Discusión acerca del Matrimonio Igualitario

2.3.1 Personalisation in online domains other than the health domain

Studies on applied personalisation predominantly include the following areas: education, entertainment, web browsing, e-commerce [77, 93, 94]. In a review of e-commerce personalisation studies published between 2000 and 2008, Adolphs and Winkelmann [93] found the following trends in this research area:

 More than half of the reviewed articles researched personalisation from the perspective of user behaviour and user perception about, e.g., trust, satisfaction, support, or expectations.

 50% of the reviewed articles focused on recommender systems.

‘Theoretical foundations’ were, however, understudied [93]. These are the type of studies that focus on identifying user needs and user groups to assist content providers in

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improving website adaptation. Identifying individual user needs – as are emotional needs,

for a specific user group - people affected by cancer, led the research presented in this thesis.

Personalisation has been broadly adopted and studied in e-commerce. The majority of e- commerce websites claim they have adopted personalisation, at least in the basic sense [95]. More than 80% of them also report their personalisation efforts to be based on broad segmentation and clustering [95]. The websites and applications that are providing personalised experiences include some of the well known Web-based services, e.g.: Amazon, Facebook, LinkedIn, Bombfell (menswear), Stitch Fix (women’s personal styling), Netflix (movies and television shows), Hulu (shows), Spotify (music), Pandora (music), and Advertising – Retargeted (Speek) and Interest-based [96].

Nevertheless, only a small fraction (10%) of retailers perceive to be highly effective in personalisation [95]. One third of retailers believe their means to support personalisation are limited or lacking [95]. The e-commerce domain can be considered a leader in the adoption of personalisation technologies, yet it shows room for improvement and further research. Understandably, therefore, research on personalisation in other online domains is lacking, specifically the lagging health domain, which is addressed in this thesis.

2.3.2 Personalisation of Web-based health services

Personalisation can be seen as an “asset” for health websites as it enables easier, more personal way for navigating the website, easier and faster access to more relevant content meeting personal health needs [57]. Studies show that personalisation is one of the two most important usability and functionality factors determining users’ preference for e- health websites [11], and a decisive factor in selecting and trusting health websites [97- 99]. Overall, online health users are interested in personalised health websites [100, 101]. However, the evaluation of 21 US hospital websites showed that personalisation adoption is lagging [23]. The adoption of personalisation technologies in online health services continues to be slow and neglected [27-30]. This is the case even with major health

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portals [102]. One of the objectives of the research in this thesis was, therefore, to

introduce personalisation to a cancer website by implementing the features desired by the target users. Moreover, user acceptance and perceptions about the implemented personalisation services were thereafter explored.

Normally, personalisation in eHealth implies personalised treatments, interventions, medicines or learning material [24-26]. The traditional approach to personalisation of eHealth has been to apply users’ explicit feedback to deliver educational material

matched to the collected preferences and health data, thereby aiming to affect health behaviour [103]. For example, personalised health systems have been used to educate patients to abide by a medical regime [104]. However, with the occurrence of Web 2.0 technologies, new opportunities for health application adaptation [103] arose with the widespread availability of user information, and a more common utilisation of Personal Health Records (PHRs). The application of personalisation in online health services has been so far in PHRs, health education, search outcomes, and recommendations of clinical trials [20].

There are some health websites, primarily US based, that have introduced a limited level of personalisation [20]. These are websites such as: TrialX [105], PatientsLikeMe [106], various Web-based PHRs, Healthy Harlem [107], WebMD [108], MedlinePlus [109] and EsTuDiabetes.org [110]. For example, WebMD, a US based but globally renowned health portal, collaborated with Wellpoint [111], and expanded to its services with personalisation based on member population segmentation [112, 113]. Other health websites incorporated [114]:

 content filtering and personalisation; for example: - Vadlo1 – search engine for life sciences

- Wellsphere2 – offering online health information, primarily a community for health bloggers

1 _{http://vadlo.com/}

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- GoPubMed3 – search engine for biomedical knowledge, based on GeneOntology used to retrieve PubMed publications sorted at the what, where, when, who levels

 websites with advanced search result manipulation and data filtering (e.g., MayoClinic.org4)

 and websites offering patient health record management (e.g., MayoClinic.org). Other online health projects that experimented with personalisation include [103]: providing users with personalised health-related promotion messages (e.g., Riskbot); use of tags and ratings for personalised health education; and use of data collected from the major PHR’s (Google and Microsoft) for health personalisation. Another interesting project is MyHealthEducator, which creates personalised recommendations of health information based on user data from the online PHRs [103]. The main problem with these personalised systems is that they rely on the existence and access to users’ online PHRs and their integration [103] with existing applications.

In addition to the mentioned websites and applications, academic research has also been undertaken in online health personalisation. Personalisation of health websites was studied from specific perspectives, e.g.: identifying personalisation requirements of Dutch senior citizens [21]; introducing personalised educational material on stroke-precaution for elderly in Taiwan [115]; proposing Web-based personalised information and educational resources for cardiovascular diseases [25]; and, more recently, exploring the effect of personalised feedback on physical activities among adults from seven European countries [116].

Promoting healthier behaviour in patients with chronic conditions - for example neck and shoulder pain – was seen feasible with the use of Personal Coaching Systems (PCS) [117]. The PCSs provide personal feedback and employ machine learning to adapt the content. Another study used a kHealth system to aggregate asthma patient data from sensors and questionnaire responses, and claimed that using patient context to process the data, along with personalised medical knowledge, results in improved decision making

3 _{http://www.gopubmed.com/web/gopubmed/} 4 _{http://www.mayoclinic.org/}

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[118]. Additionally, research applied to medical literature search (search engine for medical articles - TRIPDatabase.com) [119] indicated the effectiveness of personalised medical search results; they showed users prefer personalised rankings, in particular those related to the content viewed by other similar users [119].

Other research explored whether tailoring can predict health content elaboration (content clarification), specifically of educational health messages for adolescents [32]. The difference in scores for satisfaction and knowledge on the tailored and non-tailored website was not found. However, tailoring had a positive effect on content elaboration [32]. Nevertheless, additional research was suggested in order to understand whether the same applies to health content on websites other than the educational ones, and population groups of different attitudes [32]. Hence, the research presented in this thesis

investigated tailoring of a health website used by different categories of people affected by cancer.

Only limited personalisation research has been applied to online cancer-related services,

the area this research addressed. For example, an ontology-driven framework was proposed for providing personalised mHealth for young cancer survivors [120]. Another study [121] evaluated MijnAVL, an interactive non-personalised portal that provided educational material and other support to breast cancer survivors. The study implied that the portal’s usefulness and efficacy could be improved by introducing feature tailoring [121].

Healthy.me [122] is a research platform that also provides tailored information for breast cancer survivors, specifically those in Australia. The platform focuses on offering personal health management, connecting with peers and health professionals, and cancer survivor care information. An evaluation study [122] of Healthy.me showed that users found such a system useful, however that it presented certain barriers to usage - such as technical errors and lack of content updates - as expected given that it is still a research e- health platform. In another study that evaluated the OncoKompas eHealth application that provides follow-up support to cancer survivors, including tailored feedback and

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personalised advice, healthcare professionals highlighted the need to tailor care and to simplify the navigation [123].

Based on the reviewed literature, research on personalisation in online cancer services is in exploration stages. The questions that are open for research include: conducting a systematic review of personalisation features that can be introduced to cancer websites, obtaining target user feedback about the potential personalisation services, and addressing the needs of cancer-affected populations from understudied, non-English speaking environments. Another challenge in this area is that personalisation does not entail a one-size-fits-all solution applicable across different domains. It requires taking the unique set of target user characteristics into account. Researching the specific characteristics of the cancer-affected users requires more attention, and is thus being addressed here (Chapters 3).