Integrating Behavior Change and Persuasive Design Theories into an Example Mobile Health Recommender System

2021 ◽  
Author(s):  
Helma Torkamaan ◽  
Jürgen Ziegler
Keyword(s):  
Behavior Change ◽  
Mobile Health ◽  
Recommender System ◽  
Design Theories

scholarly journals Smartloss: A Personalized Mobile Health Intervention for Weight Management and Health Promotion

2016 ◽  
Vol 4 (1) ◽  
pp. e18
Author(s):  
Corby K Martin ◽  
L. Anne Gilmore ◽  
John W Apolzan ◽  
Candice A Myers ◽  
Diana M Thomas ◽  
...  
Keyword(s):  
Weight Loss ◽  
Body Weight ◽  
Behavior Change ◽  
Mobile Phone ◽  
Weight Management ◽  
Mobile Phones ◽  
Mobile Health ◽  
Treatment Fidelity ◽  
Health Improvement ◽  
The Individual

Background Synonymous with increased use of mobile phones has been the development of mobile health (mHealth) technology for improving health, including weight management. Behavior change theory (eg, the theory of planned behavior) can be effectively encapsulated into mobile phone-based health improvement programs, which is fostered by the ability of mobile phones and related devices to collect and transmit objective data in near real time and for health care or research professionals and clients to communicate easily. Objective To describe SmartLoss, a semiautomated mHealth platform for weight loss. Methods We developed and validated a dynamic energy balance model that determines the amount of weight an individual will lose over time if they are adherent to an energy intake prescription. This model was incorporated into computer code that enables adherence to a prescribed caloric prescription determined from the change in body weight of the individual. Data from the individual are then used to guide personalized recommendations regarding weight loss and behavior change via a semiautomated mHealth platform called SmartLoss, which consists of 2 elements: (1) a clinician dashboard and (2) a mobile phone app. SmartLoss includes and interfaces with a network-connected bathroom scale and a Bluetooth-connected accelerometer, which enables automated collection of client information (eg, body weight change and physical activity patterns), as well as the systematic delivery of preplanned health materials and automated feedback that is based on client data and is designed to foster prolonged adherence with body weight, diet, and exercise goals. The clinician dashboard allows for efficient remote monitoring of all clients simultaneously, which may further increase adherence, personalization of treatment, treatment fidelity, and efficacy. Results Evidence of the efficacy of the SmartLoss approach has been reported previously. The present report provides a thorough description of the SmartLoss Virtual Weight Management Suite, a professionally programmed platform that facilitates treatment fidelity and the ability to customize interventions and disseminate them widely. Conclusions SmartLoss functions as a virtual weight management clinic that relies upon empirical weight loss research and behavioral theory to promote behavior change and weight loss.

A Theory-based Mobile Health Physical Activity Intervention for Breast Cancer Patients during Chemotherapy:a Preliminary Evaluation (Preprint)

2020 ◽  
Author(s):  
Zhaohui Geng ◽  
Li Ning ◽  
Lingzhi Cai ◽  
Ying Liu ◽  
Jingting Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Physical Activity ◽  
Behavior Change ◽  
Health Behaviors ◽  
Cancer Patients ◽  
Mobile Health ◽  
Protective Factor ◽  
Smartphone Application ◽  
Preliminary Evaluation ◽  
Breast Cancer Patients

BACKGROUND Physical activity (PA), known as a modifiable protective factor, provides an approach to sustain physical and psycho-social health for breast cancer patients both during and after treatment. Mobile health (mHealth) application targeted promoting health behaviors demonstrates advantages in behavior tracking, knowledge sharing and social connecting and tailored intervention. However, process of mHealth application (App) development is lack of theoretical basis, restricting its sustainable benefits to cancer survivors. OBJECTIVE To construct a theory-based mHealth PA intervention program, and to determine whether this intervention would improve PA behavior change during chemotherapy for breast cancer patients, thus to capture their perspectives and experiences when participate it. METHODS Social cognitive theory (SCT), self-efficacy theory (SET) and the theory of planned behavior (TPB) are referred to construct mHealth intervention strategies. Smartphone application was chosen to implement a pre-post three-month PA intervention. A mixed method was utilized to test the preliminary effectiveness of MPAP. Quantitative results from online records and self-reported questionnaires were collected after intervention. Qualitative feedback through telephone interviewing was recorded to explore patients’ using experiences. RESULTS “Breast care” smartphone application was developed to improve self-management of breast cancer patients including PA. In the end, five main pages covering 6 functions (information delivering, disease tracking, events reminding based on calendar, online interaction, health behavior recording and self-reported assessment) were displayed in the app. In the preliminary evaluation process, twenty participants were recruited. Based on PA capability assessment and baseline PA evaluation, 12 patients were divided into active group, and 8 patients were grouped in sedentary lifestyle. Within three months, participants’ usage behavior identified at portal site indicated the accumulated app usage time is 40 minutes a week, and average login time of each participant was three times a week. The total PA increased 945.70 MET-min/w with a significant improvement(p=0.040) after 3 months. Walking displayed a significant improvement after intervention (904.20 MET-min/w) (p=0.030). Sedentary mean time declined 210 mins/w. Qualitative results showed satisfaction and willingness of breast cancer patients to use app to manage PA and relevant health behaviors. CONCLUSIONS The theory-based mHealth PA intervention has great potential to enhance breast cancer patients’ PA awareness and engagement, meanwhile to facilitate their PA behavior change.

scholarly journals A Smartphone-Based Technique to Detect Dynamic User Preferences for Tailoring Behavioral Interventions: Observational Utility Study of Ecological Daily Needs Assessment (Preprint)

2020 ◽  
Author(s):  
Ginger E Nicol ◽  
Amanda R Ricchio ◽  
Christopher L Metts ◽  
Michael D Yingling ◽  
Alex T Ramsey ◽  
...  
Keyword(s):  
Young Adults ◽  
Health Behavior ◽  
Behavior Change ◽  
Needs Assessment ◽  
Mobile Health ◽  
Health Behavior Change ◽  
Mobile App ◽  
User Needs ◽  
Self Monitoring ◽  
Psychiatric Conditions

BACKGROUND Mobile health apps are promising vehicles for delivering scalable health behavior change interventions to populations that are otherwise difficult to reach and engage, such as young adults with psychiatric conditions. To improve uptake and sustain consumer engagement, mobile health interventions need to be responsive to individuals’ needs and preferences, which may change over time. We previously created an ecological daily needs assessment to capture microprocesses influencing user needs and preferences for mobile health treatment adaptation. OBJECTIVE The objective of our study was to test the utility of a needs assessment anchored within a mobile app to capture individualized, contextually relevant user needs and preferences within the framework of a weight management mobile health app. METHODS Participants with an iOS device could download the study app via the study website or links from social media. In this fully remote study, we screened, obtained informed consent from, and enrolled participants through the mobile app. The mobile health framework included daily health goal setting and self-monitoring, with up to 6 daily prompts to determine in-the-moment needs and preferences for mobile health–assisted health behavior change. RESULTS A total of 24 participants downloaded the app and provided e-consent (22 female; 2 male), with 23 participants responding to at least one prompt over 2 weeks. The mean length of engagement was 5.6 (SD 4.7) days, with a mean of 2.8 (1.1) responses per day. We observed individually dynamic needs and preferences, illustrating daily variability within and between individuals. Qualitative feedback indicated preferences for self-adapting features, simplified self-monitoring, and the ability to personalize app-generated message timing and content. CONCLUSIONS The technique provided an individually dynamic and contextually relevant alternative and complement to traditional needs assessment for assessing individually dynamic user needs and preferences during treatment development or adaptation. The results of this utility study suggest the importance of personalization and learning algorithms for sustaining app engagement in young adults with psychiatric conditions. Further study in broader user populations is needed.

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