Technologies for Monitoring Lifestyle Habits Related to Brain Health: A Systematic Review
Abstract
:1. Introduction
- Physical exercise: The regular practice of physical exercise has been shown to have a deep impact on mood and stress tolerance, improving depression and anxiety. In addition, physical activity can improve cognitive function and improve wellbeing in a number of neurodegenerative diseases. It also has been repeatedly associated with the upregulation of neurotrophic factors. Different studies have linked being active with a lower prevalence of neurological and psychiatric diseases [10,11].
- Sleep: Sleep disorders have implications for daily life, including fatigue, low performance, and difficulties to complete professional, family or social obligations. There is also a correlation between sleep disorders and neurological disorders [12]. Even in the absence of sleep disorders, the amount and quality of sleep have a major impact on brain health, cognitive function, and mental wellbeing.
- Nutrition: How much we eat and what we eat represent an important pillar for brain health. An unbalanced diet can result in a lack of nutrients, which can have a deep impact on our overall health. In addition, nutritional factors have been linked to diseases such as dementia or Alzheimer’s disease [10]. A balanced Mediterranean diet can impact cognitive function, and certain nutritional supplements might have an effect on mood, motivation, and initiative. Furthermore, the body mass index (BMI) appears to correlate with mental wellbeing and cognitive abilities [13,14].
- Cognitive activity: As we get older, our brains require less strain to perform everyday activities. However, our brain needs to face new challenges in order to stay healthy. It is as important to “exercise” our mind as it is to exercise our bodies. Cognitive impairment can be the result of neurological diseases such as Alzheimer’s disease or Parkinson’s disease. Keeping an active brain can preserve brain plasticity and promote brain resilience and cognitive reserve. Cognitive activity can, but does not necessarily have to, involve computer-supported cognitive training [15].
- Vital plan: Meaning in life and life purpose are the focus of many psychology studies from the last decades of 20th century [16,17,18] and alterations or lack of a defined vital plan are associated with many disorders like anxiety, depression, or even mortality. These disorders are known to interfere in brain health [19]. Our human brain has a property that animals lack: It allows us to project ourselves into the future. Prospecting, the ability to imagine what it will be like to try to make a goal or a dream into a reality, is an essential function for our brain and we need to encourage it by defining a vital plan, a purpose in life that transcends us as individuals. This is so important that it seems to mediate the effect of all other pillars onto our brain health.
- Social interactions: We are social beings and our brain needs relationships. The time spent with family and friends or getting to know and relating to our neighbors and colleagues is important. Loneliness is not only bad for brain health, it is a deadly disease. Individuals with a high number of social interactions experience significantly less cognitive decline compared to those who are lonely or isolated [20,21]. It also has been shown that social interactions and environment can help to improve brain plasticity after a brain lesion [22].
- Overall health: Overall health is an important factor due to the existing strong relations between overall health and brain health. For example, there is a close link between chronic diseases and depression [23,24] and systemic diseases, such as diabetes or hypertension, pose critical risks for brain health. Therefore, we should have check-ups, go to the doctor regularly, follow their recommendations, and pay attention to the conditions and diseases we have. However, we now also know that the opposite direction is also important, good mental and brain health promote overall health and wellbeing.
2. Materials and Methods
2.1. Keywords Definition
- General: Terms that define the main field of the study. In this case, terms related to brain health or cognitive functioning, including cognitive deterioration and cognitive reserve. The terms ‘brain health’ and ‘cognitive’ (which include terms above and more) were chosen.
- Associated: Terms associated with the topic. In this case, terms associated with cognitive decline (e.g., age, aging).
- Pillars: Terms that are associated with the specific pillars of intervention (as defined in [3]) identified as critical variables that affect brain health. (e.g., nutrition, sleep or socialization).
- Techniques: Terms that are often used in projects related to interventions and monitoring of daily life activities (e.g., intervention, monitoring, adherence, etc.).
- Technologies: Technical terms that usually appear in studies related to eHealth and telemedicine (e.g., wearable, eHealth, ICT, etc.).
2.2. Identification
2.3. Screening & Eligibility
- Population: Participants had to be at least 18 years old as we aimed to focus on adults and exclude pediatric populations. Participants had to be healthy and thus could not be diagnosed with any particular disease or disability.
- Intervention: The intervention should not be related to one particular disease (e.g., Alzheimer’s disease or multiple sclerosis) or to any single particular ability or problem (e.g., driving). Rather, the intervention should be focused on habit improvement and daily life monitoring. It also must involve the use of at least one of the following technologies: (1) Web application (2) mobile phone (3) wearables (4) biosensors (5) medical devices (e.g., fMRI) (6) computer tasks.
- Comparator: Both placebo and active interventions were taken into consideration.
- Outcome: The outcome must be referred to core aspects of brain health or cognitive function, including (but not exclusively) one or more of the seven pillars (e.g., sleep or physical activity). Ideally, the publications should also contain an outcome of therapy adherence or usability.
- Timeframe: Both short- and long-term outcomes were taken into account.
- Study: Studies could be either randomized controlled trials (RCTs) or observational studies. Protocols, systematic reviews, nonsystematic reviews, case studies, commentaries, and letters or editorials were excluded.
2.4. Included
- Heavy monitoring: When people needed a hospital or a controlled site to do specific tasks or specific tests.
- Medium monitoring: When participants were monitored using smartphones, wearables or biosensors that are not intrusive.
- Light monitoring: When participants were only monitored using questionnaires and tests or providing self-report data, through web or mobile applications.
- No monitoring: When no monitoring took place or is not reported.
- Studies were also subcategorized according to how the intervention was carried out. We defined two categories:
- Dynamic intervention: When the intervention was adaptive and could change to fit the participant’s behavior patterns and evolution.
- Static intervention: When the intervention was the same for all participants, based on pre-specified criteria and rules, and was not modified throughout the study.
3. Results and Discussion
3.1. Distribution on Pillars
3.2. Monitoring
3.3. Intervention Style
3.4. Technology Used
3.5. Technologies Related to Pillars
- The vast majority (75%) of studies related to physical exercise focus on proofs of concept and use specific controlled environments, where they integrate or replicate the sensors that could be found in a wearable device. Future studies, therefore, are likely to employ wearables to capture similar outcomes.
- Nutrition is difficult to monitor with sensors, so it is usual to find that both, monitoring and intervention, are carried out with questionnaires and guidelines. This is why web and mobile applications are the most used (75% of them).
- Surprisingly, the same occurs with the cognitive pillar, where only tasks or questionnaires are used. Future studies ought to leverage mobile trackers, wearables, and phones to try to capture relevant information regarding cognitive function in a real-life setting and employing passive, non-intrusive designs.
- Although there exist some non-intrusive devices to measure brain signals (mainly EEG), these are not yet comfortable, portable or reliable enough to use in daily life tasks and in long periods.
3.6. Demographic Data
3.7. Correlation between Lifestyle Habits Factors and Brain Health
3.8. Limitations and Out of Criteria Studies
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Index | Query | Category |
---|---|---|
1 | ( “brain health” OR “cognitive” ) | General Terms |
2 | ( “brain health” OR “cognitive” OR “young elders” OR “aging” OR “older adults” OR “ageing” OR “elderly” OR “aged” OR “older person” OR “geriatrics” ) | Associated Terms |
3 | ( “nutrition” OR “diet” OR “physical” OR “physical exercise” OR “physical activity” OR “cognitive” OR “cognition” OR “cognitive activity” OR “cognitive training” OR “social” OR “socialization” OR “vital plan” OR “purpose in life” OR “psychological wellbeing” OR “mindfulness” OR “general health” OR “comprehensive health” OR “global health” OR “sleep” OR “sleeping” OR “relax” OR “rest”) | Pillar related Terms |
4 | ( “adherence” OR “motivation” OR “monitoring” OR “coaching” OR “coach” OR “treatment” OR “intervention” OR “exercise” ) | Technique related terms |
5 | ( “smartphone” OR “mobile” OR “ICT” OR “RMT”OR “mHealth” OR “eHealth” OR “data mining” OR “predictor” OR “machine learning” OR “deep learning” OR “neuronal network” OR “artificial intelligence” OR “computer” OR “biosensor” OR “wearable” OR “technology” OR “technologies”) | Technology related terms |
6 | ( “observational study” OR “controlled study” ) | Study filter |
7 | NOT ( “schizophrenia” ) AND NOT ( “cancer” ) AND NOT ( “pediatrics” ) AND NOT ( “epilepsy” ) AND NOT ( “drugs” ) AND NOT ( “diabetes” ) AND NOT ( “stroke” ) AND NOT ( “dementia” ) AND NOT ( “transplant” ) AND NOT ( “fracture” ) AND NOT ( “traumatic” ) AND NOT ( “surgical” ) AND NOT ( “EEG” ) AND NOT ( “disorder” ) | Exclusions |
8 | [1] AND [2] AND [3] AND [4] AND [5] AND [6] AND [7] | Resultant query |
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Categories | Terms |
---|---|
General | Brain Health; Cognitive |
Associated | Older Adults; Aging; Ageing; Elderly; Geriatrics; Young elders; Aged; Older Person |
Pillars | Nutrition; Physical exercise; Cognition; Social; Purpose in life; General health; Diet; Physical activity; Cognitive activity; Socialization; Psychological wellbeing; Comprehensive health; Physical; Cognitive; Cognitive training; Vital plan; Mindfulness; Rest; Sleep; Sleeping; Relax; Global health |
Techniques | Exercise; Coach; Intervention; Coaching; Treatment; Monitoring; Adherence; Motivation |
Technologies | Wearable; Computer; ICT 1; Machine learning; Data mining; RMT 2; Data mining; Artificial intelligence; Deep Learning; eHealth; mHealth; Biosensor; Neuronal network; Predictor; Mobile; Smartphone; Technology |
First Author, Year | Region | Variable | Study Design/Study Duration | Methodology and Technologies | Sample Size | Age Groups | Feedback Loop/End-User | Results | Funding Body |
---|---|---|---|---|---|---|---|---|---|
Commissaris et al. (2014) [29] | Netherlands and Germany | Physical exercise | Cohort/1 working day (7–8 h) | Heart Rate Monitor/3D kinematics measurement system | 15 | 29 years (SD 12) | Office Workers and Employers | Neutral | German Social Accident Insurance (DGUV) |
Mourad et al. (2016) [30] | Sweden | Life purpose 1 | RCT/4 weeks | Internet-delivered program with questionnaires | 15 | 22–76 | Self/User | Neutral | County Council of Östergötland/Medical Research of Southeast Sweden |
D. Wirth et al. (2015) [31] | South Carolina (USA) | Nutrition | Cohort/14 days | Phone questionnaires | 430 | 21–35 | NR | Positive | Coca Cola Company |
Pavel et al. (2016) [32] | NR | Life purpose 2 | RCT/25 weeks | Mobile phone | 204 | NR | Self/User | Positive | NR |
Ramnath et al. (2018) [33] | South Africa | Physical exercise and cognitive activity | Cohort/1 session | Questionnaires & physical tasks | 70 | 65–84 | Self/User | Neutral | NR |
Phatak et al. (2017) [34] | United States | Physical exercise | Cohort/14 weeks | Fitbit Zip/Mobile App/Personalization | 20 | 40–65 | Self/User | Positive | National Science Foundation |
Lange et al. (2018) [35] | Germany | Nutrition | Cohort/2 years | Web App | 3000 | 41,5 (SD 11.9) | Self/User | Positive | German Ministry of Education and Research |
Merriman et al. (2018) [36] | Ireland | Physical exercise | RCT/5 weeks | PC game/Wii Balance Board/Gamification/Serious Game | 70 | 65–84 | Self/User | Positive | European Commission Seventh Framework Programme ‘VERVE’ Project and by Principal Investigator award and TIDA award to FNN from Science Foundation Ireland |
Roepke et al. (2015) [37] | World | Life purpose 3 | RCT/6 weeks | Smartphone-Based/Internet-Based Self-Help Tool | 283 | 40.15 (SD 12.4) | Self/User | Neutral | Private donation |
Veronese et al. (2016) [38] | Italy | Physical exercise | Cohort/4.4 years | Data Analysis | 3099 | >65 | NR | Positive | Fondazione Cassa di Risparmio di Padova e Rovigo/University of Padova/Azienda Unità Locale Socio Sanitaria |
Konstantinidis et al. (2014) [39] | Europe | Physical exercise | Cohort/7–8 weeks | Serious Game/Computer application/Data analysis/Exergaming/Wii Balance Board | 116 | >65 | Self/User | Positive | European Union |
Rodrigues et al. (2017) [40] | Portugal | Nutrition and Physical exercise | RCT/6 months | TV app | 282 | >60 | Self/User | NR | European Economic Area |
Zielhorst et al. (2015) [41] | Netherlands | Life purpose 4 | Cohort/10–15 days | CBT/Gamification | 101 | 24–63 | Self/User | Positive | NR |
Vercelli et al. (2017) [42] | Europe, Australia, and Asia | Life purpose 5 | NR | Smartphone app/wearables | NR | >65 | Self/User | NR | European Union |
Robertson et al. (2015) [43] | United States | Cognitive activity | RCT/1 h | Mobile app/Motion sensors/Real Time Annotation Tool | 42 | 19.88 | Self/User | Positive | National Science Foundation |
First Author, Year | Region | Variable | Study Design/Study Duration | Methodology and Technologies | Sample Size | Age Groups | Feedback Loop/End-User | Results | Funding Body | Exclusion |
---|---|---|---|---|---|---|---|---|---|---|
Robert et al. (2013) [45] | France and Taiwan | Physical exercise | Cohort/1 day | Intelligent room (2D video camera, ambiance microphone, motion sensor, and tri-axial accelerometer mounted on the shoes) | 64 | >65 | Therapist | Positive | Innovation Alzheimer and ARMEP associations | Alzheimer |
Chen et al. (2013) [46] | Australia | Cognitive activity | Cohort/1 session | FaceLAB for pupil dilation and position | 15 | 20–48 | Therapist | Negative | Australian Government | No Brain Health |
Cerasa et al. (2014) [47] | Italy | Cognitive activity | RCT/6 weeks | RehaCom (Cognitive training tasks), 3T Scanner for images | 20 | 61.1 (12.4 SD) | Therapist | Positive | Ministerio Univesita’ e Ricerca | Parkinson |
Baglio et al. (2015) [48] | Italy | Stress. Multidisciplinary intervention | RCT/32 Weeks | fMRI and questionnaires | 60 | 65–85 | Therapist | Positive | Ricerca Corrente (Italian Ministry of Health) | Alzheimer |
Manzoni et al. (2016) [49] | Italy | Habits | RCT/11 weeks | Virtual Reality/CBT | 158 | 18–50 | Self/Patient | Positive | NR | Obese people |
Mehrabian et al. (2018) [50] | France | Intervention | Cohort/40 min | Interviews + web app | 92 | 54–85 | Patient/Caregiver | Positive | National Research Agency and the Foundation Mederic Alzheimer | Cognitively impaired/caregivers |
Cerasa et al. (2013) [51] | Italy | Cognitive Function | RCT/6 weeks | fMRI/cognitive computerized tasks | 26 | 32 (SD 10) | Clinicians | Positive | Fondazione Italiana Sclerosi Multipla onlus and Ministero Universita’ e Ricerca | Multiple sclerosis |
Evensen et al. (2017) [52] | Norway | Physical Activity | Cohort/3 months | accelerometers/activePal | 38 | 82.9 (SD 6.3) | Clinicians | Positive | Liaison Committee between the Central Norway Regional Health Authority and the Norwegian University of Science and Technology | Hospitalized patients |
Hacker et al. (2015) [53] | USA | Personalization | Cohort/4, 20 days | Web application | 176 | 11 to 15 | Self/User | Positive | National Science Foundation | Not health-oriented |
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Moreno-Blanco, D.; Solana-Sánchez, J.; Sánchez-González, P.; Oropesa, I.; Cáceres, C.; Cattaneo, G.; Tormos-Muñoz, J.M.; Bartrés-Faz, D.; Pascual-Leone, Á.; Gómez, E.J. Technologies for Monitoring Lifestyle Habits Related to Brain Health: A Systematic Review. Sensors 2019, 19, 4183. https://doi.org/10.3390/s19194183
Moreno-Blanco D, Solana-Sánchez J, Sánchez-González P, Oropesa I, Cáceres C, Cattaneo G, Tormos-Muñoz JM, Bartrés-Faz D, Pascual-Leone Á, Gómez EJ. Technologies for Monitoring Lifestyle Habits Related to Brain Health: A Systematic Review. Sensors. 2019; 19(19):4183. https://doi.org/10.3390/s19194183
Chicago/Turabian StyleMoreno-Blanco, Diego, Javier Solana-Sánchez, Patricia Sánchez-González, Ignacio Oropesa, César Cáceres, Gabriele Cattaneo, Josep M. Tormos-Muñoz, David Bartrés-Faz, Álvaro Pascual-Leone, and Enrique J. Gómez. 2019. "Technologies for Monitoring Lifestyle Habits Related to Brain Health: A Systematic Review" Sensors 19, no. 19: 4183. https://doi.org/10.3390/s19194183
APA StyleMoreno-Blanco, D., Solana-Sánchez, J., Sánchez-González, P., Oropesa, I., Cáceres, C., Cattaneo, G., Tormos-Muñoz, J. M., Bartrés-Faz, D., Pascual-Leone, Á., & Gómez, E. J. (2019). Technologies for Monitoring Lifestyle Habits Related to Brain Health: A Systematic Review. Sensors, 19(19), 4183. https://doi.org/10.3390/s19194183