal Recruitmentand data collection was performed by the health profes-sional that commonly attends the patientPeople suffering from terminal illnesses severe psychi-

atric disorders addiction to other psychoactive sub-stances or who did not consent to participate in thestudy were excluded Of the 1850 patients that fulfilledthe inclusion criteria 1725 agreed to participate (932)The percentage of participation was similar between menand women in each age stratum (36ndash45 years old p =0913 46ndash65 years old p = 0176 gt65 years old p =0246) except that less men (931 vs 980 p = 0008)accepted to participate among individuals aged 35 andyounger

Puigdomegravenech et al BMC Public Health 2015 152 Page 3 of 14httpwwwbiomedcentralcom1471-2458152

The study protocol was reviewed and approved by theHealth Care Ethics Committee and the Clinical ResearchEthics Committee of the Primary Health Care UniversityResearch Institute-IDIAP Jordi Gol located in BarcelonaSpain

Study variablesThe following information was obtained by healthcareprofessionals collected through face to face interviewsage sex educational level occupational social class civilstatus ICTs (email text messaging and web pages) avail-ability and use self-declared daily tobacco consumptionin cigarettes per day smoking onset age number of pre-vious attempts (of at least of 24 hours) to quit smokingmaximum abstinence time (in days) pharmacologicaltreatment used on previous attempts (nicotine substi-tutes Bupropion Vareniclina) environmental exposureto smoke from family workmates and friends and nico-tine dependence level measured by the simplified two-question Fagerstroumlm test classified as low medium andhigh [26] Educational level refers to the maximum levelof finalized studies classified into no formal studies pri-mary studies secondary and university Subsequently itwas recoded into lower than secondary and ge secondaryTo assign occupational social class we used the Span-

ish classification which is based on Goldthorpersquos schemewhich was designed to facilitate international comparisons[27] Consequently social class was assigned through thecurrent or last occupation of the patient in cases wherethe subject had not worked through the current or lastoccupation of the head of the household [28] The classifi-cation includes five well-established main social groupsbut was subsequently collapsed into smaller number ofcategories manual (social classes IV-V) and non-manualworkers (the rest) to undertake analysis [27]The information in the use of the three ICTs (E-mail

text messages and web pages) was gather by the follow-ing two questions Do you use electronic mail (or inter-netweb page or sms) Possible answers were lsquoNorsquo orlsquoYesrsquo If the participant responded yes then the inter-viewer asked for the frequency of use possible answerswere lsquoless than once a weekrsquorsquo once a weekrsquo or lsquomorethan once a weekrsquo Consequently the use of the threeICTs was grouped into four categories lsquono usersquo lsquolessthan once a weekrsquo lsquoonce a weekrsquo and lsquomore than once aweekrsquo Subsequently it was recoded into lsquono usersquo andlsquolow frequency of usersquo and lsquomidhigh frequency of usersquoThis study included other variables that are not pre-

sented in this paper

Statistical analysisResults are expressed as mean and standard deviation(SD) for quantitative variables or by frequency distribu-tion for qualitative variables Pearsonrsquos Chi-square test

for independence or homogeneity was applied to assessthe relationship between two categorical variables TheStudentrsquos t-test and ANOVA for independent sampleswere used to analyze associations between dichotomicand continuous normal qualitative variables respect-ively Mann-Whitneyrsquos U and Kruskal-Wallis test wereused to compare dichotomic and continuous variables ifthey did not follow a normal distribution Binary logisticmodels were used to assess the associations betweensociodemographic and tobacco consumption factors andICTs use The level of statistical significance was set at005 and all tests were two-tailed Statistical analyseswere conducted using SPSS version 170 (SPSS Inc Chi-cago IL)

ResultsA total of 1725 smokers participated in the study meanage 455 years (SD 136 years) and 865 (511) were maleCharacteristics of participants are shown in Table 1 Par-ticipants were more likely to be married (635) manualworkers (595) and 525 had completed at least second-ary education Mean age of starting tobacco consumptionwas 172 (SD 45) and the mean number of self-declaredcigarettes smoked per day was 154 (SD 93) Half of theparticipants declared a low dependency on nicotine 745of participants declared previous attempts to quit smok-ing and 766 of those did not use any medication incases where they had used medication a nicotine substi-tute was the most frequently used Patients includedtended to live in a non-smoke-free environment of thosewho had a partner 479 declared living with a partnerthat smoked Of those who were working or studying552 declared having co-workers that smoked 654 ofthe participants declared that their friends lived in a smok-ing environmentWhen comparing non-users of any ICT (n = 269) with

users of at least one ICT (any frequency of use) ICTsusers (n = 1456) tended to be male middleyoung (18 to45 years) non-manual workers and had a higher educa-tional level (all p lt0001) The users of at least one ICTalso reported lower consumption of tobacco had startedusing tobacco at a younger age and a higher percentageof them had lower nicotine dependence tended to livewith partners who smoke and in not smoke-free homesand consumed chronic medication No statistically sig-nificant differences were found neither regarding thenumber of previous attempts to quit smoking nor othersmoking environments (Table 1)Frequency of use of the three ICTs is specified in

Table 2 Self-reported use of E-mail text messaging andweb pages were 653 (498 for high use) 74 (508for high use) and 715 (560 for high use) respect-ively Descriptive analysis showed that more high fre-quency users were women middleyoung (18 to 45)

Table 1 Comparison of main sociodemographic features and tobacco consumption variables among non users vsusers of any ICT

Total Non users Users P-valueN () N () N ()

Participants 1725 269 (156) 1456 (844)

Gender 1725 lt0001

Male 865 (511) 180 (331) 685 (530)

Female 860 (499) 89 (669) 771 (470)

Age (years) Mean (SD) 4554 (1365) 5560 (1197) 4139 (1206) lt0001

Age group 1725 lt0001

18-35 451 (261) 6 (22) 445 (306)

36-45 402 (233) 21 (78) 381 (262)

46-65 733 (425) 159 (591) 574 (394)

gt65 139 (81) 83 (309) 56 (38)

Marital status 1725 lt0001

Married 1096 (635) 198 (736) 898 (617)

Single 395 (229) 27 (100) 368 (253)

Separate 174 (101) 18 (67) 156 (107)

Widower 60 (35) 26 (97) 34 (23)

Social class 1658 lt0001

Most favored Non-manual 672 (405) 40 (165) 632 (447)

Disadvantaged Manual 986 (595) 203 (835) 783 (553)

Educational level 1723 lt0001

Lower secondary education 819 (475) 216 (803) 603 (415)

Secondaryhigher education 904 (525) 53 (197) 851 (585)

Number cigarettesday Mean (SD) 1539 (928) 1735 (1140) 1503 (880) lt0001

Age of initiation consumption Mean (SD)Mean (SD) MEAN

1721 (455) 1862 (664) 1696 (401) lt0001

Fagerstroumlm test Mean (SD) 235 (163) 262 (174) 231 (161) 0004

Fagerstroumlm test 1693 lt0001

Low 862 (509) 130 (489) 732 (513)

Medium 691 (408) 98 (368) 593 (416)

High 140 (83) 38 (143) 102 (71)

Attempts at smoking cessation Mean (SD) 22 (24) 21 (22) 22 (25) 0480

Smoking environment of partner 1448 lt0001

Yes 695 (479) 69 (317) 626 (509)

No 753 (521) 149 (683) 604 (491)

Presence of smoking in the home 1673 0001

Yes 939 (875) 121 (471) 818 (578)

No 734 (125) 136 (529) 598 (422)

Workplacesmoking studies 1351 0970

Yes 745 (552) 73 (553) 672 (551)

No 606 (448) 59 (447) 547 (449)

Smoking environment of friends 1725 0570

Yes 1129 (654) 172 (639) 957 (657)

No 596 (346) 97 (361) 499 (343)

Puigdomegravenech et al BMC Public Health 2015 152 Page 4 of 14httpwwwbiomedcentralcom1471-2458152

Table 1 Comparison of main sociodemographic features and tobacco consumption variables among non users vsusers of any ICT (Continued)

Have made some attempt to quit smoking 1582 0830

Yes 1179 (745) 349 (749) 830 (744)

No 403 (255) 117 (251) 286 (256)

Pharmacoterapy used for smoking cessationdagger 1137 0210

Nicotine replacement therapy 132 (116) 17 (97) 115 (120)

Bupropion 27 (24) 5 (28) 22 (23)

Varenicline 43 (38) 4 (23) 39 (41)

ge2 treatments 64 (56) 5 (28) 59 (61)

No medication 871 (766) 145 (824) 726 (755)

Chronic medication intake 1137 0049

Some medication 266 (234) 31 (176) 235 (245)

No medication 871 (766) 145 (824) 726 (755)

SD Standard deviationP-value derived from the Chi-square test and ANOVA in categorical and continuous variables respectivelyNot taking into account cases of ldquonot applicablerdquowithout partner living alone or not working or studyingdaggerIn the last attempt to quitP-value derived from the Chi-square test and ANOVA in categorical and continuous variables respectively

Puigdomegravenech et al BMC Public Health 2015 152 Page 5 of 14httpwwwbiomedcentralcom1471-2458152

and individuals with a higher educational level Thoseindividuals from lower social classes tended to declareno use or lower use of E-mail text messaging and internetRegarding tobacco consumption variables the number ofcigarettes smoked per day and nicotine dependence washigher among non users of ICTs Conversely users ofboth low and high frequency declared a lower age of startof tobacco consumptionFinally we analyzed factors affecting ICTs use (Tables 3

4 and 5) by comparing no use vs low frequency of use(OR1) and no use vs midhigh frequency of use (OR2)Binary logistic adjusted analysis showed that age wasthe strongest predictor of E-mail frequency use (for in-dividuals aged 18ndash35 OR1 = 334 CI951397-8025and OR2 = 600 CI95 301-953) Higher social class(OR1 = 161 CI95108-234 and OR2 = 429 CI95311-593) and educational level (OR1 = 222 CI95 156-315 and OR2 = 408 CI95 303-548) were also posi-tively associated to the frequency of use of E-mail Lowdependence to nicotine was associated with a midhighuse of E-mail use (OR2 = 203 CI95 122-338) Furtheradjustments did not materially alter these associationsThese results are consistent with crude analysisAll these factors were also associated to SMS and inter-

net frequency of use except that dependence to nicotinedid not remain statistically significant Conversely womenwere more likely to use SMS (OR1 = 215 CI95 163-285 and OR2 = 305 CI95 240-388)When the frequency of E-mail SMS and internet use

was analyzed separately by age social class and educa-tional level the influence of the other factors was similaras among the whole sample studied (results not shown)for instance the direction of the associations of young

age and higher social class remained analogous and sta-tistically significant in both individuals with high andlow educational level

DiscussionPrincipal findingsBy means of a representative sample of the smokingpopulation attended in the public primary care systemwe have studied the differences among smokers in rela-tion to the use of different ICTs (Internet Email andSMS messages) including among the variables demo-graphic characteristics socioeconomic status and smok-ing profile This study shows that the three studied ICTsare widely used especially among the population under45 women more favoured social class of higher educa-tion level and those with lower consumption It alsoshowed that cigarette consumption had started at ayounger age in ICTs usersOur data show that 844 of smokers use some of the

ICTs studied being the use of Internet sms and E-mailand 715 (560 for high use) 740 (508 for highuse) and 653 (498 for high use) respectively Thefrequency of access to web of our study is comparable tothe general population of Spain in 2012 (708) [15] andto some international studies (655) [1429] Amongsmokers Hunt et al showed that 635 were internetusers [30] Some studies have attempted to characterizethe group of internet users (not only smokers) who areinterested in finding information about smoking onhealth-related web pages but not a clear common pro-file has been found [3132] Weaver and colleagues sug-gested that females white respondents people aged55ndash64 and computer owners were positively associated

Table 1 Comparison of main sociodemographic features and tobacco consumption variables among non users vsusers of any ICT (Continued)

Have made some attempt to quit smoking 1582 0830

Yes 1179 (745) 349 (749) 830 (744)

No 403 (255) 117 (251) 286 (256)

Pharmacoterapy used for smoking cessationdagger 1137 0210

Nicotine replacement therapy 132 (116) 17 (97) 115 (120)

Bupropion 27 (24) 5 (28) 22 (23)

Varenicline 43 (38) 4 (23) 39 (41)

ge2 treatments 64 (56) 5 (28) 59 (61)

No medication 871 (766) 145 (824) 726 (755)

Chronic medication intake 1137 0049

Some medication 266 (234) 31 (176) 235 (245)

No medication 871 (766) 145 (824) 726 (755)

SD Standard deviationP-value derived from the Chi-square test and ANOVA in categorical and continuous variables respectivelyNot taking into account cases of ldquonot applicablerdquowithout partner living alone or not working or studyingdaggerIn the last attempt to quitP-value derived from the Chi-square test and ANOVA in categorical and continuous variables respectively

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and individuals with a higher educational level Thoseindividuals from lower social classes tended to declareno use or lower use of E-mail text messaging and internetRegarding tobacco consumption variables the number ofcigarettes smoked per day and nicotine dependence washigher among non users of ICTs Conversely users ofboth low and high frequency declared a lower age of startof tobacco consumptionFinally we analyzed factors affecting ICTs use (Tables 3

4 and 5) by comparing no use vs low frequency of use(OR1) and no use vs midhigh frequency of use (OR2)Binary logistic adjusted analysis showed that age wasthe strongest predictor of E-mail frequency use (for in-dividuals aged 18ndash35 OR1 = 334 CI951397-8025and OR2 = 600 CI95 301-953) Higher social class(OR1 = 161 CI95108-234 and OR2 = 429 CI95311-593) and educational level (OR1 = 222 CI95 156-315 and OR2 = 408 CI95 303-548) were also posi-tively associated to the frequency of use of E-mail Lowdependence to nicotine was associated with a midhighuse of E-mail use (OR2 = 203 CI95 122-338) Furtheradjustments did not materially alter these associationsThese results are consistent with crude analysisAll these factors were also associated to SMS and inter-

net frequency of use except that dependence to nicotinedid not remain statistically significant Conversely womenwere more likely to use SMS (OR1 = 215 CI95 163-285 and OR2 = 305 CI95 240-388)When the frequency of E-mail SMS and internet use

was analyzed separately by age social class and educa-tional level the influence of the other factors was similaras among the whole sample studied (results not shown)for instance the direction of the associations of young

age and higher social class remained analogous and sta-tistically significant in both individuals with high andlow educational level

DiscussionPrincipal findingsBy means of a representative sample of the smokingpopulation attended in the public primary care systemwe have studied the differences among smokers in rela-tion to the use of different ICTs (Internet Email andSMS messages) including among the variables demo-graphic characteristics socioeconomic status and smok-ing profile This study shows that the three studied ICTsare widely used especially among the population under45 women more favoured social class of higher educa-tion level and those with lower consumption It alsoshowed that cigarette consumption had started at ayounger age in ICTs usersOur data show that 844 of smokers use some of the

ICTs studied being the use of Internet sms and E-mailand 715 (560 for high use) 740 (508 for highuse) and 653 (498 for high use) respectively Thefrequency of access to web of our study is comparable tothe general population of Spain in 2012 (708) [15] andto some international studies (655) [1429] Amongsmokers Hunt et al showed that 635 were internetusers [30] Some studies have attempted to characterizethe group of internet users (not only smokers) who areinterested in finding information about smoking onhealth-related web pages but not a clear common pro-file has been found [3132] Weaver and colleagues sug-gested that females white respondents people aged55ndash64 and computer owners were positively associated

Table 2 Comparison of the frequency of use of three ICTs (email sms and the web pages) according to sociodemographic variables and tobacco consumptionof the participants in the study ldquoUsage profile of ICTsrdquo

Total Donrsquot use E-mail Low use of E-mail Midhigh use ofE-mail

P-value Donrsquot use webpages

Low use of webspages

Midhigh use ofweb pages

P-value

N () N () N () N () N () N () N ()

Participants 1725 599 (347) 267 (155) 859 (498) 492 (285) 267 (155) 966 (560)

Gender (N = 1725) lt0001 lt0001

Male 865 (501) 337 (563) 135 (506) 393 (458) 279 (567) 124 (464) 462 (478)

Female 860 (499) 262 (437) 132 (494) 466 (542) 213 (433) 143 (536) 504 (522)

Age (years) (N = 1725) 455 plusmn 136 544 plusmn 122 423 plusmn 122 403 plusmn 118 lt0001 560 plusmn 119 452 plusmn 119 403 plusmn 118 lt0001

Age group lt0001 lt0001

18-35 451 (261) 42 (70) 87 (326) 322 (375) 25 (50) 57 (213) 369 (382)

36-45 402 (233) 87 (145) 66 (247) 249 (290) 65 (132) 70 (262) 267 (276)

46-65 733 (425) 355 (593) 107 (401) 271 (315) 293 (596) 132 (494) 308 (319)

gt65 139 (81) 115 (192) 7 (26) 17 (20) 109 (222) 8 (30) 22 (23)

Social class (N = 1658) lt0001 lt0001

Most favoredNM 672 (405) 101 (181) 78 (299) 493 (588) 81 (177) 80 (311) 511 (542)

Disadventaged-M 986 (595) 458 (819) 183 (701) 345 (412) 377 (823) 177 (689) 432 (458)

Educational level (N = 1723) lt0001 lt0001

ltSecondary 819 (475) 439 (517) 138 (517) 242 (282) 381 (774) 126 (472) 312 (324)

geSecondaryHigher 904 (525) 129 (483) 129 (483) 615 (718) 111 (226) 141 (528) 652 (676)

Num Cigarettes day 154 plusmn 93 169 plusmn 104 153 plusmn 87 144 plusmn 84 lt0001 172 plusmn 109 147 plusmn 81 147 plusmn 86 lt0001

Age of initiation consumption 172 plusmn 45 180 plusmn 60 165 plusmn 32 169 plusmn 36 lt0001 183 plusmn 63 169 plusmn 39 168 plusmn 35 lt0001

Fagerstroumlm test 24 plusmn 16 26 plusmn 17 24 plusmn 16 21 plusmn 16 lt0001 26 plusmn 17 24 plusmn 15 22 plusmn 16 lt0001

Attempts smoking cessation 22 plusmn 24 21 plusmn 23 22 plusmn 24 23 plusmn 25 0182 21 plusmn 23 21 plusmn 24 23 plusmn 25 0185

Total Donrsquot use sms Low use of sms Midhigh use of sms P-value

N () N () N () N ()

448 (260) 400 (232) 877 (508)

Gender (N = 1725) lt0001

Male 865 (501) 305 (681) 199 (498) 361 (412)

Female 860 (499) 143 (319) 201 (503) 516 (588)

Age (years) (N = 1725) 455 plusmn 136 544 plusmn 123 473 plusmn 119 397 plusmn 118 lt0001

Age group lt0001

18-35 451 (261) 32 (71) 66 (165) 353 (403)

36-45 402 (233) 52 (116) 100 (250) 250 (285)

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Table 2 Comparison of the frequency of use of three ICTs (email sms and the web pages) according to sociodemographic variables and tobacco consumptionof the participants in the study ldquoUsage profile of ICTsrdquo (Continued)

46-65 733 (425) 272 (607) 209 (523) 252 (287)

gt65 139 (81) 92 (205) 25 (63) 22 (25)

Social class (N = 1658) lt0001

Most favoredNM 672 (405) 102 (243) 142 (368) 428 (502)

Disadventaged-M 986 (595) 317 (757) 244 (632) 425 (498)

Educational level (N = 1723) lt0001

ltSecondary 819 (475) 302 (674) 205 (513) 312 (357)

geSecondaryHigher 904 (525) 146 (326) 195 (488) 563 (643)

Num Cigarettes day 154 plusmn 93 167 plusmn 109 160 plusmn 93 144 plusmn 83 lt0001

Age of initiation consumption 172 plusmn 45 179 plusmn 57 173 plusmn 48 168 plusmn 37 lt0001

Fagerstroumlm test 24 plusmn 16 25 plusmn 17 24 plusmn 16 22 plusmn 16 0003

Attempts smoking cessation 22 plusmn 24 22 plusmn 24 21 plusmn 22 23 plusmn 25 0755

ICT Information and Communication TechnologiesLow use le1 time per week Midhigh use gt1 time per weekp-value derived from the Chi square test and ANOVA in categorical and continuous variables respectivelyLow use le1 time per week Midhigh use gt1 time per weekp-value derived from the Chi square test and ANOVA in categorical and continuous variables respectively

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Table 3 Main predictors of the use of E-mail

Use of E-mail

Low frequency of use Midhigh frequency of use

OR1 (95 CI) P-value OR2 (95 CI) P-value

CRUDE OR

Gender

Male 100 lt0001 100 0169

Female 065 (053-081) 0822 (062-109)

Social class

Disadventaged-M 100 lt0001 100 lt0001

Most favored_NM 193 (137-272) 648 (502-837)

Educational level

ltSecondary 100 lt0001 100 lt0001

geSecondary 256 (189-346) 697 (552-881)

Age Group

gt65 100 100

46-65 495 (224-1094) lt0001 516 (303-880) lt0001

36-45 124 (545-2851) lt0001 1936 (1107-3406) lt0001

18-35 340 (1459-7940) lt0001 5186 (2839-9471) lt0001

Fagerstroumlm test

High 100 100

Medium 146 (087-246) 0152 233 (155-351) lt0001

Low 147 (088-245) 0144 274 (183-409) lt0001

ADJUSTED OR

Gender

Male 100 0613 100 0300

Female 0926 (066-127) 086 (065-114)

Social class

Disadventaged-M 100 0019 100 lt0001

Most favored_NM 161 (108-234) 4293 (311-593)

Educational level

ltSecondary 100 lt0001 100 lt0001

geSecondary 222 (156-315) 408 (303-548)

Age Group

gt65 100 100

46-65 464 (206-1045) lt0001 546 (297-1004) lt0001

36-45 119 (510-2811) lt0001 219 (114-419) lt0001

18-35 334 (1397-8025) lt0001 600 (301-953) lt0001

Fagerstroumlm test

High 100 100

Medium 125 (071-219) 0436 192 (115-320) 0013

Low 124 (071-217) 0448 203 (122-338) 0006

M =Manual NM = Non ManualLow frequency of use le1 time per week Midhigh frequency of use gt1 time per weekOR Odd Ratio OR = 1 denotes reference categoryOR1 Odds Ratio of low frequency use vs no useOR2 Odds Ratio of midhigh frequency use vs no useAdjusted OR OR adjusted for potential confounders in the case of gender by age social class and level of education Fagerstroumlm test was adjusted for all other variablesP-value derived from the Wald test

Puigdomegravenech et al BMC Public Health 2015 152 Page 8 of 14httpwwwbiomedcentralcom1471-2458152

Table 4 Main predictors of the use of SMS

Use sms

Low frequency of use Midhigh frequency of use

OR1 (95 CI) P-value OR2 (95 CI) P-value

CRUDE OR

Gender

Male 100 lt0001 100 lt0001

Female 215 (163-285) 30522 (240-388)

Social class

Disadventaged-M 100 lt0001 100 lt0001

Most favored_NM 181 (133-245) 313 (241-406)

Educational level

ltSecondary 100 lt0001 100 lt0001

geSecondary 1972 (149-260) 373 (293-475)

Age Group

gt65 100 100

46-65 282 (175-456) lt0001 387 (236-636) lt0001

36-45 708 (406-1232) lt0001 2010 (1157-3495) lt0001

18-35 759 (412-1398) lt0001 4613 (2559-8316) lt0001

Fagerstroumlm test

High 100 100

Medium 162 (101-263) 0177 219 (144-335) lt0001

Low 138 (086-221) 0047 218 (144-330) lt0001

ADJUSTED OR

Gender

Male 100 lt0001 100 lt0001

Female 18926 (140-257) 25186 (188-334)

Social class

Disadventaged-M 100 0122 100 0001

Most favored_NM 133 (093-191) 179 (128-250)

Educational level

ltSecondary 100 0017 100 lt0001

geSecondary 1502 (107-210) 231 (169-316)

Age Group

gt65 100 100

46-65 265 (156-449) lt0001 311 (179-537) lt0001

36-45 714 (388-1313) lt0001 162 (876-2980) lt0001

18-35 704 (365-1356) lt0001 338 (1787-6412) lt0001

Fagerstroumlm test

High 100 100

Medium 141 (084-236) 0432 129 (079-213) 0307

Low 128 (073-205) 0196 1473 (089-243) 0132

M =Manual NM = Non ManualLow frequency of use le1 time per week Midhigh frequency of use gt1 time per weekOR Odd Ratio OR = 1 denotes reference categoryOR1 Odds Ratio of low frequency use vs no useOR2 Odds Ratio of midhigh frequency use vs no useAdjusted OR OR adjusted for potential confounders in the case of gender by age social class and level of education Fagerstroumlm test was adjusted for all other variablesP-value derived from the Wald test

Puigdomegravenech et al BMC Public Health 2015 152 Page 9 of 14httpwwwbiomedcentralcom1471-2458152

Table 5 Main predictors of the use of web pages

Use of web pages

Low frequency of use Midhigh frequency of use

OR1 (95 CI) P-value OR2 (95 CI) P-value

CRUDE OR

Gender

Male 100 0007 100 0001

Female 151 (112-204) 1432 (115-178)

Social class

Disadventaged-M 100 lt0001 100 lt0001

Most favored_NM 210 (147-300) 550 (419-723)

Educational level

ltSecondary 100 lt0001 100 lt0001

geSecondary 384 (279-529) 717 (558-922)

Age Group

gt65 100 100

46-65 6145 (290-1295) lt0001 521 (321-846) lt0001

36-45 1467 (664-3244) lt0001 203 (1195-3465) lt0001

18-35 310 (1317-7328) lt0001 731 (2839-9471) lt0001

Fagerstroumlm test

High 100 100

Medium 2196 (122-392) 0009 225 (153-331) lt0001

Low 202 (113-361) 0017 1974 (133-293) 0001

ADJUSTED OR

Gender

Male 100 0990 100 0048

Female 101 (071-141) 074 (055-099)

Social class

Disadventaged-M 100 0148 100 lt0001

Most favored_NM 136 (090-207) 342 (242-484)

Educational level

ltSecondary 100 lt0001 100 lt0001

geSecondary 342 (236-497) 451 (329-620)

Age Group

gt65 100 100

46-65 547 (252-1188) lt0001 554 (316-972) lt0001

36-45 128 (56-294) lt0001 235 (126-437) lt0001

18-35 270 (110-662) lt0001 844 (422-1692) lt0001

Fagerstroumlm test

High 100 100

Medium 1873 (099-351) 0051 151 (096-251) 0083

Low 176 (094-330) 0077 142 (087-232) 0161

M =Manual NM = Non ManualLow frequency of use le1 time per week Midhigh frequency of use gt1 time per weekOR Odd Ratio OR = 1 denotes reference categoryOR1 Odds Ratio of low frequency use vs no useOR2 Odds Ratio of midhigh frequency use vs no useAdjusted OR OR adjusted for potential confounders in the case of gender by age social class and level of education Fagerstroumlm test was adjusted for all other variablesP-value derived from the Wald test

Puigdomegravenech et al BMC Public Health 2015 152 Page 10 of 14httpwwwbiomedcentralcom1471-2458152

Puigdomegravenech et al BMC Public Health 2015 152 Page 11 of 14httpwwwbiomedcentralcom1471-2458152

with the interest of finding information on the web[32] Our study shows that smokers from higher socialclass and educational level as well as young age are as-sociated to web use which is in accordance with someother studies [303334] Regarding gender our studyshows more women users (522-536) along with otherstudies [3335] but results remain inconclusively [3034]Chander and collaborators found that higher educationwas associated with the use of these ICTs among HIVcarriers [36]There are few studies that report sms use among the

general population According to the Forrester ResearchMobile Media Application Spending Forecast lsquomore than6 billion of SMS are sent each day and text messageusers receive an average of 35 messages per dayrsquo Con-cretely more than 80 of the US population owning amobile phone and with almost 70 of these phoneowners regularly sending or receiving text messages[3738] In Spain there are 334 millions of cell phoneusers (858 of people aged 15 or older) [15] No datahas been found regarding sms use among smokers ex-cept for the study published by Chander et al that re-ported that 39 of HIV positive smokers used sms andits use was mainly associated to higher educational level[36] Data from the control group of a clinical trial thatused text message to help smokers to quit showed thatmostly were unemployed students and manual workers(55) which was similar to our findings (632-498)but were younger and tended to be more males (55 vs439) [39]Regarding the use of email we have not found data to

inform the use of these ICTs by smokers Data concern-ing the populations that have participated in trials usingemail in treating smokers show similarities in somesocio-demographic factors (younger age and predomin-antly female participants) [4041] although Polosa andcollegues reported a higher participation of men [42]Data from our study showed more consumption of to-

bacco among non-users of the three technologies (meancigarettes per day 173 (SD 114)) compared to users(mean cigarettes per day 150 (SD 880)) Stoddard ampAuguston reported similar cigarette consumption butdid not find differences between those who used internetand those who didnrsquot [34] Besides our data showed thatage of onset of smoking we report that our study showsdifferences between among ICTs users was lower (170(SD 40)) than non users (186 (SD 66)) Neither similarfindings nor possible explanations have found in theliteratureThe three technologies used show a very high use

among smokers which suggests that they could be po-tentially very useful in interventions based on exclusiveuse use in combination or supporting face-to-face inter-ventions Each of the three technologies studied have

their own advantages and disadvantages Disadvantagesinclude the development of a private and secure envir-onment to regulate its use refusal to use them and lackof experience and time on its use [21344344] E-mailand SMS would probably be the most feasible to usesince both patient and sanitary professional can check itat their own convenience which allows certain time torespond (preferably in the first 48 hours) can diminishvisits to the primary care center are helpful on sendingreminders improve medication adherence and self-management of some chronic illnesses and can providevisual information [212225] Whilst E-mail is a quitecheap technology in Spain SMS comprise higher costsin Spain since they are not chargeless Although websitesshare some of these advantages can transmit a feeling ofimpersonality to certain users Additionally the use ofthese technologies should be tailored thus the potentialtherapeutic use rises if these technologies are used bythe sanitary professional who knows and treats the pa-tient [18] Moreover the relationship among patientand sanitary professional can be deepened and encour-aging attitudes can be generated if the experience ispositive [43]

Limitation and future directionsOne of the main limitations of the study is its design across-sectional study does not allow causal associationsThis study was conducted in a population of smokersand we were not able to acknowledge the differences be-tween this group and the general population In factparticipation in the study was offered to several refereesof primary care research of all the Spanish territory andonly those form Catalonia Aragoacuten and Salamanca ac-cepted to participate maybe the ICT use in other re-gions of Spain could be different We only studied thosewho came to the primary healthcare centres consideringthat the vast majority of the Spanish population attendthem once a year [84546] and were potential users ofthese technologies Consequently primary care can bean ideal setting to recruit participants from the generalpopulation to whom ICT based interventions can betestedWe did not evaluate barriers to the use of these tech-

nologies and did not consider the costs associated withmobile phone use and texting which may limit its use inbehavioural interventions However it is an ongoingqualitative and quantitative research by our researchgroup that will analyze the barriers and aids to the useof ICTs among smokers and health professionals Thequalitative study will also try to assess if patients wouldengage into a cessation program since we were not ableto gather this information on the present studyRegarding cell phone use we have only asked for the

use of sms in our population but mobile applications

Table 2 Comparison of the frequency of use of three ICTs (email sms and the web pages) according to sociodemographic variables and tobacco consumptionof the participants in the study ldquoUsage profile of ICTsrdquo (Continued)

46-65 733 (425) 272 (607) 209 (523) 252 (287)

gt65 139 (81) 92 (205) 25 (63) 22 (25)

Social class (N = 1658) lt0001

Most favoredNM 672 (405) 102 (243) 142 (368) 428 (502)

Disadventaged-M 986 (595) 317 (757) 244 (632) 425 (498)

Educational level (N = 1723) lt0001

ltSecondary 819 (475) 302 (674) 205 (513) 312 (357)

geSecondaryHigher 904 (525) 146 (326) 195 (488) 563 (643)

Num Cigarettes day 154 plusmn 93 167 plusmn 109 160 plusmn 93 144 plusmn 83 lt0001

Age of initiation consumption 172 plusmn 45 179 plusmn 57 173 plusmn 48 168 plusmn 37 lt0001

Fagerstroumlm test 24 plusmn 16 25 plusmn 17 24 plusmn 16 22 plusmn 16 0003

Attempts smoking cessation 22 plusmn 24 22 plusmn 24 21 plusmn 22 23 plusmn 25 0755

ICT Information and Communication TechnologiesLow use le1 time per week Midhigh use gt1 time per weekp-value derived from the Chi square test and ANOVA in categorical and continuous variables respectivelyLow use le1 time per week Midhigh use gt1 time per weekp-value derived from the Chi square test and ANOVA in categorical and continuous variables respectively

Puigdomegravenech

etalBM

CPublic

Health

2015152Page

7of

14httpw

wwbiom

edcentralcom1471-2458152

Table 3 Main predictors of the use of E-mail

Use of E-mail

Low frequency of use Midhigh frequency of use

OR1 (95 CI) P-value OR2 (95 CI) P-value

CRUDE OR

Gender

Male 100 lt0001 100 0169

Female 065 (053-081) 0822 (062-109)

Social class

Disadventaged-M 100 lt0001 100 lt0001

Most favored_NM 193 (137-272) 648 (502-837)

Educational level

ltSecondary 100 lt0001 100 lt0001

geSecondary 256 (189-346) 697 (552-881)

Age Group

gt65 100 100

46-65 495 (224-1094) lt0001 516 (303-880) lt0001

36-45 124 (545-2851) lt0001 1936 (1107-3406) lt0001

18-35 340 (1459-7940) lt0001 5186 (2839-9471) lt0001

Fagerstroumlm test

High 100 100

Medium 146 (087-246) 0152 233 (155-351) lt0001

Low 147 (088-245) 0144 274 (183-409) lt0001

ADJUSTED OR

Gender

Male 100 0613 100 0300

Female 0926 (066-127) 086 (065-114)

Social class

Disadventaged-M 100 0019 100 lt0001

Most favored_NM 161 (108-234) 4293 (311-593)

Educational level

ltSecondary 100 lt0001 100 lt0001

geSecondary 222 (156-315) 408 (303-548)

Age Group

gt65 100 100

46-65 464 (206-1045) lt0001 546 (297-1004) lt0001

36-45 119 (510-2811) lt0001 219 (114-419) lt0001

18-35 334 (1397-8025) lt0001 600 (301-953) lt0001

Fagerstroumlm test

High 100 100

Medium 125 (071-219) 0436 192 (115-320) 0013

Low 124 (071-217) 0448 203 (122-338) 0006

M =Manual NM = Non ManualLow frequency of use le1 time per week Midhigh frequency of use gt1 time per weekOR Odd Ratio OR = 1 denotes reference categoryOR1 Odds Ratio of low frequency use vs no useOR2 Odds Ratio of midhigh frequency use vs no useAdjusted OR OR adjusted for potential confounders in the case of gender by age social class and level of education Fagerstroumlm test was adjusted for all other variablesP-value derived from the Wald test

Puigdomegravenech et al BMC Public Health 2015 152 Page 8 of 14httpwwwbiomedcentralcom1471-2458152

Table 4 Main predictors of the use of SMS

Use sms

Low frequency of use Midhigh frequency of use

OR1 (95 CI) P-value OR2 (95 CI) P-value

CRUDE OR

Gender

Male 100 lt0001 100 lt0001

Female 215 (163-285) 30522 (240-388)

Social class

Disadventaged-M 100 lt0001 100 lt0001

Most favored_NM 181 (133-245) 313 (241-406)

Educational level

ltSecondary 100 lt0001 100 lt0001

geSecondary 1972 (149-260) 373 (293-475)

Age Group

gt65 100 100

46-65 282 (175-456) lt0001 387 (236-636) lt0001

36-45 708 (406-1232) lt0001 2010 (1157-3495) lt0001

18-35 759 (412-1398) lt0001 4613 (2559-8316) lt0001

Fagerstroumlm test

High 100 100

Medium 162 (101-263) 0177 219 (144-335) lt0001

Low 138 (086-221) 0047 218 (144-330) lt0001

ADJUSTED OR

Gender

Male 100 lt0001 100 lt0001

Female 18926 (140-257) 25186 (188-334)

Social class

Disadventaged-M 100 0122 100 0001

Most favored_NM 133 (093-191) 179 (128-250)

Educational level

ltSecondary 100 0017 100 lt0001

geSecondary 1502 (107-210) 231 (169-316)

Age Group

gt65 100 100

46-65 265 (156-449) lt0001 311 (179-537) lt0001

36-45 714 (388-1313) lt0001 162 (876-2980) lt0001

18-35 704 (365-1356) lt0001 338 (1787-6412) lt0001

Fagerstroumlm test

High 100 100

Medium 141 (084-236) 0432 129 (079-213) 0307

Low 128 (073-205) 0196 1473 (089-243) 0132

M =Manual NM = Non ManualLow frequency of use le1 time per week Midhigh frequency of use gt1 time per weekOR Odd Ratio OR = 1 denotes reference categoryOR1 Odds Ratio of low frequency use vs no useOR2 Odds Ratio of midhigh frequency use vs no useAdjusted OR OR adjusted for potential confounders in the case of gender by age social class and level of education Fagerstroumlm test was adjusted for all other variablesP-value derived from the Wald test

Puigdomegravenech et al BMC Public Health 2015 152 Page 9 of 14httpwwwbiomedcentralcom1471-2458152

Table 5 Main predictors of the use of web pages

Use of web pages

Low frequency of use Midhigh frequency of use

OR1 (95 CI) P-value OR2 (95 CI) P-value

CRUDE OR

Gender

Male 100 0007 100 0001

Female 151 (112-204) 1432 (115-178)

Social class

Disadventaged-M 100 lt0001 100 lt0001

Most favored_NM 210 (147-300) 550 (419-723)

Educational level

ltSecondary 100 lt0001 100 lt0001

geSecondary 384 (279-529) 717 (558-922)

Age Group

gt65 100 100

46-65 6145 (290-1295) lt0001 521 (321-846) lt0001

36-45 1467 (664-3244) lt0001 203 (1195-3465) lt0001

18-35 310 (1317-7328) lt0001 731 (2839-9471) lt0001

Fagerstroumlm test

High 100 100

Medium 2196 (122-392) 0009 225 (153-331) lt0001

Low 202 (113-361) 0017 1974 (133-293) 0001

ADJUSTED OR

Gender

Male 100 0990 100 0048

Female 101 (071-141) 074 (055-099)

Social class

Disadventaged-M 100 0148 100 lt0001

Most favored_NM 136 (090-207) 342 (242-484)

Educational level

ltSecondary 100 lt0001 100 lt0001

geSecondary 342 (236-497) 451 (329-620)

Age Group

gt65 100 100

46-65 547 (252-1188) lt0001 554 (316-972) lt0001

36-45 128 (56-294) lt0001 235 (126-437) lt0001

18-35 270 (110-662) lt0001 844 (422-1692) lt0001

Fagerstroumlm test

High 100 100

Medium 1873 (099-351) 0051 151 (096-251) 0083

Low 176 (094-330) 0077 142 (087-232) 0161

M =Manual NM = Non ManualLow frequency of use le1 time per week Midhigh frequency of use gt1 time per weekOR Odd Ratio OR = 1 denotes reference categoryOR1 Odds Ratio of low frequency use vs no useOR2 Odds Ratio of midhigh frequency use vs no useAdjusted OR OR adjusted for potential confounders in the case of gender by age social class and level of education Fagerstroumlm test was adjusted for all other variablesP-value derived from the Wald test

Puigdomegravenech et al BMC Public Health 2015 152 Page 10 of 14httpwwwbiomedcentralcom1471-2458152

Puigdomegravenech et al BMC Public Health 2015 152 Page 11 of 14httpwwwbiomedcentralcom1471-2458152

with the interest of finding information on the web[32] Our study shows that smokers from higher socialclass and educational level as well as young age are as-sociated to web use which is in accordance with someother studies [303334] Regarding gender our studyshows more women users (522-536) along with otherstudies [3335] but results remain inconclusively [3034]Chander and collaborators found that higher educationwas associated with the use of these ICTs among HIVcarriers [36]There are few studies that report sms use among the

general population According to the Forrester ResearchMobile Media Application Spending Forecast lsquomore than6 billion of SMS are sent each day and text messageusers receive an average of 35 messages per dayrsquo Con-cretely more than 80 of the US population owning amobile phone and with almost 70 of these phoneowners regularly sending or receiving text messages[3738] In Spain there are 334 millions of cell phoneusers (858 of people aged 15 or older) [15] No datahas been found regarding sms use among smokers ex-cept for the study published by Chander et al that re-ported that 39 of HIV positive smokers used sms andits use was mainly associated to higher educational level[36] Data from the control group of a clinical trial thatused text message to help smokers to quit showed thatmostly were unemployed students and manual workers(55) which was similar to our findings (632-498)but were younger and tended to be more males (55 vs439) [39]Regarding the use of email we have not found data to

inform the use of these ICTs by smokers Data concern-ing the populations that have participated in trials usingemail in treating smokers show similarities in somesocio-demographic factors (younger age and predomin-antly female participants) [4041] although Polosa andcollegues reported a higher participation of men [42]Data from our study showed more consumption of to-

bacco among non-users of the three technologies (meancigarettes per day 173 (SD 114)) compared to users(mean cigarettes per day 150 (SD 880)) Stoddard ampAuguston reported similar cigarette consumption butdid not find differences between those who used internetand those who didnrsquot [34] Besides our data showed thatage of onset of smoking we report that our study showsdifferences between among ICTs users was lower (170(SD 40)) than non users (186 (SD 66)) Neither similarfindings nor possible explanations have found in theliteratureThe three technologies used show a very high use

among smokers which suggests that they could be po-tentially very useful in interventions based on exclusiveuse use in combination or supporting face-to-face inter-ventions Each of the three technologies studied have

their own advantages and disadvantages Disadvantagesinclude the development of a private and secure envir-onment to regulate its use refusal to use them and lackof experience and time on its use [21344344] E-mailand SMS would probably be the most feasible to usesince both patient and sanitary professional can check itat their own convenience which allows certain time torespond (preferably in the first 48 hours) can diminishvisits to the primary care center are helpful on sendingreminders improve medication adherence and self-management of some chronic illnesses and can providevisual information [212225] Whilst E-mail is a quitecheap technology in Spain SMS comprise higher costsin Spain since they are not chargeless Although websitesshare some of these advantages can transmit a feeling ofimpersonality to certain users Additionally the use ofthese technologies should be tailored thus the potentialtherapeutic use rises if these technologies are used bythe sanitary professional who knows and treats the pa-tient [18] Moreover the relationship among patientand sanitary professional can be deepened and encour-aging attitudes can be generated if the experience ispositive [43]

Limitation and future directionsOne of the main limitations of the study is its design across-sectional study does not allow causal associationsThis study was conducted in a population of smokersand we were not able to acknowledge the differences be-tween this group and the general population In factparticipation in the study was offered to several refereesof primary care research of all the Spanish territory andonly those form Catalonia Aragoacuten and Salamanca ac-cepted to participate maybe the ICT use in other re-gions of Spain could be different We only studied thosewho came to the primary healthcare centres consideringthat the vast majority of the Spanish population attendthem once a year [84546] and were potential users ofthese technologies Consequently primary care can bean ideal setting to recruit participants from the generalpopulation to whom ICT based interventions can betestedWe did not evaluate barriers to the use of these tech-

nologies and did not consider the costs associated withmobile phone use and texting which may limit its use inbehavioural interventions However it is an ongoingqualitative and quantitative research by our researchgroup that will analyze the barriers and aids to the useof ICTs among smokers and health professionals Thequalitative study will also try to assess if patients wouldengage into a cessation program since we were not ableto gather this information on the present studyRegarding cell phone use we have only asked for the

use of sms in our population but mobile applications

Table 3 Main predictors of the use of E-mail

Use of E-mail

Low frequency of use Midhigh frequency of use

OR1 (95 CI) P-value OR2 (95 CI) P-value

CRUDE OR

Gender

Male 100 lt0001 100 0169

Female 065 (053-081) 0822 (062-109)

Social class

Disadventaged-M 100 lt0001 100 lt0001

Most favored_NM 193 (137-272) 648 (502-837)

Educational level

ltSecondary 100 lt0001 100 lt0001

geSecondary 256 (189-346) 697 (552-881)

Age Group

gt65 100 100

46-65 495 (224-1094) lt0001 516 (303-880) lt0001

36-45 124 (545-2851) lt0001 1936 (1107-3406) lt0001

18-35 340 (1459-7940) lt0001 5186 (2839-9471) lt0001

Fagerstroumlm test

High 100 100

Medium 146 (087-246) 0152 233 (155-351) lt0001

Low 147 (088-245) 0144 274 (183-409) lt0001

ADJUSTED OR

Gender

Male 100 0613 100 0300

Female 0926 (066-127) 086 (065-114)

Social class

Disadventaged-M 100 0019 100 lt0001

Most favored_NM 161 (108-234) 4293 (311-593)

Educational level

ltSecondary 100 lt0001 100 lt0001

geSecondary 222 (156-315) 408 (303-548)

Age Group

gt65 100 100

46-65 464 (206-1045) lt0001 546 (297-1004) lt0001

36-45 119 (510-2811) lt0001 219 (114-419) lt0001

18-35 334 (1397-8025) lt0001 600 (301-953) lt0001

Fagerstroumlm test

High 100 100

Medium 125 (071-219) 0436 192 (115-320) 0013

Low 124 (071-217) 0448 203 (122-338) 0006

M =Manual NM = Non ManualLow frequency of use le1 time per week Midhigh frequency of use gt1 time per weekOR Odd Ratio OR = 1 denotes reference categoryOR1 Odds Ratio of low frequency use vs no useOR2 Odds Ratio of midhigh frequency use vs no useAdjusted OR OR adjusted for potential confounders in the case of gender by age social class and level of education Fagerstroumlm test was adjusted for all other variablesP-value derived from the Wald test

Puigdomegravenech et al BMC Public Health 2015 152 Page 8 of 14httpwwwbiomedcentralcom1471-2458152

Table 4 Main predictors of the use of SMS

Use sms

Low frequency of use Midhigh frequency of use

OR1 (95 CI) P-value OR2 (95 CI) P-value

CRUDE OR

Gender

Male 100 lt0001 100 lt0001

Female 215 (163-285) 30522 (240-388)

Social class

Disadventaged-M 100 lt0001 100 lt0001

Most favored_NM 181 (133-245) 313 (241-406)

Educational level

ltSecondary 100 lt0001 100 lt0001

geSecondary 1972 (149-260) 373 (293-475)

Age Group

gt65 100 100

46-65 282 (175-456) lt0001 387 (236-636) lt0001

36-45 708 (406-1232) lt0001 2010 (1157-3495) lt0001

18-35 759 (412-1398) lt0001 4613 (2559-8316) lt0001

Fagerstroumlm test

High 100 100

Medium 162 (101-263) 0177 219 (144-335) lt0001

Low 138 (086-221) 0047 218 (144-330) lt0001

ADJUSTED OR

Gender

Male 100 lt0001 100 lt0001

Female 18926 (140-257) 25186 (188-334)

Social class

Disadventaged-M 100 0122 100 0001

Most favored_NM 133 (093-191) 179 (128-250)

Educational level

ltSecondary 100 0017 100 lt0001

geSecondary 1502 (107-210) 231 (169-316)

Age Group

gt65 100 100

46-65 265 (156-449) lt0001 311 (179-537) lt0001

36-45 714 (388-1313) lt0001 162 (876-2980) lt0001

18-35 704 (365-1356) lt0001 338 (1787-6412) lt0001

Fagerstroumlm test

High 100 100

Medium 141 (084-236) 0432 129 (079-213) 0307

Low 128 (073-205) 0196 1473 (089-243) 0132

M =Manual NM = Non ManualLow frequency of use le1 time per week Midhigh frequency of use gt1 time per weekOR Odd Ratio OR = 1 denotes reference categoryOR1 Odds Ratio of low frequency use vs no useOR2 Odds Ratio of midhigh frequency use vs no useAdjusted OR OR adjusted for potential confounders in the case of gender by age social class and level of education Fagerstroumlm test was adjusted for all other variablesP-value derived from the Wald test

Puigdomegravenech et al BMC Public Health 2015 152 Page 9 of 14httpwwwbiomedcentralcom1471-2458152

Table 5 Main predictors of the use of web pages

Use of web pages

Low frequency of use Midhigh frequency of use

OR1 (95 CI) P-value OR2 (95 CI) P-value

CRUDE OR

Gender

Male 100 0007 100 0001

Female 151 (112-204) 1432 (115-178)

Social class

Disadventaged-M 100 lt0001 100 lt0001

Most favored_NM 210 (147-300) 550 (419-723)

Educational level

ltSecondary 100 lt0001 100 lt0001

geSecondary 384 (279-529) 717 (558-922)

Age Group

gt65 100 100

46-65 6145 (290-1295) lt0001 521 (321-846) lt0001

36-45 1467 (664-3244) lt0001 203 (1195-3465) lt0001

18-35 310 (1317-7328) lt0001 731 (2839-9471) lt0001

Fagerstroumlm test

High 100 100

Medium 2196 (122-392) 0009 225 (153-331) lt0001

Low 202 (113-361) 0017 1974 (133-293) 0001

ADJUSTED OR

Gender

Male 100 0990 100 0048

Female 101 (071-141) 074 (055-099)

Social class

Disadventaged-M 100 0148 100 lt0001

Most favored_NM 136 (090-207) 342 (242-484)

Educational level

ltSecondary 100 lt0001 100 lt0001

geSecondary 342 (236-497) 451 (329-620)

Age Group

gt65 100 100

46-65 547 (252-1188) lt0001 554 (316-972) lt0001

36-45 128 (56-294) lt0001 235 (126-437) lt0001

18-35 270 (110-662) lt0001 844 (422-1692) lt0001

Fagerstroumlm test

High 100 100

Medium 1873 (099-351) 0051 151 (096-251) 0083

Low 176 (094-330) 0077 142 (087-232) 0161

M =Manual NM = Non ManualLow frequency of use le1 time per week Midhigh frequency of use gt1 time per weekOR Odd Ratio OR = 1 denotes reference categoryOR1 Odds Ratio of low frequency use vs no useOR2 Odds Ratio of midhigh frequency use vs no useAdjusted OR OR adjusted for potential confounders in the case of gender by age social class and level of education Fagerstroumlm test was adjusted for all other variablesP-value derived from the Wald test

Puigdomegravenech et al BMC Public Health 2015 152 Page 10 of 14httpwwwbiomedcentralcom1471-2458152

Puigdomegravenech et al BMC Public Health 2015 152 Page 11 of 14httpwwwbiomedcentralcom1471-2458152

with the interest of finding information on the web[32] Our study shows that smokers from higher socialclass and educational level as well as young age are as-sociated to web use which is in accordance with someother studies [303334] Regarding gender our studyshows more women users (522-536) along with otherstudies [3335] but results remain inconclusively [3034]Chander and collaborators found that higher educationwas associated with the use of these ICTs among HIVcarriers [36]There are few studies that report sms use among the

general population According to the Forrester ResearchMobile Media Application Spending Forecast lsquomore than6 billion of SMS are sent each day and text messageusers receive an average of 35 messages per dayrsquo Con-cretely more than 80 of the US population owning amobile phone and with almost 70 of these phoneowners regularly sending or receiving text messages[3738] In Spain there are 334 millions of cell phoneusers (858 of people aged 15 or older) [15] No datahas been found regarding sms use among smokers ex-cept for the study published by Chander et al that re-ported that 39 of HIV positive smokers used sms andits use was mainly associated to higher educational level[36] Data from the control group of a clinical trial thatused text message to help smokers to quit showed thatmostly were unemployed students and manual workers(55) which was similar to our findings (632-498)but were younger and tended to be more males (55 vs439) [39]Regarding the use of email we have not found data to

inform the use of these ICTs by smokers Data concern-ing the populations that have participated in trials usingemail in treating smokers show similarities in somesocio-demographic factors (younger age and predomin-antly female participants) [4041] although Polosa andcollegues reported a higher participation of men [42]Data from our study showed more consumption of to-

bacco among non-users of the three technologies (meancigarettes per day 173 (SD 114)) compared to users(mean cigarettes per day 150 (SD 880)) Stoddard ampAuguston reported similar cigarette consumption butdid not find differences between those who used internetand those who didnrsquot [34] Besides our data showed thatage of onset of smoking we report that our study showsdifferences between among ICTs users was lower (170(SD 40)) than non users (186 (SD 66)) Neither similarfindings nor possible explanations have found in theliteratureThe three technologies used show a very high use

among smokers which suggests that they could be po-tentially very useful in interventions based on exclusiveuse use in combination or supporting face-to-face inter-ventions Each of the three technologies studied have

their own advantages and disadvantages Disadvantagesinclude the development of a private and secure envir-onment to regulate its use refusal to use them and lackof experience and time on its use [21344344] E-mailand SMS would probably be the most feasible to usesince both patient and sanitary professional can check itat their own convenience which allows certain time torespond (preferably in the first 48 hours) can diminishvisits to the primary care center are helpful on sendingreminders improve medication adherence and self-management of some chronic illnesses and can providevisual information [212225] Whilst E-mail is a quitecheap technology in Spain SMS comprise higher costsin Spain since they are not chargeless Although websitesshare some of these advantages can transmit a feeling ofimpersonality to certain users Additionally the use ofthese technologies should be tailored thus the potentialtherapeutic use rises if these technologies are used bythe sanitary professional who knows and treats the pa-tient [18] Moreover the relationship among patientand sanitary professional can be deepened and encour-aging attitudes can be generated if the experience ispositive [43]

Limitation and future directionsOne of the main limitations of the study is its design across-sectional study does not allow causal associationsThis study was conducted in a population of smokersand we were not able to acknowledge the differences be-tween this group and the general population In factparticipation in the study was offered to several refereesof primary care research of all the Spanish territory andonly those form Catalonia Aragoacuten and Salamanca ac-cepted to participate maybe the ICT use in other re-gions of Spain could be different We only studied thosewho came to the primary healthcare centres consideringthat the vast majority of the Spanish population attendthem once a year [84546] and were potential users ofthese technologies Consequently primary care can bean ideal setting to recruit participants from the generalpopulation to whom ICT based interventions can betestedWe did not evaluate barriers to the use of these tech-

nologies and did not consider the costs associated withmobile phone use and texting which may limit its use inbehavioural interventions However it is an ongoingqualitative and quantitative research by our researchgroup that will analyze the barriers and aids to the useof ICTs among smokers and health professionals Thequalitative study will also try to assess if patients wouldengage into a cessation program since we were not ableto gather this information on the present studyRegarding cell phone use we have only asked for the

use of sms in our population but mobile applications

Table 4 Main predictors of the use of SMS

Use sms

Low frequency of use Midhigh frequency of use

OR1 (95 CI) P-value OR2 (95 CI) P-value

CRUDE OR

Gender

Male 100 lt0001 100 lt0001

Female 215 (163-285) 30522 (240-388)

Social class

Disadventaged-M 100 lt0001 100 lt0001

Most favored_NM 181 (133-245) 313 (241-406)

Educational level

ltSecondary 100 lt0001 100 lt0001

geSecondary 1972 (149-260) 373 (293-475)

Age Group

gt65 100 100

46-65 282 (175-456) lt0001 387 (236-636) lt0001

36-45 708 (406-1232) lt0001 2010 (1157-3495) lt0001

18-35 759 (412-1398) lt0001 4613 (2559-8316) lt0001

Fagerstroumlm test

High 100 100

Medium 162 (101-263) 0177 219 (144-335) lt0001

Low 138 (086-221) 0047 218 (144-330) lt0001

ADJUSTED OR

Gender

Male 100 lt0001 100 lt0001

Female 18926 (140-257) 25186 (188-334)

Social class

Disadventaged-M 100 0122 100 0001

Most favored_NM 133 (093-191) 179 (128-250)

Educational level

ltSecondary 100 0017 100 lt0001

geSecondary 1502 (107-210) 231 (169-316)

Age Group

gt65 100 100

46-65 265 (156-449) lt0001 311 (179-537) lt0001

36-45 714 (388-1313) lt0001 162 (876-2980) lt0001

18-35 704 (365-1356) lt0001 338 (1787-6412) lt0001

Fagerstroumlm test

High 100 100

Medium 141 (084-236) 0432 129 (079-213) 0307

Low 128 (073-205) 0196 1473 (089-243) 0132

M =Manual NM = Non ManualLow frequency of use le1 time per week Midhigh frequency of use gt1 time per weekOR Odd Ratio OR = 1 denotes reference categoryOR1 Odds Ratio of low frequency use vs no useOR2 Odds Ratio of midhigh frequency use vs no useAdjusted OR OR adjusted for potential confounders in the case of gender by age social class and level of education Fagerstroumlm test was adjusted for all other variablesP-value derived from the Wald test

Puigdomegravenech et al BMC Public Health 2015 152 Page 9 of 14httpwwwbiomedcentralcom1471-2458152

Table 5 Main predictors of the use of web pages

Use of web pages

Low frequency of use Midhigh frequency of use

OR1 (95 CI) P-value OR2 (95 CI) P-value

CRUDE OR

Gender

Male 100 0007 100 0001

Female 151 (112-204) 1432 (115-178)

Social class

Disadventaged-M 100 lt0001 100 lt0001

Most favored_NM 210 (147-300) 550 (419-723)

Educational level

ltSecondary 100 lt0001 100 lt0001

geSecondary 384 (279-529) 717 (558-922)

Age Group

gt65 100 100

46-65 6145 (290-1295) lt0001 521 (321-846) lt0001

36-45 1467 (664-3244) lt0001 203 (1195-3465) lt0001

18-35 310 (1317-7328) lt0001 731 (2839-9471) lt0001

Fagerstroumlm test

High 100 100

Medium 2196 (122-392) 0009 225 (153-331) lt0001

Low 202 (113-361) 0017 1974 (133-293) 0001

ADJUSTED OR

Gender

Male 100 0990 100 0048

Female 101 (071-141) 074 (055-099)

Social class

Disadventaged-M 100 0148 100 lt0001

Most favored_NM 136 (090-207) 342 (242-484)

Educational level

ltSecondary 100 lt0001 100 lt0001

geSecondary 342 (236-497) 451 (329-620)

Age Group

gt65 100 100

46-65 547 (252-1188) lt0001 554 (316-972) lt0001

36-45 128 (56-294) lt0001 235 (126-437) lt0001

18-35 270 (110-662) lt0001 844 (422-1692) lt0001

Fagerstroumlm test

High 100 100

Medium 1873 (099-351) 0051 151 (096-251) 0083

Low 176 (094-330) 0077 142 (087-232) 0161

M =Manual NM = Non ManualLow frequency of use le1 time per week Midhigh frequency of use gt1 time per weekOR Odd Ratio OR = 1 denotes reference categoryOR1 Odds Ratio of low frequency use vs no useOR2 Odds Ratio of midhigh frequency use vs no useAdjusted OR OR adjusted for potential confounders in the case of gender by age social class and level of education Fagerstroumlm test was adjusted for all other variablesP-value derived from the Wald test

Puigdomegravenech et al BMC Public Health 2015 152 Page 10 of 14httpwwwbiomedcentralcom1471-2458152

Puigdomegravenech et al BMC Public Health 2015 152 Page 11 of 14httpwwwbiomedcentralcom1471-2458152

with the interest of finding information on the web[32] Our study shows that smokers from higher socialclass and educational level as well as young age are as-sociated to web use which is in accordance with someother studies [303334] Regarding gender our studyshows more women users (522-536) along with otherstudies [3335] but results remain inconclusively [3034]Chander and collaborators found that higher educationwas associated with the use of these ICTs among HIVcarriers [36]There are few studies that report sms use among the

general population According to the Forrester ResearchMobile Media Application Spending Forecast lsquomore than6 billion of SMS are sent each day and text messageusers receive an average of 35 messages per dayrsquo Con-cretely more than 80 of the US population owning amobile phone and with almost 70 of these phoneowners regularly sending or receiving text messages[3738] In Spain there are 334 millions of cell phoneusers (858 of people aged 15 or older) [15] No datahas been found regarding sms use among smokers ex-cept for the study published by Chander et al that re-ported that 39 of HIV positive smokers used sms andits use was mainly associated to higher educational level[36] Data from the control group of a clinical trial thatused text message to help smokers to quit showed thatmostly were unemployed students and manual workers(55) which was similar to our findings (632-498)but were younger and tended to be more males (55 vs439) [39]Regarding the use of email we have not found data to

inform the use of these ICTs by smokers Data concern-ing the populations that have participated in trials usingemail in treating smokers show similarities in somesocio-demographic factors (younger age and predomin-antly female participants) [4041] although Polosa andcollegues reported a higher participation of men [42]Data from our study showed more consumption of to-

bacco among non-users of the three technologies (meancigarettes per day 173 (SD 114)) compared to users(mean cigarettes per day 150 (SD 880)) Stoddard ampAuguston reported similar cigarette consumption butdid not find differences between those who used internetand those who didnrsquot [34] Besides our data showed thatage of onset of smoking we report that our study showsdifferences between among ICTs users was lower (170(SD 40)) than non users (186 (SD 66)) Neither similarfindings nor possible explanations have found in theliteratureThe three technologies used show a very high use

among smokers which suggests that they could be po-tentially very useful in interventions based on exclusiveuse use in combination or supporting face-to-face inter-ventions Each of the three technologies studied have

their own advantages and disadvantages Disadvantagesinclude the development of a private and secure envir-onment to regulate its use refusal to use them and lackof experience and time on its use [21344344] E-mailand SMS would probably be the most feasible to usesince both patient and sanitary professional can check itat their own convenience which allows certain time torespond (preferably in the first 48 hours) can diminishvisits to the primary care center are helpful on sendingreminders improve medication adherence and self-management of some chronic illnesses and can providevisual information [212225] Whilst E-mail is a quitecheap technology in Spain SMS comprise higher costsin Spain since they are not chargeless Although websitesshare some of these advantages can transmit a feeling ofimpersonality to certain users Additionally the use ofthese technologies should be tailored thus the potentialtherapeutic use rises if these technologies are used bythe sanitary professional who knows and treats the pa-tient [18] Moreover the relationship among patientand sanitary professional can be deepened and encour-aging attitudes can be generated if the experience ispositive [43]

Limitation and future directionsOne of the main limitations of the study is its design across-sectional study does not allow causal associationsThis study was conducted in a population of smokersand we were not able to acknowledge the differences be-tween this group and the general population In factparticipation in the study was offered to several refereesof primary care research of all the Spanish territory andonly those form Catalonia Aragoacuten and Salamanca ac-cepted to participate maybe the ICT use in other re-gions of Spain could be different We only studied thosewho came to the primary healthcare centres consideringthat the vast majority of the Spanish population attendthem once a year [84546] and were potential users ofthese technologies Consequently primary care can bean ideal setting to recruit participants from the generalpopulation to whom ICT based interventions can betestedWe did not evaluate barriers to the use of these tech-

nologies and did not consider the costs associated withmobile phone use and texting which may limit its use inbehavioural interventions However it is an ongoingqualitative and quantitative research by our researchgroup that will analyze the barriers and aids to the useof ICTs among smokers and health professionals Thequalitative study will also try to assess if patients wouldengage into a cessation program since we were not ableto gather this information on the present studyRegarding cell phone use we have only asked for the

use of sms in our population but mobile applications

Table 5 Main predictors of the use of web pages

Use of web pages

Low frequency of use Midhigh frequency of use

OR1 (95 CI) P-value OR2 (95 CI) P-value

CRUDE OR

Gender

Male 100 0007 100 0001

Female 151 (112-204) 1432 (115-178)

Social class

Disadventaged-M 100 lt0001 100 lt0001

Most favored_NM 210 (147-300) 550 (419-723)

Educational level

ltSecondary 100 lt0001 100 lt0001

geSecondary 384 (279-529) 717 (558-922)

Age Group

gt65 100 100

46-65 6145 (290-1295) lt0001 521 (321-846) lt0001

36-45 1467 (664-3244) lt0001 203 (1195-3465) lt0001

18-35 310 (1317-7328) lt0001 731 (2839-9471) lt0001

Fagerstroumlm test

High 100 100

Medium 2196 (122-392) 0009 225 (153-331) lt0001

Low 202 (113-361) 0017 1974 (133-293) 0001

ADJUSTED OR

Gender

Male 100 0990 100 0048

Female 101 (071-141) 074 (055-099)

Social class

Disadventaged-M 100 0148 100 lt0001

Most favored_NM 136 (090-207) 342 (242-484)

Educational level

ltSecondary 100 lt0001 100 lt0001

geSecondary 342 (236-497) 451 (329-620)

Age Group

gt65 100 100

46-65 547 (252-1188) lt0001 554 (316-972) lt0001

36-45 128 (56-294) lt0001 235 (126-437) lt0001

18-35 270 (110-662) lt0001 844 (422-1692) lt0001

Fagerstroumlm test

High 100 100

Medium 1873 (099-351) 0051 151 (096-251) 0083

Low 176 (094-330) 0077 142 (087-232) 0161

M =Manual NM = Non ManualLow frequency of use le1 time per week Midhigh frequency of use gt1 time per weekOR Odd Ratio OR = 1 denotes reference categoryOR1 Odds Ratio of low frequency use vs no useOR2 Odds Ratio of midhigh frequency use vs no useAdjusted OR OR adjusted for potential confounders in the case of gender by age social class and level of education Fagerstroumlm test was adjusted for all other variablesP-value derived from the Wald test

Puigdomegravenech et al BMC Public Health 2015 152 Page 10 of 14httpwwwbiomedcentralcom1471-2458152

Puigdomegravenech et al BMC Public Health 2015 152 Page 11 of 14httpwwwbiomedcentralcom1471-2458152

with the interest of finding information on the web[32] Our study shows that smokers from higher socialclass and educational level as well as young age are as-sociated to web use which is in accordance with someother studies [303334] Regarding gender our studyshows more women users (522-536) along with otherstudies [3335] but results remain inconclusively [3034]Chander and collaborators found that higher educationwas associated with the use of these ICTs among HIVcarriers [36]There are few studies that report sms use among the

general population According to the Forrester ResearchMobile Media Application Spending Forecast lsquomore than6 billion of SMS are sent each day and text messageusers receive an average of 35 messages per dayrsquo Con-cretely more than 80 of the US population owning amobile phone and with almost 70 of these phoneowners regularly sending or receiving text messages[3738] In Spain there are 334 millions of cell phoneusers (858 of people aged 15 or older) [15] No datahas been found regarding sms use among smokers ex-cept for the study published by Chander et al that re-ported that 39 of HIV positive smokers used sms andits use was mainly associated to higher educational level[36] Data from the control group of a clinical trial thatused text message to help smokers to quit showed thatmostly were unemployed students and manual workers(55) which was similar to our findings (632-498)but were younger and tended to be more males (55 vs439) [39]Regarding the use of email we have not found data to

inform the use of these ICTs by smokers Data concern-ing the populations that have participated in trials usingemail in treating smokers show similarities in somesocio-demographic factors (younger age and predomin-antly female participants) [4041] although Polosa andcollegues reported a higher participation of men [42]Data from our study showed more consumption of to-

bacco among non-users of the three technologies (meancigarettes per day 173 (SD 114)) compared to users(mean cigarettes per day 150 (SD 880)) Stoddard ampAuguston reported similar cigarette consumption butdid not find differences between those who used internetand those who didnrsquot [34] Besides our data showed thatage of onset of smoking we report that our study showsdifferences between among ICTs users was lower (170(SD 40)) than non users (186 (SD 66)) Neither similarfindings nor possible explanations have found in theliteratureThe three technologies used show a very high use

among smokers which suggests that they could be po-tentially very useful in interventions based on exclusiveuse use in combination or supporting face-to-face inter-ventions Each of the three technologies studied have

their own advantages and disadvantages Disadvantagesinclude the development of a private and secure envir-onment to regulate its use refusal to use them and lackof experience and time on its use [21344344] E-mailand SMS would probably be the most feasible to usesince both patient and sanitary professional can check itat their own convenience which allows certain time torespond (preferably in the first 48 hours) can diminishvisits to the primary care center are helpful on sendingreminders improve medication adherence and self-management of some chronic illnesses and can providevisual information [212225] Whilst E-mail is a quitecheap technology in Spain SMS comprise higher costsin Spain since they are not chargeless Although websitesshare some of these advantages can transmit a feeling ofimpersonality to certain users Additionally the use ofthese technologies should be tailored thus the potentialtherapeutic use rises if these technologies are used bythe sanitary professional who knows and treats the pa-tient [18] Moreover the relationship among patientand sanitary professional can be deepened and encour-aging attitudes can be generated if the experience ispositive [43]

Limitation and future directionsOne of the main limitations of the study is its design across-sectional study does not allow causal associationsThis study was conducted in a population of smokersand we were not able to acknowledge the differences be-tween this group and the general population In factparticipation in the study was offered to several refereesof primary care research of all the Spanish territory andonly those form Catalonia Aragoacuten and Salamanca ac-cepted to participate maybe the ICT use in other re-gions of Spain could be different We only studied thosewho came to the primary healthcare centres consideringthat the vast majority of the Spanish population attendthem once a year [84546] and were potential users ofthese technologies Consequently primary care can bean ideal setting to recruit participants from the generalpopulation to whom ICT based interventions can betestedWe did not evaluate barriers to the use of these tech-

nologies and did not consider the costs associated withmobile phone use and texting which may limit its use inbehavioural interventions However it is an ongoingqualitative and quantitative research by our researchgroup that will analyze the barriers and aids to the useof ICTs among smokers and health professionals Thequalitative study will also try to assess if patients wouldengage into a cessation program since we were not ableto gather this information on the present studyRegarding cell phone use we have only asked for the

use of sms in our population but mobile applications

Puigdomegravenech et al BMC Public Health 2015 152 Page 11 of 14httpwwwbiomedcentralcom1471-2458152

with the interest of finding information on the web[32] Our study shows that smokers from higher socialclass and educational level as well as young age are as-sociated to web use which is in accordance with someother studies [303334] Regarding gender our studyshows more women users (522-536) along with otherstudies [3335] but results remain inconclusively [3034]Chander and collaborators found that higher educationwas associated with the use of these ICTs among HIVcarriers [36]There are few studies that report sms use among the

general population According to the Forrester ResearchMobile Media Application Spending Forecast lsquomore than6 billion of SMS are sent each day and text messageusers receive an average of 35 messages per dayrsquo Con-cretely more than 80 of the US population owning amobile phone and with almost 70 of these phoneowners regularly sending or receiving text messages[3738] In Spain there are 334 millions of cell phoneusers (858 of people aged 15 or older) [15] No datahas been found regarding sms use among smokers ex-cept for the study published by Chander et al that re-ported that 39 of HIV positive smokers used sms andits use was mainly associated to higher educational level[36] Data from the control group of a clinical trial thatused text message to help smokers to quit showed thatmostly were unemployed students and manual workers(55) which was similar to our findings (632-498)but were younger and tended to be more males (55 vs439) [39]Regarding the use of email we have not found data to

inform the use of these ICTs by smokers Data concern-ing the populations that have participated in trials usingemail in treating smokers show similarities in somesocio-demographic factors (younger age and predomin-antly female participants) [4041] although Polosa andcollegues reported a higher participation of men [42]Data from our study showed more consumption of to-

bacco among non-users of the three technologies (meancigarettes per day 173 (SD 114)) compared to users(mean cigarettes per day 150 (SD 880)) Stoddard ampAuguston reported similar cigarette consumption butdid not find differences between those who used internetand those who didnrsquot [34] Besides our data showed thatage of onset of smoking we report that our study showsdifferences between among ICTs users was lower (170(SD 40)) than non users (186 (SD 66)) Neither similarfindings nor possible explanations have found in theliteratureThe three technologies used show a very high use

among smokers which suggests that they could be po-tentially very useful in interventions based on exclusiveuse use in combination or supporting face-to-face inter-ventions Each of the three technologies studied have

their own advantages and disadvantages Disadvantagesinclude the development of a private and secure envir-onment to regulate its use refusal to use them and lackof experience and time on its use [21344344] E-mailand SMS would probably be the most feasible to usesince both patient and sanitary professional can check itat their own convenience which allows certain time torespond (preferably in the first 48 hours) can diminishvisits to the primary care center are helpful on sendingreminders improve medication adherence and self-management of some chronic illnesses and can providevisual information [212225] Whilst E-mail is a quitecheap technology in Spain SMS comprise higher costsin Spain since they are not chargeless Although websitesshare some of these advantages can transmit a feeling ofimpersonality to certain users Additionally the use ofthese technologies should be tailored thus the potentialtherapeutic use rises if these technologies are used bythe sanitary professional who knows and treats the pa-tient [18] Moreover the relationship among patientand sanitary professional can be deepened and encour-aging attitudes can be generated if the experience ispositive [43]

Limitation and future directionsOne of the main limitations of the study is its design across-sectional study does not allow causal associationsThis study was conducted in a population of smokersand we were not able to acknowledge the differences be-tween this group and the general population In factparticipation in the study was offered to several refereesof primary care research of all the Spanish territory andonly those form Catalonia Aragoacuten and Salamanca ac-cepted to participate maybe the ICT use in other re-gions of Spain could be different We only studied thosewho came to the primary healthcare centres consideringthat the vast majority of the Spanish population attendthem once a year [84546] and were potential users ofthese technologies Consequently primary care can bean ideal setting to recruit participants from the generalpopulation to whom ICT based interventions can betestedWe did not evaluate barriers to the use of these tech-

nologies and did not consider the costs associated withmobile phone use and texting which may limit its use inbehavioural interventions However it is an ongoingqualitative and quantitative research by our researchgroup that will analyze the barriers and aids to the useof ICTs among smokers and health professionals Thequalitative study will also try to assess if patients wouldengage into a cessation program since we were not ableto gather this information on the present studyRegarding cell phone use we have only asked for the

use of sms in our population but mobile applications

Puigdomegravenech et al BMC Public Health 2015 152 Page 12 of 14httpwwwbiomedcentralcom1471-2458152

(mobile Apps) are being developed to help smokers toquit and can post a new paradigm on smoking cessation[47] No data was gathered among the use of social net-working sites such as Twitter that can be a potentialtool to support smoking cessation [48]Our results show differences on nicotine dependence

levels among ICT users and not users (p = 0004) howeverthe clinical relevance of this difference remains incon-clusively in using ICTs to help smokers quit Possiblymore intensive ICTs interventions (such as more smsor E-mails) will be needed on those smokers withhigher nicotine dependence (with higher Fagerstroumlmtest levels and higher number of cigarettes smoked)The final model used in the binary logistic analysis in-

cludes social class and educational level that may resultin over adjustment However in the context of a globaleconomic crisis in our country nowadays education can-not be used as an indicator of occupation since manypeople with higher education are currently working injobs that do not match their educational level

ConclusionsIn conclusion the use of ICTs for smoking cessation ispromising since can reach an extensive range of popula-tion with mobile phones being the technology withbroadest potential Considering the high prevalence ofsmoking in the general population and the broad use ofICTs in smokers the health benefits are clear in termsof effectiveness and cost-effectiveness for treating thesepatients By knowing the profile of smokers who useICTs primary care health professional can offer the pos-sibility to use a specific ICT according to the smokerrsquosprofile in order to maximise the probability of success Itis necessary to develop future clinical trials to determinethe feasibility acceptability and effectiveness of thesetechnologies as individual or complementary interven-tions with pharmacotherapy

AbbreviationICT Information and communication technologies

Competing interestsThe authors declare that they have no competing interests

Authorsrsquo contributionsEP JMT CMC and LDG participated in the design coordination andexecution of the study analysis and interpretation of data writing of themanuscript and supervision of the project MMM JSF YGF BGR EB MLCJ CCand JB participated in the research team contributed to the study designinterpretation of data and critical revision of the manuscript All authors readand approved the final manuscript

AcknowledgementsThe study was supported by research grants from Fondo de InvestigacioacutenSanitaria (PI1100817) The authors gratefully acknowledge technical andscientific assistance provided by Primary Healthcare Research Unit of BarcelonaPrimary Healthcare University Research Institute IDIAP-Jordi Gol We would alsothank the Network of Preventive Activities and Health Promotion in primarycare (Red de Actividades Preventivas y Promocioacuten de la Salud en Atencioacuten

Primaria redIAPP) Programa Atencioacute Primagraveria Sense Fum (PAPSF) and SocietatCatalana de Medicina Familiar i Comunitagraveria (CAMFIC) for the diffusion of thestudy among sanitary professionals

TABATIC project investigatorsAbad Hernandez David Abad Polo Laura Abadiacutea Taira Mariacutea BegontildeaAguado Parralejo Maria del Campo Alabat Teixido Andreu Alba GranadosJoseacute Javier Alegre Immaculada Alfonso Camuacutes Jordi Aacutelvarez FernaacutendezSandra Aacutelvarez Soler Mariacutea Elena Andres Lorca Anna Anglada SellaresInmaculada Araque Pro Marta Arnaus Pujol Jaume Arribas ArribasMordfAngeles Baixauli Hernandez Montserrat Ballveacute Moreno Joseacute Luis BaraGallardo MordfJesuacutes Barbanoj Carruesco Sara Barbera Viala Julia BarceloTorras Anna Maria Barrera Uriarte Maria Luisa Bartolome Moreno CruzBelmonte Calderon Laura Benediacute Palau Maria Antonia Benedicto MordfRosaBernueacutes Sanz Guillermo Bertolin Domingo Nuria Blasco Oliete MelitoacutenBobeacute Armant Francesc Bonaventura Sans Cristina Bravo Luisa BretonesOlga Mariblanca Briones Carcedo Olga Bueno Brugueacutes Albert BuntildeuelGranados Joseacute Miguel Camats Escoda Eva Camos Guijosa Maria PalomaCampama Tutusaus Inmaculada Campanera Samitier Elena Canals CalbetGemma Cantoacute Pijoan Ana Mordf Cantildeadas Crespo Silvia Carmela RodriacuteguezMordf Carrascoacutes Goacutemez Montserrat Carreacutes Piera Marta Casals Felip RoserCasas Guumlell Gisel Casas Moreacute Ramon Casasnova Perella Ana Cascoacuten GarciacuteaMiguel Casellas Loacutepez Pilar Castantildeo MordfCarmen Castantildeo YolandaCastellano Iralde Susana Chillon Gine Marta Chuecos Molina MartaCifuentes Mora Esther Claveria Magiacute Clemente Jimeacutenez MordfLourdesClemente Jimeacutenez Silvia Cobacho Casafont Rosa Coacutelera Martiacuten Maria PilarComerma Paloma Gemma Correas Bodas Antonia Cort Miroacute Isabel CorteacutesGarciacutea MordfIsabel Cristel Ferrer Laura Crivilleacute Mauricio Silvia Cruz DomenechJose Manuel Cunillera Batlle Meritxell Danta Goacutemez Mari Carmen de CaboAngela De Juan Asenjo Jose Ramon de Pedro Picazo Beleacuten Del Pozo NiuboAlbert Delgado Diestre Carmen Diacuteaz Espallardo Trinidad Diacuteaz Gete LauraDiacuteaz Juliano Fernando Diez Diez M Amparo Digoacuten Blanco Clarisa DigonGarcia Escelita Domegravenech Bonilla MordfEncarnacion Erruz Andreacutes InmaculadaEscusa Anadoacuten Corina Espejo Castantildeo MordfTeresa Espin Cifuentes PietatEsteban Gimeno Ana Beleacuten Esteban Robledo Margarita Fabra NogueraAnna Fanlo De Diego Gemma Farre Pallars Francisca Felipe Nuevo MariaDolores Fernandez Campi Maria Dolores Fernandez de la Fuente PerezMaria Angeles Fernandez Gregorio Yolanda Fernaacutendez Maestre SorayaFernandez Martinez Mar Fernandez Moyano Juan Fernando FernaacutendezParceacutes MordfJesuacutes Ferre Antonia Ferrer Vilarnau Montserrat Figuerola GarciaMireia Florensa Piro Carme Flores Santos Raquel Gabriel Cesaacutereo GalbeRoyo Eugenio Garcia Esteve Laura Garciacutea Minguez Maria Teodora GarciaRueda Beatriz Garcia Sanchon Carlos Gardentildees Moron Lluisa GasullaGriselda Gerhard Perez Jana Gibert Sellareacutes MordfAgravengels Gineacute Vila AnnaGoacutemez Esmeralda Goacutemez Santidrian Fernando Goacutemez-Quintero Mora AnaMordf Gonzalez Casado Almudena Gonzaacutelez Ferneacutendez Yolanda Mariacutea GrasaLambea Inmaculada Grau Majo Inmaculada Grive Isern Montserrat GuumlerriBallarin Inmaculada Guillem Mesalles Moacutenica Victoria Guilleacuten AntoacutenMordfVictoria Guilleacuten Lorente Sara Hengesbach Barios Esther HernandezAguilera Alicia Hernandez Martin Teodora Hernaacutendez Moreno AnaConsuelo Hernandez Rodriguez Trinidad Herranz Fernandez Marta HerreraGarcia Adelina Herrero Rabella Maria Agravengels Huget Bea Nuria IgnacioRecio Jose Inza Henry Carolina Jarentildeo MordfJose Jericoacute Claveriacutea LauraJimenez Gomez Alicia Jou Turallas Neus Laborda Ezquerra KatherinaLaborda Ezquerra MordfRosario Lafuente Martiacutenez Pilar Lasaosa MedinaMordfLourdes Lera Omiste Inmaculada Llorente Mercedes Llort Sanso LaiaLoacutepez Barea Antonio Jose Loacutepez Borragraves Esther Loacutepez Carrique TrinidadLopez Castro Maite Lopez Luque Maite Loacutepez Mompoacute MordfCristina LopezPavon Ignacio Lopez Torruella Dolors Loreacuten Maria Teresa Lorente ZozayaAna Maria Lozano Enguita Eloisa Lozano Moreno Maribel Lucas SaacutenchezRoque Manzano Montero Moacutenica Marco Aguado MordfAngles Marco NavarroMaria Jose Mariacuten Andreacutes Fernando Marsa Benavent Eva Martiacuten CanteraCarlos Martin Montes Esperanza Martiacuten Royo Jaume Martiacuten Soria CarolinaMartinez Nuria Martiacutenez Esther Martiacutenez Abadiacuteas Blanca Martiacutenez GarciacuteaMireya Martinez Gomez Alberto Martinez Iguaz Susana Martiacutenez PeacuterezMaria Trinidad Martiacutenez Picoacute Angela Martiacutenez Romero MordfRocio MasSanchez Adoracioacuten Masip Beso Meritxell Massana Raurich Anna MataSegues Francesca Mayolas Saura Emma Mejiacutea Escolano David MejiasGuaita MordfJesus Mendioroz Lorena Mestre Ferrer Jordi Mestres LuceroJordi Migueles Garciacutea Susana Molina Albert MordfLluisa Moliner MolinsCristina Monreal Aliaga Isabel Morella Alcolea Nuria Moreno Brik Beatriz

Puigdomegravenech et al BMC Public Health 2015 152 Page 13 of 14httpwwwbiomedcentralcom1471-2458152

Morilla Tena Isabel Mostazo Muntaneacute Aliacutecia Mulero Rimbau Isabel MunneacuteGonzaacutelez Gemma Munuera Arjona Susana Navarro Echevarria MordfAntoniaNavarro Picoacute Montserrat Nevado Castejoacuten Jorge Nosas Canovas AsuncionOrtega Raquel Padiacuten Minaya Cristina Palacio Lapuente Jesuacutes Pallaacutes EspinetM Teresa Parra Gallego Olga Pascual Gonzalez Carme Pastor SantamariaMordfEncarnacion Pau Pubil Mercedes Paytubi Jodra Marta Pedrazas LoacutepezDavid Perez Lucena M Jose Perez Rodriguez Dolores Pinto Lucio PintoRodriguez Raquel Plana Mas Alexandra Planas Ruth Portillo Gantildeaacuten MariaJoseacute Pueyo Val Olga Maria Quesada Almacellas Alba Quintana VelascoCarmen Rafecas Garcia Veronica Rafols Ferrer Nuria Rambla VidalConcepcioacuten Ramos Caralt Maria Isabel Rando Matos Yolanda RasconGarcia Ana Rebull Santos Cristina Redondo Estibaliz Redondo MagdalenaReig Calpe Pere Rengifo Reyes Gloria del Rosario Riart Solans MarissaRibatallada Diez Ana Maria Robert Angelique Roca Domingo MarionaRodero Perez Estrella Rodrigo De Pablo Fani Rodriguez Moraacuten MordfJosepRodriacuteguez Saacutenchez Sonia Roura Rovira Nuacuteria Rozas Martinez MarianoRubiales Carrasco Ana Rubio Muntildeoz Felisa Rubio Ripolles Carles RuizComellas Anna Ruiz Pino Santiago Sabio Aguilar Juan Antonio SaacutenchezAna Maria Saacutenchez Benigna Saacutenchez Giralt Maria Sanchez RodriguezMordfBelen Saacutenchez Saacutenchez-Crespo Agravengela Sancho Domegravenech Laura SansCorrales Mireia Sans Rubio Merce Santsalvador Font Isabel Sarragrave ManetasNuacuteria Serrano Morales Cristina Servent Turo Josefina Server Climent MariaSilvestre Peacuterez Juliagrave Sola Casas Gemma Solagrave Cinca Teresa Soleacute BrichsClaustre Soleacute Lara M Pilar Soler Carne MordfTeresa Solis i Vidal Silvia TajadaVitales Celia Tagravepia Loacutepez Montserrat Tarongi Saleta Ana Telmo HuescoSira Tenas i Bastida Maria Dolors Trillo Calvo Eva Trujillo Goacutemez JoseacuteManuel Urpi Fernaacutendez Ana M Valbuena Moreno M Gracia Valdes PinaLaura Vallduriola Calboacute MordfCarme Valverde Trillo Pepi Vazquez MuntildeozImmaculada Vendrell Antentas Ana Maria Vera Morell Anna Vicente GarciacuteaRoveacutes Irene Vidal Cupons Anabel Vila Borralleras Montse Villagrasa GarciaMaria Pilar Vintildeas Viamonte MordfCarmen Wilke Asuncioacuten

Author details1Unidad de Soporte a la Investigacioacuten Barcelona Ciudad InstitutoUniversitario de Investigacioacuten en Atencioacuten Primaria Jordi Gol (IDIAP JordiGol) C Sardenya 375 entresol 08025 Barcelona Spain 2Centre drsquo AtencioacutePrimaria Passeig de Sant Joan Institut Catalagrave de la Salut Barcelona Spain3Departament of Medicine Universitat Autogravenoma de Barcelona BarcelonaSpain 4Centre drsquoAtencioacute Primaria La Sagrera Institut Catalagrave de la SalutBarcelona Spain 5Centre drsquoAtencioacute Primaria Horta 7F Institut Catalagrave de laSalut Barcelona Spain 6Centre drsquoAtencioacute Primaria Navarcles BarcelonaSpain 7Centro Sanitario Santo Grial (Huesca) Grupo Aragoneacutes deInvestigacioacuten en Atencioacuten Primaria Asociacioacuten para la Prevencioacuten delTabaquismo en Aragoacuten (APTA) Aragoacuten Spain 8La Alamedilla Health CentreCastilla y Leoacuten Health ServicendashSACYL redIAPP IBSAL Salamanca Spain

Received 20 January 2014 Accepted 14 December 2014Published 13 February 2015

References1 World Health Organization WHO Report on the Global TOBACCO Epidemic

2009 implementing smoke-free environments 2013 URL httpwwwwhointtobaccompower2009en Accessed January 16 2014

2 US Department of Health and Human Services How tobacco smoke causesdisease the biology and behavioral basis for smoking-attributable disease areport of the surgeon general 2010 URL httpwwwsurgeongeneralgovlibraryreportstobaccosmokeindexhtml Accessed January 16 2014

3 Comiteacute nacional para la prevencioacuten del tabaquismo Documento deconsenso para la atencioacuten cliacutenica al tabaquismo en Espantildea 2013 URLhttpwwwcnptesdocumentacionpublicacionesec34e5d56ba572d76297484cb6eb6a3f9dd91ac750db1addf646305eccae0f6apdf Accessed January16 2014

4 Ministerio de Sanidad Servicios Sociales e Igualdad Encuesta Nacional deSalud de Espantildea 201112 Nota teacutecnica-Principales resultados 2013 URLhttpwwwmsssigobesestadEstudiosestadisticasencuestaNacionalencuesta2011htm Accessed January 16 2014

5 Becontildea E Vazquez FL Effectiveness of personalized written feedbackthrough a mail intervention for smoking cessation a randomized-controlledtrial in Spanish smokers J Consult Clin Psychol 200169(1)33ndash40

6 Hughes JR Keely J Naud S Shape of the relapse curve and long-termabstinence among untreated smokers Addiction 200499(1)29ndash38

7 Ministerio de Sanidad Servicios Sociales e Igualdad Encuesta Nacional deSalud de Espantildea 2006 2013 URL httpwwwmsssigobesestadEstudiosestadisticasencuestaNacionalencuesta2006htm Accessed January16 2014

8 Fiore MC Jaeacuten CR Baker TB Bailey WC Benowitz NL Curry SJ et al A clinicalpractice guideline for treating tobacco use and dependence 2008 update AUS Public Health Service report Am J Prev Med 200835(2)158ndash76

9 Lemmens V Oenema A Knut IK Brug J Effectiveness of smoking cessationinterventions among adults a systematic review of reviews Eur J CancerPrev 200817(6)535ndash44

10 Stead LF Buitrago D Preciado N Sanchez G Hartmann-Boyce J Lancaster TPhysician advice for smoking cessation Cochrane Database Syst Rev20135CD000165

11 Marlow SP Stoller JK Smoking cessation Respir Care 200348(12)1238ndash5412 Uruentildea A Ferrari A Valdecasa A Ballestero MP Antoacuten P Castro R et al La

Sociedad en Red 2010 Informe Anual Edicioacuten 2011 ISSN 1989ndash7324 2011URL httpwwwosimgaorgexportsitesosimgagldocumentosd20110905_perfil_sociodemo_2010pdf Accessed January 16 2014

13 Weaver B Lindsay B Gitelman B Communication technology and socialmedia opportunities and implications for healthcare systems Online JIssues Nurs 201217(3)3

14 Internet World Stats Internet users in Europe Internet Usage in Europe2011 URL httpwwwinternetworldstatscomstats4htmeuropean2012Accessed January 16 2014

15 Observatorio Nacional de las Telecomunicaciones y de la SI (ONTSI) Perfilsociodemografico de los internautas Analisis de datos INE 2011 2012 URLhttpwwwontsiredesontsisitesdefaultfilesperfil_sociodemografico_de_los_internautas_analisis_datos_ine_2011pdfAccessed January 16 2014

16 Usher W Developing policies for e-health use of online health informationby Australian health professionals and their patients HIM J201140(2)15ndash22

17 Wu SJ Raghupathi W A panel analysis of the strategic association betweeninformation and communication technology and public health deliveryJ Med Internet Res 201214(5)e147

18 Civljak M Sheikh A Stead LF Car J Internet-based interventions for smokingcessation Cochrane Database Syst Rev 20109CD007078

19 Shahab L McEwen A Online support for smoking cessation a systematicreview of the literature Addiction 2009104(11)1792ndash804

20 Hutton HE Wilson LM Apelberg BJ Tang EA Odelola O Bass EB et al Asystematic review of randomized controlled trials web-based interventionsfor smoking cessation among adolescents college students and adultsNicotine Tob Res 201113(4)227ndash38

21 Kuppersmith RB Is E-mail an effective medium for physician-patientinteractions Arch Otolaryngol Head Neck Surg 1999125(4)468ndash70

22 Whittaker R1 McRobbie H Bullen C Borland R Rodgers A Gu Y Mobilephone-based interventions for smoking cessation Cochrane Database SystRev 201211CD006611 doi 10100214651858CD006611pub3

23 Chen YF1 Madan J Welton N Yahaya I Aveyard P Bauld L et alEffectiveness and cost-effectiveness of computer and other electronic aidsfor smoking cessation a systematic review and network meta-analysisHealth Technol Assess 201216(38)1ndash205 iii-v doi 103310hta16380

24 Civljak M1 Stead LF Hartmann-Boyce J Sheikh A Car J Internet-basedinterventions for smoking cessation Cochrane Database Syst Rev20137CD007078 doi 10100214651858CD007078pub4

25 Diaz-Gete L Puigdomenech E Briones EM Fagravebregas-Escurriola M FernandezS Del Val JL et al Effectiveness of an intensive E-mail based intervention insmoking cessation (TABATIC study) study protocol for a randomizedcontrolled trial BMC Public Health 201313364

26 Heatherton TF Kozlowski LT Frecker RC Fagerstroumlm KO The fagerstrom testfor nicotine dependence a revision of the fagerstrom tolerancequestionnaire Br J Addict 1991861119ndash27

27 Shaw M Galobardes B Lawlor DA Lynch J Wheeler B Davey Smith GThe handbook of inequality and socioeconomic position concepts andmeasures Bristol UK The Policy Press 2007 ISBN 978 186 1347664

28 Domingo-Salvany A Regidor E Alonso J Alvarez-Dardet C Proposal for asocial class measure working group of the Spanish Society of epidemiologyand the Spanish Society of family and community medicine Aten Primaria200025(5)350ndash63

29 The World Bank Internet users 2013 URL httpdataworldbankorgindicatorITNETUSERP2 Accessed January 16 2014

Puigdomegravenech et al BMC Public Health 2015 152 Page 14 of 14httpwwwbiomedcentralcom1471-2458152

30 Hunt Y Internet use among smokers in the US data from the 2003 and2007 Health Information National Trends Survey (HINTS) Seattle 31 AnnualMeeting amp Scientific Sessions of the Society of Behavioral Medicine 2010

31 Bundorf MK Wagner TH Singer SJ Baker LC Who searches the internet forhealth information Health Serv Res 200641(3 Pt 1)819ndash36

32 Weaver JB Mays D Lindner G Eroglu D Fridinger F Bernhardt JM Profilingcharacteristics of internet medical information users J Am Med InformAssoc 200916(5)714ndash22

33 Cobb NK Graham AL Characterizing Internet searchers of smokingcessation information J Med Internet Res 20068(3)e17

34 Stoddard JL Augustson EM Smokers who use internet and smokers whodonrsquot data from the Health Information and National Trends Survey (HINTS)Nicotine Tob Res 20068Suppl 1S77ndash85

35 Cobb NK Graham AL Bock BC Papandonatos G Abrams DB Initialevaluation of a real-world Internet smoking cessation system Nicotine TobRes 20057(2)207ndash16

36 Chander G Stanton C Hutton HE Abrams DB Pearson J Knowlton A et alAre smokers with HIV using information and communication technologyImplications for behavioral interventions AIDS Behav 201216(2)383ndash8

37 Lenhart A Cell phones and American adults they make just as many callsbut text less often than teens Pew research Center 2013 2010 URL httpwwwpewinternetorg~mediaFilesReports2010PIP_Adults_Cellphones_Report_2010pdf Accessed January 16 2014

38 Forrester Research Forrester research mobile media application spendingforecast 2012 To 2017 (EU-7) 2013 URL httpblogsforrestercommichael_ogrady12-06-19-sms_usage_remains_strong_in_the_us_6_billion_sms_messages_are_sent_each_day Accessed January 16 2014

39 Free C Knight R Robertson S Whittaker R Edwards P Zhou W et alSmoking cessation support delivered via mobile phone text messaging(txt2stop) a single-blind randomised trial Lancet 2011378(9785)49ndash55

40 Wangberg SC Nilsen O Antypas K Gram IT Effect of tailoring in aninternet-based intervention for smoking cessation randomized controlledtrial J Med Internet Res 201113(4)e121

41 Te Poel F Bolman C Reubsaet A De Vries H Efficacy of a single computer-tailored e-mail for smoking cessation results after 6 months Health EducRes 200924(6)930ndash40

42 Polosa R Russo C Di Maria A Arcidiacono G Morjaria JB Piccillo GAFeasibility of using E-mail counseling as part of a smoking-cessationprogram Respir Care 200954(8)1033ndash9

43 Baena A Quesada M El papel integrador y complementario de las nuevastecnologiacuteas de la informacioacuten y de la comunicacioacuten en el control ytratamiento del tabaquismo Trastornos Adictivos 20079(1)46ndash52

44 Atherton H Huckvale C Car J Communicating health promotion anddisease prevention information to patients via email a review J TelemedTelecare 201016(4)172ndash5

45 Jaakkimainen L Upshur RE Klein-Geltink J Leong A Maaten S Schultz Set al Ambulatory physician care for adults primary care in Ontario TorontoCES Atlas Institute for Clinical Evaluative Sciences 2006 Toronto CES AtlasInstitute for Clinical Evaluative Sciences Toronto 7-7-2013

46 Zwar NA Richmond RL Role of the general practitioner in smokingcessation Drug Alcohol Rev 200625(1)21ndash6

47 Abroms LC Padmanabhan N Thaweethai L Phillips T iPhone apps forsmoking cessation a content analysis Am J Prev Med 201140(3)279ndash85

48 Prochaska JJ Pechmann C Kim R Leonhardt JM Twitter = quitter Ananalysis of Twitter quit smoking social networks Tob Control201221(4)447ndash9

doi1011861471-2458-15-2Cite this article as Puigdomegravenech et al Information andcommunication technologies for approaching smokers a descriptivestudy in primary healthcare BMC Public Health 2015 152

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