Promoting successful aging through the prevention of non-communicable diseases, such as dementia, cardio-metabolic diseases (cardiovascular (eg stroke, angina) or metabolic (eg dyslipidemia, obesity) diseases or mental health problems among older adults has become one of the main World Health Organization (WHO) priorities.1 Indeed, non-communicable diseases represent 71% of all deaths worldwide with a higher burden among middle-aged and older adults.2 Most cardio-vascular diseases could be prevented by modifying their risk factors; for example such as improving physical activity, stopping smoking, having a healthy diet, controlling weight or blood pressure, or decreasing cholesterol, lipids or blood sugar could also have an effect on cardio-vascular disease incidence.3 Moreover, these risk factors are also known to be associated with dementia incidence.4,5 Therefore, managing these risk factors could contribute to both cognitive and cardio-metabolic disease prevention. Even though some studies suggest that many older adults have only basic health literacy,6 others suggest that middle-aged and older adults may be attentive to these factors, know the notion of risk for them7 and could be prone to initiate healthy behavior changes with the help of promotional interventions.
Information and communication technologies could help to promote healthier behaviors, particularly through the use of mobile technology tools such as smartphones. Mobile and smartphone accessibility has rapidly increased worldwide, first, in high income countries and now also in low or middle-income countries.8 The number of mobile phone was reached 7 billion in 2021 worldwide, of whom 6.3 billion were smartphone users.9 The number of mobile applications is also increasing worldwide, with, for example, 47,478 iOS applications classified as “health care” applications available in the first quarter of 2020.10 Adults over 65 years are increasingly using smartphones, but there are evident disparities, with the oldest individuals, those with lowest household income and lowest levels of education less often owning a smartphone11 and more often using simple message and weather applications rather than more interactive ones, compared to younger individuals.12,13 These disparities are also evident in the telemedicine field, although some studies have shown that telemedicine is feasible among older populations.14–18
Telephone and smartphone interventions may be interesting tools for prevention in middle-aged and older adults given their common usage, inexpensive cost, and accessibility. Moreover, mobile-phones are portable, and well-integrated in daily life. Finally, mobile phones are a potentially powerful tool for monitoring and communicating with patients (and thus providing interventions) in a continuous way. In a clinical trial setting, they may improve recruitment rates by reducing participant burden, for example by reducing/removing travel to study visits.19 They could therefore be a powerful method for encouraging behavior changes and consequently having an impact on long-term disease prevention. Mobile phones also enable “big data” approaches through the collection of medical (and other) data in a non-decontextualized (ecological) environment via text messages,20 mobile applications and smartphone sensors (accelerometer, location, etc.). Telephones could also facilitate cognitive assessment21 and improve early detection of cognitive and functional decline22 or other health issues. However, clinical trials using such tools in older populations could be particularly affected by selection bias, given the disparities in use in older populations.
Despite the many promising advantages, it is necessary to evaluate the evidence concerning the effectiveness of such strategies.
Thus, the main aim of this review was to assess the effectiveness of telephone and smartphone-based interventions to prevent cognitive decline, dementia or cardiometabolic diseases, which share many risk factors, in older adults.
When data were available, we described strategies measuring intervention adherence, factors associated with adherence and the implementation of such strategies.
Materials and Methods
Searches were run in PubMed (including MEDLINE) and the WHO International Clinical Trials Registry Platform (ICTRP), which includes various registries from around the world (eg International Standard Randomised Controlled Trials Number (ISRCTN), Clinicaltrials.gov, Australian New-Zealand Clinical Trials Registry (ANZCTR), Brazilian Clinical Trials Registry), until 17 January 2022, using the search equations outlined in the Appendix.
Eligibility Criteria (See Appendix Box A)
Studies that met the following criteria were included:
- Inclusion of individuals aged 50 and over living independently at home
- Randomized clinical trial (RCT), quasi-experimental or pilot study
- Assessment of efficacy (as a primary or secondary outcome) of mobile-phone or other telephone interventions targeting the prevention of cognitive decline, dementia or cardio-metabolic diseases or their risk factors (overweight, physical activity, sedentarity, sugar or lipid profile, diet food and nutrition)
- Articles written in English
We excluded studies focusing on other interfaces, such as tablet, computer or personal digital assistants, because we wanted to exclusively focus on telephone-interventions since they are more portable and more integrated into everyday routine. We included all kinds of telephone interventions, ie telephone calls, short messages system (SMS), applications or telephone-accessible platforms. For cognitive outcomes, as we focused on studies concerning the prevention of dementia or cognitive impairment, we excluded studies involving participants with serious diseases likely to affect cognitive function (dementia, depression, schizophrenia, Parkinson’s disease), but we did not exclude studies of participants with mild cognitive impairment or subjective cognitive decline. For cardio-metabolic outcomes, we excluded participants with congenital cardio-vascular disease. We also excluded studies of advanced disease (cancer, palliative care, malnutrition).
Each study’s eligibility was assessed by two authors (LA and CG). Study selection was firstly based on title and abstract, and the full-text was read where necessary. Publications which possibly met inclusion criteria were then assessed by both investigators independently. In cases of discordances regarding the eligibility of an article, there was discussion between the two investigators until consensus was reached.
The reference lists of the eligible articles were also checked in order to identify other studies of potential interest that were not identified in the literature search, as well as the reference lists of selected reports and papers in our own files.
Data were extracted by one author from each study regarding setting, participant characteristics, intervention description and outcomes.
We assessed the quality of completed studies using the items of the RoB2 (Version 2 of the Cochrane risk-of-bias tool for randomized trials).23 Quality is defined into 3 levels: low risk of bias, some concerns and high risk of bias. Due to a lack of existing scales, we used the TREND (Transparent Reporting of Evaluations with Nonrandomized Designs) statement items to evaluate the quality of non-randomized controlled studies.24 We divided scores into 3 groups. Poor quality was defined by a score of ≤9 criteria, good quality by a score of >18 criteria, and fair quality by a score between 10 to 18.
Telephone and Smartphone Interventions for the Prevention of Cognitive Decline or Dementia
The Pubmed searches yielded 989 papers, of which 10 were eligible for our review (Figure 1). Of the 10 included articles, 3 described completed studies, 7 detailed protocols of ongoing studies.
Figure 1 Study selection flow-chart for the cognitive outcome articles.
In the ICTRP, 16 ongoing studies were included of the 856 identified (Figure 1).
Table 1 Characteristics of Completed Studies Evaluating the Effect of Telephone and Smartphone Interventions on Cognitive Outcomes
Based on the RoB2 and TREND Checklist scoring systems, only one study was at low risk of bias.27 The other two studies had higher risks of bias. For example, Oh et al26 did not detail the allocation sequence generation and did not describe who was blinded. For the remaining study,25 details concerning location, date of inclusion and setting details of the intervention as well as side effects were missing.
Description of Completed Studies (Table 1)
Sakakibara et al27 evaluated in a low-risk of bias randomized clinical trial, the effect of telephone lifestyle coaching sessions (aiming to improve control of cardio-metabolic risk factors) among 126 Canadian stroke survivors adults aged 50 years and over without cognitive impairment. Participants received stroke risk factors manual, a kit to monitor risk factors and 7 coaching telephone sessions of 30–45 minutes to coach, motivate and help them to change lifestyle and 5 additional follow-up calls over 6 months. Control group received a memory training, an agenda to make reminder notes and 7 memory coaching telephone sessions of 30–45 minutes and 5 additional follow-up calls over 6 months. At 12 months, there was no change in MoCA (Montreal Cognitive Assessment), studied as tertiary outcome in intervention group compared with a memory training control group.
Oh and al26 evaluated, in an 8-week randomized trial, the effect of a smartphone-based brain training application (Smartphone-based brain Anti-aging and memory Reinforcement Training (SMART)) among 53 South-Korean adults with subjective memory complaints and a Mini Mental State Examination (MMSE) score greater or equal to 24 (mean baseline scores: 28.06 (2.04) in the SMART group, 28.68 (1.06) in the fit Brain group and 28.25 (1.57) in the wait list group). This application, which targeted attention and working memory, was compared with another commercialized cognitive training application and a control group (no intervention). Participants used applications 5 times a week for 15 to 20 minutes. Weekly telephone calls and text messaging assessed progress and conscientious participation. The study found significant improvement between pre and post intervention on working memory for SMART group only, but not for the other tests. It should be noted that this was a poor-quality study, with several limitations, including.
The last study,25 evaluated in an uncontrolled single arm study, a computer-based multidomain lifestyle intervention with telephone and email or text health support for a single group of 82 American aged 60–75 year-old with subjective cognitive decline. Participants received personalized coaching sessions, focusing on nutrition, physical activity, cognitive training and social engagement. The authors found significant improvement at 52 weeks compared to baseline scores for the primary outcomes on the total Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) scores (mean improvement=5.8, p<0.001), memory (immediate recall mean improvement=7.8, p<0.001, delayed recall mean improvement=5.4, p<0.001), language (mean improvement=5.7, p<0.001). Immediate recall significantly decreased at 24 weeks, but no other significant differences were found.
Description of Ongoing Studies
Australia is the country most frequently enrolling participants in the ongoing studies (7 studies). Six studies are being conducted in Asia, 4 in European countries, and 3 in North/South America (Canada, Brazil and USA). The remaining three are enrolling participants in multiple locations (Australia, Europe, Asia for one study, China and United Kingdom for the second, and Germany, the Netherlands and Norway in the third). (Appendix Table A and Figure 1)
The interventions are due to last between 4 weeks and 36 months, with a median of 12 months of follow-up. Moreover, the number of participants varies highly, from 9 for the smallest study to 3498 in the largest.
Ten of the studies are enrolling participants considered to be at risk of cognitive decline, due to the presence of memory complaints, mild cognitive impairment or stroke comorbidity (without cognitive impairment), and the others are including participants with no specific risk factors.
The interventions tested in the ongoing trials can be divided into 2 categories:
The first, and most frequent, is mobile-phone applications or mobile health interventions, which are being used by 14 studies28–31 (CTRI/2018/01/011090, NCT03058146, DRKS00010595, NCT04692974, NCT04184037, ACTRN12619001634167, DRKS00020943, JPRN-UMIN000041926, ACTRN12620001037998, JPRN-UMIN000042123). Two of these studies (CTRI/2018/01/011090, JPRN-UMIN000042123) are using mobile phone-based cognitive training, one a physical exercise program (ACTRN12620001037998), another a meditation application (NCT04184037) while the others are using mobile phone interventions for training, monitoring, tracking, supporting and/or promoting healthy behaviors28–31 (NCT03058146, DRKS00010595, NCT04692974, ACTRN12619001634167, DRKS00020943, JPRN-UMIN000041926).
The second type of intervention is telephone support and coaching which is being evaluated in 7 studies32–34 (ACTRN12617000082303, NCT01012947, ACTRN12621000977875, ACTRN12620000978965). In these studies, telephone calls or messages are used to provide advice or encouragement, improve risk factor control, or increase adherence, or are used as reminders to carry out the intervention. For instance, Cox et al32 are evaluating the effect of mentor telephone counselling. Another study (ACTRN12618000513213) is evaluating the effect of standardized reminder or reinforcement messages.
The effects of telephone interventions on cognitive function are being evaluated as a primary outcome in 12 studies, as a secondary outcome in 9 studies, using neuropsychological scores or changes on a single test (n=6) or on a battery of neuropsychological tests (n=16).
Finally, two study are evaluating the impact of a smartphone intervention on a dementia incidence, as a primary or secondary outcome (Eggink et al,31 JPRN-UMIN000041926) and 2 trials on dementia risk score (Eggink et al,31 ACTRN12621000977875)).
Few studies reported implementation results. In a 3-month pilot phase, the portability, usability and acceptability of a physical, cognitive, psychological and social mobile platform intervention were evaluated in a limited sample of 20 participants. Protocols, questionnaires and platform technical aspects were found to be good.29 This study29 concluded that the intervention platform was suitable (no additional details were provided), and another31 improved functionality of application and logistic issues before beginning the main trial. Adherence, duration, frequency of use or feedback will be recorded in several trials.28–30,32 For instance, Summer et al29 are recording duration and frequency of mobile application use and time spent on cognitive training, and another study30 is recording the number of logins and days using the mobile application.
Telephone and Smartphone-Based Interventions for Cardio-Metabolic Outcomes and Risk Factors
Of the 2680 articles identified in Pubmed, 30 (1.1%), published from 2007 onwards, were included (Figure 2). In the ICTRP, we identified 2207 records, and 15 studies were included. In total, 18 completed and 27 ongoing studies were analyzed.
Figure 2 Study selection flow-chart for the cardio-metabolic outcome articles.
Based on the Rob2 and TREND checklists, the 18 completed studies,27,35–51 4 were of good quality,27,37,44,46 and the rest were fair or poor of quality. Among the lowest quality studies, missing information included randomization allocation,35,39 or sample size calculations, for example. In one study, we did not find details concerning blinding of participants39 or if assessor was blinded during assessment of outcomes.40,42
Description of Completed Studies (Tables 2–4)
Five of the studies included participants from the USA, while the others included participants from the Australia (n=4), UK (n=2), China (n=1), Belgium (n=1), Netherland (n=1), Canada (n=1), South Korea (n=1), Singapore (n=1) and New-Zealand (n=1).
Table 2 Characteristics of Completed Studies Evaluating the Effect of Smartphone Interventions on Cardio-Vascular Outcomes
Table 3 Characteristics of Completed Studies Evaluating the Effect of Telephone Call Interventions on Cardio-Vascular Outcomes
Table 4 Characteristics of Completed Studies Evaluating the Effect of Text Messaging or Text Messaging with Telephone Calls Interventions on Cardio-Vascular Outcomes
Participants at risk of cardio-metabolic diseases were included in 15 studies, defined by the presence of stroke comorbidities,27,40 type 2 diabetes,36–38,41,42,49 obesity,35,44,46 hypertriglyceridemia and/or high blood pressure41,48 or sedentarism.45,47,50 Interventions lasted between 2 weeks and 24 months.
Telephone vocal support to motivate participants to reach a goal, increase intervention adherence or resolve problems was the most frequent type of telephone intervention used, representing 9 studies’ interventions.27,35–37,40,43–45,50
Automated motivational or reminder text messaging for adherence was used for 5 studies.39,41,46,48,51 A wired telephone-connected glucometer associated with short message service and telephone technical support was evaluated in one study,38 while another study49 studied a self-monitoring dietary intake application for weight loss. The final study42 evaluated a website with motivational sessions to increase physical activity or decrease sedentarism, associated with an optional mobile application with behavior monitoring, goal and notification reminders.
The effect of these interventions on cardio-metabolic outcomes or risk factors was evaluated as a primary outcome in eight studies using various measures, but none used hard clinical outcomes such as cardio-vascular mortality. The outcomes used include hemoglobin A1c (HbA1c),37,38,41 body weight,35,37,38,41,43,44,47 lipid and glycemic profile,35–38,41,43 blood pressure,27,35,37,41,44,48,50 waist and hip circumference,36,37,41,44,47,48 smoking status,40 fruit, vegetable or other consumption27,36,40,43,47,49,51 and physical activity (eg step count) outcomes.27,36,39,40,42–50
The effect of telephone-support or text messaging on these outcomes was discordant. For example, high blood pressure improved in a 12 week single arm study including 40 adults aged 50 years and over at risk of cardiometabolic diseases or cancer with informational and motivational text messaging in one study41 but no effect was found in a 18 months trial evaluating the effect of bi-weekly telephone counselling sessions in obese women aged 50 to 75 years.35 Moreover, interventions involving telephone support seemed to be associated with self-reported dietary changes with improvement in fruit and vegetable consumption after 3 months of risk factor information and healthy behavior with telephone support among 52 stroke or transient ischemic attack adults.40 Vita et al36 found also healthier diet with more fiber intake and lower saturated fat intake, but a high quality study27 find no effect of coaching sessions on fiber and fat intake change after 12 months of follow-up. Intervention effects on HbA1c were discordant in the highest quality studies: an 18-month telephone health coaching intervention37 was not associated with improvement in mean HbA1c compared to the control group, contrary to an 12-month telephone lifestyle counselling intervention which improved HbA1c control compared to a memory training program group.27 Results were also discordant among the highest quality studies of the effect of telephone and smartphone interventions on blood pressure27,37,44 or physical activity,25,44,46 although there were various differences in study population, type of telephone intervention and duration.
Description of Ongoing Studies
Eight studies are being conducted in Australia, four in the US, two in Thailand, one each in Spain, Malaysia, Slovenia, Poland, China, Iran, India, Finland, Singapore and Japan, and three in several locations (one in European countries, one in UK and China and the last one in Europe, Asia and Australia).
Participants are at risk of cardio-metabolic diseases in the majority of studies, defined by stroke comorbidities, type 2 diabetes, overweight or obesity, insufficient physical activity or smoking status (Appendix Table B).
Interventions are lasting between 6 weeks to 48 months and studies are including 30 to 2400 participants.
Mobile-phone applications are being used in 13 studies28–31,52,53 (NCT01307137, NCT04819256, TCTR20211021006, ACTRN12621001136897, ACTRN12621000236897, TCTR20190902004, ISRCTN31471852). For instance, participants with diabetes are using smartphones to record meals and self-monitor weight (NCT04819256). Another application is using an interactive coach supported mobile application31 among participants at risk of dementia.
Telephone vocal support is the second method used in the ongoing cardio-metabolic studies (n=12).
Finally, 2 studies are using both telephone vocal coaching and health or reminder text message interventions (ACTRN12617001022358, JPRN-UMIN000024416).
18 studies are evaluating cardio-metabolic outcomes as primary outcomes using various cardio-metabolic outcomes and risk factors, with physical activity and HbA1c being the most commonly used. Smoking status, lipid or glycemic profile, dietary outcomes or waist and hip circumference, blood pressure, BMI are also being used, but, as in the completed studies, no hard clinical outcomes.
For all completed studies, adherence was high with more than 70% of participants completing the studies. Measures of adherence, including the number of sessions completed27,36,43,44,46,48,49 or the number of participants who completed the study visit,38 were described in eleven studies.27,35–38,43,44,46–49 Vital et al36 found that 71% of the initial population attended the last follow-up visit with 75 to 77% of participants receiving all follow-up telephone calls. Lim et al38 reported higher adherence (ie 92.2 to 96.1%). Text messaging frequency (twice weekly) was also found to be appropriate.51 Moreover a second study46 evaluated the effect of text messages to support engagement with a home exercises program and found 86% of participants completed the study (94% for control group). Another study49 found that a dietary intake monitoring application was used for 92.7% of the 8-week follow-up period.
When reported, the main reasons for participants withdrawing from studies were being too busy or family/personal issues.38 In the study by Perri et al,35 participants completed a mean of 21.1±5.7 out of 26 telephone sessions. The participants who dropped-out seemed to have higher BMI, lower income, were younger and less often had private insurance, compared to those who did not drop out.
We found few high-quality completed studies evaluating the effectiveness of telephone or smartphone-based interventions on cognitive or cardio-metabolic outcomes among middle-age and older adults. Indeed, 21 completed studies were identified, of which only four had a good quality rating, and some of them included both cognitive and cardio-metabolic outcomes. A further 50 other studies are still ongoing. Based on these studies, no conclusions about the efficacy of telephone or smartphone-based interventions on cognition can be made because of a lack of high quality-data, and cardio-metabolic results varied depending on outcomes, interventions or the study population.
There were fewer completed trials evaluating the effects of telephone and notably smartphone-based interventions on cognitive or cardio-metabolic outcomes than we expected, given the large number of smartphone applications currently available. Indeed, many health applications have been developed without any scientific evaluation of their impact. Finally, the use of new technology tools differs in different age groups, and implementation data are still needed for older age groups.
There may be several reasons for the low number of studies identified. First, the mobile phone market is relatively recent, compared to other interfaces (ie computers). Second, mobile phone use initially grew mostly among young adults, although 62% of adults aged 70 and over owned smartphones in 201954 compared to 18% of adults aged 65 years and over in 201311 in the US. However, older adults are still the less likely to be smartphone users compared to younger adults.55 Furthermore, mobile and smartphones were designed for younger populations, and may not always be practical for older adults. Ergonomics should be adapted for age-related features, such as larger buttons or telephone contrast.56 This could help to increase participation and reduce attrition for smartphone interventions in older age groups. Moreover, smartphone applications should also be specifically designed for older populations. For instance, the frequency of daily smartphone access depends on age, and older individuals tend to favor weather and personal information manager applications over communication applications, such as instant messaging, compared to younger individuals.13
Concerning the interventions we reviewed, some used telephone support, but we do not know if they concerned landline and/or mobile phones. Furthermore, many of the studies included in the review were multicomponent interventions and the results relate to the efficacy of the intervention as a whole, and not just the telephone component.
Older adults are usually less often included in clinical trials,57,58 and more particularly in clinical trials using new technologies.59 Furthermore, the individuals included in the study populations might be higher-educated and healthier than the general population, since they firstly accepted to take part in a clinical trial,60 and also because telephone and smartphone ownership depends on socio-economic status.11 This selection of the population may have limited the effects of the interventions. Furthermore, the studies included in our review might be too small to detect intervention effects, since only 3 studies31,36 (JPRN-UMIN000041926) include more than 1000 participants.
High quality study results concerning short and long-term effect on cognition, cardio-metabolic diseases and their common risk factors in middle aged and older adults are therefore still needed. Given the relatively wide-scale availability of commercial applications, we expected that cognitive healthcare applications would have been well-evaluated in clinical trials. With only three completed studies,25–27 we could not draw any conclusions about the effectiveness of telephone-based interventions on cognition. Telephone call support was the main type of telephone intervention identified and none of the completed studies evaluated the effects of telephone or smartphone-based interventions on dementia incidence. Only 2 ongoing trials are evaluating dementia incidence.31 (JPRN-UMIN000041926) However, a meta-analysis61 found a small to moderate effect on cognition for web-based lifestyle intervention compared to control. Additionally, study-follow-up (maximum 24 months) might be too short to expect significant changes in this outcome.
We found discordant results for cardio-metabolic effects of telephone or smartphone-based interventions between studies, and the highest-quality studies27,37,44,46 found discrepant effects on HbA1c, physical activity and blood pressure. Previous literature reviews have also found discordant results. Indeed, Widmer et al62 reviewed the effect of any digital health intervention on cardio-metabolic outcomes and found that results depended on whether the interventions were used for primary or secondary prevention. For example, beneficial effects of digital interventions were found on systolic blood pressure for participants having cardio-metabolic risk factors (primary prevention) but not for those who had cardio-metabolic diseases (secondary prevention). However, high blood pressure decreased with telephone support after a myocardial infarction in a meta-analysis.63 We did not find different effects of telephone and smartphone interventions based on primary or secondary prevention in older adults. Moreover, mobile technology (smartphone and wearable sensor) seemed to be associated with better management of cardio-metabolic diseases or risk factors among community-dwelling adults64 and a systematic review and meta-analysis based on web-based prevention of cardio-vascular outcomes found 57 studies with significant effects on blood pressure, HbA1c and weight.59 Nevertheless, these reviews covered any kind of digital health intervention, and these studies evaluated young as well as older adults.
Our review shows that telephone and smartphone-based interventions suitable for older adults are still in the development phase, since there are relatively few eligible completed studies. However, they could be promising in older age groups since studies have showed that their use is feasible and a major application promoting health aging is currently being implemented in clinical practice.65
Our review has some limitations. First, our search equation included the term “prevention” which may have limited the number of relevant articles identified but we considered that this was in important term, since our aim was to evaluate telephone and smartphone for the prevention of cognitive and cardio-metabolic disease and their risk factors. However, we also scrutinized reference lists from the studies we identified, but this only led to the identification of one further study, which suggests that our original search was exhaustive. Secondly, the majority of included articles had poor or fair quality scores, and few results have so far been published in older populations, thus limiting the evidence base on which to draw conclusions. Moreover, for some studies (notably the ongoing studies identified in clinical trial registries), data were not always very detailed which limited the exhaustiveness of our descriptions of interventions or outcomes.
Research on telephone and smartphone interventions in older populations is in its early stages. However, some recommendations can be made to improve future randomized trials in this field. Digital tools (eg emails, social media advertisements) are effective for recruiting older participants66 even those with mild cognitive impairment.67 However, it is indispensable to take into account limitations, barriers and needs of the target population to better adapt devices and interventions.68,69 One of the main barriers to older people using mobile technology and devices should be as simple as possible and if applicable, easy to wear.68 Motivational behavior change techniques should be used to improve engagement with interventions69 and therefore efficacy. Moreover, to improve engagement and digital literacy, a session to explain how to use study device with trained-staff is recommended. Reminders (by telephone calls or text messaging) could also improve engagement as well as using supervised interventions.70 Implementation evaluation is also indispensable, in order to provide better recommendations. Finally, it is important to evaluate multiple outcomes associated with similar risk factors as was done in some of studies we identified,27,29–34 since interventions could have simultaneous benefits on multiple age-related disease outcomes by improving these factors.
Few studies on telephone and smartphone for the prevention of dementia and cardio-metabolic diseases have been specifically performed in middle-aged and older adults, and few are at low of risk of bias. Most completed studies reported no statistically significant effects of their interventions. Many studies are ongoing and can be classified as pilot or Phase 2 studies. Overall, in spite of the wealth of mobile health applications available and given a lack of research data and evaluation at the current time, we cannot demonstrate the supplementary value of such technologies compared to usual intervention strategies. Nonetheless, this is a growing area of research which will help to develop patient-centered approaches to prevention, and several new studies are underway, meaning that efficacy and implementation results for middle-aged or older adults should be available in the near future.
This review was performed as part of the Prodemos Project. The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 779238 and the National Key R&D Programme of China (2017YFE0118800). The members of the PRODEMOS group are: Edo Richard, Pim van Gool, Eric Moll van Charante, Marieke Hoevenaar-Blom, Esmé Eggink, Melanie Hafdi, Patrick Witvliet (Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands); Carol Brayne, Linda Barnes, Rachael Brooks (University of Cambridge, Cambridge, UK); Wei Wang, Wenzhi Wang, Youxin Wang, Manshu Song (Capital Medical University, Beijing, China; Edith Cowan University (ECU), Perth Australia); Anders Wimo, Ron Handels (Karolinska Institutet, Stockholm, Sweden); Sandrine Andrieu, Nicola Coley (INSERM UMR1295, Toulouse France); Jean Georges, Cindy Birck (Alzheimer Europe, Luxembourg); Bram van de Groep, Rick Mast (Vital Health Software, Ede, the Netherlands). The present work was also performed in the context of the Inspire Program, a research platform supported by grants from the Region Occitanie/Pyrénées-Méditerranée (Reference number: 1901175) and the European Regional Development Fund (ERDF) (Project number: MP0022856).
Dr Sandrine Andrieu reports grants from CHU, Université Paul Sabatier, UMR1295, Toulouse, during the conduct of the study; personal fees from Roche, outside the submitted work. The authors declare no other conflicts of interest relevant to the current work.
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