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Detecting Receptivity for mHealth Interventions in the Natural Environment

2021-06-15 , Mishra, Varun , Künzler, Florian , Kramer, Jan-Niklas , Fleisch, Elgar , Kowatsch, Tobias , Kotz, David

Just-In-Time Adaptive Intervention (JITAI) is an emerging technique with great potential to support health behavior by providing the right type and amount of support at the right time. A crucial aspect of JITAIs is properly timing the delivery of interventions, to ensure that a user is receptive and ready to process and use the support provided. Some prior works have explored the association of context and some user-specific traits on receptivity, and have built post-study machine-learning models to detect receptivity. For effective intervention delivery, however, a JITAI system needs to make in-the-moment decisions about a user’s receptivity. To this end, we conducted a study in which we deployed machine-learning models to detect receptivity in the natural environment, i.e., in free-living conditions. We leveraged prior work regarding receptivity to JITAIs and deployed a chatbot-based digital coach – Ally – that provided physical-activity interventions and motivated participants to achieve their step goals. We extended the original Ally app to include two types of machine-learning model that used contextual information about a person to predict when a person is receptive: a static model that was built before the study started and remained constant for all participants and an adaptive model that continuously learned the receptivity of individual participants and updated itself as the study progressed. For comparison, we included a control model that sent intervention messages at random times. The app randomly selected a delivery model for each intervention message. We observed that the machine-learning models led up to a 40% improvement in receptivity as compared to the control model. Further, we evaluated the temporal dynamics of the different models and observed that receptivity to messages from the adaptive model increased over the course of the study.

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The social meaning of steps: user reception of a mobile health intervention on physical activity

2020-02 , Presset, Bastien , Kramer, Jan-Niklas , Kowatsch, Tobias , Ohl, Fabien

In recent years, mobile health (mHealth) technologies have received increasing attention from industry and researchers. Such technologies have been the focus of both criticism and high expectations. In this paper, we analyze the integration of mHealth tools in everyday life. Insights into the actual use of such tools have empirical importance and could contribute to our theoretical understanding of mHealth technologies. Our research is based on 23 interviews with the participants of a smartphone-based mobile health intervention aimed at increasing physical activity. We followed the principles of grounded theory during data collection and our analysis is framed by the domestication approach. Our results reveal that the intervention design can result in the participants feeling ill-represented by the reductive nature of the data they generate. The results also reveal the inadequacy between biomedical standards and the social contexts of use. In addition, we describe how middle-class users perceive step-counting through the prism of a moralizing ethos of self-responsibility. Our research has practical implications for the developers and participants of mHealth interventions and theoretical implications regarding mHealth as a societal practice. We also suggest that mHealth-related public policies may fail to reach certain population groups, namely those who do not share the values that surround those technologies and their uses.

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Exploring the State-of-Receptivity for mHealth Interventions

2019-12 , Künzler, Florian , Varun, Mishra , Kramer, Jan-Niklas , Kotz, David , Fleisch, Elgar , Kowatsch, Tobias

Recent advancements in sensing techniques for mHealth applications have led to successful development and deployments of several mHealth intervention designs, including Just-In-Time Adaptive Interventions (JITAI). JITAIs show great potential because they aim to provide the right type and amount of support, at the right time. Timing the delivery of a JITAI such as the user is receptive and available to engage with the intervention is crucial for a JITAI to succeed. Although previous research has extensively explored the role of context in users’ responsiveness towards generic phone notifications, it has not been thoroughly explored for actual mHealth interventions. In this work, we explore the factors affecting users’ receptivity towards JITAIs. To this end, we conducted a study with 189 participants, over a period of 6 weeks, where participants received interventions to improve their physical activity levels. The interventions were delivered by a chatbot-based digital coach ś Ally ś which was available on Android and iOS platforms. We define several metrics to gauge receptivity towards the interventions, and found that (1) several participant-specific characteristics (age, personality, and device type) show significant associations with the overall participant receptivity over the course of the study, and that (2) several contextual factors (day/time, phone battery, phone interaction, physical activity, and location), show significant associations with the participant receptivity, in-the-moment. Further, we explore the relationship between the effectiveness of the intervention and receptivity towards those interventions; based on our analyses, we speculate that being receptive to interventions helped participants achieve physical activity goals, which in turn motivated participants to be more receptive to future interventions. Finally, we build machine-learning models to detect receptivity, with up to a 77% increase in F1 score over a biased random classifier.

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Effects of Charitable Versus Monetary Incentives on the Acceptance of and Adherence to a Pedometer-Based Health Intervention: Study Protocol and Baseline Characteristics of a Cluster-Randomized Controlled Trial

2016-09 , Kowatsch, Tobias , Kramer, Jan-Niklas , Kehr, Flavius , Wahle, Fabian , Elser, Niklas , Fleisch, Elgar

Background: Research has so far benefited from the use of pedometers in physical activity interventions. However, when public health institutions (eg, insurance companies) implement pedometer-based interventions in practice, people may refrain from participating due to privacy concerns. This might greatly limit the applicability of such interventions. Financial incentives have been successfully used to influence both health behavior and privacy concerns, and may thus have a beneficial effect on the acceptance of pedometer-based interventions. Objective: This paper presents the design and baseline characteristics of a cluster-randomized controlled trial that seeks to examine the effect of financial incentives on the acceptance of and adherence to a pedometer-based physical activity intervention offered by a health insurance company. Methods: More than 18,000 customers of a large Swiss health insurance company were allocated to a financial incentive, a charitable incentive, or a control group and invited to participate in a health prevention program. Participants used a pedometer to track their daily physical activity over the course of 6 months. A Web-based questionnaire was administered at the beginning and at the end of the intervention and additional data was provided by the insurance company. The primary outcome of the study will be the participation rate, secondary outcomes will be adherence to the prevention program, physical activity, and health status of the participants among others. Results: Baseline characteristics indicate that residence of participants, baseline physical activity, and subjective health should be used as covariates in the statistical analysis of the secondary outcomes of the study. Conclusions: This is the first study in western cultures testing the effectiveness of financial incentives with regard to a pedometer-based health intervention offered by a large health insurer to their customers. Given that the incentives prove to be effective, this study provides the basis for powerful health prevention programs of public health institutions that are easy to implement and can reach large numbers of people in need.

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Long-term effectiveness of mHealth physical activity interventions: a systematic review and meta-analysis of randomized-controlled trials

2021-04-30 , Mönninghoff, Annette Barbara Isabell , Kramer, Jan-Niklas , Hess, Alexander Jan , Ismailova, Kamila , Teepe, Gisbert , Tudor Car, Lorainne , Müller-Riemenschneider, Falk , Kowatsch, Tobias

Background: mHealth interventions can increase physical activity (PA), but their long-term impact is not well understood. The increasing number of primary studies reporting long-term follow-up measurements supports a meta-analysis of this evidence. Objective: This systematic review and meta-analysis aimed to understand the immediate and long-term impact of mHealth interventions on PA. The secondary objective was to explore potential effect moderators (population type, intervention design, control group type). Methods: We performed this systematic review according to the Cochrane and PRISMA guidelines. We searched PubMed, the Cochrane Library, SCOPUS and PsychINFO in July 2020. Eligible studies included randomized-controlled trials of mHealth interventions targeting PA as a primary outcome in adults. Eligible outcome measures were walking, moderate to vigorous PA (MVPA), total PA (TPA), and energy expenditure (EE). Where reported, we extracted data for three time points (ie, end of intervention, follow-up ≤6 months, follow-up >6 months). To understand effect moderators, we performed subgroup analyses by population, intervention design, and control group type. Results were summarized using random-effects meta-analysis. Risk of bias was assessed using the Cochrane Collaboration’s tool. This review is registered with PROSPERO (CRD42019124716). Results: Of the 2 828 identified citations, 117 studies were included. These studies reported on 21 118 participants with a mean age of 52.03 years (SD 14.14), of whom 59% were female. mHealth interventions significantly increased PA across all four outcome measures at the end of intervention (walking standardized mean difference 0.46, 0.36 to 0.55; p<0.001; MVPA 0.28, 0.21 to 0.35; p<0.001; TPA 0.34, 0.20 to 0.47; p<0.001; EE 0.44, 0.13 to 0.75; p=0.01). Only 33 studies reported short-term and eight studies reported long-term follow-up measurements. Effects were sustained short-term for walking (0.26, 0.09 to 0.42; p=0.002), MVPA (0.20, 0.05 to 0.35; p=0.008), and TPA (0.53, 0.13 to 0.93; p=0.009). Long-term, effects were also sustained for walking (0.25, 0.10 to 0.39; p=0.001) and MVPA (0.19, 0.11 to 0.27; p<0.001). We found study population to be an effect moderator, with higher effect scores in sick and at-risk populations. PA was increased both in scalable and non-scalable mHealth intervention designs and regardless of the control group type. The risk of bias was rated high in 94 of 117 studies. In addition, heterogeneity was significant and substantial across outcome measures, resulting in low to very low quality of evidence. Conclusions: mHealth interventions can foster small to moderate increases in PA. Effects are maintained long-term, but effect size decreases over time. The results encourage the use of mHealth interventions in at-risk and sick populations, and support the use of scalable mHealth intervention designs to affordably reach large populations. However, given low evidence quality and high heterogeneity, further methodologically rigorous studies are warranted to evaluate the long-term effects of mHealth interventions.

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Investigating Intervention Components and Exploring States of Receptivity for a Smartphone App to Promote Physical Activity: Protocol of a Microrandomized Trial

2019 , Kramer, Jan-Niklas , Künzler, Florian , Mishra, Varun , Presset, Bastien , Kotz, David , Smith, Shawna , Scholz, Urte , Kowatsch, Tobias

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Using Feedback to Promote Physical Activity: The Role of the Feedback Sign

2017 , Kramer, Jan-Niklas , Kowatsch, Tobias

Background: Providing feedback is a technique to promote health behavior that is emphasized by behavior change theories. However, these theories make contradicting predictions regarding the effect of the feedback sign—that is, whether the feedback signals success or failure. Thus, it is unclear whether positive or negative feedback leads to more favorable behavior change in a health behavior intervention. Objective: The aim of this study was to examine the effect of the feedback sign in a health behavior change intervention. Methods: Data from participants (N=1623) of a 6-month physical activity intervention was used. Participants received a feedback email at the beginning of each month. Feedback was either positive or negative depending on the participants’ physical activity in the previous month. In an exploratory analysis, change in monthly step count averages was used to evaluate the feedback effect. Results: The feedback sign did not predict the change in monthly step count averages over the course of the intervention (b=−84.28, P=.28). Descriptive differences between positive and negative feedback can be explained by regression to the mean. Conclusions: The feedback sign might not influence the effect of monthly feedback emails sent out to participants of a large-scale physical activity intervention. However, randomized studies are needed to further support this conclusion. Limitations as well as opportunities for future research are discussed.

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Which Components of a Smartphone Walking App Help Users to Reach Personalized Step Goals? Results From an Optimization Trial

2020-03-17 , Kramer, Jan-Niklas , Künzler, Florian , Mishra, Varun , Smith, Shawna N. , Kotz, David F. , Scholz, Urte , Fleisch, Elgar , Kowatsch, Tobias

Background The Assistant to Lift your Level of activitY (Ally) app is a smartphone application that combines financial incentives with chatbot-guided interventions to encourage users to reach personalized daily step goals. Purpose To evaluate the effects of incentives, weekly planning, and daily self-monitoring prompts that were used as intervention components as part of the Ally app. Methods We conducted an 8 week optimization trial with n = 274 insurees of a health insurance company in Switzerland. At baseline, participants were random-ized to different incentive conditions (cash incentives vs. charity incentives vs. no incentives). Over the course of the study, participants were randomized weekly to different planning conditions (action planning vs. coping planning vs. no planning) and daily to receiving or not receiving a self-monitoring prompt. Primary outcome was the achievement of personalized daily step goals. Results Study participants were more active and healthier than the general Swiss population. Daily cash incentives increased step-goal achievement by 8.1%, 95% confidence interval (CI): [2.1, 14.1] and, only in the no-incentive control group, action planning increased step-goal achievement by 5.8%, 95% CI: [1.2, 10.4]. Charity incentives, self-monitoring prompts, and coping planning did not affect physical activity. Engagement with planning interventions and self-monitoring prompts was low and 30% of participants stopped using the app over the course of the study. Conclusions Daily cash incentives increased physical activity in the short term. Planning interventions and self-monitoring prompts require revision before they can be included in future versions of the app. Selection effects and engagement can be important challenges for physical-activity apps. Clinical Trial Information This study was registered on ClinicalTrials.gov, NCT03384550.

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A Cluster-Randomized Trial on Small Incentives to Promote Physical Activity

2019-01-17 , Kramer, Jan-Niklas , Tinschert, Peter , Scholz, Urte , Fleisch, Elgar , Kowatsch, Tobias

Introduction: There has been limited research investigating whether small financial incentives can promote participation, behaviour change, and engagement in physical activity promotion programs. This study evaluates the effects of two types of small financial incentives within a physical activity promotion program of a Swiss health insurance company. Study Design: Three-arm cluster-randomized trial comparing small personal financial incentives and charity financial incentives (10 Swiss Francs, equal to $10.4) for each month with an average step count of at least 10,000 steps per day) to control. Insurees' federal state of residence was the unit of randomization. We collected data in 2015 and completed the analyses in 2018. Setting/participants: We invited German-speaking insurees of a large health insurer in Switzerland. Invited insurees were aged ≥ 18 years, enrolled in complementary insurance plans and registered on the insurer's online platform. Main outcome measures: Primary outcome was the participation rate. Secondary outcomes were steps per day, participant days that more than 10,000 steps were achieved and non-usage attrition over the first three months of the program. Results: Participation rate was 5.94% in the personal financial incentive group (OR: 1.96; 95% CI: 1.55 to 2.49) and 4.98% in the charity financial incentive group (OR: 1.59; 95% CI: 1.25 to 2.01) compared to 3.23% in the control group. At the start of the program, the charity financial group had a 12% higher chance to walk 10,000 steps per day than the control group (OR: 1.68; 95% CI: 1.23 to 2.30), but this effect dissipated after three months. Steps per day and non-usage attrition did not differ significantly between the groups. Conclusions: Small personal and charity financial incentives can increase participation in physical activity promotion programs. Incentives may need to be modified in order to prevent attrition and promote behaviour change over a longer period of time.

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The Potential of Mobile Apps for Improving Asthma Self-Management: A Review of Publicly Available and Well-Adopted Asthma Apps

2017 , Tinschert, Peter , Jakob, Robert , Barata, Filipe , Kramer, Jan-Niklas , Kowatsch, Tobias

Background: Effective disease self-management lowers asthma’s burden of disease for both individual patients and health care systems. In principle, mobile health (mHealth) apps could enable effective asthma self-management interventions that improve a patient’s quality of life while simultaneously reducing the overall treatment costs for health care systems. However, prior reviews in this field have found that mHealth apps for asthma lack clinical evaluation and are often not based on medical guidelines. Yet, beyond the missing evidence for clinical efficacy, little is known about the potential apps might have for improving asthma self-management. Objective: The aim of this study was to assess the potential of publicly available and well-adopted mHealth apps for improving asthma self-management. Methods: The Apple App store and Google Play store were systematically searched for asthma apps. In total, 523 apps were identified, of which 38 apps matched the selection criteria to be included in the review. Four requirements of app potential were investigated: app functions, potential to change behavior (by means of a behavior change technique taxonomy), potential to promote app use (by means of a gamification components taxonomy), and app quality (by means of the Mobile Application Rating Scale [MARS]). Results: The most commonly implemented functions in the 38 reviewed asthma apps were tracking (30/38, 79%) and information (26/38, 68%) functions, followed by assessment (20/38, 53%) and notification (18/38, 47%) functions. On average, the reviewed apps applied 7.12 of 26 available behavior change techniques (standard deviation [SD]=4.46) and 4.89 of 31 available gamification components (SD=4.21). Average app quality was acceptable (mean=3.17/5, SD=0.58), whereas subjective app quality lied between poor and acceptable (mean=2.65/5, SD=0.87). Additionally, the sum scores of all review frameworks were significantly correlated (lowest correlation: r36=.33, P=.04 between number of functions and gamification components; highest correlation: r36=.80, P<.001 between number of behavior change techniques and gamification components), which suggests that an app’s potential tends to be consistent across review frameworks. Conclusions: Several apps were identified that performed consistently well across all applied review frameworks, thus indicating the potential mHealth apps offer for improving asthma self-management. However, many apps suffer from low quality. Therefore, app reviews should be considered as a decision support tool before deciding which app to integrate into a patient’s asthma self-management. Furthermore, several research-practice gaps were identified that app developers should consider addressing in future asthma apps.