Keywords
obesity, weight loss study, gamification, patient adherence, behaviour change
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Ivezić D, Keppel J, Horneber D et al. EghiFit: Smartphone based Behaviour Monitoring and Health Recommendation in a Weight Loss Intervention Study [version 1; peer review: awaiting peer review]. F1000Research 2024, 13:1347 (https://doi.org/10.12688/f1000research.157584.1)
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Research Article
EghiFit: Smartphone based Behaviour Monitoring and Health Recommendation in a Weight Loss Intervention Study
[version 1; peer review: awaiting peer review]
PUBLISHED 11 Nov 2024
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Abstract
Corresponding authors:
Dijana Ivezić, Oliver Amft
Competing interests:
No competing interests were disclosed.
Grant information:
This work is partially funded by the German Federal Ministry of Education and Research~(BMBF) under grant number 16SV8528.
Copyright:
© 2024 Ivezić D et al.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite: Ivezić D, Keppel J, Horneber D et al. EghiFit: Smartphone based Behaviour Monitoring and Health Recommendation in a Weight Loss Intervention Study [version 1; peer review: awaiting peer review]. F1000Research 2024, 13:1347 (https://doi.org/10.12688/f1000research.157584.1)
First published: 11 Nov 2024, 13:1347 (https://doi.org/10.12688/f1000research.157584.1)
Latest published: 11 Nov 2024, 13:1347 (https://doi.org/10.12688/f1000research.157584.1)
1. Introduction
The widespread prevalence of unhealthy habits, including poor dietary choices and sedentary lifestyle, presents a critical societal challenge.28 Overweight and obese individuals are at risk of numerous serious health issues, such as cardiovascular disease, diabetes, and musculoskeletal disorders.2,10,11,18,20
There have been numerous research activities on digital health interventions, with the focus on behavioural change.8,29 With the widespread use of smartphones in our daily lives, mobile application dietary intervention and behaviour change solutions have emerged.7,12,16 The systematic review of Scarry et al.22 indicates that diet quality can be improved with the use of mobile applications, which is important for improving health and managing weight. Successful weight management for obese patients can be supported by logging consumed meals in a smartphone application.26 Smartphone applications could become a self-help resource, in particular to support daily behaviour choices.27
Environmental exposure, i.e. the exposome, and behaviour could be preferably monitored with smartphone and wearable sensors.1 However, there neither exist robust best practices nor platforms that can guide the implementation of smartphone-based behaviour monitoring and health recommendation services. Furthermore, various research reports describe systems that require active user input, thus may tire patients rapidly (see related work for details). In order to capture and react to everyday situations and behaviour, a multi-modal sensor monitoring approach is required.
In this work, we investigate a smartphone based behaviour monitoring and health recommendation system to monitor patients in a weight loss intervention study and to analyse their benefit from health recommendations that are provided via the smartphone. Our approach is based on the BEXOME cloud platform, which provides patient and data management, as well as an interface for data review. We embed our study in a regular, physician-supervised weight loss programme.
In particular, this paper makes the following contributions:
1. We introduce the BEXOME platform, a cloud-based system for behaviour monitoring and health recommendations. The BEXOME platform allows us to rapidly deploy mobile applications for health tracking, including fitness and weight loss goals set remotely by health coaches. Health coaches have access to patient data via a health dashboard to monitor patient progress, set healthy goals, and write nudges as “tips of the day”.
2. We propose a serious gaming approach to guide patients towards healthy habits. Patients can collect points for goals that they achieved. The points can be spend to take care of a virtual pet. Moreover, patients can track their overall progress and compliance with the prescribed goal-setting through the daily updated game level.
3. We perform a three months intervention study to monitor behaviour and investigate the effect of explicit (i.e. tips of the day) and implicit (i.e. gaming) health recommendations with obese patients along their weight loss programme. We collect and analyse data on patient adherence to suggested healthy behaviour, usability of our BEXOME framework, and how the gamification elements connect to weight loss. In addition to data analysis, we provide data on patient feedback from interview session conducted after the study.
2. Related work
2.1 Health monitoring
Lopes et al.13 developed a SapoFitness application, which even in the earlier days of smartphones, back in 2011, had an overall positive reception from their users as a tool for weight monitoring and assessment. Martin et al.14 developed SmartLoss, an automated mHealth system for weight loss consisting of a smartphone application and a dashboard. SmartLoss leveraged mathematical models to predict weight change and check if the user’s actual weight measurements fell within an adherence zone based their anthropometric data. The system automatically retrieved weight data from a smart scale and activity data from fitness devices like the Fitbit. The application allowed users to receive automatic and personalised feedback as well as to track their weight and steps done. We extend the work described above with additional data monitoring modalities to better understand behaviour, modern cross-platform application, and a health dashboard for managing fitness goals and data labelling to complement automatically collected data. We also include gamification elements in our smartphone application. With our approach, we intent to study conditions for patient adherence and satisfaction.
2.2 Behaviour change
Klein et al.9 introduced eMate system to support patients with HIV, diabetes or cardiovascular disease by monitoring patient behaviour and providing cooperative coaching. In order to support patients, eMate collected behavioural data, including medication adherence through smart pillbox, adherence to healthy food choices through in-app questionnaire and adherence to fitness goals. Subsequently, eMate estimated what caused the patient to not adhere to the proposed health goals with the help of questionnaires so the patient could receive personalised motivational messages to help them change their behaviour.
Scheerman et al.23 introduced WhiteTeeth, a mobile application to nudge adolescents with fixed orthodontic appliances to improve their oral hygiene. Adolescents would take a selfie on which they could click on the already coloured areas with any dental plague to provide feedback on the actual status of dental plague they may be having. The adolescents would receive positive reinforcement if they brushed their teeth or achieved their dental hygiene goals as well as reminders and timers to brush their teeth. In case the adolescent did not meet their dental health goals, they were instructed to create a coping plan so they can improve as coping is a valuable resource for long-term lifestyle change.24
SitCoach by van Dantzig et al.6 included a mobile application to suggest active breaks from sedentary activities to office workers. The physical activity was measured by on-device accelerometer sensor and the office workers were reminded to take a break via the vibration and auditory signals from their smartphones.
In the present work, we included nudges for patient behavioural change in the form of “tips of the day” provided by the patient’s health coaches. The recommendations provided by the app aligned with the in-person behaviour change advice provided by the health coaches.
2.3 Gamification
In recent years, gamification has come to light as a promising approach to promote healthy lifestyle changes, particularly in the context of health improvement and tracking.4,15 By incorporating game-like elements into health interventions, researchers aim to increase user engagement, motivation, and adherence to healthier behaviours.
Pollak et al.21 explored the potential of mobile games to encourage healthy eating habits. Their research highlighted how game-based interventions can make the process of adopting better dietary choices more engaging and enjoyable for users. By leveraging the widespread use of smartphones and the appeal of mobile gaming, there is a potential to reach a broad audience and integrate seamlessly into daily routines.
Orji et al.17 developed LunchTime, a slow-casual game designed to promote long-term dietary behaviour change. Chiu et al.5 introduced the Playful Bottle, a mobile social persuasion system designed to motivate healthy water intake to tackle another important aspect of a person’s weight loss journey. Another approach to behaviour change encouragement was presented by Soares et al.25 with their Sustainable App for encouraging sustainable choices. The Sustainable App introduced a virtual pet plant whose mood changes depending on the points collected while choosing sustainable choices.
In this work, we introduce a multi-goal concept, where patients receive specific goals according to agreements made with their health coach. Based on their goal achievement, patients receive points to be spent for the interaction with a virtual pet in the EghiFit application. Patients could receive feedback in the EghiFit app from their health coaches who review the health dashboard data (i.e., tips of the day).
3. Methodology
3.1 BEXOME cloud-based system overview
For the cloud-based behaviour and context analysis system, we consider three user types: patients, researchers and health coaches. The patients receive personalised health goals and tips in the EghiFit application as set by the health coaches. Researchers are responsible for cloud server setup and data analysis and thus have access to part or all of the patient data. Health coaches are responsible for patient management, including onboarding and off-boarding, setting patient goals, reviewing patient performance, and providing personalised feedback. Coaches could give feedback to the patients either in person or through modifying goals or tips. All data communication between the components and the cloud server are secured and controlled by access credentials. Figure 1 shows an overview of our approach.
Figure 1. Overview of the BEXOME cloud-based system used in the study.
There are three main actors considered: patients, researchers and health coaches. See main text for role details.
3.2 Smartphone-based behaviour monitoring
3.2.1 EghiFit application
We utilised an array of technologies to collect, analyse, and leverage health-related data from the enrolled patients. The data collection was achieved through a smartphone application, termed EghiFit, which is compatible with both Android and iOS devices. The application serves as a comprehensive health tracking tool, capable of monitoring various aspects of a patient’s daily life. Patients were instructed to use a smartwatch alongside the EghiFit application so the vital health information could be acquired.
EghiFit keeps track of patient’s physical activity through total amount of steps taken in a day and heart rate information which provide valuable insights into patient’s overall physical activity and cardiovascular health. Sleep monitoring is another important feature as data on sleep patterns and quality can relate to metabolism and unhealthy eating habits.3,19 Additionally, the application collects local weather information, which can be correlated with other health data to identify potential environmental influences on weight loss outcomes.
Except for automatically acquired patient data, EghiFit supported a meal journal for patients to log their food intake. Food items with barcodes could be scanned by patients to ease the meal entry burden. The patients were instructed to log their meals for at least three days in a row each week. The first meal logging day of the week was shifted by one day compared to the previous week to have more comprehensive data on patient eating habits across workdays (Monday to Friday) and weekends.
A cross-platform approach was used to implement the EghiFit application, based on the Flutter software development kit. Fitness data was collected from both major health platforms, Google Fit and Apple Health.
3.2.2 EghiFit gamification
To enhance patient engagement and motivation, we incorporated several gamification elements into the application. Patients could set personalised health goals during in-person sessions with their health coach. These goals serve as concrete targets for patients to strive towards, promoting active participation in their weight loss journey. As patients achieve their goals, they received points to be spent for the interactions with their virtual pet in the EghiFit application. The gamification feature allowed patients to feed, give water to, and play with their virtual pet. The virtual pet interaction was intended to create a positive reinforcement loop that encourages patients to maintain healthy behaviours. Furthermore, we designed a game level scoring within the application to give patients a concrete measure of their progress. Alongside interacting with their virtual pets, patients also received personalised tips of the day with detailed action suggestions on a healthy lifestyle and insights into how to lose weight for the specific day.
Patients received two goals per goal type for any study day. Goal types were all quantitative and included amount of steps, number of meals logged and amount of tips implemented. While the first goal had a comparably lower quantity and gave one point upon completion, the second goal required a higher quantity and gave two points upon completion.
The EghiFit application included an overall game score, where patients could gain one level if they achieve the second goal for all goals given for one day. Patients lost one game level, if at least the first goal level was not achieved for any of the goals set on a study day and remained on the same level if all the goals were achieved up to at least the first level. Besides the overall game score, the virtual pet had a daily point score of needs met (zero, one or two points), where the score depended on the patient’s goal achievement and if the patient used the acquired points to interact with their pet.
3.2.3 BEXOME cloud server
The data collected through the smartphone application was securely transmitted and stored in a cloud environment. The cloud system consisted of two main components: a back-end for data storage and processing, and a front-end interface for managing patient enrolment. Moreover, the back-end implemented the game logic and point scoring, while the EghiFit application provided the view on the patient’s data and progress. Our design intended to ensure a simple and straightforward introduction of new features without requiring patients to update the EghiFit application. The cloud server was implemented using the Django framework.
3.2.4 Health dashboard
To provide real time insights into patient’s progress, a web-based dashboard to be used by the health coaches was developed. The health dashboard allows health coaches to visualise the data collected from patients so they can identify trends, patterns, and potential areas of concern. Health coaches can also use the dashboard to label notable events within the patients’ data, which is particularly valuable for providing context to the collected data. The dashboard also empowers health coaches to adjust patients’ goals based on their progress and to provide the patients with personalised tips of the day. This flexibility ensures that the goals remain challenging yet achievable, maintaining patient motivation throughout the study.
3.3 Patient study
3.3.1 Study description
Ten patients of a weight loss intervention program were included in the study. Patients received detailed information about the study purpose and signed a consent form before being included. Out of a total of ten patients, eight successfully completed the onboarding process. Nearly all of the patients lost weight during the course of the study. Detailed patient statistics can be seen in Table 1.
Table 1. Statistics of the patients included in the study, i.e. using the EghiFit application.
| # | #Android | #iPhone | age | height | weight | weight | |
|---|---|---|---|---|---|---|---|
| [years] | [cm] | [kg] | [kg] | ||||
| Women | 5 | 5 | 0 | 41 ± 18 | 168 ± 8 | 101 ± 36 | -7 ± 8 |
| Men | 3 | 1 | 2 | 56 ± 11 | 176 ± 15 | 91 ± 12 | -7 ± 4 |
| Total | 8 | 6 | 2 | 47 ± 16 | 171 ± 11 | 97 ± 29 | -7 ± 6 |
Patients were enrolled during the course of three months. Each week, patients would meet with their health coaches who would give them personalised advice based on their measured body mass, information from the dashboard and patient feedback. Any concerns or technical challenges would be resolved as soon as possible. The patients were instructed to adhere to protein rich, low carbohydrate diet and to avoid sweets and unhealthy, energy packed foods.
3.3.2 Onboarding process
Detailed instructions on how to operate the EghiFit application were given to patients, including how food intake is logged, as well as how to interact with the virtual pets. Patients were also given detailed instructions on how to setup their smartwatches, connect them to their mobile device and how to install necessary 3rd party applications, if not already available on their device. Patients could use their own smartwatch, if available, and were provided with a new smartwatch otherwise.
In order to use EghiFit, patients needed to register in the application by scanning a unique QR code provided to them by the health coaches. After the first initialisation, the patient’s smartphone was registered in the BEXOME cloud server back-end and only then the patient could use the EghiFit application.
3.3.3 Interviews
To gain insight into how patients used the EghiFit application, we conducted interviews with the patients during the last two weeks of the weight loss intervention program. In the interviews, we asked patients about their experience using the EghiFit application.
4. Results
We structure the analyses of the study data into overall, cohort-level insights, followed by findings from our analyses of selected patients.
4.1 Multiple data modalities
Total number of days with successful recordings per data modality and per patient is shown in Figure 2. Some patients used the EghiFit application and followed the usage instructions diligently, while other patients had considerably reduced data than the study duration suggested, even missing some data modalities.
Figure 2. Number of days of recordings per patient per data modality.
Bar charts for each patient represent number of days with recordings. Each bar chart represents a different data modality collected through EghiFit application.
Sleep and heart rate information required the patients to wear the smartwatch during day and night and take breaks only for recharging periods during the day. As it is visible in Figure 2, most of the patients did not use their smartwatch correctly during the course of the study. We got feedback from the patients that they do not find wearing the smartwatch during the night comfortable or desirable which led to insufficient sleep data collected. Heart rate data had a similar fate with heart rate data lacking for half of the patients.
As steps data were automatically acquired and meals manually, there are more days of recordings for step counts as opposed to meal information for Patients 1, 4, 5, 6 and 8. Although meal logging required manual effort, most of the patients have at least 20 days of recordings.
Subsequently, we analysed workday and weekend data separately. Figure 3 shows average steps, average calories and average tips done in the EghiFit application scaled by the maximum value for the data modality across all patients. In other words, steps during workday and weekend are comparable with one another and also across patients, same for calories and number of tips.
Figure 3. Average scaled values per data type for workday and weekend days.
Average values for total steps done in a day, calories consumed in a day and tips completed in a day are shown across all workdays and weekends separately for each of the patients. As expected number of tips done in a day is less than ten and potential steps completed are in thousands range, average values are scaled so the maximum values per modality (regardless of being workday or weekend) is 1.0.
Steps data of Patients 3, 7, and 8 had errors due to an error in the smartphone setup. The highest number of average steps were scored by Patient 2 (2533 avg. steps/day) and Patient 4 (5712 avg. steps/day). Patients 1, 4, 5, and 6 did more steps during the workday as opposed to the weekend. All of the patients have logged their meal intake for at least some of the days with some patients not logging meals during the weekend. Patients 4, 5, 6, and 8 had strong motivation to use the tips system while other patients had relatively less tips done.
4.2 Meal data
Reported meal data binned into specific times of the day provides insights into patients’ habits. Most of the patients were not having late night meals, every patient except Patient 4, as shown in Figure 4. Some patients, on average, had imbalanced meals depending on the time of the day with the patient having eaten significantly more calories in a specific time period. On the other hand, other patients reported more consistent amount of calories eaten throughout the day.
Figure 4. Binned average calories consumed per patient across all days with recordings.
Each 24h period is divided into four bins with each bin representing an expected time of meals. Starting with the first bin from the figure (03-11h), patients are expected to have their breakfast during those hours followed by lunch, dinner and late night snacking. For each patient, each bar chart represents average calories across all days with logged meals in the specific time period.
During the course of the study, patients could select exact date and time of their reported meal with the current date and time being preselected. Figure 5 shows that most of the patients did not choose to use the option of selecting exact values in the application, instead, the patients have opted for keeping the preselected values in the EghiFit application. Primarily the Patient 5 and Patient 6 had a significant amount of backlogging done, going back more than one day with Patient 1 having done so on only a few occasions.
Figure 5. Meal intake backlogging per patient.
The violin plot shows distributions of the total hours that elapsed between actual logging and time for the meal consumption reported in the app. In addition, actual data points and the data median are illustrated. Five patients did no or negligible backlogging. Three patients had done backlogging more extensively, however medians remained below 10 h.
The lack of backlogging done by the patients is an important insight into meal logging and general effort required to log the meals. The patients resort to doing the least amount of individual steps in the application while still adhering to doing the meal logging as per the instructions the patients were given.
4.3 Application usability and adherence
Figure 6 shows percentage of the total days the patient used the EghiFit application where they achieved a certain goal. One of the hardest goals to achieve was the amount of steps taken per day, even though the amount of data collected for that data modality is high due to ease of setup and use. Only Patient 4 had more than 50% of the days achieving a steps goal. Most of the patients had at least 40% of the days meal logging goal achieved even though it is a manual effort, compared to tips where it only took a click of a button on the EghiFit application interface. In comparison to manual meal entry goals, the second goal of completing tips of the day was achieved less often for all of the patients except for Patient 4. When we asked Patient 4 about their experience with the tips of the day, they were very positive, saying that the tips were very motivating. Thus, they used it diligently, while other patients found logging their meals more attractive.
Figure 6. Percentage of days with a specific goal achieved per goal type per patient.
Out of total number of days specific patient used the EghiFit application, percentages of achieved goals per goal type are shown for each of the patients. Some points awarded for achieving goals remain unused by the patients. Patients can achieve first and easier goal or second and more difficult goal per goal type. The percentage of the days with any number of unused points is shown per patient and indicates usability of pet interaction element.
After achieving their goals, patients could use the points they have collected to interact with their virtual pet by playing with it, feeding it or giving it water. Up to 60% of rewarded points remained unused in the Patient 2 case. Percentage of days with at least one unused rewarded point is shown on Figure 6. While Patient 2 had a significant amount of unused points which indicates their lack of conviction in the pet reward system, Patient 2 was not so successful in achieving their goals either which might indicate lack of motivation. The most motivated patient was Patient 4 with more than 50% of the time having achieved more difficult step count goal and they were also motivated to log meal items and completing tips of the day around 80% and 90% of the time respectively. Patient 4 used all of their points to interact with their pet.
Figure 7 shows a quantised view of how many times per day every patient opened the EghiFit application. Timelines across study participants were aligned to the first day of use of the application. The frequency of opening the application at the beginning of the study was comparably high and reduced with time. EghiFit captured all of the patient’s attention for at least one month. Patient 6 in particular stopped using the EghiFit application, but decided to continue with the usage relatively regularly after a short break. The most impressive usage can be seen from Patient 4 who used the BEXOME system in the span of at least 120 days.
Figure 7. Quantised frequency of daily EghiFit application use per patient.
Timelines across study participants were aligned to the first day of use of the application. Each token in the graph represents when the patient opened the application at least once in a day. The tokens are coloured by frequency.
4.4 Patient interviews
Out of the patients, who participated in our study, three agreed to be interviewed (Patients 1, 4, and 6). In the interviews, all three patients praised the tip system, emphasising that the tips were appropriate for their abilities (not too hard nor too easy) and motivated them to engage in healthy behaviours. For example, Patient 1 stated: “Yes, I thought that [the tip of the day] was good too. Also things that you can implement quite well. I think there were tips like’Have you done your ten squats today?’. So that’s something where I say, no matter where I’m sitting, i.e. in the office, and then he says ten squats, then I’ll just do them.”
4.5 Case study
Here we provide in-depth analysis of selected data. Total steps taken timeline for Patient 4 and Patient 6 are shown in Figure 8 for side by side comparison. Two lines for both step count goals are shown as well as the total recorded steps per each day of the study. Patient 4 had successful steps recordings for nearly every day of the study, while Patient 6 had more spotty data with missing days and improbable values (around 0 steps). Health coaches recognised the Patient 4’s efforts and increased their step goals to keep them challenged and engaged. Patient 4 completed the required steps for most of the days. Patient 6 had their step goals set relatively consistently throughout the study with them failing to meet the step goals for most of the days.
Figure 8. Time-series representing total amount of steps done per day of the study and set goals.
For Patient 4 (a) and Patient 6 (b), time-series representing total amount of steps done per day of the study are shown as scatter plots with red crosses. More motivated Patient 4 used the EghiFit application the most, has the most data recorded and was the most successful of all patients in achieving set goals by BEXOME system. Goals as set by health coaches are visualised as grey lines. Patient 6 was less motivated and had trouble achieving their step goals.
Patient 4 was very motivated and achieved a total game level 37 at the end of the study, the progress timeline can be seen in Figure 9. All of the other patients ended the study at level 1. These findings indicate that gamification and interaction elements presented in EghiFit application have a potential to capture patient’s attention and be their partner in their fitness journey. When we asked Patient 4 about their experience with the app, they mentioned that they particularly appreciated the app’s ability to provide an overview of their diet and exercise habits, which motivated them to engage in healthy behaviours.
Figure 9. Virtual pet levels and overall game level of Patient 4.
Scatter plots for each goal type are coloured separately and represent the achieved level of the corresponding pet interaction element which uses the points from the specified goal type. Line plot represents the overall game level which depends on the levels achieved by the virtual pet each day.
5. Lessons learnt
Our study highlighted that (1) patients could successfully implement the goal achievement using the EghiFit app, as well as (2) several challenges persist that impact both data collection and patient engagement. For the sleep data recordings, the primary issue was that none of the patients volunteered to wear the smartwatch during sleep. Most of the patients found sleeping with their smartwatch uncomfortable or inconvenient. Lack of sleep data recordings is an important issue as sleep patterns offer valuable insights into health.
Patient engagement with the application’s gamification elements declined over time due to the lack of novelty after using the application for some time. While some patients were initially enthusiastic, most gradually lost interest in using the application. Continuously developing new and engaging gamification elements based on our BEXOME platform may offer a future solution to maintain patient interest. An important consideration for patient adherence are the manual tasks, e.g. food logging. While some patients demonstrated that the food logging was feasible, a more controlled use of the logging is sensible. The data collected during interview process suggest that the reason for decreased patient engagement after some time may be due to a lack of reliability or desired functionality in the EghiFit application leading to patient dissatisfaction.
The smartphone installation and setup process proved challenging, especially for less tech-savvy patients. Despite our guidance, many patients struggled with the onboarding process, potentially introducing selection bias by discouraging less technologically adept individuals from participating or continuing with the study.
Reliably collecting data in the background on current smartphone platforms has significant difficulties. First, there exist aggressive battery management policies in modern smartphone operating systems that often terminate background processes. Second, the platforms implement strict privacy management policies, including technical constraints as well as administrative hurdles in the approval process. Thus smartphone platform restrictions further complicated consistent data collection.
Future research on behaviour coaching in everyday life should consider strategies to implement the lessons learnt in this section. For example, more comfortable wearable devices could help to complement behaviour monitoring. Further options include creating evolving game elements, simplifying the onboarding process, and exploring alternative methods for consistent data collection on smartphone platforms.
6. Conclusion
Some patients were very motivated and used the EghiFit application nearly every day across the study duration of three months. The complying patients used the proposed gamification and achieved their fitness goals as set by the health coaches during the study. A considerable number of patients started using the application, but their interest waned over time with around one month retention period for most of the patients. The game scoring system and the virtual pet caught some of the patient’s attention. The varying interest across the included patients becomes clear in the data, as there are study days where patients chose not to interact with their virtual pet even though they achieved their goals and thus could spent points to interact with the pet. We conclude that our approach can capture patient behaviour, however, not all patients could yet benefit from it. Further research is needed to design adjustable, context-aware interaction options and health recommendations that retain patient interest.
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Ivezić D, Keppel J, Horneber D et al. EghiFit: Smartphone based Behaviour Monitoring and Health Recommendation in a Weight Loss Intervention Study [version 1; peer review: awaiting peer review]. F1000Research 2024, 13:1347 (https://doi.org/10.12688/f1000research.157584.1)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
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