Personalized Coaching Systems to support healthy behavior in people with chronic conditions

Exploring the Feasibility of Using ChatGPT to Create Just-in-Time Adaptive Physical Activity mHealth Intervention Content: Case Study

BACKGROUND

Achieving physical activity (PA) guidelines' recommendation of 150 minutes of moderate-to-vigorous PA per week has been shown to reduce the risk of many chronic conditions. Despite the overwhelming evidence in this field, PA levels remain low globally. By creating engaging mobile health (mHealth) interventions through strategies such as just-in-time adaptive interventions (JITAIs) that are tailored to an individual's dynamic state, there is potential to increase PA levels. However, generating personalized content can take a long time due to various versions of content required for the personalization algorithms. ChatGPT presents an incredible opportunity to rapidly produce tailored content; however, there is a lack of studies exploring its feasibility.

OBJECTIVE

This study aimed to (1) explore the feasibility of using ChatGPT to create content for a PA JITAI mobile app and (2) describe lessons learned and future recommendations for using ChatGPT in the development of mHealth JITAI content.

METHODS

During phase 1, we used Pathverse, a no-code app builder, and ChatGPT to develop a JITAI app to help parents support their child's PA levels. The intervention was developed based on the Multi-Process Action Control (M-PAC) framework, and the necessary behavior change techniques targeting the M-PAC constructs were implemented in the app design to help parents support their child's PA. The acceptability of using ChatGPT for this purpose was discussed to determine its feasibility. In phase 2, we summarized the lessons we learned during the JITAI content development process using ChatGPT and generated recommendations to inform future similar use cases.

RESULTS

In phase 1, by using specific prompts, we efficiently generated content for 13 lessons relating to increasing parental support for their child's PA following the M-PAC framework. It was determined that using ChatGPT for this case study to develop PA content for a JITAI was acceptable. In phase 2, we summarized our recommendations into the following six steps when using ChatGPT to create content for mHealth behavior interventions: (1) determine target behavior, (2) ground the intervention in behavior change theory, (3) design the intervention structure, (4) input intervention structure and behavior change constructs into ChatGPT, (5) revise the ChatGPT response, and (6) customize the response to be used in the intervention.

CONCLUSIONS

ChatGPT offers a remarkable opportunity for rapid content creation in the context of an mHealth JITAI. Although our case study demonstrated that ChatGPT was acceptable, it is essential to approach its use, along with other language models, with caution. Before delivering content to population groups, expert review is crucial to ensure accuracy and relevancy. Future research and application of these guidelines are imperative as we deepen our understanding of ChatGPT and its interactions with human input.

Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review

BACKGROUND

Sensors are increasingly used in health interventions to unobtrusively and continuously capture participants' physical activity in free-living conditions. The rich granularity of sensor data offers great potential for analyzing patterns and changes in physical activity behaviors. The use of specialized machine learning and data mining techniques to detect, extract, and analyze these patterns has increased, helping to better understand how participants' physical activity evolves.

OBJECTIVE

The aim of this systematic review was to identify and present the various data mining techniques employed to analyze changes in physical activity behaviors from sensors-derived data in health education and health promotion intervention studies. We addressed two main research questions: (1) What are the current techniques used for mining physical activity sensor data to detect behavior changes in health education or health promotion contexts? (2) What are the challenges and opportunities in mining physical activity sensor data for detecting physical activity behavior changes?

METHODS

The systematic review was performed in May 2021 using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We queried the Association for Computing Machinery (ACM), IEEE Xplore, ProQuest, Scopus, Web of Science, Education Resources Information Center (ERIC), and Springer literature databases for peer-reviewed references related to wearable machine learning to detect physical activity changes in health education. A total of 4388 references were initially retrieved from the databases. After removing duplicates and screening titles and abstracts, 285 references were subjected to full-text review, resulting in 19 articles included for analysis.

RESULTS

All studies used accelerometers, sometimes in combination with another sensor (37%). Data were collected over a period ranging from 4 days to 1 year (median 10 weeks) from a cohort size ranging between 10 and 11615 (median 74). Data preprocessing was mainly carried out using proprietary software, generally resulting in step counts and time spent in physical activity aggregated predominantly at the daily or minute level. The main features used as input for the data mining models were descriptive statistics of the preprocessed data. The most common data mining methods were classifiers, clusters, and decision-making algorithms, and these focused on personalization (58%) and analysis of physical activity behaviors (42%).

CONCLUSIONS

Mining sensor data offers great opportunities to analyze physical activity behavior changes, build models to better detect and interpret behavior changes, and allow for personalized feedback and support for participants, especially where larger sample sizes and longer recording times are available. Exploring different data aggregation levels can help detect subtle and sustained behavior changes. However, the literature suggests that there is still work remaining to improve the transparency, explicitness, and standardization of the data preprocessing and mining processes to establish best practices and make the detection methods easier to understand, scrutinize, and reproduce.

Unsupervised Early Detection of Physical Activity Behaviour Changes from Wearable Accelerometer Data

Wearable accelerometers record physical activity with high resolution, potentially capturing the rich details of behaviour changes and habits. Detecting these changes as they emerge is valuable information for any strategy that promotes physical activity and teaches healthy behaviours or habits. Indeed, this offers the opportunity to provide timely feedback and to tailor programmes to each participant's needs, thus helping to promote the adherence to and the effectiveness of the intervention. This article presents and illustrates U-BEHAVED, an unsupervised algorithm that periodically scans step data streamed from activity trackers to detect physical activity behaviour changes to assess whether they may become habitual patterns. Using rolling time windows, current behaviours are compared with recent previous ones, identifying any significant change. If sustained over time, these new behaviours are classified as potentially new habits. We validated this detection algorithm using a physical activity tracker step dataset (N = 12,798) from 79 users. The algorithm detected 80% of behaviour changes of at least 400 steps within the same hour in users with low variability in physical activity, and of 1600 steps in those with high variability. Based on a threshold cadence of approximately 100 steps per minute for standard walking pace, this number of steps would suggest approximately 4 and 16 min of physical activity at moderate-to-vigorous intensity, respectively. The detection rate for new habits was 80% with a minimum threshold of 500 or 1600 steps within the same hour in users with low or high variability, respectively.

Healthcare professionals' opinions regarding health coaching for patients with diabetes: A pilot exploration in Indonesia

Background

Diabetes management is applied for the entire patients' lives, so it requires lifelong sustainable self-management actions to have a positive impact. Integrated care as coaching intervention is considered a program that facilitates and supports patients in managing diabetes more effectively and optimally. However, there are limited studies regarding this program in Indonesia.

Objective

This study aimed to explore the opinions of healthcare professionals concerning the importance of health coaching for patients with diabetes in Indonesia.

Methods

An invitation letter via email was distributed individually to participants from the three provinces of Java, Indonesia, between June and August 2020. The open-ended questions that consist of two sections were developed to explore the matter related to health coaching for patients with diabetes. A descriptive analysis of the participants' answers was used to explain the data comprehensively and accurately reveal the complete information.

Results

A total of seven healthcare professionals from four professions participated in the study. Based on healthcare professionals' opinions, this study revealed that the most common reason health coaching needs to be implemented is related to self-management in dealing with diabetes. Health coaching as a tailored-intervention strategy in diabetes self-management requires a multidisciplinary approach and considers the local wisdom to achieve the expected goals in all aspects of patients' lives. Thus, health coaching as an integral part of diabetes self-management is considered an appropriate program to cope with this problem.

Conclusion

Health coaching for patients with diabetes is useful and reasonable to implement among patients with appropriate strategies, especially in Indonesia and beyond.

Evidence for the Effectiveness of Feedback from Wearable Inertial Sensors during Work-Related Activities: A Scoping Review

Background: Wearable inertial sensor technology (WIST) systems provide feedback, aiming to modify aberrant postures and movements. The literature on the effects of feedback from WIST during work or work-related activities has not been previously summarised. This review examines the effectiveness of feedback on upper body kinematics during work or work-related activities, along with the wearability and a quantification of the kinematics of the related device. Methods: The Cinahl, Cochrane, Embase, Medline, Scopus, Sportdiscus and Google Scholar databases were searched, including reports from January 2005 to July 2021. The included studies were summarised descriptively and the evidence was assessed. Results: Fourteen included studies demonstrated a 'limited' level of evidence supporting posture and/or movement behaviour improvements using WIST feedback, with no improvements in pain. One study assessed wearability and another two investigated comfort. Studies used tri-axial accelerometers or IMU integration (n = 5 studies). Visual and/or vibrotactile feedback was mostly used. Most studies had a risk of bias, lacked detail for methodological reproducibility and displayed inconsistent reporting of sensor technology, with validation provided only in one study. Thus, we have proposed a minimum 'Technology and Design Checklist' for reporting. Conclusions: Our findings suggest that WIST may improve posture, though not pain; however, the quality of the studies limits the strength of this conclusion. Wearability evaluations are needed for the translation of WIST outcomes. Minimum reporting standards for WIST should be followed to ensure methodological reproducibility.

Effect of a mobile just-in-time implementation intention intervention on momentary smoking lapses in smoking cessation attempts among Asian American young adults

Identifying vulnerable windows for a given problematic behavior and providing timely and appropriate support are critical for building an effective just-in-time (JIT) intervention for behavioral change. We developed and evaluated an implementation intention (II) based, JIT cessation intervention prototype to support Asian American young adult smokers to prevent lapses in their cessation attempts in real-time. We examined how a JIT II reminder may prevent lapses during self-identified high-risk smoking situation (HRSS) as a microtemporal process. We also tested whether the effect of JIT reminder changes over the course of study and differed between those who used their own versus project loan phones. Asian American young adult smokers (N = 57) who were interested in quitting or reducing smoking participated in a 4 week, mobile-based, cessation study (MyQuit USC, MQU). MQU is a JIT mobile app that deploys a user-specified II reminder at user-specified HRSS and assesses momentary lapse status. Generalized mixed linear models were conducted to assess the effect of the JIT intervention on lapse prevention. We found a significant interaction effect (p = .03) such that receiving JIT reminder reduced the likelihood of lapses for participants using their own phones but not for the loaners. The results also showed that when participants enacted the suggested II, they were less likely to lapse (p < .001). The JIT effect did not change over time in study (p = .21). This study provides evidence that receiving a reminder of a smoker's own plan just before a self-identified risky situation on a familiar device and successfully executing specified plans can be helpful in preventing lapses. Our results highlighted factors to consider when designing and refining a JIT intervention.

A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics

Background: Digital processing has enabled the development of several generations of technology for tinnitus therapy. The first digital generation was comprised of digital Hearing Aids (HAs) and personal digital music players implementing already established sound-based therapies, as well as text based information on the internet. In the second generation Smart-phone applications (apps) alone or in conjunction with HAs resulted in more therapy options for users to select from. The 3rd generation of digital tinnitus technologies began with the emergence of many novel, largely neurophysiologically-inspired, treatment theories that drove development of processing; enabled through HAs, apps, the internet and stand-alone devices. We are now of the cusp of a 4th generation that will incorporate physiological sensors, multiple transducers and AI to personalize therapies. Aim: To review technologies that will enable the next generations of digital therapies for tinnitus. Methods: A "state-of-the-art" review was undertaken to answer the question: what digital technology could be applied to tinnitus therapy in the next 10 years? Google Scholar and PubMed were searched for the 10-year period 2011-2021. The search strategy used the following key words: "tinnitus" and ["HA," "personalized therapy," "AI" (and "methods" or "applications"), "Virtual reality," "Games," "Sensors" and "Transducers"], and "Hearables." Snowballing was used to expand the search from the identified papers. The results of the review were cataloged and organized into themes. Results: This paper identified digital technologies and research on the development of smart therapies for tinnitus. AI methods that could have tinnitus applications are identified and discussed. The potential of personalized treatments and the benefits of being able to gather data in ecologically valid settings are outlined. Conclusions: There is a huge scope for the application of digital technology to tinnitus therapy, but the uncertain mechanisms underpinning tinnitus present a challenge and many posited therapeutic approaches may not be successful. Personalized AI modeling based on biometric measures obtained through various sensor types, and assessments of individual psychology and lifestyles should result in the development of smart therapy platforms for tinnitus.

Personalized mobile technologies for lifestyle behavior change: A systematic review, meta-analysis, and meta-regression

Given that the one-size-fits-all approach to mobile health interventions have limited effects, a personalized approach might be necessary to promote healthy behaviors and prevent chronic conditions. Our systematic review aims to evaluate the effectiveness of personalized mobile interventions on lifestyle behaviors (i.e., physical activity, diet, smoking and alcohol consumption), and identify the effective key features of such interventions. We included any experimental trials that tested a personalized mobile app or fitness tracker and reported any lifestyle behavior measures. We conducted a narrative synthesis for all studies, and a meta-analysis of randomized controlled trials. Thirty-nine articles describing 31 interventions were included (n = 77,243, 64% women). All interventions personalized content and rarely personalized other features. Source of data included system-captured (12 interventions), user-reported (11 interventions) or both (8 interventions). The meta-analysis showed a moderate positive effect on lifestyle behavior outcomes (standardized difference in means [SDM] 0.663, 95% CI 0.228 to 1.10). A meta-regression model including source of data found that interventions that used system-captured data for personalization were associated with higher effectiveness than those that used user-reported data (SDM 1.48, 95% CI 0.76 to 2.19). In summary, the field is in its infancy, with preliminary evidence of the potential efficacy of personalization in improving lifestyle behaviors. Source of data for personalization might be important in determining intervention effectiveness. To fully exploit the potential of personalization, future high-quality studies should investigate the integration of multiple data from different sources and include personalized features other than content.

Development of Respercise® a Digital Application for Standardizing Home Exercise in COPD Clinical Trials

Background

Pulmonary rehabilitation (PR) is an important therapy for patients with chronic obstructive pulmonary disease (COPD), yet uptake remains low. Intervention strategies which recapitulate the benefits of PR are, therefore, needed and digital, home-based therapies present opportunity in this space. Digital therapies also potentially offer an opportunity to standardize PR in clinical trials for new COPD therapies.

Aims and Methods

We aimed to create a digital application (app), Respercise^®, consisting of up to 4 strengthening exercises in conjunction with Therbands™ and a daily physical activity program with individualized step goals, and to test its feasibility in a clinical trial. App usability was surveyed qualitatively before development iterations and deployment in a 13-week interventional clinical trial. All participants who completed the study were invited for an exit interview and performed the 5-repetition sit-to-stand test amongst other measures.

Results

Feedback from clinical trial participants was positive; 97% of respondents liked the app. A total of 88% of participants reported that it was easy to fit the exercises into their daily routine, and there was over 90% adherence for entering daily step counts. Notably, on day 90 both females and males using Respercise alone demonstrated a 2.22- and 2.27-seconds improvement in time for 5-repetition sit-to-stand tests respectively, above the 1.7 second threshold that is considered clinically meaningful in COPD.

Conclusions

Respercise can be successfully deployed in clinical trials, offering the opportunity for standardization of exercise in clinical trials and, with further development, could have wider reach as a home-based intervention for individuals with COPD.

On How Chronic Conditions Affect the Patient-AI Interaction: A Literature Review

Background: Across the globe, managing chronic diseases has been recognized as a challenge for patients and healthcare providers. The state of the art in managing chronic conditions requires not only responding to the clinical needs of the patient, but also guaranteeing a comfortable state of wellbeing for them, despite living with the disease. This demands mutual effort between the patient and the physician in constantly collecting data, monitoring, and understanding the disease. The advent of artificial intelligence has made this process easier. However, studies have rarely attempted to analyze how the different artificial intelligence based health coaching systems are used to manage different types of chronic conditions. Objective: Throughout this grounded theory literature review, we aim to provide an overview for the features that characterize artificial intelligence based health coaching systems used by patients with chronic diseases. Methods: During our search and paper selection process process, we use three bibliographic libraries (PubMed, IEEE Xplore, and ACM Digital Library). Using the grounded theory, we extract overarching themes for the artificial intelligence based health coaching systems. These systems are then classified according to their role, platform, type of interaction with the patient, as well as targeted chronic conditions. Of 869 citations retrieved, 31 unique studies are included in this review. Results: The included studies assess 14 different chronic conditions. Common roles for AI-based health coaching systems are: developing adherence, informing, motivating, reminding, preventing, building a care network, and entertaining. Health coaching systems combine the aforementioned roles to cater to the needs of the patients. The combinations of these roles differ between multilateral, unilateral, opposing bilateral, complementing bilateral, one-role-missing, and the blurred role combinations. Conclusion: Clinical solutions and research related to artificial intelligence based health coaching systems are very limited. Clear guidelines to help develop artificial intelligence-based health coaching systems are still blurred. This grounded theory literature review attempted to shed the light on the research and development requirements for an effective health coaching system intended for patients with chronic conditions. Researchers are recommended to use this review to identify the most suitable role combination for an effective health coaching system development.

Experiences of people with cancer who have participated in a hospital-based exercise program: a qualitative study

PURPOSE

Exercise interventions for people with cancer and cancer survivors improve physical health, fatigue, and quality of life. Despite these benefits, poor adherence to exercise is an ongoing challenge among this population. In order to improve adherence in clinical services, this study aims to explore the benefits, challenges, barriers, and facilitators experienced by people with cancer and cancer survivors who participated in a hospital-based exercise program, specifically those who completed or did not complete the full program.

METHODS

This study involved a qualitative approach. People with a cancer diagnosis who did complete (completers, n = 11) and did not complete (non-completers, n = 4) a 12-session exercise program at a tertiary hospital were recruited. Semi-structured interviews were conducted and thematic analysis was employed to identify emergent themes.

RESULTS

Perceived benefits of exercise was the most prominent theme to emerge, with most participants recognizing improvements in physical, mental, and/or social well-being. Non-completers focused on treatment-related side effects, whereas completers saw an opportunity to return to a healthy lifestyle. The transition from a supervised environment to everyday life presented as the most significant barrier to exercise beyond the program among both program completers and non-completers.

CONCLUSIONS

Most people with cancer identified physical, mental, and social benefits from exercising. However, people with cancer and cancer survivors had difficulty maintaining exercise participation beyond completion of a supervised hospital-based program.

IMPLICATIONS

Improving exercise participation in people with cancer and cancer survivors may require supervised exercise interventions plus the implementation of strategies to manage side effects and to facilitate the transition of exercise into everyday life to enhance long-term adherence.

Developing Empirical Decision Points to Improve the Timing of Adaptive Digital Health Physical Activity Interventions in Youth: Survival Analysis

Background

Current digital health interventions primarily use interventionist-defined rules to guide the timing of intervention delivery. As new temporally dense data sets become available, it is possible to make decisions about the intervention timing empirically.

Objective

This study aimed to explore the timing of physical activity among youth to inform decision points (eg, timing of support) for future digital physical activity interventions.

Methods

This study comprised 113 adolescents aged between 13 and 18 years (mean age 14.64, SD 1.48 years) who wore an accelerometer for 20 days. Multilevel survival analyses were used to estimate the most likely time of day (via odds ratios and hazard probabilities) when adolescents accumulated their average physical activity. The interacting effects of physical activity timing and moderating variables were calculated by entering predictors, such as gender, sports participation, and school day, into the model as main effects and tested for interactions with the time of day to determine conditional main effects of these predictors.

Results

On average, the likelihood that a participant would accumulate a typical amount of moderate-to-vigorous physical activity increased and peaked between 6 PM and 8 PM before decreasing sharply after 9 PM. Hazard and survival probabilities suggest that optimal decision points for digital physical activity programs could occur between 5 PM and 8 PM.

Conclusions

Overall, the findings of this study support the idea that the timing of physical activity can be empirically identified and that these markers may be useful as intervention triggers.

Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review

Non-oncologic chronic pain is a common high-morbidity impairment worldwide and acknowledged as a condition with significant incidence on quality of life. Pain intensity is largely perceived as a subjective experience, what makes challenging its objective measurement. However, the physiological traces of pain make possible its correlation with vital signs, such as heart rate variability, skin conductance, electromyogram, etc., or health performance metrics derived from daily activity monitoring or facial expressions, which can be acquired with diverse sensor technologies and multisensory approaches. As the assessment and management of pain are essential issues for a wide range of clinical disorders and treatments, this paper reviews different sensor-based approaches applied to the objective evaluation of non-oncological chronic pain. The space of available technologies and resources aimed at pain assessment represent a diversified set of alternatives that can be exploited to address the multidimensional nature of pain.

Real-Time Remote-Health Monitoring Systems: a Review on Patients Prioritisation for Multiple-Chronic Diseases, Taxonomy Analysis, Concerns and Solution Procedure

Remotely monitoring a patient's condition is a serious issue and must be addressed. Remote health monitoring systems (RHMS) in telemedicine refers to resources, strategies, methods and installations that enable doctors or other medical professionals to work remotely to consult, diagnose and treat patients. The goal of RHMS is to provide timely medical services at remote areas through telecommunication technologies. Through major advancements in technology, particularly in wireless networking, cloud computing and data storage, RHMS is becoming a feasible aspect of modern medicine. RHMS for the prioritisation of patients with multiple chronic diseases (MCDs) plays an important role in sustainably providing high-quality healthcare services. Further investigations are required to highlight the limitations of the prioritisation of patients with MCDs over a telemedicine environment. This study introduces a comprehensive and inclusive review on the prioritisation of patients with MCDs in telemedicine applications. Furthermore, it presents the challenges and open issues regarding patient prioritisation in telemedicine. The findings of this study are as follows: (1) The limitations and problems of existing patients' prioritisation with MCDs are presented and emphasised. (2) Based on the analysis of the academic literature, an accurate solution for remote prioritisation in a large scale of patients with MCDs was not presented. (3) There is an essential need to produce a new multiple-criteria decision-making theory to address the current problems in the prioritisation of patients with MCDs.

A systematic review of just-in-time adaptive interventions (JITAIs) to promote physical activity

BACKGROUND

Progress in mobile health (mHealth) technology has enabled the design of just-in-time adaptive interventions (JITAIs). We define JITAIs as having three features: behavioural support that directly corresponds to a need in real-time; content or timing of support is adapted or tailored according to input collected by the system since support was initiated; support is system-triggered. We conducted a systematic review of JITAIs for physical activity to identify their features, feasibility, acceptability and effectiveness.

METHODS

We searched Scopus, Medline, Embase, PsycINFO, Web of Science, DBLP, ACM Digital Library, Cochrane Central Register of Controlled Trials, ClinicalTrials.gov and the ISRCTN register using terms related to physical activity, mHealth interventions and JITAIs. We included primary studies of any design reporting data about JITAIs, irrespective of population, age and setting. Outcomes included physical activity, engagement, uptake, feasibility and acceptability. Paper screening and data extraction were independently validated. Synthesis was narrative. We used the mHealth Evidence Reporting and Assessment checklist to assess quality of intervention descriptions.

RESULTS

We screened 2200 titles, 840 abstracts, 169 full-text papers, and included 19 papers reporting 14 unique JITAIs, including six randomised studies. Five JITAIs targeted both physical activity and sedentary behaviour, five sedentary behaviour only, and four physical activity only. JITAIs prompted breaks following sedentary periods and/or suggested physical activities during opportunistic moments, typically over three to four weeks. Feasibility challenges related to the technology, sensor reliability and timeliness of just-in-time messages. Overall, participants found JITAIs acceptable. We found mixed evidence for intervention effects on behaviour, but no study was sufficiently powered to detect any effects. Common behaviour change techniques were goal setting (behaviour), prompts/cues, feedback on behaviour and action planning. Five studies reported a theory-base. We found lack of evidence about cost-effectiveness, uptake, reach, impact on health inequalities, and sustained engagement.

CONCLUSIONS

Research into JITAIs to increase physical activity and reduce sedentary behaviour is in its early stages. Consistent use and a shared definition of the term 'JITAI' will aid evidence synthesis. We recommend robust evaluation of theory and evidence-based JITAIs in representative populations. Decision makers and health professionals need to be cautious in signposting patients to JITAIs until such evidence is available, although they are unlikely to cause health-related harm.

REFERENCE

PROSPERO 2017 CRD42017070849.

User Models for Personalized Physical Activity Interventions: Scoping Review

Background

Fitness devices have spurred the development of apps that aim to motivate users, through interventions, to increase their physical activity (PA). Personalization in the interventions is essential as the target users are diverse with respect to their activity levels, requirements, preferences, and behavior.

Objective

This review aimed to (1) identify different kinds of personalization in interventions for promoting PA among any type of user group, (2) identify user models used for providing personalization, and (3) identify gaps in the current literature and suggest future research directions.

Methods

A scoping review was undertaken by searching the databases PsycINFO, PubMed, Scopus, and Web of Science. The main inclusion criteria were (1) studies that aimed to promote PA; (2) studies that had personalization, with the intention of promoting PA through technology-based interventions; and (3) studies that described user models for personalization.

Results

The literature search resulted in 49 eligible studies. Of these, 67% (33/49) studies focused solely on increasing PA, whereas the remaining studies had other objectives, such as maintaining healthy lifestyle (8 studies), weight loss management (6 studies), and rehabilitation (2 studies). The reviewed studies provide personalization in 6 categories: goal recommendation, activity recommendation, fitness partner recommendation, educational content, motivational content, and intervention timing. With respect to the mode of generation, interventions were found to be semiautomated or automatic. Of these, the automatic interventions were either knowledge-based or data-driven or both. User models in the studies were constructed with parameters from 5 categories: PA profile, demographics, medical data, behavior change technique (BCT) parameters, and contextual information. Only 27 of the eligible studies evaluated the interventions for improvement in PA, and 16 of these concluded that the interventions to increase PA are more effective when they are personalized.

Conclusions

This review investigates personalization in the form of recommendations or feedback for increasing PA. On the basis of the review and gaps identified, research directions for improving the efficacy of personalized interventions are proposed. First, data-driven prediction techniques can facilitate effective personalization. Second, use of BCTs in automated interventions, and in combination with PA guidelines, are yet to be explored, and preliminary studies in this direction are promising. Third, systems with automated interventions also need to be suitably adapted to serve specific needs of patients with clinical conditions. Fourth, previous user models focus on single metric evaluations of PA instead of a potentially more effective, holistic, and multidimensional view. Fifth, with the widespread adoption of activity monitoring devices and mobile phones, personalized and dynamic user models can be created using available user data, including users’ social profile. Finally, the long-term effects of such interventions as well as the technology medium used for the interventions need to be evaluated rigorously.

Effect of smartphone application-supported self-rehabilitation for frozen shoulder: a prospective randomized control study

Objectives:

To evaluate the clinical efficacy of smartphone-assisted self-rehabilitation in patients with frozen shoulder.

Design:

A single-center, randomized controlled trial.

Setting:

Orthopedic department of a university hospital.

Subjects:

A total of 84 patients with frozen shoulder were recruited.

Intervention:

Patients were randomly divided into two groups: a smartphone-assisted exercise group ( n = 42) and a conventional self-exercise group ( n = 42). The study was performed over three months, during which each group performed home-based rehabilitation.

Main measures:

Visual analogue scale for pain and passive shoulder range of motion were assessed at baseline and after 4, 8, and 12 weeks of treatment. Technology Acceptance Model–2 and Usefulness, Satisfaction, and Ease of Use scores were evaluated in the smartphone group.

Results:

Initial visual analogue scale for pain of the smartphone group was 6.0 ± 2.2 and ended up with 1.8 ± 2.5 after 12 weeks, whereas the self-exercise group showed 5.8 ± 2.3 for the baseline visual analogue scale for pain and 2.2 ± 1.7 at the end. Significant time-dependent improvements in all measured values were observed in both groups (all Ps < 0.001), but no significant intergroup difference was observed after 4, 8, or 12 weeks of treatment. In the smartphone group, Technology Acceptance Model–2 and Usefulness, Satisfaction, and Ease of Use scores showed high patient satisfaction with smartphone-assisted exercise.

Conclusion:

There was no difference between home-based exercise using a smartphone application and a conventional self-exercise program for the treatment of frozen shoulder in terms of visual analogue scale for pain and range of motions.

Real-Time Remote Health-Monitoring Systems in a Medical Centre: A Review of the Provision of Healthcare Services-Based Body Sensor Information, Open Challenges and Methodological Aspects

Promoting patient care is a priority for all healthcare providers with the overall purpose of realising a high degree of patient satisfaction. A medical centre server is a remote computer that enables hospitals and physicians to analyse data in real time and offer appropriate services to patients. The server can also manage, organise and support professionals in telemedicine. Therefore, a remote medical centre server plays a crucial role in sustainably delivering quality healthcare services in telemedicine. This article presents a comprehensive review of the provision of healthcare services in telemedicine applications, especially in the medical centre server. Moreover, it highlights the open issues and challenges related to providing healthcare services in the medical centre server within telemedicine. Methodological aspects to control and manage the process of healthcare service provision and three distinct and successive phases are presented. The first phase presents the identification process to propose a decision matrix (DM) on the basis of a crossover of 'multi-healthcare services' and 'hospital list' within intelligent data and service management centre (Tier 4). The second phase discusses the development of a DM for hospital selection on the basis of integrated VIKOR-Analytic Hierarchy Process (AHP) methods. Finally, the last phase examines the validation process for the proposed framework.

Real-Time Fault-Tolerant mHealth System: Comprehensive Review of Healthcare Services, Opens Issues, Challenges and Methodological Aspects

The burden on healthcare services in the world has increased substantially in the past decades. The quality and quantity of care have to increase to meet surging demands, especially among patients with chronic heart diseases. The expansion of information and communication technologies has led to new models for the delivery healthcare services in telemedicine. Therefore, mHealth plays an imperative role in the sustainable delivery of healthcare services in telemedicine. This paper presents a comprehensive review of healthcare service provision. It highlights the open issues and challenges related to the use of the real-time fault-tolerant mHealth system in telemedicine. The methodological aspects of mHealth are examined, and three distinct and successive phases are presented. The first discusses the identification process for establishing a decision matrix based on a crossover of 'time of arrival of patient at the hospital/multi-services' and 'hospitals' within mHealth. The second phase discusses the development of a decision matrix for hospital selection based on the MAHP method. The third phase discusses the validation of the proposed system.

Systematic Review of Real-time Remote Health Monitoring System in Triage and Priority-Based Sensor Technology: Taxonomy, Open Challenges, Motivation and Recommendations

The new and ground-breaking real-time remote monitoring in triage and priority-based sensor technology used in telemedicine have significantly bounded and dispersed communication components. To examine these technologies and provide researchers with a clear vision of this area, we must first be aware of the utilised approaches and existing limitations in this line of research. To this end, an extensive search was conducted to find articles dealing with (a) telemedicine, (b) triage, (c) priority and (d) sensor; (e) comprehensively review related applications and establish the coherent taxonomy of these articles. ScienceDirect, IEEE Xplore and Web of Science databases were checked for articles on triage and priority-based sensor technology in telemedicine. The retrieved articles were filtered according to the type of telemedicine technology explored. A total of 150 articles were selected and classified into two categories. The first category includes reviews and surveys of triage and priority-based sensor technology in telemedicine. The second category includes articles on the three-tiered architecture of telemedicine. Tier 1 represents the users. Sensors acquire the vital signs of the users and send them to Tier 2, which is the personal gateway that uses local area network protocols or wireless body area network. Medical data are sent from Tier 2 to Tier 3, which is the healthcare provider in medical institutes. Then, the motivation for using triage and priority-based sensor technology in telemedicine, the issues related to the obstruction of its application and the development and utilisation of telemedicine are examined on the basis of the findings presented in the literature.

Telemedicine is the way forward for the management of cystic fibrosis- the case against

It is reasonable to suggest that Telemedicine could help in the management of chronic diseases by giving patients more flexibility to remain at home with opportunities to forward electronic data to healthcare professionals, reduce hospital emergency attendances and reduce overall costs. The reality, particularly in cystic fibrosis care, is this has not happened. There is concern that home-generated lung function data is of poor quality and virtually no studies show improved outcomes. The UK has a poor record in developing novel IT programmes and we need many more well designed clinical studies in Telemedicine before wading in with ill-conceived expensive plans just because the idea seems interesting.

User Compliance and Behavioral Adaptation Associated With Supine Avoidance Therapy

This study investigates behavioral adaptation to vibrotactile position-avoidance therapy during sleep in patients with obstructive sleep apnea (n =135) across 15 to 52 weeks. The overall compliance, based on nights used ≥ 4 hr, was 71%. Overall regular use, that is, ≥ 4 hr/night over 70% of nights, was 88%. Poor early compliance strongly predicted poor long-term treatment adherence, with 92% of those noncompliant across the first 12 weeks of therapy remaining noncompliant. Conversely, 21% of those with compliant utilization in the short term became noncompliant in the long term. It appears that patients do not habituate to the stimulus during sleep, nor was there a training effect associated with long-term use.

Understanding Monitoring Technologies for Adults With Pain: Systematic Literature Review

BACKGROUND

Monitoring of patients may decrease treatment costs and improve quality of care. Pain is the most common health problem that people seek help for in hospitals. Therefore, monitoring patients with pain may have significant impact in improving treatment. Several studies have studied factors affecting pain; however, no previous study has reviewed the contextual information that a monitoring system may capture to characterize a patient's situation.

OBJECTIVE

The objective of this study was to conduct a systematic review to (1) determine what types of technologies have been used to monitor adults with pain, and (2) construct a model of the context information that may be used to implement apps and devices aimed at monitoring adults with pain.

METHODS

A literature search (2005-2015) was conducted in electronic databases pertaining to medical and computer science literature (PubMed, Science Direct, ACM Digital Library, and IEEE Xplore) using a defined search string. Article selection was done through a process of removing duplicates, analyzing title and abstract, and then reviewing the full text of the article.

RESULTS

In the final analysis, 87 articles were included and 53 of them (61%) used technologies to collect contextual information. A total of 49 types of context information were found and a five-dimension (activity, identity, wellness, environment, physiological) model of context information to monitor adults with pain was proposed, expanding on a previous model. Most technological interfaces for pain monitoring were wearable, possibly because they can be used in more realistic contexts. Few studies focused on older adults, creating a relevant avenue of research on how to create devices for users that may have impaired cognitive skills or low digital literacy.

CONCLUSIONS

The design of monitoring devices and interfaces for adults with pain must deal with the challenge of selecting relevant contextual information to understand the user's situation, and not overburdening or inconveniencing users with information requests. A model of contextual information may be used by researchers to choose possible contextual information that may be monitored during studies on adults with pain.

The effectiveness of the use of a digital activity coaching system in addition to a two-week home-based exercise program in patients after total knee arthroplasty: study protocol for a randomized controlled trial

BACKGROUND

There is consistent evidence that supervised programs are not superior to home-based programs after total knee arthroplasty (TKA), especially in patients without complications. Home-based exercise programs are effective, but we hypothesize that their effectiveness can be improved by increasing the adherence to physical therapy advice to reach an adequate exercise level during the program and thereafter. Our hypothesis is that an activity coaching system (accelerometer-based activity sensor), alongside a home-based exercise program, will increase adherence to exercises and the activity level, thereby improving physical functioning and recovery. The objective of this study is to determine the effectiveness of an activity coaching system in addition to a home-based exercise program after a TKA compared to only the home-based exercise program with physical functioning as outcome.

METHODS

This study is a single-blind randomized controlled trial. Both the intervention (n = 55) and the control group (n = 55) receive a two-week home-based exercise program, and the intervention group receives an additional activity coaching system. This is a hand-held electronic device together with an app on a smartphone providing information and advice on exercise behavior during the day. The primary outcome is physical functioning, measured with the Timed Up and Go test (TUG) after two weeks, six weeks and three months. Secondary outcomes are 1) adherence to the activity level (activity diary); 2) physical functioning, measured with the 2-Minute Walk Test (2MWT) and the Knee Osteoarthritis Outcome Score; 3) quality of life (SF-36); 4) healthcare use up to one year postoperatively and 5) cost-effectiveness. Data are collected preoperatively, three days, two and six weeks, three months and one year postoperatively.

DISCUSSION

The strengths of the study are the use of both performance-based tests and self-reported questionnaires and the personalized tailored program after TKA given by specialized physical therapists. Its weakness is the lack of blinding of the participants to treatment allocation. Outcomes are generalizable to uncomplicated patients as defined in the inclusion criteria.

TRIAL REGISTRATION

The trial is registered in the Dutch Trial Register ( www.trialregister.nl , NTR 5109) (March 22, 2015).

An m-Health system for education and motivation in cardiac rehabilitation: the experience of HeartCycle guided exercise

Introduction

Home-based programmes for cardiac rehabilitation play a key role in the recovery of patients with coronary artery disease. However, their necessary educational and motivational components have been rarely implemented with the help of modern mobile technologies. We developed a mobile health system designed for motivating patients to adhere to their rehabilitation programme by providing exercise monitoring, guidance, motivational feedback, and educational content.

Methods

Our multi-disciplinary approach is based on mapping “desired behaviours” into specific system’s specifications, borrowing concepts from Fogg’s Persuasive Systems Design principles. A randomised controlled trial was conducted to compare mobile-based rehabilitation (55 patients) versus standard care (63 patients).

Results

Some technical issues related to connectivity, usability and exercise sessions interrupted by safety algorithms affected the trial. For those who completed the rehabilitation (19 of 55), results show high levels of both user acceptance and perceived usefulness. Adherence in terms of started exercise sessions was high, but not in terms of total time of performed exercise or drop-outs. Educational level about heart-related health improved more in the intervention group than the control. Exercise habits at 6 months follow-up also improved, although without statistical significance.

Discussion

Results indicate that the adopted design methodology is promising for creating applications that help improve education and foster better exercise habits, but further studies would be needed to confirm these indications.

Applying and advancing behavior change theories and techniques in the context of a digital health revolution: proposals for more effectively realizing untapped potential

As more behavioral health interventions move from traditional to digital platforms, the application of evidence-based theories and techniques may be doubly advantageous. First, it can expedite digital health intervention development, improving efficacy, and increasing reach. Second, moving behavioral health interventions to digital platforms presents researchers with novel (potentially paradigm shifting) opportunities for advancing theories and techniques. In particular, the potential for technology to revolutionize theory refinement is made possible by leveraging the proliferation of "real-time" objective measurement and "big data" commonly generated and stored by digital platforms. Much more could be done to realize this potential. This paper offers proposals for better leveraging the potential advantages of digital health platforms, and reviews three of the cutting edge methods for doing so: optimization designs, dynamic systems modeling, and social network analysis.

Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams

Monitoring and modeling biomedical, health care and wellness data from individuals and converging data on a population scale have tremendous potential to improve understanding of the transition to the healthy state of human physiology to disease setting. Wellness monitoring devices and companion software applications capable of generating alerts and sharing data with health care providers or social networks are now available. The accessibility and clinical utility of such data for disease or wellness research are currently limited. Designing methods for streaming data capture, real-time data aggregation, machine learning, predictive analytics and visualization solutions to integrate wellness or health monitoring data elements with the electronic medical records (EMRs) maintained by health care providers permits better utilization. Integration of population-scale biomedical, health care and wellness data would help to stratify patients for active health management and to understand clinically asymptomatic patients and underlying illness trajectories. In this article, we discuss various health-monitoring devices, their ability to capture the unique state of health represented in a patient and their application in individualized diagnostics, prognosis, clinical or wellness intervention. We also discuss examples of translational bioinformatics approaches to integrating patient-generated data with existing EMRs, personal health records, patient portals and clinical data repositories. Briefly, translational bioinformatics methods, tools and resources are at the center of these advances in implementing real-time biomedical and health care analytics in the clinical setting. Furthermore, these advances are poised to play a significant role in clinical decision-making and implementation of data-driven medicine and wellness care.

The Mining Minds digital health and wellness framework

Background

The provision of health and wellness care is undergoing an enormous transformation. A key element of this revolution consists in prioritizing prevention and proactivity based on the analysis of people’s conducts and the empowerment of individuals in their self-management. Digital technologies are unquestionably destined to be the main engine of this change, with an increasing number of domain-specific applications and devices commercialized every year; however, there is an apparent lack of frameworks capable of orchestrating and intelligently leveraging, all the data, information and knowledge generated through these systems.

Methods

This work presents Mining Minds, a novel framework that builds on the core ideas of the digital health and wellness paradigms to enable the provision of personalized support. Mining Minds embraces some of the most prominent digital technologies, ranging from Big Data and Cloud Computing to Wearables and Internet of Things, as well as modern concepts and methods, such as context-awareness, knowledge bases or analytics, to holistically and continuously investigate on people’s lifestyles and provide a variety of smart coaching and support services.

Results

This paper comprehensively describes the efficient and rational combination and interoperation of these technologies and methods through Mining Minds, while meeting the essential requirements posed by a framework for personalized health and wellness support. Moreover, this work presents a realization of the key architectural components of Mining Minds, as well as various exemplary user applications and expert tools to illustrate some of the potential services supported by the proposed framework.

Conclusions

Mining Minds constitutes an innovative holistic means to inspect human behavior and provide personalized health and wellness support. The principles behind this framework uncover new research ideas and may serve as a reference for similar initiatives.

Mobile and Wireless Technologies in Health Behavior and the Potential for Intensively Adaptive Interventions

Recent advances in mobile and wireless technologies have made real-time assessments of health behaviors and their influences possible with minimal respondent burden. These tech-enabled real-time assessments provide the basis for intensively adaptive interventions (IAIs). Evidence of such studies that adjust interventions based on real-time inputs is beginning to emerge. Although IAIs are promising, the development of intensively adaptive algorithms generate new research questions, and the intensive longitudinal data produced by IAIs require new methodologies and analytic approaches. Research considerations and future directions for IAIs in health behavior research are provided.