Jan-Niklas Kramer

Jan-Niklas Kramer

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Kramer

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Jan-Niklas

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Detecting Receptivity for mHealth Interventions in the Natural Environment

(ACM, 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.

Type: journal article

Journal: The Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)

Volume: 5

Issue: 2

URL: https://doi.org/10.1145/3463492

DOI: 10.1145/3463492

URI: https://www.alexandria.unisg.ch/handle/20.500.14171/110314

Scopus© Citations 35
Which Components of a Smartphone Walking App Help Users to Reach Personalized Step Goals? Results From an Optimization Trial

(Oxford University Press (OUP), 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.

Type: journal article

Journal: Annals of Behavioral Medicine

URL: https://doi.org/10.1093/abm/kaaa002

URI: https://www.alexandria.unisg.ch/handle/20.500.14171/112317
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.

Type: journal article

Journal: American Journal of Preventive Medicine

Volume: 56

Issue: 2

URI: https://www.alexandria.unisg.ch/handle/20.500.14171/98969
Exploring the State-of-Receptivity for mHealth Interventions

(ACM, 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.

Type: journal article

Journal: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)

Volume: 3

Issue: 4

URL: https://doi.org/10.1145/3369805

DOI: 10.1145/3369805

URI: https://www.alexandria.unisg.ch/handle/20.500.14171/97981

Scopus© Citations 42
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

(JMIR Publications, 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.

Type: journal article

Journal: JMIR Research Protocols

Volume: 5

Issue: 3

DOI: 10.2196/resprot.6089

URI: https://www.alexandria.unisg.ch/handle/20.500.14171/103979
Ally: A Smartphone-based Physical Activity Intervention

( 2017-12-04)
Künzler, Florian
;
Kramer, Jan-Niklas
;
Mishra, Varun
;
Presset, Bastien
;
Smith, Shawna N.
;
Kotz, David F.
;
Scholz, Urte
;
Fleisch, Elgar
;
Kowatsch, Tobias

No behavior has an impact on human health as great as physical activity (PA). We therefore developed Ally, a smartphone-based 6-week PA intervention. Ally seeks to exploit the ubiquity and sensing capabilities of mobile phones to adapt the provision of PA interventions to the context of the user. In this research we investigate the following research questions: (1) What are effective components of Ally, a mHealth physical activity intervention? and (2) Can mobile sensor data predict opportune moments for interventions?

Type: conference poster

URL: https://www.researchgate.net/publication/321825056

DOI: 10.13140/RG.2.2.13559.93605

URI: https://www.alexandria.unisg.ch/handle/20.500.14171/101726
Detecting Receptivity for mHealth Interventions in the Natural Environment

( 2020-11-16)
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 – Walkie – that provided physical-activity interventions and motivated participants to achieve their step goals. The Walkie app included 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.

Type: working paper

Journal: arXiv.org

URL: https://arxiv.org/abs/2011.08302

URI: https://www.alexandria.unisg.ch/handle/20.500.14171/111564

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