SOCIAL MEDIA USE IN HEALTHCARE: THE PATIENT’S PERSPECTIVE
“To what extent does the use of social media influence outcomes for patients and their
relationship with their healthcare provider?”
Sterre Attema
S2195038
Bloemstraat 47-74
9712LC Groningen
S.M.Attema@student.rug.nl
University of Groningen
Faculty of Economics and Business
Master Change Management
June 2016
Supervisor: Edin Smailhodzic
Co-assessor: David J. Langley
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Table of Contents
Abstract
1. Introduction 4
2. Theoretical Background 6
2.1 Social Media in Healthcare 6
2.1.1 Defining social media 6
2.1.2 Patients’ social media use 6
2.2 Self-determination Theory 7
2.3 Hypotheses Development 8
2.3.1 Social media use and self-determination 8
2.3.2 Predicting social media outcomes 10
2.4 Conceptual Model 13 3. Methods 13 3.1 Data Collection 14 3.2 Measurements 14 3.2.1 Independent variables 14 3.2.2 Mediator variable 15 3.2.3 Dependent variables 15 3.2.4 Control variables 15
3.2.5 Validity and reliability 16
3.2.6 Analysis plan 17
4. Results 18
4.1 Data Exploration 18
4.2 Assumptions of Multiple Regression Analysis 19
4.3 Testing Hypotheses 19
4.3.1 Results on the effects of social media on self-determination 19 4.3.2 Results on the mediating role of self-determination 19
5. Discussion 22
6. References 27
3 Abstract
This study’s aim was to create insight into the consequences of social media use for patients and their relationships with their healthcare providers. The theory of self-determination is leveraged to explain three outcomes for patients that are considered important in healthcare: patients’ ability to self-manage their disease; the extent to which they engage in shared decision-making (SDM); and the amount of patient-physician information seeking (PI). Data of 169 participants were collected on an online health community (OHC) by means of an online survey. The survey included scales intended to measure the amount of social support found on the OHC, patients’ determination, self-management, SDM and PI. The conducted regression analyses show that patients who use OHCs as a source of social support had significantly higher self-determination, and this in turn was positively associated with the ability to self-manage their disease, engagement in SDM, and engagement in PI. This supports the idea that self-determination is an important variable in predicting patients’ outcomes of OHC use; however the results only point to indirect effects and not to a mediation effect of self-determination. Theoretical and practical implications based on these findings are discussed.
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“Living things have needs that must be fulfilled if they are to persist and thrive.” - Ryan and Deci (2002, p. 7)
1. Introduction
Over the past few years the use of social media has expanded greatly: while in 2012 around 65% of Dutch people older than 12 years used social media, in 2014 this had increased to roughly 80% (CBS, 2015). This increase in the use of social media has had, and continues to have impact on the individuals using social media. It creates the possibility for users to generate, share, and receive information, as well as to comment on information (Kaplan & Haenlein, 2010). This is also the case in the field of healthcare with the emergence of online health communities (OHCs), which are social units that allow people with similar interest or a shared goal to interact with each other anywhere and anytime through communication technologies (Demiris, 2006). On these OHCs patients can now find and place large amounts of information about diseases, symptoms, treatments and experiences, and connect with others (Moorhead et al., 2013).
A substantial amount of research has been conducted to assess the motives and consequences of social media use (Hamm et al., 2013). An important motive for the use of social media use by patients is the availability of social support: social media enable people to connect with each other and share their information, experiences and give advice, as well as support each other with emotional issues. These types of social support can be categorized into informational and emotional support (Antheunis, Tates & Nieboer, 2013; Moorhead et al., 2013). Several benefits of social media use have been identified, such as the increased amount of information that is available, the fact that the information has become more tailored to the patients’ needs, and the fact that it has become easier to access the information (Moorhead et al., 2013). Moreover, health information technologies in general have been found to potentially improve the quality of healthcare (Agarwal, Gao, DesRoches & Jha, 2010) and of health outcomes (Bornkessel, Furberg & Lefebvre, 2014). The patient’s perspective however has not received much attention in research on the emergence of social media in healthcare (Agarwal et al., 2010).
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literature the goal of this current research is to create insight into the consequences of social media use for patients and their relationships with their healthcare providers. Hence it addresses the following question: “To what extent does the use of social media influence outcomes for patients and their relationship with their healthcare provider?”
To address this question the current research assesses the effects of OHC use on three outcomes: self-management of a disease, shared decision making between healthcare provider and patient, and the extent to which a patients seeks information when interacting with the healthcare provider. In order to explain the effects of OHCs on health outcomes the current research leverages self-determination theory (SDT) in contrast to prior studies that often have not included theories (Moorhead et al., 2013). SDT is a highly applied theory of human motivation and behaviour which has been used in previous studies to explain health outcomes and behaviours as well (Ryan et al., 2008). SDT is built on the idea that the fulfilment of the three basic needs of competence, autonomy and relatedness is essential to motivate human behaviour and maintenance of behaviour. The current research argues that OHCs are able to fulfil these three basic needs, and that in turn this will positively affect patients’ health and alter their relationship with their healthcare provider. In a broader sense it argues that SDT has the potential to explain why social media use leads to certain outcomes for patients.
By addressing the gap that exists concerning the effects of social media on patient outcomes and the healthcare provider-patient relationship this study adds to two literature streams. Firstly, a contribution is made to literature on social media by looking at its emerging role in healthcare from the patient’s perspective (Aral et al., 2013; Hamm et al., 2013). By leveraging SDT this study investigates the outcomes for the patients who use social media. Secondly, this study adds to literature on health information technology through its assessment of the effects of health related social media on the relationship between healthcare provider and patient, which has been explored but has not been fully studied in previous research (Agarwal et al., 2010).
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2. Theoretical Background
2.1 Social Media in Healthcare2.1.1 Defining social media
The term social media is frequently used in everyday life, but the definition of social media and the applications that can be considered social media are both less straightforward. Kaplan and Haenlein (2010) state that social media differ from Web 2.0 and User Generated Content (UGC). The term Web 2.0 was introduced when both creators and users of websites could all start creating and modifying content on a website, in contrast to Web 1.0 where only the creator could modify the content. Kaplan and Haenlein (2010) perceive Web 2.0 as a key enabler for the “evolution of social media”. UGC on the other hand refers to the content that is created by users, so it refers to the actual contributions of the users within media. With this in mind, Kaplan and Haenlein (2010, p. 61) propose the following definition of social media “a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of User Generated Content”.
The current research focuses on social media in the field of healthcare. Within healthcare social media have the potential to make information available to a broad range of people and to enable social support in an efficient way (Neal et al., 2007). The group of health-related social media that are investigated in the current research are OHCs. OHCs are social units that allow people with similar interests or a shared goal to interact with each other anywhere and anytime through communication technologies (Demiris, 2006). Several types of communities exist in healthcare and the current research is directed at communities that exist mainly to support patients and their caregivers. On these communities members mostly engage in “mutual problem solving, information sharing, expression of feelings, mutual support and empathy” (Demiris, 2006, p. 179). According to Neal et al. (2007) the strength of OHCs lies in their ability to make a connection between people who are geographically separated.
2.1.2 Patients’ social media use
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emotional support refers to “providing messages that involve emotional concerns such as caring, understanding, or empathy” (Liang et al., 2011, p. 72).
2.2 Self-determination Theory
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and feel that they belong somewhere, irrespective of other goals related to obtaining a certain status or other outcomes. With regards to health it would mean that people feel connected to others that are dealing with the same health issues.
2.3 Hypotheses Development
2.3.1 Social media use and self-determination
Both Ryan et al. (2008) and Ng et al. (2012) mention contextual factors as an antecedent to the fulfilment of the basic needs. The role of the contextual antecedents is of interest for the current research as the emergence of social media, such as OHCs, has altered the healthcare context to some extent over the last few years, and the development of healthcare-related social media has become a great addition to the provision of regular healthcare support (Rupert at al., 2014). Patients traditionally have relied on healthcare providers for informational support and to some extent for emotional support, hereby supporting the fulfilment of patient’s basic needs (Arora, Finney Rutten, Gustafson, Moser & Hawkins, 2007; Niemiec, Ryan, Deci, & Williams, 2009). Nowadays social media have gained an important role within the healthcare context (Antheunis et al., 2013; Hajli, Shanmugam, Hajli, Khani & Wang, 2015) and provide patients with social support (Antheunis et al., 2013). Therefore I argue that social media, such as OHCs, are able to provide patients with similar support for the fulfilment of the three basic needs. Consequently the current study expects that the use of OHCs, through its provision of social support, can serve as a contextual antecedent to fulfilment of the basic psychological needs.
The proposed conceptual model (Figure 1) positions self-determination as mediating the effects of OHC use on several outcomes. OHCs that are used for informational support are expected to have influence on self-determination as informational support affects all three needs. First of all, competence is about effectively engaging in health-related behaviours and having the possibility to act upon capabilities concerning health-related behaviours. Competence can be supported by making people fully understand and master certain behaviours, providing them with appropriate instructions and feedback that will help them master the behaviours, providing them with tools, and letting them engage in challenging activities (Niemiec & Ryan, 2009). Some of these supporting elements can be found on OHCs. Users of OHCs for example have been found to educate each other (Smailhodzic, Boonstra & Langley, 2015), hereby supporting each other in mastering the knowledge that is available about a disease. Additionally, users help each other resolving health issues and give each other advice on lifestyle (Smailhodzic et al., 2015), such as instructions and feedback that is needed to deal with a disease, and they even inspire each other to engage in challenging behaviours such as a difficult type of exercise. These kinds of support found on OHCs mostly pertain to the category informational support (Smailhodzic et al., 2015). Hence the informational aspect of social support is expected to be important for the fulfilment of the need for competence.
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feel like they have initiated and chosen their health-related behaviours. Autonomy can be supported by providing someone with relevant information, listening to someone, minimizing control, and encouraging someone to ask questions, to express opinions, and to choose between treatments (Williams & Deci, 2001). Through provision of informational support these behaviours are supported on OHCs when users give each other advice or recommendations, and teach each other (Antheunis et al., 2013; Liang et al., 2011). As users educate and interact with peers these OHC contexts are likely to be relatively low on control, in contrast to a classic patient-healthcare provider interaction where the healthcare provider is the one holding the information and giving the options (Rupert et al., 2014). Consequently informational support is expected to be associated with a person’s need for autonomy.
Lastly, informational support is expected to increase an individual’s sense of relatedness as well. The need of relatedness becomes fulfilled when a person has a sense of belonging and feels respected, understood, and cared for by others who deal with the same disease. This feeling arises for example in response to perspective-taking: when people take each other’s perspective and try to imagine what the situation of the other person is like (Ryan & Deci, 2000). Moreover, when other people show understanding and empathy for other users, this can increase a person’s sense of relatedness (Liang et al., 2011). Although support directed at providing information is foremost about providing someone with useful information, it is a form of social support that can make a person feel better. The exchange of informational support involves one person signalling willingness to help another person, and willingness to invest time to do this. Schaefer, Coyne and Lazarus (1981) argue that informational support can signal caring when it does not originate from obligation. In this way informational support can increase an individual’s feeling of being respected, understood, and cared for (Liang et al., 2011). Hence perceived relatedness is expected to be higher when someone is provided with more informational support from an OHC.
Hypothesis 1a: The use of OHCs as a source of informational support is positively associated with individual self-determination.
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showing solidarity and by valuing one another (Smailhodzic et al., 2015). This type of support relates to emotional support and is likely to increase feelings of belonging and relatedness, as others that deal with the same disease show their understanding and support. In line with this the following hypothesis is developed.
Hypothesis 1b: The use of OHCs as a source of emotional support is positively associated with individual self-determination.
2.3.2 Predicting social media outcomes
The current research proposes that the outcomes of OHC use for patients and their healthcare provider-patient relationship is related to the mechanism of SDT. The following section provides the rationale for several predictions that can be made based on SDT.
Predicting self-management. Self-management of chronic diseases is considered important in healthcare as it has positive effects on health and quality of life (Heisler, Bouknight, Hayward, Smith & Kerr, 2002). Finding both informational and emotional support online can have positive effects on the ability to self-manage a disease (Roblin, 2011). Self-management of diseases -and in specific chronic diseases- refers to having the required skills to deal with a disease. According to Heisler et al. (2002) it is important to increase understanding about both the health behaviours and the disease, and to consistently engage in the necessary health behaviours in order to become able to self-manage. For some patients it may nonetheless be very challenging to self-manage a disease, even more so when a healthcare provider does not offer adequate support or information for self-management (Glasgow, Hampson, Strycker, Ruggiero, 1997). Moreover, an often stated problem is maintaining the necessary behaviours even when a patient has the required skills to deal with the disease (Vermeire, Hearnshaw, Royen & Denekens, 2001).
In light of this approach to self-management its relationship with self-determination can be argued. First of all, an important aspect of self-determination is that it leads people to internalize and integrate behaviours into daily life (Ryan et al., 2008). When behaviours are internalized and integrated people are better able to engage in necessary behaviours over a longer period of time. Because of this, being more self-determined can help patients maintaining the necessary health behaviours such as adhering to a certain treatment, diet, or exercise plan in order to self-manage it. Secondly, patients who are more self-determined perceive themselves as more competent in dealing with a disease. When this competence is high a patient has more knowledge of the disease and a wider array of available skills to deal with the disease (Ryan and Deci, 2002) and this helps the patient to engage in the activities relevant for self-managing the disease (Heisler et al., 2002).
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obtained from their healthcare provider alone, which helps them acquiring the necessary skills and becoming competent to deal with the disease. In line with this logic the following hypotheses are developed.
Hypothesis 2a: The effect of informational support on management will be mediated by self-determination.
Hypothesis 2b: The effect of emotional support on management will be mediated by self-determination.
Predicting shared decision-making. Shared decision-making (SDM) is a type of interaction between healthcare provider and patient and refers to an approach that uses input from both the healthcare provider and patient to come to a shared decision based on the knowledge that is available. The healthcare provider stimulates patients to think about a range of topics and options regarding the treatment of a disease and to communicate their opinion (Elwyn et al., 2010). SDM is increasingly considered as the preferred healthcare model as it is linked to positive patient outcomes as satisfaction and improvement in their dealing with a disease (Charles, Gafni & Whelan, 1997; Lerman et al., 1990). Especially informational support has been found to be important for SDM as it aids decision making and helps a patient to reflect on his or her disease and the options (Elwyn et al., 2010). The SDM model has four key characteristics: at least two parties are involved; all parties share information; all parties undertake action to reach an agreement; finally an agreement is reached (Charles et al., 1997). However, for patients there are obstacles to SDM in healthcare for example when patients “feel intimidated and unable to make a difference in the relationship, are reluctant to bother the doctor, and do not understand the language or know the script” (Godolphin, 2009, p. 188). If these obstacles can be overcome one would expect there to be more SDM between patient and healthcare provider. Taking these views on SDM together it can be concluded that the availability of information together with an effective interaction with the healthcare provider is essential for SDM.
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and know the script. The feeling of being autonomous that accompanies self-determination could play an important role as well. Patients who are more autonomous feel that they are the ones ultimately responsible for dealing with the disease instead of someone else controlling them to do so (Ryan et al., 2008; Ryan & Deci, 2000) and therefore they are more likely to engage in the decision-making process. Hence they are expected not to take on a passive role vis-à-vis their healthcare provider, but rather to share their views and engage in discussion on the treatment plan.
Concluding, patients can become more self-determined through the use of OHCs, and this stimulates them to be more proactive in daily life. This proactive mind set is expected to lead them to engage more actively in the decision-making process with their healthcare provider. Moreover, as patients feel more competent and more autonomous they are more able to discuss health-related topics into detail with their healthcare provider and feel that it is their responsibility to do so. The informational aspect of social support is expected to be most important for SDM, since SDM depends highly on a patient’s knowledge and insight. In line with this logic the following hypothesis is developed.
Hypothesis 3: The effect of informational support on shared decision-making will be mediated by self-determination.
Predicting patient–physician information exchange. Patient-physician information exchange (PI) refers to the extent of information that is exchanged between doctor and patient assessed through the patient’s effort to obtain the information (Lerman et al., 1990). In other words it is the extent to which patients seek information when visiting a healthcare provider. This information exchange between patient and healthcare provider is important in modern healthcare as it improves a patient’s ability to deal with a disease and increases their satisfaction (Lerman et al., 1990). Not all patients will actively seek for information when interacting with their healthcare provider. The two important requirements of PI are that the patients have to be able to articulate a question, and that they have a sense of control related to their health.
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that patients who were more informed on their disease engaged in more PI than their less informed counterparts. Hence patients with higher competence are expected to become more able to interact with their healthcare provider and to articulate questions due to their increased amount of knowledge on the disease (Deci & Ryan, 2002).
In sum, patients who use OHCs as a source of informational support will be more self-determined, making them more actively involved in social situations and increasing their perceived control over their health. Because of this they are expected to become more proactively involved with their healthcare provider and to seek more information. In line with this the following hypothesis is developed.
Hypothesis 4: The effect of informational support on patient-physician information exchange will be mediated by self-determination.
2.4 Conceptual Model
Figure 1: conceptual model for the study
3. Methods
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discuss the procedure of data collection, the measurements that were used to measure the variables, and the analyses that have been conducted.
3.1 Data Collection
Data collection was targeted at diabetes patients and parents or other caregivers of diabetes patients who made use of the website Diabetestrefpunt. This is a Dutch online health community aimed at bringing diabetes patients together to help each other. It is part of the Dutch diabetes community (DVN) that acts on the behalf of all Dutch diabetes patients. Diabetestrefpunt has over 2000 members and a number of non-members visiting the platform as well. The participants were approached by DVN via their website and through an email to participate in the research. In order to increase the response rate we raffled ten gift certificates. This technique helps boosting the response rate, however one should be careful with the selection of the prize as differential effects are found for men and women (Laguilles, Williams & Saunders, 2011). To minimalize this the current research offered gift cards that could be used on a website for health products; they were expected to appeal equally to male and female participants as both were dealing with diabetes. The participants could click on a link that would lead them to the online survey that was created in Qualtrics. The participants were informed about the purpose of the study, the length of the study, their chance of winning a gift certificate and the fact that the data collected would be stored anonymously and confidentially. At the end of the study the participants were thanked for their participation and asked to leave their email address.
Over a timespan of three weeks 169 usable responses were received. A total of 76 of the participants were men (45%). The largest part of participants consisted of patients (96.4 %), and the rest consisted of parents or caregivers. The ages of participants ranged from 19 to 88 with an average of 57.1, with a standard deviation of 13.6. The sample included participants and caregivers dealing with a range of diabetes types, of which most participants suffered from type 1 (43.8%) or type 2 (47.9%). Most participants had been suffering or taking care of someone with diabetes for over 5 years (81.7%). 3.2 Measurements
Wherever possible validated measures where used, or validated measures were adapted to fit the purpose of this study (Table 5, Appendix A). As the study was conducted on a Dutch platform, all questionnaires have been translated to Dutch and translated back into English to guarantee adequate translation. Whenever these latter translations did not match with the official English questionnaires, the translation was reconsidered. All items of the scales can be found in Appendix A. All scales were answered on a 7-point Likert scale ranging from 1 (Strongly disagree) to 7 (Strongly agree), except for the scales measuring shared decision-making and patient-physician information exchange, which ranged from 1 (Never) to 7 (Always).
3.2.1 Independent variables
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(Shakespeare-Finch & Obst, 2011) were adapted. Although the 2-Way SSS originally measures instrumental support which encompasses informational support, only items pertaining to informational support were used as these are more important in an online context (Liang et al., 2011). Informational support refers to the provision of advice and information, hence the items assessed the extent to which patients used the forum to seek advice and learn about subjects related to the disease.
Emotional support. The 2-Way SSS (Shakespeare-Finch & Obst, 2011) was also used to measure emotional support. This construct refers to the expression of emotions such as liking or loving. In this questionnaire the participants were asked to rate the extent to which they agreed with items that assessed their use of Diabetestrefpunt as a source of emotional support.
3.2.2 Mediator variable
Self-determination. The sub-constructs of self-determination were measured using an adapted version of the Psychological Need Satisfaction in Exercise Scale (Wilson, Rogers, Rodgers & Wild, 2006) which originally measures self-determination regarding exercising. The adapted scale focussed on the key aspects of dealing with diabetes. The adaption resulted in a scale with 12 statements that covered the constructs competence, autonomy and relatedness with regards to dealing with diabetes. Competence was measured by assessing the extent to which a patient feels effective in engaging in necessary diabetes-related behaviours. Autonomy was measured by assessing the feeling that one is the origin of diabetes-related behaviours. Lastly, relatedness was measured through assessing the extent to which a patient feels related to other people who are dealing with diabetes.
3.2.3 Dependent variables
Self-management. Self-management refers to a patients engagement in self-care activities concerning a disease (Heisler et al., 2002). Self-management of diabetes was measured on five domains in accordance with Heisler et al. (2002). The participants were asked to rate the extent to which they were able to conduct five necessary behaviours such as taking medications and exercising. Shared decision-making. Four items were adapted from the Patient Decision-making Scale (Lerman et al., 1990) in order to measure SDM. These items originated from the Perceived Involvement in Care Scale (Lerman et al., 1990) and assess the extent to which patients actively engage in the decision-making process, for example by making suggestions and insisting on certain tests.
Patient-physician information exchange. The measurement of PI was based on the patient-physician information exchange, which is a subscale of the Perceived Involvement in Care Scale (Lerman et al., 1990). This scale measures the extent to which a patient actively seeks information about a disease during an appointment with his or her healthcare provider.
3.2.4 Control variables
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Age. Age was entered as a control variable as this can affect the use of OHCs. According to Prensky (2001) younger people can be viewed as “digital natives” as they grew up with digital options, and therefore are more used to interact and communicate through OHCs. As these digital natives might interact differently on OHCs, this could have effects on their self-determination.
Sex. Patients were asked whether they were male or female. According to Tannen (1991) women have a higher tendency to invest in long-term relations and building a sense of community than men. This difference could affect the extent to which OHC use is related to self-determination. Hence this control variable was entered into the study to control for possible differences in communication patterns between male and female participants.
Level of education. In order to measure education level of participants, they were asked to select one of five options indicating levels of education from the current and older Dutch education systems: 1. none/primary school; 2. VMBO/MAVO/LBO; 3. MBO; 4. HAVO/VWO; 5. HBO/WO. This control variable was added because education level could be linked to the extent to which people are able to self-manage a disease and the extent to which they are able to engage in a discussion with their healthcare provider about the disease.
Role of the participant. To assess the role of the participants they were asked to select if they were a diabetes patient or a parent and/or a caregiver for a diabetes patient, as this can influence the impact the disease has on someone’s life and hereby also the impact of the perceived outcomes.
Intensity of forum use. The frequency with which participants attended the community was measured as people who make use of the forum more often may automatically receive more social support than people who do not. Therefore it was expected to influence the mediator and the outcome variables. The following answer possibilities could be selected by the participants: 1. A few times per year; 2. Approximately 1 time per month; 3. A few times per month; 4. A few times per week; 5. Every day.
Diabetes specifics. Three specifics concerning participants’ diabetes were measured: the type of diabetes (Type 1, Type 2, Other…), the duration of disease (1. Less than 6 months; 2. Between 6 and 12 months; 3. Between 1 and 3 years; 4. Between 3 and 5 years; 5. For over 5 years.), and whether the participant suffered other diseases as these could affect the ease with which participants can engage in self-management and SDM.
3.2.5 Validity and reliability
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and validated scales were used. Lastly, participants were only informed about the general subject of the study as knowing the goal of a study could have influenced their answers.
In order to assess the possibility of using the subscales of self-determination and of social support separately, two factor analyses were conducted. The principal component analysis procedure (PCA) was used, which reveals classification among a set of items (Wold, Esbensen & Geladi, 1987). Hence it helps determining which items belong to which component or show similarity amongst each other according to the collected data. In order to structure the PCA a varimax rotation was used. Items that did not have a high enough factor-loading (<.70) or had cross-loadings higher than .40 were not included in the study. A valid set of items remained (Table 6 and Table 7, Appendix B), and the conditions for performing a factor analysis were met in both cases (KMO > .50; Bartlett test <.00) (Jolliffe, 2002). Subsequently the reliabilities of all the scales were assessed by conducting a Cronbach’s Alpha analysis (Table 8, Appendix B). All variables met the threshold of α = .70, indicating sufficient internal consistency (Moore, McCabe & Craig, 2012).
3.2.6 Analysis plan
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4. Results
4.1 Data ExplorationThe descriptive statistics (Table 9, Appendix C) and the correlation matrix (Table 1) that are shown provide information about the average scores on all the scales, the spread of the scores, and the strength of relationships between the variables. Participants in the sample scored relatively high on average on a few scales: Competence (M = 5.46, SD = 1.19), Autonomy (M = 5.33, SD = 1.35), Self-management (M = 5.81, SD = .94). Hence on average the participants in this sample felt competent and autonomous in dealing with their diabetes, and believed they were able to self-manage their diabetes.
The correlation matrix reveals several significant correlations between variables. Informational and emotional support do not show a significant correlation with the outcome variables self-management, SDM and PI, whereas the mediator variable self-determination does. Moreover, both informational and emotional support are significantly correlated with the mediator variable self-determination, as was expected. These two observations indicate that indirect effects between the predictor and the outcome variables might be present, rather than the predicted mediation effects. Several control variables are significantly related to the outcome variables as well. Firstly, age and sex are significantly related to PI, revealing that the older male participants engaged more in information seeking at the appointment with their healthcare provider. Moreover, the duration of the disease and the type of diabetes are related to self-management: participants who were suffering from diabetes for a longer time and had diabetes type 1 were more able to self-manage their diabetes. Additionally when people were suffering from another disease apart from diabetes they engaged in more SDM with their healthcare provider. Lastly, the frequency with which someone visited the forum was significantly and positively correlated to SDM.
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Multiple regression analysis requires that a dataset meets several assumptions (Cohen, Cohen, West & Aiken, 2003). The first one is normality of the residuals which is not violated based on visual inspection of figures 2 through 4 (Appendix D). The second assumption is homoscedasticity of the errors which is visually inspected using the scatterplots in figures 5 through 7 (Appendix D). These figures show that there is no pattern to be identified in the errors, and thus the errors are distributed evenly. The third assumption requires linearity between independent and dependent variables. This assumption is not violated as can be seen in the figures 8 through 16 that all show linear relationships (Appendix D). The last assumption is independence of the errors, this assumption cannot be checked, but should be warranted by the procedure of data collection. The other three assumptions were not violated, hence this provided no reason for transformation of the data. Subsequently the data was checked for any possible outliers by calculating the cook’s distance for each regression analysis (Heiberger & Holland, 2004). For the prediction of self-management the maximum cook’s distance was .21; for SDM the maximum cook’s distance was .09; and for PI the maximum observed cook’s distance was .18. In all cases the cook’s distance was below 1.0, hereby providing no reason to suspect significant outliers (Heiberger & Holland, 2004). Lastly, multicollinearity statistics were calculated to assess whether the predictor variables were not correlated too highly amongst each other. Tables 2 and 3 show that the resulting VIF scores remained under the threshold value of 5, which indicates that there is no reason to suspect multicollinearity between the variables (Heiberger & Holland, 2004). To conclude, inspection of the data has shown that a multiple regression analysis is appropriate for this dataset.
4.3 Testing Hypotheses
4.3.1 Results on the effects of social media on self-determination
A regression analysis was conducted to test hypothesis 1a and 1b concerning the effects of social support on self-determination. The model was significant (F=7.10, p=.00), and the two predictors explain 8 % of the variance in self-determination. The effects of both informational support and emotional support on self-determination are significant. Hence hypothesis 1a and 1b are both supported by this analysis, showing that the use of OHCs as a source of informational support and emotional support is related to higher levels of self-determination.
4.3.2 Results on the mediating role of self-determination
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relationship between informational support and self-management should be present as well. A separate regression analysis shows that this is not the case as the model testing the simple effect was insignificant (Table 4). Hence this does not support Hypothesis 2a “The effect of informational support on self-management will be mediated by self-determination”.
Table 3 reveals the regression analysis of self-management predicted by emotional support and self-determination. The bootstrap procedure reveals a significant indirect effect of .06, and differs significantly from zero (95% confidence interval = .01 to .12). Table 3 demonstrates that the use of an OHC as a source of emotional support leads to increased self-determination, which in turn is linked to increased ability to manage a disease. In this case the simple effect of emotional support on self-management is not present either as the model was insignificant (Table 4). Hence Hypothesis 2b “The effect of emotional support on self-management will be mediated by self-determination” is not supported. Nevertheless, the findings do show an indirect effect of both informational support and emotional support on self-management through self-determination. Figure 17 (Appendix E) illustrates these indirect effects.
Table 2 in addition shows the test of Hypothesis 3 “The effect of social support on shared decision-making will be mediated by self-determination.” The bootstrap procedure shows a significant indirect effect of .11, which differs significantly from zero (95% confidence interval = .04 to .21). Table 2 demonstrates that participants who used OHCs for informational support had higher self-determination, and this was linked to the extent to which they engaged in SDM with their healthcare providers. In order to establish the mediation effect, informational support should be significantly related to SDM. A separate regression analysis shows that this simple effect is not found, as the model was insignificant (Table 4). These findings do not support Hypothesis 3, however the indirect effect was found and is illustrated in Figure 18 (Appendix E).
21 Table 2
Regression results and indirect effects of informational support Dependent variables SDT Self-management SDM PI β β β β VIF Informational support .23** -.01 -.10 .06 1.54 SDT .45** .45** .32** 1.20 Rolec -.28 -.84 -.03 1.05 Agec .00 .00 .00 1.55 Sexc .17 .08 -.54* 1.22 Educationc .12* -.02 .01 1.14 Duration of diseasec .09 -.03 -.15 1.11 Type of diabetesc -.18 -.11 .00 1.19 Other diseasesc .03 -.59* -.34 1.14 Intensity of forum usec .03 .12 -.04 1.09 F-value 14.08** 7.07** 2.96** 2.49** R2 .08 .31 .16 .14
95% Boot confidence interval
Dependent variables
Boot indirect effect
Lower bound Upper bound
Self-management .10 .04 .21
SDM .10 .04 .21
PI .07 .02 .16
Notes: N = 169; * p <.05; ** p <.01; c= Control variable; based on 5000 bootstrap samples Table 3
22
95%Boot confidence interval
Dependent variable
Boot indirect effect
Lower bound Upper bound
Self-management .06 .01 .12
Notes: N = 169; * p <.05; ** p <.01; c= Control variable; based on 5000 bootstrap samples Table 4
Regression results of simple effects of informational and emotional support Dependent variables Self-management SDM PI β β β Informational support .06 .02 .14 Emotional support .01 F-value .56 .03 2.95 R2 .01 .00 .02
8. Discussion
This study was set out to assess the importance of social media use, such as OHCs, for several outcomes for patients and for their relationship with their healthcare provider. In addition it has introduced self-determination as a mediator in predicting outcomes of social media use in healthcare. The findings of this study demonstrate that patients who use OHCs to find social support have increased self-determination and hereby are significantly more able to self-manage their disease, are more involved in the decision-making process, and more actively seek for information during an appointment with their healthcare provider. The following sections will discuss the findings more into detail.
23
express opinions and ask questions, and become more able to make decisions with regards to a disease (Williams & Deci, 2001). Lastly, a significant increase in the need for relatedness can be explained by Schaefer et al.’s (1981) findings that relatedness can be increased when other people voluntarily offer their help and hereby signal their caring. The relationship between emotional support and self-determination was also found to be significant, which supports the idea that emotional support makes an individual feel like he or she is being cared for, loved, esteemed and valued (Cobb, 1976).
Although the study did not find support for the mediating role of self-determination, it did find significant indirect effects between social support and the three outcome variables self-management, SDM, and PI. Mediation effects differ from indirect effects as mediation effects require the presence of a direct effect between predictor variable and outcome variable. The absence of these direct relationships between social support and the three outcome variables could be due to the following explanation. More than 80% of the participants in this sample suffered from diabetes for more than five years. Patients suffering from a disease for a long time are expected to be more knowledgeable with regards to the disease (Heisler et al., 2002; Williams, Freedman & Deci, 1998). Therefore, the social support, and in particular informational support, they find on an OHC may not affect their outcomes as much as was predicted. The indirect effects through self-determination however are still present because it helps patients to engage in a range of behaviours about which they already had the knowledge, but lacked the motivation to engage in. Self-determination affects patients in a different way than the direct effects of social support, and this makes it less affected by the duration of the disease. For self-management this explanation seems to be especially plausible, as self-management is significantly correlated with the duration of the disease. However, as this is solely speculation future research should address these findings.
24
Secondly, the results support the idea that self-determination is an important variable in explaining SDM. Patients who found more informational support on an OHC reported significantly higher self-determination, and this in turn was linked to higher SDM. This indicates that patients engage more in the decision-making process with their healthcare provider as they are more self-determined through their use of an OHC for informational support. These findings support the idea that SDM relies on the extent to which a patient is proactive and constructive in social situations, which is an aspect of self-determination. People who are less self-determined tend to be more passive, tend not to accept their responsibilities and deny growth, as opposed to people with higher self-determination (Ryan & Deci, 2000). For this reason individuals with higher fulfilment of all the three basic needs will be more inclined to actively engage in decision-making with their healthcare provider.
Thirdly and lastly, informational support was indirectly related to PI through its relationship with self-determination. This means that patients who go online to find informational support have higher self-determination, and this in turn makes them more likely to actively seek for information when interacting with their healthcare provider. The explanation for this relationship is similar to that of SDM. Patients who are more self-determined engage more actively in their social development (Ryan & Deci, 2000). Moreover, they feel more effective in dealing with their disease and their social environment (Ryan & Deci, 2000). As they are more socially proactive these patients are also expected to interact more proactively with their healthcare provider and ask more questions concerning their disease. In addition when patients feel more competent they have more knowledge (Deci & Ryan, 2002) and hereby become more able to articulate questions when interacting with the healthcare provider, which is essential to PI. These aspects of self-determination can explain why it mediates the relation between informational support and PI.
The discussion of these findings together help answering the research question of this study: “To what extent does the use of social media influence outcomes for patients and their relationship with their healthcare provider?” The results show that the use of OHCs as a source of social support is associated with self-determination in patients, and that this in turn is related to three outcomes: patients reported to be more able to self-manage their disease, to engage more in shared decision-making with their healthcare provider, and to seek for more information during an appointment with the healthcare provider .
25
established the importance of self-determination in healthcare (Ng et al., 2012), but not in relation to OHC use.In future research on the effects of OHCs, or even the effects of social media on outcomes for patients the concept of self-determination theory should not be ignored. Moreover, to increase the usefulness of OHCs for patients, ways should be explored to design OHCs in a manner that optimizes users’ potential to develop themselves and their well-being. According to Ryan and Deci (2000) studies with the aim of designing such supporting social environments have already been conducted, this research however should be extended to the context of social media environments like OHCs.
This study has addressed the emergence of OHCs in healthcare by focussing on the consequences for patients and by taking their perspective into account. Although self-reported, patients claimed an increased ability to self-manage their disease. Future research should focus on assessing the consequences of OHC use in healthcare in ways that rely on other measures besides self-reported measures. Moreover, this research has answered the call for quantitative research on the consequences of patients’ social media use on their relationship with their healthcare provider (Agarwal et al., 2010; Hamm et al., 2013; Rupert et al., 2014). The results have supported the idea that patients who use social media as a source of social support have a different relationship with their healthcare provider than patients who do not use social media for support (Rupert et al., 2014). In specific, these patients more often engaged in SDM with their healthcare provider and more actively sought for information when interacting with their healthcare provider. In the future, studies should be conducted with the aim of exploring the ways in which SDM and PI could be further stimulated by OHCs, for example by creating applications or special topics that can serve as decision aids to help patients engage even more actively in the relationship (Elwyn et al., 2010).
26
should be open-minded to discuss the information that patients have found on social media. If this need is not assessed, patients will feel disempowered and this can be detrimental to the relationship. Healthcare providers are advised to be open towards information that is brought to the appointment.
Conclusions based on the findings of this research are restricted by several limitations. Firstly, the sample on which the current research is based consisted of diabetes patients or caregivers caring for diabetes patients. Diabetes is a chronic disease that requires daily attention and affects many aspects of daily life (Anderson et al., 2000), hence the results may not generalize to people suffering from other diseases. However, diabetes is not the only disease that is chronic and needs to be dealt with on a daily basis. In other chronic diseases the effects of OHC use may be similar. Nevertheless in order to assess the generalizability of this study, samples from other contexts should be used in future research. Moreover, due to the lack of a control group and the fact that this was not a longitudinal research the current research deals with the issue of causality. In light of the predictions that were made based on literature the current findings are interpreted as such that social support is related to several outcomes due to its association with self-determination, but the causality of this relationship is not established. Future research could be conducted with the aim of finding causal support for this causal relationship. Lastly, as was mentioned before this research relies on self-reported data. Therefore it is not certain whether patients who used the OHC for social support actually were better at self-managing their disease and engaged in more shared decision-making. Future research therefore could assess these outcomes in a less subjective manner by assessing medical outcomes.
27
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Appendix A
MeasurementsTable 5
Items per measurement
Measure Items
Social support (Adapted from Shakespeare-Finch & Obst, 2011)
Wanneer ik gebruik maak van het diabetestrefpunt...
Informational support
1. Krijg ik advies over hoe ik (of degene die ik verzorg) met diabetes kan omgaan 2. Leer ik over medicijnen en diëten
3. Lees of geef ik informatie over dokters en behandelingen 4. Leer ik over behandelingen
5. Lees ik over ervaringen van anderen met een bepaalde behandeling
Emotional support
6. Vind ik iemand waarbij ik steun kan vinden
7. Vind ik iemand die mij het gevoel geeft dat ik ertoe doe
8. Voel ik dat ik een kring van mensen heb die mij waardevol vinden 9. Gebruik ik het forum om mijn angsten en zorgen te delen
10. Vind ik geruststelling als ik dit nodig heb Psychological Need Satisfaction (Adapted from Wilson et al., 2006) Ik voel... Competence
1. dat ik in staat ben om met mijn diabetes om te gaan
4. dat ik mijn diabetes ook op moeilijke momenten kan behandelen 7. me zeker over de manier waarop ik mijn diabetes behandel 11. dat ik goed kan omgaan met mijn diabetes
Autonomy
2. me vrij om op mijn manier met mijn diabetes om te gaan
5. me vrij in het maken van mijn eigen keuzes in mijn diabetes behandeling 8. dat ik de baas ben over mijn keuzes over mijn diabetes
12. dat ik degene ben die bepaalt wat ik doe met diabetes
Relatedness
3. me verbonden met mijn vrienden die ook diabetes hebben 6. me verbonden met mensen die ook diabetes hebben
9. een band met mijn vrienden die ook diabetes hebben, omdat we met hetzelfde te maken hebben 10. dat ik het goed kan vinden met mensen die ook diabetes hebben
Diabetes self-management (Heisler et al., 2002)
Ik geloof dat ik in staat ben om... 1. mijn medicatie te nemen 2. regelmatig te sporten 3. mijn dieetplan te volgen 4. mijn bloedsuiker te controleren
5. mijn voeten te controleren op wonden of zweren Shared
decision-making (Adapted from Lerman et al., 1990)
Geef alstublieft aan hoe vaak de volgende dingen voorkomen wanneer u uw zorgverlener bezoekt. 1. Ik stel een bepaalde behandeling, een medicijn of hulpmiddel voor aan mijn zorgverlener 2. Ik dring aan op een bepaalde test, behandeling, medicijn of hulpmiddel
3. Ik uit mijn twijfels over een bepaalde test, behandeling, medicijn of hulpmiddel 4. Ik geef mijn mening over een bepaalde behandeling, test, medicijn of hulpmiddel Patient-physician
information exchange (Lerman et al., 1990)
Geef alstublieft aan hoe vaak de volgende dingen voorkomen wanneer u uw zorgverlener bezoekt. 1.Ik vraag extra uitleg over de behandeling
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Appendix B
Factor Analysis and ReliabilityTable 6
Rotated Component Matrix Social Support
Notes: * = deleted item
Table 7
Rotated Component Matrix Self-determination scale
Notes: * = deleted item
Component 1 Component 2 Informational support 1 .80 Informational support 2 .83 Informational support 3 .79 Informational support 4 .87 Informational support 5 * Emotional support 1 .80 Emotional support 2 .79 Emotional support 3 * Emotional support 4 .91 Emotional support 5 .81 KMO measure .84 Bartlett’s test p < .00
Component 1 Component 2 Component 3
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Appendix D
Assumption 1: normality of the residualsFigure 2: Histogram of the distribution of the standardized residuals of self-management.
36
Figure 4: Histogram of the distribution of the standardized residuals of PI.
Assumption 2: homoscedasticity of the errors
Figure 5: Scatterplot of the studentized residuals of self-management against the standardized predicted values
37
Figure 6: Scatterplot of the studentized residuals of SDM against the standardized predicted values of SDM.
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Assumption 3: linearity between independent and dependent variables
Figure 8: Scatterplot of the linear relationship between the independent variable informational support and the
dependent variable self-management.
Figure 9: Scatterplot of the linear relationship between the independent variable emotional support and the
39
Figure 10: Scatterplot of the linear relationship between the independent variable SDT and the dependent
variable self-management.
Figure 11: Scatterplot of the linear relationship between the independent variable informational support and the
40
Figure 12: Scatterplot of the linear relationship between the independent variable emotional support and the
dependent variable SDM.
Figure 13: Scatterplot of the linear relationship between the independent variable SDT and the dependent
41
Figure 14: Scatterplot of the linear relationship between the independent variable informational support and the
dependent variable PI.
Figure 15: Scatterplot of the linear relationship between the independent variable emotional support and the
42
Figure 16: Scatterplot of the linear relationship between the independent variable SDT and the dependent
43
Appendix E
Figures illustrating the indirect effects
Figure 17: Indirect effects of informational and emotional support on
self-management.
Notes: * p <.05; ** p <.01
Figure 18: Indirect effects of informational support on SDM. Notes: * p <.05; ** p <.01
