As of 2020, approximately 7.2% of the total working population was unemployed around the globe [1]. This condition was exacerbated by the COVID-19 pandemic. In Germany, the rate of unemployment rose from 5.3% in prepandemic January 2020 to 6.3% in January 2021 [2,3]. Although the unemployment rate dropped again to 5.4% in January 2022, approximately 2.5 million people of the German population were unemployed, of which 40.2% were without work for >12 months [4]. There is striking evidence that unemployment is associated with poor health outcomes (reviewed in the study by Jin et al [5]). In 2011, Roelfs et al [6] meta-analyzed that all-cause mortality was 63% higher in the unemployed population than in the working population. In addition to increases in mental health diseases, such as depression, anxiety disorders [7], psychoses [8], and substance abuse [9], physical diseases also occur more often in the unemployed than in the employed [10]. These include cancer [11] and cardiovascular events [12,13]. deBoer et al [11] compared participants who had cancer in recent years with cancer-free control participants and found statistically significant higher unemployment rates in the former, particularly in those with cancers of the gastrointestinal system (relative risk of 1.41). Moreover, Gallo et al [13] showed that the unemployed had 2.4- and 2.5-fold higher risks of stroke and myocardial infarction (MI), respectively. In this regard, the risk of MI increases with increasing length of unemployment. While the relative risk of MI was 1.49 times higher in participants who were unemployed for up to 8 months than in the employed participants, it was 3.08 times higher in those who were unemployed for >16 months [14]. Gastrointestinal cancer and cardiovascular events are also known to be influenced by poor nutrition (reviewed in the study by Wei et al [15]) and physical activity behavior (reviewed in the study by Lacombe et al [16]), which are generally common among the unemployed [17,18].

Although there is no difference in health-related behavior between long-term unemployed persons in urban areas and those in rural areas, the latter have more difficult access to health promotions from, for example, primary care physicians or offers from health insurances because of a lack of financial resources, poorly developed public transportation infrastructure, and thus lower mobility [19]. This shows that strategies to reach the long-term unemployed in rural areas are especially in need. Therefore, interventions designed in a digital format might be promising strategies to promote the health of the long-term unemployed in rural areas.

In general, digital health interventions (DHIs), particularly those on mobile devices, have been shown to be effective tools for inducing health-related behavior changes [20-23]. Moreover, conversational agents (CAs), computer programs that simulate conversations, are being increasingly used as DHIs [24], including behavior change apps to promote healthy eating [25] or physical activity [26]. However, DHIs require access to technology and sufficient knowledge to use it (digital literacy). Social health inequalities may contribute to a digital gap, as next to older age and being male, lower level of education and lower annual income are associated with a lower likelihood of owning a smartphone [27]. By contrast, Rhoades et al [28] showed that more than half of the homeless population owns smartphones and uses the internet daily. Moreover, Reinwand et al [29] reported that unemployed persons in a randomized controlled study used the intervention more frequently than employed persons, probably because it was time consuming and they had more time to use it.

The aim of this study is to elucidate whether a DHI, conceived for use on mobile devices, can improve the nutritional and physical activity behaviors of long-term unemployed in the rural areas of Germany. Therefore, a customized 9-week intervention is conceptualized and implemented in a mobile app and will be tested in a randomized controlled trial (RCT). The effectiveness of the intervention will be assessed by questionnaire assessment.

The intervention design was planned in accordance with the intervention mapping approach [30]. The intervention content and the mobile app were designed through a user-centered approach including the needs assessment [31], a participatory design workshop with long-term unemployed (N=7), and a pretest for formative evaluation.

The intervention content is based on the transtheoretical model (TTM) for health behavior change by Prochaska et al [32,33]. The 9 weeks of the conceptualized intervention are adapted to the phases of the TTM as follows: week 1, “precontemplation”; week 2, “contemplation”; week 3, “preparation”; weeks 4 to 7, “action”; and weeks 8 to 9, “maintenance.” Before the intervention, the participants were assigned to week 1 (“precontemplation”), week 2 (“contemplation”), or week 3 (“preparation”) depending on their initially reported nutritional or physical activity behavior. The initially reported behavior was assessed by answering the following questions: “Do you regularly eat a balanced diet for example, foods such as fresh fruits and vegetables several times a week, or whole grains as well as dairy products sometimes and less sausage and meat?” or “Do you exercise regularly, for example, walking, biking, swimming, or going to the grocery store, that is, for at least 30 minutes each, at least 5 days per week?” If the questions were answered with “no,” the participant will receive the following selections: (a) “...and I do not think about eating a more balanced diet/exercising more,” (b) “...but I do think about eating a more balanced diet/exercising more,” or (c) “...but I will start eating a more balanced diet/exercising more.” The participant will (1) start in week 1 if the initial question is answered with “no” and then (a) is selected, (2) start in week 2 if the initial question is answered with “no” and then (b) is selected, or (3) start in week 3 if the initial question is answered with “yes” or with “no” and then (c) is selected. Therefore, the actual intervention duration varies between 7 and 9 weeks. The TTM stage is measured only once at the beginning of the intervention. However, the participants reach the next TTM stage only when they proceed to participate in the intervention. The intervention will be adaptive, and the participants must initially decide whether they want to promote their nutritional or physical behavior. Furthermore, in week 3 (“preparation”), they can choose to pursue only one of the following aims: (n1) change drinking habits, (n2) eat more fruits and vegetables, (n3) eat more whole grain products, (pa1) increase step count, (pa2) strengthen muscles, or (pa3) improve endurance, depending on whether they initially chose to promote nutritional (n1-3) or physical activity (pa1-3) behavior change. After the completion of the chosen aim, they will have the opportunity to select a new one. The intervention contents are designed in accordance with official national and international nutritional [34,35] and physical activity [36,37] recommendations. Detailed information about the intervention contents is provided in Tables 1 and 2. The language of the intervention is German.

In addition to the theoretical model of behavior change, behavior change techniques (BCTs) are used. BCTs are active components that aim to change behaviors as a part of an intervention [38,39]. Here, the appropriate BCTs are selected in relation to the individual phases of the TTM (Tables 1 and 2).

The DHI is implemented in a mobile app using the MobileCoach intervention platform [40,41], an open-source platform for the design and deployment of DHIs based on rule-based CA. Here, the participant chooses 1 of 4 coaches before the intervention, and the intervention information is provided as text messages, graphics, and videos or by gamification and storytelling approaches by emulating human-like interactions (Figure 1). The participants receive new intervention content once a day (at midnight). Additional content is sent only after the previous day’s content is completed. Push notifications are sent once a day to motivate the participants to complete the intervention content.

The intervention content and mobile app were evaluated in a pretest [42]. In the pretest, the long-term unemployed participants (N=12) were asked to test the mobile app for 9 weeks, and feedback was recorded every 3 weeks. The intervention content and mobile app were modified as suggested by the participants.

This study was approved by the ethics committee of the State Medical Chamber of Baden-Wuerttemberg, Germany (number F-2019-106), and registered in the German Clinical Trials Register (DRKS00024805; registered on February 22, 2022). Written consent is obtained from all the participants before enrollment in this study.

In this study protocol, a customized DHI to promote the nutritional and physical activity behaviors of long-term unemployed is presented. The mobile app will be tested in an RCT with long-term unemployed volunteers in a parallel-armed design in 2022.

Improving nutrition literacy and promoting physical activity are suggested to be successful strategies for improving the health of long-term unemployed. However, health interventions for the unemployed are scarce [55]. Moreover, they often fail to improve physical health [56,57], suggesting that either (1) the intervention design of these studies was inappropriate or (2) the intervention medium was not suitable for the targeted group. Regarding the intervention design, the intervention concept in this study is based on the TTM. The TTM is a stage-based behavior change model, which was first described for the cessation of smoking [33] and was proven to be effective by a review of Spencer et al [58]. The TTM has also been successfully used in behavior change interventions regarding nutritional (reviewed in the study by Nakabayashi et al [59]) and physical activity behaviors (reviewed in the study by Adams and White [60]), suggesting that the TTM is an effective model to change the nutritional and physical activity behaviors of long-term unemployed. However, the effectiveness in changing the behaviors is also discussed controversially [61-63], especially in regard to long-term behavior changes [60].

The intervention was conceived in the form of a DHI, which has been proven earlier to be a successful intervention medium for promoting healthier eating [25] and physical activity choices [26] in vulnerable groups. However, to date, there is no DHI specifically designed to meet the needs of long-term unemployed. Although it has been shown that mobile phone ownership is not considered a serious barrier to participate in DHIs, even for unemployed individuals [64], the accessibility to a DHI, and therefore its effectiveness, can be improved by considering support for older devices, the possibility of offline use, low digital literacy requirements, and the involvement of the targeted people in the development process [65]. Thus, the current intervention addresses health-related topics identified by the participative input of the long-term unemployed in Germany through interviews and workshops [31]. The conceived intervention is then realized by low-threshold informational content, supported by graphical files, links, and videos. A key point of the DHI is the use of a rule-based CA, which emulates an informal human-like interaction and is suggested to be encouraging and motivating [24]. Another strength of the present intervention is the blended use of an easily accessible DHI in the form of a mobile app in combination with classical face-to-face appointments. Face-to-face interventions have been proven to be more effective than interventions designed solely in a digital format [66-68]. However, DHIs are easily accessible to a wide range of the population and are associated with lower financial and time commitments, as they can be used at any time at home. Blended interventions, the combination of DHIs with face-to-face interventions, have been used frequently for the treatment of mental disorders in adults (reviewed in the study by Erbe et al [69]) and are discussed to be feasible and more effective than stand-alone interventions in the treatment of substance abuses, as they significantly reduce the participant dropout rates [70-73]. In the planned study, the conception of the intervention in a blended design is expected to decrease the participant dropout and, therefore, increase the intervention effectiveness. To minimize an increased effort to participate in face-to-face appointments, they will be located in the social sector societies near to the long-term unemployed in the rural areas of Germany, where they have, in general, daily face-to-face appointments with the employees of the social societies. However, the recruitment of social societies that are willing to assist the study by providing long-term unemployed participants and the infrastructure to carry out face-to-face appointments might be difficult, especially under the complicated circumstances due to the COVID-19 pandemic (eg, hygiene and distance rules).

This study has some limitations. First, we did not adapt the intervention according to the multiphase optimization strategy, which maximizes the translation of research into effective practice [74]. However, we did run a pretest while developing the intervention to ensure the feasibility of the blended intervention. Second, the effectiveness of the intervention is based on questionnaires and, therefore, might be underlying a reporting bias. Further studies might be necessary to measure physiological adaptation to a healthier lifestyle, for example, by measures of health-related biomarkers, such as markers for metabolic diseases [75] or cardiovascular health [76]. Third, as the participants receive incentives, they might have been overly motivated to use the mobile app. To counteract a potential overestimation of the app use through the incentives, we instructed the participants that the incentives are only tied to the completion of the questionnaire and not to the use of the mobile app. Finally, although in previously conducted interviews (N=20), most of the long-term unemployed in the rural areas of southwestern Germany (75%) had smartphones and regularly used mobile apps on their devices (Mages-Torluoglu, J, unpublished data, February 2020), some may not and, therefore, cannot participate in this study. However, the ownership of smartphone devices is still rapidly growing [77]. Thus, we feel that a DHI with face-to-face appointments is a promising strategy to improve the health of long-term unemployed in the rural areas of Germany. At this point, it must also be emphasized that behavior change strategies may only address the secondary effects of long-term unemployment. In general, improving living conditions (eg, better financial support or social inclusion) may be a more comprehensive strategy for promoting health among the unemployed.

The intervention presented in this study is tailored to the needs of the long-term unemployed and is implemented in a blended intervention consisting of a DHI based on the interaction with an internet-based CA and additional face-to-face interactions. The effectiveness of the blended intervention in promoting improved nutritional and physical activity behaviors of long-term unemployed participants will be evaluated in a protocolized RCT and measured by questionnaire assessment. If we could show that the health intervention designed for the study described above is effective in promoting beneficial choices regarding nutrition (eg, eating more fruits and vegetables) and physical activity (eg, improving endurance), this would be a first and an important step in promoting the general health of the unemployed. The blended intervention is intended for implementation in community health systems and can thus contribute to an improved health status of the German population in general.