SNapp, a Tailored Smartphone App Intervention to Promote Walking in Adults of Low Socioeconomic Position: Development and Qualitative Pilot Study.

BACKGROUND: Adults of low socioeconomic position (SEP) are generally less physically active than those who are more socioeconomically advantaged, which increases their cardiovascular disease incidence risk. Moreover, individuals of low SEP are often less easily reached with physical activity (PA) interventions than individuals of higher SEP. Smartphone apps have been presented as a promising platform for delivering PA interventions to difficult-to-reach individuals of low SEP. Although PA apps are widely available, they are rarely based on health behavior theories and most predominantly offer generic PA advice. Consequently, it is unlikely that available apps are the most effective PA intervention tools. OBJECTIVE: To respond to these areas for improvement, we developed SNapp, an app-based intervention encouraging adults of low SEP to increase PA by providing tailored coaching messages targeting walking behavior. This study aimed to describe SNapp's stepwise development and pilot evaluation process. METHODS: We applied a stepwise approach: analyzing the health problem, developing a program framework, developing tailoring assessments, writing tailored messages, automating the tailoring process, and implementing and evaluating the program in a qualitative pilot study (11 participants). RESULTS: SNapp consisted of several elements. First, an app was developed to collect step count and geolocation data using smartphone sensor functionalities. In addition, a survey measure was created to assess users' behavior change technique (BCT) preferences. These 3 data types were used to tailor SNapp's coaching messages to stimulate walking. This allows SNapp to offer feedback on performance levels, contextually tailored prompts when users are near green spaces, and coaching content that aligns with individual BCT preferences. Finally, a server-based Python program that interacts with databases containing user data and tailored messages was built using Microsoft Azure to select and automatically send messages to users through Telegram messenger. Pilot study findings indicated that SNapp was rated positively, with participants reporting that its design, technical functioning, and message content were acceptable. Participants suggested additional functionalities that are worth considering for future updates. CONCLUSIONS: SNapp is an app-based intervention that aims to promote walking in adults of low SEP by offering tailored coaching messages. Its development is theory based, and it is among the first to incorporate contextualized feedback and content tailored to individual BCT preferences. The effectiveness of SNapp will be evaluated in a 12-month real-life parallel cluster-randomized controlled trial.

Understanding the complexities of Bluetooth for representing real-life social networks: A methodology for inferring and validating Bluetooth-based social network graphs.

Bluetooth (BT) data has been extensively used for recognizing social patterns and inferring social networks, as BT is widely present in everyday technological devices. However, even though collecting BT data is subject to random noise and may result in substantial measurement errors, there is an absence of rigorous procedures for validating the quality of the inferred BT social networks. This paper presents a methodology for inferring and validating BT-based social networks based on parameter optimization algorithm and social network analysis (SNA). The algorithm performs edge inference in a brute-force search over a given BT data set, for deriving optimal BT social networks by validating them with predefined ground truth (GT) networks. The algorithm seeks to optimize a set of parameters, predefined considering some reliability challenges associated to the BT technology itself. The outcomes show that optimizing the parameters can reduce the number of BT data false positives or generate BT networks with the minimum amount of BT data observations. The subsequent SNA shows that the inferred BT social networks are unable to reproduce some network characteristics present in the corresponding GT networks. Finally, the generalizability of the proposed methodology is demonstrated by applying the algorithm on external BT data sets, while obtaining comparable results.

Reducing cardiometabolic risk in adults with a low socioeconomic position: protocol of the Supreme Nudge parallel cluster-randomised controlled supermarket trial.

BACKGROUND: Unhealthy lifestyle behaviours such as unhealthy dietary intake and insufficient physical activity (PA) tend to cluster in adults with a low socioeconomic position (SEP), putting them at high cardiometabolic disease risk. Educational approaches aiming to improve lifestyle behaviours show limited effect in this population. Using environmental and context-specific interventions may create opportunities for sustainable behaviour change. In this study protocol, we describe the design of a real-life supermarket trial combining nudging, pricing and a mobile PA app with the aim to improve lifestyle behaviours and lower cardiometabolic disease risk in adults with a low SEP. METHODS: The Supreme Nudge trial includes nudging and pricing strategies cluster-randomised on the supermarket level, with: i) control group receiving no intervention; ii) group 1 receiving healthy food nudges (e.g., product placement or promotion); iii) group 2 receiving nudges and pricing strategies (taxing of unhealthy foods and subsidizing healthy foods). In collaboration with a Dutch supermarket chain we will select nine stores located in low SEP neighbourhoods, with the nearest competitor store at > 1 km distance and managed by a committed store manager. Across the clusters, a personalized mobile coaching app targeting walking behaviour will be randomised at the individual level, with: i) control group; ii) a group receiving the mobile PA app. All participants (target n = 1485) should be Dutch-speaking, aged 45-75 years with a low SEP and purchase more than half of their household grocery shopping at the selected supermarkets. Participants will be recruited via advertisements and mail-invitations followed by community-outreach methods. Primary outcomes are changes in systolic blood pressure, LDL-cholesterol, HbA1c and dietary intake after 12 months follow-up. Secondary outcomes are changes in diastolic blood pressure, blood lipid markers, waist circumference, steps per day, and behavioural factors including healthy food purchasing, food decision style, social cognitive factors related to nudges and to walking behaviours and customer satisfaction after 12 months follow-up. The trial will be reflexively monitored to support current and future implementation. DISCUSSION: The findings can guide future research and public health policies on reducing lifestyle-related health inequalities, and contribute to a supermarket-based health promotion intervention implementation roadmap. TRIAL REGISTRATION: Dutch Trial Register ID NL7064, 30th of May, 2018.

Effectiveness and cost-effectiveness of personalised dietary advice aiming at increasing protein intake on physical functioning in community-dwelling older adults with lower habitual protein intake: rationale and design of the PROMISS randomised controlled trial.

INTRODUCTION: Short-term metabolic and observational studies suggest that protein intake above the recommended dietary allowance of 0.83 g/kg body weight (BW)/day may support preservation of lean body mass and physical function in old age, but evidence from randomised controlled trials is inconclusive. METHODS AND ANALYSIS: The PRevention Of Malnutrition In Senior Subjects in the EU (PROMISS) trial examines the effect of personalised dietary advice aiming at increasing protein intake with or without advice regarding timing of protein intake to close proximity of usual physical activity, on change in physical functioning after 6 months among community-dwelling older adults (≥65 years) with a habitual protein intake of <1.0 g/kg adjusted (a)BW/day. Participants (n=264) will be recruited in Finland and the Netherlands, and will be randomised into three groups; two intervention groups and one control group. Intervention group 1 (n=88) receives personalised dietary advice and protein-enriched food products in order to increase their protein intake to at least 1.2 g/kg aBW/day. Intervention group 2 (n=88) receives the same advice as described for intervention group 1, and in addition advice to consume 7.5-10 g protein through protein-(en)rich(ed) foods within half an hour after performing usual physical activity. The control group (n=88) receives no intervention. All participants will be invited to attend lectures not related to health. The primary outcome is a 6-month change in physical functioning measured by change in walk time using a 400 m walk test. Secondary outcomes are: 6-month change in the Short Physical Performance Battery score, muscle strength, body composition, self-reported mobility limitations, quality of life, incidence of frailty, incidence of sarcopenia risk and incidence of malnutrition. We also investigate cost-effectiveness by change in healthcare costs. DISCUSSION: The PROMISS trial will provide evidence whether increasing protein intake, and additionally optimising the timing of protein intake, has a positive effect on the course of physical functioning after 6 months among community-dwelling older adults with a habitual protein intake of <1.0 g/kg aBW/day. ETHICS AND DISSEMINATION: The study has been approved by the Ethics Committee of the Helsinki University Central Hospital, Finland (ID of the approval: HUS/1530/2018) and The Medical Ethical Committee of the Amsterdam UMC, location VUmc, Amsterdam, the Netherlands (ID of the approval: 2018.399). All participants provided written informed consent prior to being enrolled onto the study. Results will be submitted for publication in peer-reviewed journals and will be made available to stakeholders (ie, older adults, healthcare professionals and industry). TRIAL REGISTRATION NUMBER: ClinicalTrials.gov Registry (NCT03712306).

Digital Health Behavior Change Technology: Bibliometric and Scoping Review of Two Decades of Research.

BACKGROUND: Research on digital technology to change health behavior has increased enormously in recent decades. Due to the interdisciplinary nature of this topic, knowledge and technologies from different research areas are required. Up to now, it is not clear how the knowledge from those fields is combined in actual applications. A comprehensive analysis that systematically maps and explores the use of knowledge within this emerging interdisciplinary field is required. OBJECTIVE: This study aims to provide an overview of the research area around the design and development of digital technologies for health behavior change and to explore trends and patterns. METHODS: A bibliometric analysis is used to provide an overview of the field, and a scoping review is presented to identify the trends and possible gaps. The study is based on the publications related to persuasive technologies and health behavior change in the last 18 years, as indexed by the Web of Science and Scopus (317 and 314 articles, respectively). In the first part, regional and time-based publishing trends; research fields and keyword co-occurrence networks; influential journals; and collaboration network between influential authors, countries, and institutions are examined. In the second part, the behavioral domains, technological means and theoretical foundations are investigated via a scoping review. RESULTS: The literature reviewed shows a clear and emerging trend after 2001 in technology-based behavior change, which grew exponentially after the introduction of the smartphone around 2009. Authors from the United States, Europe, and Australia have the highest number of publications in the field. The three most active research areas are computer science, public and occupational health, and psychology. The keyword "mhealth" was the dominant term and predominantly used together with the term "physical activity" and "ehealth". A total of three strong clusters of coauthors have been found. Nearly half of the total reported papers were published in three journals. The United States, the United Kingdom, and the Netherlands have the highest degree of author collaboration and a strong institutional network. Mobile phones were most often used as a technology platform, regardless of the targeted behavioral domain. Physical activity and healthy eating were the most frequently targeted behavioral domains. Most articles did not report about the behavior change techniques that were applied. Among the reported behavior change techniques, goal setting and self-management were the most frequently reported. CONCLUSIONS: Closer cooperation and interaction between behavioral sciences and technological areas is needed, so that theoretical knowledge and new technological advancements are better connected in actual applications. Eventually, this could result in a larger societal impact, an increase of the effectiveness of digital technologies for health behavioral change, and more insight in the relationship between behavioral change strategies and persuasive technologies' effectiveness.

Active2Gether: A Personalized m-Health Intervention to Encourage Physical Activity.

Lack of physical activity is an increasingly important health risk. Modern mobile technology, such as smartphones and digital measurement devices, provides new opportunities to tackle physical inactivity. This paper describes the design of a system that aims to encourage young adults to be more physically active. The system monitors the user's behavior, uses social comparison and provides tailored and personalized feedback based on intelligent reasoning mechanisms. As the name suggests, social processes play an important role in the Active2Gether system. The design choices and functioning of the system are described in detail. Based on the experiences with the development and deployment of the system, a number of lessons learnt are provided and suggestions are proposed for improvements in future developments.

Formal specification and analysis of intelligent agents for model-based medicine usage management.

A model-based agent system model for medicine usage management is presented and formally analysed. The model incorporates an intelligent ambient agent model that has an explicit representation of a dynamical system model to estimate the medicine level in the patient's body by simulation, is able to analyse whether the patient intends to take the medicine too early or too late, and can take measures to prevent this.