RevieweHealth interventions to promote objectively measured physical activity in community-dwelling older people
Introduction
Continuing or commencing an active lifestyle with ageing is associated with health benefits. It is well-documented that higher levels of daily physical activity (PA) are associated with better physical and mental well-being in older people [1], [2], [3]. Adopting an active lifestyle at old age has also shown strong positive effects for older people, such as improved functioning [4], reduced fall risk [5], and improved quality of life[6]. In addition, physical inactivity can boost physical decline as a result of ageing [7]. Given its potential for counteracting or slowing down detrimental outcomes, interventions for promoting an active lifestyle are widely considered in aging populations [8].
Over the past decades, the use of information and communication technology (ICT) to deliver lifestyle interventions has grown exponentially. The use of ICT solutions in healthcare services is often called electronic health or eHealth [9]. eHealth interventions that use electronic devices, such as computers, smartphones or tablets, for promoting an active lifestyle have shown positive results on PA in the general population [10], as well as in older people [9]. eHealth interventions are presumed to have great potential to increase access to interventions, increase compliance, lessen the burden on healthcare staff, and are highly scalable. Moreover, the use of a digital environment allows for delivery of continuous feedback and application of additional behaviour change techniques within the technology [11]. It further facilitates the tailoring of the intervention to the individual [12]. Those aged ā„55 years may be more familiar with using electronic devices and wearable technology than previous generations [13], and prior evidence has shown that this generation finds electronic devices promoting PA acceptable [14].
When evaluating the health benefits of lifestyle interventions for older people, it is essential to consider theories underlying the intervention to understand working mechanisms [15]. Besides piloting feasibility of newly developed eHealth interventions in a small sample, evaluating intermediate outcomes related to health, such as PA, is considered crucial to prove effectiveness and establish the causal pathways of long-term health benefits [15]. A recent systematic review showed that eHealth lifestyle interventions are effective in promoting PA in people above 50 years; however, the majority of studies in this review measured PA self-reported by questionnaires [9]. Although questionnaires are inexpensive, quick and easy to administer, they are prone to recall bias, might lead to variable and socially desirable answers and generally do not assess light PA or ordinary activities in daily life [16], [17]. Questionnaires therefore do not provide a very accurate reflection of a personās daily PA. Increased availability of wearable devices, such as pedometers or inertial sensors, allows collection of objective PA data in daily life [18]. Pedometers count steps while walking, whereas inertial sensors collect and store data over longer periods, later analysed to extract multiple features of PA. Inertial sensors, particularly tri-axial accelerometers, have shown better reliability in capturing daily PA than pedometers and uniaxial accelerometers due to their ability to detect light PA [19].
This review presents an overview of recent eHealth interventions for promoting PA in community-dwelling older people with objective measurements of PA (i.e. by pedometer, uni-axial or tri-axial accelerometers). We discuss the eHealth interventions developed for promoting sPA in the older target population, as well as the employed methods to assess PA objectively. Finally, we discuss the effectiveness of the interventions on PA behaviour.
Section snippets
Methods
For this narrative review, we followed the guidelines for database search, selection of studies and data extraction from Cochrane [20]. We searched PubMed (from January 1990 to January 2018) with key search terms and synonyms for āolder peopleā, ātelemedicineā, āexerciseā, āambulatory monitoringā, and ārandomized trialsā (see Supplementary Table 1 for the search syntax). Studies were included in the current review if they: 1) included community-dwelling people with a mean or median age
eHealth interventions for promoting physical activity
Twelve different studies met our inclusion criteria [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32] (Table 1 for details and Fig. 1 for a flowchart of the search). Sample sizes of studies varied from 40 [28] to 263 [21], with eight out of twelve studies including <100 participants. The ICT modalities that were used by studies to deliver the interventions differed considerably, and four studies compared multiple interventions with one control condition [23], [24], [29],
Conclusions
This overview shows that eHealth interventions for promoting an active lifestyle, delivered in a wide variety of modalities, appear to be acceptable for older populations and have positive effects on increasing PA in the short-term. However, caution is warranted since many studies were underpowered and long-term effects have not yet been established. Larger studies with theory-based interventions and a longer follow-up are needed to fully understand the potential and effective components of
Contributors
All authors were involved in the study design.
Nini H Jonkman performed the data collection and analysis, and drafted the manuscript.
Kimberley S van Schooten, Andrea B Maier and Mirjam Pijnappels provided input with the interpretation of the data, and contributed to critical revision of the manuscript.
All authors approved the final version of the manuscript.
Nini H Jonkman had full access to all data and had final responsibility for the decision to submit for publication.
Nini H Jonkman, Andrea B
Conflict of interest
The authors declare that they have no conflict of interest.
Funding
This work was supported by funding from the European Unionās Horizon 2020 research and innovation programme [grant agreement number 689238]. Kimberley S van Schooten was supported by the Human Frontier Science program [HFSP long-term fellowship number LT001080/2017]. The funding source had no involvement in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.
Provenance and peer review
This article has undergone peer review.
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