Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol

STUDY PROTOCOL Open Access Tailo ing mo i a ional heal h messages o smoking cessa ion using an mHeal h ecommende sys em in eg a ed wi h an elec onic heal h eco d: a s udy p o ocol San iago Ho s-F aile 1,2* , F ancine Schneide 2 , Luis Fe nandez-Luque 3,4 , F ancisco Luna-Pe ejon 1 , An on Ci i 1 , Dimi is Spachos 5 , Panagio is Bamidis 5 and Hein de V ies 2 Abs ac Backg ound: Smoking is one o he mos a oidable heal h isk ac o s, and ye he qui ing success a es a e low. The usage o ailo ed heal h messages o suppo qui ing has been p o ed o inc ease qui ing success a es. Technology can p o ide con enien means o deli e ailo ed heal h messages. Heal h ecommende sys ems a e in o ma ion- il e ing algo i hms ha can choose he mos ele an heal h- ela ed i ems— o ins ance, mo i a ional messages aimed a smoking cessa ion— o each use based on his o he p o ile. The goals o his s udy a e o analyze he pe cei ed quali y o an mHeal h ecommende sys em aimed a smoking cessa ion, and o assess he le el o engagemen wi h he messages deli e ed o use s ia his medium. Me hods: Pa ien s pa icipa ing in a smoking cessa ion p og am will be p o ided wi h a mobile app o ecei e ailo ed mo i a ional heal h messages selec ed by a heal h ecommende sys em, based on hei p o ile e ie ed om an elec onic heal h eco d as he ini ial knowledge sou ce. Pa ien s’ eedback on he messages and hei in e ac ions wi h he app will be analyzed and e alua ed ollowing an obse a ional p ospec i e me hodology o a) assess he pe cei ed quali y o he mobile-based heal h ecommende sys em and he messages, using he p ecision and ime- o- ead me ics and an 18-i em ques ionnai e deli e ed o all pa ien s who comple e he p og am, and b) measu e pa ien engagemen wi h he mobile-based heal h ecommende sys em using agg ega ed da a analy ic me ics like session equency and, o de e mine he indi idual-le el engagemen , he a e o ead messages o each use . This pape de ails he implemen a ion and e alua ion p o ocol ha will be ollowed. Discussion: This s udy will explo e whe he a heal h ecommende sys em algo i hm in eg a ed wi h an elec onic heal h eco d can p edic which ailo ed mo i a ional heal h messages pa ien s would p e e and conside o be o a good quali y, encou aging hem o engage wi h he sys em. The ou comes o his s udy will help u u e esea che s design be e ailo ed mo i a ional message-sending ecommende sys ems o smoking cessa ion o inc ease pa ien engagemen , educe a i ion, and, as a esul , inc ease he a es o smoking cessa ion. T ial egis a ion: The ial was egis e ed a clinical ials.o g unde he ClinicalT ials.go iden i ie NCT03206619 on July 2nd 2017. Re ospec i ely egis e ed. Keywo ds: Recommende sys em, Tailo ed messages, Smoking cessa ion, Mobile app, Pa ien , mHeal h * Co espondence: [email p o ec ed] 1 Depa men o A chi ec u e and Compu e Technology, Uni e sidad de Se illa, ETSII, A enida Reina Me cedes S/N, 41012 Se ille, Spain 2 Depa men o Heal h P omo ion, School o Public Heal h and P ima y Ca e (Caph i), Maas ich Uni e si y, P. Debyeplein 1, 6229, HA, Maas ich , The Ne he lands Full lis o au ho in o ma ion is a ailable a he end o he a icle © The Au ho (s). 2018 Open Access This a icle is dis ibu ed unde he e ms o he C ea i e Commons A ibu ion 4.0 In e na ional License (h p://c ea i ecommons.o g/licenses/by/4.0/), which pe mi s un es ic ed use, dis ibu ion, and ep oduc ion in any medium, p o ided you gi e app op ia e c edi o he o iginal au ho (s) and he sou ce, p o ide a link o he C ea i e Commons license, and indica e i changes we e made. The C ea i e Commons Public Domain Dedica ion wai e (h p://c ea i ecommons.o g/publicdomain/ze o/1.0/) applies o he da a made a ailable in his a icle, unless o he wise s a ed. Ho s-F aile e al. BMC Public Heal h (2018) 18:698 h ps://doi.o g/10.1186/s12889-018-5612-5 Backg ound New echnologies such as sma phones and wea ables can be used o suppo beha io change among pa ien s, as many s udies ha e al eady shown [1–7]. One o he waysinwhich echnologyisused odosoisbydesign- ing ailo ed heal h messages a ge ed a pa ien s. Some such pla o ms use expe sys ems [8] ha use he ules o human expe easoning and in e esul s based on people’s answe s o ques ions abou beha io knowledge and mo i a ional aspec s like a i ude and sel -e icacy. Ye ano he ype o sys em is a ecommende sys em, which aims a sending messages ailo ed o use s’p e e ences [9–11]. These pla o ms employ algo i hms o p edic which message is mos simila o i s use s’p e iously p e- e ed messages. A ecommende sys em is a piece o so wa e ha lea ns o p edic he bes i em o each use om a se o i ems [12]. I ems can be ex messages, mo ies, books, people, o any hing else ha can be ecommended. Rec- ommende sys ems ha e been exploi ed mos ex ensi ely in he sphe es o e-comme ce and leisu e, h ough he ecommenda ion o , o ins ance, mo ies, books, and music [13]. Fo example, i a sys em knows he books you ha e liked in he pas , i aims o o ecas he books you may also like in he u u e. This can be done using di e en ech- niques like compa ing he ea u es o he books you ha e liked wi h he ea u es o o he books you ha e no ead, as shown in Fig. 1, o by conside ing books ha people wi h simila as es as you ha e also liked. Recommende sys ems ha e also been used in he heal hca e domain. Heal h ecommende sys ems a e especially aimed a p o iding eedback and ecommen- da ions on heal h s a us and hea h beha io s, such as li es yle, nu i ion [14], obesi y [15], diabe es [16,17], d ug side e ec s [18], and smoking cessa ion [19]. The ype o eedback is based on algo i hms p edic ing he ype o message needed on he basis o p e iously mea- su ed a iables. The equi ed inpu can be aken om elec onic heal h eco ds as desc ibed by Wiesne e al. [20] o may be based on a pe son’s in e es in speci ic leisu e- ime beha io s. Howe e , ew s udies use his echnique, and hei po- en ial is s ill o be exploi ed [21–23]. In addi ion, heal h ecommende sys ems need o be p ecise and accu a e in o de o be e ec i e. Tha op imiza ion can al eady be ound in ecommende sys ems used o ecomme ce and leisu e. Howe e , assessing he quali y o heal h ecommende sys ems in e ms o app ecia ion o he ecommenda ions by pa ien s and hei beha io al e - ec s has so a no been explo ed in-dep h. This can be done using expe imen al designs aimed a measu ing hei objec i e accu acy [24]o by indingou hei use s’subjec i e opinions as desc ibed in he ResQue amewo k [25]. The goal o his s udy is de elop and e alua e he quali y o a heal h ecommende sys em o ill his exis ing gap. The heal h ecommende sys em chosen o he s udy is aimed a suppo ing smoking cessa ion, because smoking is di ec ly associa ed wi h a numbe o diseases: ch onic obs uc i e pulmona y disease, as hma exace ba ion, ca a- ac s, pulmona y ib osis, o al ca i y cance , pha ynx cance , la ynx cance , esophagus cance , lung cance , and bladde cance , among o he s [26]. Consequen ly, qui ing smoking is he mos impo an decision smoke s can ake o op imize hei chances o educe heal h isks and in- c ease he longe i y o hei li es [27]. Se e al s udies explo ed he e ec s o inno a i e ailo ed beha io al change me hods o smoking cessa ion and demons a ed Fig. 1 Concep diag am o a simple ecommende sys em. In his example, he ecommended i ems a e books. The use ecei es sugges ions on wha o ead nex based on he gen es and ea u es o books he o she has liked in he Ho s-F aile e al. BMC Public Heal h (2018) 18:698 Page 2 o 10 ha hey can con ibu e o a highe success a es [28–30]. O he s udies showed ha he deli e y o ailo ed mo i - a ional messages using mobile phones as a pla o m could be an e ec i e way o enable smoking cessa ion [31–35] as well as o impac o he aspec s o heal h, such as p omo ing physical ac i i y and exe cise [36,37], men al heal hca e [38], and alcohol- ela ed ha m [39], among o he s. These ailo ed messages a e usually pieces o ex based on he use ’s esponses o ce ain ques ions, assessing hei a i ude, social suppo , sel -e icacy, and ype o ac ion planning. Ye , qui ing smoking is o en accompanied by se e al ba ie s, such as nico ine abs inence synd ome ha may ha e consequences like headaches, c a ings, in es inal dis- o de s, weigh gain, insomnia, es lessness, ne ousness, dep ession, and i i abili y, among o he s [40,41]. As a con- sequence, he a e o smoking elapses is high and success a es a e o en low [42–46], illus a ing a clea need o p o- iding messages ha a e highly pe sonalized and ailo ed o he needs o each pe son a ha poin in ime. Heal h ec- ommende sys ems may ha e speci ic added alue he e as hey use algo i hms o op imally choose no only he ype o message o be sen bu also when i is sen . In he cu en s udy, we will combine he p inciples o ailo ing wi h wo ecommende sys ems, which cus omize messages and p edic he bes ime o send he ailo ed mo i a ional heal h messages o a coho o pa ien s who a e ying o qui smoking using a mobile app. To selec he mos ele an message opic ha can os e heal hie habi s in each pa ien , a heal h ecommenda ion sys em algo i hm (HRSA) accesses each pa ien ’s pe sonal heal h eco ds and eeds he algo i hm wi h his heal h da a. Since he messages a e deli e ed using sma phones and a e ailo ed as pe each pa ien ’s heal h da a, we call ou sys em an mHeal h Recommende Sys em (m-HRS). Wendel e al. [47] sugges ed ou basic equi emen s o educe po en ial ba ie s o using ecommende sys ems: 1. E o inpu minimiza ion o educe use s’bu den; 2. P i acy assu ance; 3. Op imizing message use ulness; and 4. Enjoymen . The quali y o he sys em is s ongly associa ed o he pa ien s’a i udes owa d he sys em and, consequen ly, o i s usage [48]. A heal h sys em ha pa ien s eel is no o a good quali y and one hey canno us will no be used [49]. By inc easing he pe cei ed quali y o he sys em, pa ien s will be encou aged o adop he sys em and use i mo e, which will lead o be e heal h ou comes [50,51]. A i ion is a well-known p oblem in digi al heal hca e in e en ions [52]. I is impo an o inc ease app eci- a ion and hus aise pa ien engagemen , he eby keeping a i ion o a minimum. This is especially ele an in m-HRS because he mo e use engagemen wi h he sys- em, he mo e he sys em lea ns abou he use , and, con- sequen ly, he s onge he sys em becomes. Alkhaldi e al. explained he impo ance o use engagemen in digi al heal h in e en ions [53]. Al hough he e a e di e en de - ini ions o engagemen , he one used in his s udy was p oposed by Alkhaldi e al. in he s udy “Use s e isi ing he digi al in e en ion”. A high engagemen in digi al heal h in e en ions is also associa ed wi h be e pa ien heal h ou comes [54,55]. This is because engaged pa ien s ead mo e ailo ed mo i a ional heal h messages and he e o e ecei e mo e such p omp s. This has a posi i e impac on hei beha io change, such as qui ing smoking, as some s udies ha e al eady shown [56,57]. Howe e , he no el y e ec may also in luence hei a i ion. Consequen ly, i would be necessa y o com- pa e how pa ien s’opinions ega ding he HRS mes- sages e ol e h oughou he in e en ion. In conclusion, he i s goal o his s udy is o desc ibe he pa ien s’pe cei ed quali y o he m-HRS. The second goal is o assess he pa ien s’le el o engagemen wi h he messages gene a ed by he sys em. Iden i ying which sys em ea u es and use cha ac e is ics de e mine di e ences in use engagemen and quali y pe cep ion may help imp o e he design o u u e m-HRS pla o ms. Me hods/Design Pa icipan s Fo his obse a ional p ospec i e s udy, we will analyze pa ien s pa icipa ing in he SmokeF eeB ain p ojec [58] o a pe iod o 12 mon hs. The inclusion c i e ia o he s udy a e pa ien s who a e a ending he smoking cessa ion p og am Smoke F ee B ain [59,60] a he Vi gen del Rocio Uni e si y Hospi al, a e a leas 18 yea s old, a e willing o s a ea men o qui smoking, own an And oid sma phone and know how o use i , ha e ins alled he smoking ces- sa ion app ecommended by he doc o , and who ha e no p e iously had any known ad e se e ec s o he pha macological ea men . T ial design Pa ien s will be p o ided wi h a pu pose-buil And oid-based mobile applica ion ha allows hem o ecei e messages and a e hem on hei sma phones. The app is called “Lib e de humos”,o “Smoke ee”in English. F om he e onwa d, we will e e o i as “ he app”.Theapp’so he ea u es include a goal achie emen dashboa d, physical exe cise eco ds, a elapse dia y, a elaxa ion ool, mini-games o help pa ien s o e come c a ings h ough dis ac ions—one o which is based on he webFi Fo All exe gaming pla o m p o ocol [61,62]—and an in o m- a i e sec ion wi h con en on a ious opics ela ed o smoking cessa ion. As no o mal me hodology has ye been used o assess mobile heal h ecommende sys ems, we will use he message a ings, pa ien s’app usage beha io , and an Ho s-F aile e al. BMC Public Heal h (2018) 18:698 Page 3 o 10 adap ed se o ques ions o he pa ien s o compa e hei le els o quali y app ecia ion and engagemen . This will allow us o compa e di e ences be ween he m-HRS pa ien s’opinions o e ime. App ea u es and clinical in eg a ion The p esen s udy will ocus on exploi ing ea u es o sma phones ha allow use s o ecei e messages om a se e and ack hei ac i i y and in e ac ion. The app can connec wi h he smoking cessa ion uni o he hospi al’s elec onic heal h eco d using Mi h Connec so wa e. I in e connec s he ollowing: The hospi al use da abase o access he pa ien s’ demog aphic da a The hospi al clinical da a base o access he pa ien s’ clinical in o ma ion The Ligh weigh Di ec o y Access P o ocol o alida e he c eden ials o he heal hca e p o essionals who access he hospi al sys em The in o ma ion eques ed by he app is p ocessed and selec ed om he elec onic heal h eco d da abase, o ma ed using he ISO13606 s anda d, and sen back by he Mi h Connec pla o m. The e o e, all he in o ma ion ega ding a pa ien ’sp o- ile—name, age, gende , da e o qui ing smoking, ype o pha macological ea men —is au oma ically loaded in he app wi hou pa ien s needing o inpu any hing apa om a code p o ided by hei clinicians. Fu he mo e, using he clinical s a ion— he sys em in e ace a he hospi al— heal hca e p o essionals can moni o he pa ien s’ac i i y using he app. Fo ins ance, i a pa ien logs a elapse, his o he doc o s will be able o access i , p o ided hey ha e he pa ien ’s consen o do so. The app will allow pa ien s o a e he messages hey ha e ecei ed using bu ons o “Like”(posi i e), “Dislike” (nega i e), o “Don’ mind”(neu al), as shown in Fig. 2. The messages hey will ecei e all unde one o he ol- lowing i e opics: gene al mo i a ion, die ips, physical exe cise ips, pe sonal pe o mance, and he bene i s o being a non-smoke . Each o hese opics has a pool o 150 di e en messages, along wi h use ul ailo ed in o ma ion o he pa ien s. These opicsandmessagesha ebeenapp o edbyasmok- ing cessa ion psychologis as well as a pulmonologis om he hospi al. Thus, his design will show whe he pa ien s p e e he messages selec ed by he m-HRS a he end o he pilo as a consequence o he HRSA being ained o be e ma ch he pa ien ’s p e e ences, as opposed o he mes- sages sen a he beginning when he HRSA was no con igu ed o lea n and imp o e messages based on he pa ien s’ eedback. Use -cen ic conside a ions in he heal h ecommende sys em Based on Wendel e al.’s s udy [47] on os e ing heal h ecommenda ion sys ems, we ook in o accoun he ollowing: E o : Any e o equi ed om he pa ien ’s side has been minimized by emo ing he en y ba ie o ha ing o manually inpu hei p o ile de ails. These de ails a e au oma ically loaded om he hospi al elec onic heal h eco d. P i acy isk: The app only s o es he pa ien ’s name and all communica ion ollows he MD5 enc yp ion p o ocol as equi ed o mee he hospi al’s p i acy equi emen s. The app and he elec onic heal h eco d exchange XML documen s ollowing he HL7 p o ocol using he Mi h Connec engine. Use ulness: The opics o he messages and he messages hemsel es we e alida ed by a psychologis and pulmonologis o make su e hey con ain ele an scien i ic in o ma ion. Enjoymen : The use o iendly and amilia language combined wi h he equency and iming o he messages makes he app eel pe sonal and less obo ic. This ollows he gami ica ion p inciple o “unp edic abili y and andomness”as pe he Oc alysis gami ica ion amewo k [63]. Finally, Wendel e al.’s s udy also e e ed o he in e - media y ha in oduces he sys em o he pa ien s. In his case, i is he heal hca e p o essional om he hospi- al’s smoking cessa ion uni who p esen s and endo ses he m-HRS. This ac may also posi i ely con ibu e o pa ien s using i . Choosing he message opic The da a on he message a ings and he pa ien s’in e - ac ion wi h he app will be sen o he hospi al se e , whe e i will be p ocessed by an HRSA o enable i o choose he nex message o each use as well as he ime a which i should be sen . Figu e 3shows a concep ualiza ion o his mHeal h Recommende Sys em a chi ec u e. The HRSA is an algo i hm ha ollows a hyb id app oach o imp o e i s pe o mance, as sug- ges ed by Bu ke [64]. The algo i hm combines he ol- lowing h ee ac o s o compu e he esul s: Pa ien ’s demog aphic simila i y: Demog aphic in luence will be based on he simila i y o pa ien s in e ms o hei age, gende , employmen s a us, da e hey s opped smoking, and sco es on he Fage s öm [65] and Richmond es s [66]. Pe cei ed u ili y o he message opics: The u ili y o he messages will be measu ed as ollows: Pa ien s Ho s-F aile e al. BMC Public Heal h (2018) 18:698 Page 4 o 10 will be p omp ed o a e each message wi h “like”, “dislike”,o “neu al”. In case o mul iple a ings gi en o he same message, he old a ings will be o e idden. Visi s o sec ions o he app on he same opic as he messages o e- ead messages on a gi en opic will also be conside ed. S a emen o ini ial in e es : Pa ien s need o ill ou a ques ionnai e consis ing o i e ques ions in which hey s a e hei in e es in he i e message opics. This in e es can be a ed as posi i e, neu al, o nega i e. Pa ien s can modi y hei answe s any ime h ough he app. Fig. 3 Desc ip ion o he mHeal h Recommende Sys em’s a chi ec u e and low o da a in o ma ion Fig. 2 Sc een cap u es o he message lis and he a ing mechanism o he “Lib e de humos”smoking cessa ion app. Pa ien s can p o ide eedback on he messages hey ecei e ia he app. They can p o ide eedback on each message by indica ing “like”,“dislike”,o “indi e en ” Ho s-F aile e al. BMC Public Heal h (2018) 18:698 Page 5 o 10 Once he opic has been selec ed, he sys em selec s a p e iously unsen message om he da abase and ailo s i o he use so ha i is mo e pe sonalized. Fo in- s ance, he message may include he name o he use . Sending he messages The equency a which pa ien s will ecei e messages is based on he conclusions o Ab oms e al. [67] consid- e ed in combina ion wi h he ans heo e ical beha io change model [68]. This model was chosen because he counseling o e ed a he hospi al’s smoking cessa ion uni is pa ially based on i , and we wan ed he messages o be in line wi h his counseling. We s a ed conside - ing pa ien s in he p epa a ion phase, which is when hey can join he p og am. P epa a ion: One message a day un il he day o qui ing— he opic o hese messages is no p edic ed by he HRSA bu chosen a andom, and hey canno be a ed, so hey ha e no in luence on he HRSA Ac ion: Fou messages on he day o qui ing, and one message a day o he ollowing week. Main enance: Th ee messages a week a e ha , o one yea . In he main enance phase, as pe he ans heo e ical beha io al change model, he messages will be sen a andom days du ing he week. In o al, a pa ien who comple es he yea long ea men will ecei e a o al o 150 messages om he m-HRS. The ime a which he messages will be sen is also calcula ed by he HRSA, based on he ime di e ence be ween when a message is sen and when i is ead. The day will be di ided in 12 wo-hou pe iods du ing which he message can be sen a any andom ime. The soone he pa ien eads he message a e eceip , he mo e likely i is ha he nex message will be sen in he same wo-hou pe iod. Pa- ien s can deac i a e messages du ing a speci ic pe iod o ime h ough a do-no -dis u b se ing. Jus like he inal decision o he message opic, he messages’ iming is also based on a p obabili y ec o . Ou comes Ou p ima y ou comes a e he pa ien s’pe cei ed qual- i y o he m-HRS and hei engagemen wi h i . We de- ine quali y as a se o ea u es ha makes he sys em ecommend imely and ele an messages o each pa- ien . As no o mal me hodology has ye been used o assess he quali y o and engagemen wi h HRS, we will use bo h objec i e and subjec i e quali y measu es along wi h engagemen me ics a an agg ega ed and indi idual le el. Quali y Objec i e quali y Since he HRSA p edic s he message opic and iming o deli e y, he objec i e pe cei ed quali y will be mea- su ed using wo me ics: he p ecision o he message opics, and how long a e ecei ing he message he use s ead i . The p ecision me ic is de ined as he ela ion be ween he numbe o hi s pe message in he i s mon h (base- line) and he ex en o “posi i e”,“neu al”,and“nega i e” eedback pe message in mon h 3, mon h 6, mon h 9, and mon h 12. Hi s a e messages wi h only posi i e eedback (1) P ecision p and wi h bo h posi i e and neu al eedback (2) P ecision p&n . P ecisionP¼jmessages wi h posi i e eedback j jall messages wi h any eedback jð1Þ P ecisionpn ¼jmessages wi h posi i e and neu al eedback j jall messages wi h any eedback j ð2Þ We expec he accep ance a e o be highe in mon h 12, as compa ed o he p e ious mon hs, as by his ime he HRSA will ha e mo e pa ien s and enough in o ma- ion o make accu a e decisions. We will calcula e he p ecision o he sys em a di e en poin s o ime, which will e lec he use s’ eedback and hei in e ac ions wi h speci ic sec ions o he app. All his in o ma ion is s o ed in he da abase o he m-HRS. The ime- o- ead me ic is de ined as he ime di e - ence be ween when he message was sen and when i was ead by he use . We will use i o assess he e olu- ion in he quali y o he HRSA o p edic he bes ime a which o send a use messages—we heo ize ha he lesse he ime- o- ead, he be e i is o he use o e- cei e messages a ha ime. Subjec i e quali y The subjec i e quali y will be e alua ed h ough eigh een ques ions assessed using a i e-poin Like scale answe ed by all pa ien s a he end o hei smoking cessa ion p og am. The ques ionnai e is adap ed o he con ex o smoking cessa ion and o i wi hin he expec ed ime ame in which he pa ien s will ha e o comple e i du ing hei consul a ion a he hospi al. The i s wel e ques- ions a e a selec ion o hose p oposed in he ResQue amewo k [25] in ended o measu e he quali y o he use ’s expe ience wi h a ecommende sys em and i s in luence on he use ’s beha io s and in en ions. The emaining six ques ions a e designed on he basis o he i-Change beha io change model [69–71] o iden i y whe he he messages also ha e an impac on he pa- ien s’mo i a ions. Ho s-F aile e al. BMC Public Heal h (2018) 18:698 Page 6 o 10 Engagemen Engagemen a an agg ega ed le el This will be measu ed h ough i e ac o s using he an- aly ics so wa e Yahoo Flu y, which is in eg a ed in he m-HRS app. As desc ibed by he so wa e de elope s hemsel es [72], hese ac o s a e as ollows: 1. Rolling e en ion: The pe cen age o use s s ill ac i e N days a e ins alla ion. This is a a io o he numbe o use s whose las day o ac i i y is pas day N o he numbe o use s who could ha e been ac i e on day N (i.e., he sum o new use s up un il day N). 2. Session leng h dis ibu ion: The session leng h is de ined simply as he leng h o ime be ween he s a app e en and he end app e en . The session leng h de e mines engagemen in e ms o how much ime pa ien s spend on he app pe session. 3. Session equency: F equency o Use is a measu e o how o en each unique use used he app wi hin a gi en ime in e al. 4. Sessions pe use : A session is de ined as one use o he app by a pa ien . This begins when he applica ion is launched and ends when he applica ion is e mina ed. 5. Re u n a e: Re u n a e measu es he pe cen age o use s who e u n o he app a a speci ic ime a e ins alla ion. We look a his alue by coho g oup, ha is, based on when use s i s opened he app. I is calcula ed as he a io o he numbe o use s ac i e on a gi en day, week, o mon h o he size o he coho . The ins all da e is conside ed as Day 0. Fo example, he Re u n Ra e o day 7 is he pe cen age o use s ha opened he app on he 7 h day a e ins alla ion. Use ac i i y on day 8, 9, and so on does no impac his alue. Engagemen a an indi idual le el This will be assessed based on he a e o messages ead by he use . I is calcula ed as he a io o he messages a use has ead and he o al numbe o messages he sys- em has sen o he use . Sample size We aim o ec ui 120 pa ien s. No powe analysis was done o his s udy, as we ollowed a con enien sample [73] me hod: ou ec ui ing pa ne Hospi al Vi gen del Rocío es ima ed hey could only access 120 pa ien s wi hin he ime limi s o he p ojec . The expec ed d op- ou a e is 50%. This means he sys em will ha e sen a leas 9000 messages o pa ien s be o e he s udy ends (a minimum o 60 pa ien s ending he ea men mul iplied by 150 messages sen h oughou he ea men ). E hics The Social Local and Mobile in e en ion his s udy is based on has been app o ed by he E hical Commi ee o he Vi gen del Rocío and he Vi gen Maca ena Uni e si y Hospi als (app o al numbe SFB-APP_EC-2016-01). The esea ch ac i i ies will s ic ly ollow hospi al egula ions. Rega ding he use o da a, his p ojec will con o m o he egula ions o he Pe sonal Da a Ac (based on he Eu opean Da a P o ec ion Di ec i e). Discussion This s udy will explo e how pa ien s en olled in a smoking cessa ion p og am pe cei e and engage wi h an m-HRS ha sends hem pe iodic mo i a ional messages encou - aging hem o s op smoking. We also expec he m-HRS o help inc ease he low a i ion a e ha is ypically seen in eHeal h ials [74]. The answe s o his s udy’s esea ch ques ions will bene i u u e ecommende sys em-based beha io al change in e en ions. Resea che s will be able o e-use ou p oposed pe cei ed quali y and engagemen me ics and exploi he knowledge de i ed in o de o design new and imp o ed heal h ecommende sys ems ha ake in o accoun use app ecia ion and engagemen . Thus, he heal h messages sen o he use s will be mo e in e es ing, ele an , and engaging o hem. We expec he pa ien s’pe cei ed quali y o he sys em o inc ease o e ime, ha is, o he p ecision o he messages o inc ease and he pa ien s’ ime o ead he messages o dec ease. Howe e , we should conside ha , a some poin du ing he in e en ion, pa ien s may s op eading he messages because hey ha e unins alled he app. This may be because hey ha e success ully qui smoking and do no wan any smoking- ela ed e- minde s, because hey ha e elapsed, o because hey ne e engaged wi h he app in he i s place and ha e decided o emo e i om hei sma phone. The e o e, hese me ics will need o be ca e ully analyzed o a oid simplis ic conclusions. The use s’in-app ac i i y analy ics will p o ide an insigh in o how pa ien s eac o he messages he HRSA selec s o hem. This opens up se e al u u e esea ch oppo uni ies, such as compa ing whe he he heo e ical in e es s pa ien s claim o ha e a e in ac hei in e es s, no ing he ime hey s op a ing messages because hey a e no longe in e es ed in ecei ing mo e messages, and ha ing an ini ial m-HRS-gene a ed message pa ien ac- cep ance da a epo o u u e implemen a ion in smok- ing cessa ion p og ams. Howe e , his s udy may su e om he us p oblem like all ecommende agen s. Despi e he elabo a e design o he HRSA, should a pa ien ecei e a single message ha is ou o con ex , i may uin he con idence in he sys em and she may s op using he m-HRS al oge he Ho s-F aile e al. BMC Public Heal h (2018) 18:698 Page 7 o 10 [75]. In o de o minimize his, he messages ha e been designed such ha hey a e applicable o almos all ci - cums ances and expe iences o a pa ien . The p emise behind his s udy using an m-HRS in- s ead o a s anda d ini ial ques ionnai e o an expe sys em is he high on- he-go adap abili y po en ial o pa ien s wi hou bo he ing hem wi h ques ionnai es o upda e hei p e e ences. Ano he eason o his is he po en ial scalabili y and obus ness he m-HRS can achie e in he long e m when he HRSA da abase con- ains in o ma ion on a la ge numbe o pa ien s. This will p o ide he basis o heal h beha io change s udies o be conduc ed a a la ge scale and o become mo e ad anced since he ecommenda ions o pa ien s will be selec ed om a mo e complex se o op ions. Limi a ions and gene alizabili y This s udy is limi ed by he design o he HRSA, which canno dis inguish be ween longi udinal in e es changes in he pa ien coho du ing he smoking cessa ion p ocess. In addi ion, he HRSA is also limi ed because i does no di e en ia e be ween wo king days and holidays. The imposed maximum sample size and he lack o a-p io i powe analysis may also be a limi ing ac o o he gene alizabili y o he esul s o he s udy. Howe e , o minimize his, we will do powe calcula ions o mini- mum de ec able e ec sizes o key ou comes. The pa ien engagemen analysis is limi ed because i is no possible o c oss-compa e he indi idual-le el en- gagemen me ics o he agg ega ed-le el engagemen me ics. Since he s udy will only be conduc ed in he ci y o Se ille, i may no be possible o he ou comes o be di - ec ly gene alized o he Spanish popula ion as a whole. Abb e ia ions HRSA: Heal h ecommenda ion sys em algo i hm; m-HRS: mHeal h Recommende Sys em Acknowledgemen s We app ecia e he con ibu ions o he pa ne s o he SmokeF eeB ain p ojec who di ec ly wo ked in his s udy: he Vi gen del Rocío Uni e si y Hospi al o i s suppo in in eg a ing he HRSA wi h hei se e s and synch onizing i wi h hei elec onic heal h eco ds, Salumedia Tecnologías, he A is o le Uni e si y o Thessaloniki, and he No he n G eece Neu oscience Cen e o hei suppo in de eloping he app. Funding The p esen s udy was unded h ough he P ojec SmokeF eeB ain “Mul idisciplina y ools o imp o ing he e icacy o public p e en ion measu es agains smoking”o he Eu opean Union’s Ho izon 2020 esea ch and inno a ion p og amme unde he g an ag eemen No 681120. Au ho s’con ibu ions SHF concei ed and designed he m-HRS s udy and also d a ed and e ised he manusc ip . LFL, FS, and HV specially con ibu ed o he p o ocol design, e alua ion me hods, and c i ically e isi ing he manusc ip . FLP, AC, DS, and PB con ibu ed du ing he d a ing o he manusc ip de ailing echnical and me hodological aspec s. All au ho s ha e ead and app o ed he inal manusc ip . E hics app o al and consen o pa icipa e The E hical Re iew Boa d “CEI de los Hospi ales Uni e si a ios Vi gen Maca ena yVi gendelRocío”, a ilia ed o he Vi gen Maca ena and Vi gen del Rocío Uni e si y Hospi als, app o ed he SoLoMo s udy wi h code SFB-APP_EC- 2016-01. This E hical Re iew Boa d also e iewed he in o med consen , which all pa icipan s we e gi en in w i en ex , and had o ead and sign o be included in he s udy. Compe ing in e es s LFL is he owne o Salumedia Tecnologías, SHF is i s adminis a o . Salumedia Tecnologías is he company ha de eloped he app used in his s udy. The au ho s decla e ha hey ha e no compe ing in e es s. Publishe ’sNo e Sp inge Na u e emains neu al wi h ega d o ju isdic ional claims in published maps and ins i u ional a ilia ions. Au ho de ails 1 Depa men o A chi ec u e and Compu e Technology, Uni e sidad de Se illa, ETSII, A enida Reina Me cedes S/N, 41012 Se ille, Spain. 2 Depa men o Heal h P omo ion, School o Public Heal h and P ima y Ca e (Caph i), Maas ich Uni e si y, P. Debyeplein 1, 6229, HA, Maas ich , The Ne he lands. 3 Qa a Compu ing Resea ch Ins i u e, Hamad bin Khali a Uni e si y, Educa ion Ci y, Doha, Qa a . 4 Salumedia Tecnologías, A enida República A gen ina 24, Edi icio To e de los Remedios, Plan a 5, Módulo A, Se ille, Spain. 5 Medical School, Facul y o Heal h Sciences, A is o le Uni e si y o Thessaloniki, Thessaloniki, G eece. Recei ed: 16 Oc obe 2017 Accep ed: 25 May 2018 Re e ences 1. Fjeldsoe BS, Ma shall AL, Mille YD. Beha io change in e en ions deli e ed by mobile elephone sho -message se ice. 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