Making good decisions is hard when you are at your worst: Investigating the degree of users’ decision-making in an e-Mental health service

doi:10.21203/rs.3.rs-1994721/v1

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Research Article

Making good decisions is hard when you are at your worst: Investigating the degree of users’ decision-making in an e-Mental health service

https://doi.org/10.21203/rs.3.rs-1994721/v1

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Background. Empowering people to decide on their health has proven to be beneficial and to enable the creation of a therapeutic alliance. This could be the same in an e-Mental health service. However, little is known about the degree of decision-making people should have when using such services and when they are seriously depressed or in a life-threatening situation.

Method. The topic was explored through two studies. The first study was a quantitative study to investigate how much decision-making freedom the self-help e-Mental health service allowed and in what situation (serious or less serious mental complaints) the service could be used. Participants were randomly assigned to one of four prototypes of a self-help e-Mental health service (for elderly people) with a different degree of decision-making and level of gravity of the situation. Afterwards, they were asked to fill in a survey to measure autonomy, competence, relatedness, privacy, safety, patient-technology alliance and intention to use. To analyse the data, ANOVAs and regression analyses were performed. In a second, qualitative study, 10 (clinical) experts with different backgrounds were interviewed about the degree of decision-making elderly people should have when using an e-Mental health service. The interviews were analysed via open and axial coding.

Results. For the first study, 72 elderly people were recruited. No significant effect of decision-making and level of gravity was found. Relatedness significantly influences patient-technology alliance and intention to use. Additionally, patient-technology alliance significantly influences intention to use. For the second study, it was found that control is central for users, even if it is more difficult for people who are seriously depressed or in a critical situation. Nonetheless, design and technical suggestions on how to support users of e-Mental health services who have more serious symptoms are presented, including personalization, a three-steps approach to control, and setting goals.

Conclusions. The results of this study can be applied to other self-help e-Mental health services with therapeutic purposes. Additionally, further research is needed to understand which other factors, together with relatedness, can influence the creation of a therapeutic alliance and how to foster intention to use.

E-Mental health technology

decision-making

empowerment

therapeutic alliance

intention to use

In the last decades, therapists have increased patients’ freedom for deciding how they want to manage their health, with the ultimate goal of improving patients’ recovery (Barello et al., 2016). In the mental health care sector, however, there has been more difficulty in finding the right degree of decision-making patients should have (Simmons & Gooding, 2017). From a systematic review on decision-making capacity in depressed individuals, Hindmarch and colleagues (2013) concluded that little is known about decision-making when a person is seriously depressed or in a life-threatening situation. More recent research on the subject also showed that it is still difficult to assess decision-making capacity in people with depression (Bishop & Gagne, 2018). This might be because individuals in these situations may have difficulty concentrating, which could diminish their ability to think critically and they may prefer decisions that lead to minimal contact with the therapist (van Randenborgh, de Jong-Meyer, & Hüffmeier, 2010). Nonetheless, informing patients about their choices and, thus, giving them the possibility to decide about their health has been proven to be beneficial, for instance by increasing patient satisfaction (Simmons & Gooding, 2017). Additionally, it can facilitate therapeutic alliance: the bond and shared understanding between patients and therapists (Cook & Doyle, 2002); generally considered one of the central factors in therapy leading to optimal recovery.

It has been stated that the use of technology during therapy might facilitate patient empowerment (Akeel & Mundy, 2018; Risling, Martine, Young, & Thorp-Froslie, 2017) due to, for example, technologies which increase patients’ self-management (Risling et al., 2017), such as patient portals where patients can directly see their data. This can, in turn, increase the degree of decision-making for a patient (Akeel & Mundy, 2019). Giving control to the patient via technology is also occurring in the mental health sector, where a big variety of self-help eHealth technology – e-Mental health services - allows patients to decide on how to treat and manage their illnesses without the presence of a therapist (Barazzone, Cavanagh, & Richards, 2012; Zaman, Hossain, Ahammed, & Ahmed, 2017). One can wonder whether it is possible to create a therapeutic alliance with a technology. Several studies have shown that this is the case (Barazzone et al., 2012; Bickmore, Gruber, & Picard, 2005). Accordingly, Barazzone and colleagues (2012) created a framework of factors that support therapeutic alliance in an e-Mental health service. Patient empowerment, of which shared decision-making is an important aspect (Akeel & Mundy, 2019), is a fundamental part of this framework (Barazzone et al., 2012). Nonetheless, as stated above, decision-making for people with depression or other mental issues could be difficult and this could even be more difficult in a self-help technology when a therapist is not involved. It is therefore of crucial importance that e-Mental health services offer the right functionalities and design for patient decision-making.

This study has two aims. First, we aim to uncover how decision-making given to the user and decision-making given to the system influence therapeutic alliance with a technology - patient-technology alliance - and the intention to use a self-help technology. Second, we aim to uncover the level of decision-making an e-Mental health service for people with depressive symptoms should have, according to experts in psychology and health technology. With the insights from both studies, we will have a good base to support developers who would like to offer shared decision-making functionalities in e-Mental health interventions.

Method

To assess the influence of decision-making on therapeutic alliance and intention to use, an online experiment with a 2x2 between-subjects design was conducted. The experiment was supported by a prototype of an e-Mental health service for older mourners, based on the LEAVES service: a self-help e-Mental health service with a virtual agent (‘Sun’) for helping older adults process the loss of their spouse (van Velsen et al., 2020). In line with van Randenborgh et al. (2010), the level of gravity of a situation likely affects a mental health patient’s ability for decision making and may therefore also affect their eagerness to give away control over the situation. Therefore, we have selected decision-making (by the patient or the system) and level of gravity as our independent variables. The prototype included four different options (see Fig. 1). In prototypes 1 and 2, users were given control over their decisions while the gravity of the situation was either low – activity part (i.e., users were given control whilst choosing for activities they could do, like going to the cinema) or high - escalation part (i.e., a person was advised to contact a professional because his/her mental health was deteriorating quickly). In prototypes 3 and 4, decision-making control resided with the technology (system), either while the gravity of the situation was low – activity part (i.e., the system organised an activity for the user, like going to the cinema) or high – escalation part (i.e., the system contacted a professional because his/her mental health was deteriorating quickly). As an example, the activity part of prototype 3 and the escalation part of prototype 4 are shown in Fig. 2 and Fig. 3.

For each condition, we assessed seven outcome measures as dependent variables: perceived autonomy, perceived relatedness, perceived competence, perceived privacy, perceived safety, patient technology alliance, and the intention to use. Figure 4 depicts our research model. Here, decision-making and level of gravity are expected to affect the factors perceived autonomy, perceived relatedness, perceived competence, perceived privacy, and perceived safety (Feathermana & Pavloub, 2003; van Randenborgh et al., 2010; Ryan, Rigby, & Przybylski, 2006). These factors, in turn, make up the concept of patient technology alliance (following Akeel & Mundy 2019; Beldad, De Jong, & Steehouder, 2010; Cook & Doyle 2002; Feathermana & Pavloub, 2003; Lee & Choi 2017; Quin & Dutton, 2005) and they are expected to affect intention to use (Akeel & Mundy 2019; Proudfoot et al., 2010). Finally, it is expected that the presence of therapeutic alliance in self-help e-Mental health services influences intention to use (Barazzone et al., 2012). Please refer to Table 1 for a definition of each factor.

Data collection

Dutch older people (55+) were approached through the snowball method and by using two research panels. Participants were randomly assigned to one of the four conditions. Respectively, for each prototype, these numbers of participants participated: 28 19, 14, and 11. When participants clicked on the link, they were presented with an online informed consent form. On a new page, a scenario of a potential user, called Heleen, was shown and participants were asked to go through the prototype. Finally, they were asked to fill out an online survey. First, open questions elicited participants’ general opinions on the prototype and we inventoried demographics. Second, the relevant factors were assessed on a 5-point Likert scale (going from 1 (strongly disagree) to 5 (strongly agree)). The sources for these scales can be found in Table 1.

The reliability of the rating scales was assessed via Cronbach's alpha, which was in all cases sufficient (Perceived autonomy = .83; Perceived relatedness = .84; Perceived competence = .67, Perceived privacy = .74; Perceived safety = .69; Perceived patient-technology alliance = .89; Intention to use = .88).

Table 1

Overview of variables and questionnaire items
Variable	Definition	Items (translated in English)	Source
Autonomy, Relatedness, Competence	Individuals, in a relationship, need to feel autonomous, relatedness, and competent in order to create strong relationships (Quin & Dutton, 2005).	Perceived Autonomy: • I had the feeling that I could decide for myself whether I wanted to carry out the proposed tasks. • I had the feeling that I could decide for myself how I would plan my grieving process. • I had the feeling that I had control over my grieving process. Perceived Relatedness: • I had the feeling that my needs were understood by the virtual agent ‘Sun’ (LEAVES program) • I had the feeling that the virtual agent ‘Sun’ (LEAVES program) treated me with respect. • I liked the interaction with the virtual agent ‘Sun’ (LEAVES program). Perceived Competence: • I felt capable/ skilled enough to understand what the virtual agent ‘Sun’ (LEAVES program) was asking of me. • I felt overwhelmed by the request from ‘Sun’ (LEAVES program). • I felt skilled enough to understand how ‘Sun’ (LEAVES program) works.	The Player Experience of Need Satisfaction (PENS) (Ryan et al., 2006). The Cronbach’s alpha of the perceived autonomy, perceived relatedness and perceived competence were respectively .66, .72, and .79 (Ryan et al., 2006). For perceived relatedness, another source was also used. This was benevolence towards a virtual agent (Philip et al., 2020). The Cronbach’s alpha of this construct was .71 (Philip et al., 2020). The items for relatedness were created by combining items from both sources.
Privacy	Perceived privacy: when users believe that their personal information is safe and that they have control of it (Beldad et al., 2010; Chen & Dibb, 2010; Feathermana & Pavloub, 2003).	Perceived Privacy: • I trust ‘Sun’ (LEAVES program) to treat my data confidentially. • I am concerned about the security of my personal information when using ‘Sun’ (LEAVES-program). • I found it inconvenient to give my personal information to ‘Sun’ (LEAVES program).	Perceived level of privacy and confidentiality’ in e-commerce, which had a Cronbach’s alpha of .66 (Belanger, Hiller, & Smith, 2002).
Safety	Perceived (physical) safety: the service could pose a threat to the life of the users (Feathermana & Pavloub, 2003; Milne, Pettinico, Hajjat, & Markos, 2017).	Perceived Safety: • I felt safe using the LEAVES program. • Using the LEAVES program could compromise my physical safety. • I do not think the LEAVES program can help me well in difficult situations.	‘Perceived risk of purchase’ something online (Dean & Biswas, 2001). The Chronbach’s alpha was calculated as .88 (Dean & Biswas, 2001).
Therapeutic alliance	The degree to which health professionals and patients interact with each other in order to achieve an attachment bond and a shared understanding about therapeutic goals and tasks (Cook & Doyle, 2002; Kowatsch et al., 2018, p. 2).	Perceived therapeutic alliance: • It became clear to me how I can improve my mental health with the LEAVES program. • The goals of the LEAVES program were clear to me. • The suggested tasks clearly showed how I can improve my health. • I believe the suggested tasks can improve my health. • I believe that the virtual agent ‘Sun’ (LEAVES program) has my best interest in mind. • I felt that, in collaboration with the virtual agent ‘Sun’ (LEAVES program), I could decide for myself how to improve my health.	Working Alliance Inventory survey (Paap, Schrier, & Dijkstra, 2019). The Cronbach’s alpha of the whole construct was calculated as .91.
Intention to use	The strength of one's intention to perform a specified behavior (Fishbein & Ajzen, 1977, p. 288).	Intention to use: • If necessary, I would consider using the LEAVES program itself. • I plan to use the LEAVES program if necessary. • If necessary, I will recommend the LEAVES program to a friend.	Items about the intention to use a website (Bart, Shankar, Sultan, & Urban, 2005). ‘Behavioural intent’ had a Chronbach’s alpha of .88.

Preparation for analysis

Data were analysed with SPSS (IBM, SPSS statistics version 27, n.d.). For the demographics and variables, descriptive statistics were calculated. Seven two-way Analyses of Variance (ANOVA) were conducted to investigate and compare the effects of decision-making and level of gravity on perceived autonomy, perceived relatedness, perceived competence, perceived privacy, perceived safety, perceived patient-technology alliance, and intention to use. Furthermore, to explore the effect of perceived autonomy, perceived relatedness, perceived competence, perceived privacy, and perceived safety on patient-technology alliance and on intention to use, two multiple linear regressions were conducted. Finally, a linear regression analysis was conducted to measure the effect of patient-technology alliance on intention to use.

Results

Descriptive Statistics and Frequencies of variables

The total number of participants was 72. Their mean age was 68.56 (SD = 7.12). The oldest participant was 84 years old and the youngest participant was 53 years old. 95.8% of the participants had lost someone in their life. 13.8% said that they had lost their spouse: the most recent loss was 1.5 years ago and the least recent loss was 36 years ago.

In the table below (Table 2), the mean and standard deviation values for all dependent and independent variables are shown and grouped for each study condition.

Table 2

Descriptive statistics and Frequencies
	Condition 1: Decision-making: users; level of gravity: low (N = 28)	Condition 2: Decision-making: users; level of gravity: high (N = 19)	Condition 3: Decision-making: system; level of gravity: low (N = 14)	Condition 4: Decision-making: system; level of gravity: high (N = 11)
Perceived Autonomy	M = 3.54 (SD = .87)	M = 3.28 (SD = 1.12)	M = 3.14 (SD = 1.34)	M = 3.12 (SD = 1.02)
Perceived Relatedness	M = 3.49 (SD = .98)	M = 3.70 (SD = .99)	M = 3.41 (SD = 1.46)	M = 3.30 (SD = .74)
Perceived Competence	M = 3.65 (SD = 1.21)	M = 3.74 (SD = .83)	M = 3.69 (SD = .71)	M = 3.79 (SD = .75)
Perceived Privacy	M = 3.04 (SD = 1.21)	M = 3.28 (SD = .82)	M = 2.81 (SD = 1.21)	M = 3.33 (SD = .56)
Perceived Safety	M = 3.38 (SD = .87)	M = 3.35 (SD = .93)	M = 3.19 (SD = 1.22)	M = 3.24 (SD = .47)
Patient-Technology Alliance	M = 3.60 (SD = .96)	M = 3.58 (SD = .82)	M = 3.26 (SD = 1.13)	M = 3.21 (SD = .94)
Intention to Use	M = 2.96 (SD = 1.18)	M = 3.04 (SD = 1.14)	M = 2.83 (SD = 1.56)	M = 2.58 (SD = .98)

Analyses

Seven analyses of Variance (ANOVA) were conducted to test the effects of decision-making and level of gravity on perceived autonomy, perceived relatedness, perceived competence, perceived privacy, perceived safety, perceived patient-technology alliance, and intention to use. However, none of these analyses showed significant results as F values were less than 1.

Two multiple linear regressions were planned to measure the effect of perceived autonomy, perceived relatedness, perceived competence, perceived privacy, and perceived safety on perceived patient-technology alliance and on intention to use. The first multiple linear regression had perceived autonomy, perceived relatedness, perceived competence, perceived privacy and perceived safety as independent variables, and perceived patient-technology alliance as the dependent variable. After checking for assumptions, using the plots and histograms, it was found that all assumptions, but the one of no outliers were met. The proportion of variance, thus what was explained by the model, was 62%. This was significant, F(5, 66) = 21.95, p < .001. The individual effect of perceived autonomy, perceived competence, perceived privacy and perceived safety on perceived patient-technology alliance was not significant (Table 3). However, the individual effect of perceived relatedness on perceived patient-technology alliance is significant, b = .51, t(66) = 4.92, p < .001. To check the robustness of the analysis, we also conducted the bootstrap method without the two outliers (these did not differ much from the rest of the sample). Similar results were found.

Table 3

Multiple Linear Regression Analysis with Perceived Patient-Technology Alliance as Dependent Variable
					95% CI
	b	SE	t	p	LL	UL
(Constant)	.58	.34	1.68	.10	− .11	1.26
Perceived Autonomy	.08	.10	.84	.40	− .12	.28
Perceived Relatedness	.51	.10	4.92	< .001	.31	.72
Perceived Competence	.02	.09	.16	.87	− .17	.19
Perceived Privacy	.05	.10	.52	.60	− .15	.25
Perceived Safety	.18	.15	1.18	.24	− .13	.49
Notes. a. Dependent variable: Perceived Patient-Technology Alliance
b. CI = confidence interval; LL = Lower Limit, UL = Upper Limit

The second multiple linear regression had perceived autonomy, perceived relatedness, perceived competence, perceived privacy, and perceived safety as independent variables and intention to use as dependent variable. After checking for assumptions, using the plots and histograms, it was found that all assumptions were met. The proportion of variance, thus what was explained by the model, was 53%. This was significant, F(5, 66) = 14.70, p < .001. The individual effect of perceived autonomy, perceived competence, perceived privacy and perceived safety on intention to use was not significant (Table 4). However, the individual effect of perceived relatedness on intention to use is significant, b = .62, t(66) = 4.21, p < .001. To also check for the robustness of the analysis, the bootstrap method was conducted. Similar results were found.

Table 4

Multiple Linear Regression Analysis with Intention to Use as Dependent Variable
					95% CI
	b	SE	t	p	LL	UL
(Constant)	.24	.48	.50	.62	− .72	1.21
Perceived Autonomy	.23	.14	1.61	.11	− .05	.51
Perceived Relatedness	.62	.15	4.21	< .001	.32	.91
Perceived Competence	− .23	.13	-1.81	.08	− .48	.02
Perceived Privacy	.16	.14	1.11	.27	− .13	.44
Perceived Safety	.03	.22	.15	.88	− .40	.46
Notes. a. Dependent variable: Intention to Use
b. CI = confidence interval; LL = Lower Limit, UL = Upper Limit

Eventually, a linear regression was run to test the effect of perceived patient-technology alliance on intention to use. After checking for assumptions, using the plots and histograms, it was found that all assumptions were met. The proportion of variance, thus what was explained by the model, was 51%. This was significant, F(1, 70) = 73.18, p < .001. There is a significant effect of perceived patient-technology alliance on intention to use, b = .90, t(70) = 8.56, p < .001 (Table 5). To also check for the robustness of the analysis, the bootstrap method was conducted. Similar results were found.

Table 5

Linear Regression Analysis with Intention to Use as Dependent Variable
					95% CI
	b	SE	t	p	LL	UL
(Constant)	− .20	.38	− .54	.59	− .95	.55
Perceived Patient-Technology Alliance	.90	.11	8.56	< .001	.69	1.10
Notes. a. Dependent variable: Intention to Use
b. CI = confidence interval; LL = Lower Limit, UL = Upper Limit

Method

The first study investigated the degree of decision-making a self-help e-Mental health service should have in order to influence the creation of therapeutic alliance and intention to use. This was also done by researching if the level of gravity had an influence. However, the first study was quantitative and it did not consider the personal situations or mental state of participants. With the second study, we wanted to further understand, qualitatively, the degree of decision-making that users in a stressful situation or with lower mental capacity should have when using self-help e-Mental health services. This was done with online semi-structured interviews with experts in psychology and health technology.

Procedure and participants

Experts were recruited via the network of the researchers. Ten experts were willing to participate and provide informed consent. Table 6 shows the participants’ information. At the beginning of the session, participants looked at prototypes 2 (decision-making to users and high level of gravity) and 4 (decision-making to the system and high level of gravity) to get a better idea of the service. With these prototypes, we wanted to give participants examples of how users (prototype 2) or the system (prototype 4) can react when the latter mental health is deteriorating quickly. Afterwards, the semi-structured interview was held. These interviews focused on eHealth technologies and experts’ experience with it; decision-making in e-Mental health services; the degree of decision-making users should have when using this technology with or without a therapist; and the degree of decision-making when a person has lower mental capability due to, for example, depression.

Table 6

Experts’ information
	Provenience	Expertise	eHealth technology experience
Participant 1	Portugal	Clinical psychology, social psychology, decision-making	App to help elderly people quit smoking. Technology to promote health in the working environment.
Participant 2	Germany	Human-factor psychology, usability	Medical devices, robot faces, car driving
Participant 3	The Netherlands	Clinical psychology, ‘schema therapy’	No, but participant knows Minddistrict (e-Mental health application) due to work
Participant 4	Portugal	(Geriatric) psychiatry	Informatic systems
Participant 5	Italy	Clinical and health psychology	No
Participant 6	The Netherlands	Occupational therapy with elderly people	No
Participant 7	Switzerland	Clinical psychology	Not in practice, participant will work in a e-Mental health project
Participant 8	Switzerland	Psychotherapy (a big range of psychiatric disorders)	No, but participant showed interest in it
Participant 9	The Netherlands	eHealth in mental health care, forensic psychiatry	VR, web-based modules, apps, wearables
Participant 10	The Netherlands	Psychology: design for behavioural change	Serious games and apps for interactive experience

Data analyses

All interviews were transcribed. To ensure the reliability of data, two researchers worked together in creating the final coding scheme through open and axial coding (Boeije, 2009). The main theme was decision-making, which included: the degree of decision-making that users should have when using e-Mental health service, the importance of control for the user, the capability of people to decide in serious situations, suggestions on how to tailor decision-making, suggestions on how to include decision-making within an e-Mental health service, and the mediating role of technology for decision-making. Finally, the researchers applied the final coding scheme to 20% of the transcripts by using Atlas.ti (Atlas.ti, n.d.) and Cohen's Kappa for interrater reliability of decision-making was calculated. This was .94, so interrater reliability was high. Consequently, the first researcher finished coding and analysed all the data with the agreed coding scheme.

Results

Decision-making in e-Mental health services

Overall, most of the participants stated that it is best to always give patients the freedom to decide, and that patients should always be included in the decision-making process. Additionally, some participants said that it is really hard for technology to make the right decisions. Because of this, all the suggestions which are given need to be ‘solid’ (participant 2) in order to avoid a counterproductive effect. Some participants said that if patients are given too many options, they can find it ‘overwhelming’ (participant 7). A few participants said that it is important that patients are also guided to make the right decisions by, for instance, explaining to them why something needs to be done: ‘when people understand why something is important [...], people can make the decisions for themselves’ (participant 1).

Decision-making and control

Many participants stressed the importance of control and autonomy for patients. According to all participants, decision-making is associated with the level of control that a patient has. To quote a few participants: ‘the user should have the freedom for everything.’ (participant 2) and ‘I think that if a system would do something for them, then it could be forceful. I think, from my own perspective, like you need to go to the theatre, I would feel, a person would lose control.’ (participant 9).

Some participants explained how this control can be increased. For instance, by stating that the suggestions given by the service are meant to help the patient and that they can still decide what to do, or by tailoring decision-making (see next section).

Finally, an expert who works on implementing the feeling of autonomy in technology explained how, according to self-determination theory, it is really important to give autonomy as, otherwise, it might be harder to have people use the technology: ‘If you take away autonomy then resistance is increased […]. It feels really bad if some program wants to take away your autonomy.’ (participant 10).

Decision-making in serious situations

Opinions on this topic were contrasting among participants. Some participants thought that, also in serious situations, patients should still be given the chance to decide. A few participants were also a bit sceptical about the help that a self-help e-Mental health service could give in serious situations. For instance, participant 5 said that ‘a web application is not right’ and participant 2 said that ‘the emergency part is exactly the part where a human needs to come in’.

Nonetheless, some participants said that when the level of gravity is high, the system should take action. Participant 8 said that ‘if you feel very bad, then you are not good at decision-making’. When the researcher asked the reason, this participant said that ‘it is a symptom of depression that you are not good at decision-making’ (participant 8). Additionally, a few participants mentioned that when other people, like a child, could be harmed, there should be something or someone to act immediately. Finally, participant 1 said that ‘when the arousal level is high, no one can make an intuitive rational decision’. This means that, in serious situations, having the system decide for the user might be the best option.

Control is central in serious situations. Some participants said that patients might already feel hopeless and taking control away from them could just worsen their situation. If it is mentioned that the system is calling a therapist, they could even get more ‘agitated’ (participant 10). Because of this, a few participants said that it is important to give guidance and support in these situations, but not take away the opportunity to decide.

Personalising decision-making

Many participants mentioned that decision-making needs to be personalised depending on the patient’s characteristics, situation, and grief symptoms. To quote some participants: ‘It could be amazing to ask at the beginning what each person would prefer’ (participant 7); ‘The kind of people and grieving style can be so different’ (participant 9).

Some participants said that the reason to need tailored decision-making is that the system should be able to work for everyone. Nonetheless, participant 9 also explained that ‘if there are people with lower literacy or health literacy or low technology skills, then personalisation can become overwhelming and unnecessary. Research showed that if people are in high need of autonomy, then they need personalisation, if they are low, then it is not necessary.’.

Some participants explained how decision-making could be personalised. For example, participant 2 said that being advised to do an activity in a social context, like going to the cinema, could make some patients uncomfortable because they could see other people enjoying their time whilst they do not. Because of this, this participant suggested that the user should be asked ‘do you experience loneliness when you are in a crowd?’ (participant 2). If the answer is positive, the system should suggest activities which do not make the patient feel uncomfortable. Participant 9 also suggested having a system which can readapt according to the feedback received, so that it is always up to date.

Stepwise decision-making

Some participants gave suggestions on how to have patients decide wisely when using a self-help service. The most common suggestion was a two-step approach. Patients should first be given the chance to choose, for example, which activity to do or if calling someone. Afterwards, the system should ask if they did the action. If they did not, then the system should take action for them. This is how participant 3 explained this approach: ‘To combine both: to first make the suggestions: why don’t you call someone, who are you going to call? And then, after a while, the program asks ideally if the person called and if not, the system is going to call’ (participant 3). Moreover, if the system calls someone for the patients, the latter should agree about it.

However, a few participants said that if the patient starts using the service in a really difficult situation, the system should be the one to take decisions to activate him or her. Afterwards, the patient should be given the chance to decide. Nonetheless, according to participant 9, another way to help the patient is to suggest him or her to set goals and to ‘have the system reminding the user to do it until they do it’.

From our analysis of the first, quantitative study, one of the expectations that was met was that perceived relatedness can be a factor influencing the creation of therapeutic alliance and can motivate patients to use a specific technology. Literature shows that telling patients the reasons behind the suggestions given can make them feel relatedness (Cook & Doyle, 2002) and can enhance the creation of an alliance. Moreover, empathic features were part of the framework developed by Barazzone et al. (2012) on how to influence the creation of an alliance between a self-help computerised cognitive behavioural program and a person.

From the quantitative study, we also found that if patients are given the possibility to decide and an alliance is formed with the technology, patients would be more motivated to use the service and adhere to treatment. This is also following Goldberg, Davis, & Hoyt (2013) and Cohen Rodrigues et al. (2021). Adherence increases the chance that patients might get better (Cohen Rodrigues et al., 2021; Cook & Doyle, 2002) and, consequently, this finding could be important in the development of a self-help e-Mental health service to meet its purpose and for future research.

From the interviews, we found that a patient should always be able to choose, as it happens in a traditional therapeutic setting. This is also because, from literature, a paternalistic approach in the doctor-patient relationship, as it is enhanced in the prototype where decision-making is given to the system, is not beneficial (Grünloh, Myreteg, Cajander, & Rexhepi, 2018). Moreover, the feeling of control was considered by participants a fundamental factor. This is also in literature as perceived control is central in shared decision-making and self-determination theory to reinforce the relationship with the other entity (Elwyn et al., 2012; Quin & Dutton, 2005).

It is still unclear if people with depressive symptoms or are in a critical situation can make rational or good decisions. From the interviews, an argument to give decision-making to patients was that no one can make rational decisions when the level of arousal is high, not only people with depressive symptoms. Nonetheless, the research by Mazzocco, Masiero, Carriero, and Pravettoni (2019) on how emotions influence decision-making, highlighted that the capacity of people to make decisions also depends on other factors, like situation or the person's mental state. Therefore, people in a vulnerable situation with depressive symptoms may still have more difficulty making decisions than people with a high level of arousal but good mental health.

Nonetheless, interviewees also said that, in an e-Mental health service, it can still be possible to support people with more serious symptoms. This is also proved in shared decision-making which implies the involvement of the patient in making decisions by giving the right information and guidance (Elwyn et al., 2012). Moreover, in the interviews, more elaborated suggestions were also given. These were compared to literature and the following recommendations were formulated:

A three steps approach to decision-making. Firstly, people need to be made aware of the choices they have. Secondly, they need to be given specific information and explanations about these choices. Lastly, people need to be supported in their choices by highlighting the possible benefits. This approach is a combination of the three-stepwise process of decision-making by Elwyn et al. (2012) and the interviews’ results.
Having users set goals for themselves. Setting goals is also an intervention which is used in positive psychology. This is proved to enhance wellbeing and reduce depressive symptoms levels in people (Bolier et al., 2013).
Personalisation. In literature, personalisation means developing the technology to meet the needs of a single user in order to enhance a positive experience (Ameen, Tarhini, Reppel, & Anand, 2021). Nonetheless, as some experts said and according to the literature, the downside of this strategy could be that too much personalisation is overwhelming due to the many options and information that the user needs to choose from (Mugge, Schoormans, & Schifferstein, 2009). Nevertheless, as it was emphasized in the interview, if the technology is not developed according to different users’ characteristics and situations, it could be counterproductive.

Limitations and Strengths

The limitations only regard the quantitative study. The first is the small number of participants and the inequality of participants among groups. This might be a possible reason for the insignificant results. The inequality among groups could be due to the randomisation done via a web-based software which counted incomplete answers to the questionnaire as valid. This also means that people who were presented prototypes 3 and 4 are also the ones who dropped the most. We could presume that they did not like that the system was deciding for the user, to the point that they decided to quit. Another limitation of the first study may be that participants may not have been able to empathize enough with the situation presented in the prototypes. Consequently, they may not have had enough information to properly answer the questions.

A strength of this paper is that these studies were conducted with an already developed self-help e-Mental health service. This gave us the possibility to test the influence of decision-making and level of gravity on the other variables by using an already existing prototype. Another strength regards the variety of participants’ backgrounds and experiences in the second study. Due to this, different perspectives regarding decision-making were gathered. Additionally, many answers were the same for more than one participant, no matter the background. This makes the results of this study more valid and generalisable. The final strength was the combination of two studies to investigate the degree of decision-making in a self-help e-Mental health service. These allowed us to have insights from two different angles, and therefore, to give more complete answers on the topic.

For further research

A general implication for e-Mental health services is that users should be given the opportunity to decide and be in control of how to manage their health. As a follow-up of this paper, it is advised to investigate, with a similar study but with a bigger sample, which features can create a patient-technology alliance in a self-help e-Mental health service with therapeutic purposes.

Afterwards, to confirm that patient-technology alliance influences intention to use, a longitudinal study should be conducted to give time to participants to establish an alliance with a self-help e-Mental health service with therapeutic purposes.

Practical implications

During the interviews, some practical suggestions were made on how to introduce the right degree of decision-making in a self-help e-Mental health service. For instance, the three steps approach could be a way to calibrate the right degree of decision-making according to the situation and users’ needs and characteristics. This is linked to personalisation, which can be a motivator factor for people to use the technology. Zanker, Rook, and Jannach, (2019) categorise personalisation in three dimensions: the user interface, the content, and the interaction processes. Firstly, the personalisation of the user interface refers to the layout and design of the technology. Secondly, content personalisation is the information which is shown according to users’ preferences. Lastly, personalising the interaction processes means creating an algorithm which is capable of making decisions and approach users according to their feedback or needs (Chen, Chiang, & Storey as cited in Ameen et al., 2021).

By looking at which of these three dimensions are more important to personalise and at which level, it might be possible to reach the right level of personalisation. For instance, regarding the content and tailoring decision-making, users could decide which activity they would like to do or the information that they would like to see. Or personalisation could also be automatic: after getting feedback or inputs from the user (interaction processes personalisation), personalised messages and content could be presented.

Finally, relatedness could facilitate the creation of an alliance with the technology and, in turn, for intention to use. Accordingly, it is advised to developers of self-help e-Mental health services to implement features which could enhance relatedness, like having a system which can empathise with the user via notifications, or a virtual agent.

This paper investigated the degree of decision-making users should have when using a self-help e-Mental health service to create a therapeutic alliance and foster intention to use the technology. This was also done by considering the capacity of people with depressive symptoms or in critical situations to make decisions. Consequently, from these studies, we gave recommendations on how to facilitate decision-making in e-Mental Health services.

As it would happen in traditional therapeutic settings, we found that the creation of a patient-technology alliance is fundamental to promote usage. Nonetheless, it is still unknown which factors, together with relatedness and control, can influence the creation of an alliance between a patient and a technology. Moreover, it was confirmed that, in general, people should be given the chance to decide and have control over their health when using a self-help e-Mental Health service.

This research brings some answers on the degree of decision-making a person with depressive symptoms or in critical situations should have. We found that if people have difficulty deciding because of depressive symptoms or because they are in a critical situation, there are ways in which they can still be given the chance to decide.

The findings of this paper can be generally applied to self-help e-Mental health services which have a therapeutic purpose. As for future research, we recommend conducting more research on which features can be used to create an alliance between a person and a technology. Additionally, a general implication is that personalisation is needed to meet users’ needs, situations, and characteristics.

Ethics approval and consent to participate

All methods were performed in accordance with the relevant guidelines and regulations.

These studies were approved by the ethics committee of the Faculty of Behavioural, Management, and Sciences at the University of Twente, the Netherlands. Before starting the first study, participants were presented with an online informed consent form. By clicking on the 'next' button in this form, participants agreed on participating and gave consent to use the data collected for research purposes. Before starting the second study, participants received an informed consent form. By signing this form, they agreed to be interviewed and gave consent to use the data collected for research purposes.

Consent for publication

Not Applicable

Availability of data and materials

The data can be provided by the researcher Bartali V. (correspondence) upon reasonable request.

Competing interests

The authors declare to not have any competing interests.

Funding

Not Applicable for the studies of this paper

Authors’ contributions

Bartali V. designed, conducted the studies and analysed the data. Additionally, this author wrote the major part of this paper. Brandl L. helped with the design and analysis of the data. Karreman J. helped in the design of the studies, supervised the study, and contributed in the revision of the paper. Van Velsen L. was a major contributor in the designing of the study and the writing of the paper.

Acknowledgments

This research was carried out under the AAL Programme under project number AAL-2019-6-168-CP with funding by the European Union and the national funding agencies from the Netherlands, Portugal, and Switzerland: The Netherlands Organisation for Health Research and Development (ZonMW), Fundação para a Ciência e Tecnologia (FCT), and Innosuisse – Swiss Innovation Agency.

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No competing interests reported.

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Making good decisions is hard when you are at your worst: Investigating the degree of users’ decision-making in an e-Mental health service

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Decision-making and control

Decision-making in serious situations

Personalising decision-making

Stepwise decision-making

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