Users, usage, and user experience
Overall, the respondent demographic is a broad sample of the German population and confirms recent findings on mHealth/ DiGA usage in Germany, with around 1/3 of respondents having previous experience with mHealth apps and 5% with DiGAs [
55]. The observed link between lower age, higher electronic literacy and mHealth/ DiGA usage has also been proven to be significant by past research [
36,
56].
Looking into shorter usage times for DiGAs as compared to mHealth, it is important to note that the former have been available for a shorter period of time. Thus, it would be misleading to draw any conclusions as to user experience or adherence based on usage time alone. When comparing the reasons to stop using mHealth apps to DiGAs, it becomes clear that experiences differ depending on whether the app in question is a prescription-based DiGA or a non-prescribed mHealth app. The main reasons to stop mHealth app usage revolve around lack of need, helpfulness, and time constraints, with app quality and ease of use playing a minor role. Contrary to that, DiGA users mainly stop using their app due to lack of need or time and data security concerns, with app functionality or ease of use playing no role at all. This can be explained by the different motivations for using mHealth apps/ DiGAs: prescription-based DiGAs are used for a very specific need, sometimes for limited timeframes, for a diagnosed medical condition exclusively. The data input for DiGAs is often highly sensitive and personal, such as specific daily symptoms, increasing user concerns regarding data privacy and security. Compared to that, many mHealth apps are rather used to fulfill lifestyle needs and are often less targeted towards specific conditions (e.g. nutrition apps, cycle-tracking). As previous research shows[
41], when the medical need for mHealth apps is less pronounced, ease of use becomes more important.
It would be interesting to further investigate methods to increase patient adherence: a recent study [
57] found healthcare professionals to have the greatest potential to promote patient adherence to digital therapeutics, however, the correlation between patient adherence and app design remains relatively unexplored.
Willingness to use, prescription, certification, and willingness to pay
The overall high willingness to use mHealth apps/ DiGAs (76%) is a positive sign for app providers and proponents of digitalization in the healthcare space. The highest adoption rates can be expected for the ages 30 – 49, with no significant differences between genders. High self-assessed electronic literacy additionally supports willingness to use, stressing the need for patient education. It is interesting to note that physician prescription plays a minor role only, with 67% of respondents willing to use mHealth apps/ DiGAs that are not prescribed. This is particularly interesting considering the rather skeptical stance of German physicians regarding mHealth apps/ DiGAs – in a recent survey among German healthcare professionals, only 30.3% (393/1299) planned to prescribe DiGA [
14]; another found 31% believe digitization endangers the trust in the doctor-patient relationship [
55]. As app reimbursement is possible either through physician prescription or through approval by the health insurer, app providers could focus on strengthening relationships with insurers and building patient awareness to improve adoption rates.
On the other side, quality certification by a government or other entity is more important to patients (53% would only be willing to use mHealth apps/ DiGAs when their quality is certified by the government, see Fig.
4). This might be due to the fact that multiple applications for the same indication are available, and patients lack the specialized knowledge to identify the appropriate app with proven medical benefit. The need for more transparency and quality checks within mHealth has been highlighted by numerous studies [
8,
47,
48], especially in light of some cases of misleading statements by app providers and the lack of proven medical benefits of apps [
9]. This transparency can be provided by different stakeholders: e.g., in Germany, the Central Institute for the Provision of Health Care by Statutory Health Insurance (German: “Zentralinstitut für die kassenärztliche Versorgung in der Bundesrepublik Deutschland”) provided a KV app radar (a portal which synthesizes and aggregates app store reviews and where both users of mHealth apps/ DiGAs and physicians can additionally review and comment on applications). Additional actions could include introducing an open-source directory of app evidence or standardized facts labels for health apps.
Despite the willingness to pay out of pocket for mHealth apps/ DiGAs being relatively low (only one in four participants would be willing to pay, on average), it is interesting to note that it increases for users experienced with mHealth apps and then again for users experienced with DiGAs. This is particularly important in light of the current discussions on pricing dynamics for DiGAs in Germany: currently, app developers set prices for DiGAs through negotiations with the GKV-SV (“Gesetzlicher Krankenversicherungs Spitzenverband”, German statutory health insurance association), with price limits sets based on comparators within one indication group (prices being considered too high if they exceed 80% of comparators). Although there are few published studies on actual mHealth app/ DiGA usage and adherence, the GKV-SV reported that between September 2020 – 2021, only 80% of prescribed apps were activated [
58]. This has led to increased calls from payors to adapt the current reimbursement model to incentivize adherence – be it through the inclusion of value-based elements, co-pay for patients, or other alternatives. Existing research points toward an inverse association between co-pay and medication adherence for pharmaceutical therapies [
59]; however, the effects of co-pay on adherence to mHealth apps yet remain to be explored. A recent study found a positive relationship between higher up-front costs and health club attendance [
60], mainly due to higher perceived loss for non-attendance.
Predictors of mHealth usage
According to our findings, only performance expectancy, self-efficacy and attitude have a significant effect on the intention to use. Previous research has consistently identified performance expectancy as one of the core predictors of intention to use mHealth apps [
22,
28‐
36]. A recent meta-analysis of 67 studies on patient acceptance models within the mHealth space [
40] confirms PE as the second-strongest indicator for behavioral intention (β = 0.41); another finds that especially compared with fitness/ wellness applications, PE is a stronger indicator for use within purely medical apps [
41]. This highlights the need for mHealth app developers and regulators to focus on clearly communicating the expected benefits of mHealth app/ DiGA usage first and foremost.
Increasingly, more recent studies have focused on self-efficacy as a significant predictor of intention to use [
18,
31]. Improving electronic literacy and health education, which have been identified as determinants of self-efficacy [
18] are thus key to boosting mHealth/ DiGA adoption rates. This could be done by traditional methods (training, demonstrations, free trials), but also through targeted marketing communication showcasing effortless usage of mHealth services across age groups and demographics. Considering the situation in the German market, ensuring health care professionals’ remuneration for training patients is key. Under the current system, despite being the key access point [
14] to prescription-based DiGAs, remuneration for initial DiGA prescription amounts to only 2€ per patient (GOP 01,470) and 7.21 € for progress monitoring (GOP 01,471/ 01,472 and “Pauschale” 86,700).
The last significant predictor of intention to use identified in our model is attitude. This latent construct is based on trust, technology resistance, and other personal impediments (such as belief about the ability to integrate usage into daily routines) and was confirmed through EFA. Despite the rather loose definition of this dimension, previous research indicates a strong relationship between pre-conceived notions regarding mHealth and the intention to use [
18,
30,
40]. A recent study among 2011 German citizens [
55] found that almost 1 in 4 respondents believes technology creates more problems than it solves, pointing toward a high overall technology skepticism among the German population. It is important to note here that as previous research points out, technology skepticism tends to be country-specific [
61], meaning the results obtained might not translate to different geographies. Nonetheless, a key takeaway for regulators, providers of mHealth apps/ DiGAs, and other stakeholders involved in mHealth adoption is the importance of addressing negative beliefs early on.
Considering the factors identified as not significant on intention to use mHealth technologies/ DiGAs, perhaps the only puzzling aspect is the non-significance of data security and quality concerns. Germany tends to be seen as one of the countries with the strongest attitudes toward data privacy and protection [
62], translating to particularly restrictive GDPR. The non-significance of this dimension could be explained by two factors: first, mHealth technology remains relatively new, meaning most respondents in our sample are not experienced in using it. This could translate to a lack of importance placed on data security as users are not aware of which data would be collected and thus do not consider it worthy of protection. Second, users with existing conditions might place more weight on the perceived benefit of digital interventions and be less concerned with data privacy, as mHealth solutions cover a previously unmet need. Additionally, a cross-sectional survey of 1003 adults in Germany revealed a high willingness to share health-related data for research purposes [
63] during the Covid-19 pandemic, pointing toward changing attitudes toward data privacy.
Looking into the other non-significant UTAUT2 factors (effort expectancy, social influence, facilitating conditions, and hedonic motivation), previous research is ambiguous as to their impact on intention to use. Effort expectancy is in some instances considered significant for older populations only [
43] and often found to not affect the intention to use [
30,
32,
34,
44]. This could be explained by the increased penetration of mobile technologies across all age groups and demographics, further strengthened by the Covid-19 pandemic.
Social influence has been less extensively researched within the mHealth space. Some papers indicate a weak positive relationship [
22,
33,
34,
43,
44] between the degree to which an individual perceives others believe they should use a technology and the intention to use it. However, this could be explained by most of these studies being conducted in Asian geographies, where the impact of social influence is considered greater due to higher power distance and a less individualistic culture [
64].
Facilitating conditions gain importance with increased technology complexity, which leads to a higher need for support infrastructure. In line with previous findings [
22,
43], mobile apps have become increasingly integrated into daily routines and smartphone usage has reached sufficient penetration to negate the importance of such support systems; with users placing value on self-efficacy (i.e., the extent to which they believe themselves to be able to perform a behavior that leads to a valued outcome) instead on the ability to simply use mHealth technologies. This is further supported by the positive ratings respondents give for the ease to obtain and receiving reimbursement for a prescription-based DiGA (means of 3.4 and 3.3 out of 5, see Fig.
4).
As to hedonic motivation, there is discord regarding the inclusion of this dimension in the context of health behavior, as this usually is not connected to pleasurable experiences. The primary outcome is not geared toward entertainment, but positive health outcomes. Despite some existing research indicating a weak positive impact on intention to use [
46], this ambivalence could help explain the lack of a significant relationship between hedonic motivation and intention to use mHealth apps/ DiGAs in our model.
Limitations & further research
Although the present study reveals important findings, it has several limitations, the first of which is selection bias, as common for web-based research. This may have resulted in a bias towards populations with higher electronic literacy and exacerbated self-selection bias (skew towards respondents with higher interest in mHealth topics). Second, social influence and hedonic motivation were found not to have a significant effect on the intention to use mHealth, which should be re-examined through further research. Additionally, it would be interesting to examine the effect of price value on intention to use, as users’ awareness of mHealth cost increases with rising mHealth adoption rates.
As opposed to the original UTAUT study, which was a longitudinal study, this research only measures the respondents’ perceptions and intention to use at a single time point. Further research examining perceptions and intention to use over time would be required, especially given the large impact Covid-19 had on patient attitudes toward digital therapies, data sharing, and mHealth.
Finally, although data was collected from a broad population sample in Germany, we cannot claim validity in other countries.
Conclusion
In conclusion, acceptance of mHealth interventions in Germany is high, with age, high electronic literacy, and prior experience being predictors of the intention to use. Performance expectancy, self-efficacy of the app, and attitude are major levers in improving mHealth adoption, as they have a significant effect on the intention to use. A key takeaway for regulators, providers of mHealth apps/ DiGAs, and other stakeholders involved in mHealth adoption is the importance of addressing negative beliefs early on, targeted communication around effortless usage of mHealth services across age groups and demographics, and focusing on highlighting expected benefits of mHealth app/ DiGA usage.