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Here are 8 methods that you should invest your time and resources for producing organic user engagement on your website. Have you ever heard of the story about a Rabbit and a Tortoise that go for a race? However, in the online web industry, it is quite the opposite when it comes to speed. Being faster means faster here and slowing down even for a second can mean your death.
Slow website loading is the biggest culprit in destroying user engagement on websites. No matter how updated or elegantly designed your website might be, it will still suffer increased bounce rates because the user will never get the chance to see it. In order to fix this, have your developers look at your website performance and remove any bugs or glitches that might be causing the lag. Internal linking is an effective technique to improve user engagement on your website. It connects your visitors to important content that helps increase page views of your website.
Creating content that actually helps the user inform with helpful aspects and then anchoring it with keywords is the best means to generate views for SEO rankings while maintaining a reliable internal linking structure for navigation of your website. Avoid overdoing it as it will appear as spamming and damage your website image. It is quite similar to internal linking except that you display the link at the end of the page.
Customers who are eager to learn more about your products or services will most definitely find the related content interesting and click on the links at the end of the page. According to research, most customers who have already read your post till the end have the tendency to read more if presented with a related content. Cash in this opportunity and introduce links to help your users navigate to new pages.
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Having a reliable and self-spoken menu for your website navigation is imperative for generating user engagement. Like everyone else, your audience is composed of a human being who will judge you on every inch of your website and that is why you must never give them a reason to dislike you when getting the first impression. The reason why some websites are resilient to user engagement is their poor navigation features that prevent customers from finding what are looking for.
Your website menu should be simple enough to interact, yet elegant enough to attract visitors into its fold. Ask your developers to throw in a few extra graphic effects and speed up the loading time so the user has no excuse to abandon the page.
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Make a neat work out of your website without spending too much on the design. Remember, a website is only strongest as its weakest feature — and that sir is the navigation menu. Keeping your website updated with the latest trends is essential for leading the competition. Every trend has a significant effect on the SEO rankings of your website if you execute them properly. With technology on its peak of development, cellular technology has given birth to a new prospect of digital marketing.
Smartphones have emerged with all sorts of sizes and features well within the affordable range of everyone. With their use and popularity skyrocketing, the need for responsive design has grown exponentially. Businesses need their websites to be accessible in every means possible and responsive design ensures the website is scalable and functional on every screen size from hand held devices to desktop screens.
By integrating responsive design, you are not only securing the customers that use smartphones more often, but also retaining the ones who use other devices such as tablets, laptops and desktops. According to a survey report by Statistics, Learn to adapt to the changing needs of your audience. It has also been hypothesised that mechanisms of action, such as accountability to a healthcare practitioner and relatedness to other individuals, might positively influence engagement with DBCIs [ 68 , 77 , 86 , 96 ].
An unmeasured third variable , such as higher baseline motivation or self-efficacy, may be responsible for the observed association between increased engagement and positive DBCI outcomes. Alternatively, those who engage with DBCIs might simply be more inclined to behave healthily in general [ 11 ]. It has also been argued that the target behaviour itself might influence engagement [ ]. For example, smokers who relapse might be more likely to stop engaging with the DBCI, while those who successfully manage their cravings might be more likely to continue engaging with the DBCI.
Optimal dose refers to a pre-defined level of engagement at which specific DBCIs are effective. It has been hypothesised that the receipt of an optimal dose may explain the relationship between engagement and intervention effectiveness but that the optimal dose for particular DBCIs may vary depending on user characteristics [ 70 , ]. The use of specific intervention features has been found to be associated with better DBCI outcomes [ 70 ].
It has been suggested that there may be a mismatch between features that participants choose to engage with frequently and effective features that are causally linked to intervention outcomes [ ]. For example, although users may enjoy engaging with a particular feature e. The final aim of the review was to develop a conceptual framework specifying potential direct and indirect influences on engagement and relationships between engagement and intervention effectiveness.
As the framework proposed by Ritterband and colleagues [ 29 ] and the ontology proposed by West and Michie [ 4 ] explicitly linked engagement to behaviour change, we drew on these to structure our conceptual framework, mapping the other existing frameworks onto it. Additional factors identified in the reviewed literature not otherwise specified were also mapped onto the conceptual framework. We propose a conceptual framework in which engagement with a DBCI influences the target behaviour through specific mechanisms of action; box 4, box 1, box 3 and box 2, respectively.
Content has been found to directly influence engagement with DBCIs; box a. Delivery has been hypothesised to directly influence engagement with DBCIs; box b. The context and the target behaviour are hypothesised to directly influence engagement; box 5 and box 3, respectively. Mechanisms of action are hypothesised to indirectly influence engagement; box 2. The population e. The setting has been hypothesised to directly influence engagement; box d.
Engagement is hypothesised to be indirectly influenced by the moderating influence of the context on the influence of the DBCI; box 4, box 5 and box 1, respectively. Hypothesised influences are marked with stars. Conceptual framework of direct and indirect influences on engagement with DBCIs. Transparent boxes indicate concepts. Concepts can be defined as abstract ideas that are derived from either direct or indirect evidence [ ]. Blue boxes indicate attributes of concepts.
Attributes can be defined as properties that characterise a concept [ ]. Solid black arrows indicate relationships between concepts and attributes. Arrows with transparent heads indicate an influence of a concept. An integrative conceptualisation of engagement with DBCIs has been developed; engagement is defined here as a multidimensional construct which can be measured through self-report questionnaires, verbal reports, automatic recording of DBCI use or recording of psychophysical manifestations. A conceptual framework was developed, which suggests that the context of use influences engagement with DBCIs either directly or indirectly by moderating the influence of the DBCI on engagement.
Mechanisms of action might indirectly influence engagement and the target behaviour might directly influence engagement with DBCIs, suggesting the presence of a positive feedback loop. The proposed relationships between engagement and intervention effectiveness are tentative, as these have not been studied extensively.
The suggested behavioural and experiential dimensions of engagement can in principle be measured or inferred in every instance of a DBCI. The content, structure, length and design of specific DBCIs tend to vary, and hence, the relevance of the different dimensions of engagement will vary accordingly. Thus, the proposed definition of engagement allows for direct comparison across different kinds of DBCIs by including multiple dimensions of engagement at its core.
This has been lacking in previous conceptualisations. If this is the case, the proposed definition of engagement should provide a means of generalising findings from particular DBCIs to other similar DBCIs. It may not be possible to evaluate the usefulness of the proposed definition prior to empirical work [ ]. Although some self-report questionnaires designed to measure engagement demonstrate good validity and reliability [ 64 , ], these typically rely on measuring engagement after, as opposed to during, the event.
However, the advent of new technologies allows self-reports of engagement to be measured in real-time rather than through paper-and-pencil questionnaires [ ]. Although physiological measures have been used to measure engagement, notably in the HCI literature, associations between physiological and self-reported measures of engagement are weak [ 65 ]. The nature of these associations should be investigated further. Previous conceptual frameworks have been based on theoretical predictions only or have been derived from the literature within one scientific domain [ 4 , 28 — 30 ].
In contrast, our conceptual framework is derived from theoretical predictions and empirical observations within multiple, interrelated disciplines. This endeavour was facilitated by the use of principles from CIS, which allowed the combination of a diverse set of research findings. The proposed conceptual framework of engagement is a synthesis of existing ontologies, frameworks and models and incorporates factors not previously included.
The negative association between poor mental health and engagement might be explained by the observation that those with poor mental health e. Experience of well-being might be negatively associated with engagement due to being related to the belief that one does not need any support. Familiarity with the design of DBCIs and guidance might positively influence engagement because familiar examples, design conventions or stepped how-to-use guides may inculcate feelings of comfort and ease of use.
A narrative might draw users in, increasing their interest and enjoyment. Moreover, this review identified a trend towards a positive association between engagement and older age, higher educational attainment and being a woman, which merits further investigation. Although these demographic characteristics have been included in existing frameworks of engagement, the direction of influence has not been previously discussed.
Through the use of a systematic, interdisciplinary approach, the proposed conceptual framework offers a comprehensive overview of the factors that may influence engagement with DBCIs and hence provides a starting point for reducing the observed fragmentation of research findings. The lack of evidence supporting the claim that setting of use e. This might either reflect the search terms used or indicate that this has not been investigated in the literature; we cannot distinguish between these explanations.
There was also a lack of evidence in support of the claim that the context of use i. For example, the setting of use may vary depending on the mode of delivery e. Hence, the DBCI might indirectly influence engagement through determining the setting of use; while computers may predominantly be used at home or in a clinic, mobile phones might mainly be used on the go, which may influence the amount or depth of engagement. Future research should test this hypothesis. Another limitation is that no formal quality assessment of the included articles was conducted.
However, this was in line with the chosen method, which suggests that the articles should be judged on the basis of their relevance to the research question rather than their methodological rigour. This method was selected due to the conceptual nature of the research questions. Finally, the end date for the literature search i. November constitutes a limitation; with the pace of technological advances and the proliferation of digital health research, it is likely that relevant literature has since been published.
The proposed integrative definition and conceptual framework of engagement with DBCIs have implications for clinical practice: the use of a shared terminology and measurement techniques will ensure more rapid advance in understanding engagement with DBCIs and developing methods to improve it. A shared conceptualisation of engagement can be used to help policymakers and commissioners to set evaluation standards for DBCIs.
Moreover, the proposed conceptual framework can be used to generate testable hypotheses about how to improve engagement with DBCIs. For example, according to the conceptual framework, the presence of rewards might influence engagement with a DBCI due to increased motivation. This hypothesised link between rewards, motivation and engagement can be tested using an experimental design.
Future avenues for research include the assessment of what dimensions of engagement e. Engagement with DBCIs is conceptualised here in terms of both experience and behaviour. The authors would like to thank Jacqui Smith, librarian at University College London, for helping to build the search strategy; Nicola Newhouse for validating the coding frame and commenting on an early draft; and Holly Walton for helping to screen articles.
Olga Perski is a Ph. The funders played no role in the design, conduct or analysis of the study nor in the interpretation and reporting of study findings. Statements on human rights, the welfare of animals, informed consent, and the Declaration of Helsinki are not applicable to this manuscript. IRB approval is not applicable.
All authors have approved the final manuscript and agree with its submission to Translational Behavioural Medicine. All authors have contributed equally to the scientific work and are responsible and accountable for the results. We confirm that this manuscript has not been previously published partly or in full and that the manuscript is not being simultaneously submitted elsewhere.
We confirm that the data have not been previously reported elsewhere and that no data have been fabricated or manipulated to support our conclusions. The authors have full control of all data, which are accessible upon request. RW undertakes research and consultancy and receives fees for speaking from companies that develop and manufacture smoking cessation medications. Practice : The use of a shared conceptual framework for engagement with digital behaviour change interventions DBCIs should promote more rapid advances in developing methods to improve it.
Research : The proposed conceptual framework can be used to generate testable hypotheses about how to improve engagement. Electronic supplementary material. National Center for Biotechnology Information , U. Journal List Transl Behav Med v. Transl Behav Med. Published online Dec Author information Copyright and License information Disclaimer.
Corresponding author. This article has been cited by other articles in PMC. Electronic supplementary material The online version of this article doi The aims of this review are threefold; the second and third build on output from the first: To synthesise past work on engagement, addressing the following research questions: How has engagement been defined in the selected literatures? How has engagement been measured?
What factors have been found or hypothesised to influence engagement? Criteria for considering studies for this review All types of study designs were included except position papers. Searching for other resources Articles from adjacent fields not immediately or obviously relevant to the research questions were identified through expertise within the review team [ 32 ].
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Data collection and analysis Selection of studies Articles identified through the electronic and hand searches were merged using EndNote X7 [ 38 ] to ensure consistency. Data extraction and management A pro-forma was developed by the first author to extract information about the study setting, participant characteristics, study design, data collection method and study findings [ 32 ]. Quality appraisal CIS suggests the prioritisation of seemingly relevant articles rather than favouring particular study methodologies [ 40 ].
Data synthesis Based on the principles from CIS, the data synthesis comprised the following steps: Concepts identified in the full texts of included articles were labelled with codes by the first author.
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The synthesising arguments were refined through discussion between all co-authors. Results Summary of search results The electronic database search yielded published articles. Open in a separate window. Fig 1.
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How has engagement been defined in the literature? Engagement as subjective experience Engagement has been conceptualised as the subjective experience that emerges in the momentary interaction with a system [ 18 , 28 , 43 ]. Engagement as behaviour The majority of articles reviewed from the behavioural science literature conceptualised engagement in behavioural terms, suggesting that it is identical to the usage of a DBCI or its components.
Development of an integrative definition of engagement An integrative definition of engagement with DBCIs was developed through the merging of overlapping conceptualisations as outlined above, in addition to the integration of the two overarching synthetic constructs. Subjective measures In research settings, self-report questionnaires have frequently been used to measure engagement with digital games and DBCIs [ 51 , 58 — 67 ]. Objective measures Automatic tracking of use patterns, including number of logins, time spent online and the amount and type of content used during the intervention period, was the most commonly used measure of engagement in the behavioural science literature [ 11 , 19 , 20 , 26 , 44 , 70 — 82 ].
Measures relating to the integrated conceptualisation of engagement Based on the literature synthesis, we suggest that all facets of engagement proposed in the integrative definition of engagement can in principle be measured or inferred through the following: 1 user-reported interaction with the DBCI through self-report questionnaires, interview studies or think aloud studies; 2 automated recording of DBCI use e. What factors have been hypothesised or found to influence engagement?
Context Population Psychological characteristics Motivation was found to be positively associated with engagement across many studies, with none indicating a negative association [ 20 , 68 , 83 — 87 ]. Demographic characteristics Age [ 20 , 57 , 63 , 68 — 70 , 73 , 76 , 79 , 91 , 95 , 96 , 99 , — ], gender [ 20 , 69 , 73 , 90 , 95 , , , , ], education [ 20 , 69 , 91 , 92 , 96 , 99 , , , , , ], employment [ 91 , 92 , ] and ethnicity [ 57 , ] were found to be significantly associated with engagement. Physical characteristics Physique , including baseline weight and the presence of comorbidities, was found to be negatively associated with engagement [ 68 , 70 , 71 , 91 — 94 , , ].
Setting The social and physical environments in which a DBCI is used, have been hypothesised to influence engagement [ 4 , 29 , 30 ]. DBCI Content DBCIs that include particular behaviour change techniques BCTs , such as action plans [ 78 ], goal setting [ ], feedback [ 59 ] and self-monitoring tools [ 78 ], have been found to be associated with higher engagement [ 78 ]. Delivery Mode of delivery , which includes face-to-face, telephone, text message, smartphone app, website and mass media delivery, has been hypothesised to influence engagement with DBCIs [ 4 ]. What are the proposed relationships between engagement and the effectiveness of DBCIs?
Mechanisms of action Mechanisms of action proposed to mediate the effect of engagement with DBCIs on intervention effectiveness [ 4 ] include increased knowledge, motivation, affect management, cognitive restructuring, skill building [ 29 ], comprehension and practice of programme content, and increased self-efficacy [ 19 ]. Unmeasured third variable An unmeasured third variable , such as higher baseline motivation or self-efficacy, may be responsible for the observed association between increased engagement and positive DBCI outcomes.
Optimal dose Optimal dose refers to a pre-defined level of engagement at which specific DBCIs are effective. Effective features The use of specific intervention features has been found to be associated with better DBCI outcomes [ 70 ]. Development of a conceptual framework of engagement with DBCIs The final aim of the review was to develop a conceptual framework specifying potential direct and indirect influences on engagement and relationships between engagement and intervention effectiveness.
Fig 2. Acknowledgements The authors would like to thank Jacqui Smith, librarian at University College London, for helping to build the search strategy; Nicola Newhouse for validating the coding frame and commenting on an early draft; and Holly Walton for helping to screen articles. Compliance with ethical standards Statements on human rights, the welfare of animals, informed consent, and the Declaration of Helsinki are not applicable to this manuscript. Ethical responsibilities of authors All authors have approved the final manuscript and agree with its submission to Translational Behavioural Medicine.
Footnotes Implications Practice : The use of a shared conceptual framework for engagement with digital behaviour change interventions DBCIs should promote more rapid advances in developing methods to improve it. References 1. Rock Health.
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Green, Eds. Krishnan A What are academic disciplines? Krishnan A Five strategies for practising interdisciplinarity. Csikszentmihalyi M. Flow: the Psychology of Optimal Performance. New York: Cambridge University Press; Defining participant exposure measures in web-based health behavior change programs. Engagement and retention: measuring breadth and depth of participant use of an online intervention. Eysenbach G. The law of attrition. Consumer Health Information Corporation. Motivating patients to use smartphone health apps.