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CNMlab » The tectonics of music
The tectonics of music
The groundswelling movements of the music industry
By Thomas Paris
Thomas Paris is a researcher at the CNRS (GREGHEC) and an associate professor at HEC Paris. He holds a doctorate in management and he specializes in the creative economy and the creative industry. He studies these sectors (along with cinema and audiovisual, music, fashion, publishing, architecture, video games, gastronomy, design) from managerial, organizational, and sector-specific perspectives, in partnership with key players. He also works on innovation management, entrepreneurship, the digital economy, and cultural politics.
The recorded music sector seems to have come to the end of a crisis that has lasted fifteen years. The revival of its economy has come at the cost of major transformations, though these can barely be perceived from the point of view of consumption, which (or will have) a major influence on the music offered. In order to highlight the stakes at play, this essay takes a look at the fundamental laws of the music economy and the underlying trends which are likely to divert its course. It analyzes the effects generated by the three major trends, which are: the de-linearization of consumption, the transformation of prescription, and the globalization of the music economy.
To the extent that music has been produced and listened to by people since prehistory and is intimately bound to our humanity, it is hardly surprising to remark upon its resilience. From a revenue of US$23 billion in 2000, the world market for recorded music dropped to US$14 billion in 2014, before undertaking a revival in order to find itself in the early 2020s at a level close to what it was prior to this long crisis.1Data from IFPI During this same period, we may witness an explosion of consumption, which has arisen thanks to the penetration of smartphones. As for concerts and festivals, the enthusiasm which they have generated during this period supports the idea that music plays a part in our humanity. Thus, between 2014 and 2019, the world market for live music grew by 18 %, reaching US$28,6 billion, prior to the pandemic.2Global Entertainment and Media Outlook, PricewaterhouseCoopers (PWC), July 2021.
Was this only a passing crisis, erased in a few years by our need for music? The reality is much more complicated, because behind this remediation and this explosion of "consumption," one should recognize that everything has changed. The music economy acknowledged a transition towards digital – or more precisely of an "age of access,"3Rifkin J., L'Âge de l'accès. La nouvelle culture du capitalisme, Paris, La Découverte, 2005. seeing as digital music initially arose with the Compact Disc – gradually transforming itself. Digital music and the Internet have sent such telluric shock waves, unleashing tectonic movements of such amplitude, that they are still reverberating today, in particular if one considers the tremors announced by livestreaming and the metaverse. The subject of this essay is to reveal the groundswell which has influenced the reconfiguration that the musical planet is undergoing.
The arrival of the Internet, at the end of the previous millennium, was accompanied by the perspective of important mutations, perceived, according to different points of view, as both dangerous or liberating. It should be noted that the first major case to mark the merging of the Internet with music, pitted the American rap group, Public Enemy, against their record label, thus announcing that a frontal attack between the island of the music industry and offshore pirates should not be expected, but instead a restructuring at the very heart of this sector. Subsequently, digital transformations gave rise to several concepts, offered successively in order to describe what was perceived as the phenomenon beneath this major transformation. Some have spoken of a disintermediation and short loop economies, with the idea that the Internet, in a fashion similar to today's use of blockchain, would do away with all of the intermediaries who insert themselves between artists and consumers.4Benghozi P.-J. and Paris T., "L'industrie de la musique à l'âge Internet. Nouveaux enjeux, nouveaux modèles, nouvelles stratégies", Gestion 2000, 2001, p. 41-60; Hadida A. L. and Paris T., "Managerial Cognition and the Value Chain in the Digital Music Industry", Technological Forecasting and Social Change, vol. 83, 2014, p. 84-97. Some have spoken of a "long tail," in order to evoke that the Internet would at last allow for the liberation of an important amount of artists and consumers from the diktat of intermediaries – them again – who in this instance referred to record distributors, limited according to their storage capacities.5Anderson C., The Long Tail. Why the Future of Business is Selling Less of More, New York, Hyperion, 2006. The concepts of 360° deals and the convergence of channels and content, have also had their moment of glory; both arose as a response to the loss of value which the Internet has caused. 360° deals were meant to allow record labels, by signing wider rights contacts with artists, to gain revenue through concerts and merchandise. The convergence between channels and content relied upon the exploitation of synergies between channels (media and telecommunication/stakeholders in distribution), who were expected to be able to offer content that would generate subscriptions, in particular for content where monetization was becoming increasingly complex. It was also a matter of a "global license," bringing about the end of paid-for music and advocating for a free-access model for the consumer, with an additional tax on subscriptions.
Looking back on the past twenty years of crisis one may learn that few of these previsions have come to fruition: at this time, long tail has not been substantiated,6Benghozi P.-J. and Benhamou F., "Longue traîne levier numérique de la diversité culturelle?", Culture prospective, no 1, 2008, p. 1-11. 360° deals have not become the norm as, at the same time, an increasing number of artists have retained their rights. This convergence has once again undergone the major capitalistic movements of a merging between channels and content which have, for the most part, disintegrated since. A global license has seemingly been buried, whilst bearing witness to a similar approach becoming established within each sector, in a commercial format: it is no longer a matter of a global tax that is redistributed between the different producers of content, but of unlimited consumption by each sector, controlled by – with regards to music – services such as Spotify, Deezer, and Apple Music, among others. This informs us that forecasting, with regards to cultural industries, is a difficult art, comparable to that of seismology. At the most, we may underline the fundamental rules and identify the tectonic plates at stake.
The fundamental rules of the cultural economy, its Newtonian mechanics which no innovation, however disruptive it may be, may allow us to forget, have for many been stated by Howard Becker.7Becker H. S., Art Worlds, Berkeley, University of California Press, 1982. He claims that a work's value is established both conventionally and socially. This implies that intermediaries are not in fact intermediaries, but agents who participate in the production of this value, through the choices they make, in signing a particular artist, in promoting a specific work. These intermediaries play a role in the foundation of this value by directing the consumer's choice. This orientation, which we may refer to as prescription,8Benghozi P.-J. et Paris T., "De l'intermédiation à la prescription: le cas de la télévision", Revue française de gestion, vol. 29, no 142, 2003, p. 205-227. is not the distortion of an improbable perfect market, but a necessity in order to trace the guidelines which will direct the consumer's choices.9Karpik L., L'économie des singularités, Paris, Gallimard, 2007, p. 12-13. Nonetheless, this intermediation can have negative effects, notably with regards to cultural diversity. Indeed, those responsible for prescribing are numerous and varied: editorial choices, marketing, algorithms, etc. This variety is a source of diversity, but it may also be placed in danger due to the market logic. The types of fee-based prescriptions – marketing, space purchasing, keyword purchasing, etc. – will render mute other types that rely upon subjective choices, if they take up too much space. Prescribers can also see the economic model that they rely upon be placed in danger due to competitors, such is the case for traditional record stores compared with online providers.
Music also has a social value and it is vain to hope for a system whereby all artists are listened to equally. As long as music exists as a cultural phenomena, listening to the same music as others will have a value, and this consumption will result in a group phenomena, not to say concentration. In addition to these rules, we can add that of abundance: to the extent to which there is no intrinsic value to works, numerous propositions are brought to market with the aim of finding an audience, the previous rule thus suggests that very few will in fact reach this audience. This abundance implies two types of strategies: that of following a trend, or that of differentiating, in order to stand out. This is translated via the dynamics at work in any cultural domain, consisting of dominant movements and avant-garde movements, which either remain on the fringes, or eventually become mainstream. From disco, which followed on from rock 'n' roll, arose punk, which was dethroned by new wave… These movements are also permitted or amplified by the logic of distinction which Bourdieu highlights: for reasons of identifying with social classes and generations, listeners either have a tendency to listen to the same music as a certain category of person or, on the contrary, to do everything to avoid it. These are some of the Newtonian rules which govern the music economy and it is hard to see how they may be challenged by any innovation.
The dematerialization of music and its availability via the Internet have opened the way for wide-ranging tectonic movements. Whilst they do not challenge the rules which have just been stated, they must be taken into account alongside them, in order to understand how the music economy may evolve. The effects of these mutations are very much apparent, in the manner of a shift towards of a form of consumerism which relies upon access and the central role that is occupied by platforms. This surface change has been accompanied by several tectonic mutations: the de-linearisation of consumption, the transformation of prescription, and the globalization of the music economy.
Prior to the era of platforms, the consumption of music adopted a logic of programming which implied collective consumption. Pre-Internet media (radio, television, concerts and festivals) functioned upon the principle of prescriptions around which they attempted to unite a certain number of listeners or spectators. Individual consumption through the acquisition of physical media aligned itself as the prolongation of this collective consumption. Streaming has potentially erased not only the proposition of collective consumption, but also its inscription within time, to such an extent that one may witness specific works being recommended at a specific time. If the perspective of a perfectly individual recommendation seems illusory, there is the new possibility of seeing subcultures of consumption emerge. In succession to a singular long tail, multiple long tails might arise, based upon categories of consumers, giving rise to a fragmentation of the audience. For example, the era of mass media has created common cultural references on a nationwide level. The era of streaming might be translated by another form of organization of consumption, disconnected from national territories, thus questioning the social bond created by culture.
The way platforms prescribe content relies greatly upon algorithms and barely upon that of editorial selection. The relationship between these two approaches will be decisive in the music economy: will platforms become aggregators of radios (playlists) chosen in a traditional manner? Will listening, with the objective of discovery, allowing for the exposure of rarer pieces, existing outside of any playlist, give way to algorithmic recommendations? The resulting equilibrium, will depend upon the habits consumers adopt, but the principle of streaming opens the way for an automated prescription. If the effects of this prescription currently remain ambiguous,10See Coavoux S. and Beuscart J.-S., in this volume: "Paranoid android : algorithmes de recommandation et autonomie des auditeurs", cnmlab.fr. one may easily see how its substitution of traditional, editorial forms of prescription, will no longer allow for innovative propositions to arise, those which escape existing categories and whose success relies upon a willingness that is dependent on one or more individuals. Prescription also occurs upstream, in the choices made by labels. The Internet offers the possibility to "test the market" and to see how listeners react to new propositions. Previously, labels had to make choices and defend them. Today, the major trend is to sign artists who have already grown an audience. This has two consequences. On the first hand, for artists to be able to generate an "organic" audience, they must either be capable of marketing themselves, to have people help them, or to be established within networks. Thus, the opening provided by the Internet is also accompanied, in a muted fashion, by a form of "locking out" of artists who might not have the means to make themselves known. On the other hand, this ability to obtain an immediate response from the marketplace favors artists who are representative of current trends, to the detriment of more innovative propositions, that require being assisted.
The globalization of the music economy is the third underlying and foundational movement which plays out in the background of an implementation of a form of consumption, based upon subscription platforms. The Internet allows for these platforms to have a global approach, hence to offer music for all of the territories in which they are present. This results in wide ranging career dynamics. In the pre-Internet economy, an artist's success was built upon concentric circles: first they had to prove themselves within a local scene, then grow to a national level, which might finally serve as a launchpad, in order to become known internationally. Today, an artist from a small provincial town, for example, can assemble a large international audience without having to pass via the local scene. This mutation is far from insignificant, because it transforms a plurality of markets, or scenes, into one single entity, within which it is harder to break through, requiring the mobilization of a larger audience, in less time. The rise of such platforms may weaken the "incubators" which made up local and national scenes. The key question behind this trend is what place a physical economy will hold, through local concerts and – to a lesser degree – record stores, with regards to a dematerialized economy. The role that livestreaming will possess, will thus prove to be decisive, because this will transfer a live economy, towards that of platforms. If it may retain a dimension of being programmed, then it might also emancipate itself.
Recorded music has never been as healthy as it is now, or at the least it has never been consumed as much. Technology, such as the Internet and smartphones, have liberated it from any lethargy: we can carry our record collection wherever we go and listen to whatever we want. Streaming has established itself as the new dominant economic model for recorded music, whilst we wait for it to find its place within the concert economy. The gatekeepers,11Cultural industries highlight numerous "gatekeepers" whose actions result in the filtration of recommendations: initially this is undertaken by publishers and producers who make choices from the many propositions they receive, but also through critics and distributors. who hindered diversity, have less influence than before. In a certain manner, music has moved towards a greater liberalism: anybody can create music, publish it, and maybe find an audience. This has served as a breath of fresh air for many artists, urban music in particular, which had previously been partly deprived of exposure.12Charbonnier R., Poinsignon P. and Paris T., "Du BoP dans le beat, une analyse des transformations numériques dans la musique", Revue française de gestion, no 294, 2021, p. 115-134.However, these apparent liberations may bring about other stakes, and streaming will not be synonymous with an unlikely pure and perfect market. Its accession is accompanied by major tectonic movements and these are liable – in the future – to have a notable affect on the market. Due to streaming, everything has changed. Nonetheless, it is not easy to foresee the future: this is built upon an interaction between the strategies of major players, market innovations, and consumer reactions. The expectations of consumers, with regards to editorial prescription, will depend upon the willingness of labels and music programmers who will not make their choices according to initial market reactions (number of views and likes), but by continuing to engage in subjective choices, thus deciding the place that live performances will hold when an online alternative – such as livestreaming – will have become established… These are some of the questions whose answers will redraw a new musical world map.
Translated from French by Dom Savage
Data from IFPI
Global Entertainment and Media Outlook, PricewaterhouseCoopers (PWC), July 2021.
Rifkin J., L'Âge de l'accès. La nouvelle culture du capitalisme, Paris, La Découverte, 2005.
Benghozi P.-J. and Paris T., "L'industrie de la musique à l'âge Internet. Nouveaux enjeux, nouveaux modèles, nouvelles stratégies", Gestion 2000, 2001, p. 41-60; Hadida A. L. and Paris T., "Managerial Cognition and the Value Chain in the Digital Music Industry", Technological Forecasting and Social Change, vol. 83, 2014, p. 84-97.
Anderson C., The Long Tail. Why the Future of Business is Selling Less of More, New York, Hyperion, 2006.
Benghozi P.-J. and Benhamou F., "Longue traîne levier numérique de la diversité culturelle?", Culture prospective, no 1, 2008, p. 1-11.
Becker H. S., Art Worlds, Berkeley, University of California Press, 1982.
Benghozi P.-J. et Paris T., "De l'intermédiation à la prescription: le cas de la télévision", Revue française de gestion, vol. 29, no 142, 2003, p. 205-227.
Karpik L., L'économie des singularités, Paris, Gallimard, 2007, p. 12-13.
See Coavoux S. and Beuscart J.-S., in this volume: "Paranoid android : algorithmes de recommandation et autonomie des auditeurs", cnmlab.fr.
Cultural industries highlight numerous "gatekeepers" whose actions result in the filtration of recommendations: initially this is undertaken by publishers and producers who make choices from the many propositions they receive, but also through critics and distributors.
Charbonnier R., Poinsignon P. and Paris T., "Du BoP dans le beat, une analyse des transformations numériques dans la musique", Revue française de gestion, no 294, 2021, p. 115-134.
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Nissan has launched "Built To Thrill", an integrated advertising campaign focusing on the inspiration behind the design of the Nissan JUKE crossover car built for men. The campaign brings together original design elements, a dune buggy (originally chosen for its high arched wheel design) and a motorbike (the inspiration for the interior design and console) to build a 3D image of a JUKE. The car seat fabric was inspired by wet suit material. A canoe and snowboard are also featured to appeal to Nissan JUKE's target audience who seek excitement and fun in the city.
The Built To Thrill campaign was developed at TBWA\G1, Paris, TBWA London, TBWA\Else, Paris, by creative director Alasdhair MacGregor-Hastie, creative team Fabio Abram & Braulio Kuwabara, account managers Ewan Veitch, Celina Eude, Eva Gotteland, Thien-Huong Pham, Gaelle Guillou, agency producer (TV) Maxime Boiron, working with Nissan Europe marketing team Bruno Mattucci, Arnaud Charpentier, Timothée Gazeau, Thomas Rodier, Elena Karpenko.
Filming was shot by director Lieven Van Baelen via Henry De Czar with director of photography Nicolas Karakatsanis, executive producer Jean Ozannat and line producer Louis Saint Calbre.
Visual effects were produced at Mikros Image by executive producer Jean-Lin Roig, producer Christophe Huchet, VFX supervisor Antoine Carlon, VFX shoot supervisors Antoine Carlon and Laurent Creusot, head of 3D studio (commercials) Julien Meesters.
Sound was produced by Benoit Dunaigre at TBWA\Else.
Music, "The Kraken", was composed and performed by The Horrors, represented by Chrysalis, Beggars / XL.
The print campaign was produced by art buyers/producers Louise Cripps, Claire Delafons and Trudy Waldron, photographer Luke Kirwan, set designers Lyndsay Milne and Andy Knight.


text:
Synagoga Nowostrojeńska w Dyneburgu (ros. Большая Новостроенская синагога) – żydowska bóżnica zbudowana w dzielnicy nowostrojeńskiej ówczesnego Dźwińska przy obecnej ul. K. Skryndy 79.

Synagogę zbudowano w 1865 na terenie nowej dzielnicy Dyneburga. Nazywana była wielką synagogą, choć mogła pomieścić tylko 84 osoby.

Była jedną z ponad 40 synagog w Dyneburgu (1920-1940).

Zobacz też
 Synagoga Cyna w Dyneburgu
 Synagoga Nabrzeżna w Dyneburgu
 Synagoga Kopszer Szul w Dyneburgu

Linki zewnętrzne

Nowostrojeńska

redpajama_set_name: RedPajamaWikipedia


Want to manage your website with a CMS? No problem.
Most websites are built on Content Management Systems (CMS) which allow web site owners to update content easily through a simple, intuitive interface.
Using a content management system gives you the independence and freedom to update your website yourself once it goes live; but it doesn't matter if you have no experience managing a website as we will train and support you and your team to feel comfortable doing this.
At the heart of the platform, it is user focused but behind the scenes it allows us to build anything from a simple website through to a feature rich web application.
We may recommend OctoberCMS as the platform of choice, but it's not our only focus. A large portion of clients we work with use WordPress with a few others on MODX. We have a wealth of knowledge in each of these content management systems ready to help with your website design and development projects.
Once we know you, your technical capabilities and the general functionality requirements for your next project, you can be rest assured that we will recommend the best possible solution for your needs.
With support and assistance on tap through optional monthly retainers, you can safely sleep at night knowing we've got you covered throughout the entire process and our on-going relationship.


text:
Three Cardinals earn D2CCA All-Midwest Region Honors
Click here for full release
ASHLAND, Ohio -- Three Saginaw Valley State University Men's Soccer had three student-athletes earn Division II Conference Commissioners' Association (D2CCA) All-Midwest Region honors following their play for the 2018 season.
Omar Sinclair was named to the D2CCA All-Midwest Region First Team as a defender while Michael Shaikly (MF) and Azaad Liadi (F) earned All-Midwest Region Second Team honors.
Sinclair garners another honor after being named the GLIAC Defensive Player of the Year and All-GLIAC First Team. He helped anchor an SVSU defense that led the league with a 0.62 goals against average, allowing only 11 goals total this season. The Cardinals racked-up seven clean sheets in the 2018 regular season while securing the program's fourth GLIAC title and second in the past three years. This marks Sinclair's second All-Midwest Region honor after being named to the second team in 2017.
Shaikly earned All-GLIAC honors in 2018 for the third time in his career and was named All-GLIAC Second Team at the midfielder position last season. He scored twice and added two assists for the Cardinals while ranking third on the team in shots and second in shots on goal. He scored a goal this past Saturday in the team's 4-1 victory at Southern Indiana to secure a berth in the NCAA Round of 16.
Liadi, a 2017 All-GLIAC Second Team honoree and 2018 First Team member, led the Cardinals offensively with seven goals and 16 points, adding two helpers during the regular season. His seven scores ranked him fourth in the GLIAC and his 16 points were fifth. He knocked-in two game-winning goals this regular season as well. He recently had a goal and two helpers in the team's 4-1 win Saturday at Southern Indiana in the second round of the national tournament.
The Cardinals have advanced to the NCAA Division II Round of 16 for the third time and are competing in the national postseason tournament for the seventh time. SVSU faces Ohio Valley Thursday (Nov. 15) at 5:00 p.m. eastern time at Fort Hays State University in Hays, Kansas. Follow the match live at www.svsucardinals.com.
@SVSUsoccer | #svsumsoc | @svsuathletics | #BeaksUp

redpajama_set_name: RedPajamaCommonCrawl


The UAV Robotics Lab at the College of Charleston is located in Harbor Walk East, room 105C and features a variety of drone technology and opportunities for students to conduct research work in visual and voice guided robotics. The lab contains DJI Phantom 4 drones, Arduino technologies, Raspberry Pis, wireless microphones, and other fun gadgets that are used to implement research ideas.
Students who have completed CSCI 230 would possess enough of a background to join the lab. A student must be able to work both independently and in a team environment, and might be able to receive course credit (CSCI 397 or CSCI 490) for their work in the lab. Projects involve mobile app development using Java (Android Studio) or control software using C++ (Visual Studio) which integrate speech recognition or computer vision libraries to develop visual and voice guided robotics applications. To learn more about developing software for the DJI Phantom series, visit: https://developer.dji.com/.
Interested in joining the lab, contact Dr. Sebastian van Delden.


text:
Q: Failed to combine array of object react native Im trying to join the data into the existing array of oject in state. in console.log(data) i get the data as expected but its not join together with this.state.listData.
"react-native": "0.61.5",
"@react-native-firebase/firestore": "^6.3.4",
retrieveMore = async () => {
        const { navigation }  = this.props;
        const title2 = navigation.getParam('title2', null);
        userEmail = firebase.auth().currentUser.email;
        try {

                const data = [];
                await database.collection("All Recipes").orderBy("reviewCount", "desc").startAfter(this.state.lastVisible).limit(this.state.limit).get().then(doc => {
                    doc.forEach( async (doc) =>{
                        const {filter, id , name, image} = doc.data();
                        lastVisible = doc;
                        let recipeId = doc.data().id;
                        let test = await database.collection("All Recipes").doc(recipeId).collection("Reviews").onSnapshot(function(querySnapshot) {
                            let data2 = [];
                            querySnapshot.forEach(function(doc) {
                                const { Rating } = doc.data();
                                data2.push({
                                    Rating: Rating,
                                });
                            });
                            const totalRating = data2.reduce((prev,next) => prev + next.Rating,0);
                            let averageRating = parseInt((Math.round(totalRating/data2.length * 100) / 100).toFixed(1));
                            let totalReview = Math.abs(data2.length) > 999 ? Math.sign(data2.length)*((Math.abs(data2.length)/1000).toFixed(1)) + 'k' : Math.sign(data2.length)*Math.abs(data2.length);

                            data.push({
                                filter: filter,
                                id:id,
                                name:name,
                                image:image,
                                averageRating: averageRating,
                                totalReview: totalReview,
                            });
                        });
                    });
                    console.log(data);
                    this.setState({
                      listData:[...this.state.listData, ...data],
                      lastVisible: lastVisible,
                      loading: false,
                    });
                })

        }
        catch (error) {
          console.log(error);
        }
      };

if i commented the the code like below,the data and the this.state.listData join perfectly but without averageRating and totalReview.
const data = [];
                await database.collection("All Recipes").orderBy("reviewCount", "desc").startAfter(this.state.lastVisible).limit(this.state.limit).get().then(doc => {
                    doc.forEach( async (doc) =>{
                        const {filter, id , name, image} = doc.data();
                        lastVisible = doc;
                        let recipeId = doc.data().id;
                        // let test = await database.collection("All Recipes").doc(recipeId).collection("Reviews").onSnapshot(function(querySnapshot) {
                        //     let data2 = [];
                        //     querySnapshot.forEach(function(doc) {
                        //         const { Rating } = doc.data();
                        //         data2.push({
                        //          Rating: Rating,
                        //          });
                        //     });
                        //     const totalRating = data2.reduce((prev,next) => prev + next.Rating,0);
                        //  let averageRating = parseInt((Math.round(totalRating/data2.length * 100) / 100).toFixed(1));
                        //  let totalReview = Math.abs(data2.length) > 999 ? Math.sign(data2.length)*((Math.abs(data2.length)/1000).toFixed(1)) + 'k' : Math.sign(data2.length)*Math.abs(data2.length);

                            data.push({
                                filter: filter,
                                id:id,
                                name:name,
                                image:image,
                               //  averageRating: averageRating,
                                // totalReview: totalReview,
                            });
                        });
                    // });
                    console.log(data);
                    this.setState({
                      listData:[...this.state.listData, ...data],
                      lastVisible: lastVisible,
                      loading: false,
                    });
                })

A: this.setState(state=>({
    listData:[...state.listData, ...data],
    lastVisible: lastVisible,
    loading: false,
}));

try this it may help you

redpajama_set_name: RedPajamaStackExchange


The 12 Step LEDs and display can definitely be controlled from an external source. Please see the External Control of 12 Step section of the 12 Step Reference Manual for more detailed information.
Can I rip presets from a 12 Step into the editor?


This may sound like a funny name for a congregation, especially since it's not the name of the town we're in! In Scripture, Horeb is the place where people meet God. Moses meets God in the burning bush at Horeb, water comes from the rock at Horeb, and Mt. Horeb the place where the Israelites are given the Ten Commandments. Our congregation in Augusta county is also a place where people - young and old - meet God. God comes to meet our community of faith in Jesus Christ and by his Spirit to claim us as his own and call us as his witnesses in this place of the world.
Why do you seem to pay special attention to the pulpit, table and font?
Signs and symbols serve us all the time in life. We use street signs to know where we are and where we're supposed to go. A wedding ring may serve to symbolize unending love and commitment. A warning sign tells us to keep away for our safety. So too, in worship, we come and gather around liturgical "furniture" to remind us what is really going on.
We gather around the font and water, remembering that for Christians, here, water is thicker than blood. For it is in our baptism, by the Holy Spirit, that we are united to Christ and adopted into the family of God.
We gather around the Bible and pulpit, remembering that Jesus Christ is the living Word of God who has spoken and who continues to speak to us, especially through the written word of Scripture.
We gather around the table to taste and see again and again that our God is not far off but is present with us in Christ by the Spirit, spreading a table before us and inviting us to come and eat the bread of life and drink the cup of salvation. The communion table is an invitation to come and not only remember Christ's work, but to be nourished in faith by Christ himself, through the Spirit, and to do so knowing that one day we will feast together with him in the glory of the new creation.
We gather for worship around each of these signs to be reminded of God's grace and God's presence with us.
Why does Rev. Ytterock often pour water or stand by the font when we're reminded of God's grace?
After confessing our brokenness and sin, we remember our baptism as it is the sign that everything old in us has been washed away because of Christ and we have been raised to new life. The water is the symbol of that washing and the symbol of the living water which Christ offers us that we may thirst no more.
Why do the colors in the sanctuary change sometimes?
The colors in the sanctuary change to reflect the current season of the church (or, Christian) year. For Christians, the year begins with the season of Advent (four weeks before Christmas day) and ends with Christ the King Sunday, in November. Between Advent and Christ the King, we celebrate Christmastide, Epiphany, Lent, Holy Week, Eastertide, and the Season After Pentecost (or, Ordinary Time). The seasons help to reorder our lives, letting them sync to God's story instead of our own.


text:
Q: Select On COUNT < x in a relational database I have the following 3 tables:
TestGroup
RecordNumber (pk)
Test
RecordNumber (pk)
TestGroupRecordNumber (fk)
Result
RecordNumber (pk)
TestRecordNumber (fk)
Basically, TestGroup is a group of tests, and each test contains a number of results. I want to select all TestGroups that have less than X results. What query will achieve this?

A: You should use having clause:
SELECT count(1), tg.recordNumber FROM testgroup tg
LEFT JOIN test t ON t.TestGRoupRecordNumber = tg.recordNumber
LEFT JOIN result r ON r.testRecordNumber = t.recordNumber
GROUP by tg.recordNumber
HAVING count(1) < 10

A: You just need to use GROUP BY and HAVING:
SELECT TG.RecordNumber
FROM TestGroup TG
   LEFT JOIN Test T ON TG.RecordNumber = T.TestGroupRecordNumber
   LEFT JOIN Result R ON T.RecordNumber = R.TestRecordNumber
GROUP BY TG.RecordNumber
HAVING COUNT(R.TestRecordNumber) < X

I'd also use a LEFT JOIN in case the Test Groups have no results or no tests.


Within the grounds of Eggleston Hall are the ruins of the former Chapel of Ease. There had been a Chapel of Ease on the site since 1539. Dating to the 18th century, the small church is constructed of irregular coursed sandstone and ashlar.
The former chapel featured a three bay nave and three bay chancel each with round-headed central doorways.
The private chapel was closed in 1868 with the roof stripped and used on other estate buildings.
A surviving wall monument (below) features three cherubs heads carved in relief flanked by wide scroll brackets. The ruins of the chapel are Grade II Listed.


Guess Centanni thought it was warm enough to peel off some clothes! I'd better buy some more leg straps!
The downside to 70 degree weather after 4 inches of snow….
The weather can't decide what to do today (snow or freezing rain) but either way the horses plan to use the run-in today apparently.
You'd think with this sunrise it would be a beautiful day. But no…..its cloudy and depressing out. Bring on May please!!!!
Well Delilah makes ONE of us that was happy it was so crappy out this weekend!!!!
Its apparently my first day….
THIS isn't Patty!!!! This is CLEARLY the wrong halter!! Ugh, someone needs to fire me! I seem to be having a bad week….. Good girl Delilah!!


text:
BMC Medical Informatics and Decision Making
Mobile Technology for Community Health in Ghana: what happens when technical functionality threatens the effectiveness of digital health programs?
Amnesty E. LeFevre1,2,
Diwakar Mohan1,
David Hutchful3,
Larissa Jennings1,
Garrett Mehl4,
Alain Labrique1,2,
Karen Romano3 &
Anitha Moorthy3
BMC Medical Informatics and Decision Making volume 17, Article number: 27 (2017) Cite this article
Despite the growing use of technology in the health sector, little evidence is available on the technological performance of mobile health programs nor on the willingness of target users to utilize these technologies as intended (behavioral performance). In this case study of the Mobile Technology for Health (MOTECH) program in Ghana, we assess the platform's effectiveness in delivering messages, along with user response across sites in five districts from 2011 to 2014.
MOTECH is comprised of "Client Data Application" (CDA) which allows providers to digitize and track service delivery information for women and infants and "Mobile Midwife" (MM) which sends automated educational voice messages to the mobile phones of pregnant and postpartum women. Using a naturalist study design, we draw upon system generated data to evaluate message delivery, client engagement, and provider responsiveness to MOTECH over time and by level of facility.
A total of 7,370 women were enrolled in MM during pregnancy and 14,867 women were enrolled postpa1rtum. While providers were able to register and upload patient-level health information using CDA, the majority of these uploads occurred in Community-based facilities versus Health Centers. For MM, 25% or less of expected messages were received by pregnant women, despite the majority (>77%) owning a private mobile phone. While over 80% of messages received by pregnant women were listened to, postpartum rates of listening declined over time. Only 25% of pregnant women received and listened to at least 1 first trimester message. By 6–12 months postpartum, less than 6% of enrolled women were exposed to at least one message.
Caution should be exercised in assuming that digital health programs perform as intended. Evaluations should measure the technological, behavioral, health systems, and/or community factors which may lead to breaks in the impact pathway and influence findings on effectiveness. The MOTECH platform's technological limitations in 'pushing' out voice messages highlights the need for more timely use of data to mitigate delivery challenges and improve exposure to health information. Alternative message delivery channels (USSD or SMS) could improve the platform's ability to deliver messages but may not be appropriate for illiterate users.
Mobile phones are the leading form of communication worldwide [1]. Their widespread and increasing use, particularly in low and middle income countries where the disease burden is highest, has led to growing calls to harness the potential of mobile and wireless technology to improve health and health care delivery. Mobile-health (mHealth)—defined as the use of mobile and wireless technology for health [2]—aims to improve health outcomes by addressing critical health systems constraints to service delivery, coverage, and utilization [3].
Throughout the last decade, over 600 mHealth pilot strategies and programs have been introduced globally [4]. Despite the proliferation of mHealth programs, evidence on their effectiveness is limited [5–7] and only modest gains have been observed for interventions aiming to improve provider diagnosis and management, and/or increased user demand through messaging using automated voice or SMS [7]. While calls to address broader evidence gaps in linking digital technologies to outcome and impact level health indicators are emerging, little to no attention has been paid to improving program monitoring, including measuring critical processes on the technological and behavioral performance of the program.
The technological performance aims to determine if the technology platforms performs as intended, whether in aiding program implementation or generating data critical to monitoring and evaluation. Depending on program design, technology platforms may aim to deliver health information messages, alerts and reminders, and/or support the collection of data, including patient-level information on the timing and content of services provided. Variability in how the technology platforms are designed and maintained, including decisions on which data elements are collected, with what frequency, by whom, and for what purpose, greatly influences the consistent delivery of messages and routine monitoring of programs.
The behavioral performance of a program is characterised by the interaction of users, including health care providers and patients, with the technology and program delivery strategy in a dynamic and evolving health systems and community context. For mobile technologies aiming to improve supply side dimensions of service delivery, providers must be willing and able to utilize the technology; a factor which is influenced by perceptions on usability, feasibility and acceptability within a given health systems context [8]. For mHealth solutions which aim to improve demand for and utilization of services through messaging apps, users must not only receive messages (technological performance) which may require negotiating use of the phone if shared (community/household factors) but then decide to open, listen to and/or read them once received (behavioral performance). Whether the technology corresponds to a change in health behaviour and practices requires a complex negotiation of social norms/practices, household and community dynamics, as well as overcoming financial and physical barriers to care.
Collective consideration of the technological and behavioral performance of an mHealth program is essential for accurately measuring program effectiveness and protecting against 'program drift'—defined as the deviation from intended design in real-world delivery of programs resulting in decreased benefit for patients [9]. In the absence of digital technologies, including mobile phones, data derived from (paper-based) special surveys or health information systems may be used to measure effectiveness, including coverage, or inform process evaluations aimed at assessing fidelity and implementation quality, amongst other factors [10]. The use of mobile and other digital health strategies have the potential to generate data in real-time on essential processes as well as patient careseeking and health status, enhancing not only program implementation and monitoring but evidence needed for summative evaluations.
The Mobile Technology for Community Health (MOTECH) program was initiated in 2009 in collaboration with the Grameen Foundation and the Ghana Health Services (GHS) to utilize mobile technology to improve uptake and quality of care of maternal, newborn and child health services (MNCH). The MOTECH platform consists of two interconnected mobile applications– "Mobile Midwife" and the "Client Data Application" (hereafter referred to as Client Data App) (Fig. 1). Mobile Midwife enables pregnant women and mothers of children under age one to receive pre-recorded audio messages on MNCH education and care in local languages or SMS messages on their mobile phones timed to a women's gestational age or that of her infant's. The Client Data App enables frontline health workers to use mobile phones to identify women and infants in their area that are due or overdue for care and improve data reporting processes by digitally recording care given to patients. In this case study, we draw from system generated data collected across five districts in Ghana from 2009 to 2014 to explore two research questions: (1) does the MOTECH platform perform as intended in delivering audio messages to registered users across study sites and over time? and (2) how do rates of active listening among users differ across study sites, during pregnancy and postpartum, and across thematic content areas? By exploring these research questions on the technological and behavioral performance of MOTECH respectively, we hope to inform efforts to optimize the use of technology for health in low resource settings where disease burden is highest.
Mobile Technology for Health in Ghana: program overview
Study context and population
Ghana is home to a population of almost 25 million disbursed across 216 districts in 10 administrative regions [11]. Over 40% of Ghanaians are under the age of 15 and over half live in urban areas [11]. While child mortality rates have declined by 50% since 1990, for every 1,000 live births 62 children die; 47% within the first 28 days of life [12]. Similar declines in maternal mortality have been observed over the last three decades, however, the lifetime risk of maternal death remains high at 1 in 66, and for every 100,000 live births an estimated 380 women die [12]. Observed declines in mortality have been driven by concurrent increases in the utilization of critical MNCH services, yet gaps in continuity of care during pregnancy, delivery, and postpartum persist. Despite near universal attendance of at least 1 antenatal care (ANC) session and high utilization of 4 or more ANC visits (84%), nearly 30% of deliveries are not attended by a skilled birth attendant, and nearly 20% do not receive postnatal care (PNC) [12].
Field level implementation of the MOTECH program launched in August of 2010 in Kassena-Nankana West (KNW) district in the Upper Each Region of Ghana, and replicated in 2011 in Awutu Senya District in the Central Region. In 2012, with added funding from USAID and the Bill and Melinda Gates Foundation (BMGF), MOTECH was expanded into new districts: Gomoa West in Central region, Dangme East in Greater Accra region, South Tongu in the Volta region. Administrative division of two districts occurred during implementation; effectively splitting Awutu Senya into Awutu Senya East and West and Dangme East into Ada East and West and raising the total number of program districts to seven across four regions.
Figure 1 provides an overview of the MOTECH program and its two inter-related components: Client Data App (supply side) and Mobile Midwife (demand side).
The Client Data App consists of simplified digital and paper registers consolidating information previously collected in over a dozen paper based registers in Health Centers and Community-based Health Planning and Services (CHPS) facilities. The Client Data App is a supply side intervention which allows facility and community based providers to record all care provided into five simplified paper registers, and digitize clinical care information pertaining to MNCH and other essential care to better track and deliver care to women and infants. Frontline health workers record patient care data onto mobile devices and upload these data to the server (Additional file 1: Figure S1). Data collected by health workers using Client Data App are uploaded into MOTECH's central database and cross-checked against GHS guidelines on routine care needs for pregnant women, infants, and lactating mothers to trigger a system of alerts about upcoming and missed care sent to both clients in Mobile Midwife program and health workers [13]. These alerts are sent as weekly short message service (SMS) lists containing Mobile Midwife clients with their IDs and care they require. Field teams, composed of Grameen Foundation and district health data staff, routinely monitored and scored health facilities on the accuracy and completion of clinical information uploaded into the MOTECH server compared to care captured in the paper-based registers. Facilities in MOTECH districts that attained 85% completion and accuracy ratings for reporting for three consecutive months were eligible to be 'automated.' Automated facilities received auto-generated monthly reports containing aggregate care. This threshold for automation was determined in collaboration with GHS, and based on government standards for data quality.
Mobile Midwife is a demand side component of the MOTECH program which aims to improve client knowledge and awareness of key health information during pregnancy and postpartum period, with the goal of stimulating best practices and encouraging timely and appropriate service utilization. Mobile Midwife is comprised of a maximum of 88 stage-based educational messages and care alerts for pregnant and postpartum women timed to their gestational age or age of their infants, respectively. Educational content was developed based on global and national MNCH guidelines and varied slightly between districts in order to debunk local cultural beliefs and practices. Clients received messages as automated voice recordings in local languages or SMS messages at a day and time of their choosing every week. The voice messaging service was persistent. A weekly message was resent to a registered phone number if a client listened to the call for less than 30 s, either because they missed the call, the device was powered off, there were call congestion issues during message delivery, or if the mobile network was temporarily down. In this case, the system was designed to send that message multiple times over the subsequent two hours, and once every other day until the next message is due.
Mobile Midwife and Client Data App are underpinned by a larger technology infrastructure which includes platform support and server hosting. The MOTECH platform is based on a modular, extensible open-source software comprised of a core platform and several modules, which were developed in 2009 and upgraded in 2012. Specific details on system architecture and evolution over time are available elsewhere, including security aspects and available modules [13, 14]. The software development responsibilities were split between two teams: the server-side components were designed by a group from the University of Southern Maine, and the mobile phone components were designed by a young company based in Ghana [13]. Following the initial development of the overall system architecture, the platform and server hosting were maintained at the Grameen Foundation offices in Accra, Ghana.
Program start-up and implementation
The MOTECH program was developed and implemented in collaboration with GHS, regional and District Health and Management Teams (DHMT). Table 1 summarizes program activities and inputs required to develop, startup and sustain implementation of the MOTECH program, including Mobile Midwife and Client Data App. To initiate project activities at a district level, extensive profiling was first undertaken to gather data on the health systems and telecommunications infrastructure. Content initially developed at a national level was localized to specific regional deployments and mobile phonesFootnote 1 were procured (Nokia 1680c-2, Nokia 2330c-2, Nokia C1-01, Nokia Asha 200, Nokia 2330c-2 s) for facility-based providers. Orientation of district-level leadership and facility-based provider training was undertaken. As part of Mobile Midwife, community health volunteers and facility level providersFootnote 2 were trained to register clients to receive stage-based actionable advice as well as service delivery alerts and reminders. Training in the use of Client Data App included DHMT, regional, and national level staff as well as facility-based providers in 69 CHPS facilities and 23 Health Centers/Hospitals. Trainings were followed by a three-month practice interval which allowed providers time to practice, adjust to the five simplified paper registers, and acclimate to data entry and uploading on mobile phones. To facilitate the trouble-shooting of technical and/or programmatic issues, a customer support service was established and maintained at the Grameen Foundation offices in Accra, Ghana. This service provided support for health workers using Client Data App, and tracked overall data uploads, and flagged changes in data upload trends.Footnote 3 Once facility-based providers demonstrated proficiency in using MOTECH's Client Data App, Mobile Midwife activities were initiated. At a community level, district launch events inclusive of key political figures, religious leaders, health workers and community members were held through durbars (festivals) and other marketing events to promote the program and encourage registration. Posters and murals were additionally installed on the exterior walls of community structures to raise awareness and promote registration into Mobile Midwife.
Table 1 Description of MOTECH program activities and inputs
Following start-up activities, health facilities began patient registration and enrollment into MOTECH. Providers were encouraged to enter and upload patient level information on a daily basis to facilitate the sending of alerts and reminders. In two districts (Awutu Senya East and Awutu Senya West), high performing facilities were automated and thus given the opportunity to receive monthly feedback reports from Grameen Foundation, summarizing service delivery statistics. To support facility-based activities routine district level meetings were held and supervision visits made to support facility level providers. Where possible, DHMT members sought to include program tasks in their current workflow. However, the Grameen Foundation provided additional program support including device replacement, refresher training, and routine data entry, management and feedback.
Study design and measurement
Using a naturalistic study design, this case study sought to describe variation across study sites over time in the receipt of and engagement with health information messages [15]. Figure 2 presents our conceptual framework for assessing the optimal pathway between health care providers who identify and register clients; the technological platform that receives and sends health information messages as well as alerts and reminders for care; end-users who receive and access messages; service delivery and careseeking; and ultimately, improved health status. In practice, multiple breaks are likely to have occurred at each point along this pathway as a result of technological (network coverage, feasibility/usability of the device, size of the data bundle, phone functionality/access); behavioral (user's willingness to listen to/access messages, motivation/satisfaction); health systems (workload, organizational and structural inputs); and/or community constraints (financial/physical barriers to care, social norms, power dynamics). In this analysis, we draw upon system generated data to explore the chain of events denoted by a dotted arrow linking provider registration, message delivery, and receipt.
Measuring program fidelity: was the program delivered as it was intended?. The dotted line denotes the pathway assessed as part of this manuscript. Yellow boxes denote factors which are influenced by health systems and/or providers, the light green represents technological factors, and the light blue community/client level factors
Technological performance was assessed by exploring the MOTECH platform's effectiveness in 'pushing' out messages across the continuum of care and by content area to registered women. Technological indicators included the proportion of messages 'pushed out' out of the total number of messages expected to be 'pushed' (Additional file 1: Figure S1 and Additional file 2: Table S1). Behavioral performance was assessed by capturing trends in provider and client engagement with Client Data App and MM, respectively. To measure provider engagement with Client Data App and ascertain the broader feasibility of recording patient-level health information, we assessed the frequency and volume of data uploads over time and by level of facility. To explore client engagement, we identified the proportion of and characteristics of message recipients who listened to at least 50% the length of each message received (defined as 'active listeners').
Data collection extraction and analysis
Data from Mobile Midwife and Client Data App were obtained from MOTECH server housed at Grameen Foundation offices in Accra, Ghana. Data elements specific to Client Data App included health facility data uploads, while data from Mobile Midwife included data on health information messages, including messages sent and accessed by pregnant women and mothers of infants. Messaging access was defined by the number of clients who 'listened' to stage-based health information messages successfully 'pushed out.' Since sharing of phones and phone numbers is common practice in Ghana, many clients were listed under the same phone numbers. For the purposes of this analysis, we extracted details of 'listening' for unique phone numbers to facilitate client level analysis. We further excluded registered women who did not have their own or have access to a mobile phone in their household and insteadhad to rely on a community phone to call into the system to retrieve their weekly messages. As part of the Client Data App, facility based providers are required to upload data on the careseeking practices of women registered into MM. Data on the number of uploads per month were analyzed using proportions and frequencies. We used confidence intervals at the 95% level to assess statistical differences in rates of active listening across thematic content areas. Data were analysed using Stata 13.2 for five districts: Awutu Senya West, Awutu Senya East, Gomoa West, Ada East and Ada West. The timeline for implementation varied across sites, and spanned from July 2011 to September 2014 overall.
Client data application
Figure 3 summarizes mean quarterly uploads on patient level data as part of the Client Data App by district from July 2011 to September 2014. In Awutu Senya East, uploads ranged from 3,534 from July to October 2011 to 2,291 over the same time period in 2013; gradually tampering off as the program drew to a close in September 2014. In Awutu Senya West, the frequency of uploads ranged from 10,044 to 4,647 over the same time period. Trends in Ada West and East were nearly identical at just over 4,000 for July to September 2013 before declining to 3,126 for the same window in 2014. Overall data uploads were highest for Gomoa West ranging from 11,063 in April to June 2013 to 18,403 from October to December 2013, before declining to 10,619 for the period of July to September 2014.
Total data uploads on service utilization by district (Calendar year). Findings highlight variations in data uploads across geographic areas
With the exception of Ada West, all CHPS facilities accounted for the over half of data uploads, followed by health centers (Additional file 3: Figure S2). In Gomoa West other health facilities constituted 20% of data uploads, while in Awutu Senya East, a periurban district with a mix of public and private facilities, private clinics accounted for nearly one-quarter of uploads. In Awutu Senya East and Awutu Senya West, automated CHPS facilities (n = 16), which receive routine data summaries on trends in data uploading as well as service statistics, were observed to have a greater number of and more stable patterns of data uploading across all facilities and study sites over time (Fig. 4).
Average data uploads for automated (n = 16) vs. non-automated facilities (n = 17) from July/Sept 2011–2014 in Awutu Senya East & West
Enrollment into mobile midwife
Table 2 presents data on the characteristics of women enrolled into Mobile Midwife from October 2011 to September 2014 in five districts. A total of 7,370 women were enrolled during pregnancy; 22% during the first trimester, 53% during the second and 25% during the third. Among those enrolled during pregnancy, 85% had a private phone and 71% had had one or more previous children. Across geographic areas, the proportion of women enrolled in the 1st trimester—a factor which has been shown to increase the number of antenatal care visits— was highest in Ada West (37%), followed by Ada East (27%) and Gomoa West (24%). Differences in phone ownership were observed across geographic areas, with greater than 1 in 5 pregnant women in Ada East (22%) and Ada West (27%) sharing phones as compared to 8-19% elsewhere.
Table 2 Characteristics of women enrolled into Mobile Midwife from October 2011 to September 2014 in five districts
During the postpartum period, 14,867 women were newly enrolled: 24% within 6 weeks of delivery, 50% between 7 weeks and 6 months postpartum, and 26% between 6 and 12 months postpartum. An estimated 77% of those enrolled postpartum owned a personal mobile phone. Enrollment trends and participant characteristics were similar across geographic areas for postpartum women.
Message delivery
Figure 5 presents data on the proportion of messages sent out of the total expected for each stage of the continuum of care and by thematic area from October 2011 to September 30, 2014 in 5 districts of Ghana. Across the continuum of care, on average less than one-third of expected messages were pushed out of the system to intended recipients. The largest proportion of messages received occurred in the first trimester (29%). The fewest number of expected messages (9%) were received during the extended postpartum period when an estimated 26 messages were intended to be pushed out from 6 to 12 months following a birth outcome. By thematic area, an average of 12 to 23% of expected messages were received. The fewest number of expected messages received fell under the thematic areas of infant care (12%), postpartum care danger signs (12%), and infant feeding and nutrition (13%).
Program effectiveness and technological performance for each stage of the continuum of care from October 2011 to September 30, 2014 in 5 districts of Ghana. The dark blue bars reflect the proportion of messages received out of those expected, while the light grey is the proportion of messages that each woman listened out of the total they were expected to receive including those not received
Figures 6 and 7 and Additional file 4: Figure S3 presents data on the percentage of messages successfully pushed out to pregnant and postpartum women, respectively, over time and across geographic areas from October 2011 to September 2014. Message delivery varied across sites and over time, falling under 35% for messages to pregnant women and 20% for messages to postpartum women across all geographic areas (Additional file 4: Figure S3).
The percentage of messages successfully pushed out to pregnant women from October 2011 to September 2014. The grey area denotes the percentage of messages sent by geographic area over the period of implementation in each site
The percentage of messages successfully pushed out to postpartum women from October 2011 to September 2014. The grey area denotes the percentage of messages sent by geographic area over the period of implementation in each site. Message delivery varied across sties and over time, falling under a threshold of 30%
User engagement with messages
In addition to data on message delivery, we reviewed the behavioral performance of users by assessing rates of active listening amongst message recipients (Table 3 and Fig. 8). Across the continuum of care from the first trimester to postpartum, 44% to 86% of women who received messages listened to at least 50% the length of each message. Among pregnant women, over 80% chose to listen to messages received; however, postpartum rates of active listening ranged from 44% from birth to post-partum to 54% at 6–12 months postpartum. By thematic area, rates of active listening were significantly higher for messages on malaria (71%), danger signs during pregnancy (79%) and postpartum (73%) than for other content areas (Fig. 8).
Table 3 Program effectiveness, technological and behavioral performance
The behavioral performance of Mobile Midwife users assessed by the proportion of messages that each woman listened to out of those received. The yellow dot denotes the mean whilst the black dots reflect the upper and lower bounds of the 95% confidence interval
To estimate the overall effectiveness of the program, we estimated overall exposure to messaging content by assessing the proportion of messages that each woman listened to out of the total they were expected to receive including those not received (Table 3). Study findings suggest that exposure to messaging content declined from 25% in the 1st trimester to 6% at 6–12 months postpartum. This decline occurred as the number of eligible MOTECH users increased from 1,618 in the 1st trimester to 22,237 at 6–12 months postpartum. Across thematic areas, the mean proportion of women exposed varied from 19% for pregnancy care danger signs to 8% for infant care and development milestones.
Study findings sought to generate critical insights into the implementation and effectiveness of the MOTECH program—one of the largest, and most comprehensive demand and supply side MNCH mHealth programs ever to be implemented in a low resource setting globally. Program effectiveness was assessed by exploring technological and behavior elements of the program. Technological elements were defined as the MOTECH platform's ability to 'push out' Mobile Midwife messages to pregnant and postpartum women, while behavioral elements included health workers' willingness to upload patient level data into Client Data App and the willingness of pregnant and postpartum women to listen to Mobile Midwife messages if received (Table 4). The latter, when considered in conjuction with the platform's ability to deliver messages, provided a measure of overall exposure to critical program content.
Table 4 Case study characteristics for investigating the technological and behavioral performance of MOTECH in Ghana
MOTECH's Client Data App was designed to produce automated reminders and alerts, searchable lists of clients needing care, and in two districts (Awutu Senya East and West) where the program had been implemented without encountering prolonged programmatic or political issues, CHPS facilities received automated monthly aggregate reports for upwards reporting. Findings suggest that health care providers were able to upload health information on pregnant and postpartum women enrolled into MOTECH and that the majority of these uploads occurred in frontline CHPS facilities. Variability in the volume and frequency of uploads were observed across all sites, particularly within the first and last quarters of implementation. Facilities that received automated summary reports tended to have more consistent data uploads, however, the usability and reliability of the data in these reports depended heavily on health workers' mastering clinical data entry and uploading at a high standard of accuracy and consistency. In practice, this proved difficult for the higher-client-volume health centers and hospitals, where overburdened midwives providing much of antenatal, delivery and early postnatal care, and siloed treatment areas meant digitizing patient level data was a low priority. Moreover, the small size of feature phones, specifically their screen and keypad size, likely contributed to inability to community health nurses and other staff to maintain consistent data uploads. Because of these challenges, none of the district health centers achieved the 85% accuracy and completeness level required to allow meaningful use of automated reports [13]. In contrast, at the community level, health facilities were better able to attain accuracy and completeness level targets as demonstrated in Awutu Senya, where all the facilities achieved the accuracy threshold and nurses even went on to train others in the system. Overall, a mobile-based client data system like MOTECH's Client Data App can be of real value, particularly at the community-level among health workers that are manageable patient volumes. At the facility-level, maintaining data accuracy and completeness is likely to hinge upon added human resources/support given that certain cadres of health workers are currently overburdened with patient volumes, as well as larger mobile devices for data entry. Strong district leadership, consistent monitoring of data quality, and reliable network access were all essential optimizing the use of Client Data App [13].
Mobile Midwife sought to improve knowledge, awareness, and utilization of MNCH services through exposure to health information messages, and care reminders and alerts. In contrast to other mHealth programs which have used SMS delivery channels [16] to push out information messages, the majority of MOTECH participants opted to receive information in local languages through voice delivery channels. Delivery through this modality may improve access and awareness among women with limited to no literacy—an estimated 30% of all Ghanians. Despite this potential, and message delivery system's ability to be persistent when calls are listened to for less than 30 s, quarterly fluctuations were observed in the proportion of expected messages pushed out of the system and overall, only two-thirds of expected messages were delivered. The precise factors underpinning system failures in message delivery are unknown due to the way the system logs the call failures [13]. However, they are likely due to one or more of the following reasons: inconsistent network connectivity, call congestion in the E1 phone lines as more clients were enrolled in the program, client phones' being powered off because they were not charged or unable to be charged because of inconsistent access to the power grid, and, finally, technical problems within the MOTECH system [13].
Message delivery challenges occurred despite extensive efforts to conduct performance testing and upgrade the software and devices used. In 2012, Grameen Foundation significantly revised the open-source MOTECH software platform to expand the range of features and make it more robust for larger-scale deployments by multiple organizations internationally. At this time, performance/stress testing was conducted by sampling 20,000 subscribers and sending messages out to them all at the same time. While findings from the performance testing suggested that the system performed well and was capable of sending 120 messages per minute for SMS and data communications, voice calls were identified as a bottleneck. In particular, the program was constrained by the availability of E1 phone lines to deliver voice messages. Services drew upon two active lines available in Ghana, which together supported 60 simultaneous calls. As a way to accommodate a growing number of clients enrolling into the program, outbound calls were distributed more evenly over the course of a day, and eventually a "queue" system was introduced in the software to ensure that the maximum number of simultaneous calls were not exceeded. Grameen Foundation also housed a customer support center with staff capable to dealing with low tech issues in both Client Data App and Mobile Midwife, and a streamlined process of moving complicated software issues to the tech/IT department. In the first half of the program period, staff received inquires from clients and health workers not just on registration, data uploads, and message delivery, but also on MNCH-related care, something Grameen Foundation staff were not trained to handle. As a result of this, as well as commitment to build district-level capacity to manage Mobile Midwife, responsibilities of client feedback and inquiries were transferred to the DHMTs, and the customer support center only dealt with technological issues with Mobile Midwife raised by the districts. Given that nearly all MOTECH enrollees opted to receive automated voice over SMS messages—a factor which when considered with the number of available phone lines and passive monitoring of message delivery, may explain MOTECH-system related delivery challenges. Future programs should more carefully weigh the options on messaging format given to clients with the feasibility of message delivery based on consideration of the number of available phone lines, message size, and number. Systems for the continuous extraction and assessment of data and simple dashboards tracking message delivery should additionally be instituted from the program's inception to identify trends in the proportion of expected messages received over time by delivery channels (SMS, voice, USSD), and content area in real-time. Unfortunately, our evaluation occurred retrospectively in 2015 and not in time enough to yield meaningful changes in the MOTECH program or platform.
Beyond the MOTECH platform's technical performance in message delivery, we sought to derive a measure of overall 'exposure' by determining the proportion of women who actively listened to the messages received. To measure the dose of exposure, we defined 'active listening' as the proportion of women who listened to at least 50% of content of 50% of messages. While we explored alternative thresholds, we opted for the 50% threshold based on the assumption that individuals would have the capacity to glean the bulk of topical content in that window of time. Future analyses may wish to assume more stringent thresholds of 100% and additionally interview clients to identify whether messages were shared with family members and/or listened to multiple times. Findings on active listening suggested that overall willingness to listen to messages exceeded 60% for all topical areas. However, rates of active listening were observed to decline during the postpartum window which may suggest that there is a 'novelty' effect associated with mobile messaging and/or that alternative content formats are needed to rekindle interest (e.g. quizzes, narrative stories, etc.). Future programs may also need to consider an abbreviated number of messages better targeted to address critical gaps in practices and/or careseeking.
To explore the effectiveness of exposure to messaging we initially sought to evaluate the careseeking patterns of women enrolled into Mobile Midwife, including the timing and utilization of services across the continuum of care. However, these data have not been presented because a large proportion of data were missing, and/or unable to be linked to the data on listening. This is part due to the fact that clients move between public and private facilies for MNCH care, and the difficulty of maintaining data uploads in high patient volume facilities, such as health centers and hospitals, where much of that critical care is provided. Future efforts to evaluate the effectiveness of health information messages in bolstering careseeking, should address this critical evidence gap by assessing the effects of specific bundles of messaging based on enrollment during pregnancy or postpartum, by thematic area and across the continuum of care.
This is first analysis of its kind to examine large volumes of back-end data to draw conclusions about the effectiveness of a large-scale mHealth program. Study findings aim to encourage transparency in presenting data on digital health platforms and foster wider dialogue on how to optimize these systems to improve the accessibility and use of data to inform implementation and evidence on their effectiveness. The increased availability of data as part of mHealth programs offers much promise for improving and measuring the exposure of target users to critical program content and allowing for the routine use of patient-level health information at multiple levels of the health system. Study findings highlight importance of real-time program monitoring to protect against 'program drift' and ensure the functionality of the underlying technical platform as well as the continued engagement of target users. Efforts to scale-up and sustain the use of mobile phones and other digital devices to improve service delivery in health facilities will need to ensure the adequacy of human resources along with strong district leadership, consistent monitoring of data quality, and reliable network access. Challenges in the delivery of health information messages to pregnant and postpartum women limited the program's reach and exposure. While over 80% of messages received by pregnant women were listened to, declines in postpartum rates of active listening highlight the need for further research to identify the factors underpinning user engagement over time.
Comparison with other studies
Calls to improve the rigor of mHealth evidence generation and reporting are emerging [17]. Findings from a systematic review of mHealth solutions on coverage and use of ANC, PNC, and childhood immunizations in low- and middle-income countries suggests that there is some evidence of effectiveness at changing behavior [18]. Among specific studies, two RCTs—one in Zanzibar and one in Kenya— which explored the effectiveness of text message reminders and education delivered to pregnant women's mobile phones, found evidence of statistically significant increases in ANC in their intervention groups relative to their control groups [16, 19]. In Zanzibar, program activities represent one deployment of the Mobile Alliance for Maternal Action (MAMA) project which has additional sites in Bangladesh, Malawi, India, and South Africa and like MOTECH aims to improve MNCH outcomes through the delivery of timed health information messages and appointment reminders. While evaluation activities are still underway for many MAMA deployments, published findings from Zanzibar have emphasized summative findings only; suggesting not only increases in the proportion of women receiving the recommended four ANC visits during pregnancy as well as a trend towards improved quality of careFootnote 4 [16]. In Kenya, a similar initiative of health information messages and service prompts corresponded to an increase in adherence to recommended ANC visits [19]. In both settings, the absence of details on the technological platforms which serve as the backbone of these programs greatly limits understanding of the factors influencing effects observed, their comparability with alternative resource uses, and generalizability to other settings. While the word count limitations of peer review manuscripts may hinder efforts to report the details of all evaluation activities, robust evaluations should consider the technological performance of the program as a key component of its impact pathway and in particular, its ability to perform as intended. As findings from MOTECH demonstrate, the technological performance of a program is subject to change over time and as scale increases. The failure to monitor this may impact exposure to the program and in turn findings on its effectiveness.
Elsewhere in the literature, a similar pattern of mHealth program evaluations providing limited details on the technological performance of mHealth programs is emerging. Evaluation findings of Text2Floss—a 7-day text messaging intervention which sought to improve oral health behavior and knowledge in the US— suggest that the program led to improvements in knowledge and oral health outcomes [20]. While the characteristics of the automated, multichannel, two-way SMS gateway text messaging service are described, evaluation findings do not provide details on the performance of the technology platform, including messages sent and received out of those expected. Elsewhere in the US, a report on the lessons learnt from a randomized controlled trial of an mHealth smoking cessation program does define the technology functionality by the "pathing of participants from one stage to the next" and report recording these data by the software program, however, no additional data or details are presented [21]. Similarly in Australia, findings from a process evaluation of the TXT2BFIT mHealth program designed to improve weight management in young adults (18–35 years) presented details on the number of text messages delivered and replied to from MessageMedia® web platform; suggesting high rates (98%) of message delivery [22]. However, broader details on the platform were not well detailed [22]. Beyond summative evaluations, qualitative studies have sought to explore user perceptions of mobile tools and/or health information messaging programs in the US [23, 24], New Zealand [25], and India [26–29]. However, none of these present quantitiative data on the technological performance of the program; instead focusing on reported perceptions of the technology, including feasibility, usability and/or acceptability.
There are several limitations to our analyses (Table 4). Implementation of the MOTECH program relies on an initial period of client registration by health workers. While data on the number of individuals enrolled into MOTECH are presented, the true denominator from which these individuals are drawn remains unknown. Similarly, study findings are limited by the absence of details on pregnant and postpartum women not enrolled into Mobile Midwife. Research at a household level is recommended to better measure the population level coverage and sustained engagement in the program. Client level analyses of active listening were restricted to unique phone numbers and women with their own phones or access to a phone in their household. Given the common practice of sharing phones and phone numbers, this may have introduced selection bias and led to an overestimate of active listening status. Health worker use of the Client Data App is limited in this analysis to absolute numbers on the numbers of patients seeking care uploaded into the system. Details on the frequency and timing of these uploads coupled with the completeness of records is missing. Given the challenges reported with the Mobile Midwife message delivery platforms, it is feasible that similar challenges were present in the uploading of patient level information by health workers.
Future research is recommended to more closely track the flow of data across recipients and levels of the health system over time, particularly within and across districts. While we sought to extract data on reminders sent to clients and nurses, these efforts were unsuccessful. Future mHealth platforms should consider ways to improve routine data extraction and use of data to allow for continuous program monitoring. Finally, with regard to message delivery platforms, considerable challenges were noted in the preceding sections. Beyond these, added details on the length of each message, in addition to the number and format, are needed to formulate recommendations on the optimal data package appropriate for use in a program. Since messaging programs can be costly, it is further recommended that linkages between messaging exposure and careseeking be assessed and the value for money of this component weighed against alternative resource uses.
Digital health programs should not assume that messaging programs perform as intended both with regard to technical functionality and the behavioral responses of end-users. Evaluations of mobile health programs should measure technological, behavioral, health systems, and/or community factors which may lead to breaks in the impact pathway and ultimately, influence findings on program delivery and effectiveness.
Early deployments in 2010 relied on a Nokia 1680c-2 feature phone. These were upgraded to the Nokia 2330c-2 and Nokia C1-01, and later to the Nokia Asha 200 to enhance functionality and usability through features such as dual SIM capacity and a larger screen and full keyboard that made it easier to enter data and reduced entry errors. To replace aging Nokia 2330c-2 s and to test a smartphone (Android) version of the Client Data AppClient Data AppClient Data AppClient Data AppClient Data Application, Samsung Galaxy S4 phones were used in the Upper East region.
Facility-level health workers include Community Health Nurses, Midwives, data entry staff, and health extension workers working at CHPS, Health Centers and Hospitals
Initially, this service also provided support for clients enrolled in Mobile Midwife, however, midway through the program, monitoring and feedback from clients were transferred to the districts as part of program capacity building.
Measured by increasing the identification and referral of antepartum complications, increases in the number of women receiving preventive health services and attending ANC late in pregnancy.
ANC:
CHN:
CHPS:
Community-based health planning and services
GHS:
Ghana health services
MAMA:
Mobile alliance for maternal action
mHealth:
MNCH:
Maternal, newborn, and child health
MOTECH:
Mobile technology for community health
Short messaging service
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We are grateful to the support provided by Ghana Health Services throughout the implementation of MOTECH. Funding for MOTECH was provided by the Bill and Melinda Gates Foundation, USAID's Saving Lives at Birth, and the United Nations Foundation. Funding to support the publication of this manuscript was provided by the Bill and Melinda Gates Foundation. Without their continued support to dissemination of results in all forms, analyses such as these would not be possible. The data contained within this manuscript were generously collected by health care providers with support from District officials in Ada East and West, Awutu Senya East and West, and Gomoa West—we thank them for their participation in MOTECH. The fourth author's work on this manuscript was supported by the National Institute of Mental Health (K01MH107310). Additional support for the JHSPH team came from the World Health Organization's Department of Reproductive Health and Research under the United Nations Innovation Working Group Catalytic mHealth Grant Mechanism, managed by the United Nations Foundation with funding from the Norwegian Agency for Development Cooperation (NORAD).
MOTECH in Ghana was a partnership of the Ghana Health Service, Grameen Foundation, and the Columbia University Mailman School of Public Health. The initiative was cofinanced by the Bill & Melinda Gates Foundation; Saving Lives at Birth: A Grand Challenge for Development (SLAB), a multinational partnership of the United States Agency for International Development (USAID), the Norwegian Ministry of Foreign Affairs, Grand Challenges Canada, the UK Department for International Development (DFID), the Korea International Cooperation Agency (KOICA), and the Bill & Melinda Gates Foundation; and the United Nations Foundation. Technical support to the Monitoring and Evaluation of MOTECH was provided by Johns Hopkins Bloomberg School of Public Health with support from the World Health Organization's Department of Reproductive Health and Research and United Nations Foundation, under the Every Women, Every Child Innovations Working Group mHealth Catalytic Grant Mechanism.
The datasets supporting the conclusions of this article are available upon individual request from the Grameen Foundation Ghana. Please contact the corresponding author for additional queries.
Evaluation efforts were conducted retrospectively by the Grameen Foundation and Johns Hopkins Bloomberg School of Public Health and with support from the World Health Organization. KR is the overall study PI. AM and AEL conceived the idea for this analysis with KR, DH and DM. Data were provided by DH and analyzed by DM and AEL with inputs from AM, LJ, AL, and GM. AEL wrote the first draft of this manuscript with inputs from DM, KR, AM, LJ, AL, and GM. All authors approved the final manuscript.
Given the secondary nature of this analyses, JHU faculty leading this analysis were considered exempt by the institutional review board at the Johns Hopkins Bloomberg School of Public Health in Baltimore, Maryland, U.S.A. The Ghanaian Ministry of Health in Accra, Ghana also approved the study's activities as part of routine monitoring and evaluation. Users' registered into MOTECH consented to the collection of patient level health information at the time of registration into the program, however, these data are not presented.
Department of International Health, Johns Hopkins School of Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA
Amnesty E. LeFevre, Diwakar Mohan, Larissa Jennings & Alain Labrique
Department of International Health, Johns Hopkins University Global mHealth Initiative, 615 N. Wolfe Street, Baltimore, MD, USA
Amnesty E. LeFevre & Alain Labrique
Grameen Foundation Ghana, OSDTD5041 No. 25 Labone Cresent, Accra, Ghana
David Hutchful, Karen Romano & Anitha Moorthy
World Health Organization, Geneva, Switzerland
Garrett Mehl
Amnesty E. LeFevre
Diwakar Mohan
David Hutchful
Larissa Jennings
Alain Labrique
Karen Romano
Anitha Moorthy
Correspondence to Amnesty E. LeFevre.
Data flows through the MOTECH system reproduced with permission from [1]. (DOCX 150 kb)
Summary metrics for assessing program effectiveness including technological and behavioral components. (DOCX 31 kb)
Proportion of data uploads by facility type and district. Trends in system generated data on uploads by facility type and district. (DOCX 133 kb)
Proportion of messages sent out of those expected across the continuum of care by district. Trends in system generated data on message delivery. (DOCX 73 kb)
LeFevre, A.E., Mohan, D., Hutchful, D. et al. Mobile Technology for Community Health in Ghana: what happens when technical functionality threatens the effectiveness of digital health programs?. BMC Med Inform Decis Mak 17, 27 (2017). https://doi.org/10.1186/s12911-017-0421-9
Frontline health workers
IVR messaging
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